Fuzzy control method for IGV attendance amount based on operation demand prediction, and medium

Abstract

The invention provides an IGV attendance fuzzy control method based on operation demand prediction, and the method comprises the steps: S1, obtaining a first IGV allocation amount D1 based on experience optimization, and obtaining a second IGV allocation amount D2 adaptive to the dynamic operation of a wharf; s2, obtaining the IGV notch amount; s3, synthesizing an IGV control quantity according to the formulated fuzzy rule; s4, performing defuzzification on the IGV control quantity to obtain an IGV quantity accurate value; s5, performing numerical adjustment on the IGV attendance according to the accurate value of the IGV number obtained in the step S4; s6, judging whether the IGV attendance amount adjusted in the step S5 meets the operation requirement of the automatic container terminal, if yes, completing fuzzy control over the IGV attendance amount, and if not, executing the step S1 in a circulating mode; the invention further provides a computer readable storage medium applying the fuzzy control method for the IGV attendance amount based on operation demand prediction. According to the method, the IGV operation dispatch quantity of the automatic container terminal can be accurately controlled, and the port operation resource utilization rate is improved.

Description

Technical Field

[0001] The invention relates to the technical field of port operation equipment dispatching management, and in particular to a fuzzy control method and medium for IGV attendance based on operation demand prediction. Background Art

[0002] Intelligent Guided Vehicle (IGV) is one of the important equipment responsible for horizontal transportation in automated container terminals. It undertakes many key tasks and is widely used in cargo loading, unloading and handling. As a key link connecting quay cranes, yards and rail cranes, IGVs need to perform multiple tasks in a complex terminal operating environment, including cargo handover with quay cranes, yard operations in collaboration with rail cranes, and dynamic avoidance of other IGVs. At the same time, IGVs also need to interact with the scheduling system in real time to complete designated tasks according to task priorities, job requirements and path planning. Their operating efficiency and accuracy of task completion directly affect the overall throughput capacity of the terminal. In the operating scenario of an automated container terminal, IGVs involve interactions with multiple devices and systems, including automatic interaction with quay cranes, automatic interaction with rail cranes, interaction with charging systems, position confirmation with other IGVs, and human-computer interaction when disassembling and installing locks. Therefore, optimizing the attendance rate of IGVs and reasonably allocating the number of IGVs in real time according to job requirements are of great significance to improving the work efficiency of the terminal.

[0003] At present, the IGV scheduling strategy at automated container terminals is usually based on preset rules or empirical formulas, and the number of vehicles is mainly allocated according to the daily work shift plan. However, this traditional scheduling method has obvious limitations: on the one hand, it cannot flexibly respond to the dynamic changes in operation demand. For example, there may be insufficient resource allocation during peak demand periods, while vehicles will be idle and resources will be wasted during low demand periods. On the other hand, other influencing factors are not fully considered during the scheduling process, such as the urgency of IGV operation tasks, equipment health, and path congestion, resulting in low resource utilization efficiency. In addition, the real-time requirements in IGV scheduling are high, and traditional linear programming is difficult to meet the needs of dynamic adjustment.

[0004] Existing IGV control schemes are usually relatively simple, mainly using fixed allocation rules, and lack of detailed consideration of complex working conditions. When demand fluctuates greatly or the operating environment is complex, the scheduling strategy may show large lag and inefficiency, resulting in the impact on terminal operation efficiency. Especially during peak missions, insufficient vehicle allocation may lead to operation delays, while during low demand periods, excessive vehicle allocation will increase energy consumption and equipment wear.

[0005] The existing IGV control scheme still has the following disadvantages:

[0006] 1. The number of IGV dispatches currently used by the terminal is a pre-defined scheduling rule. This method lacks flexibility when demand changes and is difficult to respond to emergencies;

[0007] 2. Poor adaptability to uncertainties (such as demand fluctuations, AGV failures), and low robustness of scheduling results;

[0008] 3. The control is not precise enough, and there are problems of insufficient and inappropriate resource utilization. Summary of the invention

[0009] In view of this, it is necessary to propose a fuzzy control method and medium for IGV attendance based on job demand prediction to address the above problems, so as to overcome several shortcomings of the above background technology and solve the following technical problems:

[0010] How to accurately control the number of IGV operations dispatched at automated container terminals and improve the utilization rate of port operation resources.

[0011] To achieve the above object, the present invention adopts the following technical solutions:

[0012] The present invention proposes a fuzzy control method for IGV attendance based on operation demand prediction, which is applied to an automated container terminal including multiple IGVs, a TOS system and an automated equipment management system communicating with the TOS system. The TOS system is used to manage the operation of the automated container terminal, and the automated equipment management system is used to obtain the actual attendance of the IGV. The fuzzy control method includes:

[0013] Step S1, obtaining a first IGV allocation amount D1 based on experience optimization according to the historical situation of terminal operations, obtaining a second IGV allocation amount D2 adapted to the dynamic operation of the terminal according to the fluctuation of terminal operations, and then executing step S2;

[0014] Step S2, obtaining the IGV gap amount: judging whether the second IGV allocation amount D2 is greater than the first IGV allocation amount D1, if it is judged to be yes, the IGV gap amount is equal to the first IGV allocation amount D1 minus the IGV actual attendance amount, if it is judged to be no, the IGV gap amount is equal to the second IGV allocation amount D2 minus the IGV actual attendance amount, and the IGV gap amount is used as the fuzzy control input; then executing step S3;

[0015] Step S3, synthesizing the IGV control amount according to the formulated fuzzy rules; then executing step S4;

[0016] Step S4, then defuzzify the IGV control amount into the precise value of the IGV quantity; then execute step S5;

[0017] Step S5, adjusting the IGV attendance value according to the accurate value of the IGV quantity obtained in step S4; then executing step S6;

[0018] Step S6, judging whether the IGV attendance adjusted in step S5 meets the operation requirements of the automated container terminal, if it is judged to be yes, then the fuzzy control of the IGV attendance is completed, if it is judged to be no, then the step S1 is executed repeatedly.

