Method and System for Selecting Target Path of Terminal Process Flow Based on Big Data

Abstract

The present invention discloses a method and system for selecting a target path of a terminal process flow based on big data, which relates to the technical field of target path selection management of terminal process flows. The method for selecting a target path of a terminal process flow based on big data includes the following steps: dynamic assessment of terminal operations, static path assessment, and split-combination path assessment. The present invention selects the target path of the terminal process flow through the obtained dynamic change index of terminal operations. If a static path is selected, it is judged whether to switch the static path to a split-combination path based on the obtained static path efficiency assessment index. If a split-combination path is selected, it is judged whether to adopt a standby combination path through the obtained initial combination path efficiency index, achieving the effect of improving the accuracy of selecting the target path of the terminal process flow and solving the problem that the dynamic change situation of terminal operations is not fully considered in the prior art when selecting the target path of the terminal process flow.

Description

Technical Field

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

[0002] With the continuous improvement of the global port automation and intelligence levels, the optimization of terminal process flows has become a key direction for improving port operation efficiency. The method and system for selecting target paths for terminal process flows based on big data utilize massive data and advanced algorithms to achieve precise decision-making for loading and unloading, yard scheduling, and equipment path optimization, improve resource utilization rate, and reduce operation costs. This technology combines the requirements of smart port construction, logistics efficiency improvement, and sustainable development, and is an important support for the digital and intelligent transformation of modern ports.

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

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

[0005] For example, a path planning method for vehicles in an automated container terminal disclosed in an invention patent announcement with announcement number: CN110826819B includes decomposing the path planning problem into a main problem and sub-problems, and using different algorithms to solve the main problem and sub-problems, that is, the main problem adopts the framework of the branch and price algorithm, and the sub-problems adopt the genetic algorithm to solve.

[0006] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems:

[0007] In the prior art, the dock operation environment is complex and dynamically variable. Existing path selection methods often fail to respond to these changes in real time, resulting in overly static planned paths that are difficult to meet actual requirements. There is a problem that the dynamic changes in dock operations are not fully considered when selecting the target path of the dock process flow. Summary of the Invention

[0008] Embodiments of the present application provide a method and system for selecting the target path of the dock process flow based on big data, which solves the problem in the prior art that the dynamic changes in dock operations are not fully considered when selecting the target path of the dock process flow, and realizes the improvement of the accuracy of selecting the target path of the dock process flow.

[0009] Embodiments of the present application provide a method for selecting the target path of the dock process flow based on big data, including the following steps: obtaining the dynamic change index of the dock operation environment by acquiring the dock operation environment data of a preset process cycle, and selecting the target path of the dock process flow accordingly. The dynamic change index of the dock operation environment is used to quantify the degree of dynamic change of the dock operation environment. The target path of the dock process flow includes a static path and a split-combination path; if the static path is selected, the transportation efficiency data of the preset process cycle obtained in real time is combined with the dynamic change index of the dock operation environment to obtain the static path efficiency evaluation index, and based on the static path efficiency evaluation index, it is judged whether to switch the static path to the split-combination path. The static path efficiency evaluation index is used to evaluate the transportation efficiency of the static path; if the 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 dynamic change index of the dock operation environment to obtain the initial combination path efficiency index, and based on the initial combination path efficiency index, it is judged whether to adopt the alternative combination path. The initial combination path efficiency index is used to evaluate the transportation efficiency of the initially adopted split-combination path.

[0010] Furthermore, the dock operation environment data includes material deviation, material arrival time delay, available yard capacity, and yard cargo occupancy ratio; the material deviation represents the difference between the actual quantity of materials arriving at the dock and the expected quantity of materials to arrive at the dock; the transportation efficiency data includes material throughput, average material turnover time, dock equipment utilization rate, material transportation loss rate, and dock operation completion rate; the material throughput represents the total quantity of materials transported within a preset process cycle; the transportation feedback data includes average single-transport time, path usage frequency, average path congestion time, average material throughput, and transport vehicle utilization rate; the average path congestion time represents the ratio of the total path congestion time within a preset process cycle to the number of transports.

