A sequential liquid pipeline scheduling method based on multi-time-scale nesting

Abstract

The present invention relates to a scheduling method for sequential liquid pipelines based on multi-time-scale nesting, which obtains input information including: pipeline and station information, scheduling period, and preset batch plan information; uses days as the scheduling time unit, and solves the rough scheduling plan of the operation volume of each station, that is, the daily scheduling plan, with the first objective function and the first constraint condition; divides a fine scheduling time window from the current rough scheduling time; for the fine scheduling time window, uses a preset time unit less than one day, and for the remaining rough scheduling time, still uses days as the time unit, and solves the fine scheduling plan of the operation volume of each station within the fine time window and the rough scheduling plan within the remaining rough time window with the second objective function and the second constraint condition; updates the daily scheduling plan; repeats the iteration until the current rough scheduling time can no longer be divided into a fine scheduling time window; and superimposes and outputs the fine scheduling plans of each station in chronological order.

Description

Technical Field

[0001] The present invention relates to the technical field of refined oil transportation, and in particular to a scheduling method for batch transportation of liquid pipelines based on multi-time scale nesting. Background Art

[0002] Refined oil pipelines are important transportation tools for completing the spatial transfer of oil resources on land. After the market-oriented operation of refined oil pipelines, they face requests for changes in the demand plans of various shippers. Therefore, quickly formulating a scheduling plan that meets market demands and ensures the safe and efficient transportation of the pipeline network is the core link in the operation and management of refined oil pipeline networks. The optimized scheduling plan can achieve an accurate match between the oil source supply side and the market demand side. Due to the strong uncertainty in the upstream production plan and the downstream demand plan, during the operation process, dispatchers also need to adjust the scheduling plan in real time according to changes in production, demand, and pipeline status.

[0003] The scheduling optimization model for refined oil pipelines is usually constructed as a mixed integer linear programming (MILP) model, which requires a large number of binary variables to represent the operation decisions of different stations at different stages. The number of these variables increases exponentially with the length of the planning period, the scale of the stations, and the number of batches. When facing long-cycle and large-scale pipeline systems, a large number of binary variables are prone to cause a "combinatorial explosion" in the search space. The traditional branch and bound method relied on by traditional scheduling optimization takes more than half an hour when facing a pipeline with 7 stations, and its timeliness cannot meet the requirements of dynamic adjustment. Delayed adjustment will lead to production accidents such as emergency pipeline shutdown, a sharp increase in the amount of mixed oil, and subsequent oil products not being delivered on time. Summary of the Invention

[0004] In view of the above problems, the purpose of the present invention is to provide a scheduling method for batch transportation of liquid pipelines based on multi-time scale nesting, which can be applied to the rapid preparation of batch transportation plans for large-scale and long-cycle refined oil pipelines, greatly reduce the scale of binary variables in large-scale scheduling optimization models, thereby achieving the effect of accelerating the solution and realizing the rapid response of pipeline plans to the dynamic market.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present application provides a scheduling method for batch transportation of liquid pipelines based on multi-time scale nesting, the method comprising:

[0007] (1) Obtaining input information, including: pipeline and station information of the batch transportation liquid pipeline, scheduling period, and preset batch plan information;

[0008] (2) Based on the input information, with days as the basic time unit for rough scheduling, and with a preset first objective function and first constraint conditions, solve the daily scheduling plan for each station yard within the rough scheduling time window. The initial value of the rough scheduling time window is the scheduling period;

[0009] (3) Perform the following iterative processing:

[0010] Based on the current daily scheduling plan, update the rough scheduling time window by subtracting the time value of a fine scheduling time window with a set length from the current rough scheduling time window;

[0011] Among them, for the fine scheduling time window, use a preset time unit less than one day, and for the updated rough scheduling time window, still use days as the time unit. With the second objective function and second constraint conditions, solve the fine scheduling plan for the operation volume of each station yard within the fine time window and the rough scheduling plan within the rough scheduling time window;

[0012] Update the daily scheduling plan according to the rough scheduling plan within the rough scheduling time window;

[0013] Repeat the processing of this step until the current rough scheduling time window can no longer be divided into fine scheduling time windows. For the remaining time of the rough scheduling time window, use a preset time unit less than one day, and with the second objective function and second constraint conditions, solve the fine scheduling plan for the operation volume of each station yard;

[0014] (4) Stack the fine scheduling plans of each station yard obtained from the above iterative processing in chronological order, and output the final scheduling plan covering the entire scheduling period.

