Port energy-traffic bilateral flexible resource intelligent scheduling method and system

Abstract

The invention discloses an intelligent scheduling method and system for port energy-traffic bilateral flexible resources, relates to the technical field of port scheduling, and aims to solve the problem that the resource utilization efficiency is reduced due to the fact that an existing port scheduling system cannot realize collaborative optimization of energy and traffic resources when facing equipment performance fluctuation and traffic flow imbalance. The method comprises the following steps: sensing and evaluating operation completion time information of equipment in real time and generating a dispatching instruction of a transport vehicle; and acquiring operation completion time information updated in real time and an arrival time adjustment request of the transport vehicle when the operation equipment executes the operation task, and generating an adjustment instruction of the transport vehicle according to the updated operation completion time information and the arrival time adjustment request. The problem of mismatching of energy and traffic resources caused by information lag and lack of flexible adjustment capability of a traditional scheduling system is effectively solved, and the overall operation efficiency and the resource utilization rate of a port are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of port scheduling technology, specifically a port energy-transportation dual-side flexible resource intelligent scheduling method and system. Background Technology

[0002] In modern port operations, dispatching systems rely on preset equipment performance parameters and traffic models trained on historical data. They coordinate the operations of quay cranes, yard cranes, and container trucks through a "timing-based dispatching instruction logic" to improve loading and unloading efficiency and reduce energy consumption. This logic often employs a "filling the gap" strategy, issuing high-load task instructions during brief periods of equipment idleness to maximize equipment utilization. However, this strategy overlooks the instantaneous impact on the local power supply network caused by the simultaneous startup of multiple high-power inductive loads (such as quay crane motors). This can lead to a sudden voltage drop, resulting in reduced motor output torque and actual operating speeds lower than the system's preset values.

[0003] Because the existing dispatching system assumes a stable power grid and constant equipment performance, it lacks a feedback mechanism for voltage fluctuations caused by its own dispatching behavior and their impact on equipment performance. Therefore, it still calculates the operation completion time based on ideal operating conditions and issues positioning instructions to the trucks in advance accordingly. When the trucks arrive on time as instructed, the quay cranes have not yet completed their operations due to actual delays, causing the trucks to idle for extended periods. This not only increases fuel or electricity consumption and raises operating costs but also causes localized traffic congestion and disrupts subsequent operations.

[0004] In summary, existing technologies are unable to achieve dynamic and coordinated optimization of energy supply and traffic flow. Their scheduling logic lacks the ability to adapt to fluctuations in equipment performance and power grid disturbances. Instead, the centralized scheduling strategy exacerbates the imbalance in energy use and traffic flow disorder, ultimately leading to a decline in the overall resource utilization efficiency of the port. Summary of the Invention

[0005] The purpose of this application is to address the problem that existing port scheduling systems cannot achieve coordinated optimization of energy and transportation resources when faced with fluctuations in equipment performance and uneven traffic flow, resulting in decreased resource utilization efficiency. A flexible intelligent scheduling method and system for port energy and transportation resources is proposed. By sensing the operating status of operating equipment and the dynamic demand of transport vehicles in real time, scheduling instructions are dynamically adjusted. This effectively solves the problem of energy and transportation resource mismatch caused by information lag and lack of flexible adjustment capabilities in traditional scheduling systems, significantly improving the overall operational efficiency and resource utilization rate of the port.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a port energy-transportation dual-side flexible resource intelligent scheduling method, the method comprising: Send task information to the working equipment to start the working equipment to perform a task; The system acquires the operation completion time information of the operating equipment based on its real-time perception and evaluation of its own operating status, and generates a dispatch instruction for the transport vehicle based on the operation completion time information. The dispatch instruction includes an arrival time range. Obtain real-time updated task completion time information during the execution of tasks by the operating equipment; Obtain the arrival time adjustment request of the transport vehicle during the execution of the dispatch instruction; Based on the updated job completion time information and arrival time adjustment request, generate adjustment instructions for the transport vehicles.

[0007] This solution effectively solves the problem of energy and transportation resource mismatch caused by information lag and lack of flexible adjustment capabilities in traditional scheduling systems by dynamically adjusting dispatch instructions through real-time perception of the operating status of operating equipment and the dynamic demand of transport vehicles. This significantly improves the overall operational efficiency and resource utilization of the port.

[0008] Optionally, obtaining the real-time updated job completion time information during the execution of the job by the operating equipment includes: Collect instantaneous power waveform data at the output of the hoisting motor frequency converter when the operating equipment performs the operation task; The instantaneous input power of the hoisting motor is calculated based on the instantaneous power waveform data, and the harmonic components in the instantaneous power waveform data are identified. The harmonic loss coefficient is determined based on the harmonic components, and then combined with the actual mechanical output power of the hoisting motor is determined based on the instantaneous input electrical power. The operation time compensation is calculated based on the deviation between the actual mechanical output power and the theoretical mechanical power required for the task. The initial operation completion time information is then corrected based on the operation time compensation and used as the updated operation completion time information.

[0009] In this solution, by accurately analyzing the instantaneous power wave of the hoisting motor and considering the impact of harmonic losses on the actual output power of the motor, the actual working capacity of the equipment can be assessed more accurately, the work completion time can be corrected, and the deviation of the traditional scheduling system based on ideal parameters can be avoided, making the scheduling more precise.

[0010] Optionally, determining the harmonic loss coefficient based on harmonic components, in conjunction with determining the actual mechanical output power of the hoisting motor based on the instantaneous input electrical power, includes: The local control system of the operating equipment collects instantaneous acceleration data of the container spreader and calculates the instantaneous dynamic impact load of the container based on the instantaneous acceleration data. The instantaneous conversion efficiency of the frequency converter under the current dynamic operating conditions is evaluated based on the output frequency, output voltage and output current of the frequency converter. The harmonic loss calculated by the harmonic loss coefficient is subtracted from the instantaneous input power, and the inherent losses of the frequency converter and motor corrected according to the instantaneous dynamic impact load and the instantaneous conversion efficiency are then subtracted to obtain the actual mechanical output power of the hoisting motor.

[0011] In this solution, by introducing the instantaneous dynamic impact load of the container and the instantaneous conversion efficiency of the frequency converter, the calculation of the actual mechanical output power of the motor is more refined, making the evaluation of equipment performance closer to the actual working conditions and further improving the accuracy of the prediction of the operation completion time.

