Wharf technological process target path selection method and system based on big data

Abstract

The invention discloses a wharf process flow target path selection method and system based on big data, and relates to the technical field of wharf process flow target path selection management. The wharf process flow target path selection method based on the big data comprises the following steps of dynamic evaluation of wharf operation, static path evaluation and split and combined path evaluation. According to the method, the wharf process flow target path is selected through the obtained wharf operation dynamic variability index, if the static path is selected, whether the static path is switched to the split and combined path or not is judged based on the obtained static path efficiency evaluation index, and if the split and combined path is selected, the static path is switched to the split and combined path. If yes, whether the standby combined path is adopted or not is judged through the obtained initial combined path efficiency index, the effect of improving the wharf technological process target path selection accuracy is achieved, and the problem that in the prior art, the wharf operation dynamic change situation is not fully considered when the wharf technological process target path is selected is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of terminal process target path selection management, and in particular to a terminal process target path selection method and system based on big data. Background Art

[0002] With the continuous improvement of the level of automation and intelligence of global ports, the optimization of terminal process has become a key direction for improving port operation efficiency. The target path selection method and system of terminal process based on big data use massive data and advanced algorithms to achieve accurate decision-making on loading and unloading, yard scheduling, and equipment path optimization, improve resource utilization, and reduce operating costs. This technology, combined with the needs of smart port construction, logistics efficiency improvement, and sustainable development, is an important support for the transformation of modern ports to digitalization and intelligence.

[0003] The existing path selection of terminal process mainly relies on manual experience or traditional optimization algorithms, which are difficult to handle complex and dynamic operating environments, resulting in low efficiency, waste of resources and other problems. Some systems use static models for path planning, but lack the ability to dynamically analyze and make decisions on real-time data. In addition, existing technologies often ignore the global optimization of equipment scheduling, yard layout and transportation paths, and are difficult to adapt to the requirements of smart port construction for efficient and precise operations. Therefore, there is an urgent need for intelligent path optimization methods based on big data to break through existing bottlenecks.

[0004] For example, the patent application with publication number: CN114386652A discloses an automated terminal AGV intelligent scheduling method and system, including: obtaining the transfer information of the goods to be transferred; based on the cargo box position information, selecting all transfer equipment within a preset range centered on the cargo box position, and marking them as candidate transfer equipment; obtaining the first remaining loading capacity of the candidate transfer equipment, screening out the candidate transfer equipment whose first remaining loading capacity is greater than the cargo box size, and marking them as preferred transfer equipment; obtaining the location information of the preferred transfer equipment, and obtaining the optimal path for all transfer tasks; calculating the work completion time of the preferred transfer equipment, and selecting the preferred transfer equipment with the earliest work completion time to perform the transfer work of the goods to be transferred.

[0005] For example, the invention patent with announcement number: CN110826819B announces a path planning method for vehicles at an automated container terminal, which includes decomposing the path planning problem into a main problem and sub-problems, and using different algorithms to solve the main problem and the sub-problems, that is, the main problem uses the framework of a branch pricing algorithm, and the sub-problems are solved using a genetic algorithm.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the prior art, the terminal operating environment is complex and dynamic, and the existing path selection methods are often unable to respond to these changes in real time, resulting in the planned path being too static and difficult to meet actual needs. There is also a problem that the dynamic changes in terminal operations are not fully considered when selecting the target path for the terminal process flow. Summary of the invention

[0007] The embodiments of the present application provide a method and system for selecting a target path for a terminal process flow based on big data, thereby solving the problem in the prior art that the dynamic changes of terminal operations are not fully considered when selecting the target path for the terminal process flow, and improving the accuracy of selecting the target path for the terminal process flow.

[0008] The embodiment of the present application provides a method for selecting a target path of a terminal process flow based on big data, comprising the following steps: obtaining a terminal operation dynamic variability index by acquiring terminal operation environment data of a preset process cycle, and selecting a terminal process flow target path accordingly, wherein the terminal operation dynamic variability index is used to quantify the degree of dynamic change of the terminal operation environment, and the terminal process flow target path includes a static path and a split combination path; if a static path is selected, the transportation efficiency data of the preset process cycle acquired in real time is combined with the terminal operation dynamic variability index to obtain a static path efficiency evaluation index, and based on the static path efficiency evaluation index, it is determined whether to switch the static path to the split combination path, and the static path efficiency evaluation index is used to evaluate the transportation efficiency of the static path; if a split combination path is selected, the transportation feedback data of the initial combination path of the preset process cycle is obtained and combined with the terminal operation dynamic variability index to obtain an initial combination path efficiency index, and based on the initial combination path efficiency index, it is determined whether to adopt a backup combination path, and the initial combination path efficiency index is used to evaluate the transportation efficiency of the initially adopted split combination path.

[0009] Furthermore, the terminal operating environment data includes material deviation, material arrival time delay, yard available capacity and yard cargo occupancy ratio; the material deviation represents the difference between the number of materials actually arriving at the terminal and the number of materials expected to arrive at the terminal; the transportation efficiency data includes material throughput, average material turnover time, terminal equipment utilization, material transportation loss rate and terminal operation completion rate; the material throughput represents the total amount of materials transported within a preset process cycle; the transportation feedback data includes average single transportation time, path usage frequency, average path congestion time, average material throughput and transportation vehicle utilization; the average path congestion time represents the ratio of total path congestion time to transportation times within a preset process cycle.

[0010] Furthermore, the specific process of obtaining the dynamic variability index of terminal operations is as follows: number the preset process cycles, and obtain the estimated arrival time, reference environment weight and reference yard weight from the preset database, the reference environment weight includes material deviation weight, time delay weight and yard weight, and the reference yard weight includes capacity weight and proportion weight; perform ratio processing on the material arrival time delay and the corresponding estimated arrival time to obtain the material time value; process the yard available capacity and the yard cargo occupancy ratio with the corresponding reference yard weight respectively and sum them to obtain the yard occupancy value; process the material deviation, material time value and yard occupancy value with the corresponding reference environment weight respectively to obtain the dynamic variability index of terminal operations.

