Artificial Intelligence-Based Container Terminal Loading and Unloading Resource Scheduling System and Method

Abstract

The present invention discloses a container terminal loading and unloading resource scheduling system and method based on artificial intelligence, which relates to the field of loading and unloading resource scheduling. By constructing the mapping relationship between the numerical values of the state parameters of each type of container loading and unloading resource at the start and end moments of each loading and unloading stage, the state parameters of each type of container loading and unloading resource at the end moment of each stage in the current loading and unloading process are mapped subsequently, and then the loading and unloading priority data of multiple containers are adjusted, ensuring that the state parameters of the resources at the end moment of a certain stage in the current loading and unloading process do not reach the corresponding limit values, thus ensuring the smooth progress of the subsequent loading and unloading process and improving the loading and unloading efficiency. Among them, by introducing random variables, it is possible to make a certain degree of prediction for these uncertain events, and to a certain extent, improve the robustness of the resource scheduling and planning scheme carried out in advance.

Description

Technical Field

[0001] The present invention belongs to the field of handling resource scheduling. Specifically, it particularly relates to a container terminal handling resource scheduling system and method based on artificial intelligence. Background Art

[0002] The existing handling priorities for containers are often set in advance. However, such settings do not consider the dynamic changes in handling resources during the handling process, which may lead to situations where resources are already occupied or insufficient during the handling process, thus unable to ensure the smooth progress of the entire handling process and affecting the handling efficiency. Summary of the Invention

[0003] In view of the problems in the related art, the present invention proposes a container terminal handling resource scheduling system and method based on artificial intelligence to overcome the above-mentioned technical problems existing in the existing related technologies.

[0004] To solve the above technical problems, the present invention is realized through the following technical solutions:

[0005] The present invention is a container terminal handling resource scheduling method based on artificial intelligence, including the following steps:

[0006] S1. Collect the state parameters of container handling resources and the scale parameters of corresponding containers at the start and end times of each handling stage of historical container handling to obtain a historical container handling resource state parameter matrix set and a historical container scale parameter matrix;

[0007] S2. Use the historical container handling resource state parameter matrix set and the historical container scale parameter matrix to construct a final container handling resource state parameter moment mapping equation matrix;

[0008] S3. Set the handling priority data of each current container to obtain a current initial container handling priority data set;

[0009] S4. Through a loop, input the state parameters of handling resources and the scale data of corresponding containers at the start time of each current handling stage into the corresponding mapping equations in the final container handling resource state parameter moment mapping equation matrix to obtain a mapping data set; then adjust the current initial container handling priority data set according to the number of resource state parameters reaching the corresponding resource state limit in the mapping data set to obtain a current final container handling priority data set;

[0010] In this solution, first, a mapping relationship is constructed between the numerical values of the state parameters of each type of container handling resource at the start and end times of each handling stage, so as to map the state parameters of each type of container handling resource at the end time of each stage in the current handling process subsequently. Then, according to the state parameters of each type of container handling resource at the end time of each stage obtained by the mapping, the handling priority data of multiple containers are adjusted, ensuring that the state parameter of the resource at the end time of a certain stage in the current handling process does not reach the corresponding limit value, thus ensuring the smooth progress of the subsequent handling process and improving the handling efficiency.

[0011] Preferably, S1 includes the following steps:

[0012] S11. Set the state parameters of several types of terminal container handling resources to obtain a set of container handling resource state parameter types; divide the process of handling the current container into stages to obtain the current container handling stage set; then set the scale parameters of several types of containers to obtain a set of container scale parameter types;

[0013] S12. In cooperation with the set of container handling resource state parameter types, the set of container scale parameter types, and the current container handling stage set, collect the state parameters of the container handling resources and the scale parameters of the corresponding containers at the start and end times of each handling stage of several groups of historical container handling at the same terminal, to obtain a set of historical container handling resource state parameter matrices and a historical container scale parameter matrix;

[0014] By setting the set of container handling resource state parameter types, which is used for the quantitative description of the resources required for handling various types of containers at the terminal, it provides a basis for collecting the corresponding resource type parameters for the disassembly of containers at the terminal subsequently; since the resources required in different stages are different during the process of handling containers; based on this, the process of handling containers is divided into stages, thereby improving the accuracy of subsequent resource scheduling; in addition, by collecting the set of historical container handling resource state parameter matrices and the historical container scale parameter matrix, it provides a basis for constructing the mapping equation between the state parameters of container handling resources at the start and end times of different stages subsequently.

[0015] Preferably, S2 includes the following steps:

[0016] S21. In cooperation with the set of container handling resource state parameter types, the current container handling stage set, and the set of container scale parameter types, construct an initial mapping equation for each state parameter of the container handling resource corresponding to the end time of each container handling stage, to obtain an initial container handling resource state parameter end time mapping equation matrix;

[0017] S22. Adjust each initial container handling resource status parameter engraving mapping equation in the initial container handling resource status parameter engraving mapping equation matrix by using the historical container handling resource status parameter matrix set and the historical container scale parameter matrix, so as to obtain the final container handling resource status parameter engraving mapping equation matrix;

[0018] By using the container handling resource status parameter data at the start time of the container loading and unloading phase and the scale parameters of various types of containers as the independent variables of the initial container handling resource status parameter engraving mapping equation, it is convenient to subsequently realize the direct mapping from the container handling resource status parameter data and the scale parameters of various types of containers at the start time to the container handling resource status parameter data at the end time; Due to the uncertainty of the container handling resource status parameter data, such as events like ship delays and equipment failures, etc.; Based on this, by introducing random variables, it is possible to make a certain degree of prediction for these uncertain events, thereby improving the robustness of the resource scheduling plan for early resource scheduling to a certain extent; Among them, by using the collected historical data to adjust the initial container handling resource status parameter engraving mapping equation, the fitting degree of the final container handling resource status parameter engraving mapping equation to the container handling resource scheduling in reality is improved, and further the accuracy of the subsequent resource scheduling plan specified by the final container handling resource status parameter engraving mapping equation is improved.

