Dynamic task planning system for yard crane under multi-factor constraints

Abstract

This invention relates to the field of container terminal management technology, specifically a dynamic task planning system for yard cranes under multi-factor constraints. The system includes a yard crane data acquisition module, a yard crane multi-task analysis module, a yard crane information processing module, and a yard crane collaborative scheduling module. Based on the errors and influencing factors of the data acquisition module, the sampling time is adjusted to obtain a yard crane monitoring database. Based on the yard crane task time quantification analysis system of the yard crane multi-task analysis module, time analysis results and multi-task comprehensive analysis results are obtained for different task stages during yard crane operations. The yard crane information processing module classifies the database to obtain data subsets for different task stages during operations. The yard crane collaborative scheduling module combines the time analysis results and data subsets of different task stages with the multi-task comprehensive analysis results to adjust and plan yard crane tasks. This invention's data-driven dynamic scheduling method enables the system to adapt to different operational scenarios and task requirements, enhancing system adaptability and stability.

Description

Technical Field

[0001] This invention relates to the field of container terminal management technology, specifically a dynamic task planning system for yard cranes under multi-factor constraints. Background Technology

[0002] In automated container terminals, yard cranes are among the key pieces of equipment, and their operational efficiency and scheduling strategies play a decisive role in the terminal's overall operational efficiency. Traditional yard crane planning methods have several shortcomings: Firstly, existing methods mostly employ static work plans and scheduling rules, failing to fully consider the impact of real-time operational demands, equipment status, and the yard environment. This can lead to excessively long waiting times for yard cranes during actual operations, ultimately affecting yard operational efficiency and economic benefits. Secondly, in the complex and ever-changing environment of container terminals, real-time monitoring data is crucial for achieving automated scheduling and task planning. However, traditional data acquisition methods are susceptible to interference from factors such as fluctuations in monitoring equipment performance, unstable network environments, and communication conflicts. This often results in inaccurate data collection results and missing node data, hindering dynamic data acquisition, reducing the accuracy of monitoring results, and negatively impacting yard crane task planning.

[0003] Therefore, a dynamic task planning system for yard cranes under multi-factor constraints is needed to adapt to the complex and ever-changing operating environment of automated container terminals, obtain complete and comprehensive dynamic monitoring information, and facilitate the dynamic adjustment and scientific planning of yard crane operation tasks. Summary of the Invention

[0004] Given the numerous limitations of existing traditional yard crane planning methods, which struggle to match and meet the actual operational needs of automated container terminals when dealing with real-time operational demands, equipment status changes, and dynamic adjustments to the yard environment, this invention provides a yard crane dynamic task planning system under multi-factor constraints. This system aims to adapt to the operational environment of automated container terminals, comprehensively acquire dynamic monitoring information, and adjust yard crane operation tasks based on a multi-factor consideration of various influencing factors, thereby achieving dynamic optimization and planning of yard crane operation tasks. The system includes a yard crane data acquisition module, a yard crane multi-task analysis module, a yard crane information processing module, and a yard crane collaborative scheduling module. The system also considers the errors and influencing factors of the yard crane data acquisition module. The sampling time is adjusted, and data is re-collected based on the adjusted sampling time to obtain the yard bridge monitoring database. A yard bridge task time quantification analysis system is established in the yard bridge multi-task analysis module. Through this system, time analysis results for different task stages during yard bridge operations and multi-task comprehensive analysis results are obtained. The yard bridge information processing module is used to classify and organize the yard bridge monitoring database and output data subsets for different task stages during yard bridge operations. The yard bridge collaborative scheduling module dynamically adjusts and plans yard bridge tasks by combining the time analysis results for different task stages, the multi-task comprehensive analysis results, and the data subsets for different task stages.

[0005] This invention combines time analysis results from different task stages, multi-task comprehensive analysis results, and data subsets from different task stages to dynamically adjust and plan yard crane tasks. It can adjust the start time and resource allocation of different tasks in a timely manner. The collaborative work of each module can optimize the yard crane operation process, reduce waiting time and ineffective operations, improve yard crane operation efficiency, and thus improve the overall operational efficiency of the container terminal.

[0006] Optionally, adjusting the sampling time in the yard bridge data acquisition module based on errors and influencing factors, and re-collecting data based on the adjusted sampling time to obtain the yard bridge monitoring database includes: setting up multiple parallel modular rotor channels in the yard bridge data acquisition module; analyzing errors in the yard bridge data acquisition module based on the multiple parallel modular rotor channels; introducing a monitoring signal spectrum analysis function; and analyzing influencing factors in the yard bridge data acquisition module based on the monitoring signal spectrum analysis function and the errors. The system of this invention can dynamically adjust the sampling time according to changes in yard bridge operating conditions, enabling the system to better adapt to the dynamic changes in yard bridge operations.

[0007] Optionally, adjusting the sampling time in the yard crane data acquisition module based on errors and influencing factors, and then re-collecting data based on the adjusted sampling time to obtain the yard crane monitoring database includes: adjusting the sampling time in the yard crane data acquisition module according to the influencing factors to obtain an adjusted sampling time; and re-collecting data based on the adjusted sampling time to obtain initial monitoring data for rotor channels of different moduli at different time points. This invention, by collecting initial monitoring data for rotor channels of different moduli at different time points based on the adjusted sampling time, can more accurately reflect the operating status of the yard crane under different operational stages and environmental conditions, providing an information foundation for subsequent multi-task analysis of the yard crane.

[0008] Optionally, the step of re-collecting data based on the adjusted sampling time to obtain the yard bridge monitoring database includes: setting a monitoring data optimization function in the yard bridge data acquisition module based on the error and the influencing factors; optimizing the initial monitoring data through the monitoring data optimization function to obtain yard bridge data sequences for rotor channels of different moduli at different time points; and the yard bridge data acquisition module performing aggregation processing on the yard bridge data sequences to obtain the yard bridge monitoring database. The monitoring data optimization function of this invention can reduce redundant information and noise interference in the data.

[0009] Optionally, establishing a quantitative analysis system for yard crane task time in the yard crane multi-task analysis module includes: establishing a quantitative analysis system for yard crane task time based on the yard crane monitoring database; and setting prediction formulas for total unloading operation time, truck queuing time at quay cranes, truck queuing time at yard cranes, total quay crane operation time, and total yard crane operation time in the quantitative analysis system based on the terminal operation scenario and the yard crane monitoring database. This invention's quantitative analysis system for yard crane task time can provide data support for terminal management decisions, compare different operation scenarios, and help improve the scientific nature of yard crane decisions.

