Port management method, system and equipment based on digital intelligent algorithm

Abstract

The invention discloses a port management method, system and equipment based on a digital intelligent algorithm, and belongs to the field of intelligent ports, and the method comprises the following steps: collecting cargo information, ship information and vehicle information, and carrying out the preprocessing; performing intelligent berth planning according to the preprocessed ship information to obtain berth information, and performing intelligent stowage planning 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 stockpiling plan by combining the stowage information with the port storage information to obtain stockpiling information; intelligent position selection is carried out according to the berth information, the stowage information and the stockpiling information, position selection parameters and position selection tasks are obtained, and automatic box distribution is carried out according to the position selection parameters and the position selection tasks to achieve port real-time scheduling. The problem that port dynamic management cannot be realized in the prior art can be solved.

Description

Technical Field

[0001] The present invention belongs to the field of smart ports, and specifically relates to a port management method, system and equipment based on a digital intelligent algorithm. Background Art

[0002] In today's era of information explosion, digitalization, networking and intelligence have penetrated into all walks of life. In the wave of global trade, ports, as important nodes in the logistics chain, their operational efficiency and management level are crucial to the smooth operation of the entire supply chain. With the development of digitalization and intelligence, the port industry is undergoing an unprecedented transformation.

[0003] However, with the continuous increase in international trade volume, the challenges faced by port management are becoming increasingly severe, with problems such as cargo accumulation, complex scheduling and environmental pollution emerging one after another. The traditional port management model has incomplete data collection, unreasonable resource allocation and low loading and unloading efficiency. It cannot achieve dynamic management of ports and cannot monitor real-time changes in ports. Therefore, the traditional port management model can no longer meet the requirements of modern society for port efficiency, safety and environmental protection. Summary of the invention

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

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

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

[0007] Collect cargo information, ship information, and vehicle information and perform pre-processing;

[0008] Perform intelligent berth planning according to the preprocessed ship information to obtain berth information, and then perform intelligent loading planning according to the preprocessed cargo information, the preprocessed vehicle information and the berth information to obtain loading information; collect port storage information, and perform intelligent storage planning in combination with the loading information to obtain storage information;

[0009] Intelligent location selection is performed according to the berth information, stowage information and storage information, location selection parameters and location selection tasks are obtained, and automatic container dispatch is performed according to the location selection parameters and location selection tasks to realize real-time port scheduling.

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

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

[0012] According to the cargo location information, input the loading and unloading time of the corresponding cargo to complete the cargo loading and unloading reservation.

[0013] In some embodiments, the following steps are also included:

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

[0015] Real-time image information is collected, and port monitoring is performed based on the real-time image information.

[0016] In some embodiments, the intelligent berth plan, intelligent loading plan and intelligent storage plan are implemented based on a back propagation neural network model;

[0017] The berth information, loading information and storage information are intelligently selected through a back propagation neural network model;

[0018] The steps for establishing the BP neural network model are as follows:

[0019] Collecting predicted values ​​and actual values ​​of port throughput, and performing differential calculation based on the predicted values ​​and actual values ​​to obtain port throughput prediction residual data;

[0020] According to the predicted value of the port throughput, an ARIMA model is established after ARIMA stationarity judgment is performed;

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

[0022] In some embodiments, the step of automatically sending containers according to the location selection parameters and location selection tasks to achieve real-time port scheduling specifically includes:

[0023] Selecting a slot according to the slot selection parameters and the slot selection task, updating the historical status information of the corresponding slot, and obtaining the current status information of the corresponding slot;

[0024] Automatically dispatch containers based on the current status information of the corresponding container slots to achieve real-time port scheduling;

[0025] The container location selection includes: export container location selection, transit container location selection, import container location selection, stack transfer location selection, unloading container location selection, inbound container location selection and pick-up container location selection;

[0026] The current status information includes slot locking, slot confirmation and slot updating.

[0027] In some embodiments, 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:

[0028] The entry unloading sequence and the pickup retrieval sequence are collected, and stored according to the entry unloading sequence through the BRP strategy to obtain an initial storage position, and then the initial storage position is optimized according to the pickup retrieval sequence through the BSS-BRP strategy to obtain an optimized storage position.

