Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides an intelligent wharf horizontal transportation scheduling method.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the intelligent wharf horizontal transportation scheduling method comprises the following steps:
s1: collecting GPS coordinates and speed information of a wharf transport vehicle and a container through a sensor, synchronously acquiring temperature, humidity and cargo conditions in the container, screening key environmental factors and time nodes, and obtaining an environmental and time sensitivity analysis result;
S2: based on the environmental and time sensitivity analysis results, the loading and unloading priority of each transportation task is adjusted by combining the weather conditions and traffic flow of the current wharf, the transportation path is adjusted according to the loading and unloading priority adjustment results, the loading and unloading time is redistributed, and the real-time operation environment of the wharf is matched, so that a wharf dispatching route and a time optimization scheme are formed;
s3: according to the dock dispatching route and the time optimization scheme, vehicle allocation and a job schedule in the dock are adjusted, the job plan is dynamically adjusted in combination with real-time feedback, the actual demand of each job task is analyzed and confirmed according to the dynamic adjustment result of the job plan, and the operation condition is subjected to targeted optimization adjustment to generate a dispatching execution plan;
S4: and executing the dispatching execution plan, continuously monitoring the state changes of the container and the transport vehicle through the sensor, dynamically adjusting and optimizing the operation plan according to the real-time acquired data, and generating a continuously optimized wharf transport dispatching scheme.
As a further aspect of the present invention, the environmental and time sensitivity analysis results are obtained by the steps of,
Capturing GPS coordinates and speed information through sensors installed on a wharf transport vehicle and a container, monitoring the temperature, humidity and cargo condition inside the container, recording data and a time stamp, and generating original environment data and a time tag data set;
Analyzing the original environment data and the time tag data set, screening key environment factors, and marking key time nodes to obtain the key environment factors and the time node data set;
comprehensively analyzing the key environmental factors and the time node data set, and adopting the formula:
；
computing an environmental sensitivity score for each time nodeObtaining environmental and time sensitivity analysis results, wherein,Representing the actual environmental parameter measurement values,Indicating the exposure time of the cargo to the target environmental conditions,AndThe environmental sensitivity adjustment coefficient, the exponential growth rate of the environmental factor and the impact weight of the time factor,Is the total data points analyzed.
As a further aspect of the present invention, the step of adjusting the loading and unloading priority of the transportation task is,
Collecting the environmental and time sensitivity analysis results, and analyzing the weather adaptability of each task by combining the weather data of the current wharf to generate a task preliminary influence assessment result;
Based on the task preliminary influence assessment result, integrating current dock real-time traffic flow data, assessing the direct influence of vehicle blockage and cargo loading and unloading efficiency on transportation tasks, and analyzing the traffic adaptability of each task to obtain comprehensive environment and traffic influence results;
According to the comprehensive environment and the traffic influence result, adopting the formula:
；
Computing a comprehensive priority score for a target taskGenerating an adjusted loading and unloading priority result, wherein,A weather fitness score representing the task,A traffic suitability score representing the mission,AndIt is the weight that is to be adjusted,Is a normalized coefficient.
As a further aspect of the present invention, the dock dispatch route and time optimization scheme obtaining step is,
Analyzing the adjusted loading and unloading priority result, identifying bottleneck and efficiency problems in the transportation path, evaluating the matching degree of the predicted loading and unloading time and the actual demand by combining the congestion condition and the availability of the current transportation path, and carrying out transportation path adjustment to obtain a transportation path adjustment record;
redistributing the loading and unloading time according to the adjusted loading and unloading priority result, optimizing task time distribution, balancing resource utilization and avoiding task conflicts, and obtaining loading and unloading time adjustment records;
And according to the loading and unloading time adjustment record and the transportation path adjustment record, matching with the real-time operation environment of the wharf, and outputting a wharf dispatching route and a time optimization scheme by combining weather conditions, staff shifts and equipment availability factors.