[0010] Optionally, establishing a quantified analysis system for yard crane task time in the multi-task analysis module, and obtaining time analysis results for different task stages during yard crane operations through this system includes: obtaining the total unloading operation time using the total unloading operation time prediction formula within the quantified analysis system; obtaining the queuing waiting time of container trucks at quay cranes using the quantified analysis system; obtaining the queuing waiting time of container trucks at yard cranes using the quantified analysis system; obtaining the queuing waiting time of container trucks at yard cranes using the quantified analysis system; obtaining the total operation time of all quay cranes using the quantified analysis system; and obtaining the total operation time of the yard cranes using the quantified analysis system. The time analysis results for different task stages of this invention provide a basis for the rational allocation of yard crane resources.

[0011] Optionally, obtaining the time analysis results and multi-task comprehensive analysis results of different task stages during the yard crane operation through the yard crane task time quantification analysis system includes: deriving and analyzing the total unloading operation time, the queuing waiting time of the container trucks at the quay crane, the queuing waiting time of the container trucks at the yard crane, the total operation time of all quay cranes, and the total operation time of the yard crane, and establishing a yard crane multi-task objective function; and using the yard crane multi-task objective function to obtain the multi-task comprehensive analysis results.

[0012] This invention provides a comprehensive analysis of the time required for different task stages, which helps to improve the coordination of the overall work process and thus enhance the overall operational efficiency of the port.

[0013] Optionally, the step of classifying and organizing the yard crane monitoring database using the yard crane information processing module and outputting data subsets for different task stages during yard crane operations includes: the yard crane information processing module classifying and organizing the yard crane monitoring database according to the yard crane task type, and obtaining data subsets for the loading stage, unloading stage, and yard handling stage during yard crane operations. Each data subset in this invention contains detailed information for the corresponding task stage, providing information for optimizing the yard crane operation process.

[0014] Optionally, the yard crane collaborative scheduling module dynamically adjusts and plans yard crane tasks by combining the time analysis results of different task stages, the multi-task comprehensive analysis results, and the data subsets of different task stages. This includes: setting a priority index analysis function in the yard crane collaborative scheduling module; and obtaining the priority index of different task stages during yard crane operations based on the priority index analysis function and the time analysis results of different task stages. This invention arranges tasks using priority indices, which can avoid mutual waiting and conflicts between tasks and reduce the idle time of yard cranes and container trucks.

[0015] Optionally, the yard bridge collaborative scheduling module dynamically adjusts and plans yard bridge tasks by combining the time analysis results of different task stages, the multi-task comprehensive analysis results, and the data subsets of different task stages. This includes: setting weight coefficients for different task stages based on the priority index of different task stages; and the yard bridge collaborative scheduling module dynamically adjusts and plans yard bridge tasks based on the time analysis results, priority index, weight coefficients, and data subsets of different task stages, combined with the multi-task comprehensive analysis results.

[0016] The weighting coefficients of this invention quantify the importance of different task stages, so that scheduling decisions are not based on subjective judgment and experience, but on objective data and quantitative indicators, which helps to achieve efficient coordination and rational planning among multiple tasks of the field bridge. Attached Figure Description

[0017] Figure 1 This is a flowchart of the field bridge dynamic task planning system under multi-factor constraints of the present invention; Figure 2 This is a structural diagram of the field bridge dynamic task planning system under multi-factor constraints according to the present invention. Detailed Implementation

[0018] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0019] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0020] To adapt to the operating environment of automated container terminals and fully utilize dynamic monitoring information, this invention rationally plans yard crane operation tasks under the premise of multiple influencing factors, thereby achieving dynamic optimization and scientific planning of yard crane operation tasks. Please refer to the multi-factor constraint-based dynamic task planning system for yard cranes provided in this invention. Figure 1 And includes the following steps: This embodiment sets up a yard bridge data acquisition module, a yard bridge multi-task analysis module, a yard bridge information processing module, and a yard bridge collaborative scheduling module in the yard bridge dynamic task planning system under multi-factor constraints.

[0021] S1. Based on the errors and influencing factors of the yard bridge data acquisition module, the sampling time in the yard bridge data acquisition module is adjusted, and data is re-acquired according to the adjusted sampling time to obtain the yard bridge monitoring database. The specific implementation details are as follows: In the operation of automated container terminals, the yard crane data acquisition module plays a crucial role, and the accuracy and real-time performance of its data collection directly affect subsequent decision-making and control. However, in practical applications, the module is subject to interference from various errors and influencing factors. Therefore, this embodiment first analyzes the errors and influencing factors of the yard crane data acquisition module.

[0022] The first step is to set up multiple parallel modular rotor channels in the field bridge data acquisition module.

[0023] In order to improve the data acquisition efficiency and real-time performance of the yard bridge data acquisition module, multiple ( ) are set in the yard bridge data acquisition module. The system comprises (1) parallel modular rotor channels, capable of simultaneously acquiring field bridge monitoring data at different time points. Each channel has an independent data input path. In this embodiment, the acquired data is processed using... To represent, where Indicates the sequence number of the modular rotor channel, and , Indicates the time point at which the data was sampled.

[0024] In this embodiment, the sampling rates of rotor channels with different moduli must be kept consistent, and their phases must be staggered. This primarily utilizes a time-alternating parallel sampling method, increasing the data acquisition rate of the field bridge data acquisition module to that of a single channel. Times. In one specific embodiment, the sampling rate of a single channel is . Then the sampling rate of the overall data stream of the field bridge data acquisition module can reach This significantly improves the amount and real-time nature of monitoring data collected from the yard bridge, enabling more timely capture of status changes and dynamic information during yard bridge operation.

[0025] Next, the data collected from the rotor channels of different modules are spliced ​​and combined using a digital multiplexer to obtain the overall data stream of the field bridge. The aforementioned digital multiplexer can integrate the data collected from each parallel channel in an orderly manner according to specific rules and timing to meet the needs of high-frequency monitoring at the port and provide data support for subsequent data processing and analysis.

[0026] The second step involves analyzing the errors in the field bridge data acquisition module based on the multi-parallel modular rotor channel analysis.

[0027] The aforementioned multi-parallel modular rotor channels improve data acquisition efficiency. However, due to differences in the operating performance of related equipment in the field bridge data acquisition module, mismatch errors between different modular rotor channels can affect data quality, causing deviations between the acquired data and the true values, ultimately affecting the accuracy and reliability of the data. The aforementioned mismatch errors mainly include bias error, gain error, and clock error.

[0028] In order to accurately analyze the relevant errors, this embodiment defines multiple error parameters based on the parameter information of the field bridge data acquisition module and the information collection status.

[0029] The error parameters for rotor channels of different module sizes in the field bridge data acquisition module are set as follows: bias error This refers to the fixed deviation of the channel output signal when there is no input signal. Its dimension is the same as the signal amplitude. When there is no input to the channel, the output signal is 0. Since the offset error output result may be a fixed non-zero value, the above value is the offset error. .