[0019] Preferably, S22 includes the following steps:

[0020] S221. Substitute the data at the start time of each phase of the historical container handling resource status parameter matrix set and the data in the historical container scale parameter matrix into the corresponding initial container handling resource status parameter engraving mapping equation in the initial container handling resource status parameter engraving mapping equation matrix for mapping, so as to obtain the historical engraving container handling resource status parameter matrix set;

[0021] S222. Set the engraving container handling resource status parameter mapping error threshold; Calculate the error data between the historical engraving container handling resource status parameter matrix set and the container handling resource status parameter at the end time of the same phase and the same type in the historical container handling resource status parameter matrix set, so as to obtain the historical engraving container handling resource status parameter mapping error data matrix;

[0022] S223. When there is mapping error data greater than or equal to the mapping error threshold of the historical engraved container handling resource status parameter in the historical engraved container handling resource status parameter mapping error data matrix, use the initial container handling resource status parameter engraved mapping equation corresponding to this mapping error data as the container handling resource status parameter engraved mapping equation to be adjusted; adjust the container handling resource status parameter engraved mapping equation to be adjusted until there is no mapping error data greater than or equal to the mapping error threshold of the historical engraved container handling resource status parameter in the historical engraved container handling resource status parameter mapping error data matrix; otherwise, there is no need to adjust the container handling resource status parameter engraved mapping equation to be adjusted.

[0023] By substituting the collected historical data into the corresponding initial container handling resource status parameter engraved mapping equation for mapping, and then calculating the error between the mapping result and the actual data, the mapping accuracy rate of the initial container handling resource status parameter engraved mapping equation can be detected; and then it is determined whether it is necessary to adjust the initial container handling resource status parameter engraved mapping equation; among them, by setting the mapping error threshold of the engraved container handling resource status parameter, a quantitative determination index is provided for determining whether it is necessary to adjust the initial container handling resource status parameter engraved mapping equation.

[0024] Preferably, in S223, the Harris hawk optimization algorithm is used to adjust the container handling resource status parameter engraved mapping equation to be adjusted.

[0025] Preferably, S3 includes the following steps:

[0026] S31. Take the first current container handling stage in the current container handling stage set as the current container handling stage to be adjusted; set several containers that need to be handled currently to obtain the current container set.

[0027] S32. Set the handling priority data of each container in the current container set to obtain the current initial container handling priority data set; set the value limit corresponding to the status parameter of each type of container handling resource to obtain the container handling resource status parameter limit value set; in combination with the current initial container handling priority data set, take the container with the highest priority as the current container to be handled.

[0028] Since it is necessary to determine the order of loading and unloading the containers, setting the current initial container loading and unloading priority data set determines the order for subsequent loading and unloading of the current containers; by setting the value limits corresponding to the loading and unloading resource status parameters of each type of container, when a certain loading and unloading stage is completed, if a certain status parameter reaches the limit value, the subsequent loading and unloading process is completed, resulting in an increase in the loading and unloading time, providing a quantitative basis for subsequent determination of whether it is necessary to adjust the loading and unloading priorities of each current container.

[0029] Preferably, S4 includes the following steps:

[0030] S41. In cooperation with the container loading and unloading resource status parameter type set and the container scale parameter type set, obtain various types of loading and unloading resource status parameters at the start time of the current container to be adjusted for loading and unloading, and obtain the container loading and unloading resource status parameter set at the current start time; then collect the scale parameters of each container in the current container set to obtain the current container scale parameter matrix;

[0031] S42. Input the scale parameter corresponding to the current container to be loaded and unloaded in the current container scale parameter matrix and each status parameter in the container loading and unloading resource status parameter set at the current start time into the corresponding mapping equation in the final container loading and unloading resource status parameter end-time mapping equation matrix for mapping, and obtain the container loading and unloading resource status parameter set at the current end time;

[0032] When there is a resource status parameter in the container loading and unloading resource status parameter set at the current end time that is the same as the corresponding resource status parameter limit value in the container loading and unloading resource status parameter limit value set, adjust the priority data corresponding to the containers that have not started loading and unloading or have not been completed in the current initial container loading and unloading priority data set until there is no resource status parameter in the container loading and unloading resource status parameter set at the current end time that is the same as the corresponding resource status parameter limit value in the container loading and unloading resource status parameter limit value set; otherwise, there is no need to adjust the current initial container loading and unloading priority data set; after the adjustment is completed, use the next loading and unloading stage of the current container to be adjusted for loading and unloading as the current container to be adjusted for loading and unloading, replace the container loading and unloading resource status parameter set at the current start time with the container loading and unloading resource status parameter set at the current end time, and repeat S41 and S42 until the current container to be adjusted for loading and unloading is the last loading and unloading stage in the current container loading and unloading stage set;

[0033] S43. After the current container to be loaded and unloaded is loaded and unloaded, use the current container corresponding to the priority data that is second only to the current container to be loaded and unloaded in the current initial container loading and unloading priority data set as the current container to be loaded and unloaded, and repeat S41, S42, and S43 until all the containers in the current container set are loaded and unloaded. Use the current adjusted container loading and unloading priority data set obtained after adjusting the priority data during the loading and unloading process of the last current container in the current container set as the current final container loading and unloading priority data set;

[0034] Since the sizes of different containers are different, the loading and unloading order has a certain impact on the entire loading and unloading process. For example, combining large containers and small containers for transportation results in higher utilization of the transportation space. Conversely, the space utilization rate is lower. Based on this, by cycling through each current container and each loading and unloading stage, and according to the loading and unloading conditions of the corresponding containers in each stage, dynamically optimize the loading and unloading order of the current containers to ensure that all current containers can be successfully loaded and unloaded, which not only ensures the successful completion of the loading and unloading but also improves the efficiency of the loading and unloading process.