As a further aspect of the present invention, the dynamic adjustment step of the operation plan is,
Acquiring current vehicle use data and operation progress based on the wharf scheduling route and the time optimization scheme, analyzing all data, analyzing vehicle use conditions, constructing an operation schedule real-time view, and generating a real-time operation state record;
and utilizing the real-time operation state record to adopt the formula:
；
calculating the current optimal allocation index of the target vehicleRe-optimizing the vehicle allocation and the job schedule, generating an adjusted vehicle allocation plan, wherein,Is a vehicleIs used in the service life of the vehicle,Is a vehicleThe overall priority score of the current task,Is a vehicleIs used for the operation of the device,Is a vehicleIs used for the time period of the scheduled operation,Is a vehicleA predetermined travel distance;
According to the adjusted vehicle distribution scheme, the real-time feedback of the wharf operation site is combined, the operation execution condition is monitored, the operation plan is subjected to necessary dynamic adjustment, and the dynamic operation adjustment plan is generated in response to an emergency or delay.
As a further aspect of the present invention, the step of obtaining the schedule execution plan includes,
According to the dynamic job adjustment plan, integrating the completion state of each task and the code head site feedback information, evaluating the actual demand and resource matching condition of the task, determining the resource utilization effectiveness and adjustment demand, and generating a job demand analysis record;
utilizing the operation demand analysis record to identify key operation conditions including equipment performance limitation, working environment difficulty and personnel configuration problems, optimizing resource configuration and generating an operation condition optimization scheme;
And implementing the operation condition optimization scheme, monitoring the adjustment effect in real time, confirming that each operation accurately reflects the current operation requirement and environment change, and generating a dispatching execution plan.
As a further aspect of the invention, the continuously optimized dock transportation scheduling scheme is obtained by the steps of,
Executing the dispatching execution plan, and collecting key transportation data in real time by using a deployment sensor, transmitting the key transportation data to a central data processing center in real time, and generating a real-time data stream;
according to the real-time data flow, performing data cleaning and preprocessing, analyzing data to identify abnormal modes and predicting potential transportation risks, and generating an optimized transportation scheduling decision suggestion;
And updating and adjusting the transportation scheduling plan of the wharf according to the optimized transportation scheduling decision proposal, and integrating the current operation condition and the prediction result, and automatically adjusting the transportation path and the loading and unloading time to generate a continuously optimized wharf transportation scheduling scheme.
An intelligent dock horizontal transport scheduling system, comprising:
the sensor information acquisition module captures GPS coordinates and speed information of the transport vehicle and the container through the sensor, monitors the internal condition of the container, screens key environmental factors, marks key time nodes, calculates an environmental sensitivity score corresponding to each time node, and obtains an environmental and time sensitivity analysis result;
The adaptability analysis module collects the environmental and time sensitivity analysis results, combines the weather data of the current wharf, analyzes the weather adaptability of each task, combines the current wharf real-time traffic flow data, analyzes the traffic adaptability of each task, calculates the comprehensive priority score of the target task, and generates an adjusted loading and unloading priority result;
The path and time optimization module analyzes the adjusted loading and unloading priority result, identifies bottleneck and efficiency problems in the transportation path, adjusts the transportation path, optimizes task time allocation, matches with the real-time operation environment of the wharf, and outputs a wharf dispatching route and a time optimization scheme;
The vehicle dispatching optimization module analyzes the service condition of the vehicle based on the wharf dispatching route and the time optimization scheme, calculates the current optimizing distribution index of the target vehicle, re-optimizes the vehicle distribution and the operation schedule, and combines the real-time feedback of the wharf operation site to generate a dynamic operation adjustment plan;
The resource allocation optimization module evaluates the actual requirements and resource matching conditions of tasks according to the dynamic job adjustment plan, identifies key operation conditions, optimizes resource allocation, confirms that each operation accurately reflects the current job requirements and environment changes, and generates a dispatching execution plan;
And the transportation risk management module executes the dispatching execution plan, collects key transportation data in real time, analyzes the data, identifies abnormal modes and predicts potential transportation risks, updates and adjusts the transportation dispatching plan of the wharf, and generates a continuously optimized wharf transportation dispatching scheme.