[0030] Gain error This reflects the deviation between the channel's amplification or attenuation of the input signal and the ideal value. Ideally, the channel should amplify or attenuate the input signal with a fixed gain. However, in practical applications, the amplitude of the output signal will differ from the ideal due to the influence of gain error. In one embodiment, the ideal gain is 2, while the actual gain is 2.2, therefore the gain error... .

[0031] Clock error This explains that inaccuracy in the channel sampling clock can lead to deviations in sampling time points. Inaccurate sampling clocks can cause sampling time to be advanced or delayed, thus affecting the accuracy of the collected data.

[0032] The third step is to introduce a spectrum analysis function for the monitoring signal.

[0033] The embodiment performed a spectrum analysis on the monitoring signal output by the field bridge data acquisition module, and the complex form of the monitoring data signal can be represented by the monitoring signal spectrum analysis function, satisfying the following relationship: , in, This represents the complex form of the monitoring signal from the field bridge data acquisition module. Represents the imaginary unit. This indicates the initial phase parameters of the monitoring signal acquired by the monitoring bridge data acquisition module. This represents the time variable of the field bridge data acquisition module. This indicates the period of the monitoring signal by the field bridge data acquisition module. This represents the amplitude coefficient of the signal monitored by the field bridge data acquisition module.

[0034] The complex form described above mainly contains the amplitude and phase information of the signal; the imaginary unit satisfies the following relationship: In the complex domain, it is used to represent phase information; the amplitude coefficient of the field bridge data acquisition module monitors the signal, reflecting the signal strength.

[0035] The fourth step involves analyzing the influencing factors in the field bridge data acquisition module based on the monitoring signal spectrum analysis function and error analysis.

[0036] Spectral analysis of monitored data signals can reveal the signal characteristics and the impact of errors on the signal. Based on the reference spectral analysis results output by the spectral analysis function of the monitored signal in this embodiment, we can see that: Gain error and clock error The generated spectral spurious emissions are mainly distributed in Within the range, It is a parameter related to signal analysis, which mainly depends on various factors such as the signal frequency, sampling rate, and signal processing algorithm.

[0037] bias error The spectrum is concentrated in Near a frequency point means that the offset error mainly affects this specific frequency component in the signal spectrum, causing a deviation in the signal amplitude at that frequency point.

[0038] Analysis results indicate clock error The impact on spectral purity is most significant. Clock errors lead to inaccurate signal sampling timing, which in turn alters the energy distribution of the signal in the frequency domain, generating additional spectral spurious signals, and is a major factor contributing to data distortion. Therefore, in the process of reducing errors and improving data quality, clock error correction should be a key focus, as it helps improve the data quality of the field bridge data acquisition module.

[0039] Based on the above error analysis results and error parameter information, this embodiment mainly addresses the sampling time deviation in the field bridge data acquisition module. Perform the analysis.

[0040] In this embodiment, the bridge data acquisition module has a unified reference sampling time series, wherein the reference sampling time interval is: (All channels should be sampled at this time interval), and then the... Each reference sampling time can be represented as: , in, Indicates the reference sampling time. Indicates the sampling time sequence number. Indicates the reference sampling time interval.

[0041] This embodiment sets the first The first modular rotor channel in the... The actual sampling time for each sampling point is Therefore, their relationship can be expressed as: , in, Indicates the first The first modular rotor channel in the... The actual sampling time for each sampling point Indicates the sampling time sequence number. Indicates the reference sampling time interval. Indicates the first Clock error coefficient for each analog-to-digital rotor channel, Indicates the sampling clock period. Indicates the serial number of the modular rotor channel.

[0042] Based on the above benchmark sampling time The reference sampling time for rotor channels with different module numbers can be determined. Therefore, the sampling time deviation of rotor channels with different moduli in the embodiment must satisfy the following relationship: , in, This indicates the sampling time deviation of rotor channels with different module numbers. Indicates the first The first modular rotor channel in the... The actual sampling time for each sampling point Indicates the first The first modular rotor channel in the... The reference sampling time for each sampling point Indicates the serial number of the modular rotor channel.

[0043] The above sampling time deviation can be further simplified as follows: , in, When representing the sampling of rotor channels with different moduli deviation, Indicates the first Clock error coefficient for each analog-to-digital rotor channel, This indicates the sampling clock period.

[0044] The above formula directly demonstrates the relationship between the actual sampling time and the reference sampling time. The simplified sampling time deviation formula more clearly illustrates the impact of clock error on sampling time deviation. Based on this, the time deviation of different modulus rotor channels in the module during data acquisition can be understood, further providing a basis for error correction and data optimization.

[0045] Then, the sampling time in the field bridge data acquisition module is adjusted to obtain the adjusted sampling time.

[0046] This embodiment primarily addresses the sampling time deviation of rotor channels with different module values ​​by considering various factors affecting the sampling time of the field bridge data acquisition module. Deviation from time.

[0047] The implementation can change the rhythm of the sampling clock by adjusting the frequency or phase of the clock source, so that the sampling time of different multi-parallel analog-to-digital rotor channels tends to be consistent. If the unstable clock source frequency causes sampling time deviation, phase-locked loop technology can be used to lock and adjust the clock source frequency to ensure that it is stable at the preset value, thereby reducing the deviation of sampling time.

[0048] After the above adjustments, it is also necessary to calculate the sampling time deviation of each modular rotor channel after adjustment, and further compensate and calibrate the sampling clock to accurately calibrate different time nodes of different modular rotor channels. (Sampling time node), thus obtaining the adjusted sampling time.

[0049] Finally, data was recollected based on the adjusted sampling time to obtain the bridge monitoring database.

[0050] The first step is to re-collect data based on the adjusted sampling time to obtain the initial monitoring data of rotor channels with different moduli at different time points.

[0051] In this embodiment, the field bridge data acquisition module re-acquires the initial monitoring data of different module rotor channels at different time points based on the adjusted sampling time. During the acquisition process, it ensures that each channel strictly follows the adjusted sampling time to collect data, so as to guarantee the synchronization and accuracy of the data.

[0052] The second step is to set up a monitoring data optimization function in the field bridge data acquisition module based on the error and influencing factors.

[0053] The monitoring data optimization function in this embodiment comprehensively considers bias error. Gain error and clock error Regarding the impact on the collected data, the above monitoring data optimization function satisfies the following relationship: , in, Indicates the first The first modular rotor channel in the... The series of field bridge data collected at each time point, Indicates the sequence number of the modular rotor channel. Represents the input signal function. Indicates the first The first modular rotor channel in the... Initial monitoring data collected at each time point, Indicates the first The offset error of each modular rotor channel Indicates the first Gain error of each analog-to-digital rotor channel Indicates the first The sampling clock cycle of each analog-to-digital rotor channel, Represents the imaginary unit. Indicates the first Clock error of each analog-to-digital rotor channel.