[0019] Further, in step S1, obtaining the first IGV distribution amount D1 based on experience optimization includes the following steps performed in sequence:

[0020] Step S111, calculate the number of IGVs K corresponding to a single frontier device configuration t for:

[0021]

[0022] In formula (1), N is the model coefficient, T m is the change of transport capacity over time. The operation time of a single transport includes loading and unloading and driving time. a The time delay ratio caused by traffic impact, T c is the operation time of a single transport, V l is the transport capacity of the vehicle, η e is the loading and unloading efficiency of the vehicle, T m (t) 2 The change in transport capacity due to the increase in operation time, is the ratio of operation time to cycle time; γ is the time lag benefit coefficient, γ·D t-1 Considering the time lag effect, D t-1 It is the transportation capacity at the previous moment, which is used to reflect the impact of the previous cycle on the current transportation capacity;

[0023] Step S112, according to formula (1), calculate the first IGV distribution amount D1:

[0024]

[0025] In formula (2), α ij represents the influence coefficient of frontier equipment and container distribution; exp(β ij ·K ij ) Modeling K through exponential function ij With α ij The nonlinear relationship between K ij The influence of γ is more sensitive; t is the time coefficient of IGV demand; γ t ·D t-1is the IGV demand introduced at a past time point to consider the time lag effect; δ t ·sin(ω t ·t) is the time added to capture periodic changes;∈ t is the error term, which accounts for random fluctuations or uncertainty in the model.

[0026] Further, in step S1, obtaining the second IGV allocation amount D2 adapted to the dynamic operation of the terminal includes the following steps performed in sequence:

[0027] Step S121, establish a basic model formula, which is a formula based on time, ship size, operation box quantity and equipment capacity, and is used to quantify the impact of ships about to berth and leave on the demand for IGVs; the basic model formula is:

[0028] IGV=f(C,T,S,P,ΔQ) (3)

[0029] In formula (3), C is the number of containers that the current ship has handled, T is the berthing time of the ship, S is the size of the ship, P is the capacity of the port equipment, and ΔQ is the number of containers that the ship is about to handle;

[0030] Step S122, establishing a linear model:

[0031] D 21 =k1·ΔQ+k2·T+k3·S-k4·P (4)

[0032] In formula (4), k1 is the coefficient to be optimized, which indicates the influence of the increase or decrease of the operation box quantity of the ship about to berth and leave on the IGV; k2 is the berthing time coefficient, which indicates the influence of the berthing time on the IGV; k3 is the ship size coefficient, which indicates the influence of the ship size on the demand; k4 is the frontier equipment coefficient, which indicates the influence of the frontier equipment capacity; T is the berthing time of the ship, S is the size of the ship, and P is the capacity of the port equipment;

[0033] Step S123, calculate the required workload D of the horizontal transportation of the yard rail crane 22 :

[0034] D 22 =k 11 Q ARMG +k 12 ·Ak 13 ·N (5)

[0035] In formula (5), Q ARMG is the horizontal transport operation volume of the yard rail crane, A is the attendance number of the yard rail crane, N is the number of tasks of the rail crane, k 11 ,k 12 ,k13 are the influence coefficients of workload, attendance number and task number on IGV demand respectively;

[0036] Step S124, calculate the required operation volume D of the number of towing vehicles entering and leaving the gate 23 :

[0037] D 23 =k 21 ·Ek 22 ·L+k 23 ·C (6)

[0038] In formula (6), E is the number of trailers entering the gate, L is the number of trailers leaving the gate, c is the yard capacity, and k is the 21 , k 22 , k 23 are the influence coefficients of the number of incoming towing, the number of outgoing towing and the yard capacity on the IGV demand;

[0039] Step S125, dynamically predict the demand for IGVs based on the number of berthing and unberthing at the front of the integrated wharf, horizontal transportation by rail cranes in the yard, and the number of towing vehicles entering and leaving the gate to obtain the second IGV allocation amount D2 for dynamic allocation:

[0040] D2=λ 21 ·D 21 +λ 22 ·D 22 +λ 23 ·D 23 (7)

[0041] In formula (7), λ 21 The change in the demand for berthing and unberthing at the front of the wharf D 21 Conciliation coefficient, λ 22 The change in demand for horizontal transportation of the yard rail crane D 22 Conciliation coefficient, λ 23 The required operation volume D is the change in the number of tugs entering and leaving the gate 23 Conciliation coefficient.

[0042] The present invention further proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the fuzzy control method of IGV attendance based on job demand prediction as described in any of the above items are implemented.