[0015] In one implementation, the first objective function is: the deviation between the cumulative actual injection / transmission volume and the planned injection / transmission volume of the station yard within the rough scheduling time window is minimized.

[0016] In one implementation, the first constraint conditions include: batch volume constraint, maximum daily injection / transmission volume constraint, maximum daily pipeline transportation flow constraint, operation logic constraint.

[0017] In one implementation, the second objective function is: the deviation between the cumulative actual injection / transmission volume and the planned injection / transmission volume of the station yard within the fine scheduling time window and the rough scheduling time window is minimized.

[0018] In one implementation, the second constraint conditions include: batch volume constraint, maximum injection / transmission flow constraint, minimum injection / transmission flow constraint, maximum pipeline transportation flow constraint, minimum pipeline transportation flow constraint, operation logic constraint.

[0019] In one implementation, the preset time unit less than one day is divisible by the fine scheduling time window.

[0020] In a second aspect, a sequential liquid pipeline scheduling method system based on multi-time scale nesting is provided. The system includes:

[0021] An input module for obtaining input information, including: pipeline and station information of the sequential liquid pipeline, scheduling period, and preset batch plan information;

[0022] An initial calculation module for, according to the input information, taking a day as the basic time unit for coarse scheduling, and using a preset first objective function and first constraint conditions to solve the daily scheduling plan of each station within the coarse scheduling time window. The initial value of the coarse scheduling time window is the scheduling period;

[0023] An iterative processing module for performing the following iterative processing: Based on the current daily scheduling plan, subtract a time value of a fine scheduling time window with a set length from the current coarse scheduling time window to update the coarse scheduling time window; wherein, for the fine scheduling time window, a time unit less than one day is used, and for the updated coarse scheduling time window, a day is still used as the time unit. Using a second objective function and second constraint conditions, solve the fine scheduling plan of the operation volume of each station within the fine time window and the coarse scheduling plan within the coarse scheduling time window; update the daily scheduling plan according to the coarse scheduling plan within the coarse scheduling time window; repeat the processing of this step until the current coarse scheduling time window can no longer be divided into fine scheduling time windows, and for the remaining time of the coarse scheduling time window, use a time unit less than one day, and use the second objective function and second constraint conditions to solve the fine scheduling plan of the operation volume of each station;

[0024] An output module for superimposing the fine scheduling plans of each station obtained from the aforementioned iterative processing in chronological order and outputting the final scheduling plan covering the entire scheduling period.

[0025] The present invention constructs a sequential liquid pipeline scheduling decision acceleration solution method and system based on multi-time scale nesting by using a "fine + coarse" hybrid time expression, aiming to effectively solve the problem of "combinatorial explosion" in the search space caused by a large number of binary variables when compiling the scheduling plan of a large-scale refined oil pipeline system, thereby laying a technical foundation for the dynamic adjustment of the refined oil pipeline scheduling plan. The proposed method uses the solution information of a small-scale coarse scheduling period to quickly construct dynamic sets of time, stations, and batches, and embeds them in the process of constructing decision variables and logical constraints in the next round of fine scheduling period, guiding the pruning of a large amount of redundant and invalid search space within the fine scheduling period, accelerating the convergence of the fine scheduling model without sacrificing optimality, and greatly improving the solution speed of large-scale, long-term, and multi-batch scheduling plans. Through case verification, the solution speed can be increased by 99.40%. Description of the Drawings

[0026] Figure 1It is a schematic diagram of a rolling solution framework with hybrid time-scale nesting provided by an embodiment of the present application;

[0027] Figure 2 It is a schematic diagram of a detailed scheduling plan obtained in an example of the present application;

[0028] Figures 3(a) to (g) are schematic diagrams of the flow rate changes of each station obtained in an example of the present application;

[0029] Figures 4(a) to (f) are schematic diagrams of the flow rate changes of each pipeline section obtained in an example of the present application. Detailed implementation manners

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention fall within the scope of protection of the present invention.