[0035] Preferably, obtaining various types of loading and unloading resource status parameters at the start time of the current container to be adjusted for loading and unloading stage in S41 to obtain the current start time container loading and unloading resource status parameter set includes the following steps:

[0036] S411. Set several historical time points before the start of the current loading and unloading process to obtain a historical time point set; in conjunction with the container loading and unloading resource status parameter type set and the historical time point set, collect each type of container loading and unloading resource status parameter corresponding to each historical time point to obtain the current historical container loading and unloading resource status parameter matrix;

[0037] S412. According to the current historical container loading and unloading resource status parameter matrix and using a BP neural network model, predict various types of loading and unloading resource status parameters at the start time of the current container to be adjusted for loading and unloading stage to obtain the current start time container loading and unloading resource status parameter set;

[0038] The BP neural network model can map input data to output data. This mapping relationship can be linear or non-linear and is suitable for processing complex data patterns. Mathematical theory proves that a three-layer neural network can approximate any non-linear continuous function with arbitrary accuracy, which makes it particularly suitable for solving problems with complex internal mechanisms. After training, it can predict data that has not been seen before because it has a memory function and can remember the data patterns in training. Based on the above advantages, this solution uses the BP neural network model to predict various types of loading and unloading resource status parameters at the start time of the current loading and unloading stage to be adjusted, ensuring the accuracy of the predicted data, and further ensuring the accuracy of adjusting the loading and unloading priority data of the container according to the predicted data.

[0039] Preferably, the adjustment of the priority data corresponding to the containers that have not started loading and unloading or have not completed loading and unloading in the current initial container loading and unloading priority data set in S42 includes the following steps:

[0040] S421. Set the value range of each priority data corresponding to the containers that have not started loading and unloading or have not completed loading and unloading in the current initial container loading and unloading priority data set to obtain the current priority data value range set; construct a Harris hawk population for loading and unloading priority adjustment; set the maximum number of iterations of the Harris hawk population for loading and unloading priority adjustment to and the current number of iterations to , which are respectively recorded as the maximum number of loading and unloading adjustment iterations and the current number of loading and unloading adjustment iterations;

[0041] S422. Set the initial position of each Harris hawk in the Harris hawk population for loading and unloading priority adjustment according to the current priority data value range set to obtain the second initial position matrix;

[0042] S423. Construct the fitness function of the Harris hawk population for loading and unloading priority adjustment;

[0043] S424. Start iteration. Before iteration, set the current number of loading and unloading adjustment iterations to 1; in the first round of iteration, calculate the fitness value of the initial position of each Harris hawk in the second initial position matrix using the fitness function of the Harris hawk population for loading and unloading priority adjustment to obtain the third fitness value set; take the maximum fitness value in the third fitness value set and the corresponding initial position of the Harris hawk as the third global best fitness and the third global best position respectively; update the initial position of each Harris hawk in the second initial position matrix according to the third global best fitness and the third global best position; after the update is completed, add 1 to the current number of loading and unloading adjustment iterations and enter the next round of iteration;

[0044] In each other iteration process, the fitness function of the Harris hawk population adjusted by the loading and unloading priority is used to calculate the fitness value of the position of each Harris hawk in the Harris hawk population adjusted by the loading and unloading priority updated in the previous iteration process, and a fourth fitness value set is obtained; the maximum fitness value in the fourth fitness value set and the position of the corresponding Harris hawk are respectively used as the fourth global best fitness and the fourth global best position; according to the fourth global best fitness and the fourth global best position, the position of each Harris hawk in the Harris hawk population adjusted by the loading and unloading priority updated in the previous iteration process is updated; after the update is completed, the current iteration number of the loading and unloading adjustment is incremented by 1 and the next iteration is entered;

[0045] S425. When the iteration stops, and the second final global best fitness and the second final global best position are obtained; otherwise, the iteration continues until it reaches; the second final global best fitness is used as the optimized number of loading and unloading limits reached. When the optimized number of loading and unloading limits reached is 0, the adjustment is completed, and the second final global best position is used as the current adjusted container loading and unloading priority data set; otherwise, return to S424 to continue the iteration until the optimized number of loading and unloading limits reached is 0;

[0046] In this solution, the Harris hawk optimization algorithm is used to adjust each priority data corresponding to the containers that have not started loading and unloading or have not completed loading and unloading in the current initial container loading and unloading priority data set, and the number of resource state parameters in the current container loading and unloading resource state parameter set at the end moment that are the same as the resource state parameter limit values in the resource state parameter and container loading and unloading resource state parameter limit value set is used as the fitness function; therefore, as the iteration progresses, the number of resource state parameters in the current container loading and unloading resource state parameter set at the end moment that are the same as the resource state parameter limit values in the resource state parameter and container loading and unloading resource state parameter limit value set becomes fewer and fewer, and finally there are no resource state parameters that are the same as the corresponding limit values, thus ensuring the smooth progress of the subsequent loading and unloading process.

[0047] The container terminal loading and unloading resource scheduling system based on artificial intelligence includes a current loading and unloading stage setting module, a container loading and unloading related parameter setting module, a historical container loading and unloading data collection module, a container loading and unloading resource state parameter moment mapping equation construction module, a current container loading and unloading priority set data setting module, and a current container loading and unloading priority data adjustment module.

[0048] The present invention has the following beneficial effects:

[0049] 1. In the present invention, by constructing the mapping relationship between the numerical values of the status parameters of each type of container handling resource at the start and end moments of each handling stage, it is possible to map the status parameters of each type of container handling resource at the end moment of each stage in the current handling process, and then adjust the handling priority data of multiple containers according to the status parameters of each type of container handling resource at the end moment obtained by the mapping, ensuring that the status parameter of a certain stage at the end moment in the current handling process does not reach the corresponding limit value, thus ensuring the smooth progress of the subsequent handling process and improving the handling efficiency.

[0050] 2. In the present invention, by dividing the process of container handling into stages, the accuracy of subsequent resource scheduling is improved.

[0051] 3. In the present invention, by introducing random variables, it is possible to make a certain degree of prediction for these uncertain events, thereby improving the robustness of the resource scheduling plan for advance planning to a certain extent.

[0052] 4. In the present invention, by using the Harris Hawk Optimization Algorithm to adjust each priority data corresponding to the containers that have not started handling or have not completed handling in the current initial container handling priority dataset, as the iteration progresses, the number of status parameters in the current container handling resource status parameter set at the end moment that are the same as the corresponding status parameter limit values in the container handling resource status parameter limit value set becomes fewer and fewer, and finally there are no status parameters that are the same as the corresponding limit values, thus ensuring the smooth progress of the subsequent handling process.