[0011] Furthermore, the specific process of obtaining the dynamic variability index of terminal operations is as follows: Number the preset process cycle, and obtain the expected 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. The reference yard weight includes capacity weight and occupancy ratio weight; Process the ratio of the material arrival time delay to the corresponding expected arrival time to obtain the material time value; Process the available yard capacity and the yard cargo occupancy ratio with the corresponding reference yard weights 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 weights respectively to obtain the dynamic variability index of terminal operations.

[0012] 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 terminal operations: If the dynamic variability index of terminal operations is within the division range, select the static path as the target path of the terminal process flow; If the dynamic variability index of terminal operations is not within the division range, select the split-combination path as the target path of the terminal process flow; The static path means automatically planning the target path of the terminal process flow using the static path planning algorithm; The split-combination path means optimizing the initial terminal process flow into a simple process.

[0013] Furthermore, the specific process of obtaining the static path efficiency evaluation index is as follows: Obtain the reference transportation efficiency weight and reference transportation efficiency data from the preset database. The reference transportation efficiency weight includes material throughput weight, average material turnover time weight, terminal equipment utilization rate weight, material transportation loss rate weight, and terminal operation completion rate weight. The reference transportation efficiency data includes the expected value of material throughput, the expected value of average turnover time, and the upper limit of the highest transportation loss rate; Perform data preprocessing on the transportation efficiency data, and the data preprocessing is used to remove the unit of the transportation efficiency data and unify the dimension; Process the transportation efficiency data with the corresponding reference transportation efficiency data respectively and perform a multiplication operation with the corresponding reference transportation efficiency weight, and combine the dynamic variability index of terminal operations to obtain the static path efficiency evaluation index.

[0014] Furthermore, the specific process of determining 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 give a path switching warning.

[0015] Further, the specific process for obtaining the initial combined path efficiency index is as follows: Obtain the transportation feedback weight and reference transportation feedback data from a preset database; process the transportation feedback data, its corresponding transportation feedback weight, and the reference transportation feedback data respectively, and combine them with the terminal operation dynamic variability index to obtain the initial combined path efficiency index; the transportation feedback weight includes transportation time weight, path usage frequency weight, path congestion time weight, material throughput weight, and transportation vehicle utilization rate weight; the reference transportation feedback data includes the expected time for a single transportation, 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 transportation vehicle utilization rate.

[0016] Further, the method for obtaining the initial combined path efficiency index is as follows:

[0017] ;

[0018] In the formula, t represents the number of the preset process cycle, , represents the total number of the preset process cycles, represents the average single transportation time of the t-th preset process cycle, represents the path usage frequency of the t-th 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 transportation vehicle utilization rate of the t-th preset process cycle, represents the expected time for a single transportation, represents the minimum limit of path usage frequency, represents the maximum value of path congestion time, represents the minimum limit of material throughput, represents the minimum value of transportation vehicle utilization rate, represents the transportation time weight, represents the path usage frequency weight, represents the path congestion time weight, represents the material throughput weight, represents the transportation vehicle utilization rate weight, represents the terminal operation dynamic variability index of the t-th preset process cycle, represents the initial combined path efficiency index of the t-th preset process cycle, and e represents the natural constant.

[0019] Further, the specific process of determining whether to adopt the alternative combined path based on the initial combined path efficiency index is as follows: Obtain the combined path evaluation value from the preset database and compare 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 alternative 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 alternative combined path is adopted in the next process cycle.