[0054] In the monitoring data optimization function This item is primarily used to compensate for phase shifts caused by clock errors.

[0055] The model is designed with corresponding processing methods for different error types, which involves subtracting the bias error. To eliminate the bias effect, divide by the gain error. Gain bias can be corrected by utilizing The phase shift caused by the compensation clock error is effectively eliminated or reduced by the above mathematical processing, which can make the optimized field bridge sequence more accurate.

[0056] Most existing technologies only focus on the impact of a single type of error on the data. This embodiment further clarifies the impact of bias error, gain error and clock error on the data, and sets up a monitoring data optimization function to optimize the initial monitoring data collected. This can effectively eliminate or reduce the impact of various errors on the data, and more realistically reflect the various error situations encountered in the actual data collection process, providing a more accurate basis for subsequent data optimization.

[0057] The third step is to optimize the initial monitoring data using a monitoring data optimization function, which will yield the field bridge sequence for rotor channels of different moduli at different time points.

[0058] This embodiment utilizes a monitoring data optimization function to optimize the initial monitoring data output by rotor channels of different module numbers. Optimization is performed by monitoring the data and optimizing the function to eliminate or reduce the impact of bias error, gain error, and clock error on the data, thus obtaining the [the desired result]. The first modular rotor channel in the... More accurate field bridge data series were collected at each time point.

[0059] Furthermore, by using monitoring data to optimize functions, the field bridge data series of rotor channels with different moduli at different time points are obtained, providing a data foundation for subsequent field bridge equipment status monitoring, fault diagnosis, performance evaluation and other applications, which helps to improve the operational stability and reliability of the entire field bridge system.

[0060] The fourth step, the yard bridge data acquisition module, aggregates the yard bridge data series to obtain the yard bridge monitoring database of the embodiment.

[0061] In this embodiment, the field bridge data acquisition module aggregates the optimized field bridge data series collected by rotor channels of different modules at different time points. The aggregation process must ensure the integrity and consistency of the data, which can effectively avoid data loss or corruption.

[0062] After aggregation and processing, the final site bridge monitoring database is obtained, which can be represented as: , in, This indicates that the yard bridge data acquisition module outputs a yard bridge monitoring database. This indicates the number of modular rotor channels in the field bridge data acquisition module. Indicates the sequence number of the modular rotor channel. Indicates the first The first modular rotor channel in the... The sequence of field bridge data collected at each time point.

[0063] The yard crane data acquisition module aggregates and processes the yard crane data series of different module rotor channels, ensuring data integrity. In the yard crane dynamic task planning system under multi-factor constraints, complete data can comprehensively reflect the operation status of the yard crane, which helps the system to conduct comprehensive analysis and planning, avoids the one-sidedness of task planning due to data loss, and ensures that all relevant factors are taken into account.

[0064] The aforementioned yard crane monitoring database facilitates subsequent data analysis and dynamic planning. Furthermore, this embodiment employs a hierarchical storage structure, allowing data collected from different channels to be categorized and stored according to time series and task type. In one embodiment, data is stored at different levels based on different time periods (hours, days, months, etc.) and different tasks (loading and unloading operations, equipment maintenance, etc.) for rapid retrieval. Simultaneously, the database features real-time updates, connecting in real-time with the yard crane data acquisition module to promptly update newly collected and optimized data, supporting terminal operation management and scheduling planning.

[0065] An accurate yard crane monitoring database can truly reflect the status changes and dynamic information of yard cranes during operation, which helps to more accurately understand the workload and health status of yard cranes, thereby formulating task plans that conform to the actual situation, improving the utilization efficiency of yard cranes, and reducing the risk of equipment failure.

[0066] The above implementation steps successfully solved the problem of incomplete monitoring data nodes for yard cranes in complex terminal environments, improved the performance of the yard crane data acquisition module, and obtained an accurate and reliable yard crane monitoring database, providing strong support for the efficient and safe operation of container terminals.

[0067] S2. To comprehensively measure and optimize the yard crane operation process and improve the overall operational efficiency of the wharf, this embodiment establishes a yard crane task time quantification analysis system in the yard crane multi-task analysis module. The system is used to analyze the time analysis results of different task stages during yard crane operations, as well as the comprehensive multi-task analysis results. The specific implementation steps and content are as follows: First, a quantitative analysis system for the time of yard crane tasks was established in the multi-task analysis module of the yard crane.

[0068] The embodiment establishes a quantitative analysis system for the time of field bridge tasks in the field bridge multi-task analysis module based on the field bridge monitoring database. The database provides data support for the construction of the system and can ensure the reliability of the analysis results.

[0069] To accurately quantify and analyze the time of different tasks, the embodiment extracts task parameters from the yard crane monitoring database, including but not limited to the start and end times of different task stages and the arrival and departure times of container trucks. The above parameter information lays the information foundation for the construction of the yard crane task time prediction system. Furthermore, based on the terminal operation scenario and the yard crane monitoring database, time prediction formulas for different operation stages are introduced into the yard crane task time prediction system, which helps to achieve accurate prediction and scientific quantification of the yard crane operation process, making the measurement of task time more precise and providing a scientific basis for dynamic programming.

[0070] This implementation of the on-site crane task time quantitative analysis system includes formulas for predicting the total time of unloading operations, the queuing time of container trucks at quay cranes, the queuing time of container trucks at on-site cranes, the total time of quay crane operations, and the total time of on-site crane operations.

[0071] I. Set up a formula for predicting the total time of unloading operations in the on-site bridge task time quantitative analysis system.

[0072] The formula for predicting the total unloading time in this embodiment satisfies the following relationship: , in, This indicates the total time for unloading operations. Represents the maximum value function. This indicates the number of yard cranes involved in the unloading operation. Indicates the first The time it takes for each bridge to complete the unloading task at different stages of the mission. Describes the minimum value function. Indicates the first The timing of the start of unloading operations at each phase of the mission. Indicates different stages of a task. This represents the set of stages in the unloading task.

[0073] In the formula for predicting the total time of unloading operations It can calculate the total time for all yard cranes to complete the unloading task in each task phase, and can find the total time of the phase with the longest total unloading task completion time among all task phases, because the completion time of the entire unloading operation is limited by the slowest task.

[0074] In the formula for predicting the total time of unloading operations The minimum value of the sum of the start times of all bridges in all different task phases can be taken and used as the reference point for the start time.

[0075] Finally, by subtracting the relatively earlier start time from the total critical path time, we can obtain the overall time taken for the unloading operation.