[0012] Optionally, the evaluation of the instantaneous conversion efficiency of the frequency converter under the current dynamic operating conditions based on the output frequency, output voltage, and output current of the frequency converter includes: The local control system deploys multiple frequency, voltage and current sensors with electromagnetic shielding and signal filtering functions, and installs each sensor in a redundant configuration at the output terminal of the inverter to collect the inverter's output frequency, output voltage and output current data. Real-time consistency verification is performed on similar data from the same sensor. When the data deviation exceeds a preset threshold, it is marked as abnormal data. Time-series-based smoothing and trend forecasting are performed on data marked as outliers to obtain corrected data; Based on the corrected data and the inverter's operating parameters, the instantaneous conversion efficiency of the inverter under the current dynamic operating conditions is evaluated.

[0013] In this solution, the reliability and accuracy of inverter output data acquisition are effectively improved through redundant sensor configuration and real-time data verification. Furthermore, the accuracy of instantaneous conversion efficiency assessment is ensured through an abnormal data correction mechanism, providing a high-quality data foundation for subsequent power calculations.

[0014] Optionally, the step of performing time-series-based smoothing and trend prediction on the data marked as anomalies to obtain corrected data includes: After receiving abnormal data, the local control system identifies the type of abnormality, which includes discrete spike anomalies caused by electromagnetic interference and continuous fluctuation anomalies caused by dynamic impact loads. Based on the anomaly type of the data, a corresponding anomaly correction strategy is matched to correct discrete spike anomaly data and persistent fluctuation anomaly data, and the corrected data is obtained.

[0015] In this solution, different correction strategies are matched according to the specific type of abnormal data (discrete spikes or persistent fluctuations), which enables refined processing of data anomalies, improves the pertinence and effectiveness of data correction, and thus ensures the accuracy of subsequent evaluation.

[0016] Optionally, the step of matching corresponding anomaly correction strategies based on the anomaly type of the data to correct discrete spike anomaly data and persistent fluctuation anomaly data, and obtaining corrected data, includes: For the discrete spike anomaly data, smoothing is performed based on median filtering, and linear interpolation prediction is performed in combination with the normal data points adjacent to the discrete spike anomaly data to generate corrected data for the discrete spike anomaly data. For the persistently fluctuating abnormal data, a smoothing process is performed based on an adaptive sliding window to generate corrected data for the persistently fluctuating abnormal data; The corrected data is formed by integrating the corrected data of the discrete spike anomaly data and the corrected data of the persistent fluctuation anomaly data.

[0017] In this solution, customized correction methods are adopted for different types of abnormal data, such as median filtering and linear interpolation to handle discrete spikes, and adaptive sliding window to handle continuous fluctuations. These methods significantly improve the accuracy and robustness of data correction, ensure data quality, and provide more reliable input for subsequent efficiency evaluation.

[0018] Optionally, the corrected data formed by fusing the discrete spike anomaly data and the persistent fluctuation anomaly data to form the final corrected data includes: Identify the temporal overlap between the corrected data of the discrete spike anomaly data and the corrected data of the persistent fluctuation anomaly data; Determine whether there is a logical conflict or inconsistent smoothness in the overlapping area. When there is a logical conflict or inconsistent smoothness in the overlapping area, use the corrected data with a time density greater than the preset time density threshold and an amplitude change rate consistent with the instantaneous acceleration change rate of the container spreader as the first target corrected data. The remaining corrected data is locally adjusted within the overlapping area to maintain a consistent trend and smooth amplitude transition with the first target corrected data, thereby obtaining the second target corrected data; The final corrected data is formed by integrating the corrected data of the first target, the corrected data of the second target, and the corrected data of various abnormal data in the non-overlapping areas.

[0019] In this solution, the intelligent fusion of correction results for different types of abnormal data, especially the conflict judgment and local adjustment of overlapping areas, ensures the overall consistency and smoothness of the final corrected data, avoids inconsistencies between corrected data, and further improves the quality of data correction.

[0020] Optionally, the step of locally adjusting the remaining corrected data within the overlapping region to maintain a consistent trend and smooth amplitude transition with the first target corrected data, and obtaining the second target corrected data, includes: A first weighting factor is determined based on the magnitude difference between the remaining corrected data and the first target corrected data within the overlapping region; The second weighting factor is determined based on the trend matching degree between the remaining corrected data and the first target corrected data within the overlapping area; Based on the first weighting factor and the second weighting factor, obtain the weighting factor for the weighted average; Based on the aforementioned weighting factors, a weighted average method is used to locally adjust the remaining corrected data to achieve a smooth transition in amplitude and consistent trend.

[0021] In this scheme, a weighted average adjustment is performed using weighting factors, which achieves a smooth transition and consistent trend among different corrected data in overlapping areas. This effectively solves the problem of abrupt transitions that may occur during data fusion, making the final data more natural and accurate.

[0022] Optionally, obtaining the arrival time adjustment request of the transport vehicle during the execution of the dispatch instruction includes: When the onboard system of the transport vehicle detects an interruption in the wireless communication link with the central dispatch system, the offline emergency decision engine built into the onboard system of the transport vehicle is activated. The offline emergency decision engine loads preset high-risk area map data, alternative route rules, safe waiting area coordinates, and priority decision trees for common emergencies in the port area. It also fuses data from the vehicle's millimeter-wave radar, forward-looking camera, inertial measurement unit, and wheel speed sensor to obtain local environmental information and the status of the transport vehicle itself. Based on the local environmental information and the priority decision tree, autonomous decision-making suggestions are generated, and the driver's actual operation, suggestion adoption results, and environmental change information are recorded according to the autonomous decision-making suggestions to form a local transaction log. When the onboard system of the transport vehicle detects that the wireless communication link with the central dispatch system has been restored to stability, it triggers the fast synchronization protocol and sends a summary of the local transaction log to the central dispatch system. The central dispatch system compares the summary information with the contingency instruction log generated by the transport vehicle during the communication interruption, identifies the differences, and obtains the arrival time adjustment request based on the differences.

[0023] This solution addresses the problem of transport vehicles being unable to respond promptly to scheduling changes when communication is interrupted by combining an onboard offline emergency decision engine with a rapid synchronization mechanism after communication is restored. This ensures the vehicle's autonomous decision-making capability in complex environments and its ultimate coordination with the central system, significantly improving the flexibility and safety of traffic scheduling.