Compared with the prior art, the invention has the advantages and positive effects that:
According to the invention, the fine management of the dock transportation task is realized through comprehensive real-time monitoring data and environmental sensitivity analysis, the continuously-changing weather conditions and traffic flow are effectively adapted through dynamic adjustment of the transportation path and the loading and unloading time, the resource allocation is optimized, the invalid waiting and interruption in the transportation process are reduced, the operation cost is directly reduced, the transportation efficiency is improved, the continuity and adaptability of the operation plan are ensured through continuous state monitoring and data analysis, the speed and the safety of cargo handling are improved through accurate adjustment response to real-time change, and the cargo throughput of the dock is remarkably increased.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Referring to fig. 1, the intelligent dock horizontal transportation scheduling method includes the following steps:
s1: collecting GPS coordinates and speed information of a wharf transport vehicle and a container through a sensor, synchronously acquiring temperature, humidity and cargo conditions in the container, screening key environmental factors and time nodes, and obtaining an environmental and time sensitivity analysis result;
S2: based on the analysis results of environment and time sensitivity, the loading and unloading priority of each transportation task is adjusted by combining the weather conditions and traffic flow of the current wharf, the transportation path is adjusted according to the adjustment results of the loading and unloading priorities, the loading and unloading time is redistributed, and the real-time operation environment of the wharf is matched, so that a wharf dispatching route and a time optimization scheme are formed;
s3: according to the wharf dispatching route and the time optimization scheme, vehicle allocation and operation time schedule in the wharf are adjusted, the operation plan is dynamically adjusted by combining real-time feedback, the actual demand of each operation task is analyzed and confirmed according to the operation plan dynamic adjustment result, and the operation condition is subjected to targeted optimization adjustment to generate a dispatching execution plan;
S4: executing the dispatching execution plan, continuously monitoring the state changes of the container and the transport vehicle through the sensor, dynamically adjusting and optimizing the operation plan according to the real-time collected data, and generating a continuously optimized wharf transport dispatching scheme.
The environment and time sensitivity analysis results comprise environment factor screening results, time node determination records and sensitivity measurement results, the dock dispatching route and time optimization scheme comprises weather condition adaptation analysis results, traffic flow adjustment records and task loading and unloading priority adjustment records, the dispatching execution plan comprises vehicle allocation results, operation time tables and operation condition optimization records, and the continuously optimized dock transportation dispatching scheme comprises operation plan dynamic adjustment results, operation environment matching records, dispatching continuity and accuracy confirmation records.
Referring to fig. 2, the environmental and time sensitivity analysis results are obtained by the steps of,
S111: capturing GPS coordinates and speed information through sensors installed on a wharf transport vehicle and a container, monitoring the temperature, humidity and cargo condition inside the container, recording data and a time stamp, and generating original environment data and a time tag data set;
The GPS coordinate and speed information are captured through the sensors arranged on the wharf transport vehicle and the container, meanwhile, the temperature, the humidity and the cargo condition inside the container are monitored, data and time stamps are recorded, the sensors are accurately calibrated in the process, the real-time performance and the accuracy of the data are guaranteed, the sensor is selected to consider the environmental adaptability and the failure rate, the sensor can work stably under various climatic conditions, the GPS data are used for tracking the real-time positions of the vehicle and the container, the speed information is used for helping to monitor the dynamics in the transport process, the temperature and the humidity are monitored to ensure that the cargo is transported in a proper environment, the cargo damage caused by environmental changes is avoided, the data and the time stamps are recorded to provide basic time clues for analysis, and accordingly, the original environmental data and the time tag data set can be related to specific events or state changes in subsequent processing.
S112: analyzing the original environment data and the time tag data set, screening key environment factors, and marking key time nodes to obtain the key environment factors and the time node data set;
Analyzing original environment data and a time tag data set, cleaning and removing wrong readings by screening key environment factors such as temperature extreme values, humidity fluctuation and speed change, adopting data normalization processing to ensure data comparability among different sensors, and matching a log file with a time stamp to ensure that each time node can accurately reflect the corresponding environment state, wherein in the process, the accurate matching of the integrity and time of the data is particularly noted so as to avoid the problem of time dislocation in subsequent analysis, and obtaining the key environment factors and the time node data set.
