A port management method, system and device based on digital intelligent algorithm

Abstract

The application discloses a port management method, system and equipment based on a digital intelligent algorithm, belongs to the field of smart ports, and comprises the following steps: collecting cargo information, ship information and vehicle information and performing pretreatment; performing intelligent berth planning according to the pretreated ship information to obtain berth information, then performing intelligent stowage planning according to the pretreated cargo information, the pretreated vehicle information and the berth information to obtain stowage information; collecting port storage information, combining the stowage information with the port storage information to perform intelligent stacking planning to obtain stacking information; performing intelligent site selection according to the berth information, the stowage information and the stacking information to obtain site selection parameters and site selection tasks, and automatically sending containers according to the site selection parameters and the site selection tasks to realize real-time scheduling of the port. The application can solve the problem that the prior art cannot realize dynamic management of the port.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of smart port, and particularly relates to a port management method, system and equipment based on digital intelligent algorithm. BACKGROUND

[0002] In today's era of information explosion, digitalization, networking and intelligentization have penetrated into various industries. In the wave of globalization trade, the port as an important node of the logistics chain, its operation efficiency and management level are crucial for the smooth operation of the entire supply chain. With the development of digitalization and intelligentization, the port industry is undergoing an unprecedented revolution.

[0003] However, with the continuous increase of international trade volume, the challenges faced by port management are becoming increasingly severe, and problems such as cargo accumulation, complex scheduling and environmental pollution are emerging in an endless stream. The traditional port management mode has the problems of incomplete data collection, unreasonable resource allocation and low unloading efficiency, and cannot realize the dynamic management of the port, and cannot adapt to the real-time changes of the port, so the traditional port management mode has been unable to meet the requirements of modern society for the efficiency, safety and environmental protection of the port. SUMMARY

[0004] The purpose of the present application is to provide a port management method, system and equipment based on digital intelligent algorithm to solve the problem that the prior art cannot realize dynamic management of the port.

[0005] To achieve the above-mentioned purpose, the following technical solutions are adopted in the present application:

[0006] In a first aspect, a port management method based on digital intelligent algorithm comprises the following steps:

[0007] Collecting cargo information, ship information and vehicle information and preprocessing;

[0008] According to the preprocessed ship information, an intelligent berth plan is made to obtain berth information, and then according to the preprocessed cargo information, the preprocessed vehicle information and the berth information, an intelligent stowage plan is made to obtain stowage information; port storage information is collected, and the stowage information is combined with the port storage information to make an intelligent stacking plan to obtain stacking information;

[0009] According to the berth information, the stowage information and the stacking information, an intelligent site selection is made to obtain site selection parameters and site selection tasks, and according to the site selection parameters and the site selection tasks, automatic case sending is realized to achieve real-time scheduling of the port.

[0010] In some embodiments, the following steps are further included:

[0011] Collecting port operation information, and querying the cargo location information through the port operation information;

[0012] According to the cargo location information, the loading and unloading time of the corresponding cargo is input, and the cargo is reserved for loading and unloading.

[0013] In some embodiments, the method further comprises the following steps:

[0014] Collecting environmental parameter information, the environmental parameter information including temperature information and humidity information, and outputting an alarm signal if the environmental parameter information has an abnormal value;

[0015] Collecting real-time image information, and performing port monitoring according to the real-time image information.

[0016] In some embodiments, the intelligent berth plan, intelligent stowage plan and intelligent stacking plan are realized based on a back propagation neural network model;

[0017] The berth information, stowage information and stacking information are intelligently selected by the back propagation neural network model;

[0018] The establishment steps of the BP neural network model are as follows:

[0019] Collecting the predicted value and the true value of the port throughput, and performing difference operation to obtain port throughput prediction residual data according to the predicted value and the true value;

[0020] According to the predicted value of the port throughput, an ARIMA stationary judgment is performed to establish an ARIMA model;

[0021] According to the port throughput prediction residual data, cargo information, ship information and vehicle information, and through ARIMA model optimization, a BP neural network model is constructed.

[0022] In some embodiments, the step of realizing port real-time scheduling according to the automatic case sending based on the selection parameter and the selection task specifically comprises:

[0023] According to the selection parameter and the selection task, a case location is selected, the historical state information of the corresponding case location is updated, and the current state information of the corresponding case location is obtained;

[0024] According to the current state information of the corresponding case location, automatic case sending is performed to realize port real-time scheduling;

[0025] The case location selection includes export case location selection, transfer case location selection, import case location selection, transfer stacking location selection, unloading case location selection, approach case location selection and case picking location selection;

[0026] The current state information includes case location locking, case location confirmation and case location updating.