[0043] The present invention further proposes an electronic device, comprising a storage device, a processor, and a computer program stored in the storage device and executable by the processor. When the processor executes the computer program, the steps of the fuzzy control method of IGV attendance based on job demand prediction as described in any of the above items are implemented.

[0044] The present invention further proposes a fuzzy control system for IGV attendance based on operation demand prediction, which is applied to an automated container terminal with multiple IGVs. The fuzzy control system includes an automated container terminal operation management software module and an automated equipment management software module for obtaining the actual attendance of the IGV. The automated container terminal operation management software module interacts with the automated equipment management software module, and the automated container terminal operation management software module performs the following steps:

[0045] Step S1, obtaining a first IGV allocation amount D1 based on experience optimization according to the historical situation of terminal operations, obtaining a second IGV allocation amount D2 adapted to the dynamic operation of the terminal according to the fluctuation of terminal operations, and then executing step S2;

[0046] Step S2, obtaining the IGV gap amount: judging whether the second IGV allocation amount D2 is greater than the first IGV allocation amount D1, if it is judged to be yes, the IGV gap amount is equal to the first IGV allocation amount D1 minus the IGV actual attendance amount, if it is judged to be no, the IGV gap amount is equal to the second IGV allocation amount D2 minus the IGV actual attendance amount, and the IGV gap amount is used as the fuzzy control input; then executing step S3;

[0047] Step S3, synthesizing the IGV control amount according to the formulated fuzzy rules; then executing step S4;

[0048] Step S4, then defuzzify the IGV control amount into the precise value of the IGV quantity; then execute step S5;

[0049] Step S5, adjusting the IGV attendance value according to the accurate value of the IGV quantity obtained in step S4; then executing step S6;

[0050] Step S6, judging whether the IGV attendance adjusted in step S5 meets the operation requirements of the automated container terminal, if it is judged to be yes, then the fuzzy control of the IGV attendance is completed, if it is judged to be no, then the step S1 is executed repeatedly.

[0051] The beneficial effects of the present invention are:

[0052] The present invention can accurately control the number of IGVs dispatched for operation in an automated container terminal, and significantly improves the utilization rate of port operation resources; by introducing a fuzzy control strategy, the present invention can flexibly respond to fluctuations in operation demand, realize dynamic allocation of vehicles, optimize resource utilization, reduce energy waste, ensure efficient operation of the terminal under high load conditions, provide strong support for the intelligent development of automated terminals, and improve the overall operational efficiency and competitiveness of the terminal; the present invention can predict the IGV allocation demand according to the actual terminal operation, and can also dynamically determine the investment or reduction of the number of IGVs according to the real-time operation demand, reasonably allocate task resources, and optimize operation efficiency; the present invention can dynamically adjust the number of IGVs according to actual demand, reduce energy waste, and improve the overall system efficiency of the port, and can also respond quickly according to the expected operation demand, reduce the calculation time cost, and can combine multiple factors to obtain vehicle demand to meet the operation demand. IGV can be deployed on a large scale and simply, so that the fuzzy control vehicle dispatch number is more refined and the calculation result is in line with reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are included to provide a further understanding of the present invention, and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate exemplary embodiments of the present invention and, together with the description, are used to explain the principles of the present invention. These drawings are for illustration only and are not intended to limit the present invention.

[0054] Figure 1 This is a workflow diagram of the fuzzy control method for IGV attendance based on job demand prediction of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be further clearly and completely described in combination with the embodiments of the present invention. It should be noted that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] The terms "first", "second", "third", "fourth", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the definition of "first", "second", "third", "fourth" features may explicitly or implicitly include one or more of the features.

[0057] The following is a detailed description of an embodiment of the invention depicted in the accompanying drawings. The embodiments are detailed in order to clearly convey the invention. However, the number of details provided is not intended to limit the intended variations of the embodiments; on the contrary, the intention is to cover all modifications, equivalents, and substitutes that fall within the spirit and scope of the invention as defined by the appended claims.

[0058] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.

[0059] Embodiments of the present invention include various steps, which are described below. These steps can be performed by hardware components, or can be contained in machine executable instructions, which can be used for general or special purpose processors programmed with instructions to perform these steps. Alternatively, the steps can be performed by a combination of hardware, software and firmware and / or a human operator.

[0060] The various methods described herein can be practiced by combining one or more machine-readable storage media containing codes according to the present invention with appropriate standard computer hardware to execute the codes contained therein. Apparatus for implementing various embodiments of the present invention may include one or more computers (or one or more processors within a single computer) and a storage system containing or having network access to computer programs encoded according to the various methods described herein, and the method steps of the present invention may be completed by modules, routines, subroutines or sub-parts of a computer program product.

[0061] If the specification states that a component or feature "may," "could," "might," or "might" include or have a feature, that particular component or feature is not required to be included or have that feature.

[0062] As used in the specification herein and the claims that follow, the meanings of “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.

