Unmanned container truck intelligent scheduling method and system based on quay crane area load

Abstract

The invention relates to an unmanned container truck intelligent scheduling method and system based on quay crane area loads, and the method comprises the steps: U1, dividing a quay crane area into a plurality of sub-areas, setting an independent monitoring point for each sub-area, and monitoring the data information of the number and position of unmanned container trucks in the quay crane sub-areas in real time; and U2, on the basis of the data information of the number and the positions of the unmanned container trucks in the quay crane subareas, constructing an unmanned container truck congestion state prediction model based on a decision tree algorithm, predicting the congestion state of the unmanned container trucks, and obtaining the predicted data information of the congestion state of the unmanned container trucks. According to the method, the quay crane area is effectively prevented from being congested, the operation efficiency is improved, manual intervention is reduced, automatic scheduling is realized, and the labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned container trucks, and in particular to an intelligent dispatching method and system for unmanned container trucks based on quay crane area load. Background Art

[0002] In modern port operations, the quayside crane area is the core area for container loading and unloading. With the adoption of unmanned driving technology, automated guided vehicles (AGVs) are increasingly being used in port operations. However, when a large number of AGVs simultaneously enter the quayside crane area, congestion can easily occur, impacting operational efficiency. Existing technologies lack an effective scheduling method to control the number of AGVs entering the quayside crane area. Summary of the Invention

[0003] In view of the above problems, the present invention provides an unmanned container truck intelligent scheduling method and system based on the load of the quay crane area, which not only effectively prevents congestion in the quay crane area and improves operating efficiency, but also reduces manual intervention, realizes automated scheduling, and reduces labor costs.

[0004] In order to achieve the above-mentioned and other related purposes, the present invention provides the following technical solutions:

[0005] An unmanned container truck intelligent dispatching method based on quay crane area load, the method comprising:

[0006] U1. Divide the quay crane area into multiple sub-areas, set up independent monitoring points in each sub-area, and monitor the number and location of unmanned container trucks in the quay crane sub-area in real time;

[0007] U2. Based on the data information on the number and location of unmanned container trucks in the quayside sub-area, a prediction model for the unmanned container truck congestion state based on a decision tree algorithm is constructed to predict the unmanned container truck congestion state and obtain the predicted data information on the unmanned container truck congestion state;

[0008] U3. Based on the predicted data information on the congestion status of unmanned container trucks and the area of ​​the corresponding sub-region, a basic threshold function H is established to characterize the carrying capacity of unmanned container trucks in the sub-region and obtain data information on the basic threshold of the sub-region;

[0009] U4. Based on the basic threshold data information of the sub-region, combined with the data information of peak and low working periods, weather conditions, and equipment operating status, the basic threshold is optimized using the improved dung beetle optimization algorithm to obtain the optimized sub-region threshold data information;

[0010] U5. Based on the data information of the threshold of the optimized sub-area, if the number of unmanned trucks in the sub-area is greater than the threshold of the optimized sub-area, the vehicles enter the waiting area; if the number of unmanned trucks in the sub-area is less than the threshold of the optimized sub-area, the vehicles in the waiting area are dispatched to enter.

[0011] Furthermore, in step U2, the unmanned container truck congestion prediction model based on the decision tree algorithm is constructed to predict the unmanned container truck congestion state, including:

[0012] U21. Based on the data information of the number and location of unmanned container trucks in the quay crane sub-area, establish the information entropy function Q of the operating status of unmanned container trucks,

[0013]

[0014] Among them, x1 is the data information of the number of unmanned container trucks in the quay bridge sub-area, and x2 is the data information of the position of the unmanned container trucks in the quay bridge sub-area. The information entropy of the operating status of the unmanned container trucks is characterized to obtain the data information of the information entropy of the operating status of the unmanned container trucks;

[0015] U22. Based on the data information of the information entropy of the operating state of the unmanned truck, an information gain function W for unmanned truck congestion is established.

[0016]

[0017] Among them, y is the data information of the information entropy of the unmanned container truck operation state, α1, α2 and α3 are the gain coefficients of the unmanned container truck state, and the information gain value of the unmanned container truck congestion is calculated to obtain the data information of the information gain value of the unmanned container truck congestion;

[0018] U23. Based on the data information of the information gain value of the unmanned truck congestion, a prediction function R of the unmanned truck congestion state is established.