[0011] Furthermore, the specific process of selecting the target path of the terminal process flow is as follows: a division range is obtained from a preset database, and compared with the obtained dynamic variability index of the terminal operation: if the dynamic variability index of the terminal operation is within the division range, a static path is selected as the target path of the terminal process flow; if the dynamic variability index of the terminal operation is not within the division range, a split and combined path is selected as the target path of the terminal process flow; the static path indicates that a static path planning algorithm is used to automatically plan the target path of the terminal process flow; the split and combined path indicates that the initial terminal process flow is optimized into a simple process.

[0012] Furthermore, the specific acquisition process of the static path efficiency evaluation index is as follows: obtain reference transportation efficiency weights and reference transportation efficiency data from a preset database, the reference transportation efficiency weights include material throughput weights, material average turnover time weights, terminal equipment utilization weights, material transportation loss rate weights and terminal operation completion rate weights, and the reference transportation efficiency data include expected material throughput values, expected average turnover time values ​​and maximum transportation loss rate limits; perform data preprocessing on the transportation efficiency data, and the data preprocessing is used to de-unitize the transportation efficiency data and unify the dimensions; process the transportation efficiency data and the corresponding reference transportation efficiency data separately and perform product operations with the corresponding reference transportation efficiency weights, and at the same time combine the terminal operation dynamic variability index to obtain the static path efficiency evaluation index.

[0013] Furthermore, the specific process of judging whether to switch the static path to the split combined path based on the static path efficiency evaluation index is as follows: obtain the static efficiency evaluation value from the preset database, and compare it with the obtained static path efficiency evaluation index: if the static path efficiency evaluation index is not less than the static efficiency evaluation value, continue to use the static path; if the static path efficiency evaluation index is less than the static efficiency evaluation value, switch the static path to the split combined path in the next preset process cycle and issue a path switching warning.

[0014] Furthermore, the specific acquisition process of the initial combined path efficiency index is as follows: obtaining transport feedback weights and reference transport feedback data from a preset database; processing the transport feedback data and its corresponding transport feedback weights and reference transport feedback data respectively and combining them with the terminal operation dynamic variability index to obtain the initial combined path efficiency index; the transport feedback weights include transport time weights, path usage frequency weights, path congestion time weights, material throughput weights and transport vehicle utilization weights; the reference transport feedback data include the expected time of a single transport, the minimum limit of path usage frequency, the maximum value of path congestion time, the minimum limit of material throughput and the minimum value of transport vehicle utilization.

[0015] Furthermore, the method for obtaining the initial combined path efficiency index is as follows: ; Where t represents the number of the preset process cycle, , Indicates the total number of preset process cycles, represents the average single transport time of the t-th preset process cycle, represents the path usage frequency of the tth preset process cycle, represents the average path congestion time of the t-th preset process cycle, represents the average material throughput of the t-th preset process cycle, represents the utilization rate of transport vehicles in the tth preset process cycle, Indicates the expected time for a single transport. Indicates the minimum limit of path usage frequency. represents the maximum congestion time of the path, Indicates the minimum limit of material throughput. represents the minimum utilization rate of transport vehicles, represents the transportation time weight, represents the path usage frequency weight, represents the path congestion time weight, represents the material throughput weight, represents the utilization weight of transport vehicles, represents the dynamic variability index of terminal operations in the t-th preset process cycle, represents the initial combined path efficiency index of the tth preset process cycle, and e represents a natural constant.

[0016] Furthermore, the specific process of judging whether to adopt a backup combined path based on the initial combined path efficiency index is as follows: obtaining a combined path evaluation value from a preset database and comparing it with the initial combined path efficiency index: if the obtained initial combined path efficiency index is not less than the combined path evaluation value obtained from the preset database, the backup combined path is not adopted; if the obtained initial combined path efficiency index is less than the combined path evaluation value obtained from the preset database, the backup combined path is adopted in the next process cycle.

[0017] The embodiment of the present application provides a terminal process target path selection system based on big data, which is characterized by: comprising a terminal operation dynamic evaluation module, a static path evaluation module and a split combination path evaluation module; wherein the terminal operation dynamic evaluation module is used to obtain the terminal operation environment data of a preset process cycle to obtain a terminal operation dynamic variability index, and select the terminal process target path accordingly, the terminal operation dynamic variability index is used to quantify the degree of dynamic change of the terminal operation environment, the terminal process target path includes a static path and a split combination path; the static path evaluation module is used to obtain the preset process cycle of the real-time acquired dynamic variability index if the static path is selected. The transport efficiency data is combined with the terminal operation dynamic variability index to obtain a static path efficiency evaluation index, and based on the static path efficiency evaluation index, it is determined whether to switch the static path to a split combined path, and the static path efficiency evaluation index is used to evaluate the transport efficiency of the static path; the split combined path evaluation module is used to obtain the transport feedback data of the initial combined path of a preset process cycle and obtain the initial combined path efficiency index in combination with the terminal operation dynamic variability index if the split combined path is selected, and based on the initial combined path efficiency index, it is determined whether to adopt a backup combined path, and the initial combined path efficiency index is used to evaluate the transport efficiency of the initially adopted split combined path.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The target path of the terminal process flow is selected by the obtained dynamic variability index of the terminal operation. If a static path is selected, whether to switch the static path to the split combination path is determined based on the obtained static path efficiency evaluation index. If a split combination path is selected, whether to adopt the backup combination path is determined by the obtained initial combination path efficiency index, so as to timely select the target path of the terminal process flow, thereby improving the accuracy of the target path selection of the terminal process flow, and effectively solving the problem that the dynamic changes of the terminal operation are not fully considered when selecting the target path of the terminal process flow in the prior art.