[0027] In some embodiments, the intelligent storage plan includes cargo storage, and the steps of implementing the BRP strategy and the BSS-BRP strategy include:

[0028] collecting an entry unloading sequence and a pick-up retrieval sequence, storing according to the entry unloading sequence through the BRP strategy to obtain an initial storage position, and then optimizing the initial storage position according to the pick-up retrieval sequence through the BSS-BRP strategy to obtain an optimized storage position.

[0029] In a second aspect, a port management system based on a digital intelligent algorithm includes

[0030] a data collection module for collecting and preprocessing cargo information, ship information, and vehicle information;

[0031] an intelligent decision module for performing an intelligent berth plan according to the preprocessed ship information to obtain berth information, performing an intelligent stowage plan according to the preprocessed cargo information, the preprocessed vehicle information, and the berth information to obtain stowage information, collecting port storage information, and performing an intelligent storage plan on the stowage information in combination with the port storage information to obtain storage information;

[0032] a real-time scheduling module for performing intelligent positioning according to the berth information, the stowage information, and the storage information to obtain positioning parameters and positioning tasks, and automatically sending containers according to the positioning parameters and the positioning tasks to realize real-time scheduling of the port.

[0033] In some embodiments, the system further includes:

[0034] a cargo query module for collecting port operation information and querying cargo location information through the port operation information;

[0035] a loading and unloading reservation module for inputting loading and unloading times of corresponding cargo according to the cargo location information to complete cargo loading and unloading reservation.

[0036] In some embodiments, the system further includes:

[0037] a real-time alarm module for collecting environmental parameter information including temperature information and humidity information, and outputting an alarm signal if the environmental parameter information has an abnormal value;

[0038] a video monitoring module for collecting real-time image information and performing port monitoring according to the real-time image information.

[0039] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, and the processor implements the steps of the port management method based on the digital intelligent algorithm when executing the computer program.

[0040] Compared with the prior art, the present application has the following beneficial effects:

[0041] The present application provides a port management method based on a digital intelligent algorithm, which obtains berth information by intelligent berth planning based on preprocessed ship information, and then obtains stowage information by intelligent stowage planning based on preprocessed cargo information, preprocessed vehicle information and the berth information; collects port storage information, and combines the stowage information with the port storage information to obtain storage information by intelligent storage planning, and finally performs intelligent positioning based on the berth information, the stowage information and the storage information to obtain positioning parameters and positioning tasks, and realizes automatic case sending based on the positioning parameters and the positioning tasks to achieve real-time scheduling of the port. The present application can respond to various changes of the port in real time according to the collected information, dynamically adjust the operation strategy, maximize the port operation efficiency, reduce the possibility of errors in manual management, and improve the port service quality. In addition, the present application can collect cargo information, ship information and vehicle information in real time, and provide accurate real-time port reports, so that subsequent manual strategic decision-making based on expert experience can be made on the current situation of the port, and the problem of dynamic management of the port that cannot be realized by the prior art is solved.

[0042] Further, the intelligent berth planning, the intelligent stowage planning and the intelligent storage planning in the present application are realized based on a back propagation neural network algorithm, the berth information, the stowage information and the storage information are positioned intelligently by a back propagation neural network algorithm (BP neural network algorithm), a highly intelligent decision-making process can be realized, manual intervention and errors are reduced, work efficiency and accuracy are improved, in addition, a large amount of real-time port data can be processed, and through learning and iteration optimization, reasonable allocation of berth, stowage and storage resources can be realized, resource waste is reduced, resource utilization rate is improved, and operation cost is reduced. The present application adopts the BP neural network algorithm in port management, which can quickly respond to demand changes and flexibly adjust the berth, stowage and storage plans.