[0063] Exemplary embodiments will now be described more fully below with reference to the accompanying drawings, in which exemplary embodiments are shown. These exemplary embodiments are provided for illustrative purposes only, and so that the present invention is thorough and complete, and the scope of the present invention is fully conveyed to those of ordinary skill in the art. However, the disclosed invention can be implemented in many different forms and should not be interpreted as being limited to the embodiments set forth herein. Various modifications are apparent to those skilled in the art. Without departing from the spirit and scope of the present invention, the general principles defined herein can be applied to other embodiments and applications. In addition, all statements of the embodiments of the present invention and their specific examples described herein are intended to cover their structural and functional equivalents. In addition, these equivalents are intended to include currently known equivalents and equivalents developed in the future (i.e., any element developed to perform the same function, regardless of the structure). Moreover, the terms and wording used are for the purpose of describing exemplary embodiments and should not be considered restrictive. Therefore, the present invention will be given the widest scope, including multiple replacements, modifications and equivalents consistent with the disclosed principles and features. For the sake of clarity, the details of the technical materials known in the technical field related to the present invention are not described in detail, so as not to unnecessarily obscure the present invention.

[0064] Therefore, for example, it will be understood by those of ordinary skill in the art that schematic diagrams, schematic diagrams, diagrams, etc. represent conceptual views or processes that embody the systems and methods of the present invention. The functions of the various elements shown in the figures can be provided by using dedicated hardware and hardware capable of executing related software. Similarly, any switches shown in the figures are only conceptual. Their functions can be performed by the operation of program logic, by dedicated logic, by the interaction of program control and dedicated logic, or even manually, and specific techniques can be selected by the entity that implements the present invention. It will be further understood by those of ordinary skill in the art that the exemplary hardware, software, processes, methods and / or operating systems described herein are for illustrative purposes and are therefore not intended to be limited to any specific named elements.

[0065] Embodiments of the present invention may be provided as a computer program product, which may include a machine-readable storage medium on which instructions are tangibly implemented, which may be used to program a computer (or other electronic device) to perform a process. The term "machine-readable storage medium" or "computer-readable storage medium" includes, but is not limited to, fixed (hardware) drives, tapes, floppy disks, optical disks, optical disk read-only memories (CD-ROMs)) and magneto-optical disks, semiconductor memories such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other types of media / machine-readable media suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware). Machine-readable media may include non-transitory media in which data may be stored and does not include carrier waves and / or transient electronic signals propagated by wireless or wired connections. Examples of non-transitory media may include, but are not limited to, disks or tapes, optical storage media such as compact disks (CDs) or digital versatile disks (DVDs), flash memory, memory or memory devices. A computer program product may include code and / or machine executable instructions, which may represent any combination of a process, function, subprogram, program, routine, subroutine, module, software package, class, or instruction, data structure, or program statement. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, variables, parameters, or memory contents. Information, variables, parameters, data, etc. may be passed, forwarded, or transmitted by any suitable means, including memory sharing, message passing, token passing, network transmission, etc.

[0066] In addition, the embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description language, or any combination thereof. When implemented in software, firmware, middleware, or microcode, the program code or code segments (e.g., computer program products) that perform the necessary tasks may be stored in a machine-readable medium. A processor may perform the necessary tasks.

[0067] The systems depicted in some of the figures may be provided in various configurations. In some embodiments, the system may be configured as a distributed system, where one or more components of the system are distributed over one or more networks in a cloud computing system.

[0068] Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer only to certain specific embodiments. In other cases, it should be recognized that references to the "invention" will refer to the subject matter recited in one or more, but not necessarily all, of the claims.

[0069] Unless otherwise specified herein or the context is clearly contradictory, all methods described herein can be performed in any suitable order. The use of any and all examples or exemplary language (e.g., "such as") provided with respect to certain embodiments herein is intended only to better illustrate the present invention, rather than to limit the scope of the claimed invention. Any language in the specification should not be interpreted as indicating any unclaimed element essential to the implementation of the present invention.

[0070] Various terms used herein are listed below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the relevant art have given that term as reflected in printed publications and issued patents at the time of filing the application.

[0071] Example 1

[0072] like Figure 1 As shown:

[0073] This embodiment proposes a fuzzy control method for IGV attendance based on operation demand prediction, which is applied to an automated container terminal including multiple IGVs, a TOS system, and an automated equipment management system communicating with the TOS system. The TOS system is used to manage the operation of the automated container terminal, and the automated equipment management system (which can be a port RCMS system or a port ECS system) is used to obtain the actual attendance of the IGV. The fuzzy control method includes:

[0074] Step S1, obtaining a first IGV allocation amount D1 based on experience optimization according to the historical situation of terminal operations, obtaining a second IGV allocation amount D2 adapted to the dynamic operation of the terminal according to the fluctuation of terminal operations, and then executing step S2;

[0075] Step S2, obtaining the IGV gap amount: judging whether the second IGV allocation amount D2 is greater than the first IGV allocation amount D1, if it is judged to be yes, the IGV gap amount is equal to the first IGV allocation amount D1 minus the IGV actual attendance amount, if it is judged to be no, the IGV gap amount is equal to the second IGV allocation amount D2 minus the IGV actual attendance amount, and the IGV gap amount is used as the fuzzy control input; then executing step S3;

[0076] Step S3, synthesizing the IGV control amount according to the formulated fuzzy rules; then executing step S4;

[0077] Step S4, then defuzzify the IGV control amount into the precise value of the IGV quantity; then execute step S5;

[0078] Step S5, adjusting the IGV attendance value according to the accurate value of the IGV quantity obtained in step S4; then executing step S6;

[0079] Step S6, determines whether the IGV attendance after adjustment in step S5 meets the operational requirements of the automated container terminal. If it is judged to be yes, the fuzzy control of the IGV attendance is completed. If it is judged to be no, step S1 is executed in a loop (i.e., if it is not satisfied, the IGV real-time demand is recalculated and a new IGV control quantity is obtained to determine whether the IGV attendance quantity should be increased or decreased).