[0019] 2. By numbering the preset process cycles and obtaining the estimated arrival time, reference environment weight and reference yard weight from the preset database, the material arrival time delay is then ratioed with the corresponding estimated arrival time to obtain the material time value, and the yard available capacity and the yard cargo occupancy ratio are respectively processed with the corresponding reference yard weight and summed to obtain the yard occupancy value, and then the material deviation, material time value and yard occupancy value are respectively processed with the corresponding reference environment weight to obtain the dynamic variability index of terminal operations, so as to more accurately evaluate the dynamic situation of terminal operations and thus achieve more accurate selection of the corresponding target path.

[0020] 3. By obtaining the transport feedback weights and reference transport feedback data from the preset database, and then processing the transport feedback data and its corresponding transport feedback weights and reference transport feedback data respectively and combining them with the dynamic variability index of terminal operations, the initial combined path efficiency index is obtained, thereby more accurately evaluating the operating efficiency of the initial combined path, and then achieving a more accurate selection of the split combination path. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a method for selecting a target path for a terminal process flow based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiment of the present application solves the problem that the dynamic changes of terminal operations are not fully considered when selecting the target path of the terminal process flow in the prior art by providing a method and system for selecting the target path of the terminal process flow based on big data. The method is to number the preset process cycles, obtain the estimated arrival time, the reference environment weight and the reference yard weight from the preset database, and then perform ratio processing on the material arrival time delay and the corresponding estimated arrival time to obtain the material time value. The available capacity of the yard and the occupancy ratio of the yard are respectively processed with the corresponding reference yard weight and summed to obtain the yard occupancy value. The material deviation, material time value and yard occupancy value are then compared with the reference yard weight. The corresponding reference environment weights are processed separately to obtain the dynamic variability index of terminal operations, and the target path of the terminal process flow is selected accordingly. If a static path is selected, it is determined whether to switch the static path to a split combination path based on the obtained static path efficiency evaluation index. If a split combination path is selected, the transportation feedback weight and reference transportation feedback data are obtained from the preset database, and then the transportation feedback data and its corresponding transportation feedback weight and reference transportation feedback data are processed separately and combined with the dynamic variability index of terminal operations to obtain the initial combination path efficiency index to determine whether to adopt the alternative combination path, thereby improving the accuracy of the target path selection of the terminal process flow.

[0023] The technical solution in the embodiment of the present application is to solve the problem that the dynamic changes of the terminal operation are not fully considered when selecting the target path of the above-mentioned terminal process flow. The overall idea is as follows: The target path of the terminal process flow is selected by the obtained dynamic variability index of terminal operations. If a static path is selected, whether to switch the static path to a split combination path is determined based on the obtained static path efficiency evaluation index. If a split combination path is selected, whether to adopt an alternative combination path is determined by the obtained initial combination path efficiency index, thereby achieving the effect of improving the accuracy of target path selection for the terminal process flow.

[0024] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] like Figure 1 As shown, it is a flowchart of a method for selecting a target path of a terminal process flow based on big data provided by an embodiment of the present application, the method comprising the following steps: obtaining a terminal operation dynamic variability index by acquiring terminal operation environment data of a preset process cycle, and selecting a terminal process flow target path accordingly, the terminal operation dynamic variability index is used to quantify the degree of dynamic change of the terminal operation environment, and the terminal process flow target path includes a static path and a split combination path; if a static path is selected, the transport efficiency data of the preset process cycle acquired in real time is combined with the terminal operation dynamic variability index to obtain a static path efficiency evaluation index, and based on the static path efficiency evaluation index, it is determined whether to switch the static path to the split combination path, and the static path efficiency evaluation index is used to evaluate the transport efficiency of the static path; if a split combination path is selected, the transport feedback data of the initial combination path of the preset process cycle is obtained and combined with the terminal operation dynamic variability index to obtain an initial combination path efficiency index, and based on the initial combination path efficiency index, it is determined whether to adopt a backup combination path, and the initial combination path efficiency index is used to evaluate the transport efficiency of the initially adopted split combination path.

[0026] In this embodiment, big data not only provides basic data support, but also improves the scientificity and efficiency of path selection through real-time monitoring and data analysis. Moreover, through big data technology, it can realize refined management of terminal process flow, reduce costs, improve efficiency, and adapt to complex and changing operating environments. In addition, through real-time acquisition and analysis of terminal operating environment data, transportation efficiency data and transportation feedback data, it also realizes intelligent selection and dynamic adjustment of the target path of the terminal process flow, so as to improve the accuracy of target path selection of the terminal process flow and improve the transportation efficiency of terminal operations.

[0027] It should be supplemented that the terminal operating environment data includes material deviation, material arrival time delay, yard available capacity and yard cargo occupancy ratio; material deviation refers to the difference between the number of materials actually arriving at the terminal and the number of materials expected to arrive at the terminal; transportation efficiency data includes material throughput, average material turnover time, terminal equipment utilization, material transportation loss rate and terminal operation completion rate; material throughput refers to the total amount of materials transported within the preset process cycle; transportation feedback data includes average single transportation time, path usage frequency, average path congestion time, average material throughput and transportation vehicle utilization; average path congestion time refers to the ratio of total path congestion time to the number of transportation times within the preset process cycle.

[0028] Among them, material deviation is obtained by calculating the absolute value of the difference between the estimated material quantity in the terminal operating system and the actual material quantity collected and measured by the RFID reader or weight sensor at the unloading point. The material arrival time delay is obtained by recording the material arrival time in the ship tracking system and subtracting it from the estimated arrival time in the terminal operating system. The available capacity of the yard and the cargo occupancy rate of the yard are both directly obtained through the terminal operating system; the material throughput is obtained through the records of the terminal operating system, and the average material turnover time is obtained by calculating the ratio of the material turnover time between various transfer points recorded by the terminal operating system to the number of turnovers. The terminal equipment utilization rate is obtained through the statistical calculation of the records of the equipment management system. The terminal operating system compares the material quantity and quality before and after transportation. The material transportation loss rate is calculated by recording the quantity, and the terminal operation system is used to compare the terminal loading and unloading plan with the actual completion status to statistically calculate the terminal operation completion rate; the average single transportation time is obtained by extracting the transportation time information from the system log and performing mean calculation, the path usage frequency is calculated by the path planning system to calculate the usage frequency of the current path, the average path congestion time is calculated by installing traffic flow monitoring sensors at the path nodes to calculate the vehicle waiting time and perform mean calculation, the average material throughput is obtained by extracting the material transportation volume of each path through the terminal operation system, and the transportation tasks and idle status of the transportation vehicles are recorded through the vehicle scheduling system to calculate the utilization rate of the transportation vehicles; the acquisition of the above data is helpful for more accurate analysis of subsequent data.