[0043] Further, the application combines the BRP (Based on Receive Priority) strategy and the BSS-BRP (Best Storage Sequence Based on Retrieval Priority) strategy in goods storage, and through the unloading sequence and the retrieval sequence, the high-priority goods can be stored preferentially, the waste of storage time caused by waiting for the low-priority goods to be stored can be reduced, the storage sequence can be optimized, the high-priority goods can be quickly found and taken out, the number and distance of goods handling in the storage and retrieval process can be reduced, the goods loss can be reduced, and the logistics efficiency can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 A schematic diagram of the intelligent planning stage and the real-time scheduling stage of the port management method based on the digital intelligent algorithm provided in the embodiment;

[0045] Figure 2 A flowchart of the intelligent site selection in the embodiment;

[0046] Figure 3 A schematic diagram of the intelligent stacking by the BRP and the BSS-BRP in the embodiment;

[0047] Figure 4 An execution flowchart of the port management system based on the digital intelligent algorithm provided in the embodiment;

[0048] Figure 5 A BP neural network model establishment flowchart provided in the embodiment;

[0049] Figure 6 A specific flowchart of the port management system based on the digital intelligent algorithm provided in the embodiment;

[0050] Figure 7 A structural schematic diagram of the port management system based on the digital intelligent algorithm provided in the embodiment. DETAILED DESCRIPTION

[0051] In order to make the skilled in the art better understand the application scheme, the technical scheme of the application will be further described in detail below with reference to the drawings, and the content is an explanation of the application but not a limitation.

[0052] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of the present application are intended to cover the non-exclusive inclusion, for example, a process, method, system, product or apparatus that includes a list of steps or units without being limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, systems, products or apparatuses.

[0053] The traditional port management has the following disadvantages: (1) the data collection is not comprehensive, which cannot guide the actual activities of port management; (2) the algorithm model is not accurate, which cannot be used for dynamic management of the port; (3) the algorithm model cannot carry out autonomous deep learning, which cannot adapt to the development and changes of the port; (4) the security of the algorithm model is not high, which is easy to cause relevant information leakage. The traditional port management mode has been unable to meet the requirements of modern society on efficiency, safety and environmental protection and the like, therefore, the embodiment provides a port relationship method, system and equipment based on digital intelligent algorithm, which is not only the inevitable result of technological progress, but also the internal demand of the development of the port industry, realizes effective integration and optimized management of port resources through real-time monitoring and decision algorithm, and significantly improves the operation efficiency and service quality of the port.

[0054] A port management method based on digital intelligent algorithm, specifically comprising the following steps:

[0055] Step 1: deploying a sensor network, installing sensors including RFID tags, cameras, temperature sensors and humidity sensors and the like at key positions of the port, for monitoring real-time state and position information of ships, goods, equipment and vehicles, and environmental parameter information;

[0056] Step 2: the sensors in step 1 collect goods information, ship information and vehicle information in real time, and transmit the information to a central database through a network, to ensure the timeliness and accuracy of the data;

[0057] Step 3: preprocessing the collected goods information, ship information and vehicle information, the preprocessing operation including cleaning, denoising and standardization, to ensure that the data quality meets the subsequent analysis requirements;

[0058] Step 4: performing intelligent berth planning according to the preprocessed ship information to obtain berth information, then performing intelligent stowage planning according to the preprocessed goods information, the preprocessed vehicle information and the berth information to obtain stowage information; collecting port storage information, and combining the stowage information with the port storage information to perform intelligent stacking planning to obtain stacking information;

[0059] The intelligent berth allocation plan, the intelligent stowage plan and the intelligent stacking plan are realized based on a BP neural network algorithm, and the decision model is established by inputting cargo information, ship information and vehicle information into the BP neural network model, and the intelligent berth allocation plan, the intelligent stowage plan and the intelligent stacking plan are realized through the decision model.

[0060] Taking berth allocation, cargo stowage, stacking management and site selection as examples, by constructing a decision model capable of accurately predicting and responding to these demands, the overall operation efficiency of the port can be significantly improved.

[0061] 1) Construction of the BP neural network model: the input layer of the model includes parameters such as historical operation data, ship arrival time, cargo type, etc., and the output layer is the prediction of berth utilization, stowage scheme, stacking strategy and site selection decision.

[0062] 2) Network intelligent learning: the number of training times in the BP neural network algorithm is set to T=60000 times, the learning efficiency is set to L=0.035, the error threshold is set to E=0.65*10(-3), the number of input variables is INPUT=6, the output is OUTPUT=1, and the number of hidden layer neurons is H=8. The data from 2020 to 2023 is used as the training sample, and the data from 2024 is used as the test sample. After training the sample, the BP neural network model of this study is determined, the test sample is brought into the trained model, and the result is obtained. Through the learning and training of a large amount of historical data, the BP neural network can gradually optimize its weights and biases, so that the final decision is more accurate and efficient. The construction process of the BP neural network model is shown in Figure 5 .