[0019]

[0020] Among them, z is the data information of the information gain value of the unmanned container truck congestion, δ1, δ2 and δ3 are weight coefficients, and the unmanned container truck congestion state is predicted to obtain the data information of the predicted unmanned container truck congestion state.

[0021] Furthermore, the constraints of the weight coefficients δ1, δ2 and δ3 are:

[0022]

[0023] Furthermore, the gain coefficients α1, α2 and α3 of the unmanned truck state are:

[0024]

[0025] Among them, y is the data information of the information entropy of the operating status of the unmanned container truck.

[0026] Furthermore, the basic threshold function H is,

[0027]

[0028] Among them, a1 is the data information of the predicted unmanned truck congestion status, and a2 is the area of ​​the corresponding sub-area.

[0029] Furthermore, in step U4, the optimization of the basic threshold using the improved dung beetle optimization algorithm includes:

[0030] U41. Based on the data information of the basic threshold of the sub-region, construct the chaotic sequence function P of the basic threshold of the sub-region,

[0031]

[0032] Where t is the data information of the basic threshold of the sub-region, r(t) is the normalization function of the sub-region threshold, and h is any constant parameter between 0 and 1. The chaotic sequence of the sub-region threshold is characterized to obtain the data information of the chaotic sequence of the sub-region threshold;

[0033] U42. Based on the chaotic sequence data of the sub-region threshold, peak and trough periods of operation, weather conditions, and equipment operating status data, the dung beetle population is initialized, the population parameters and the maximum number of iterations K are determined, and the data of the initialized dung beetle population is obtained;

[0034] U43. Based on the data information of the initialized dung beetle population, a position update function S is established.

[0035]

[0036] Among them, G is the data information of the initialized dung beetle population, g is the individual of the population, and the basic threshold is optimized to obtain the data information of the threshold of the optimized sub-region.

[0037] Furthermore, the normalized function r(t) of the sub-region threshold is:

[0038]

[0039] Wherein, t is the data information of the basic threshold of the sub-region.

[0040] In order to achieve the above-mentioned and other related objectives, the present invention further provides an unmanned container truck intelligent dispatching system based on quay crane area load for implementing any of the above-mentioned items, the system comprising:

[0041] The regional monitoring module is used to divide the quay crane area into multiple sub-areas, set up independent monitoring points in each sub-area, and monitor the number and location of unmanned container trucks in the quay crane sub-area in real time;

[0042] A prediction module, connected to the regional monitoring module, is used to construct a prediction model for the congestion state of unmanned container trucks based on a decision tree algorithm, predict the congestion state of unmanned container trucks, and obtain data information on the predicted congestion state of unmanned container trucks;

[0043] A sub-region basic threshold module is connected to the prediction module and is used to establish a basic threshold function H, characterize the carrying capacity of unmanned container trucks in the sub-region, and obtain data information of the basic threshold of the sub-region;

[0044] A sub-region basic threshold optimization module is connected to the sub-region basic threshold module and is used to optimize the basic threshold using an improved dung beetle optimization algorithm based on data information of operation peak and trough periods, weather conditions, and equipment operating status, thereby obtaining data information of the optimized sub-region threshold;

[0045] The dispatching control module is connected to the sub-area basic threshold optimization module and is used to dispatch vehicles in the waiting area to enter if the number of unmanned container trucks in the sub-area is greater than the threshold of the optimized sub-area based on the data information of the threshold of the optimized sub-area; if the number of unmanned container trucks in the sub-area is less than the threshold of the optimized sub-area, the vehicles in the waiting area are dispatched to enter.

[0046] Furthermore, the system also includes a communication module connected to each module in the system, which is responsible for information transmission and system integration between various components of the system.

[0047] In order to achieve the above-mentioned objectives and other related objectives, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the unmanned container truck intelligent scheduling methods based on quay crane area load.

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

[0049] 1. The present invention constructs an unmanned container truck congestion prediction model based on a decision tree algorithm to predict the unmanned container truck congestion state. In combination with the establishment of a basic threshold function H, the carrying capacity of unmanned container trucks in a sub-area is characterized. This not only solves the congestion problem caused by the concentrated entry of unmanned container trucks into the quay crane area in the prior art, but also ensures that the number of unmanned container trucks in the quay crane area is always within a reasonable range through dynamic threshold control.