[0080] Optimally, in step S1, obtaining the first IGV distribution amount D1 based on experience optimization includes the following steps performed in sequence:

[0081] Step S111, calculate the number of IGVs K corresponding to a single frontier device configuration t for:

[0082]

[0083] In formula (1), N is the model coefficient, T m is the change of transport capacity over time. The operation time of a single transport includes loading and unloading and driving time. a The time delay ratio caused by traffic impact, T c is the operation time of a single transport, V l is the transport capacity of the vehicle (in boxes / time), η e is the loading and unloading efficiency of the vehicle, T m (t) 2 is the change in transport capacity (speeding up or slowing down) caused by the increase in operation time, is the ratio of operation time to cycle time; γ is the time lag benefit coefficient, γ·D t-1 Considering the time lag effect, D t-1 is the transport capacity at the previous moment, which is used to reflect the impact of the previous cycle on the current transport capacity; specifically, it corresponds to the number of IGVs K configured for a single frontier device t According to the operation scenario, each quay crane, light bridge and gantry crane at the front of the terminal is allocated a fixed number of K i The IGV is obtained by considering various factors such as vehicle transportation capacity, operation time, loading and unloading efficiency, different transportation distances, and traffic impact; specifically, T m (t) 2 Taking into account the nonlinear effect of transportation operation time on transportation capacity, T m (t) squared term; specifically, a single frontier device is allocated a fixed number K i IGV, according to the above value range K t, quay crane K1∈[6,8]; light bridge K2∈[5,7]; gantry crane K3∈[4,7]; specifically, the single frontier equipment is a quay crane, a light bridge or a gantry crane;

[0084] Step S112, according to formula (1), calculate the first IGV distribution amount D1:

[0085]

[0086] In formula (2), α ij represents the influence coefficient of frontier equipment and container distribution; exp(β ij ·K ij ) Modeling K through exponential function ij With α ij The nonlinear relationship between K ij The influence of γ is more sensitive; t is the time coefficient of IGV demand; γ t ·D t-1 is the IGV demand introduced at a past time point to consider the time lag effect; δ t ·sin(ω t ·t) is the addition of time (such as seasonal fluctuations) to capture periodic changes; ∈ t is the error term, which accounts for random fluctuations or uncertainty in the model.

[0087] Optimally, in step S1, obtaining the second IGV allocation amount D2 adapted to the dynamic operation of the terminal includes the following steps performed in sequence:

[0088] Step S121, establish a basic model formula, which is a formula based on time, ship size, operation box quantity and equipment capacity, and is used to quantify the impact of ships about to berth and leave on the demand for IGVs; the basic model formula is:

[0089] IGV=f(C,T,S,P,ΔQ) (3)

[0090] In formula (3), C is the number of containers that the current ship has handled, T is the berthing time of the ship, S is the size of the ship, P is the capacity of the port equipment (for example, the number of quay cranes, light bridges and gantry cranes, etc.), and ΔQ is the number of containers that will be handled by the ship that is about to berth or leave. Specifically, the second IGV allocation D2 takes into account the dynamic changes in operations, and the dynamic forecast of IGV demand for berthing and leaving at the front of the terminal, horizontal transportation by rail cranes in the yard, and the number of towing vehicles entering and leaving the gate.

[0091] Step S122, establishing a linear model:

[0092] D 21 =k1·ΔQ+k2·T+k3·S-k4·P (4)

[0093] In formula (4), k1 is the coefficient to be optimized, which indicates the influence of the increase or decrease of the operation box quantity of the ship about to berth and leave on the IGV; k2 is the berthing time coefficient, which indicates the influence of the berthing time on the IGV; k3 is the ship size coefficient, which indicates the influence of the ship size on the demand; k4 is the frontier equipment coefficient, which indicates the influence of the frontier equipment capacity; T is the berthing time of the ship, S is the size of the ship, and P is the capacity of the port equipment;

[0094] Step S123, calculate the required workload D of the horizontal transportation of the yard rail crane 22 :

[0095] D 22 =k 11 Q ARMG +k 12 ·Ak 13 ·N (5)

[0096] In formula (5), Q ARMG is the horizontal transport operation volume of the yard rail crane (unit: number of containers), A is the attendance number of the yard rail crane (unit: number of units), N is the number of tasks of the rail crane (unit: number of tasks), k 11 ,k 12 ,k 13 are the influence coefficients of workload, attendance number and task number on IGV demand respectively; Specifically, formula (5) is a linear model; Specifically, D 22 The influence of the yard rail crane operation volume, attendance number and task number is taken into account, so it is necessary to calculate formula (5);

[0097] Step S124, calculate the required operation volume D of the number of towing vehicles entering and leaving the gate 23 :

[0098] D 23 =k 21 ·Ek 22 ·L+k 23 ·C (6)

[0099] In formula (6), E is the number of trailers entering the gate (in units), L is the number of trailers leaving the gate (in units), C is the yard capacity (in units of containers), k 21 , k 22 , k 23 are the influence coefficients of the number of incoming towing, the number of outgoing towing and the yard capacity on the IGV demand;