S113: comprehensively analyzing the key environmental factors and the time node data set, adopting a formula,
；
Computing an environmental sensitivity score for each time nodeObtaining environmental and time sensitivity analysis results, wherein,Representing the actual environmental parameter measurement values,Indicating the exposure time of the cargo to the target environmental conditions,AndThe environmental sensitivity adjustment coefficient, the exponential growth rate of the environmental factor and the impact weight of the time factor,Is the total data points analyzed.
Three data points (in degrees Celsius, as an example, temperature) were obtained, wherein
，，，(Degrees celsius),(Minutes) the calculation procedure is as follows:
；
the results indicate that the sensitivity score for the current environment (temperature) versus time sensitivity analysis isIndicating the average sensitivity level faced by the cargo at a given time and temperature, helps determine whether special protective measures or adjustments to the transportation plan are needed.
Referring to fig. 3, the adjustment of the loading and unloading priorities of the transportation tasks is performed by,
S211: collecting environmental and time sensitivity analysis results, and analyzing weather adaptability of each task by combining weather data of a current wharf to generate a task preliminary influence assessment result;
Firstly, the environmental and time sensitivity analysis results provide key environmental factor data such as temperature, humidity and time variation, the weather conditions of the current wharf such as temperature fluctuation and rainfall condition are combined, the possible environmental risk of each task is analyzed according to the environmental sensitivity data and the real-time weather data, the real-time measured temperature and humidity are compared with the historical contemporaneous data, the variation range under the current condition is estimated, the weather adaptability score (weather adaptability score) of the task influence is allocated according to the variation, the task preliminary influence assessment result is generated, and the subsequent priority adjustment decision is directly influenced.
S212: based on the task preliminary influence assessment result, integrating current dock real-time traffic flow data, assessing the direct influence of vehicle blockage and cargo loading and unloading efficiency on transportation tasks, and analyzing the traffic adaptability of each task to obtain comprehensive environment and traffic influence results;
Taking real-time traffic flow data of a current wharf into consideration, evaluating how factors such as vehicle blockage, loading and unloading efficiency and the like affect a transportation task, analyzing possible delay and conflict of each task by combining traffic data and task scheduling conditions, evaluating negative influences of traffic conditions on task execution, such as delay time of a peak period of the traffic flow, distributing scores (traffic adaptability scores) on traffic adaptability of the task influence, and integrating with a task primary influence evaluation result, so that the execution priority of the task under the current condition is conveniently judged, and optimal allocation of resources and maximization of transportation efficiency are ensured.
S213: according to the comprehensive environment and the traffic influence result, adopting a formula,
Computing a comprehensive priority score for a target taskGenerating an adjusted loading and unloading priority result, wherein,A weather fitness score representing the task, representing the task's fitness ability under the current weather conditions,A traffic suitability score representing the mission, representing the mission's suitability under the current traffic flow conditions,AndIs to adjust weights for enhancing sensitivity to differing task environmental adaptations,Is a normalized coefficient;
A certain task under the target condition
The calculation process is as follows:
；
；
The results indicate that the overall priority score for a given task is 11.34486, the score will be used to determine the loading and unloading priorities of the task, high meaning higher priority, priority handling.
Referring to fig. 4, the dock dispatch route and time optimization scheme is obtained by the steps of,
S221: analyzing the adjusted loading and unloading priority result, identifying bottleneck and efficiency problems in the transportation path, evaluating the matching degree of the predicted loading and unloading time and the actual demand by combining the congestion condition and the availability of the current transportation path, and carrying out transportation path adjustment to obtain a transportation path adjustment record;
The method comprises the steps of deeply analyzing the adjusted loading and unloading priority results, evaluating congestion points and low-efficiency areas in the existing transportation route in detail through real-time traffic management and transportation scheduling data, particularly focusing on key nodes influenced by priority adjustment, identifying bottlenecks possibly causing delay, such as overcrowding of loading and unloading sites or insufficient loading and unloading equipment, collecting and analyzing actual operation data of each loading and unloading point, comparing historical synchronous data to identify and optimize potential problems of the points, and formulating specific path adjustment suggestions for improving the overall efficiency and response speed of a transportation chain through optimizing paths.