[0029] Furthermore, the specific process of obtaining the dynamic variability index of terminal operations is as follows: number the preset process cycles, and obtain the estimated arrival time, reference environment weight and reference yard weight from the preset database, the reference environment weight includes material deviation weight, time delay weight and yard weight, and the reference yard weight includes capacity weight and proportion weight; the material arrival time delay is processed with the corresponding estimated arrival time to obtain the material time value; the yard available capacity and the yard cargo occupancy ratio are processed with the corresponding reference yard weight respectively and summed to obtain the yard occupancy value; the material deviation, material time value and yard occupancy value are processed with the corresponding reference environment weight respectively to obtain the dynamic variability index of terminal operations.

[0030] The specific limiting expression of the dynamic variability index of terminal operations is as follows: ; Where t represents the number of the preset process cycle, , Indicates the total number of preset process cycles, Indicates the material deviation of the tth preset process cycle, represents the material arrival time delay of the tth preset process cycle, represents the available capacity of the yard in the tth preset process cycle, represents the cargo occupancy ratio of the yard in the tth preset process cycle, Indicates the estimated time of arrival. Indicates the material deviation weight, represents the time delay weight, represents the yard weight, represents the capacity weight, represents the weight of proportion, Represents the dynamic variability index of terminal operations in the tth preset process cycle.

[0031] In this embodiment, the algorithm combines terminal operation environment data, estimated arrival time, reference environment weight and reference yard weight for comprehensive analysis to obtain the terminal operation dynamic variability index. The estimated arrival time is obtained through the terminal operating system, which indicates the time it takes for the materials received by the terminal to be loaded and transported to the terminal. In the formula, material deviation, material arrival time delay and yard available capacity are positively correlated with the terminal operation dynamic variability index, while the yard cargo occupancy rate is negatively correlated with the terminal operation dynamic variability index. With the increase of material deviation, material arrival time delay and yard available capacity, the terminal operation dynamic variability index also increases, while with the increase of yard cargo occupancy rate, the terminal operation dynamic variability index decreases. By analyzing the terminal operation dynamic variability index, it is helpful to timely understand the dynamic changes of the terminal, and then more accurately select the corresponding terminal process flow target path to improve the accuracy of terminal process flow target path selection.

[0032] Specifically, the parameters involved in the processing of the dynamic variability index of terminal operations in this algorithm are interrelated and do not exist independently. Among them, material deviation and material arrival time delay may lead to an increase in the yard cargo occupancy rate, further reducing the scheduling flexibility of the yard, thereby aggravating the deviation or delay of subsequent arrival. Under optimized scheduling and resource allocation, the reasonable use of yard capacity and deviation control can mitigate the chain reaction caused by delays; material deviation may cause the actual arrival of more materials than the planned capacity of the yard, thereby reducing the available capacity of the yard and even overloading, and if the arrival time of materials is delayed, the quantity or type of materials may not arrive in time as planned, thereby causing material deviation; when the available capacity of the yard decreases, the yard cargo occupancy rate will increase, and the higher the cargo occupancy rate, the negative impact on the yard efficiency (such as overload); the change of the available capacity of the yard may be affected by the delay of material arrival time. For example, when the material arrives late, the available capacity of the yard may be idle during the waiting period.

[0033] Specifically, the reference environment weight is obtained from the preset database, and the material deviation weight, time delay weight and yard weight respectively represent the numerical values ​​of the influence of material deviation, material arrival time delay and yard occupancy on the dynamic variability index of terminal operations. The reference environment weight can be directly obtained by looking up the weights corresponding to the material deviation, material arrival time delay and yard occupancy in the preset database. The mapping relationship between material deviation, material arrival time delay and yard occupancy and weight is pre-set in the preset database, and this relationship can be regarded as a mapping set. For example, the real-time material deviation, material arrival time delay and yard occupancy are respectively input into the corresponding mapping set to output the corresponding material deviation weight, time delay weight and yard weight. This mapping relationship is one-to-one, that is, each material deviation, material arrival time delay and yard occupancy has a unique corresponding weight. In this example, the value of the material deviation weight is limited to between 0 and 1, and the sum of the material deviation weight, time delay weight and yard weight is 1.

[0034] Specifically, the capacity weight is obtained from the preset database, which represents the numerical value of the degree of influence of the available capacity of the yard on the occupancy of the yard. This weight can be obtained directly by looking up the weight corresponding to the available capacity of the yard in the preset database. The mapping relationship between the available capacity of the yard and the weight is pre-set in the preset database, and this relationship can be regarded as a mapping set. For example, just input the real-time available capacity of the yard into this mapping set, and you can find the corresponding weight value. This mapping relationship is one-to-one, that is, each available capacity of the yard has a unique corresponding weight. In this example, the value of the capacity weight is limited to between 0 and 1.

[0035] Specifically, in this example, the sum of the capacity weight and the proportion weight is 1.

[0036] Furthermore, the specific process of selecting the target path of the terminal process flow is as follows: obtain the division range from the preset database and compare it with the obtained dynamic variability index of the terminal operation: if the dynamic variability index of the terminal operation is within the division range, then select the static path as the target path of the terminal process flow; if the dynamic variability index of the terminal operation is not within the division range, then select the split combination path as the target path of the terminal process flow; the static path means that the static path planning algorithm is used to automatically plan the target path of the terminal process flow, and the static path planning algorithm includes Dijkstra, A*, and Floyd algorithms; the split combination path means optimizing the initial terminal process flow into a simple process, the initial terminal process flow means a process flow with equipment that can serve as both the starting point and the end point of the material, and the simple process means a process that does not have equipment that serves as both the starting point and the end point of the material.