[0024] Secondly, embodiments of this application provide a port energy-transportation dual-side flexible resource intelligent scheduling system, comprising: The task sending module is used to send task information to the working equipment to start the working equipment to execute a work task; The dispatch instruction generation module is used to obtain the operation completion time information of the operating equipment based on its real-time perception and evaluation of its own operating status, and to generate dispatch instructions for the transport vehicles based on the operation completion time information. The dispatch instructions include an arrival time range. The job completion update module is used to obtain the job completion time information updated in real time during the execution of the job by the working equipment; The arrival adjustment request module is used to obtain the arrival time adjustment request of the transport vehicle during the execution of the scheduling instruction; The transport instruction adjustment module is used to generate adjustment instructions for transport vehicles based on the updated job completion time information and arrival time adjustment requests.

[0025] The beneficial effects of this application are: By sending task information to the operating equipment and obtaining its real-time perceived task completion time, dispatch instructions for transport vehicles, including arrival time ranges, can be dynamically generated. During the operation of the equipment, updated task completion time information is continuously acquired, and combined with adjustments to the arrival times of transport vehicles, refined adjustment instructions are generated. This effectively solves the "supply-demand mismatch" problem caused by the lack of real-time feedback and flexible adjustment capabilities in existing port dispatch systems when facing equipment performance fluctuations and traffic flow imbalances. Specifically, traditional systems assume constant equipment performance and a stable power grid. This leads to the inability to promptly detect and adjust dispatch when power grid fluctuations cause a slowdown in the actual operating speed of the equipment, resulting in prolonged idling of transport vehicles and traffic congestion. This application, by sensing the operating status of the operating equipment and the dynamic needs of transport vehicles in real time, can dynamically adjust dispatch instructions, avoiding energy and transportation resource mismatch caused by information lag and lack of flexible adjustment capabilities. This significantly improves the overall operational efficiency and resource utilization of the port, and reduces unnecessary energy consumption and traffic congestion. Attached Figure Description

[0026] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0027] Figure 1 A flowchart of a port energy-transportation dual-side flexible resource intelligent scheduling method provided in this application embodiment.

[0028] Figure 2 A flowchart illustrating a method for obtaining real-time job completion time information of a work equipment, as provided in an embodiment of this application.

[0029] Figure 3 This is a schematic diagram of a port energy-transportation dual-side flexible resource intelligent scheduling system module provided in an embodiment of this application. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] Example 1: As Figure 1 As shown, a port energy-transportation dual-side flexible resource intelligent scheduling method includes steps S1-S5, wherein: S1. Send task information to the working equipment to start the working equipment to perform a work task.

[0032] In one specific embodiment, a task message is sent to the operating equipment to initiate the operation of a task. For example, a command can be sent to a quay crane to lift a container from a ship to a designated location.

[0033] S2. Obtain the operation completion time information of the operating equipment based on its real-time perception and evaluation of its own operating status, and generate a dispatch instruction for the transport vehicle based on the operation completion time information, wherein the dispatch instruction includes an arrival time range.

[0034] In some embodiments, the operating status of the operating equipment includes, but is not limited to, motor load, lifting speed, and current operation progress. Based on this data, the estimated operation completion time is assessed. For example, the local control system of the quay crane can calculate a preliminary operation completion time based on the weight of the container being lifted, the height and distance of the target location, combined with its rated power and efficiency. Based on this operation completion time information, and considering factors such as traffic conditions within the port area and the scheduling of other operating equipment, a dispatch instruction is generated for the transport vehicle. This dispatch instruction specifies the time for the transport vehicle to travel to the quay crane operation point and provides an arrival time range, such as "arrive within the next 15-20 minutes".

[0035] Furthermore, during the execution of tasks by the operating equipment, its operating status may change, such as encountering sudden malfunctions, changes in the operating environment, or power grid fluctuations. To cope with these uncertainties, the operating equipment continuously monitors its own status and updates its estimated task completion time in real time. Meanwhile, transport vehicles may encounter various unforeseen circumstances en route to the work site, such as traffic congestion within the port area, temporary road closures, or vehicle malfunctions, preventing them from arriving within the originally scheduled timeframe. Therefore, it is necessary to obtain a request for adjustment of the transport vehicle's arrival time. Specifically, when the operating equipment initiates a task, the system sends task information. During task execution, the operating equipment generates preliminary task completion time information based on its real-time perception and assessment of its own operating status. Based on this information, a transport vehicle dispatch instruction containing the arrival time range is generated. At this stage, the system begins to consider the actual status of the operating equipment, rather than relying solely on ideal parameters. However, the port environment is complex and variable, and the operating efficiency of the equipment may change due to factors such as power grid fluctuations and equipment wear during task execution. The key to this embodiment is to continuously acquire real-time updated job completion time information during the execution of job tasks by the working equipment, ensuring that the working equipment is no longer operating in a "black box" manner, but can instead feed back its latest job progress and estimated completion time to the scheduling system.

[0036] In this embodiment, by real-time sensing and evaluation of the operating equipment's own operational status, and by receiving arrival time adjustment requests from transport vehicles during the execution of dispatch instructions, real-time acquisition of bidirectional information on both the port's energy side (operating equipment efficiency) and traffic side (transport vehicle arrival time) is achieved. This enables timely detection and response to decreases in operating efficiency caused by power grid fluctuations. Operating equipment and transport vehicles no longer execute instructions in isolation but participate in dispatch decisions through real-time information interaction. This intelligent dispatching of flexible resources on both sides allows the port to more effectively cope with various uncertainties, reduce unnecessary waiting and resource waste, and achieve a synergistic solution to the problems of energy supply volatility and traffic flow imbalance, thereby improving overall operational efficiency and energy utilization efficiency.

[0037] Furthermore, by comprehensively considering the completion time information of the equipment update and the arrival time adjustment requests of the transport vehicles, more accurate and flexible transport vehicle adjustment instructions can be generated.

[0038] S3. Obtain the real-time updated task completion time information during the execution of the task by the operating equipment.

[0039] In an optional embodiment, combined with Figure 2 As shown, step S3 includes: S31. Collect instantaneous power waveform data at the output terminal of the hoisting motor frequency converter when the operating equipment performs the operation task; S32. Calculate the instantaneous input power of the hoisting motor based on the instantaneous power waveform data, and identify the harmonic components in the instantaneous power waveform data; S33. Determine the harmonic loss coefficient based on the harmonic components, and combine it with the instantaneous input electrical power to determine the actual mechanical output power of the hoisting motor; S34. Calculate the operation time compensation amount based on the deviation between the actual mechanical output power and the theoretical mechanical power required for the operation task. Based on the operation time compensation amount, correct the initial operation completion time information and use it as the updated operation completion time information.