[0031] In view of the problems of the prior art, an embodiment of the present invention provides a method for scheduling a liquid pipeline in batch transportation based on multi-time-scale nesting. The method includes:

[0032] (1) Obtain input information, including: pipeline and station information of the liquid pipeline in batch transportation, a scheduling period, and preset batch plan information;

[0033] (2) According to the input information, with days as the basic time unit for coarse scheduling, and with a preset first objective function and first constraint conditions, solve the daily scheduling plan of each station within the coarse scheduling time window. The initial value of the coarse scheduling time window is the scheduling period;

[0034] (3) Perform the following iterative processing:

[0035] Based on the current daily scheduling plan, update the coarse scheduling time window by subtracting the time value of a set-length fine scheduling time window from the current coarse scheduling time window;

[0036] Among them, for the fine scheduling time window, a time unit less than one day is preset, and for the updated coarse scheduling time window, days are still used as the time unit. With a second objective function and second constraint conditions, solve the fine scheduling plan of the operation volume of each station within the fine time window and the coarse scheduling plan within the coarse scheduling time window;

[0037] Update the daily scheduling plan according to the coarse scheduling plan within the coarse scheduling time window;

[0038] Repeat the processing of this step until the current coarse scheduling time window can no longer be divided into fine scheduling time windows. For the remaining time of the coarse scheduling time window, use a preset time unit less than one day, and solve the fine scheduling plan of the operation volume of each station according to the second objective function and the second constraint conditions.

[0039] (4) Superimpose the fine scheduling plans of each station obtained from the above iterative processing in chronological order, and output the final scheduling plan covering the complete scheduling cycle.

[0040] The following is based on Figure 1 , and illustrate the above method in a more detailed embodiment.

[0041] Such as Figure 1 , a rolling solution method with nested coarse and fine mixed time scales.

[0042] Specifically, the steps of the solution method include:

[0043] (1) For the batch plan with the number of days TD, split the scheduling cycle at intervals of one day (24h). The daily injection / transmission volume of each station is the decision variable. Considering the batch volume constraint, the maximum daily injection / transmission volume constraint, the maximum pipeline transportation flow constraint, the operation logic constraint, etc., and taking the minimum deviation between the actual injection / transmission volume of the station and the planned injection / transmission volume as the objective function, construct a crude oil pipeline coarse scheduling optimization model to obtain the daily scheduling plan, that is, the daily injection / transmission volume of each batch at each station along the pipeline.

[0044] (2) Based on the daily scheduling plan, construct a rolling solution framework with nested "fine + coarse" mixed time scales. Divide the daily scheduling cycle TD into "fine + coarse" cycles. The total number of days of the fine scheduling is Δd, and the time interval is Δdt hours (Δdt < 24h and can divide 24 evenly). The total number of days of the coarse scheduling is TD - Δd, and the time interval is one day (24h). For the fine scheduling cycle, first define the dynamic sets of time windows, stations, and batches based on the batch migration situation and the station download volume in the first Δd days of the daily scheduling plan. Take the injection / transmission flow of each station in each fine time window as the decision variable, and construct the corresponding batch volume constraint, maximum injection / transmission flow constraint, minimum injection / transmission flow constraint, maximum pipeline transportation flow constraint, minimum pipeline transportation flow constraint, and operation logic constraint; then for the coarse scheduling cycle (TD - Δd), take the daily injection / transmission volume of each station in the cycle as the decision variable, construct the coarse scheduling related constraints in step (1), and take the minimum injection / transmission deviation of the station as the objective function, and finally form a scheduling optimization model with "fine + coarse" mixed time scales to obtain the mixed scheduling plan, where the first Δd days are the fine scheduling plan and the last (TD - Δd) days are the daily scheduling plan.

[0045] (3) If the coarse scheduling period TD - Δd > 0, with the daily scheduling plan obtained in step (2) as the known condition, update the daily scheduling period TD = TD - Δd, the total number of days of the fine scheduling Δd = min(Δd, TD), and the planned injection / transmission volume of each station, and repeat step (2); otherwise, end the calculation, splice the fine scheduling plans of each iteration in time, and combine them into a scheduling plan for a complete cycle.