[0050] 2. The present invention optimizes the basic threshold by adopting an improved dung beetle optimization algorithm, which not only solves the problem in the existing technology that it cannot be dynamically adjusted according to actual conditions, but also enables the system to adapt to different operating scenarios through a multi-factor threshold adjustment mechanism, thereby effectively preventing congestion in the quay crane area, improving operating efficiency, reducing manual intervention, realizing automated scheduling, and reducing labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Schematic diagram of the method flow of the present invention;

[0052] Figure 2 A schematic diagram of a process for constructing a congestion prediction model for unmanned container trucks based on a decision tree algorithm according to the present invention;

[0053] Figure 3 Schematic diagram of the process of the improved dung beetle optimization algorithm of the present invention;

[0054] Figure 4 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0055] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0056] Example 1: Figure 1 As shown, an unmanned container truck intelligent scheduling method based on the regional load of the quay crane is provided, the method comprising:

[0057] U1. Divide the quay crane area into multiple sub-areas, set up independent monitoring points in each sub-area, and monitor the number and location of unmanned container trucks in the quay crane sub-area in real time;

[0058] U2. Based on the data information on the number and location of unmanned container trucks in the quayside sub-area, a prediction model for the unmanned container truck congestion state based on a decision tree algorithm is constructed to predict the unmanned container truck congestion state and obtain the predicted data information on the unmanned container truck congestion state;

[0059] U3. Based on the predicted data information on the congestion status of unmanned container trucks and the area of ​​the corresponding sub-region, a basic threshold function H is established to characterize the carrying capacity of unmanned container trucks in the sub-region and obtain data information on the basic threshold of the sub-region;

[0060] U4. Based on the basic threshold data information of the sub-region, combined with the data information of peak and low working periods, weather conditions, and equipment operating status, the basic threshold is optimized using the improved dung beetle optimization algorithm to obtain the optimized sub-region threshold data information;

[0061] U5. Based on the data information of the threshold of the optimized sub-area, if the number of unmanned trucks in the sub-area is greater than the threshold of the optimized sub-area, the vehicles enter the waiting area; if the number of unmanned trucks in the sub-area is less than the threshold of the optimized sub-area, the vehicles in the waiting area are dispatched to enter.

[0062] In this embodiment, if Figure 2 As shown, in step U2, the unmanned container truck congestion state prediction model based on the decision tree algorithm is constructed to predict the unmanned container truck congestion state, including:

[0063] U21. Based on the data information of the number and location of unmanned container trucks in the quay crane sub-area, establish the information entropy function Q of the operating status of unmanned container trucks,

[0064]

[0065] Among them, x1 is the data information of the number of unmanned container trucks in the quay bridge sub-area, and x2 is the data information of the position of the unmanned container trucks in the quay bridge sub-area. The information entropy of the operating status of the unmanned container trucks is characterized to obtain the data information of the information entropy of the operating status of the unmanned container trucks;

[0066] U22. Based on the data information of the information entropy of the operating state of the unmanned truck, an information gain function W for unmanned truck congestion is established.

[0067]

[0068] Among them, y is the data information of the information entropy of the unmanned container truck operation state, α1, α2 and α3 are the gain coefficients of the unmanned container truck state, and the information gain value of the unmanned container truck congestion is calculated to obtain the data information of the information gain value of the unmanned container truck congestion;

[0069] U23. Based on the data information of the information gain value of the unmanned truck congestion, a prediction function R of the unmanned truck congestion state is established.

[0070]

[0071] Among them, z is the data information of the information gain value of the unmanned container truck congestion, δ1, δ2 and δ3 are weight coefficients, and the unmanned container truck congestion state is predicted to obtain the data information of the predicted unmanned container truck congestion state.

[0072] In this embodiment, the constraints of the weight coefficients δ1, δ2 and δ3 are:

[0073]

[0074] In this embodiment, the gain coefficients α1, α2 and α3 in the unmanned truck state are:

[0075] Among them, y is the data information of the information entropy of the operating status of the unmanned container truck.

[0076] In this embodiment, the basic threshold function H is,

[0077]

[0078] Among them, a1 is the data information of the predicted unmanned truck congestion status, and a2 is the area of ​​the corresponding sub-area.