[0100] Step S125, dynamically predict the demand for IGVs based on the number of berthing and unberthing at the front of the integrated wharf, horizontal transportation by rail cranes in the yard, and the number of towing vehicles entering and leaving the gate to obtain the second IGV allocation amount D2 for dynamic allocation:

[0101] D2=λ 21 ·D 21 +λ 22 ·D 22 +λ 23 ·D 23 (7)

[0102] In formula (7), λ 21 The change in the demand for berthing and unberthing at the front of the wharf D 21 Conciliation coefficient, λ 22 The change in demand for horizontal transportation of the yard rail crane D 22 Conciliation coefficient, λ 23 The required operation volume D is the change in the number of tugs entering and leaving the gate 23 Conciliation coefficient.

[0103] Further optimized, step S3 includes the following sub-steps performed in sequence:

[0104] Step S31, according to the predicted supply quantity, define fuzzy sets and membership functions, convert input and output variables, and divide the demand gap into seven fuzzy sets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB);

[0105] Step S32, describe the fuzzy rules; design the fuzzy rules according to the experience of the operators, the rule form is "IF A THEN B", the specific meaning of the rule is that when the demand gap is negative, the control amount is also negative, indicating a reduction in IGV vehicles; when the demand gap is positive, the control amount is positive, indicating an increase in IGV vehicles; if the demand gap is NB, the control amount is NB; if the demand gap is NM, the control amount is NM; if the demand gap is NS, the control amount is NS; if the demand gap is ZO, the control amount is 0; if the demand gap is PS, the control amount is PS; if the demand gap is PM, the control amount is PM; if the demand gap is PB, the control amount is PB;

[0106] Step S32, finding the fuzzy relationship; according to the fuzzy rules, the fuzzy control rules are expressed as the relationship between the input and output fuzzy sets, and the fuzzy matrices are used to describe these rules; each fuzzy matrix represents the fuzzy relationship between the input variables and the output variables under a certain condition; finally, by finding the union of the fuzzy matrices, the maximum membership function matrix is ​​obtained as the result of the fuzzy relationship;

[0107] Step S33, fuzzy decision; the output of the fuzzy controller is a synthesis of the error vector and the fuzzy relationship; according to the current error value, the corresponding fuzzy matrix is ​​selected, and the fuzzy output vector is calculated through a synthesis operation; for example, when the error is NB, the synthesis result of the output vector and the fuzzy relationship is calculated;

[0108] Step S34, defuzzification of the control quantity. According to the result of fuzzy decision, defuzzification is performed through the "maximum membership principle" to determine the final control quantity; for example, if NB is selected after defuzzification, the number of IGVs is reduced; if PB is selected, the number of IGVs is increased. This process ensures that the control output can meet the actual needs. For example, it can be seen from the fuzzy decision that when the error is negative and large, the number of IGVs is sufficient, e = NB, and the output of the controller is a fuzzy vector, which can be expressed as:

[0109]

[0110] According to the "maximum membership principle" for defuzzification, the control amount is selected as u=-6, which can accurately reduce the number of IGVs, which is in line with the actual situation that the IGV maintenance needs are met when the operation is not busy; according to the final control amount, the number of IGV attendance is increased or decreased.

[0111] The fuzzy control method applied in this embodiment is based on a control system composed of control rules containing fuzzy information, which has better stability and robustness than conventional control systems. When improving system characteristics, the fuzzy control system does not need to only adjust parameters like conventional control systems, but can also correct system characteristics by changing control rules, membership functions, reasoning methods and decision-making methods; the real-time demand involved in this embodiment is calculated, or to be precise, it is an approximate demand equivalent, the demand gap is also calculated, and the real-time attendance is actual; the fuzzy control method of this embodiment estimates the demand gap, and uses a fuzzy algorithm to timely and accurately adjust the number of IGVs dispatched for operation, thereby achieving precise control of IGV attendance and finding a balance in the game between terminal operation needs and maintenance.

[0112] The fuzzy control method of this embodiment also has the following advantages:

[0113] 1. Set reasonable membership functions for different demand changes, and dynamically adjust the IGV operation strategy in combination with other factors (such as the urgency of the current task and the equipment usage status) to avoid insufficient resources under high demand or waste of resources under low demand;

[0114] 2. Through fuzzy algorithms, comprehensive analysis is conducted on real-time demand and IGV attendance status, and the IGV increase and decrease strategy is dynamically adjusted to ensure rapid increase in investment when demand is high, reduce resource waste when demand is low, and maintain smooth and efficient operation of the system.

[0115] 3. Rationally allocate operating resources to avoid delays in terminal operations or increased energy consumption due to excessive or insufficient IGV investment, while ensuring that the equipment is in a reasonable state of use and extending the service life of the equipment.

[0116] 4. Adopt fuzzy algorithm technology, low investment cost, easy deployment and simple later maintenance;

[0117] 5. The calculation process of fuzzy control is simple, and it can quickly respond to dynamic demand changes through direct reasoning of fuzzy rules;

[0118] 6. Fuzzy control systems can maintain stable performance when demand fluctuates or other unpredictable factors occur;

[0119] 7. According to the operating conditions of the automated terminal, establish the demand for intelligent guided vehicles based on experience optimization or dynamic allocation of operations, and then fuzzy control the overall solution of the number of dispatched intelligent guided vehicles.