[0076] II. Set up a formula for predicting the queuing and waiting time of container trucks at the quay bridge in the quantitative analysis system of on-site bridge task time.

[0077] The formula for predicting the waiting time of container trucks queuing at quay cranes satisfies the following relationship: , in, This indicates the queuing time for container trucks at the quay crane. Indicates the number of container trucks participating in the operation. Indicates the number of times the quay crane has been operated. Indicates the first The truck was in the first The time of departure during secondary quay crane operations. Show the first The truck was in the first Arrival time during secondary quay crane operations.

[0078] The formula for predicting the queuing time of container trucks at quay cranes can be used to calculate the total queuing time of all container trucks at quay cranes, thereby reflecting the impact of container truck queuing at quay cranes on the efficiency of terminal operations.

[0079] III. Set up a formula for predicting the queuing and waiting time of trucks at the on-site bridge in the quantitative analysis system of on-site bridge tasks.

[0080] The queuing time of container trucks at the yard has a significant impact on the terminal operation process. The calculation formula for the above-mentioned formula for predicting the queuing time of container trucks at the yard is as follows: , in, This indicates the queuing time for trucks at the yard bridge. Indicates the number of container trucks participating in the operation. Indicates the number of times the quay crane has been operated. Indicates the first The truck was in the first The time of departure during the secondary bridge operation. Indicates the first The truck was in the first Arrival time during secondary bridge operations.

[0081] Based on this formula, the total queuing time of all container trucks at the yard crane can be calculated, further quantifying the impact of container trucks queuing at the yard crane on the terminal operation process.

[0082] IV. Set up a formula for predicting the total time of quay crane operations in the on-site bridge task time quantitative analysis system.

[0083] The total operation time of all quay cranes reflects the working time of the quay cranes throughout the entire operation process. The above formula for predicting the total operation time of quay cranes satisfies the following relationship: , in, This represents the total operation time of all quay cranes. Indicates the number of quay cranes. The number of related equipment or tasks involved in quay crane operations. Indicates the first Under the bridge The start time of operation for each related device or task. Indicates the first Under the bridge The time when the relevant equipment or task ends.

[0084] The formula for predicting the total time of quay crane operations can calculate the sum of all quay crane operation times, thus accurately reflecting the working time of the quay crane throughout the entire operation process.

[0085] V. Set up a formula for predicting the total time of bridge operations in the bridge task time quantitative analysis system.

[0086] The total time for yard crane operations reflects the time spent by the yard crane in completing all tasks. The formula for predicting the total time for yard crane operations is as follows: , in, This indicates the total operation time for the yard bridge. Indicates the number of tasks for the field bridge. Indicates the first The end time of each task. Indicates the first The formula calculates the total time taken for the yard crane to complete all tasks, thus aiding in the analysis of the yard crane's operational efficiency.

[0087] Then, the time analysis results of different task stages in the yard bridge operation are obtained through the yard bridge task time quantification analysis system.

[0088] In one embodiment, the total unloading operation time prediction formula in the yard crane task time quantification analysis system is used. By substituting relevant parameters into the formula, the total unloading operation time can be obtained, and the overall time consumption of the unloading operation can be measured based on this.

[0089] In one embodiment, the queuing time of container trucks at the quay crane is predicted by substituting the corresponding parameters into the formula of the quay crane task time quantification analysis system, which is helpful to understand the waiting status of container trucks at the quay crane.

[0090] In one embodiment, based on the prediction formula for truck queuing time at the yard bridge in the yard bridge task time quantification analysis system, the queuing time of trucks at the yard bridge can be obtained, and the waiting situation of trucks at the yard bridge can be grasped.

[0091] In one embodiment, by substituting the parameters into the total operation time prediction formula of the quay crane in the quay crane task time quantitative analysis system, the total operation time of all quay cranes can be obtained, which is conducive to accurately evaluating the working efficiency of the quay cranes.

[0092] In one embodiment, the total operation time of the yard crane is calculated based on the yard crane task time quantification analysis system using the yard crane operation total time prediction formula, which can reflect the time consumed by the yard crane to complete all tasks.

[0093] Next, we analyze the results of the multi-task comprehensive analysis.

[0094] The embodiment combines the total unloading operation time, the queuing time of container trucks at the quay crane, the queuing time of container trucks at the yard crane, the total operation time of all quay cranes, and the total operation time of the yard crane to derive and analyze, and establishes a multi-task objective function for the yard crane.

[0095] The above multi-task objective function for the field bridge satisfies the following relationship: , in, This represents the multi-task objective function of the field bridge. The weights are the total unloading time, the queuing time of container trucks at the quay cranes, the queuing time of container trucks at the yard cranes, the total operation time of all quay cranes, and the total operation time of the yard cranes, respectively, and the sum of each weight is 1. In practical applications, the above different weights can be reasonably allocated according to the actual operation needs of the terminal and the key optimization directions.

[0096] In one specific implementation, the main objective of the terminal is to shorten the time ships spend in port and increase cargo turnover speed, so the weight of the total unloading operation time can be appropriately increased. The terminal focuses on resolving truck queuing congestion and improving internal transportation efficiency, which can correspondingly increase the weighting of truck waiting time at quay cranes and yard cranes. and .

[0097] Based on the above multi-task objective function for the yard crane, it can be seen that this embodiment comprehensively considers several key indicators closely related to wharf operations when conducting multi-task integrated analysis: Total unloading time is one of the core indicators for measuring the efficiency of unloading operations at a terminal. It covers the time spent from the moment a ship docks until all cargo is unloaded. The length of the total unloading time directly affects the time a ship spends in port, which in turn affects the terminal's cargo turnover capacity and overall operational efficiency.

[0098] Trucks are an important tool for cargo transportation at the terminal. The queuing time of trucks at the quay cranes reflects the degree of matching between the quay crane operation efficiency and the truck transportation capacity. If the queuing time is too long, it will not only increase the operating cost of trucks, but also cause traffic congestion inside the terminal and reduce the overall operation efficiency.

[0099] Trucks also need to queue at the yard crane to load and unload goods. The yard crane is mainly responsible for the storage and handling of goods in the dock yard. The queuing time of trucks at the yard crane reflects the coordination between the yard crane operation efficiency and the truck transportation demand.

[0100] Quay cranes are key equipment for ship loading and unloading operations at the terminal. The total operating time of all quay cranes comprehensively reflects the terminal's ship loading and unloading capacity over a period of time. Analyzing this indicator helps to understand the utilization of quay crane resources and to identify whether there are problems such as uneven quay crane operations or idle resources.

[0101] Yard cranes are responsible for loading, unloading, handling, and storing goods in the terminal yard. Their total operating time directly affects the operational efficiency of the terminal yard. Reasonable arrangement of the total operating time of yard cranes can ensure the orderly flow of goods in the yard, improve the space utilization of the yard, and improve the efficiency of goods entering and leaving the warehouse.