[0053] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0055] Figure 1 is a schematic flow chart of the container terminal handling resource scheduling method based on artificial intelligence of the present invention;

[0056] Figure 2 is a schematic flow chart of constructing the final container handling resource status parameter end moment mapping equation matrix of the present invention;

[0057] Figure 3Schematic diagram of the process for adjusting the current initial container loading and unloading priority data set in the present invention;

[0058] Figure 4 Schematic diagram of the modules of the container terminal loading and unloading resource scheduling system based on artificial intelligence in the present invention. Detailed implementation manners

[0059] Next, the technical solutions in the embodiments of the invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the invention. Obviously, the described embodiments are only a part of the embodiments of the invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the invention without creative efforts shall fall within the scope of protection of the invention.

[0060] Embodiment 1

[0061] Please refer to Figures 1-3 , this embodiment is an artificial intelligence-based container terminal loading and unloading resource scheduling method, including the following steps:

[0062] S1. Collect the container loading and unloading resource status parameters and the corresponding container scale parameters at the start time and end time of each loading and unloading stage of several groups of historical container loading and unloading, and obtain the historical container loading and unloading resource status parameter matrix set and the historical container scale parameter matrix;

[0063] The S1 includes the following steps:

[0064] S11. Set several types of terminal container loading and unloading resource status parameters to obtain the container loading and unloading resource status parameter type set; divide the process of current container loading and unloading into stages to obtain the current container loading and unloading stage set; then set several types of container scale parameters to obtain the container scale parameter type set;

[0065] The container loading and unloading resource status parameter type set includes quay crane / gantry crane busy / idle status, truck position and load, yard bin occupancy rate, ship operation priority, etc.; the container scale parameter type set includes total length, total width, total height, door frame opening length, and door frame opening width, etc.; among them, the process of current container loading and unloading can be divided into stages according to time or according to the steps of loading and unloading.

[0066] Among them, the busy / idle status of quay cranes / yard cranes can be represented by binary coding (0 - idle, 1 - in operation, 2 - faulty), such as [Quay Crane 1: 1, Quay Crane 2: 0, Yard Crane: 2]. Data can be obtained using a PLC controller or device IoT sensors (such as motor current monitoring); The position of the container truck can be represented by longitude and latitude, and the load of the container truck is represented as 0: empty load, 1: full load; The position and load of the container truck can be obtained through GPS / UWB (Ultra-Wideband) and on-vehicle weight sensors respectively; The occupancy rate of the yard storage positions can be divided by block. For example, the number of occupied storage positions / total number of storage positions in Area A01 and Area B02 is represented as {"A01": 0.85, "B02": 0.3}, and data can be collected through yard block scanners (lidar or cameras); The priority of ship operations: The priority score can be calculated based on the ship type (liner > bulk carrier).

[0067] S12. In coordination with the set of container handling resource status parameter types, the set of container scale parameter types, and the set of current container handling stages, collect several groups of historical container handling resource status parameters and the scale parameters of the corresponding containers at the start and end times of each handling stage during container handling at the same terminal, to obtain the historical container handling resource status parameter matrix set and the historical container scale parameter matrix a 2, a 1i indicating the historical container handling resource status parameter matrix corresponding to the i th group of historical container handling collected, indicating the total number of groups of historical container handling processes collected; a 1i and a 2 are as follows respectively,

[0068] ;

[0069] ;

[0070] Among them, and respectively represent a 1i the j th type of quay container handling resource status parameter at the start and end times of the k th handling stage in indicating the total number of set quay container handling resource status parameter types, indicating the total number of set container handling stages; Table indicates the i th type of scale parameter of the container corresponding to the th group of historical container handling collected;

[0071] S2. Construct the final container handling resource status parameter moment mapping equation matrix by using the historical container handling resource status parameter matrix set and the historical container scale parameter matrix;

[0072] The said S2 includes the following steps:

[0073] S21. Cooperate with the container handling resource status parameter type set, the current container handling stage set and the container scale parameter type set to construct the initial mapping equation of each type of container handling resource status parameter corresponding to the end moment of each container handling stage, and obtain the initial container handling resource status parameter moment mapping equation matrix ; as follows,

[0074] ;

[0075] Wherein, represents the initial mapping equation of the j th type of container handling resource status parameter corresponding to the end moment of the k th container stuffing and handling stage; as follows,

[0076] ;

[0077] Wherein, b jk1 is the dependent variable, representing the j th type of container handling resource status parameter data at the end moment of the k th container stuffing and handling stage; b jk2 is the mapping relationship, representing the combined relationship of each independent variable in , such as product, addition, exponentiation, direct proportion and inverse proportion, etc.; b jk2 is the st independent variable, representing the j th type of container handling resource status parameter data at the start moment of the k th container stuffing and handling stage; is the th independent variable, representing the th type of scale parameter of the container; is a random variable constructed for , representing a random perturbation, such as the probability of quay crane shutdown increasing due to sudden weather changes;

[0078] S22. Adjust each initial container handling resource status parameter engraving mapping equation in the initial container handling resource status parameter engraving mapping equation matrix by using the historical container handling resource status parameter matrix set and the historical container scale parameter matrix to obtain the final container handling resource status parameter engraving mapping equation matrix;

[0079] The S22 includes the following steps:

[0080] S221. Substitute the data at the start time of each stage of the historical container handling resource status parameter matrix set and the data in the historical container scale parameter matrix into the corresponding initial container handling resource status parameter engraving mapping equation in the initial container handling resource status parameter engraving mapping equation matrix for mapping to obtain the historical engraving container handling resource status parameter matrix set , indicating the historical engraving container handling resource status parameter matrix obtained by mapping the historical container handling resource status parameter matrix corresponding to the i th group of historical container handling; as follows,

[0081] ;

[0082] Among them, indicates the j rd type of quay container handling resource status parameter data at the end time of the k th handling stage obtained by mapping;