[0079] In this embodiment, three typical sub-areas A, B, and C were selected for testing, and each sub-area was configured with 5 monitoring points. Initially, the basic threshold of each sub-area was set at 20 unmanned container trucks. After a month of data collection and analysis, it was found that the average number of unmanned container trucks in sub-area A during the peak operation period reached 25, while it was only 10 during the off-peak period; sub-area B showed a relatively stable distribution, with about 18 vehicles during the peak period and 8 vehicles during the off-peak period; sub-area C was close to the yard entrance, with a peak period of up to 30 vehicles and a off-peak period of 12 vehicles. Based on the above data, the improved dung beetle optimization algorithm was used to dynamically adjust the basic threshold, and the optimized thresholds of sub-areas A, B, and C were finally determined to be 22, 16, and 28 vehicles, respectively. The experimental results show that compared with the traditional fixed threshold method, the method of the present invention reduces the average waiting time of unmanned container trucks in the sub-area by about 30%, and improves the overall operating efficiency by 15%.

[0080] Example 2: Based on the unmanned container truck intelligent scheduling method based on the quay crane area load in Example 1, the present invention is further illustrated and described below.

[0081] like Figure 1 As shown, an unmanned container truck intelligent scheduling method based on the regional load of the quay crane is provided, the method comprising:

[0082] U1. Divide the quay crane area into multiple sub-areas, set up independent monitoring points in each sub-area, and monitor the number and location of unmanned container trucks in the quay crane sub-area in real time;

[0083] U2. Based on the data information on the number and location of unmanned container trucks in the quayside sub-area, a prediction model for the unmanned container truck congestion state based on a decision tree algorithm is constructed to predict the unmanned container truck congestion state and obtain the predicted data information on the unmanned container truck congestion state;

[0084] U3. Based on the predicted data information on the congestion status of unmanned container trucks and the area of ​​the corresponding sub-region, a basic threshold function H is established to characterize the carrying capacity of unmanned container trucks in the sub-region and obtain data information on the basic threshold of the sub-region;

[0085] U4. Based on the basic threshold data information of the sub-region, combined with the data information of peak and low working periods, weather conditions, and equipment operating status, the basic threshold is optimized using the improved dung beetle optimization algorithm to obtain the optimized sub-region threshold data information;

[0086] U5. Based on the data information of the threshold of the optimized sub-area, if the number of unmanned trucks in the sub-area is greater than the threshold of the optimized sub-area, the vehicles enter the waiting area; if the number of unmanned trucks in the sub-area is less than the threshold of the optimized sub-area, the vehicles in the waiting area are dispatched to enter.

[0087] In this embodiment, if Figure 3 As shown, in step U4, the optimization of the basic threshold using the improved dung beetle optimization algorithm includes:

[0088] U41. Based on the data information of the basic threshold of the sub-region, construct the chaotic sequence function P of the basic threshold of the sub-region,

[0089]

[0090] Where t is the data information of the basic threshold of the sub-region, r(t) is the normalization function of the sub-region threshold, and h is any constant parameter between 0 and 1. The chaotic sequence of the sub-region threshold is characterized to obtain the data information of the chaotic sequence of the sub-region threshold;

[0091] U42. Based on the chaotic sequence data of the sub-region threshold, peak and trough periods of operation, weather conditions, and equipment operating status data, the dung beetle population is initialized, the population parameters and the maximum number of iterations K are determined, and the data of the initialized dung beetle population is obtained;

[0092] U43. Based on the data information of the initialized dung beetle population, a position update function S is established.

[0093]

[0094] Among them, G is the data information of the initialized dung beetle population, g is the individual of the population, and the basic threshold is optimized to obtain the data information of the threshold of the optimized sub-region.

[0095] In this embodiment, the normalized function r(t) of the sub-region threshold is:

[0096]

[0097] Wherein, t is the data information of the basic threshold of the sub-region.

[0098] In this example, the port handles over 100,000 TEUs daily and faces complex scheduling requirements. By introducing the method presented in this invention, the port has achieved refined management of unmanned container trucks in the quay crane area. For example, during a typhoon, the system automatically adjusted the optimization thresholds for each sub-area based on weather data, avoiding congestion caused by inclement weather. Furthermore, by monitoring the operating status of equipment in real time, the system can provide early warning of potential failure risks, thereby reducing the probability of safety incidents. Statistics show that since the system went live, the port's overall operating costs have been reduced by approximately 20%, and customer satisfaction has significantly improved.