[0037] In this embodiment, through precise path planning, terminal resources such as loading and unloading equipment, warehouse space, etc. can be used more effectively, reducing resource waste and waiting time. Combined with modern information technology (such as big data analysis), more accurate and efficient process planning and control can be achieved.

[0038] Take the following 10 simple processes as an example: 1. BC1→BC13→BC6→SR1 2. BC1→BC3→BC4→BC7→SR2 3. BC1→BC3→BC4→BC8→SR3 4. BC2→BC13→BC6→SR1 5. BC2→BC3→BC4→BC7→SR2 6. BC2→BC3→BC4→BC8→SR3 7. BC1→OBC2→BC5→BC7→SR2 8. BC1→OBC2→BC5→BC8→SR3 9. BC1→OBC2→OBC3→SR4 10. OBC1→OBC2→BC5→BC7→SR2 From this, it can be seen that the purpose of the splitting is to split any equipment process flow that can serve as both the material starting point and the material end point into multiple simple processes that do not have material starting points and material end points; for example: the combined process from unloading from ship to loading train = the simple process from unloading from ship to storage yard + the simple process from storage yard to storage yard + the simple process from storage yard to loading train.

[0039] The combination fully utilizes the concepts of modern information sharing and integration, and innovates process sharing and integration technologies, to achieve the use of 292 simple process flows in this project, completely replacing thousands of processes previously calculated using static methods.

[0040] The embodiments of the present application fill the gap in the technology of combining process flows in port terminals, improve efficiency, and because the number of processes is reduced, the reliability of system operation is improved, the probability of system errors is greatly reduced, debugging time is saved, and a technical foundation is laid for switching of operation flows without stopping production.

[0041] Specifically, the division range is obtained from a preset database. In a specific embodiment, the division range is obtained by professional staff according to specific terminal process target path transportation requirements and historical data analysis to obtain a set range as the division range.

[0042] Furthermore, the specific acquisition process of the static path efficiency evaluation index is as follows: obtain reference transportation efficiency weights and reference transportation efficiency data from the preset database, the reference transportation efficiency weights include material throughput weights, material average turnover time weights, terminal equipment utilization weights, material transportation loss rate weights and terminal operation completion rate weights, and the reference transportation efficiency data include expected material throughput values, expected average turnover time values ​​and maximum transportation loss rate limits; perform data preprocessing on the transportation efficiency data, and the data preprocessing is used to de-unitize the transportation efficiency data and unify the dimensions; process the transportation efficiency data and the corresponding reference transportation efficiency data separately and multiply them with the corresponding reference transportation efficiency weights, and at the same time combine them with the terminal operation dynamic variability index to obtain the static path efficiency evaluation index.

[0043] The specific restricted expression of the static path efficiency evaluation index is as follows: ; Where t represents the number of the preset process cycle, , Indicates the total number of preset process cycles, represents the material throughput of the tth preset process cycle, represents the average material turnover time of the tth preset process cycle, represents the terminal equipment utilization rate of the tth preset process cycle, Represents the material transportation loss rate of the tth preset process cycle, represents the completion rate of terminal operations in the tth preset process cycle, represents the expected value of material throughput, represents the expected value of the average turnaround time, Indicates the maximum limit of transportation loss rate. represents the material throughput weight, Indicates the weight of the average material turnover time, represents the weight of terminal equipment utilization, Represents the weight of material transportation loss rate, represents the weight of terminal operation completion rate, represents the dynamic variability index of terminal operations in the t-th preset process cycle, Represents the static path efficiency evaluation index of the tth preset process cycle.

[0044] In this embodiment, the algorithm combines the transportation efficiency data, the reference transportation efficiency weight and the reference transportation efficiency data for comprehensive analysis to obtain a static path efficiency evaluation index, wherein, when the material throughput is greater than the expected material throughput, the average material turnover time is less than the expected average turnover time, the material transportation loss rate is less than the maximum limit of the transportation loss rate, and the terminal equipment utilization rate is higher, the corresponding static path efficiency evaluation index is greater; when the terminal operation completion rate is lower, the corresponding static path efficiency evaluation index is greater; by analyzing the static path efficiency evaluation index, it is helpful to more accurately evaluate the efficiency of the static path, thereby improving the accuracy of the target path selection of the terminal process flow.

[0045] Specifically, the parameters involved in the processing of the static path efficiency evaluation index in this algorithm are interrelated and do not exist independently. Among them, the increase in material throughput usually increases the demand for terminal resources. If the equipment and yard capacity are limited, the average turnover time of materials may be extended. The throughput directly affects the equipment utilization. When the throughput is higher, the operating frequency of the equipment will increase, and the utilization rate will increase accordingly; when the throughput is lower, the equipment utilization rate may be insufficient and the resources will be idle. When the equipment utilization rate is higher, it may lead to reduced operating efficiency or queuing, thereby extending the turnover time. The throughput and equipment utilization are usually positively correlated, but the higher the throughput, the more likely the equipment is to reach saturation, the longer the average turnover time or the higher the transportation loss rate, and the lower the job completion rate. At the same time, the shortening of the average turnover time is usually accompanied by an increase in the job completion rate.

[0046] Specifically, the reference transportation efficiency data is obtained from a preset database. In a specific embodiment, the reference transportation efficiency data is set by a preset staff according to the transportation requirements of a specific terminal process target path. For example, it can be assumed that the expected material throughput value is 1000, the expected average turnover time value is 50, and the maximum limit of the transportation loss rate is 0.02.