[0046] Furthermore, the above solution process is represented by a mathematical model:

[0047] (1) Dynamic sets

[0048] This model uses a hybrid discrete time representation, where: T represents the set of time points, T D represents the set of fine time nodes; T A represents the set of coarse time nodes, T = T D ∪T A . T D The last time node in the set is the first node of T A , and the number of time windows is one less than the number of time nodes.

[0049] N represents the set of stations, N R represents the set of injection stations, N D represents the set of transmission stations, N = N R ∪N D .

[0050] I represents the set of batches. I n represents the set of dynamic batches that may pass through station n during the entire cycle, I t,n represents the set of dynamic batches that may pass through station n within the time window t. The dynamic sets I n and I t,n need to be updated according to the results of the daily scheduling plan of the previous iteration. Use the parameters and to represent the batch head and batch tail positions of each time node in the daily scheduling plan of the previous iteration. If in the previous round of daily scheduling plan, the batch tail of batch i did not exceed station n at the start time of the fine scheduling period and its batch head exceeded station n at the end time, then this batch i is in the set of dynamic batches of station n, as shown in Equation (1). If the batch tail of batch i did not exceed station n at the start time of the fine scheduling time window t and its batch head exceeded station n at the end time, then this batch i is in the dynamic set I t,n , as shown in Equation (2). The purpose of introducing this set of dynamic batches is to reduce the binary variables related to subscripts t, n, and i in the subsequent modeling, reduce the complexity of model solution, and improve the solution efficiency.

[0051]

[0052] (2) Objective function

[0053] The objective function is to minimize the sum of the injection and download deviations for each batch at the station, where respectively represent the volumes of injection and sub - transportation batches i at station n within the time window t; respectively represent the planned injection and sub - transportation volumes of batch i at station n within the time window t.

[0054]

[0055] (3) Batch volume constraints

[0056] The variables RC t,i and LC t,i in Equation (4) are used to represent the leading - edge coordinate and trailing - edge coordinate of batch i at time point t. By summing the volumes of all batches i′≥i in the same pipeline, the leading - edge coordinate of batch i is calculated. Equation (5) obtains its left - hand coordinate by subtracting the batch volume from the batch right - hand coordinate. Equation (6) is used to characterize the conservation of batch quantity. Specifically, for non - initial time nodes t>0, the volume of batch i at time point t is equal to the volume of batch i at the previous time point plus the total injection volume of batch i within the time window t - 1, minus the total sub - transportation volume. The volume of batch i at the initial time node t = 0 is equal to the known parameters. Due to the incompressibility of the oil product, Equation (7) limits the total volume of all batches to be equal to the pipeline capacity v L .

[0057]

[0058]

[0059] (4) Fine - scheduling period operation logic constraints

[0060] Use the binary variable to represent whether station n is injecting or sub - transporting batch i within the time window t. For the fine - scheduling time window t<|T D |, only when the leading - edge of batch i at the start time node t exceeds station n and the trailing - edge at the end node t + 1 does not exceed station n, can station n inject or sub - transport this batch. Therefore, each station can only inject or sub - transport a single batch within the same time window, and the operating volume cannot exceed the product of the upper and lower limits of the operating flow rate and the length Δdt of the fine time window. Equations (8) - (10) are the injection operation logic constraints within the fine - scheduling time window, and Equations (11) - (13) are the sub - transportation operation logic constraints, where respectively represent the upper and lower limits of the injection flow rate and the upper and lower limits of the sub - transmission flow rate of station yard n. Equation (14) represents the start - sub - transmission logic constraint. If station yard n does not have sub - transmission batch i in time window t - 1, but is in the process of sub - transmitting batch i in time window t, it is regarded as station yard n starting sub - transmission batch i at time node t, and the binary variable equals 1. In order to reduce the start - stop sub - transmission operations of pipeline segments and improve the smooth operation of the pipeline, the number of times each station yard starts a single sub - transmission batch needs to be controlled within m times, as shown in Equation (15).