[0102] The aforementioned multi-task objective function for yard cranes comprehensively considers the impact of various indicators on terminal operation efficiency, unifies multiple task indicators into a single function for evaluation, and can weigh the importance of different tasks as a whole, thereby achieving comprehensive optimization of the multi-task operation process of yard cranes and making dynamic task planning more in line with the actual operational needs of the terminal.

[0103] In this embodiment, the impact of various indicators on the efficiency of terminal operations is comprehensively considered based on the multi-task objective function of the yard crane. This helps to achieve comprehensive optimization of the multi-task operation process of the yard crane, thereby obtaining the comprehensive analysis results of the multi-task, providing a basis for terminal operation scheduling and planning management, and helping to improve the overall operational efficiency of the terminal.

[0104] In practical applications, various task indicators are interconnected and influence each other during dock operations. The multi-task objective function of the yard crane unifies multiple task indicators into one function for evaluation, which can comprehensively and holistically consider the impact of various indicators on dock operation efficiency, effectively avoid the one-sidedness and limitations of single indicator evaluation, realize the rational allocation and optimal configuration of resources, and improve the scientificity and rationality of planning results.

[0105] S3. In container terminal operations, yard cranes play a crucial role in loading, unloading, and handling tasks. Their monitoring data is diverse and complex. To provide clear and organized data support for subsequent dynamic task planning of yard cranes and improve terminal operation efficiency and safety, this embodiment utilizes a yard crane information processing module to classify and organize the yard crane monitoring database, and outputs data subsets for different task stages during yard crane operations. The specific implementation details are as follows: First, the yard bridge information processing module preprocesses the yard bridge monitoring database.

[0106] After the construction of the bridge monitoring database is completed, this embodiment uses the bridge information processing module to preprocess the bridge monitoring database in order to ensure data quality and consistency, providing a reliable information foundation for subsequent task planning and analysis.

[0107] Because yard crane monitoring data is susceptible to environmental factors and equipment performance fluctuations, the yard crane information processing module uses a filtering algorithm to smooth the yard crane monitoring database, which can reduce noise interference and improve data quality. In a specific embodiment, abnormal fluctuation values ​​in the yard crane operating speed monitoring data are made closer to the true values ​​through a filtering algorithm.

[0108] In practical applications, the dock operating environment is complex, and some monitoring data may be missing. The yard crane information processing module introduces an interpolation method based on time series prediction, which infers and fills in missing values ​​based on historical data and information from adjacent time points. In a specific embodiment, if the yard crane lifting height data is missing for a certain time period, it can be filled in based on the lifting height data from previous and subsequent time points and historical change patterns, which helps to improve the completeness of the data.

[0109] Because data dimensions differ across dimensions, to avoid data bias, the yard crane information processing module uses a minimum-maximum normalization method to map the data in the database to a unified range. In a specific embodiment, data with different dimensions, such as yard crane operating speed and container weight, are normalized to... The interval is used to facilitate subsequent comparative analysis.

[0110] The second step is to classify and organize the preprocessed bridge monitoring database and output data subsets for different task stages during bridge operations.

[0111] I. Data subset during the loading stage Loading is a crucial part of container terminal crane operations. This stage involves transferring containers from the yard to ships. Monitoring data during this stage reflects the crane's operational status and provides a basis for task analysis and optimization.

[0112] The example extracts data records related to the loading task from the yard crane monitoring database.

[0113] The key parameters and explanations of the data subset for the further loading stage are as follows: The lifting height records the vertical displacement change of the yard crane spreader from picking up a container in the yard to the designated loading position. It is one of the indicators for evaluating the efficiency of yard crane operations. By analyzing the changes in lifting height, the smoothness of operation and time consumption of the yard crane during the loading process can be accurately grasped.

[0114] Operating speed reflects the movement characteristics of the yard crane in the horizontal and vertical directions. Its variation pattern can reveal the working habits of the yard crane operator and the performance status of the equipment. Sudden changes in operating speed indicate that there may be abnormalities in operation or equipment.

[0115] The trolley position describes the horizontal movement trajectory of the yard crane spreader between the quay front and the yard, which helps to understand the trolley position and facilitates subsequent optimization of the yard crane movement path.

[0116] Container weight helps identify potential safety risks. If overloading or uneven loading occurs, the container weight needs to be adjusted to a reasonable range to ensure the safe operation of loading.

[0117] Finally, by organizing the above key parameter information according to dimensions such as task number and time, a subset of data for the loading stage can be obtained, providing real-time decision-making basis for the planning system and enabling the system to reasonably arrange loading tasks based on the actual operation of the yard crane.

[0118] II. Data Subset for the Unloading Phase Unloading refers to the process of transferring containers from ships to the container yard, and is one of the operational procedures at a container terminal. In this embodiment, the unloading task table is first retrieved from the yard crane monitoring database. This table mainly records basic information for each unloading task, including but not limited to the task number, start time, end time, and details of the ships and containers involved. Then, each unloading operation is numbered according to its task number, facilitating rapid location and retrieval of relevant data later. Simultaneously, the interaction between the yard crane and other equipment during the unloading process needs to be recorded, such as the coordination time with container trucks, waiting time, and number of operational conflicts, to reflect the collaborative efficiency of the unloading operation and provide a reference for optimizing terminal traffic flow organization. Furthermore, equipment information during the unloading phase needs to be obtained, including but not limited to the real-time operating status of the yard crane, energy consumption data, and fault alarm records, which helps to more accurately understand the status of the yard crane equipment and ensure the smooth progress of the unloading operation.

[0119] The key parameters and explanations of the data subset for the unloading phase are as follows: The lifting height reflects the vertical movement trajectory of the yard crane lifting equipment as it picks up containers from the ship's hold and places them in the yard. By analyzing the lifting height during the unloading phase, the operation of the yard crane during the unloading process can be evaluated.

[0120] Container location information can effectively avoid container stacking conflicts, improve yard space utilization, and ensure that containers are accurately placed in designated locations.

[0121] Data on the speed of the main trolley and the trajectory of the auxiliary trolley can be used to evaluate the efficiency and safety of the yard crane in transferring containers between the quayhead and the yard. Reasonable control of the speed and trajectory of the main trolley and auxiliary trolley can help improve the overall efficiency of unloading operations.

[0122] Container status information helps ensure the smooth operation of subsequent yard operations, promptly detects abnormal container status, and avoids affecting subsequent continuous operations.

[0123] In this embodiment, the data acquired and recorded above are aggregated and organized according to dimensions such as task number and time to output a data subset for the unloading stage. Based on the data subset for the unloading stage, the collaborative efficiency of the unloading operation and the operating status of the equipment can be accurately understood, which is conducive to optimizing the unloading operation process.