[0040] In this embodiment, the instantaneous power waveform data includes instantaneous voltage waveform data and instantaneous current waveform data. By performing signal processing such as Fourier transform on the acquired instantaneous voltage and current waveforms, the instantaneous active power and reactive power of the motor are calculated, and the fundamental component and harmonic component are separated from them. The presence of harmonic components usually means a decrease in power quality and additional energy loss.

[0041] Furthermore, based on the amplitude and frequency of the identified harmonic components, and combined with the characteristic curves of the motor and inverter, the impact of harmonics on motor efficiency is quantified, thereby obtaining a harmonic loss coefficient. This coefficient is used to subtract the additional losses caused by harmonics from the instantaneous input power of the motor, in order to more accurately estimate the actual output power of the motor converted into mechanical energy.

[0042] Furthermore, a work time compensation amount is calculated based on the deviation between the actual mechanical output power and the theoretical mechanical power required for the task. This compensation amount is then used to correct the initial work completion time information, which is then used as the updated work completion time information. The purpose is to accurately calculate the work time deviation caused by insufficient or excessive power by comparing the actual mechanical power provided by the motor with the theoretically required mechanical power to complete the current task. For example, if the actual mechanical output power is lower than the theoretically required power, the work time needs to be increased to complete the task; conversely, the work time can be shortened. This deviation amount is the work time compensation amount, used to correct the initially estimated work completion time in real time, ensuring that the scheduling system always makes decisions based on the most accurate work progress information.

[0043] In this embodiment, by meticulously collecting and analyzing the instantaneous power waveform data of the hoisting motor, the dynamic energy consumption and power output of the motor during actual operation can be captured in real time and accurately. The impact of harmonic losses on the actual mechanical output power is also considered, making the assessment of the actual working efficiency of the equipment more precise. Identifying harmonic components based on instantaneous power waveform data allows for the quantification of additional losses caused by power quality issues, thereby more accurately calculating the actual mechanical output power of the hoisting motor. By comparing the actual mechanical output power with the theoretically required mechanical power, the amount of work time compensation can be accurately calculated, effectively compensating for the shortcomings of traditional methods in assessing the operating status of the equipment. This correction mechanism based on actual physical quantity measurements ensures the real-time nature and accuracy of work completion time information, thus providing more reliable data support for subsequent transportation vehicle scheduling.

[0044] In an optional embodiment, step S33 includes: The local control system of the operating equipment collects instantaneous acceleration data of the container spreader and calculates the instantaneous dynamic impact load of the container based on the instantaneous acceleration data. The instantaneous conversion efficiency of the frequency converter under the current dynamic operating conditions is evaluated based on the output frequency, output voltage and output current of the frequency converter. The harmonic loss calculated by the harmonic loss coefficient is subtracted from the instantaneous input power, and the inherent losses of the frequency converter and motor corrected according to the instantaneous dynamic impact load and the instantaneous conversion efficiency are then subtracted to obtain the actual mechanical output power of the hoisting motor.

[0045] In some embodiments, the instantaneous acceleration data of the container spreader can directly reflect the instantaneous dynamic impact load borne by the container during lifting or lowering. For example, when a container sways in the air or has a slight collision with a ship or storage yard, instantaneous acceleration changes occur. The resulting instantaneous dynamic impact load consumes additional energy and causes additional mechanical stress and power loss to the motor and inverter. Collecting this instantaneous acceleration data through the local control system of the operating equipment and calculating the instantaneous dynamic impact load of the container based on physical models or empirical formulas aims to quantify the additional energy consumption under these non-ideal operating conditions.

[0046] Furthermore, the energy conversion efficiency of a frequency converter fluctuates in real time with changes in operating parameters such as output frequency, output voltage, and output current, as well as load conditions. Therefore, by monitoring the output frequency, output voltage, and output current of the frequency converter in real time, the instantaneous conversion efficiency of the frequency converter under the current dynamic operating conditions can be evaluated to more accurately reflect the efficiency of the frequency converter in converting electrical energy into mechanical energy in actual operation, thereby accurately calculating the energy loss of the frequency converter itself.

[0047] In this embodiment, by considering the instantaneous dynamic impact load on the container during operation and the instantaneous conversion efficiency of the frequency converter under dynamic operating conditions, the correction for the inherent losses of the motor and frequency converter is made more precise and real-time. The evaluation of the instantaneous conversion efficiency of the frequency converter ensures that the actual energy loss of the frequency converter under different loads and operating conditions can be accurately calculated, rather than relying on average or rated efficiency. This makes the loss term deducted from the instantaneous input electrical power more accurate, resulting in a more accurate actual mechanical output power of the hoisting motor. This improves the calculation accuracy of the operation time compensation, making the updated operation completion time information more reliable.

[0048] In an optional embodiment, the evaluation of the inverter's instantaneous conversion efficiency under the current dynamic operating conditions based on the inverter's output frequency, output voltage, and output current includes: The local control system deploys multiple frequency, voltage and current sensors with electromagnetic shielding and signal filtering functions, and installs each sensor in a redundant configuration at the output terminal of the inverter to collect the inverter's output frequency, output voltage and output current data. Real-time consistency verification is performed on similar data from the same sensor. When the data deviation exceeds a preset threshold, it is marked as abnormal data. Time-series-based smoothing and trend forecasting are performed on data marked as outliers to obtain corrected data; Based on the corrected data and the inverter's operating parameters, the instantaneous conversion efficiency of the inverter under the current dynamic operating conditions is evaluated.

[0049] In some embodiments, electromagnetic shielding effectively blocks electromagnetic interference to sensor signals, while signal filtering removes high-frequency noise, ensuring the purity of the original data. Real-time consistency verification of similar data from the same sensor involves comparing the same type of data collected simultaneously by multiple redundantly configured sensors. When data deviation exceeds a preset threshold—for example, if the average reading of a sensor deviates by more than 5% from the average readings of other sensors—the data point is marked as abnormal. This ensures timely detection of sensor malfunctions, transient interference, or data transmission errors. Combined with corrected high-quality data and inverter operating parameters, the instantaneous conversion efficiency of the inverter under complex dynamic conditions can be more accurately assessed. This provides a solid foundation for calculating the actual mechanical output power of the hoisting motor, significantly improving the accuracy and robustness of the instantaneous conversion efficiency assessment of inverters in port operation equipment.

[0050] In some embodiments, the inverter operating parameters include, but are not limited to, the inverter's rated power, rated voltage, rated current, power factor, and its internal loss model.