[0020] The embodiment of the present application provides a target path selection system for the terminal process flow based on big data, which is characterized in that it includes a terminal operation dynamic evaluation module, a static path evaluation module, and a split and combined path evaluation module. Among them, the terminal operation dynamic evaluation module is used to obtain the terminal operation dynamic variability index by acquiring the terminal operation environment data of the preset process cycle, and accordingly select the target path of the terminal process flow. The terminal operation dynamic variability index is used to quantify the dynamic change degree of the terminal operation environment. The target path of the terminal process flow includes a static path and a split and combined path. The static path evaluation module is used to, if the static path is selected, combine the real-time acquired transportation efficiency data of the preset process cycle with the terminal operation dynamic variability index to obtain the static path efficiency evaluation index, and based on the static path efficiency evaluation index, determine whether to switch the static path to the split and combined path. The static path efficiency evaluation index is used to evaluate the transportation efficiency of the static path. The split and combined path evaluation module is used to, if the split and combined path is selected, obtain the transportation feedback data of the initial combined path of the preset process cycle and combine it with the terminal operation dynamic variability index to obtain the initial combined path efficiency index, and based on the initial combined path efficiency index, determine whether to adopt the alternative combined path. The initial combined path efficiency index is used to evaluate the transportation efficiency of the initially adopted split and combined path.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. The target path of the terminal process flow is selected through the obtained terminal operation dynamic variability index. If the static path is selected, it is determined whether to switch the static path to the split and combined path based on the obtained static path efficiency evaluation index. If the split and combined path is selected, it is determined whether to adopt the alternative combined path through the obtained initial combined 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 change situation of the terminal operation is not fully considered in the prior art when selecting the target path of the terminal process flow.

[0023] 2. By numbering the preset process cycle, obtaining the expected arrival time, reference environment weight, and reference yard weight from the preset database, then processing the ratio of the material arrival time delay to the corresponding expected arrival time to obtain the material time value, and then processing and summing the available yard capacity and the yard cargo occupancy ratio with the corresponding reference yard weights respectively to obtain the yard occupancy value. Then, processing the material deviation, material time value, and yard occupancy value with the corresponding reference environment weights respectively to obtain the dynamic variability index of terminal operations, thus more accurately evaluating the dynamic situation of terminal operations, and further achieving a more accurate selection of the corresponding target path.

[0024] 3. By obtaining the transportation feedback weight and reference transportation feedback data from the preset database, then processing the transportation feedback data, its corresponding transportation feedback weight, and the reference transportation feedback data respectively and combining with the dynamic variability index of terminal operations to obtain the initial combined path efficiency index, thus more accurately evaluating the operation efficiency of the initial combined path, and further achieving a more accurate selection of the split and combined path. Brief Description of the Drawings

[0025] Figure 1 It is a flowchart of the method for selecting the target path of the terminal process flow based on big data provided by the embodiment of the present application. Detailed Embodiments

[0026] The embodiment of the present application provides a method and system for selecting the target path of the terminal process flow based on big data, which solves the problem that the dynamic change situation of terminal operations is not fully considered in the selection of the target path of the terminal process flow in the prior art. By numbering the preset process cycle, obtaining the expected arrival time, reference environment weight, and reference yard weight from the preset database, then processing the ratio of the material arrival time delay to the corresponding expected arrival time to obtain the material time value, and then processing and summing the available yard capacity and the yard cargo occupancy ratio with the corresponding reference yard weights respectively to obtain the yard occupancy value. Then, processing the material deviation, material time value, and yard occupancy value with the corresponding reference environment weights respectively to obtain the dynamic variability index of terminal operations, and selecting the target path of the terminal process flow accordingly. If a static path is selected, it is judged whether to switch the static path to the split and combined path based on the obtained static path efficiency evaluation index. If the split and combined path is selected, the transportation feedback weight and reference transportation feedback data are obtained from the preset database, and then the transportation feedback data, its corresponding transportation feedback weight, and the reference transportation feedback data are processed respectively and combined with the dynamic variability index of terminal operations to obtain the initial combined path efficiency index to judge whether to adopt the standby combined path, achieving an improvement in the accuracy of the selection of the target path of the terminal process flow.

[0027] 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 terminal process flow. The general idea is as follows:

[0028] Select the target path of the terminal process flow through the obtained dynamic change index of the terminal operation. If a static path is selected, it is judged 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, it is judged whether to adopt a standby combination path through the obtained initial combination path efficiency index, achieving the effect of improving the accuracy of the target path selection of the terminal process flow.