[0083] S222. Set the engraving container handling resource status parameter mapping error threshold; calculate the error data between the container handling resource status parameters at the end time of the same stage and the same type in the historical engraving container handling resource status parameter matrix set and the historical container handling resource status parameter matrix set to obtain the historical engraving container handling resource status parameter mapping error data matrix ; as follows,

[0084] ;

[0085] Among them, indicates the error data between the j th type of container handling resource status parameter corresponding to the end time of the k th container handling stage between the historical engraving container handling resource status parameter matrix set and the historical container handling resource status parameter matrix set; the calculation formula is as follows,

[0086] ;

[0087] S223. When there is mapping error data greater than or equal to the engraving container handling resource status parameter mapping error threshold in the historical engraving container handling resource status parameter mapping error data matrix, use the initial container handling resource status parameter engraving mapping equation corresponding to this mapping error data as the container handling resource status parameter engraving mapping equation to be adjusted; adjust the container handling resource status parameter engraving mapping equation to be adjusted until there is no mapping error data greater than or equal to the engraving container handling resource status parameter mapping error threshold in the historical engraving container handling resource status parameter mapping error data matrix; otherwise, there is no need to adjust the container handling resource status parameter engraving mapping equation to be adjusted;

[0088] The adjustment of the container handling resource status parameter engraving mapping equation to be adjusted in S223 includes the following steps:

[0089] S2231. Set the value range of each constant coefficient in the container handling resource status parameter engraving mapping equation to be adjusted to obtain a set of value ranges for the constant coefficients of the engraving mapping equation to be adjusted c 1; as follows,

[0090] ;

[0091] Among them, , represents the lower limit and upper limit of the value of the i -th constant coefficient in the container handling resource status parameter engraving mapping equation to be adjusted, represents the total number of constant coefficients of the container handling resource status parameter engraving mapping equation to be adjusted;

[0092] Construct a Harris hawk population for adjusting the engraving mapping of container handling resource status parameters; set the maximum number of iterations of the Harris hawk population for adjusting the engraving mapping of container handling resource status parameters to be and the current number of iterations to be , which are respectively recorded as the maximum number of iterations of engraving mapping and the current number of iterations of engraving mapping; the number of search space dimensions of the Harris hawk population for adjusting the engraving mapping of container handling resource status parameters is the same as ;

[0093] S2232. Generate the initial position of each Harris hawk in the Harris hawk population for adjusting the engraving mapping of container handling resource status parameters according to the set of value ranges for the constant coefficients of the engraving mapping equation to be adjusted to obtain a first initial position matrix ; as follows,

[0094] ;

[0095] Among them, represents the initial position of the j th Harris hawk in the Harris hawk population for the adjustment of the moment mapping of the container handling resource status parameters on the i th constant coefficient dimension of the moment mapping equation of the container handling resource status parameters to be adjusted, d 1 represents the size of the Harris hawk population for the adjustment of the moment mapping of the container handling resource status parameters; The generation formula of

[0096] ;

[0097] In the formula, rand 1ji represents a random number generated between 0 and 1 for ;

[0098] S2233. Construct the fitness function of the Harris hawk population for the adjustment of the moment mapping of the container handling resource status parameters ; as follows,

[0099] ;

[0100] In the formula, represents substituting a set of constant coefficients obtained in each round of iteration into the moment mapping equation of the container handling resource status parameters to be adjusted, and then substituting the data at the start time of each stage of the historical container handling resource status parameter matrix set and the corresponding data in the historical container scale parameter matrix into the moment mapping equation of the container handling resource status parameters to be adjusted for mapping to obtain the error between the obtained data and the corresponding actual data;

[0101] S2234. Start the iteration. Before the iteration, set the current iteration number of the moment mapping to 1; in the first round of iteration, use the fitness function of the Harris hawk population for the adjustment of the moment mapping of the container handling resource status parameters to calculate the fitness values of the initial positions of each Harris hawk in the first initial position matrix to obtain the first fitness value set; take the maximum fitness value in the first fitness value set and the corresponding initial position of the Harris hawk as the first global best fitness and the first global best position respectively; update the initial positions of each Harris hawk in the first initial position matrix according to the first global best fitness and the first global best position; after the update is completed, add 1 to the current iteration number of the moment mapping and enter the next round of iteration;

[0102] In each other round of iteration, use the fitness function of the Harris hawk population for the adjustment of the moment mapping of the container handling resource status parameters Calculate the fitness value of each Harris hawk's position in the Harris hawk population adjusted by the container handling resource status parameter knot mapping updated in the previous iteration process to obtain the second fitness value set; take the maximum fitness value in the second fitness value set and the corresponding position of the Harris hawk as the second global best fitness and the second global best position respectively; update the positions of each Harris hawk in the Harris hawk population adjusted by the container handling resource status parameter knot mapping updated in the previous iteration process according to the second global best fitness and the second global best position; after the update is completed, add 1 to the knot mapping current iteration number and enter the next iteration;

[0103] S2235. When is satisfied, stop the iteration to obtain the first final global best fitness and the first final global best position; otherwise, continue the iteration until is satisfied; take the first final global best fitness as the optimized mapping error data; when the optimized mapping error data is less than the knot container handling resource status parameter mapping error threshold, substitute each position component of the first final global best position into the knot mapping equation of the container handling resource status parameter to be adjusted and replace the corresponding mapping equation in the initial container handling resource status parameter knot mapping equation matrix, and the adjustment is completed; otherwise, return to S2234 to continue the iteration until the optimized mapping error data is less than the knot container handling resource status parameter mapping error threshold;

[0104] The Harris hawk optimization algorithm can conduct extensive searches within the entire search space during the exploration stage, effectively avoiding the problem of falling into local optimal solutions and increasing the possibility of finding the global optimal solution; during the exploitation stage, the algorithm can conduct fine searches in the area near the current optimal solution according to different strategies, further improving the quality of the solution and having strong local search capabilities; it has good adaptability to different types of optimization problems and different initial conditions, and can still show good performance when facing complex optimization problems; based on the above advantages, the Harris hawk optimization algorithm is adopted in this solution to iteratively adjust multiple constant coefficients of the knot mapping equation of the container handling resource status parameter to be adjusted, and the mapping accuracy rate of the knot mapping equation of the container handling resource status parameter to be adjusted is used as the fitness function; therefore, as the iteration progresses, the mapping accuracy rate of the knot mapping equation of the container handling resource status parameter to be adjusted becomes higher and higher, and finally meets the mapping requirements;

[0105] S3. Set the handling priority data of each current container to obtain the current initial container handling priority data set;

[0106] S3 includes the following steps:

[0107] S31. Take the first current container loading and unloading stage in the current container loading and unloading stage concentration as the current container loading and unloading stage to be adjusted; set a number of containers to be loaded and unloaded currently to obtain the current container set.