[0124] III. Data Subset for the Yard Handling Stage In-yard handling is an integral part of container terminal operations, which involves rearranging or transferring containers within the yard to designated storage areas.

[0125] This embodiment filters data related to yard handling tasks from the yard crane monitoring database.

[0126] The key parameters and explanations in the data subset for the yard handling phase are as follows: Lifting height and operating speed are fundamental indicators for evaluating the efficiency of yard crane operations. A proper combination of lifting height and operating speed can improve handling efficiency.

[0127] The location information of the trolleys and the running trajectory data of the trucks help track the movement path of containers in the yard, which can avoid operational conflicts or wasted time caused by improper path planning. Analyzing the running trajectories of the trolleys and trucks is beneficial for subsequent optimization of handling paths.

[0128] Information on the number of layers of containers stacked reflects the utilization rate of yard space and helps to rationally arrange subsequent handling tasks.

[0129] By organizing the aforementioned key parameter data according to dimensions such as task number and time, a subset of data for the yard handling stage can be obtained. Based on this subset of data, the execution process of handling tasks within the yard can be quantified, providing information for the efficient utilization of terminal yard resources.

[0130] The embodiment obtains data subsets for different task stages by classifying and organizing them, enabling the yard crane dynamic task planning system to comprehensively consider multiple factors such as the yard crane's operating status, coordination efficiency, and equipment status at different task stages, thereby formulating a more scientific and reasonable task planning scheme and avoiding unreasonable task planning due to insufficient consideration of a single factor.

[0131] The yard crane information processing module can classify and organize the yard crane monitoring database through the above implementation steps, and finally output data subsets of different task stages in the yard crane operation process, providing data support for the planning and management of container terminals.

[0132] S4, the yard crane collaborative scheduling module, combines time analysis results from different task stages, multi-task comprehensive analysis results, and data subsets from different task stages to dynamically adjust and plan yard crane tasks, which helps improve the overall operational efficiency of the terminal. The specific implementation details are as follows: The first step is to set a priority index analysis function in the yard bridge collaborative scheduling module. The yard bridge collaborative scheduling module obtains the priority index of different task stages during the yard bridge operation based on the priority index analysis function and the time analysis results of different task stages.

[0133] This embodiment pre-determines the criteria for judging task priority based on the actual operational needs of the terminal. Specifically, tasks that have a significant impact on the overall operational efficiency of the terminal or tasks with a high degree of urgency should be given priority.

[0134] The yard crane collaborative scheduling module obtains various time prediction results from the yard crane multi-task analysis module, which includes the total unloading operation time ( ), waiting time for container trucks at the quay crane ( ), waiting time for container trucks at the yard bridge ( Total operating time of all quay cranes ) and the total operation time of the yard bridge ( Furthermore, the dataset also includes key time metrics for different task stages and their corresponding weight information.

[0135] The embodiment sets up a priority index analysis function in the yard crane collaborative scheduling module, which needs to be referenced from the yard crane multi-task objective function. The priority index for different task stages is calculated separately, with the specific calculation details as follows: Ship unloading operation priority index ; Priority index of container trucks queuing at quay cranes ; Priority index of trucks queuing at the yard bridge ; Total Time Priority Index for Quay Crane Operations ; Total Time Priority Index for Bridge Operations .

[0136] The above calculation results take into account a variety of factors that affect dock operations, so that the priority index can accurately reflect the importance and urgency of the task.

[0137] By referring to the priority indices of different task stages mentioned above, weight coefficients for different task stages can be set accordingly. In this embodiment, different comprehensive priority weights are assigned to different task stages based on the terminal's operational situation, and the final comprehensive priority index satisfies the following relationship: , Indicates the overall priority index. These represent the priority weights for different task stages, and The sum of the priorities is 1. In this embodiment, different tasks are sorted according to the calculated priority index. In this embodiment, tasks with higher priority indices need to be prioritized and processed first to ensure that critical tasks can be processed in a timely manner during the terminal operation.

[0138] The third step, the yard bridge collaborative scheduling module, dynamically adjusts and plans yard bridge tasks based on the time analysis results, priority index, weight coefficient, and data subsets of different task stages, combined with the comprehensive analysis results of multiple tasks.

[0139] In one optional embodiment, the yard crane resources are allocated reasonably by combining information such as the current location, load status, and operating speed of the yard cranes; at the same time, based on available resource information such as the number of yard cranes and the number of container trucks, corresponding yard cranes and container trucks are allocated to different task stages to ensure reasonable resource allocation and avoid resource conflicts and waste.

[0140] In an optional embodiment, a time plan is developed for each task based on the predicted time and priority of the task; at the same time, the order and dependencies between different tasks need to be considered, and the start and end times of different tasks should be reasonably arranged to minimize the waiting time and idle time between different tasks.

[0141] In an optional embodiment, the system's path planning algorithm is used to plan driving routes for yard cranes and container trucks by combining the terminal map and real-time traffic information; at the same time, it is also necessary to consider avoiding obstacles and other equipment when planning the route to ensure the safety and feasibility of the planned route.

[0142] Finally, the yard bridge collaborative scheduling module outputs dynamic planning results. At the same time, it can also generate corresponding bridge scheduling instructions, including but not limited to information such as the yard bridge number, task type, target location, start time, and end time. This provides yard bridge operators with clear execution guidelines, enabling them to complete various tasks accurately and improve operational efficiency.

[0143] In this embodiment, relevant data and scheduling schemes for yard crane coordination can also be stored and archived so that relevant personnel can query, analyze and trace them. At the same time, the relevant data can provide a reference for the planning and management of the terminal, optimize the terminal operation process and improve the overall operational efficiency.

[0144] The yard crane collaborative scheduling module realizes dynamic planning of yard crane tasks through the above implementation steps, effectively coordinates the yard crane operation process, and improves the overall operational efficiency and safety of the terminal.

[0145] The multi-factor constraint-based dynamic task planning system for yard bridges in this embodiment includes a yard bridge data acquisition module, a yard bridge multi-task analysis module, a yard bridge information processing module, and a yard bridge collaborative scheduling module. These modules are interconnected and work together. The complete system architecture described above can ensure the smooth transmission and processing of data, enabling each module to fully utilize its functions and jointly achieve dynamic planning of yard bridge tasks.

[0146] This implementation system can effectively cope with various complex situations and multi-factor constraints in dock operations, further enhancing the stability and reliability of the system and improving the overall quality and safety of dock operations.