S222: redistributing the loading and unloading time according to the adjusted loading and unloading priority result, optimizing task time distribution, balancing resource utilization and avoiding task conflicts, and obtaining loading and unloading time adjustment records;
According to the adjusted loading and unloading priority results, loading and unloading time is redistributed, loading and unloading time tables of each task are finely arranged, time distribution is dynamically adjusted according to the real-time monitored wharf operation conditions and traffic flow data, waiting time is reduced, resource conflict is avoided, high-priority tasks can be executed preferentially, reasonable arrangement of low-priority tasks is considered, optimal solutions are selected through simulation of different scheduling schemes to achieve maximum utilization of resources and minimization of operation time, and each task is guaranteed to be processed at the best opportunity, so that overall efficiency of wharf operation is remarkably improved.
S223: according to the loading and unloading time adjustment record and the transportation path adjustment record, matching with the real-time operation environment of the wharf, and outputting a wharf dispatching route and a time optimization scheme by combining weather conditions, staff shifts and equipment availability factors;
Matching the adjusted transportation path and the redistributed loading and unloading time with the real-time operation environment of the wharf, integrating the data acquired from weather forecast service and human resource management and the input of a wharf equipment monitoring system by taking the factors of weather change, human resource configuration, equipment state and the like into consideration, forming a comprehensive scheduling and time optimizing scheme, fine tuning the scheme according to the actual operation environment, ensuring that high-efficiency and flexible response can be maintained under various conditions, and the optimizing scheme is based on data-driven decision support and aims at improving the adaptability and efficiency of wharf operation and reducing any possible operation interruption or delay.
Referring to fig. 5, the dynamic adjustment of the operation plan is performed by,
S311: based on a wharf dispatching route and a time optimization scheme, acquiring current vehicle use data and operation progress, analyzing all data, analyzing vehicle use conditions, constructing an operation schedule real-time view, and generating a real-time operation state record;
Based on the existing dock dispatching route and time optimization scheme, logistics management is utilized to carry out data grabbing, the current state and the operation progress of vehicles are monitored in a centralized mode, the specific utilization rate and the task completion condition of each vehicle are analyzed, the operation time table is updated in real time to reflect the actual operation condition, deviation and possible adjustment points in execution are identified through comparison of real-time data and a preset plan, a detailed real-time operation state record is generated, real-time dynamics of dock transportation is displayed in detail, and decision support is provided for adjusting vehicle allocation and operation time.
S312: using real-time job status records, using formulas,
；
Calculating the current optimal allocation index of the target vehicleRe-optimizing the vehicle allocation and the job schedule, generating an adjusted vehicle allocation plan, wherein,Is a vehicleAnd (c) availability weights, representing the vehicle's ability to be used for the current task,Is a vehicleThe overall priority score of the current task,Is a vehicleIs used for the operation of the device,Is a vehicleIs used for the time period of the scheduled operation,Is a vehicleA predetermined travel distance;
for the target vehicle, the following parameters are collected: indicating that the availability of the vehicle is high,Indicating that the task priority is medium,Indicating that the running efficiency of the vehicle is high,The time of the operation is preset in an hour,Kilometers are predetermined travel distances. Substituting the formula, the calculation process is as follows:
；
The results indicate that the optimal allocation index for a given vehicle under the current parameters is 0.321, which is used to evaluate the suitability and priority of the vehicle for a particular mission and condition, assisting in making more accurate vehicle allocation decisions.