[0029] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0030] As Figure 1 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 includes the following steps: Obtain the dynamic change index of the terminal operation by acquiring the terminal operation environment data of a preset process cycle, and select the target path of the terminal process flow based on this. The dynamic change index of the terminal operation is used to quantify the dynamic change degree of the terminal operation environment. The target path of the terminal process flow includes a static path and a split-combination path; if a static path is selected, the real-time acquired transport efficiency data of the preset process cycle is combined with the dynamic change index of the terminal operation to obtain a static path efficiency evaluation index, and it is judged whether to switch the static path to a split-combination path based on the static path efficiency evaluation index. The static path efficiency evaluation index is used to evaluate the transport efficiency of the static path; if a split-combination path is selected, obtain the transport feedback data of the initial combination path of the preset process cycle and combine it with the dynamic change index of the terminal operation to obtain an initial combination path efficiency index, and judge whether to adopt a standby combination path based on the initial combination path efficiency index. The initial combination path efficiency index is used to evaluate the transport efficiency of the initially adopted split-combination path.

[0031] 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. And through big data technology, it is possible to achieve refined management of the terminal process flow, reduce costs, improve efficiency, and adapt to complex and changeable operating environments; in addition, by real-time acquiring and analyzing terminal operation environment data, transport efficiency data, and transport feedback data, it also realizes the intelligent selection and dynamic adjustment of the target path of the terminal process flow to improve the accuracy of the target path selection of the terminal process flow and improve the transport efficiency of the terminal operation.

[0032] It should be added that the dock operation environment data includes material deviation, material arrival time delay, available yard capacity, and yard cargo occupancy ratio; material deviation represents the difference between the actual quantity of materials arriving at the dock and the expected quantity of materials arriving at the dock; the transportation efficiency data includes material throughput, average material turnover time, dock equipment utilization rate, material transportation loss rate, and dock operation completion rate; material throughput represents the total quantity of materials transported within a preset process cycle; the transportation feedback data includes average single - trip transportation time, path usage frequency, average path congestion time, average material throughput, and transportation vehicle utilization rate; average path congestion time represents the ratio of the total path congestion time to the number of transports within a preset process cycle.

[0033] Among them, the material deviation is obtained by calculating the absolute value of the difference between the expected quantity of materials in the dock operation system and the actual quantity of materials measured by the RFID reader or weight sensor at the ship - unloading point. The material arrival time delay is obtained by recording the material arrival time in the ship tracking system and taking the difference from the expected arrival time in the dock operation system. The available yard capacity and yard cargo occupancy ratio are directly obtained through the dock operation system; the material throughput is obtained through the records of the dock operation system. The average material turnover time is obtained by calculating the ratio of the time when materials are transferred between various transfer points recorded in the dock operation system to the number of transfers. The dock equipment utilization rate is obtained through statistical calculation based on the records of the equipment management system. The material transportation loss rate is calculated by comparing the quantity and quality records of materials before and after transportation in the dock operation system. The dock operation completion rate is obtained through statistical calculation by comparing the dock loading and unloading plan with the actual completion situation in the dock operation system; the average single - trip transportation time is obtained by performing a mean operation on the transportation time information extracted from the system log. The path usage frequency is counted by the path planning system for the current usage frequency of the used path. The average path congestion time is obtained by installing traffic flow monitoring sensors at path nodes to count the vehicle waiting time and performing a mean operation. The average material throughput is obtained by extracting the material transportation volume of each path in the dock operation system. The transportation vehicle utilization rate is counted by recording the transportation tasks and idle status of transportation vehicles through the vehicle scheduling system; through the acquisition of the above data, it is helpful to conduct more accurate analysis of subsequent data.

[0034] Further, the specific process of obtaining the dynamic variability index of terminal operations is as follows: Number the preset process cycle, and obtain the expected 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. The reference yard weight includes capacity weight and occupancy ratio weight; Process the ratio of the material arrival time delay to the corresponding expected arrival time to obtain the material time value; Process the available yard capacity and the yard cargo occupancy ratio with the corresponding reference yard weights 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 weights respectively to obtain the dynamic variability index of terminal operations.

[0035] The specific limit expression of the dynamic variability index of terminal operations is as follows:

[0036] ;

[0037] In the formula, t represents the number of the preset process cycle, , represents the total number of preset process cycles, represents the material deviation of the t-th preset process cycle, represents the material arrival time delay of the t-th preset process cycle, represents the available yard capacity of the t-th preset process cycle, represents the yard cargo occupancy ratio of the t-th preset process cycle, represents the expected arrival time, represents the material deviation weight, represents the time delay weight, represents the yard weight, represents the capacity weight, represents the occupancy ratio weight, represents the dynamic variability index of terminal operations of the t-th preset process cycle.