[0108] S32. Set the loading and unloading priority data for each container in the current container set to obtain the current initial container loading and unloading priority data set; the priority data in the current initial container loading and unloading priority data set is represented by natural numbers, and the larger the value, the higher the priority; set the value limits corresponding to the state parameters of each type of container loading and unloading resource to obtain the container loading and unloading resource state parameter limit value set; for example, the yard space occupancy rate reaches 100%, and the busy / idle status of the quay crane / yard crane is 1 (in operation) or 2 (fault), etc.; in combination with the current initial container loading and unloading priority data set, take the container with the highest priority as the current container to be loaded and unloaded.

[0109] S4. Input the state parameters of the loading and unloading resources at the start time of each current loading and unloading stage and the scale data of the corresponding containers into the corresponding mapping equations in the final container loading and unloading resource state parameter end-time mapping equation matrix through a loop to obtain a mapping data set; then adjust the current initial container loading and unloading priority data set according to the number of resource state parameters that reach the corresponding resource state limit in the mapping data set to obtain the current final container loading and unloading priority data set.

[0110] S4 includes the following steps:

[0111] S41. In combination with the container loading and unloading resource state parameter type set and the container scale parameter type set, obtain the state parameters of various types of loading and unloading resources at the start time of the current container loading and unloading stage to be adjusted to obtain the current start-time container loading and unloading resource state parameter set; then collect the scale parameters of each container in the current container set to obtain the current container scale parameter matrix.

[0112] The steps for obtaining the state parameters of various types of loading and unloading resources at the start time of the current container loading and unloading stage to be adjusted in S41 to obtain the current start-time container loading and unloading resource state parameter set include the following steps:

[0113] S411. Set a number of historical time points before the start of the current loading and unloading process to obtain the historical time point set; in combination with the container loading and unloading resource state parameter type set and the historical time point set, collect the state parameters of each type of container loading and unloading resource corresponding to each historical time point to obtain the current historical container loading and unloading resource state parameter matrix.

[0114] S412. Predict various types of handling resource status parameters at the start time of the current container handling stage to be adjusted based on the current historical container handling resource status parameter matrix and using a BP neural network model, and obtain the container handling resource status parameter set at the current start time;

[0115] S42. Input the scale parameter corresponding to the current container to be handled in the current container scale parameter matrix and each status parameter in the container handling resource status parameter set at the current start time into the corresponding mapping equation in the final container handling resource status parameter end-time mapping equation matrix for mapping, and obtain the container handling resource status parameter set at the current end time;

[0116] When there is a resource status parameter in the container handling resource status parameter set at the current end time that is the same as the corresponding resource status parameter limit value in the container handling resource status parameter limit value set, adjust the priority data corresponding to the containers that have not started handling or have not completed handling in the current initial container handling priority data set until there is no resource status parameter in the container handling resource status parameter set at the current end time that is the same as the corresponding resource status parameter limit value in the container handling resource status parameter limit value set; otherwise, there is no need to adjust the current initial container handling priority data set; after the adjustment is completed, use the next handling stage of the current container handling stage to be adjusted as the current container handling stage to be adjusted, replace the container handling resource status parameter set at the current start time with the container handling resource status parameter set at the current end time, and repeat S41 and S42 until the current container handling stage to be adjusted is the last handling stage in the current container handling stage set;

[0117] The adjustment of the priority data corresponding to the containers that have not started handling or have not completed handling in the current initial container handling priority data set in S42 includes the following steps:

[0118] S421. Set the value range of each priority data corresponding to the containers that have not started handling or have not completed handling in the current initial container handling priority data set to obtain the current priority data value range set c 2. As follows,

[0119] ;

[0120] Among them, 、 represents the lower limit and upper limit of the value of the i th priority data corresponding to the containers that have not started handling or have not completed handling in the current initial container handling priority data set, Indicates the total number of priority data corresponding to the containers that have not started or completed loading and unloading in the current initial container loading and unloading priority dataset;

[0121] Construct a Harris hawk population for adjusting loading and unloading priorities; set the maximum number of iterations of the Harris hawk population for adjusting loading and unloading priorities to and the current number of iterations to , denoted as the maximum number of iterations for loading and unloading adjustment and the current number of iterations for loading and unloading adjustment respectively; the number of search space dimensions of the Harris hawk population for adjusting loading and unloading priorities is the same as ;

[0122] S422. Set the initial position of each Harris hawk in the Harris hawk population for adjusting loading and unloading priorities according to the current priority data value range set, and obtain the second initial position matrix ;

[0123] ;

[0124] where represents the position component of the initial position of the j th Harris hawk in the Harris hawk population for adjusting loading and unloading priorities on the i th priority data dimension corresponding to the containers that have not started or completed loading and unloading in the current initial container loading and unloading priority dataset, d 2 represents the size of the Harris hawk population for adjusting loading and unloading priorities; The generation formula of

[0125] is as follows,

[0126] In the formula, rand 2ji represents a random number generated between 0 and 1 for ;

[0127] S423. Construct the fitness function of the Harris hawk population for adjusting loading and unloading priorities ; as follows,

[0128] ;

[0129] In the formula, represents the total number of resource status parameters that are the same as the resource status parameter limit values in the container loading and unloading resource status parameter limit set corresponding to the set of resource status parameters of the container loading and unloading resources at the current end time obtained by applying a set of priority data obtained in each iteration process to S32 and executing S41 and S42;

[0130] S424. Start the iteration. Set the current iteration count of the handling adjustment to 1 before the iteration; during the first round of iteration, use the handling priority adjustment to adjust the fitness function of the Harris hawk population. Calculate the fitness value of the initial position of each Harris hawk in the second initial position matrix to obtain the third fitness value set; take the maximum fitness value in the third fitness value set and the corresponding initial position of the Harris hawk as the third global best fitness and the third global best position respectively; update the initial position of each Harris hawk in the second initial position matrix according to the third global best fitness and the third global best position; after the update is completed, increment the current iteration count of the handling adjustment by 1 and enter the next round of iteration.