[0051] Understandably, in practical applications, different types of anomalous data may be caused by different reasons and have different characteristics. For example, discrete spike anomalies caused by electromagnetic interference and persistent fluctuation anomalies caused by dynamic impact loads have significantly different data forms and generation mechanisms. If a single general smoothing and trend prediction method is used, it may not be able to effectively correct all types of anomalous data, resulting in insufficient accuracy of the corrected data, which in turn affects the accuracy of subsequent inverter instantaneous conversion efficiency assessments. Therefore, time-series-based smoothing and trend prediction are performed on the data marked as anomalous to obtain corrected data.

[0052] In an optional embodiment, the step of performing time-series-based smoothing and trend prediction on the data marked as anomalies to obtain corrected data includes: After receiving abnormal data, the local control system identifies the type of abnormality, which includes discrete spike anomalies caused by electromagnetic interference and continuous fluctuation anomalies caused by dynamic impact loads. Based on the anomaly type of the data, a corresponding anomaly correction strategy is matched to correct discrete spike anomaly data and persistent fluctuation anomaly data, and the corrected data is obtained.

[0053] Specifically, the local control system refers to the control unit integrated inside the operating equipment, which is responsible for real-time acquisition, processing, and analysis of data from various sensors. Discrete spike anomalies caused by electromagnetic interference typically manifest as short-duration, high-amplitude transient changes, with an extremely short duration and a significant amplitude difference from surrounding normal data points. These anomalies are often caused by external electromagnetic field interference, power transients, or sensor noise. On the other hand, persistent fluctuation anomalies caused by dynamic impact loads manifest as continuous, regular, or irregular fluctuations in data amplitude over a certain period of time, with a relatively long duration, and may be related to the mechanical impact or vibration experienced by the operating equipment during the operation, such as the impact generated when a container spreader grabs or releases a container. By matching correction strategies based on the anomaly type of the data, the accuracy and effectiveness of anomaly data correction are improved, avoiding the problem that a single correction method is not effective in processing all anomaly data. This ensures that the corrected data more accurately reflects the actual operating status of the frequency converter under the current dynamic operating conditions, thereby improving the accuracy of the calculation of the actual mechanical output power of the hoisting motor. This provides more accurate operation completion time information for the intelligent scheduling of flexible resources on both the energy and transportation sides of the port, thus optimizing the accuracy and response speed of the overall scheduling decision.

[0054] In an optional embodiment, the step of matching corresponding anomaly correction strategies based on the anomaly type of the data to correct discrete spike anomaly data and persistent fluctuation anomaly data, and obtaining the corrected data, includes: For the discrete spike anomaly data, smoothing is performed based on median filtering, and linear interpolation prediction is performed in combination with the normal data points adjacent to the discrete spike anomaly data to generate corrected data for the discrete spike anomaly data. For the persistently fluctuating abnormal data, a smoothing process is performed based on an adaptive sliding window to generate corrected data for the persistently fluctuating abnormal data; The corrected data is formed by integrating the corrected data of the discrete spike anomaly data and the corrected data of the persistent fluctuation anomaly data.

[0055] In some embodiments, discrete spike anomaly data are typically caused by transient electromagnetic interference. Therefore, median filtering can be used for smoothing to effectively remove these isolated spikes while preserving the original trend of the data as much as possible. Median filtering, by taking the median value within a window to replace the center point, has a good suppression effect on impulse noise. Furthermore, to improve the accuracy of the corrected data, linear interpolation prediction is performed using adjacent normal data points of the discrete spike anomaly data. Linear interpolation estimates the reasonable value at the anomaly point by constructing a straight line using known normal data points before and after the anomaly point, thereby generating smoother corrected data that conforms to the overall trend of the data.

[0056] In some embodiments, persistent fluctuation anomaly data refers to data exhibiting continuous, non-periodic fluctuations over a period of time, possibly caused by dynamic impact loads or system instability. This data can be smoothed using an adaptive sliding window. The advantage of an adaptive sliding window is that its window size can be dynamically adjusted based on the local characteristics of the data or a preset fluctuation threshold. For example, when a large fluctuation amplitude is detected, the window can be appropriately increased to enhance the smoothing effect; when the fluctuation is small, the window can be decreased to retain more details. This adaptability allows the smoothing process to better adapt to different degrees of persistent fluctuations, generating corrected data for persistent fluctuation anomaly data and avoiding over-smoothing or under-smoothing problems.

[0057] Furthermore, by integrating the corrected data from discrete spike anomaly data and the corrected data from persistent fluctuation anomaly data, we can ensure that the correction results of different types of anomalies can be seamlessly connected in the same time series, thus guaranteeing the integrity and consistency of the data series. This provides high-quality input for subsequent instantaneous conversion efficiency evaluation of frequency converters, thereby ensuring the accurate updating of work completion time information.

[0058] In an optional embodiment, the corrected data formed by fusing the discrete spike anomaly data and the corrected data of the persistent fluctuation anomaly data to form the final corrected data includes: Identify the temporal overlap between the corrected data of the discrete spike anomaly data and the corrected data of the persistent fluctuation anomaly data; Determine whether there is a logical conflict or inconsistent smoothness in the overlapping area. When there is a logical conflict or inconsistent smoothness in the overlapping area, use the corrected data with a time density greater than the preset time density threshold and an amplitude change rate consistent with the instantaneous acceleration change rate of the container spreader as the first target corrected data. The remaining corrected data is locally adjusted within the overlapping area to maintain a consistent trend and smooth amplitude transition with the first target corrected data, thereby obtaining the second target corrected data; The final corrected data is formed by integrating the corrected data of the first target, the corrected data of the second target, and the corrected data of various abnormal data in the non-overlapping areas.

[0059] In some embodiments, the two types of anomalous data and their corrected data may overlap in time. Simply fusing them may lead to logical conflicts or inconsistent smoothness within the overlapping area, affecting the accuracy and reliability of the final corrected data, and consequently, the precise assessment of the operating status of the equipment. After identifying the overlapping area, it is necessary to determine whether the data within that area exhibits logical conflicts or inconsistent smoothness. Logical conflicts can manifest as the two corrected data showing diametrically opposed trends or excessively large amplitude differences at the same point in time; inconsistent smoothness refers to a significant difference in the curve smoothness of the two corrected data within the overlapping area, for example, one data is very smooth while the other fluctuates wildly. When these problems are detected, further correction processing is required to ensure the quality of the final corrected data.

[0060] Specifically, time density refers to the number of data points per unit time. High time density usually means a higher data acquisition frequency and richer information. The consistency between the amplitude change rate and the instantaneous acceleration change rate of the container spreader indicates that the corrected data more accurately reflects the actual physical process, because the instantaneous acceleration of the container spreader is a key factor causing dynamic impact loads. Data selected through these conditions is considered more reliable and closer to reality, and therefore is used as the primary target correction data.