[0038] In this embodiment, the algorithm comprehensively analyzes the dock operation environment data, the expected arrival time, the reference environment weight, and the reference yard weight to obtain the dock operation dynamic variability index. The expected arrival time is obtained through the dock operating system and represents the duration from when the dock receives the materials after being loaded and transported to the dock. In the formula, the material deviation, the material arrival time delay, and the available yard capacity are positively correlated with the dock operation dynamic variability index, while the yard cargo occupancy ratio is negatively correlated with the dock operation dynamic variability index. As the material deviation, the material arrival time delay, and the available yard capacity increase, the dock operation dynamic variability index also increases. However, as the yard cargo occupancy ratio increases, the dock operation dynamic variability index decreases. By analyzing the dock operation dynamic variability index, it helps to timely understand the dynamic changes of the dock, and then more accurately select the corresponding target path of the dock process flow to improve the accuracy of the target path selection of the dock process flow.

[0039] Specifically, the parameters involved in the processing of the dock operation dynamic variability index in this algorithm are interrelated and do not exist independently. Among them, the material deviation and the material arrival time delay may lead to an increase in the yard cargo occupancy ratio, further reducing the scheduling flexibility of the yard, thus exacerbating the deviation or delay of subsequent arrivals. Under optimized scheduling and resource allocation, the reasonable utilization of the yard capacity and deviation control can mitigate the chain reaction caused by the delay. The material deviation may cause the actual arriving materials to be more than the planned yard capacity, thus reducing the available yard capacity and even resulting in overloading. Moreover, if the material arrival time is delayed, it may cause the quantity or type of materials to fail to arrive in a timely manner as planned, thus triggering a material deviation. When the available yard capacity decreases, the yard cargo occupancy ratio will increase, and the higher the cargo occupancy ratio, the more negative impact it will have on the yard efficiency (such as overloading). The change in the available yard capacity may be affected by the material arrival time delay. For example, when the materials arrive late, the available yard capacity may be idle during the waiting period.

[0040] Specifically, the reference environment weight is obtained from a preset database. The material deviation weight, time delay weight, and yard weight respectively represent the numerical values of the influence degrees 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 relationships between the material deviation, material arrival time delay, and yard occupancy and the weights are preset in the preset database, and such relationships can be regarded as a mapping set. For example, by inputting the real-time material deviation, material arrival time delay, and yard occupancy into the corresponding mapping sets respectively, the corresponding material deviation weight, time delay weight, and yard weight can be output. Such mapping relationships are 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 range 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.

[0041] Specifically, the capacity weight is obtained from a preset database and represents the numerical value of the influence degree of the available yard capacity on the yard occupancy. This weight can be directly obtained by looking up the weight corresponding to the available yard capacity in the preset database. The mapping relationship between the available yard capacity and the weight is preset in the preset database, and such a relationship can be regarded as a mapping set. For example, by inputting the real-time available yard capacity into this mapping set, the corresponding weight value can be found. Such a mapping relationship is one-to-one, that is, each available yard capacity has a unique corresponding weight. In this example, the value range of the capacity weight is limited to between 0 and 1.

[0042] Specifically, in this example, the sum of the capacity weight and the proportion weight is 1.

[0043] 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 terminal operations. If the dynamic variability index of terminal operations is within the division range, select the static path as the target path of the terminal process flow; if the dynamic variability index of terminal operations is not within the division range, select the split-combination path as the target path of the terminal process flow. The static path means automatically planning the target path of the terminal process flow using the static path planning algorithm, 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 represents the equipment process flow that serves as both the starting point and the ending point of materials, and the simple process represents the process that does not have equipment serving as both the starting point and the ending point of materials at the same time.

[0044] In this embodiment, through precise path planning, dock resources such as loading and unloading equipment and warehouse space can be utilized more effectively, reducing resource waste and waiting time. Combining with modern information technologies (such as big data analysis), more accurate and efficient process planning and control can be achieved.