[0099] In this embodiment, firstly, by dividing sub-areas and setting independent monitoring points, the status of unmanned container trucks can be accurately grasped; secondly, by combining the decision tree algorithm with the improved dung beetle optimization algorithm, the problem of insufficient adaptability of traditional methods during peak and trough periods is effectively solved; thirdly, by dynamically adjusting the threshold, the port operation efficiency is significantly improved and the operating costs are reduced.

[0100] In this embodiment, if Figure 4 As shown, the present invention further provides an unmanned container truck intelligent dispatching system based on quay crane area load for implementing any of the above, the system comprising:

[0101] The regional monitoring module is used to divide the quay crane area into multiple sub-areas, set up independent monitoring points in each sub-area, and monitor the number and location of unmanned container trucks in the quay crane sub-area in real time;

[0102] A prediction module, connected to the regional monitoring module, is used to construct a prediction model for the congestion state of unmanned container trucks based on a decision tree algorithm, predict the congestion state of unmanned container trucks, and obtain data information on the predicted congestion state of unmanned container trucks;

[0103] A sub-region basic threshold module is connected to the prediction module and is used to establish a basic threshold function H, characterize the carrying capacity of unmanned container trucks in the sub-region, and obtain data information of the basic threshold of the sub-region;

[0104] A sub-region basic threshold optimization module is connected to the sub-region basic threshold module and is used to optimize the basic threshold using an improved dung beetle optimization algorithm based on data information of operation peak and trough periods, weather conditions, and equipment operating status, thereby obtaining data information of the optimized sub-region threshold;

[0105] The dispatching control module is connected to the sub-area basic threshold optimization module and is used to dispatch vehicles in the waiting area to enter if the number of unmanned container trucks in the sub-area is greater than the threshold of the optimized sub-area based on the data information of the threshold of the optimized sub-area; if the number of unmanned container trucks in the sub-area is less than the threshold of the optimized sub-area, the vehicles in the waiting area are dispatched to enter.

[0106] In this embodiment, the system further includes a communication module connected to each module in the system and responsible for information transmission between system components and system integration.

[0107] In this embodiment, the present invention provides a computer-readable storage medium storing a computer program programmed or configured to execute any one of the unmanned container truck intelligent scheduling methods based on quay crane area load.

[0108] Any reference to memory, storage, database or other media used in the embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0109] In summary, the present invention not only effectively prevents congestion in the quay crane area and improves operating efficiency, but also reduces manual intervention, realizes automated scheduling, and reduces labor costs.

[0110] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. An intelligent dispatching method for unmanned container trucks based on quay crane area load, characterized in that: The method comprises: U1. Divide the quay crane area into multiple sub-areas, set up independent monitoring points in each sub-area, and monitor the number and location of unmanned container trucks in the quay crane sub-area in real time; U2. Based on the data information on the number and location of unmanned container trucks in the quayside sub-area, a prediction model for the unmanned container truck congestion state based on a decision tree algorithm is constructed to predict the unmanned container truck congestion state and obtain the predicted data information on the unmanned container truck congestion state; U3. Based on the predicted data information on the congestion status of unmanned container trucks and the area of ​​the corresponding sub-region, a basic threshold function H is established to characterize the carrying capacity of unmanned container trucks in the sub-region and obtain data information on the basic threshold of the sub-region; U4. Based on the basic threshold data information of the sub-region, combined with the data information of peak and low working periods, weather conditions, and equipment operating status, the basic threshold is optimized using the improved dung beetle optimization algorithm to obtain the optimized sub-region threshold data information; U5. Based on the data information of the threshold of the optimized sub-area, if the number of unmanned trucks in the sub-area is greater than the threshold of the optimized sub-area, the vehicles enter the waiting area; if the number of unmanned trucks in the sub-area is less than the threshold of the optimized sub-area, the vehicles in the waiting area are dispatched to enter.