[0061] Furthermore, for data points not selected as the first target correction data, their trends and magnitudes within the overlapping region will be adjusted to align with the first target correction data, achieving a smooth transition, avoiding abrupt data changes, and obtaining the second target correction data. This local adjustment can be achieved using methods such as interpolation, weighted averaging, or curve fitting.

[0062] Furthermore, the correction data for non-overlapping regions can directly adopt their respective correction results, while the correction data for overlapping regions ensures logical consistency and smoothness through the synergistic effect of the first target correction data and the second target correction data.

[0063] In this embodiment, by identifying overlapping areas, judging data consistency, and prioritizing the selection of more reliable data as a benchmark based on data characteristics (such as time density and correlation with physical processes), other data are locally adjusted, thereby ensuring the overall quality of the final corrected data, improving the accuracy and reliability of the assessment of the operating status of the working equipment, providing more accurate input for scheduling, and thus optimizing the efficiency and safety of scheduling decisions.

[0064] In an optional embodiment, the step of locally adjusting the remaining corrected data within the overlapping region to maintain a consistent trend and smooth amplitude transition with the first target corrected data, and obtaining the second target corrected data, includes: A first weighting factor is determined based on the magnitude difference between the remaining corrected data and the first target corrected data within the overlapping region; The second weighting factor is determined based on the trend matching degree between the remaining corrected data and the first target corrected data within the overlapping area; Based on the first weighting factor and the second weighting factor, obtain the weighting factor for the weighted average; Based on the aforementioned weighting factors, a weighted average method is used to locally adjust the remaining corrected data to achieve a smooth transition in amplitude and consistent trend.

[0065] In some embodiments, when locally adjusting the remaining corrected data to obtain the second target corrected data, it is first necessary to quantify the difference between the remaining corrected data and the first target corrected data. The first weighting factor can be understood as the degree of importance attached to the amplitude matching degree. The greater the amplitude difference, the larger the first weighting factor may be, and vice versa. When determining the second weighting factor, the consistency of their dynamic changes is evaluated by analyzing the trend characteristics such as the direction, slope, or second derivative of the data changes in the overlapping area. Based on this, a second weighting factor reflecting the importance of trend matching is generated. The lower the trend matching degree, the larger the second weighting factor may be, and vice versa.

[0066] Furthermore, the weighted average weighting factor comprehensively reflects the influence of amplitude difference and trend matching degree on local adjustments. After obtaining the first weighting factor and the second weighting factor, a weighted average calculation is performed to obtain a comprehensive weighting factor. For example, different importance can be assigned to amplitude difference and trend matching degree according to the actual application scenario, thereby determining their respective weighting coefficients. Then, the first weighting factor and the second weighting factor are multiplied by their corresponding weighting coefficients, summed, and then divided by the sum of the weighting coefficients to obtain the final weighted average weighting factor.

[0067] In this embodiment, a quantification and adaptive mechanism is provided for the local adjustment of the remaining corrected data within the overlapping region through a first weighting factor and a second weighting factor. Specifically, the first weighting factor quantifies the amplitude difference between the remaining corrected data and the first target corrected data, enabling the adjustment to prioritize compensating for amplitude inconsistencies and ensuring that the corrected data is numerically closer to the target trend. Simultaneously, the second weighting factor assesses the trend matching degree between the two, guiding the adjustment process to maintain a smooth amplitude transition while ensuring that the trend of the corrected data remains consistent with the first target corrected data. By weighted averaging these two weighting factors, the matching requirements of both amplitude and trend dimensions can be comprehensively considered to generate a comprehensive weighting factor. Based on this weighting factor, a weighted averaging method is used to locally adjust the remaining corrected data, achieving more refined data correction and avoiding distortions that may result from simple interpolation or smoothing, thereby ensuring a smooth transition and trend consistency of the corrected data within the overlapping region.

[0068] S4. Obtain the arrival time adjustment request of the transport vehicle during the execution of the scheduling instruction.

[0069] In an optional embodiment, obtaining the arrival time adjustment request of the transport vehicle during the execution of the scheduling instruction includes: When the onboard system of the transport vehicle detects an interruption in the wireless communication link with the central dispatch system, the offline emergency decision engine built into the onboard system of the transport vehicle is activated. The offline emergency decision engine loads preset high-risk area map data, alternative route rules, safe waiting area coordinates, and priority decision trees for common emergencies in the port area. It also fuses data from the vehicle's millimeter-wave radar, forward-looking camera, inertial measurement unit, and wheel speed sensor to obtain local environmental information and the status of the transport vehicle itself. Based on the local environmental information and the priority decision tree, autonomous decision-making suggestions are generated, and the driver's actual operation, suggestion adoption results, and environmental change information are recorded according to the autonomous decision-making suggestions to form a local transaction log. When the onboard system of the transport vehicle detects that the wireless communication link with the central dispatch system has been restored to stability, it triggers the fast synchronization protocol and sends a summary of the local transaction log to the central dispatch system. The central dispatch system compares the summary information with the contingency instruction log generated by the transport vehicle during the communication interruption, identifies the differences, and obtains the arrival time adjustment request based on the differences.

[0070] S5. Generate adjustment instructions for transport vehicles based on the updated job completion time information and arrival time adjustment requests.

[0071] In this embodiment, the on-board system of the transport vehicle refers to the intelligent control unit installed on the transport vehicle. It integrates a communication module, a computing processing unit, sensor interfaces, and a storage module, enabling real-time monitoring of vehicle status and environmental information, and interaction with the central dispatch system. When the on-board system detects an interruption in the wireless communication link with the central dispatch system—for example, by determining a communication interruption through a persistent signal strength below a preset threshold, a high data packet loss rate, or a heartbeat timeout—it immediately activates its built-in offline emergency decision-making engine. The offline emergency decision-making engine is a pre-configured software module used to take over some dispatch decision-making functions during communication interruptions, ensuring that the vehicle can still operate safely and efficiently without central commands.

[0072] In some embodiments, once the offline emergency decision engine is activated, it immediately loads a series of preset data, including map data of high-risk areas in the port area to identify potentially dangerous areas; alternative route rules to provide alternative solutions when the original route is blocked; coordinates of safe waiting areas to guide vehicles to safe locations when they cannot continue driving; and priority decision trees for common emergencies to guide the vehicle's decision priorities in different emergency situations.