[0147] Please see Figure 2 In an optional embodiment, the present invention also provides a dynamic task planning system for airfield bridges under multi-factor constraints. This system includes an airfield bridge data acquisition module, an airfield bridge multi-task analysis module, an airfield bridge information processing module, and an airfield bridge collaborative scheduling module. These modules are interconnected, implementing the specific steps of the relevant embodiments of the dynamic task planning system for airfield bridges under multi-factor constraints provided by the present invention. The dynamic task planning system for airfield bridges under multi-factor constraints of the present invention has a complete structure, is objective and stable, and improves the overall applicability and practical application capability of the present invention.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention.

Claims

1. A dynamic task planning system for field bridges under multi-factor constraints, characterized in that, The system includes a yard crane data acquisition module, a yard crane multi-task analysis module, a yard crane information processing module, and a yard crane collaborative scheduling module; Based on the errors and influencing factors of the field bridge data acquisition module, the sampling time in the field bridge data acquisition module is adjusted, and data is re-acquired according to the adjusted sampling time to obtain the field bridge monitoring database. A time quantification analysis system for yard bridge tasks is established in the yard bridge multi-task analysis module. The time analysis results of different task stages and the comprehensive analysis results of multiple tasks are obtained through the yard bridge operation through the time quantification analysis system for yard bridge tasks. The yard bridge information processing module is used to classify and organize the yard bridge monitoring database, and output data subsets for different task stages during yard bridge operations; The yard bridge collaborative scheduling module dynamically adjusts and plans yard bridge tasks by combining the time analysis results of different task stages, the multi-task comprehensive analysis results, and the data subsets of different task stages.

2. The field bridge dynamic task planning system under multi-factor constraints according to claim 1, characterized in that, The process of adjusting the sampling time in the field bridge data acquisition module based on errors and influencing factors, and then re-collecting data according to the adjusted sampling time to obtain the field bridge monitoring database includes: Multiple parallel modular rotor channels are configured in the field bridge data acquisition module; Errors in the field bridge data acquisition module are analyzed based on the multi-parallel modular rotor channel analysis. Introduce a spectrum analysis function for the monitoring signal; Based on the monitoring signal spectrum analysis function and the influencing factors in the error analysis field bridge data acquisition module.

3. The field bridge dynamic task planning system under multi-factor constraints according to claim 1, characterized in that, The process of adjusting the sampling time in the field bridge data acquisition module based on errors and influencing factors, and then re-collecting data according to the adjusted sampling time to obtain the field bridge monitoring database includes: The sampling time in the field bridge data acquisition module is adjusted according to the aforementioned influencing factors to obtain the adjusted sampling time; Based on the adjusted sampling time, data is collected again to obtain the initial monitoring data of rotor channels with different moduli at different time points.

4. The field bridge dynamic task planning system under multi-factor constraints according to claim 3, characterized in that, The method of re-collecting data based on the adjusted sampling time to obtain the bridge monitoring database includes: Based on the aforementioned errors and influencing factors, a monitoring data optimization function is set in the field bridge data acquisition module; The initial monitoring data is optimized using the monitoring data optimization function to obtain the field bridge sequence of rotor channels with different moduli at different time points; The yard bridge data acquisition module aggregates the yard bridge data series to obtain a yard bridge monitoring database.

5. The field bridge dynamic task planning system under multi-factor constraints according to claim 1, characterized in that, The establishment of a time-quantitative analysis system for yard bridge tasks in the yard bridge multi-task analysis module includes: Based on the aforementioned bridge monitoring database, a bridge task time quantification analysis system is established in the bridge multi-task analysis module. Based on the dock operation scenario and the aforementioned yard crane monitoring database, the following formulas are set in the yard crane task time quantification analysis system to predict the total unloading operation time, the queuing time of container trucks at the quay crane, the queuing time of container trucks at the yard crane, the total quay crane operation time, and the total yard crane operation time.

6. The field bridge dynamic task planning system under multi-factor constraints according to claim 5, characterized in that, The establishment of a time quantification analysis system for yard bridge tasks in the multi-task analysis module, and the time analysis results obtained through the time quantification analysis system for yard bridge tasks at different task stages during yard bridge operations, include: The total unloading time is obtained by using the total unloading time prediction formula in the yard crane task time quantification analysis system. The queuing time of trucks at the quay crane is obtained by using the prediction formula of the queuing time of trucks at the quay crane in the quay crane task time quantitative analysis system. The queuing time of trucks at the yard bridge is obtained based on the prediction formula of the queuing time of trucks at the yard bridge in the quantification analysis system of yard bridge task time. The total operation time of all quay cranes is obtained based on the total operation time prediction formula of the quay crane task time quantitative analysis system. The total operation time of the yard bridge is obtained based on the prediction formula of the total operation time of the yard bridge in the quantitative analysis system of yard bridge task time.

7. The field bridge dynamic task planning system under multi-factor constraints according to claim 6, characterized in that, The time analysis results for different task stages during the bridge operation and the comprehensive multi-task analysis results obtained through the bridge task time quantification analysis system include: The total unloading time, the queuing time of the container trucks at the quay cranes, the queuing time of the container trucks at the yard cranes, the total operation time of all quay cranes, and the total operation time of the yard cranes are combined to derive and analyze the data, and a multi-task objective function for the yard cranes is established. The multi-task integrated analysis results are obtained using the aforementioned multi-task objective function for the field bridge.

8. The field bridge dynamic task planning system under multi-factor constraints according to claim 1, characterized in that, The process of classifying and organizing the yard bridge monitoring database using the yard bridge information processing module, and outputting data subsets for different task stages during yard bridge operations, includes: The yard crane information processing module classifies and organizes the yard crane monitoring database according to the yard crane task type, and obtains data subsets for the loading stage, unloading stage, and yard handling stage during the yard crane operation process.

9. The field bridge dynamic task planning system under multi-factor constraints according to claim 1, characterized in that, The yard bridge collaborative scheduling module dynamically adjusts and plans yard bridge tasks by combining the time analysis results of different task stages, the multi-task comprehensive analysis results, and the data subsets of different task stages, including: A priority index analysis function is set in the aforementioned yard bridge collaborative scheduling module; The yard bridge collaborative scheduling module obtains the priority index for different task stages during yard bridge operations based on the priority index analysis function and the time analysis results of different task stages.

10. The field bridge dynamic task planning system under multi-factor constraints according to claim 1, characterized in that, The yard bridge collaborative scheduling module dynamically adjusts and plans yard bridge tasks by combining the time analysis results of different task stages, the multi-task comprehensive analysis results, and the data subsets of different task stages, including: The weighting coefficients for different task stages are set according to the priority index of the different task stages. The site bridge collaborative scheduling module dynamically adjusts and plans site bridge tasks based on time analysis results, priority index, weight coefficient, and data subsets at different task stages, combined with the multi-task comprehensive analysis results.

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