2. The unmanned container truck intelligent dispatching method based on quay crane area load according to claim 1 is characterized in that: In step U2, the construction of the unmanned container truck congestion prediction model based on the decision tree algorithm and the prediction of the unmanned container truck congestion state include: U21. Based on the data information of the number and location of unmanned container trucks in the quay crane sub-area, establish the information entropy function Q of the operating status of unmanned container trucks, Among them, x1 is the data information of the number of unmanned container trucks in the quay bridge sub-area, and x2 is the data information of the position of the unmanned container trucks in the quay bridge sub-area. The information entropy of the operating status of the unmanned container trucks is characterized to obtain the data information of the information entropy of the operating status of the unmanned container trucks; U22. Based on the data information of the information entropy of the operating state of the unmanned truck, an information gain function W for unmanned truck congestion is established. Among them, y is the data information of the information entropy of the unmanned container truck operation state, α1, α2 and α3 are the gain coefficients of the unmanned container truck state, and the information gain value of the unmanned container truck congestion is calculated to obtain the data information of the information gain value of the unmanned container truck congestion; U23. Based on the data information of the information gain value of the unmanned truck congestion, a prediction function R of the unmanned truck congestion state is established. Among them, z is the data information of the information gain value of the unmanned container truck congestion, δ1, δ2 and δ3 are weight coefficients, and the unmanned container truck congestion state is predicted to obtain the data information of the predicted unmanned container truck congestion state.

3. The unmanned container truck intelligent dispatching method based on quay crane area load according to claim 2 is characterized by: The constraints of the weight coefficients δ1, δ2 and δ3 are:

4. The unmanned container truck intelligent dispatching method based on quay crane area load according to claim 2 is characterized by: The gain coefficients α1, α2 and α3 of the unmanned truck state are: Among them, y is the data information of the information entropy of the operating status of the unmanned container truck.

5. The unmanned container truck intelligent dispatching method based on quay crane area load according to claim 1 is characterized by: The basic threshold function H is, Among them, a1 is the data information of the predicted unmanned truck congestion status, and a2 is the area of ​​the corresponding sub-area.

6. The unmanned container truck intelligent dispatching method based on quay crane area load according to claim 1 is characterized in that: In step U4, the optimization of the basic threshold using the improved dung beetle optimization algorithm includes: U41. Based on the data information of the basic threshold of the sub-region, construct the chaotic sequence function P of the basic threshold of the sub-region, Where t is the data information of the basic threshold of the sub-region, r(t) is the normalization function of the sub-region threshold, and h is any constant parameter between 0 and 1. The chaotic sequence of the sub-region threshold is characterized to obtain the data information of the chaotic sequence of the sub-region threshold; U42. Based on the chaotic sequence data of the sub-region threshold, peak and trough periods of operation, weather conditions, and equipment operating status data, the dung beetle population is initialized, the population parameters and the maximum number of iterations K are determined, and the data of the initialized dung beetle population is obtained; U43. Based on the data information of the initialized dung beetle population, a position update function S is established. Among them, G is the data information of the initialized dung beetle population, g is the individual of the population, and the basic threshold is optimized to obtain the data information of the threshold of the optimized sub-region.

7. The unmanned container truck intelligent dispatching method based on quay crane area load according to claim 6 is characterized by: The normalized function r(t) of the sub-region threshold is, Wherein, t is the data information of the basic threshold of the sub-region.

8. An unmanned container truck intelligent dispatching system based on quay crane area load for implementing any one of claims 1-7, characterized in that: The system comprises: The regional monitoring module is used to divide the quay crane area into multiple sub-areas, set up independent monitoring points in each sub-area, and monitor the number and location of unmanned container trucks in the quay crane sub-area in real time; A prediction module, connected to the regional monitoring module, is used to construct a prediction model for the congestion state of unmanned container trucks based on a decision tree algorithm, predict the congestion state of unmanned container trucks, and obtain data information on the predicted congestion state of unmanned container trucks; A sub-region basic threshold module is connected to the prediction module and is used to establish a basic threshold function H, characterize the carrying capacity of unmanned container trucks in the sub-region, and obtain data information of the basic threshold of the sub-region; A sub-region basic threshold optimization module is connected to the sub-region basic threshold module and is used to optimize the basic threshold using an improved dung beetle optimization algorithm based on data information of operation peak and trough periods, weather conditions, and equipment operating status, thereby obtaining data information of the optimized sub-region threshold; The dispatching control module is connected to the sub-area basic threshold optimization module and is used to dispatch vehicles in the waiting area to enter if the number of unmanned container trucks in the sub-area is greater than the threshold of the optimized sub-area based on the data information of the threshold of the optimized sub-area; if the number of unmanned container trucks in the sub-area is less than the threshold of the optimized sub-area, the vehicles in the waiting area are dispatched to enter.

9. The system according to claim 8, characterized in that The system also includes a communication module connected to each module in the system, which is responsible for information transmission and system integration between various components of the system.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program programmed or configured to execute the unmanned container truck intelligent scheduling method based on quay crane area load as described in any one of claims 1 to 7.

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