[0029] Second, a port management system based on digital intelligent algorithms, including

[0030] Data collection module, used to collect cargo information, ship information, vehicle information and perform pre-processing;

[0031] The intelligent decision-making module is used to perform intelligent berth planning according to the pre-processed ship information to obtain berth information, and then perform intelligent loading planning according to the pre-processed cargo information, the pre-processed vehicle information and the berth information to obtain loading information; collect port storage information, and perform intelligent storage planning in combination with the loading information to obtain storage information;

[0032] The real-time scheduling module is used to perform intelligent site selection according to the berth information, loading information and storage information, obtain site selection parameters and site selection tasks, and automatically send containers according to the site selection parameters and site selection tasks to realize real-time port scheduling.

[0033] In some embodiments, it further comprises:

[0034] A cargo query module is used to collect port operation information and obtain cargo location information through querying the port operation information;

[0035] 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 to complete the cargo loading and unloading reservation.

[0036] In some embodiments, it further comprises:

[0037] A real-time alarm module is used to collect environmental parameter information, including temperature information and humidity information, and output an alarm signal if an abnormal value appears in the environmental parameter information;

[0038] The video monitoring module is used to collect real-time image information and perform port monitoring based on the real-time image information.

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

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

[0041] The present invention provides a port management method based on a digital intelligent algorithm, which performs intelligent berth planning through pre-processed ship information to obtain berth information, and then performs intelligent stowage planning according to pre-processed cargo information, pre-processed vehicle information and the berth information to obtain stowage information; collects port storage information, and performs intelligent storage planning in combination with the port storage information to obtain storage information, and finally performs intelligent location selection according to the berth information, stowage information and storage information to obtain location selection parameters and location selection tasks, and automatically sends boxes according to the location selection parameters and location selection tasks to realize real-time port scheduling. The present invention can respond to various changes in 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, it can collect cargo information, ship information and vehicle information in real time, and can provide accurate real-time port reports, so that subsequent manual strategic decisions based on expert experience can be made on the current situation of the port, solving the problem that the existing technology cannot realize dynamic port management.

[0042] Furthermore, the intelligent berth plan, intelligent loading plan and intelligent stockpile plan in the present invention are implemented based on the back propagation neural network algorithm. The berth information, loading information and stockpile information are intelligently selected through the back propagation neural network algorithm (BP neural network algorithm), which can realize a highly intelligent decision-making process, reduce human intervention and errors, and improve work efficiency and accuracy. In addition, it can process a large amount of real-time port collection data, and through learning iterative optimization, realize the reasonable allocation of berth, loading and stockpile resources, reduce resource waste, improve resource utilization, and reduce operating costs. The present invention adopts the BP neural network algorithm in port management to quickly respond to changes in demand and flexibly adjust berth, loading and stockpile plans.

[0043] Furthermore, the present invention combines the BRP (Based on Receive Priority) strategy and the BSS-BRP (Best Storage Sequence Based on Retrieval Priority) strategy in cargo storage. Through the entry unloading sequence and the pickup retrieval sequence, it can ensure that high-priority goods are given priority in storage, reducing the waste of storage time caused by waiting for low-priority goods to enter the warehouse. At the same time, it can optimize the storage sequence to ensure that high-priority goods can be found and retrieved quickly, reducing the number and distance of cargo transportation during storage and retrieval, reducing cargo loss, and improving logistics efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0045] Figure 2 This is a schematic diagram of the process of performing intelligent location selection in this embodiment;

[0046] Figure 3 This is a schematic diagram of the intelligent storage using BRP and BSS-BRP in this embodiment;

[0047] Figure 4 An execution flow chart of a port management system based on a digital intelligent algorithm provided in this embodiment;

[0048] Figure 5 A flow chart for establishing the BP neural network model provided in this embodiment;

[0049] Figure 6 A specific flow chart of a port management system based on a digital intelligent algorithm provided in this embodiment;

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

[0051] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings, and the described content is intended to explain the present invention rather than to limit it.

[0052] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or are inherent to these processes, methods, systems, products or apparatus.