[0131] During each subsequent round of iteration, use the handling priority adjustment to adjust the fitness function of the Harris hawk population. Calculate the fitness value of the position of each Harris hawk in the Harris hawk population adjusted by the handling priority updated during the previous round of iteration to obtain the fourth fitness value set; take the maximum fitness value in the fourth fitness value set and the corresponding position of the Harris hawk as the fourth global best fitness and the fourth global best position respectively; update the position of each Harris hawk in the Harris hawk population adjusted by the handling priority updated during the previous round of iteration according to the fourth global best fitness and the fourth global best position; after the update is completed, increment the current iteration count of the handling adjustment by 1 and enter the next round of iteration.

[0132] S425. When Stop the iteration to obtain the second final global best fitness and the second final global best position; otherwise, continue the iteration until At this time; take the second final global best fitness as the optimized handling limit reached quantity. When the optimized handling limit reached quantity is 0, the adjustment is completed, and take the second final global best position as the current adjusted container handling priority data set; otherwise, return to S424 to continue the iteration until the optimized handling limit reached quantity is 0.

[0133] S43. After the current container to be handled is loaded and unloaded, take the container corresponding to the priority data that is second only to the current container to be handled in the current initial container handling priority data set as the current container to be handled, and repeat S41, S42, and S43 until all the containers in the current container set are loaded and unloaded. Take the current adjusted container handling priority data set obtained after adjusting the priority data during the handling process of the last current container in the current container set as the current final container handling priority data set.

[0134] Embodiment 2

[0135] Please refer to Figure 4 , this embodiment discloses a container terminal loading and unloading resource scheduling system based on artificial intelligence. The system can implement the method of the above embodiment, including a current loading and unloading stage setting module, a container loading and unloading associated parameter setting module, a historical container loading and unloading data collection module, a container loading and unloading resource status parameter engraving mapping equation construction module, a current container loading and unloading priority set data setting module, and a current container loading and unloading priority data adjustment module;

[0136] The current loading and unloading stage setting module divides the process of loading and unloading the current container into stages to obtain the current container loading and unloading stage set;

[0137] The container loading and unloading associated parameter setting module sets several types of terminal container loading and unloading resource status parameters and container scale parameters to obtain a container loading and unloading resource status parameter type set and a container scale parameter type set;

[0138] The historical container loading and unloading data collection module cooperates with the container loading and unloading resource status parameter type set and the container scale parameter type set to collect the container loading and unloading resource status parameters and the corresponding container scale parameters at the start time and end time of each loading and unloading stage of several groups of historical container loading and unloading, to obtain a historical container loading and unloading resource status parameter matrix set and a historical container scale parameter matrix;

[0139] The container loading and unloading resource status parameter engraving mapping equation construction module uses the historical container loading and unloading resource status parameter matrix set and the historical container scale parameter matrix to construct a final container loading and unloading resource status parameter engraving mapping equation matrix;

[0140] The current container loading and unloading priority set data setting module sets the loading and unloading priority data of each current container to obtain a current initial container loading and unloading priority data set;

[0141] The current container loading and unloading priority data adjustment module inputs the loading and unloading resource status parameters and the corresponding container scale data at the start time of each current loading and unloading stage into the corresponding mapping equation in the final container loading and unloading resource status parameter engraving mapping equation matrix through a loop to obtain the container loading and unloading resource status parameter set at the current end time; then adjusts the current initial container loading and unloading priority data set according to the number of resource status parameters that reach the limit in the container loading and unloading resource status parameter set at the current end time to obtain the current final container loading and unloading priority data set.

[0142] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0143] The preferred embodiments of the invention disclosed above are only used to help illustrate the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the invention, so that those skilled in the art can well understand and utilize the invention.

Claims

1. An artificial intelligence-based container terminal loading and unloading resource scheduling method, characterized in that, It includes the following steps: S1. Collect the container handling resource status parameters and the corresponding container scale parameters at the start and end times of each handling stage of several groups of historical container handling operations, to obtain a set of historical container handling resource status parameter matrices and a historical container scale parameter matrix; S2. Use the set of historical container handling resource status parameter matrices and the historical container scale parameter matrix to construct a final container handling resource status parameter end-time mapping equation matrix; S3. Set the handling priority data for each current container to obtain a current initial container handling priority data set; S4. By looping, input the handling resource status parameters and the corresponding container scale data at the start time of each current handling stage into the corresponding mapping equation in the final container handling resource status parameter end-time mapping equation matrix, to obtain a mapping data set; Then, adjust the current initial container handling priority data set according to the number of resource status parameters reaching the corresponding resource status limit in the mapping data set, to obtain a current final container handling priority data set; S2 specifically includes: Construct an initial mapping equation for each type of container handling resource status parameter corresponding to the end time of each container handling stage, substitute the data of the set of historical container handling resource status parameter matrices and the historical container scale parameter matrix into each constructed initial mapping equation for mapping and adjust each initial mapping equation; S4 specifically includes: Iteratively adjust the current initial container handling priority data set by constructing a handling priority adjustment Harris hawk population; when the maximum number of iterations is reached or the number of optimized handling limits reaches 0, the adjustment is completed.

2. The method for scheduling loading and unloading resources of a container terminal based on artificial intelligence according to claim 1, wherein The said S1 includes the following steps: S11. Set several types of terminal container handling resource status parameters to obtain a set of container handling resource status parameter types; divide the process of current container handling into stages to obtain a set of current container handling stages; then set several types of container scale parameters to obtain a set of container scale parameter types; S12. In cooperation with the set of container handling resource status parameter types, the set of container scale parameter types, and the set of current container handling stages, collect the container handling resource status parameters and the corresponding container scale parameters at the start and end times of each handling stage of several groups of historical container handling operations at the same terminal, to obtain a set of historical container handling resource status parameter matrices and a historical container scale parameter matrix.