[0073] In some examples, the offline emergency decision engine generates a series of autonomous decision-making suggestions based on the fused local environmental information and the transport vehicle's own status, combined with a pre-defined priority decision tree. For instance, when road congestion and communication interruption are detected ahead, the decision tree may suggest the vehicle choose a pre-defined alternative route; or when the vehicle is about to enter a high-risk area and cannot obtain central instructions, it may suggest the vehicle move to the nearest safe waiting area. These autonomous decision-making suggestions, along with the driver's actual actions, the driver's adoption of the suggestions, and environmental change information, are recorded to form a local transaction log. The log details all key events and decision-making processes of the vehicle during the communication interruption, providing a basis for subsequent system synchronization and problem tracing.

[0074] In some embodiments, a fast synchronization protocol is used to quickly send summary information of local transaction logs during communication interruptions to the central dispatch system. The contingency plan instruction log is a potential dispatch instruction or emergency plan generated by the central dispatch system for the transport vehicle during the communication interruption, based on the expected behavior of the transport vehicle and preset rules. Upon receiving the summary information, the central dispatch system compares it with the contingency plan instruction log generated by the transport vehicle during the communication interruption to identify discrepancies—specifically, deviations between the actual behavior of the transport vehicle during the offline period and the contingency plan instructions expected by the central system. Based on these discrepancies, the central dispatch system can accurately obtain the transport vehicle's arrival time adjustment requests. For example, if the vehicle changes its route or waits longer due to autonomous decision-making, these discrepancies will directly reflect as arrival time adjustment needs.

[0075] In this embodiment, by embedding an offline emergency decision engine into the onboard system of the transport vehicle, the problem of traditional scheduling methods being unable to obtain the arrival time adjustment request of the transport vehicle when the wireless communication link is interrupted is solved. When communication is interrupted, the application of the offline emergency decision engine enables the transport vehicle to generate autonomous decision suggestions based on the actual situation even without central instructions. When communication is restored, the arrival time adjustment request is accurately obtained based on log comparison differences. This mechanism of combining offline decision-making with online synchronization enables the scheduling system to maintain effective perception of the transport vehicle status and scheduling adjustment capabilities even when facing communication instability. Even in the harsh environment of wireless communication link interruption, the transport vehicle can make autonomous decisions through the onboard offline emergency decision engine, avoiding scheduling blind spots and decision stagnation caused by communication interruption. This not only ensures the safe operation of transport vehicles in complex port environments and reduces potential accident risks, but also, by recording local transaction logs and performing rapid synchronization, enables the central scheduling system to obtain the real status and adjustment needs of the vehicle during offline periods in a timely and accurate manner, thereby generating more reasonable adjustment instructions based on more comprehensive information.

[0076] Based on the same inventive concept, this application also provides a port energy-transportation dual-side flexible resource intelligent scheduling system corresponding to a port energy-transportation dual-side flexible resource intelligent scheduling method, such as... Figure 3 As shown, it includes: The task sending module is used to send task information to the working equipment to start the working equipment to execute a work task; The dispatch instruction generation module is used to obtain the operation completion time information of the operating equipment based on its real-time perception and evaluation of its own operating status, and to generate dispatch instructions for the transport vehicles based on the operation completion time information. The dispatch instructions include an arrival time range. The job completion update module is used to obtain the job completion time information updated in real time during the execution of the job by the working equipment; The arrival adjustment request module is used to obtain the arrival time adjustment request of the transport vehicle during the execution of the scheduling instruction; The transport instruction adjustment module is used to generate adjustment instructions for transport vehicles based on the updated job completion time information and arrival time adjustment requests.

[0077] In this embodiment, the task sending module serves as the starting point of the scheduling process, ensuring that tasks are accurately distributed to the operating equipment, thus initiating the entire scheduling cycle. Subsequently, the scheduling instruction generation module, through sensing and evaluating the real-time operating status of the operating equipment, transforms abstract task completion time information into specific transport vehicle scheduling instructions, achieving initial collaboration between the operation side and the transportation side. During task execution, the task completion update module continuously acquires and corrects task completion time information, enabling the system to dynamically monitor task progress and provide real-time basis for subsequent scheduling adjustments. Simultaneously, the adjustment request module can promptly capture uncertainties encountered by transport vehicles during the execution of scheduling instructions, feeding back dynamic demands from the vehicle side to the central scheduling system. Finally, the transport instruction adjustment module merges the updated task completion information with the transport vehicle's adjustment request to generate a new adjustment instruction, thereby achieving flexible and real-time scheduling of port energy and transportation resources, ensuring the smoothness and efficiency of the entire port operation process.

[0078] The above-described embodiments are preferred embodiments of this application and are not intended to limit the specific scope of this application. The scope of this application includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape, structure, and method of this application are within the protection scope of this application.

Claims

1. A port energy-transportation dual-side flexible resource intelligent scheduling method, characterized in that, Includes the following steps: Send task information to the working equipment to start the working equipment to perform a task; The system acquires the operation completion time information of the operating equipment based on its real-time perception and evaluation of its own operating status, and generates a dispatch instruction for the transport vehicle based on the operation completion time information. The dispatch instruction includes an arrival time range. Obtain real-time updated task completion time information during the execution of tasks by the operating equipment; Obtain the arrival time adjustment request of the transport vehicle during the execution of the dispatch instruction; Based on the updated job completion time information and arrival time adjustment request, generate adjustment instructions for the transport vehicles.

2. The intelligent scheduling method for flexible resources on both the energy and transportation sides of a port, as described in claim 1, is characterized in that... The acquisition of real-time updated job completion time information during the execution of a job by the operating equipment includes: Collect instantaneous power waveform data at the output of the hoisting motor frequency converter when the operating equipment performs the operation task; The instantaneous input power of the hoisting motor is calculated based on the instantaneous power waveform data, and the harmonic components in the instantaneous power waveform data are identified. The harmonic loss coefficient is determined based on the harmonic components, and then combined with the actual mechanical output power of the hoisting motor is determined based on the instantaneous input electrical power. The operation time compensation is calculated based on the deviation between the actual mechanical output power and the theoretical mechanical power required for the task. The initial operation completion time information is then corrected based on the operation time compensation and used as the updated operation completion time information.

3. The intelligent scheduling method for flexible resources on both the energy and transportation sides of a port, as described in claim 2, is characterized in that... The process of determining the harmonic loss coefficient based on harmonic components, and combining this with determining the actual mechanical output power of the hoisting motor based on the instantaneous input electrical power, includes: The local control system of the operating equipment collects instantaneous acceleration data of the container spreader and calculates the instantaneous dynamic impact load of the container based on the instantaneous acceleration data. The instantaneous conversion efficiency of the frequency converter under the current dynamic operating conditions is evaluated based on the output frequency, output voltage and output current of the frequency converter. The harmonic loss calculated by the harmonic loss coefficient is subtracted from the instantaneous input power, and the inherent losses of the frequency converter and motor corrected according to the instantaneous dynamic impact load and the instantaneous conversion efficiency are then subtracted to obtain the actual mechanical output power of the hoisting motor.