[0063] 3) In terms of intelligent berth allocation, the BP neural network can intelligently recommend the most suitable berth according to the ship size, expected docking time and other factors, avoiding subjective judgment errors in manual scheduling.

[0064] At the same time, for the intelligent stowage system, it can consider factors such as cargo weight, volume, destination, etc., to automatically generate the optimal loading scheme, reduce the loading and unloading time, and improve the utilization rate of the ship.

[0065] In terms of cargo stacking management, by predicting the turnover speed and storage demand of various cargos, the BP neural network can help decision makers to reasonably arrange the storage space and optimize the inventory management.

[0066] As for intelligent site selection, it is a particularly important link in container terminals, and correct site selection can greatly shorten the handling distance and speed up the loading and unloading speed.

[0067] The intelligent stacking plan includes cargo storage, such as Figure 3As shown, the implementation of the BRP strategy and the BSS-BRP strategy includes the following specific steps: collecting the unloading sequence and the retrieval sequence, storing the unloading sequence according to the BRP strategy to obtain an initial storage position, and optimizing the initial storage position according to the retrieval sequence by the BSS-BRP strategy to obtain an optimized storage position.

[0068] Step 5: as shown in Figure 2 According to the berth information, stowage information and storage information, intelligent positioning is performed to obtain positioning parameters and positioning tasks, the container bay is selected according to the positioning parameters and positioning tasks, the historical state information of the corresponding container bay is updated to obtain the current state information of the corresponding container bay, and automatic container delivery is performed according to the current state information of the corresponding container bay to realize real-time scheduling of the port.

[0069] The berth information, stowage information and storage information are used for intelligent positioning by a BP neural network algorithm.

[0070] The container bay selection includes export container bay selection, transfer container bay selection, import container bay selection, transfer storage bay selection, unloading container bay selection, approach container bay selection and pickup container bay selection; and the current state information includes container bay locking, container bay confirmation and container bay updating.

[0071] As shown in Figure 1 The entire port management is divided into a planning stage and a real-time scheduling stage. In the planning stage, intelligent berth planning is performed first, and after the berth is determined, the port management system performs intelligent stowage planning according to the berth information, etc. Finally, the port management system performs intelligent storage planning according to the intelligent stowage planning, as well as the type, quantity, storage requirements and other factors of the goods. In the real-time scheduling stage, the port management system performs automatic container delivery according to the planning, and after the goods container is taken out, the system performs intelligent positioning according to the real-time transportation demand, inventory situation and other factors to select the best storage position.

[0072] With the above intelligent positioning, intelligent real-time bridge crane operation planning, intelligent horizontal transportation machinery scheduling and intelligent yard crane scheduling are further realized.

[0073] Step 6: Collect port operation information, and obtain the goods location information by querying the port operation information; according to the goods location information, input the loading and unloading time of the corresponding goods to complete the reservation of the goods loading and unloading.

[0074] Collect environmental parameter information, which includes temperature information and humidity information. If the environmental parameter information has an abnormal value, an alarm signal is output.

[0075] Collect real-time image information, and perform port monitoring according to the real-time image information.

[0076] The method provided by the embodiment can process massive data based on the BP neural network algorithm, establish a decision model, and predict traffic congestion and improve resource utilization through intelligent berth planning, intelligent stowage planning and intelligent stacking planning based on the BP neural network algorithm; the data-driven intelligent algorithm is used in the intelligent stacking, and the BRP strategy and the BSS-BRP strategy are used to provide stacking services for port cargos, improve the stacking scientificity and the loading and unloading efficiency, the intelligent stacking plan uses the unloading sequence and the cargo retrieval sequence to provide basis for the stacking plan and the site selection, the intelligent stacking plan can automatically arrange the stacking plan, reduce the time of external trucks on site and the conflict of on-site mechanical operation, improve the loading and unloading efficiency, and actively arrange the pre-palletizing and the stacking merging in the operation gap to prepare for subsequent operations.

[0077] The intelligent stacking provided by the embodiment supports automatic site selection, stacking distribution and conflict detection, supports different business rules and scenarios, and can assist manual rapid designation of a stacking plan, increase the yard utilization rate by 10%, and reduce the palletizing and unloading rate by 5%.

[0078] The embodiment provides a port management system based on a digital intelligent algorithm, and a specific execution process is as shown in Figure 4 and Figure 6 The port management system includes a data acquisition module, an intelligent decision module, a real-time scheduling module, a cargo query module, a loading and unloading reservation module, a real-time alarm module and a video monitoring module.