[0045] Taking the following 10 simple processes as examples:

[0046] 1. BC1 → BC13 → BC6 → SR1

[0047] 2. BC1 → BC3 → BC4 → BC7 → SR2

[0048] 3. BC1 → BC3 → BC4 → BC8 → SR3

[0049] 4. BC2 → BC13 → BC6 → SR1

[0050] 5. BC2 → BC3 → BC4 → BC7 → SR2

[0051] 6. BC2 → BC3 → BC4 → BC8 → SR3

[0052] 7. BC1 → OBC2 → BC5 → BC7 → SR2

[0053] 8. BC1 → OBC2 → BC5 → BC8 → SR3

[0054] 9. BC1 → OBC2 → OBC3 → SR4

[0055] 10. OBC1 → OBC2 → BC5 → BC7 → SR2

[0056] It can be seen from this that the purpose of splitting is to split any one equipment process flow that can be both the starting point and the ending point of materials into multiple simple processes that do not have the starting point and ending point of materials; for example: the combined process of unloading from a ship to loading onto a train = the simple process of unloading from a ship to a yard + the simple process of yard to yard + the simple process of yard to loading onto a train.

[0057] Combination, on the other hand, makes full use of the concept of modern information sharing and integration, innovates process sharing and integration technologies, and realizes that in this project, 292 simple process flows can completely replace thousands of process flows calculated by the previous static method.

[0058] Through the embodiment of this application, the blank of the process combination technology for port terminals is filled, the efficiency is improved, and due to the reduction in the number of processes, the reliability of the system operation is improved, the probability of system errors is greatly reduced, the debugging time is saved, and a technical foundation is laid for the switching of non-stop production operation processes.

[0059] Specifically, the division range is obtained from a preset database. In a specific embodiment, the division range is obtained by professional staff based on the specific target path transportation requirements of the terminal process flow and historical data analysis, and a set range is denoted as the division range.

[0060] Furthermore, the specific process of obtaining the static path efficiency evaluation index is as follows: Obtain the reference transportation efficiency weights and reference transportation efficiency data from a preset database. The reference transportation efficiency weights include the material throughput weight, the average material turnover time weight, the terminal equipment utilization rate weight, the material transportation loss rate weight, and the terminal operation completion rate weight. The reference transportation efficiency data includes the expected value of material throughput, the expected value of average turnover time, and the maximum limit value of transportation loss rate. Perform data preprocessing on the transportation efficiency data. The data preprocessing is used to remove the units of the transportation efficiency data and unify the dimensions. Process the transportation efficiency data and the corresponding reference transportation efficiency data respectively and perform a multiplication operation 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.

[0061] The specific limiting expression of the static path efficiency evaluation index is as follows:

[0062] ;

[0063] In the formula, t represents the number of the preset process cycle, , represents the total number of preset process cycles, represents the material throughput of the t-th preset process cycle, represents the average material turnover time of the t-th preset process cycle, represents the terminal equipment utilization rate of the t-th preset process cycle, represents the material transportation loss rate of the t-th preset process cycle, represents the terminal operation completion rate of the t-th preset process cycle, represents the expected value of material throughput, represents the expected value of average turnover time, represents the maximum limit value of transportation loss rate, represents the material throughput weight, represents the average material turnover time weight, represents the terminal equipment utilization rate weight, represents the material transportation loss rate weight, represents the terminal operation completion rate weight, represents the terminal operation dynamic variability index of the t-th preset process cycle, represents the static path efficiency evaluation index of the t-th preset process cycle.

[0064] In this embodiment, the algorithm comprehensively analyzes the transportation efficiency data, the reference transportation efficiency weight, and the reference transportation efficiency data to obtain the static path efficiency evaluation index. In the formula, when the material throughput is greater than the expected value of the material throughput, the average material turnover time is less than the expected value of the average turnover time, the material transportation loss rate is less than the highest limit of the transportation loss rate, and the utilization rate of the terminal equipment is higher, the corresponding static path efficiency evaluation index is larger; when the terminal operation completion rate is lower, the corresponding static path efficiency evaluation index is larger; by analyzing the static path efficiency evaluation index, it helps to more accurately evaluate the efficiency of the static path, thereby improving the accuracy of the target path selection of the terminal process flow.