[0047] Specifically, the reference transportation efficiency weight is obtained from the preset database, representing the numerical value of the influence of the transportation efficiency data (material throughput, average material turnover time, terminal equipment utilization, material transportation loss rate and terminal operation completion rate) on the static path efficiency evaluation index. The reference transportation efficiency weight can be directly obtained by looking up the weight corresponding to each transportation efficiency data in the preset database. The mapping relationship between the transportation efficiency data and the corresponding reference transportation efficiency weight is pre-set in the preset database and recorded as the corresponding mapping set. For example, the real-time transportation efficiency data is input into the corresponding mapping set, and the corresponding weight value can be found. This mapping relationship is one-to-one, that is, each transportation efficiency data has a unique corresponding weight. In this example, the value of each reference transportation efficiency weight is limited to between 0 and 1, and the sum of the material throughput weight, the material average turnover time weight, the terminal equipment utilization weight, the material transportation loss rate weight and the terminal operation completion rate weight in this example is 1.

[0048] Furthermore, the specific process of judging whether to switch the static path to the split combination path based on the static path efficiency evaluation index is as follows: obtain the static efficiency evaluation value from the preset database, and compare it with the obtained static path efficiency evaluation index: if the static path efficiency evaluation index is not less than the static efficiency evaluation value, continue to use the static path; if the static path efficiency evaluation index is less than the static efficiency evaluation value, switch the static path to the split combination path in the next preset process cycle and issue a path switching warning; the path switching warning is used to send a reminder to the preset staff that the target path of the terminal process flow is about to switch.

[0049] In this embodiment, when it is necessary to switch to a split combined path, the method can provide an early warning, reserve sufficient time for staff to prepare and adjust, ensure a smooth transition of the switching process, and by regularly updating the static efficiency evaluation values ​​in the preset database, help adapt to the ever-changing terminal environment and business needs, and improve the flexibility and adaptability of the overall path.

[0050] Specifically, the static efficiency evaluation value is obtained from a preset database. In a specific embodiment, the static efficiency evaluation value is obtained by professional staff according to the specific terminal process target path transportation requirements and historical data analysis to obtain a set value recorded as the static efficiency evaluation value.

[0051] Furthermore, the specific process of obtaining the initial combined path efficiency index is as follows: obtaining the transport feedback weight and reference transport feedback data from the preset database; processing the transport feedback data and its corresponding transport feedback weight and reference transport feedback data respectively and combining them with the dynamic variability index of terminal operations to obtain the initial combined path efficiency index; the transport feedback weight includes the transport time weight, the path usage frequency weight, the path congestion time weight, the material throughput weight and the transport vehicle utilization weight; the reference transport feedback data includes the expected time of a single transport, the minimum limit of the path usage frequency, the maximum value of the path congestion time, the minimum limit of the material throughput and the minimum value of the transport vehicle utilization.

[0052] The method for obtaining the initial combined path efficiency index is as follows: ; Where t represents the number of the preset process cycle, , Indicates the total number of preset process cycles, represents the average single transport time of the t-th preset process cycle, represents the path usage frequency of the tth preset process cycle, represents the average path congestion time of the t-th preset process cycle, represents the average material throughput of the t-th preset process cycle, represents the utilization rate of transport vehicles in the tth preset process cycle, Indicates the expected time for a single transport. Indicates the minimum limit of path usage frequency. represents the maximum congestion time of the path, Indicates the minimum limit of material throughput. represents the minimum utilization rate of transport vehicles, represents the transportation time weight, represents the path usage frequency weight, represents the path congestion time weight, represents the material throughput weight, represents the utilization weight of transport vehicles, represents the dynamic variability index of terminal operations in the t-th preset process cycle, represents the initial combined path efficiency index of the tth preset process cycle, and e represents a natural constant.

[0053] In this embodiment, the algorithm combines the transport feedback data, the transport feedback weight, the reference transport feedback data and the terminal operation dynamic variability index for comprehensive analysis to obtain the initial combination path efficiency index, wherein, when the ratio between the transport feedback data and the reference transport feedback data is larger, the corresponding initial combination path efficiency index is larger, indicating that the transport efficiency of the initial combination split path is higher; however, the smaller the ratio of the average path congestion time to the maximum path congestion time, the larger the corresponding initial combination path efficiency index is, indicating that the transport efficiency of the initial combination split path is higher; at the same time, when the terminal operation dynamic variability index is larger, it indicates that the impact of the terminal operation dynamic conditions on the transport efficiency of the initial combination split path is greater, and it is negatively correlated with the initial combination path efficiency index. As the terminal operation dynamic variability index increases, the initial combination path efficiency index decreases accordingly; through the above analysis of the initial combination path efficiency index, it is helpful to more accurately judge the transport efficiency of the initial split combination path, so as to switch the path in time according to the actual efficiency to improve the accuracy of the target path of the terminal process flow.

[0054] Specifically, the parameters involved in the processing of the initial combined path efficiency index in this algorithm are interrelated and do not exist independently. Among them, the increase in the frequency of path use (especially the frequency during peak hours) usually leads to an increase in the average single transportation time, because vehicles need to wait or slow down to pass through congested paths. The increase in material throughput may lead to an increase in transportation tasks, an increase in the load of paths and transportation resources, and thus extend the single transportation time; the frequency of path use is positively correlated with the congestion time. The more frequently used paths are often accompanied by longer congestion times. The increase in the frequency of path use is usually accompanied by an increase in vehicle utilization. However, if the frequency of path use becomes higher and higher, it may cause vehicles to queue and wait, thereby reducing the actual efficiency of the vehicles. The increase in throughput will lead to more transportation tasks, increase the path load, and thus extend the congestion time. At the same time, the increase in throughput will aggravate path congestion and extend the transportation time, which will affect the vehicle utilization rate and form a vicious cycle of reduced efficiency.

[0055] Specifically, the reference transport feedback data is obtained from a preset database. In a specific embodiment, the reference transport feedback data is set by a preset staff according to the transport requirements of a specific terminal process target path.