[0061]

[0062] (5) Coarse - scheduling cycle operation logic constraints

[0063] Since the results of the coarse - scheduling cycle will be brought into the fine - scheduling model for further update in the next iteration process, the coarse - scheduling cycle only considers the rough injection and sub - transmission process constraints, relaxes or ignores some of the fine - scheduling logic constraints, so as to reduce the complexity of model solution. For example, for the coarse - scheduling time window t ∈ T A |T|, only when the end of the oil of batch i at the start time node t does not exceed station yard n and the start of the oil of batch i at the end node t + 1 exceeds station yard n, station yard n can inject or sub - transmit this batch. Therefore, each station yard can inject and sub - transmit multiple batches within the same time window, and the total volume of operations within this time window cannot exceed the upper limit of the operation flow rate multiplied by the length of the coarse time window Δt. Equations (16) - (19) are the injection operation logic constraints within the fine - scheduling time window, and equations (20) - (23) are the sub - transmission operation logic constraints. Equation (24) limits the maximum volume of sub - transmission of batch i by station yard n within the coarse - scheduling time window t, and this volume cannot exceed the total volume of the previous batch before the station plus the total injection volume of the upstream station yard of station yard n minus the total sub - transmission volume.

[0064]

[0065]

[0066] (4) Pipeline segment flow constraints

[0067] Within any time window t < |T|, the transportation volume FS of the first pipeline segment t,0 equals the total injection volume of the first station, as shown in Equation (25); for the remaining pipeline segments 1 ≤ n < |N|, the transportation volume FS t,n equals the transportation volume FS of the upstream pipeline segment t,n-1 plus the total injection volume of station yard n minus the total sub - transmission volume of station yard n, as shown in Equation (26). Equation (27) limits the pipeline segment transportation flow rate within the fine - scheduling time window t < |T D | within the upper and lower limits of the pipeline segment flow rate Inside. Equation (28) restricts the flow rate of the pipeline segment within the coarse scheduling time window to be less than the upper limit of the pipeline segment flow rate, and the lower limit is no longer restricted. Therefore, binary variables do not need to be introduced in the coarse scheduling period. The scale of discrete variables is reduced.

[0068]

[0069] Next, in a specific numerical example, the above method is used for solution and compared with the existing technology to illustrate the technical effect of the present method.

[0070] Taking a certain refined oil pipeline system in the western region as the object, a numerical example analysis is carried out. There are 7 stations along the pipeline, among which S0 is the injection station; the rest are distribution stations. The basic data of the stations are shown in Table 1, and the range of pipeline operation flow rate limits is shown in Table 2.

[0071] Table 1 Basic data of stations

[0072] Station number Station type <![CDATA[Volume coordinates (m 3 )]]> <![CDATA[Flow upper limit (m 3 / h)]]> <![CDATA[Lower flow limit (m 3 / h)]]> S0 Injection station 0 750 750 S1 Offtake station 20001 200 200 S2 Offtake station 43484 200 100 S3 Offtake station 56842 300 100 S4 Offtake station 84808 120 120 S5 Offtake station 152652 1100 200 S6 Offtake station 221124 1471 525

[0073] Table 2 Range of pipeline segment operation flow rate limits

[0074] Pipeline section number <![CDATA[Flow upper limit (m 3 / h)]]> <![CDATA[Lower flow limit (m 3 / h)]]> Pipe0 1471 525 Pipe1 1471 525 Pipe2 1471 350 Pipe3 1471 120 Pipe4 1471 525 Pipe5 1471 525

[0075] The scheduling period is about 25 days. There are 4 old batches in the pipeline at the initial moment, and the order of the batch oils is 0# diesel (B0: 16960 m 3 ), 92# gasoline (B1: 1040 m 3 ), 0# diesel (B2: 8956 m 3 ), 92# gasoline (B3: 1015 m 3 ), 0# diesel (B4: 89658 m 3 ), 92# gasoline (B5: 36607 m 3 ), 0# diesel (B6: 66888 m 3 ). In this period, the planned injection batch order is 0# diesel (B6) - 92# gasoline (B7) - 92# component gasoline (B8) - 0# diesel (B9) - 92# component gasoline (B10). The planned oil injection volume and distribution volume of each station for each batch are shown in Table 3.