[0065] 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, it may extend the average turnover time of materials. The throughput directly affects the equipment utilization rate. When the throughput is higher, the working 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 resources will be idle. When the equipment utilization rate is higher, it may lead to a decrease in operating efficiency or queuing phenomena, thus extending the turnover time. The throughput and the equipment utilization rate are usually positively correlated. However, when the throughput is higher, the equipment is more likely to reach a saturated state, the average turnover time is too long or the transportation loss rate is higher, and usually the operation completion rate will be lower. At the same time, the shortening of the average turnover time is usually accompanied by an increase in the operation completion rate.

[0066] Specifically, the reference transportation efficiency data is obtained from a preset database. In a specific embodiment, the reference transportation efficiency data is set by preset staff according to the transportation requirements of the target path of the specific terminal process flow. For example, it can be assumed that the expected value of the material throughput is 1000, the expected value of the average turnover time is 50, and the highest limit of the transportation loss rate is 0.02.

[0067] Specifically, the reference transportation efficiency weight is obtained from a preset database, which represents the numerical values of the influence degrees of transportation efficiency data (material throughput, average material turnover time, terminal equipment utilization rate, material transportation loss rate, and terminal operation completion rate) on the static path efficiency evaluation index respectively. The reference transportation efficiency weight can be directly obtained by looking up the weights corresponding to each transportation efficiency data in the preset database. A mapping relationship between the transportation efficiency data and the corresponding reference transportation efficiency weight is preset in the preset database and recorded as the corresponding mapping set. For example, by inputting the real-time transportation efficiency data into the corresponding mapping set, 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 values of each reference transportation efficiency weight are limited to between 0 and 1, and the sum of the material throughput weight, average material turnover time weight, terminal equipment utilization rate weight, material transportation loss rate weight, and terminal operation completion rate weight in this example is 1.

[0068] Further, the specific process of determining 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 be switched.

[0069] In this embodiment, when it is necessary to switch to the split-combination path, the method can give an early warning, reserve enough time for the staff to prepare and adjust, ensure a smooth transition during the switching process, and by regularly updating the static efficiency evaluation value in the preset database, it helps to adapt to the changing terminal environment and business requirements, and improve the flexibility and adaptability of the overall path.

[0070] Specifically, the static efficiency evaluation value is obtained from the preset database. In a specific embodiment, the static efficiency evaluation value is obtained by professional staff based on the transportation requirements of the specific terminal process flow target path and historical data analysis, and a set value is recorded as the static efficiency evaluation value.

[0071] Further, the specific process for obtaining the initial combined path efficiency index is as follows: Obtain the transportation feedback weights and reference transportation feedback data from a preset database; process the transportation feedback data, its corresponding transportation feedback weights, and the reference transportation feedback data respectively, and combine them with the quay operation dynamic variability index to obtain the initial combined path efficiency index; the transportation feedback weights include transportation time weight, path usage frequency weight, path congestion time weight, material throughput weight, and transportation vehicle utilization rate weight; the reference transportation feedback data includes the expected time for a single transportation, 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 transportation vehicle utilization rate.

[0072] The method for obtaining the initial combined path efficiency index is as follows:

[0073] ;

[0074] In the formula, t represents the number of the preset process cycle, , represents the total number of the preset process cycles, represents the average single - transportation time of the t - th preset process cycle, represents the path usage frequency of the t - th 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 transportation vehicle utilization rate of the t - th preset process cycle, represents the expected time for a single transportation, represents the minimum limit of path usage frequency, represents the maximum value of path congestion time, represents the minimum limit of material throughput, represents the minimum value of transportation vehicle utilization rate, represents the transportation time weight, represents the path usage frequency weight, represents the path congestion time weight, represents the material throughput weight, represents the transportation vehicle utilization rate weight, represents the quay operation dynamic variability index of the t - th preset process cycle, represents the initial combined path efficiency index of the t - th preset process cycle, and e represents the natural constant.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] Specifically, the transportation feedback weight is obtained from a preset database and represents the numerical value of the influence degree of each transportation feedback data (average single transportation time, path usage frequency, average path congestion time, average material throughput, and transportation vehicle utilization rate) on the initial combined path efficiency index. By querying the weight values corresponding to each transportation feedback data in the preset database, these weights can be directly obtained. A mapping relationship between the transportation feedback data and the weights has been established in the preset database, and this relationship can be regarded as a mapping set. Specifically, by simply inputting the real-time transportation feedback data into this mapping set, the corresponding weight value can be found. This mapping relationship is one-to-one, that is, each transportation feedback data corresponds to only one unique weight. In this example, the values of all transportation feedback weights are limited to between 0 and 1, and the sum of the transportation time weight, path usage frequency weight, path congestion time weight, material throughput weight, and transportation vehicle utilization rate weight is 1.