[0056] Specifically, the transport feedback weight is obtained from the preset database, which represents the numerical value of the influence of each transport feedback data (average single transport time, path usage frequency, average path congestion time, average material throughput and transport vehicle utilization) on the initial combined path efficiency index. These weights can be directly obtained by querying the weight value corresponding to each transport feedback data in the preset database. The mapping relationship between transport feedback data and weights has been established in the preset database, and this relationship can be regarded as a mapping set. Specifically, the corresponding weight value can be found by simply inputting the real-time transport feedback data into the mapping set. This mapping relationship is one-to-one, that is, each transport feedback data corresponds to only a unique weight. In this example, the values ​​of all transport feedback weights are limited to between 0 and 1, and the sum of the transport time weight, path usage frequency weight, path congestion time weight, material throughput weight and transport vehicle utilization weight is 1.

[0057] Furthermore, the specific process of judging whether to adopt a backup combination path based on the initial combination path efficiency index is as follows: obtain a combination path evaluation value from a preset database and compare it with the initial combination path efficiency index: if the obtained initial combination path efficiency index is not less than the combination path evaluation value obtained from the preset database, the backup combination path is not adopted; if the obtained initial combination path efficiency index is less than the combination path evaluation value obtained from the preset database, the backup combination path is adopted in the next process cycle; the backup combination path represents other combination paths provided by the split combination path after the initial combination path.

[0058] In this embodiment, the existence of the backup combination path provides a redundant mechanism for the selection of the target path of the process flow. When the efficiency of the initial combination split path decreases, it can be switched to the backup path in time to ensure the continuous operation of the target path of the process flow.

[0059] Specifically, the combined path evaluation value is obtained from a preset database. In a specific embodiment, the combined path evaluation value is obtained by professional staff according to the specific terminal process target path transportation requirements and historical data analysis to obtain a set value recorded as the combined path evaluation value.

[0060] The embodiment of the present application also provides a terminal process target path selection system based on big data, which is characterized by comprising a terminal operation dynamic evaluation module, a static path evaluation module and a split combination path evaluation module; wherein the terminal operation dynamic evaluation module is used to obtain the terminal operation environment data of a preset process cycle to obtain the terminal operation dynamic variability index, and select the terminal process target path accordingly, the terminal operation dynamic variability index is used to quantify the degree of dynamic change of the terminal operation environment, and the terminal process target path includes a static path and a split combination path; the static path evaluation module is used to combine the real-time transportation efficiency data of the preset process cycle with the terminal operation dynamic variability index to obtain a static path efficiency evaluation index if the static path is selected, and judge whether to switch the static path to the split combination path based on the static path efficiency evaluation index, and the static path efficiency evaluation index is used to evaluate the transportation efficiency of the static path; the split combination path evaluation module is used to obtain the transportation feedback data of the initial combination path of the preset process cycle and obtain the initial combination path efficiency index in combination with the terminal operation dynamic variability index if the split combination path is selected, and judge whether to adopt the backup combination path based on the initial combination path efficiency index, and the initial combination path efficiency index is used to evaluate the transportation efficiency of the initially adopted split combination path.

[0061] In this embodiment, by integrating the three modules of dynamic evaluation of terminal operations, static path evaluation and split combined path evaluation, the intelligent selection and dynamic adjustment of the target path of the terminal process flow are realized, thereby realizing the selection of the terminal process flow path without fully considering the dynamic changes of terminal operations.

[0062] To summarize, the embodiment of the present application selects the target path of the terminal process flow through the obtained dynamic variability index of the terminal operation. If a static path is selected, whether to switch the static path to a split combination path is determined based on the obtained static path efficiency evaluation index. If a split combination path is selected, whether to adopt a backup combination path is determined through the obtained initial combination path efficiency index, thereby timely selecting the target path of the terminal process flow, thereby improving the accuracy of the selection of the target path of the terminal process flow, and effectively solving the problem in the prior art that the dynamic changes of the terminal operation are not fully considered when selecting the target path of the terminal process flow.

[0063] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0068] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A target path selection method for terminal process flow based on big data, characterized in that: The following steps are involved: The terminal operation dynamic variability index is obtained by acquiring the terminal operation environment data of a preset process cycle, and a terminal process flow target path is selected accordingly. The terminal operation dynamic variability index is used to quantify the degree of dynamic change of the terminal operation environment. The terminal process flow target path includes a static path and a split combination path. If a static path is selected, the transport efficiency data of the preset process cycle acquired in real time is combined with the dynamic variability index of the terminal operation to obtain a static path efficiency evaluation index, and based on the static path efficiency evaluation index, it is determined whether to switch the static path to a split combined path. The static path efficiency evaluation index is used to evaluate the transport efficiency of the static path; If a split combined path is selected, the transport feedback data of the initial combined path of the preset process cycle is obtained and combined with the dynamic variability index of the terminal operation to obtain the initial combined path efficiency index, and based on the initial combined path efficiency index, it is determined whether to adopt an alternative combined path. The initial combined path efficiency index is used to evaluate the transport efficiency of the initially adopted split combined path.

2. The method for selecting a target path for a terminal process flow based on big data as claimed in claim 1, characterized in that: The terminal operation environment data includes material deviation, material arrival time delay, yard available capacity and yard cargo occupancy ratio; The material deviation represents the difference between the quantity of materials actually arriving at the terminal and the quantity of materials expected to arrive at the terminal; The transportation efficiency data include material throughput, average material turnover time, terminal equipment utilization rate, material transportation loss rate and terminal operation completion rate; The material throughput refers to the total amount of materials transported within a preset process cycle; The transport feedback data includes average single transport time, route usage frequency, average route congestion time, average material throughput and transport vehicle utilization rate; The average path congestion time represents the ratio of the total path congestion time to the number of transportation times within a preset process cycle.