S313: according to the adjusted vehicle distribution scheme, the real-time feedback of the wharf operation site is combined, the operation execution condition is monitored, the operation plan is subjected to necessary dynamic adjustment, and a dynamic operation adjustment plan is generated in response to an emergency or delay;
After the adjusted vehicle allocation and operation schedule is obtained, the actual execution condition of the wharf operation is continuously monitored, the operation schedule is dynamically adjusted to adapt to sudden changes such as weather influence, equipment failure or addition of emergency tasks by combining with real-time feedback of field staff and equipment, the vehicle allocation and the time schedule are re-optimized, each adjustment is ensured to be based on the latest operation information, a comprehensively updated dynamic operation adjustment schedule is formed, the wharf operation efficiency is continuously optimized, any delay or cost increase caused by unforeseen events is reduced, and the wharf transportation smoothness and efficiency are ensured.
Referring to fig. 6, the step of acquiring the schedule execution plan is,
S321: according to the dynamic job adjustment plan, integrating the completion state of each task and the code head field feedback information, evaluating the actual demand and resource matching condition of the task, determining the resource utilization effectiveness and adjustment demand, and generating a job demand analysis record;
Firstly, real-time data about the use state of vehicles and the progress of tasks are automatically extracted from a comprehensive management system of a wharf, the data comprise the positions of the vehicles, the cargo carrying capacity and the operation efficiency of the vehicles, the use frequency of each vehicle and the time standard for completing the tasks are recorded in detail, the efficiency of different vehicles and the real-time change of task demands are evaluated, the links of insufficient and overload in resource allocation are helped to be identified, the update of a work schedule and the adjustment of a vehicle allocation scheme are ensured to be based on the most accurate operation data, thereby a targeted improvement measure is made, a comprehensive work demand analysis record is generated, and optimization suggestions and expected resource saving points are listed in detail.
S322: utilizing the operation demand analysis record to identify key operation conditions including equipment performance limit, working environment difficulty and personnel configuration problems, optimizing resource configuration, and generating an operation condition optimization scheme;
According to the operation requirement analysis record, detailed operation condition examination is carried out, including performance assessment of existing equipment of a wharf, challenge analysis of working environment and actual deployment condition of human resources, systematic adjustment is carried out on key operation, such as adjustment of working rhythm of loading machinery, optimization of cargo flow path and reconfiguration of personnel shift, the adjustment process generates a detailed operation condition optimization scheme by simulation and confirmation of specific contribution of the adjustment process to improvement of operation efficiency, the scheme clearly indicates which specific measures can be implemented to eliminate efficiency bottlenecks, overall operation adaptability and response speed are improved, and the method further includes a detailed description of expected efficiency improvement index and resource configuration optimization.
S323: implementing an operation condition optimization scheme, monitoring an adjustment effect in real time, confirming that each operation accurately reflects the current operation requirement and environment change, and generating a scheduling execution plan;
The method comprises the steps of implementing a formulated operation condition optimization scheme on site, accurately tracking the actual effect of each adjustment through a continuous real-time monitoring and data feedback mechanism, dynamically adjusting an operation plan to match the requirements and challenges in actual operation, analyzing and confirming the effectiveness of all adjustments through real-time data in the process, ensuring that each time of resource allocation and change of a scheduling strategy can maximally improve the operation efficiency, reducing the downtime, reducing the cost increase caused by emergency adjustment, generating a final scheduling execution plan, recording the adjustment process and effect evaluation in detail, ensuring that the transportation of a wharf reaches the optimal state, and continuously adapting to complex and changeable operation environments.
Referring to fig. 7, the continuously optimized dock transport scheduling scheme is obtained by the steps of,
S411: executing a dispatch execution plan, deploying sensors to collect key transportation data in real time, and transmitting the key transportation data to a central data processing center in real time to generate a real-time data stream;
Firstly, a high-precision sensor is deployed to monitor various key indexes in real time, such as temperature, humidity, position, speed, weight and internal pressure of a container and the like, acquired data are transmitted to a central monitoring center in real time through a secure wireless network, meanwhile, data from a GPS and other navigation equipment are integrated to track the accurate position of a transport vehicle in real time, the continuous inflow of the data enables an operation center to obtain a comprehensive transport state view at any given time point, an information basis is provided for subsequent data processing and real-time decision making, the reliability and timeliness of the data are enhanced, the response speed and the accuracy of the whole logistics monitoring system are improved, any abnormality occurring in the transport process is ensured to be detected and processed in real time, and accordingly, a real-time data stream is generated and stored in a database for further analysis and use.