[0079] As shown in Figure 7 , the port management system includes a data acquisition module, an intelligent decision module, a real-time scheduling module, a cargo query module, a loading and unloading reservation module, a real-time alarm module and a video monitoring module.

[0080] The data acquisition module acquires cargo information, ship information and vehicle information by using the deployed sensors and performs preprocessing on the acquired information.

[0081] The intelligent decision module is configured to generate an intelligent berth plan based on the preprocessed ship information, obtain berth information, generate an intelligent stowage plan based on the preprocessed cargo information, the preprocessed vehicle information and the berth information, obtain stowage information, acquire port storage information, combine the stowage information with the port storage information to generate an intelligent stacking plan, and obtain stacking information.

[0082] a real-time scheduling module configured to intelligently select a location according to the berth information, the stowage information, and the stacking information, obtain selection parameters and a selection task, and automatically send a container according to the selection parameters and the selection task to realize real-time scheduling of the port;

[0083] a cargo query module configured to collect port operation information and obtain cargo location information through the port operation information;

[0084] a loading and unloading reservation module configured to input a loading and unloading time of corresponding cargo according to the cargo location information, and complete reservation of loading and unloading of the cargo.

[0085] The cargo query module and the loading and unloading reservation module are deployed in a mobile application, which can display a real-time port operation state and provide online services, and port staff can query a cargo location and reserve loading and unloading services according to the mobile application.

[0086] a real-time alarm module configured to collect environmental parameter information including temperature information and humidity information, and output an alarm signal if the environmental parameter information has an abnormal value;

[0087] a video monitoring module configured to collect real-time image information and monitor the port according to the real-time image information.

[0088] The port management system provided in the embodiment encrypts transmitted data, sets different levels of permission control, and ensures port information security.

[0089] The port management system provided in the embodiment can respond to various changes in the port in real time, dynamically adjust operation strategies, and thus maximally improve port operation efficiency. Secondly, since all operations are automatically completed, the possibility of human errors is greatly reduced, and service quality is improved. Finally, since all data are collected and processed in real time, accurate real-time reports can be provided for management, helping them make better strategic decisions.

[0090] The port management system provided in the embodiment based on digital intelligent algorithms can efficiently process massive data, optimize cargo loading and unloading processes, improve ship scheduling efficiency, realize accurate data monitoring, intelligent analysis, optimized decision-making, and real-time feedback, and thus improve port operation efficiency, reduce costs, and provide better service experience.

[0091] The division of the modules in the embodiments of the present application is illustrative, and is merely a logical function division. In actual implementation, another division manner can be used. In addition, the function modules in each of the embodiments of the present application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module.

[0092] The embodiment further provides a computer device including a processor and a memory. The memory is used to store a computer program (the computer program includes a calculation component and an iteration component, and can perform model calculation and model updating). The computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be another general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or another programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and the like. The processor is a computing core and a control core of the terminal, and is suitable for implementing one or more instructions. Specifically, the processor is suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function. The processor in the embodiment of the present application can be used for operation of the method for intelligent collaborative operation of a thermal power plant.

[0093] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0094] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0095] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0096] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0097] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not limit the technical solutions of the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the present application, and any modification or equivalent replacement should be covered in the protection scope of the claims of the present application.

Claims

1. A port management method based on digital intelligent algorithm, characterized in that: The following steps are involved: Collect cargo information, ship information and vehicle information and perform pre-processing; Performing intelligent berth planning based on the pre-processed ship information to obtain berth information, and then performing intelligent loading planning based on the pre-processed cargo information, the pre-processed vehicle information, and the berth information to obtain loading information; Collecting port storage information, combining the stowage information with the port storage information to perform intelligent storage planning to obtain storage information; Perform intelligent location selection based on the berth information, stowage information, and storage information, obtain location selection parameters and location selection tasks, and automatically dispatch containers based on the location selection parameters and location selection tasks to achieve real-time port scheduling; The intelligent berth plan, intelligent loading plan and intelligent storage plan are implemented based on the BP neural network model; The berth information, loading information and storage information are intelligently selected through the BP neural network model; The steps for establishing the BP neural network model are as follows: Collecting predicted values ​​and actual values ​​of port throughput, and performing a differential operation based on the predicted values ​​and the actual values ​​to obtain port throughput prediction residual data; Based on the predicted value of the port throughput, an ARIMA model is established after ARIMA stationarity judgment is performed; Based on the port throughput forecast residual data, cargo information, ship information and vehicle information, and after optimization through the ARIMA model, a BP neural network model is constructed; The step of automatically sending containers according to the location selection parameters and the location selection tasks to realize real-time port scheduling specifically includes: Select a slot according to the slot selection parameters and the slot selection task, update historical status information corresponding to the slot, and obtain current status information corresponding to the slot; Automatically dispatch containers based on the current status information of the corresponding container slots to achieve real-time port scheduling; The container location selection includes: export container location selection, transit container location selection, import container location selection, stacking location selection, unloading container location selection, inbound container location selection and pick-up container location selection; The current status information includes slot locking, slot confirmation, and slot update.