3. The method for scheduling loading and unloading resources at a container terminal based on artificial intelligence according to claim 2, wherein The said S2 includes the following steps: S21. In cooperation with the set of container handling resource status parameter types, the set of current container handling stages, and the set of container scale parameter types, construct an initial mapping equation for each type of container handling resource status parameter corresponding to the end time of each container handling stage, to obtain an initial container handling resource status parameter end-time mapping equation matrix; S22. Adjust each initial container handling resource status parameter engraving mapping equation in the initial container handling resource status parameter engraving mapping equation matrix by using the historical container handling resource status parameter matrix set and the historical container scale parameter matrix, so as to obtain the final container handling resource status parameter engraving mapping equation matrix.

4. The method for scheduling container terminal handling resources based on artificial intelligence according to claim 3, characterized in that The S22 includes the following steps: S221. Substitute the data at the start time of each stage of the historical container handling resource status parameter matrix set and the data in the historical container scale parameter matrix into the corresponding initial container handling resource status parameter engraving mapping equations in the initial container handling resource status parameter engraving mapping equation matrix for mapping, so as to obtain the historical engraving container handling resource status parameter matrix set; S222. Set the engraving container handling resource status parameter mapping error threshold, and adjust the initial container handling resource status parameter engraving mapping equation matrix by calculating the error data between the historical engraving container handling resource status parameter matrix set and the historical container handling resource status parameter matrix set.

5. The method for scheduling loading and unloading resources at a container terminal based on artificial intelligence according to claim 4, characterized in that, The S3 includes the following steps: S31. Take the first current container handling stage in the current container handling stage set as the current container handling stage to be adjusted; set several containers that need to be handled currently to obtain the current container set; S32. Set the handling priority data of each container in the current container set to obtain the current initial container handling priority data set; set the value limit corresponding to the status parameter of each type of container handling resource to obtain the container handling resource status parameter limit value set; cooperate with the current initial container handling priority data set, and take the container with the highest priority as the current container to be handled.

6. The method for scheduling loading and unloading resources at a container terminal based on artificial intelligence according to claim 5, wherein The S4 includes the following steps: S41. Cooperate with the container handling resource status parameter type set and the container scale parameter type set to obtain the status parameters of various types of handling resources at the start time of the current container handling stage to be adjusted, so as to obtain the current start time container handling resource status parameter set; then collect the scale parameters of each container in the current container set to obtain the current container scale parameter matrix; S42. Substitute the scale parameter corresponding to the current container to be handled in the current container scale parameter matrix and each status parameter in the current start time container handling resource status parameter set into the corresponding mapping equation in the final container handling resource status parameter engraving mapping equation matrix for mapping, so as to obtain the current end time container handling resource status parameter set; When there is a resource status parameter in the current end-time container handling resource status parameter set that is the same as the corresponding resource status parameter limit value in the container handling resource status parameter limit value set, adjust the priority data corresponding to the containers that have not started handling or have not completed handling in the current initial container handling priority data set until there is no resource status parameter in the current end-time container handling resource status parameter set that is the same as the corresponding resource status parameter limit value in the container handling resource status parameter limit value set; otherwise, there is no need to adjust the current initial container handling priority data set; after the adjustment is completed, take the next handling stage of the current container handling stage to be adjusted as the current container handling stage to be adjusted, replace the current start-time container handling resource status parameter set with the current end-time container handling resource status parameter set, and repeat S41 and S42 until the current container handling stage to be adjusted is the last handling stage in the current container handling stage set; S43. After the current container to be handled is handled, take the current container corresponding to the priority data that is second only to the current container to be handled in the current initial container handling priority data set as the current container to be handled, and repeat S41, S42, and S43 until all the containers in the current container set are handled. Take the current adjusted container handling priority data set obtained after the adjustment of the priority data during the handling of the last current container in the current container set as the current final container handling priority data set.

7. The method for scheduling loading and unloading resources of a container terminal based on artificial intelligence according to claim 6, characterized in that: In S41, a BP neural network model is used to obtain various types of handling resource status parameters at the start time of the current container handling stage to be adjusted.

8. The method for scheduling loading and unloading resources at a container terminal based on artificial intelligence according to claim 7, wherein, The adjustment of the priority data corresponding to the containers that have not started handling or have not completed handling in the current initial container handling priority data set in S42 includes the following steps: S421. Set the value range of each priority data corresponding to the containers that have not started or completed loading and unloading in the current initial container loading and unloading priority dataset to obtain the current priority data value range set; construct a Harris hawk population for adjusting the loading and unloading priority; set the maximum number of iterations of the Harris hawk population for adjusting the loading and unloading priority to be and the current number of iterations to be , which are respectively recorded as the maximum number of iterations for loading and unloading adjustment and the current number of iterations for loading and unloading adjustment; S422. Set the initial position of each Harris hawk in the Harris hawk population for handling priority adjustment according to the current priority data value range set to obtain a second initial position matrix; S423. Construct the fitness function of the Harris hawk population for handling priority adjustment; S424. Start the iteration; in each iteration process, use the fitness function of the Harris hawk population for handling priority adjustment to calculate the fitness value of the position of each Harris hawk in the Harris hawk population for handling priority adjustment updated in the previous iteration process and update the position of each Harris hawk in the Harris hawk population for handling priority adjustment updated in the previous iteration process; S425. When occurs, stop the iteration to obtain the second final global best fitness and the second final global best position; otherwise, continue the iteration until occurs; use the second final global best fitness as the optimized number of handling limits reached. When the optimized number of handling limits reached is 0, the adjustment is completed, and use the second final global best position as the current adjusted container handling priority data set; otherwise, return to S424 to continue the iteration until the optimized number of handling limits reached is 0.

9. A system for implementing the artificial intelligence-based container terminal handling resource scheduling method according to any one of claims 1-8.

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