[0120] 8. The use of fuzzy control balances the relationship between the operation needs and maintenance of the intelligent guided vehicle.

[0121] 9. Based on the demand for the number of intelligent guided vehicles for each quay crane, light bridge and gantry crane at the front of the terminal, and considering various factors such as vehicle transportation capacity, operation time, loading and unloading efficiency, different transportation distances, and traffic impact, a new method for evaluating the demand for intelligent guided vehicles is proposed.

[0122] Example 2

[0123] This embodiment proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the fuzzy control method of IGV attendance based on job demand prediction as described in any technical solution of Example 1 are implemented.

[0124] Example 3

[0125] This embodiment proposes an electronic device, including a storage device, a processor, and a computer program stored in the storage device and executable by the processor. When the processor executes the computer program, the steps of the fuzzy control method of IGV attendance based on job demand prediction as described in any technical solution of Example 1 are implemented.

[0126] Example 4

[0127] Embodiment 4 is further optimized on the basis of Embodiment 1;

[0128] The present invention further proposes a fuzzy control system for IGV attendance based on operation demand prediction, which is applied to an automated container terminal with multiple IGVs. The fuzzy control system includes an automated container terminal operation management software module and an automated equipment management software module for obtaining the actual attendance of the IGV. The automated container terminal operation management software module interacts with the automated equipment management software module, and the automated container terminal operation management software module performs the following steps:

[0129] Step S1, obtaining a first IGV allocation amount D1 based on experience optimization according to the historical situation of terminal operations, obtaining a second IGV allocation amount D2 adapted to the dynamic operation of the terminal according to the fluctuation of terminal operations, and then executing step S2;

[0130] Step S2, obtaining the IGV gap amount: judging whether the second IGV allocation amount D2 is greater than the first IGV allocation amount D1, if it is judged to be yes, the IGV gap amount is equal to the first IGV allocation amount D1 minus the IGV actual attendance amount, if it is judged to be no, the IGV gap amount is equal to the second IGV allocation amount D2 minus the IGV actual attendance amount, and the IGV gap amount is used as the fuzzy control input; then executing step S3;

[0131] Step S3, synthesizing the IGV control amount according to the formulated fuzzy rules; then executing step S4;

[0132] Step S4, then defuzzify the IGV control amount into the precise value of the IGV quantity; then execute step S5;

[0133] Step S5, adjusting the IGV attendance value according to the accurate value of the IGV quantity obtained in step S4; then executing step S6;

[0134] Step S6, judging whether the IGV attendance adjusted in step S5 meets the operation requirements of the automated container terminal, if it is judged to be yes, then the fuzzy control of the IGV attendance is completed, if it is judged to be no, then the step S1 is executed repeatedly.

[0135] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A fuzzy control method for IGV attendance based on job demand prediction is applied to an automated container terminal including multiple IGVs, a TOS system and an automated equipment management system communicating with the TOS system, wherein the TOS system is used to manage the automated container terminal operations, and the automated equipment management system is used to obtain the actual attendance of the IGVs, and is characterized in that: The fuzzy control method includes: Step S1, obtaining a first IGV allocation amount D1 based on experience optimization according to the historical situation of terminal operations, obtaining a second IGV allocation amount D2 adapted to the dynamic operation of the terminal according to the fluctuation of terminal operations, and then executing step S2; Step S2, obtaining the IGV gap amount: judging whether the second IGV allocation amount D2 is greater than the first IGV allocation amount D1, if it is judged to be yes, the IGV gap amount is equal to the first IGV allocation amount D1 minus the IGV actual attendance amount, if it is judged to be no, the IGV gap amount is equal to the second IGV allocation amount D2 minus the IGV actual attendance amount, and the IGV gap amount is used as the fuzzy control input; then executing step S3; Step S3, synthesizing the IGV control amount according to the formulated fuzzy rules; then executing step S4; Step S4, then defuzzify the IGV control amount into the precise value of the IGV quantity; then execute step S5; Step S5, adjusting the IGV attendance value according to the accurate value of the IGV quantity obtained in step S4; then executing step S6; Step S6, judging whether the IGV attendance adjusted in step S5 meets the operation requirements of the automated container terminal, if it is judged to be yes, the fuzzy control of the IGV attendance is completed, if it is judged to be no, the step S1 is executed repeatedly.

2. The fuzzy control method of IGV attendance based on job demand prediction according to claim 1 is characterized in that: In step S1, obtaining the first IGV allocation D1 based on experience optimization includes the following steps performed in sequence: step S111, calculating the number of IGVs K corresponding to the configuration of a single frontier device t for: In formula (1), N is the model coefficient, T m is the change of transport capacity over time. The operation time of a single transport includes loading and unloading and driving time. a The time delay ratio caused by traffic impact, T c is the operation time of a single transport, V l is the transport capacity of the vehicle, η e is the loading and unloading efficiency of the vehicle, T m (t) 2 The change in transport capacity due to the increase in operation time, is the ratio of operation time to cycle time; γ is the time lag benefit coefficient, γ·D t-1 Considering the time lag effect, D t-1 is the transport capacity at the previous moment, which is used to reflect the impact of the previous cycle on the current transport capacity; step S112, according to formula (1), calculate the first IGV allocation D1: In formula (2), α ij represents the influence coefficient of frontier equipment and container distribution; exp(β ij ·K ij ) Modeling K through exponential function ij With α ij The nonlinear relationship between K ij The influence of γ is more sensitive; t is the time coefficient of IGV demand; γ t ·D t-1 is the IGV demand introduced at a past time point to consider the time lag effect; δ t ·sin(ω t ·t) is the time added to capture periodic changes;∈ t is the error term, which accounts for random fluctuations or uncertainty in the model.