[0079] Further, the specific process of determining whether to adopt the alternative combined path based on the initial combined path efficiency index is as follows: Obtain the combined path evaluation value from the preset database and compare 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 alternative 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 alternative combined path is adopted in the next process cycle. The alternative combined path refers to other combined paths provided by the split combined path after the initial combined path.

[0080] In this embodiment, the existence of the alternative combined path provides a redundancy mechanism for the selection of the process target path. When the efficiency of the initial combined split path decreases, it can be switched to the alternative path in a timely manner to ensure the continuous operation of the process target path.

[0081] Specifically, the combined path evaluation value is obtained from the preset database. In a specific embodiment, the combined path evaluation value is obtained by professional staff based on the transportation requirements of the specific terminal process target path and historical data analysis, and a set value is recorded as the combined path evaluation value.

[0082] The embodiment of the present application further provides a target path selection system for the terminal process flow based on big data, which is characterized in that it includes 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 dynamic change index by acquiring the terminal operation environment data in a preset process cycle, and accordingly select the target path of the terminal process flow. The terminal operation dynamic change index is used to quantify the dynamic change degree of the terminal operation environment. The target path of the terminal process flow includes a static path and a split-combination path; the static path evaluation module is used to, if the static path is selected, combine the real-time acquired transportation efficiency data in the preset process cycle with the terminal operation dynamic change index to obtain the static path efficiency evaluation index, and based on the static path efficiency evaluation index, judge whether to switch the static path to the split-combination path. 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, if the split-combination path is selected, acquire the transportation feedback data of the initial combination path in the preset process cycle and combine it with the terminal operation dynamic change index to obtain the initial combination path efficiency index, and based on the initial combination path efficiency index, judge whether to adopt the standby combination path. The initial combination path efficiency index is used to evaluate the transportation efficiency of the initially adopted split-combination path.

[0083] In this embodiment, by integrating the three major modules of terminal operation dynamic evaluation, static path evaluation, and split-combination path evaluation, the intelligent selection and dynamic adjustment of the target path of the terminal process flow are realized, and furthermore, the situation that the dynamic changes of terminal operations are not fully considered during the selection of the terminal process flow path is realized.

[0084] To sum up, the embodiment of the present application selects the target path of the terminal process flow through the obtained terminal operation dynamic change index. If the static path is selected, it is judged whether to switch the static path to the split-combination path based on the obtained static path efficiency evaluation index. If the split-combination path is selected, it is judged whether to adopt the standby combination path through the obtained initial combination path efficiency index, so as to timely select the target path of the terminal process flow, and furthermore improve the accuracy of the selection of the target path of the terminal process flow, and effectively solve the problem that the dynamic changes of terminal operations are not fully considered during the selection of the target path of the terminal process flow in the prior art.

[0085] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can 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.

[0086] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0087] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0089] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0090] Obviously, those skilled in the art can make various changes and variations 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 equivalent technologies, 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 terminal operation dynamic variability index to obtain the initial combined path efficiency index, 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; 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 transportations within a preset process cycle; 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; 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.

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 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.

3. 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 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.

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 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.

5. The method for selecting a target path for a terminal process flow based on big data as claimed in claim 4, 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.

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 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.

7. A terminal process flow target path selection system based on big data, used to execute the method described in any one of claims 1 to 6, characterized in that: 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.

Images