S412: according to the real-time data flow, performing data cleaning and preprocessing, analyzing data to identify abnormal modes and predicting potential transportation risks, and generating an optimized transportation scheduling decision suggestion;
In a central data processing center, a received real-time data stream is first subjected to a series of preprocessing steps, including data cleansing to filter noise and non-critical information, data normalization to unify data formats of different sensors and sources, then deep mining of the data using time series analysis and pattern recognition, automatic recognition of abnormal patterns in the data, prediction of potential transportation risk and possible delays, evaluation of current transportation status, determination of whether an adjustment plan is needed to cope with any detected problems, detailed listing of all proposed scheduling adjustments and precautions, laying a solid foundation for dynamic adjustment of transportation plans, and generation of a series of specific scheduling suggestions.
S413: according to the optimized transportation scheduling decision proposal, updating and adjusting a transportation scheduling plan of the wharf, synthesizing the current operation condition and the prediction result, automatically adjusting a transportation path and loading and unloading time, and generating a continuously optimized wharf transportation scheduling scheme;
Based on the dispatch advice provided by the central data processing center, the dock dispatch system automatically updates the transportation dispatch plan, considers the current transportation operation state, the forecast data and the optimization advice, comprehensively evaluates the possible optimal transportation path and loading and unloading time points to maximize transportation efficiency and safety, optimizes the transportation path and reduce waiting time, and can dynamically respond to sudden events such as traffic jams or bad weather, adjust the plan in real time to avoid delay, the whole decision process not only comprises optimization of the path and time, but also relates to optimal allocation of resources such as vehicles, loading and unloading equipment and human resources, the automated decision process ensures continuity and accuracy of the transportation dispatch plan, and finally generates a comprehensive and continuously optimized dock transportation dispatch scheme, and meanwhile, the scheme is updated in real time and is ready to cope with possible transportation challenges in the future.
An intelligent dock horizontal transport scheduling system, comprising:
the sensor information acquisition module captures GPS coordinates and speed information of the transport vehicle and the container through the sensor, monitors the internal condition of the container, screens key environmental factors, marks key time nodes, calculates an environmental sensitivity score corresponding to each time node, and obtains an environmental and time sensitivity analysis result;
The adaptability analysis module collects environmental and time sensitivity analysis results, combines weather data of the current wharf, analyzes weather adaptability of each task, combines current wharf real-time traffic flow data, analyzes traffic adaptability of each task, calculates comprehensive priority scores of target tasks, and generates an adjusted loading and unloading priority result;
The path and time optimization module analyzes the adjusted loading and unloading priority result, identifies bottleneck and efficiency problems in the transportation path, adjusts the transportation path, optimizes task time allocation, matches with the real-time operation environment of the wharf, and outputs a wharf dispatching route and a time optimization scheme;
The vehicle dispatching optimization module analyzes the service condition of the vehicle based on the wharf dispatching route and the time optimization scheme, calculates the current optimizing distribution index of the target vehicle, re-optimizes the vehicle distribution and the operation schedule, and combines the real-time feedback of the wharf operation site to generate a dynamic operation adjustment plan;
The resource allocation optimization module evaluates actual demands of tasks and resource matching conditions according to the dynamic job adjustment plan, identifies key operation conditions, optimizes resource allocation, confirms that each operation accurately reflects current job demands and environment changes, and generates a dispatching execution plan;
the transportation risk management module executes the dispatching execution plan, collects key transportation data in real time, analyzes the data to identify abnormal modes and forecast potential transportation risks, updates and adjusts the transportation dispatching plan of the wharf, and generates a continuously optimized wharf transportation dispatching scheme.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.