3. The method for selecting a target path for a terminal process flow based on big data as claimed in claim 2, characterized in that: The specific process of obtaining the dynamic variability index of terminal operations is as follows: Numbering the preset process cycles, and obtaining the estimated arrival time, reference environment weight, and reference yard weight from the preset database, wherein the reference environment weight includes material deviation weight, time delay weight, and yard weight, and the reference yard weight includes capacity weight and proportion weight; The material arrival time delay is processed by ratio processing with the corresponding estimated arrival time to obtain the material time value; The available capacity of the yard and the occupancy ratio of the goods in the yard are processed with the corresponding reference yard weights respectively and summed to obtain the yard occupancy value; The material deviation, material time value and yard occupancy value are processed with the corresponding reference environment weights to obtain the dynamic variability index of terminal operations.

4. The method for selecting a target path for a terminal process flow based on big data as claimed in claim 1, characterized in that: The specific process of selecting the target path of the terminal process flow is as follows: Get the division range from the preset database and compare it with the obtained terminal operation dynamic variability index: If the dynamic variability index of the terminal operation is within the division range, the static path is selected as the target path of the terminal process flow; If the dynamic variability index of the terminal operation is not within the division range, the split combination path is selected as the target path of the terminal process flow; The static path means that a static path planning algorithm is used to automatically plan the target path of the terminal process flow; The split combination path represents the optimization of the initial dock process flow into a simple process.

5. The method for selecting a target path for a terminal process flow based on big data as claimed in claim 2, characterized in that: The specific process of obtaining the static path efficiency evaluation index is as follows: Obtain reference transportation efficiency weights and reference transportation efficiency data from a preset database, wherein the reference transportation efficiency weights include material throughput weights, material average turnover time weights, terminal equipment utilization weights, material transportation loss rate weights, and terminal operation completion rate weights, and the reference transportation efficiency data include material throughput expected value, average turnover time expected value, and transportation loss rate maximum limit value; Performing data preprocessing on the transportation efficiency data, wherein the data preprocessing is used to de-unitize the transportation efficiency data and unify the dimensions; The transport efficiency data and the corresponding reference transport efficiency data are processed separately and multiplied with the corresponding reference transport efficiency weights. At the same time, the static path efficiency evaluation index is obtained by combining the dynamic variability index of terminal operations.

6. The method for selecting a target path for a dock process flow based on big data as claimed in claim 1, characterized in that: The specific process of judging whether to switch the static path to the split combined path based on the static path efficiency evaluation index is as follows: Get the static efficiency evaluation value from the preset database and compare it with the obtained static path efficiency evaluation index: If the static path efficiency evaluation index is not less than the static efficiency evaluation value, the static path will continue to be used; If the static path efficiency evaluation index is less than the static efficiency evaluation value, the static path is switched to the split combination path in the next preset process cycle and a path switching warning is issued.

7. The method for selecting a target path for a dock process flow based on big data as claimed in claim 1, characterized in that: The specific process of obtaining the initial combined path efficiency index is as follows: Obtaining transport feedback weight and reference transport feedback data from a preset database; The transport feedback data and its corresponding transport feedback weights and reference transport feedback data are processed separately and combined with the terminal operation dynamic variability index to obtain the initial combined path efficiency index; The transport feedback weights include transport time weights, route usage frequency weights, route congestion time weights, material throughput weights and transport vehicle utilization weights; The reference transport feedback data includes the expected time of a single transport, the minimum limit of the path usage frequency, the maximum value of the path congestion time, the minimum limit of the material throughput and the minimum value of the transport vehicle utilization rate.

8. The method for selecting a target path for a dock process flow based on big data as claimed in claim 7, characterized in that: The method for obtaining the initial combined path efficiency index is as follows: ； Where t represents the number of the preset process cycle, , Indicates the total number of preset process cycles, represents the average single transport time of the t-th preset process cycle, represents the path usage frequency of the tth preset process cycle, represents the average path congestion time of the t-th preset process cycle, represents the average material throughput of the t-th preset process cycle, represents the utilization rate of transport vehicles in the tth preset process cycle, Indicates the expected time for a single transport. Indicates the minimum limit of path usage frequency. represents the maximum congestion time of the path, Indicates the minimum material throughput limit, represents the minimum utilization rate of transport vehicles, represents the transportation time weight, represents the path usage frequency weight, represents the path congestion time weight, represents the material throughput weight, represents the utilization weight of transport vehicles, represents the dynamic variability index of terminal operations in the t-th preset process cycle, represents the initial combined path efficiency index of the tth preset process cycle, and e represents a natural constant.

9. The method for selecting a target path for a dock process flow based on big data as claimed in claim 1, characterized in that: The specific process of judging whether to adopt the backup combined path based on the initial combined path efficiency index is as follows: Get the combined path evaluation value from the preset database and compare it with the initial combined path efficiency index: If the efficiency index of the initial combined path obtained is not less than the combined path evaluation value obtained from the preset database, the backup combined path is not used; If the obtained initial combined path efficiency index is less than the combined path evaluation value obtained from the preset database, the backup combined path is used in the next process cycle.

10. The target path selection system for the terminal process flow based on big data is characterized by: It includes a terminal operation dynamic assessment module, a static path assessment module and a split and combined path assessment module; The terminal operation dynamic evaluation module is used to obtain the terminal operation environment data of a preset process cycle to obtain the terminal operation dynamic variability index, and select the terminal process flow target path accordingly. The terminal operation dynamic variability index is used to quantify the degree of dynamic change of the terminal operation environment. The terminal process flow target path includes a static path and a split combination path. The static path evaluation module is used to combine the real-time acquired transportation efficiency data of the preset process cycle with the terminal operation dynamic variability index to obtain a static path efficiency evaluation index if a static path is selected, and judge whether to switch the static path to a split combination path based on the static path efficiency evaluation index, wherein the static path efficiency evaluation index is used to evaluate the transportation efficiency of the static path; The split combined path evaluation module is used to obtain the transportation feedback data of the initial combined path of the preset process cycle and obtain the initial combined path efficiency index in combination with the terminal operation dynamic variability index if the split combined path is selected, and to determine whether to adopt the backup combined path based on the initial combined path efficiency index. The initial combined path efficiency index is used to evaluate the transportation efficiency of the initially adopted split combined path.

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