2. A port management method based on digital intelligent algorithm according to claim 1, characterized in that: The following steps are also included: Collecting port operation information and obtaining cargo location information through querying the port operation information; According to the cargo location information, enter the loading and unloading time of the corresponding cargo to complete the cargo loading and unloading reservation.

3. The port management method based on digital intelligent algorithm according to claim 1 is characterized in that: The following steps are also included: Collecting environmental parameter information, including temperature information and humidity information, and outputting an alarm signal if the environmental parameter information has an abnormal value; Real-time image information is collected, and port monitoring is performed based on the real-time image information.

4. The port management method based on digital intelligent algorithm according to claim 1 is characterized in that: The intelligent stockpiling plan includes cargo storage, and the steps of implementing the cargo storage through the BRP strategy and the BSS-BRP strategy include: The entry unloading sequence and the pickup retrieval sequence are collected, and stored according to the entry unloading sequence using the BRP strategy to obtain an initial storage location. Then, the initial storage location is optimized according to the pickup retrieval sequence using the BSS-BRP strategy to obtain an optimized storage location.

5. A port management system based on digital intelligent algorithm, characterized in that: include: Data acquisition module, used to collect cargo information, ship information, vehicle information and perform pre-processing; An intelligent decision-making module is used to perform intelligent berth planning based on the pre-processed ship information to obtain berth information, and then perform intelligent loading planning based on the pre-processed cargo information, pre-processed vehicle information and the berth information to obtain loading information; Collecting port storage information, combining the stowage information with the port storage information to perform intelligent storage planning to obtain storage information; A real-time scheduling module is used to perform intelligent berth selection based on the berth information, stowage information, and storage information, obtain berth selection parameters and tasks, and automatically dispatch containers based on the berth selection parameters and tasks to achieve real-time port scheduling. The intelligent berth plan, intelligent loading plan and intelligent storage plan are implemented based on the BP neural network model; The berth information, loading information and storage information are intelligently selected through the BP neural network model; The steps for establishing the BP neural network model are as follows: Collecting predicted values ​​and actual values ​​of port throughput, and performing a differential operation based on the predicted values ​​and the actual values ​​to obtain port throughput prediction residual data; Based on the predicted value of the port throughput, an ARIMA model is established after ARIMA stationarity judgment is performed; Based on the port throughput forecast residual data, cargo information, ship information and vehicle information, and after optimization through the ARIMA model, a BP neural network model is constructed; The step of automatically sending containers according to the location selection parameters and the location selection tasks to realize real-time port scheduling specifically includes: Select a slot according to the slot selection parameters and the slot selection task, update historical status information corresponding to the slot, and obtain current status information corresponding to the slot; Automatically dispatch containers based on the current status information of the corresponding container slots to achieve real-time port scheduling; The container location selection includes: export container location selection, transit container location selection, import container location selection, stacking location selection, unloading container location selection, inbound container location selection and pick-up container location selection; The current status information includes slot locking, slot confirmation, and slot update.

6. A port management system based on digital intelligent algorithm according to claim 5, characterized in that: Also includes: A cargo query module is used to collect port operation information and obtain cargo location information through querying the port operation information; The loading and unloading reservation module is used to input the loading and unloading time of the corresponding goods according to the cargo location information and complete the cargo loading and unloading reservation.

7. The port management system based on digital intelligent algorithm according to claim 5 is characterized in that: Also includes: A real-time alarm module is used to collect environmental parameter information, including temperature and humidity information, and output an alarm signal if the environmental parameter information has an abnormal value; The video monitoring module is used to collect real-time image information and perform port monitoring based on the real-time image information.

8. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein when the processor executes the computer program, the steps of a port management method based on a digital intelligent algorithm as described in any one of claims 1 to 4 are implemented.

Images