[0076] Table 3 Planned oil injection volume and distribution volume Unit: m 3

[0077]

[0078] The fine time window period is set to 3 days, the fine time window length is 3h, and a total of 6 iterations are performed. Python and the Gurobi 11.0.3 solver are used for solving, and the total solving time is 13s. Compared with the prior art (Table 4), the present invention greatly shortens the scheduling plan compilation time and can achieve no deviation, greatly improving the scheduling decision-making efficiency. The scale comparison between the present invention and the complete model is shown in Table 5. It can be seen that the number of binary variables in the single-iteration model of the present invention is much lower than that of the complete model. This is because in each round of iteration, the proposed method will make full use of the solution information of the previous coarse scheduling period to construct the dynamic sets of time, stations, and batches, guiding the pruning of a large number of redundant and invalid decision variables and constraint conditions within the fine scheduling period, accelerating the convergence of the fine scheduling model without loss of optimality, and greatly improving the solution speed of large-scale, long-cycle, and multi-batch scheduling plans.

[0079] The obtained complete and detailed scheduling plan is shown in Figure 2 . The horizontal axis in the figure represents time, the left vertical axis represents the batch distribution state along the pipeline at the initial moment, the right vertical axis represents the distance between each station and the first station, and the black diagonal line represents the migration process of the batch interface. The rectangular bars represent the batch injection / transmission operations at the stations along the line, the color represents the oil products injected / transmitted, and the time span on the corresponding horizontal axis represents the start and end time nodes of the operation. From Figure 2 it can be seen that the intermediate transmission station basically completes the transmission tasks of each batch through one operation, which is beneficial to the stable operation of the pipeline. The variation of the operation flow at each station and each pipe section along the pipeline is shown in Figure 3-4. The flow at each station and pipe section always varies within the allowable limit range, meeting the process requirements.

[0080] Table 4 Comparison of Solving Results

[0081] The present invention (decomposition iteration) Full model solution Manually prepared Computing time 13 seconds 2161 seconds 1 - 2 days Planning deviation 0 6525 0

[0082] Table 5 Comparison of Model Scales

[0083]

[0084] The present invention constructs a method and system for accelerating the solution of sequential liquid pipeline scheduling decisions based on multi-time scale nesting by using "fine + coarse" mixed time expression, aiming to effectively solve the problem of "combinatorial explosion" in the search space caused by a large number of binary variables when compiling the scheduling plan of a large-scale refined oil pipeline system, thereby laying a technical foundation for the dynamic adjustment of the refined oil pipeline scheduling plan. The proposed method uses the solution information of a small-scale coarse scheduling period to quickly construct dynamic sets of time, stations, and batches, and embeds them in the process of constructing decision variables and logical constraints in the next round of fine scheduling period, guiding the pruning of a large number of redundant and invalid search spaces within the fine scheduling period, accelerating the convergence of the fine scheduling model without sacrificing optimality, and greatly improving the solution speed of large-scale, long-term, and multi-batch scheduling plans. Through case verification, the solution speed can be increased by 99.40%.

[0085] The system provided in the above embodiment obtains the pipeline basic information and preset transportation batch information of a multi-injection point refined oil pipeline, forms batch arrangement information according to the pipeline basic information and preset transportation batch information, and then inputs the batch arrangement information into a preset scheduling model for time series simulation, and solves it in combination with the set objective function and constraint conditions to calculate the remaining transportation capacity information that meets the preset objectives. Therefore, compared with the prior art, it can fully consider the complexity of the multi-injection point pipeline system and constraint conditions such as the shipper's transportation volume, arrival time, and adjacent batch requirements, quickly and accurately calculate the remaining transportation capacity information, facilitate timely announcement to the society, and improve social and economic benefits.

[0086] The above system can be implemented in a computer device in the form of hardware or software, so that the computer device can implement the method for evaluating the remaining transportation capacity of a multi-injection point refined oil pipeline in the embodiments of the present application. The specific details of the method can refer to the description of the foregoing embodiments and will not be repeated here.

[0087] In the embodiments of the present application, a computer-readable storage medium is also provided accordingly. The computer-readable storage medium stores a computer program, and when the computer device executes the computer program, the method for evaluating the remaining transportation capacity of a multi-injection point refined oil pipeline in the embodiments of the present application is implemented.