3. The fuzzy control method of IGV attendance based on job demand prediction according to claim 1 is characterized in that: In step S1, obtaining the second IGV allocation D2 adapted to the dynamic operation of the terminal includes the following steps performed in sequence: Step S121, establish a basic model formula, which is a formula based on time, ship size, operation box quantity and equipment capacity, and is used to quantify the impact of ships about to berth and leave on the demand for IGVs; the basic model formula is: IGV=f(C, T, S, P, ΔQ) (3) In formula (3), C is the number of containers that the current ship has handled, T is the berthing time of the ship, S is the size of the ship, P is the capacity of the port equipment, and ΔQ is the number of containers that the ship is about to handle; Step S122, establishing a linear model: D 21 =k1·ΔQ+k2·T+k3·S-k4·P (4) In formula (4), k1 is the coefficient to be optimized, which indicates the influence of the increase or decrease of the operation box quantity of the ship about to berth and leave on the IGV; k2 is the berthing time coefficient, which indicates the influence of the berthing time on the IGV; k3 is the ship size coefficient, which indicates the influence of the ship size on the demand; k4 is the frontier equipment coefficient, which indicates the influence of the frontier equipment capacity; T is the berthing time of the ship, S is the size of the ship, and P is the capacity of the port equipment; Step S123, calculate the required workload D of the horizontal transportation of the yard rail crane 22 : D 22 =k 11 ·Q ARMG +k 12 ·A-k 13 ·N (5) In formula (5), Q ARMG is the horizontal transport operation volume of the yard rail crane, A is the attendance number of the yard rail crane, N is the number of tasks of the rail crane, k 11 , k 12 , k 13 are the influence coefficients of workload, attendance number and task number on IGV demand respectively; Step S124, calculate the required operation volume D of the number of towing vehicles entering and leaving the gate 23 : D 23 =k 21 ·E-k 22 ·L+k 23 ·C (6) In formula (6), E is the number of trailers entering the gate, L is the number of trailers leaving the gate, C is the yard capacity, and k 21 , k 22 , k 23 They are the influence coefficients of the number of incoming towing, the number of outgoing towing and the yard capacity on the IGV demand; Step S125, dynamically predict the demand for IGVs based on the number of berthing and unberthing at the front of the integrated wharf, horizontal transportation by rail cranes in the yard, and the number of towing vehicles entering and leaving the gate to obtain the second IGV allocation amount D2 for dynamic allocation: D2=λ 21 ·D 21 +λ 22 ·D 22 +λ 23 ·D 23 (7) In formula (7), λ 21 The change in the demand for berthing and unberthing at the front of the wharf D 21 Conciliation coefficient, λ 22 The change in demand for horizontal transportation of the yard rail crane D 22 Conciliation coefficient, λ 23 The required operation volume D is the change in the number of tugs entering and leaving the gate 23 Conciliation coefficient.

4. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the fuzzy control method of IGV attendance based on job demand prediction as described in any one of claims 1 to 3 are implemented.

5. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable by the processor. When the processor executes the computer program, the steps of the fuzzy control method of IGV attendance based on job demand prediction as described in any one of claims 1 to 3 are implemented.

6. A fuzzy control system for IGV attendance based on job demand prediction, applied to an automated container terminal with multiple IGVs, characterized in that: The fuzzy control system includes an automated container terminal operation management software module and an automated equipment management software module for obtaining the actual attendance of IGVs. The automated container terminal operation management software module interacts with the automated equipment management software module. The automated container terminal operation management software module performs the following steps: Step S1, obtaining a first IGV allocation amount D1 based on experience optimization according to the historical situation of terminal operations, obtaining a second IGV allocation amount D2 adapted to the dynamic operation of the terminal according to the fluctuation of terminal operations, and then executing step S2; Step S2, obtaining the IGV gap amount: judging whether the second IGV allocation amount D2 is greater than the first IGV allocation amount D1, if it is judged to be yes, the IGV gap amount is equal to the first IGV allocation amount D1 minus the IGV actual attendance amount, if it is judged to be no, the IGV gap amount is equal to the second IGV allocation amount D2 minus the IGV actual attendance amount, and the IGV gap amount is used as the fuzzy control input; then executing step S3; Step S3, synthesizing the IGV control amount according to the formulated fuzzy rules; then executing step S4; Step S4, then defuzzify the IGV control amount into the precise value of the IGV quantity; then execute step S5; Step S5, adjusting the IGV attendance value according to the accurate value of the IGV quantity obtained in step S4; then executing step S6; Step S6, judging whether the IGV attendance adjusted in step S5 meets the operation requirements of the automated container terminal, if it is judged to be yes, the fuzzy control of the IGV attendance is completed, if it is judged to be no, the step S1 is executed repeatedly.

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