4. The intelligent scheduling method for flexible resources on both the energy and transportation sides of a port, as described in claim 3, is characterized in that... The evaluation of the instantaneous conversion efficiency of the frequency converter under the current dynamic operating conditions based on the output frequency, output voltage, and output current of the frequency converter includes: The local control system deploys multiple frequency, voltage and current sensors with electromagnetic shielding and signal filtering functions, and installs each sensor in a redundant configuration at the output terminal of the inverter to collect the inverter's output frequency, output voltage and output current data. Real-time consistency verification is performed on similar data from the same sensor. When the data deviation exceeds a preset threshold, it is marked as abnormal data. Time-series-based smoothing and trend forecasting are performed on data marked as outliers to obtain corrected data; Based on the corrected data and the inverter's operating parameters, the instantaneous conversion efficiency of the inverter under the current dynamic operating conditions is evaluated.

5. The intelligent scheduling method for flexible resources on both the energy and transportation sides of a port, as described in claim 4, is characterized in that... The step of performing time-series-based smoothing and trend prediction on the data marked as anomalies to obtain corrected data includes: After receiving abnormal data, the local control system identifies the type of abnormality, which includes discrete spike anomalies caused by electromagnetic interference and continuous fluctuation anomalies caused by dynamic impact loads. Based on the anomaly type of the data, a corresponding anomaly correction strategy is matched to correct discrete spike anomaly data and persistent fluctuation anomaly data, and the corrected data is obtained.

6. The intelligent scheduling method for flexible resources on both the energy and transportation sides of a port, as described in claim 5, is characterized in that... The step of matching corresponding anomaly correction strategies based on the anomaly type of the data to correct discrete spike anomaly data and persistent fluctuation anomaly data, and obtaining corrected data, includes: For the discrete spike anomaly data, smoothing is performed based on median filtering, and linear interpolation prediction is performed in combination with the normal data points adjacent to the discrete spike anomaly data to generate corrected data for the discrete spike anomaly data. For the persistently fluctuating abnormal data, a smoothing process is performed based on an adaptive sliding window to generate corrected data for the persistently fluctuating abnormal data; The corrected data is formed by integrating the corrected data of the discrete spike anomaly data and the corrected data of the persistent fluctuation anomaly data.

7. The intelligent scheduling method for flexible resources on both the energy and transportation sides of a port, as described in claim 6, is characterized in that... The corrected data, formed by fusing the discrete spike anomaly data and the corrected data of the persistent fluctuation anomaly data, constitutes the final corrected data, including: Identify the temporal overlap between the corrected data of the discrete spike anomaly data and the corrected data of the persistent fluctuation anomaly data; Determine whether there is a logical conflict or inconsistent smoothness in the overlapping area. When there is a logical conflict or inconsistent smoothness in the overlapping area, use the corrected data with a time density greater than the preset time density threshold and an amplitude change rate consistent with the instantaneous acceleration change rate of the container spreader as the first target corrected data. The remaining corrected data are locally adjusted within the overlapping area to maintain a consistent trend and smooth amplitude transition with the first target corrected data, thereby obtaining the second target corrected data; The final corrected data is formed by integrating the corrected data of the first target, the corrected data of the second target, and the corrected data of various abnormal data in the non-overlapping areas.

8. The intelligent scheduling method for flexible resources on both the energy and transportation sides of a port, as described in claim 7, is characterized in that... The step of performing local adjustments on the remaining corrected data within the overlapping region to maintain a consistent trend and smooth amplitude transition with the first target corrected data, and obtaining the second target corrected data, includes: A first weighting factor is determined based on the magnitude difference between the remaining corrected data and the first target corrected data within the overlapping region; The second weighting factor is determined based on the trend matching degree between the remaining corrected data and the first target corrected data within the overlapping area; Based on the first weighting factor and the second weighting factor, obtain the weighting factor for the weighted average; Based on the aforementioned weighting factors, a weighted average method is used to locally adjust the remaining corrected data to achieve a smooth transition in amplitude and consistent trend.

9. The intelligent scheduling method for flexible resources on both the energy and transportation sides of a port, as described in claim 1, is characterized in that... The step of obtaining the arrival time adjustment request of the transport vehicle during the execution of the dispatch instruction includes: When the onboard system of the transport vehicle detects an interruption in the wireless communication link with the central dispatch system, the offline emergency decision engine built into the onboard system of the transport vehicle is activated. The offline emergency decision engine loads preset high-risk area map data, alternative route rules, safe waiting area coordinates, and priority decision trees for common emergencies in the port area. It also fuses data from the vehicle's millimeter-wave radar, forward-looking camera, inertial measurement unit, and wheel speed sensor to obtain local environmental information and the status of the transport vehicle itself. Based on the local environmental information and the priority decision tree, autonomous decision-making suggestions are generated, and the driver's actual operation, suggestion adoption results, and environmental change information are recorded according to the autonomous decision-making suggestions to form a local transaction log. When the onboard system of the transport vehicle detects that the wireless communication link with the central dispatch system has been restored to stability, it triggers the fast synchronization protocol and sends a summary of the local transaction log to the central dispatch system. The central dispatch system compares the summary information with the contingency instruction log generated by the transport vehicle during the communication interruption, identifies the differences, and obtains the arrival time adjustment request based on the differences.

10. A port energy-transportation dual-side flexible resource intelligent scheduling system, applicable to the port energy-transportation dual-side flexible resource intelligent scheduling method as described in any one of claims 1-9, characterized in that, include: The task sending module is used to send task information to the working equipment to start the working equipment to execute a work task; The dispatch instruction generation module is used to obtain the operation completion time information of the operating equipment based on its real-time perception and evaluation of its own operating status, and to generate dispatch instructions for the transport vehicles based on the operation completion time information. The dispatch instructions include an arrival time range. The job completion update module is used to obtain the job completion time information updated in real time during the execution of the job by the working equipment; The arrival adjustment request module is used to obtain the arrival time adjustment request of the transport vehicle during the execution of the scheduling instruction; The transport instruction adjustment module is used to generate adjustment instructions for transport vehicles based on the updated job completion time information and arrival time adjustment requests.

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