[0088] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above system (device) and module units can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0089] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system device embodiments described above are merely illustrative. For example, the division of the above-mentioned module units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0090] The above-mentioned integrated units implemented in the form of software function units can be stored in a computer-readable storage medium. The above-mentioned software function units stored in a storage medium include several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0091] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A scheduling method for liquid pipelines in sequential transportation based on multi-time-scale nesting, characterized in that, The method includes: (1) Obtain input information, including: pipeline and station information of the liquid pipeline for sequential transportation, scheduling period, and preset batch plan information; (2) According to the input information, with days as the basic time unit for rough scheduling, and with a preset first objective function and first constraint conditions, solve the daily scheduling plan of each station within the rough scheduling time window. The initial value of the rough scheduling time window is the scheduling period; (3) Perform the following iterative processing: Based on the current daily scheduling plan, update the rough scheduling time window by subtracting the time value of a fine scheduling time window with a set length from the current rough scheduling time window; Among them, for the fine scheduling time window, use a preset time unit less than one day, and for the updated rough scheduling time window, still use days as the time unit. With a second objective function and second constraint conditions, solve the fine scheduling plan of the operation volume of each station within the fine time window and the rough scheduling plan within the rough scheduling time window; Update the daily scheduling plan according to the rough scheduling plan within the rough scheduling time window; Repeat the processing of this step until the current rough scheduling time window can no longer be divided into fine scheduling time windows. For the remaining time of the rough scheduling time window, with a preset time unit less than one day, and with a second objective function and second constraint conditions, solve the fine scheduling plan of the operation volume of each station; (4) Stack the fine scheduling plans of each station obtained from the above iterative processing in chronological order, and output the final scheduling plan covering the entire scheduling period.

2. The sequential liquid pipeline scheduling method based on multi-time-scale nesting according to claim 1, wherein The first objective function is: the deviation between the cumulative actual injection / transmission volume and the planned injection / transmission volume of the stations within the rough scheduling time window is minimized.

3. The sequential liquid pipeline scheduling method based on multi-time-scale nesting according to claim 2, wherein, The first constraint conditions include: batch volume constraint, maximum daily injection / transmission volume constraint, maximum daily pipeline transportation flow rate constraint, operation logic constraint.

4. The sequential liquid pipeline scheduling method based on multi-time-scale nesting according to claim 1, characterized in that The second objective function is: the deviation between the cumulative actual injection / transmission volume and the planned injection / transmission volume of the stations within the fine scheduling time window and the rough scheduling time window is minimized.

5. The sequential liquid pipeline scheduling method based on multi-time-scale nesting according to claim 4, characterized in that The second constraint conditions include: batch volume constraint, maximum injection / transmission flow rate constraint, minimum injection / transmission flow rate constraint, maximum pipeline transportation flow rate constraint, minimum pipeline transportation flow rate constraint, operation logic constraint.

6. A sequential liquid pipeline scheduling method and system based on multi-time-scale nesting, characterized in that, The system includes: An input module for obtaining input information, including: pipeline and station information of the liquid pipeline for sequential transportation, scheduling period, and preset batch plan information; An initial calculation module for, according to the input information, with days as the basic time unit for rough scheduling, and with a preset first objective function and first constraint conditions, solve the daily scheduling plan of each station within the rough scheduling time window. The initial value of the rough scheduling time window is the scheduling period; An iterative processing module is used to perform the following iterative processing: Based on the current daily scheduling plan, subtract the time value of a fine scheduling time window with a set length from the current coarse scheduling time window to update the coarse scheduling time window; wherein, for the fine scheduling time window, a time unit less than one day is preset, while for the updated coarse scheduling time window, the time unit is still in days. With the second objective function and the second constraint condition, solve the fine scheduling plan of the operation volume of each station yard within the fine time window and the coarse scheduling plan within the coarse scheduling time window; update the daily scheduling plan according to the coarse scheduling plan within the coarse scheduling time window; repeat the processing of this step until the current coarse scheduling time window can no longer be divided into fine scheduling time windows, and for the remaining time of the coarse scheduling time window, with a time unit less than one day preset, with the second objective function and the second constraint condition, solve the fine scheduling plan of the operation volume of each station yard. An output module is used to stack the fine scheduling plans of each station yard obtained from the aforementioned iterative processing in chronological order and output the final scheduling plan covering the entire scheduling cycle.

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