[0053] Traditional port management has the following shortcomings: (1) Data collection is not comprehensive and cannot guide the actual activities of port management; (2) The algorithm model is inaccurate and cannot be used for dynamic port management; (3) The algorithm model cannot perform autonomous deep learning and cannot adapt to the development and changes of the port; (4) The algorithm model is not secure and can easily lead to relevant information leakage. The traditional port management model can no longer meet the requirements of modern society in terms of efficiency, safety and environmental protection. Therefore, this embodiment provides a port relationship method, system and equipment based on digital intelligent algorithms, which is not only the inevitable result of scientific and technological progress, but also the inherent demand of the port industry's own development. Through real-time monitoring and decision-making algorithms, it can realize the effective integration and optimization management of port resources, and significantly improve the port's operating efficiency and service quality.

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

[0055] Step 1: Deploy a sensor network and install sensors at key locations in the port, including RFID tags, cameras, temperature sensors, and humidity sensors, to monitor the real-time status and location information of ships, cargo, equipment, and vehicles, as well as environmental parameter information;

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

[0057] Step 3: Preprocess the collected cargo information, ship information and vehicle information. The preprocessing operations include cleaning, denoising and standardization to ensure that the data quality meets the needs of subsequent analysis;

[0058] Step 4: Perform intelligent berth planning according to the preprocessed ship information to obtain berth information, and then perform intelligent loading planning according to the preprocessed cargo information, the preprocessed vehicle information and the berth information to obtain loading information; collect port storage information, and perform intelligent storage planning in combination with the loading information to obtain storage information;

[0059] Intelligent berth planning, intelligent loading planning and intelligent stockpiling planning are realized based on BP neural network algorithm. The present invention collects cargo information, ship information and vehicle information and inputs them into BP neural network model to establish a decision model, and then carries out intelligent berth planning, intelligent loading planning and intelligent stockpiling planning through the decision model.

[0060] Taking berth allocation, cargo loading, storage management and site selection operations as an example, by building a decision-making model that can accurately predict and respond to these needs, the overall operational efficiency of the port can be significantly improved.

[0061] 1) Construction of BP neural network model: The input layer of the model includes parameters such as historical operation data, ship arrival time, cargo type, etc., while the output layer is the prediction of berth utilization, loading plan, storage strategy and location decision.

[0062] 2) Network intelligent learning: Set the number of training times in the BP neural network algorithm to T = 60,000 times, the learning efficiency to L = 0.035, the error threshold positioning E = 0.65*10(-3), the number of input variables to INPUT = 6, the output to OUTPUT = 1, and the number of hidden layer neurons to H = 8. The data from 2020 to 2023 are used as training samples, and the data from 2024 are used as test samples. After sample training, the BP neural network model for this study was determined, and the test samples were brought into the trained model to obtain the results. Through the learning and training of a large amount of historical data, the BP neural network can gradually optimize its weights and biases, making the final decision more accurate and efficient. The specific process of building the BP neural network model is as follows Figure 5 shown.

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

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

[0065] In cargo storage management, by predicting the turnover speed and storage requirements of various types of cargo, the BP neural network can help decision makers arrange cargo locations reasonably and optimize inventory management.

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

[0067] Smart stockpiling planning includes cargo storage, e.g. Figure 3As shown, it is implemented by the BRP strategy and the BSS-BRP strategy, and the specific steps include: collecting the entry unloading sequence and the pickup retrieval sequence, storing according to the entry unloading sequence through the BRP strategy to obtain the initial storage position, and then optimizing the initial storage position according to the pickup retrieval sequence through the BSS-BRP strategy to obtain the optimized storage position;

[0068] Step 5: Figure 2 As shown, intelligent location selection is performed according to the berth information, stowage information and storage information, location selection parameters and location selection tasks are obtained, container slots are selected according to the location selection parameters and location selection tasks, historical status information of corresponding container slots is updated, current status information of corresponding container slots is obtained, and automatic container dispatch is performed according to the current status information of corresponding container slots, thereby realizing real-time port scheduling;

[0069] Berth information, loading information and storage information are intelligently selected through BP neural network algorithm;

[0070] Container slot selection includes: export container slot selection, transit container slot selection, import container slot selection, stack transfer slot selection, unloading container slot selection, inbound container slot selection and pick-up container slot selection; current status information includes container slot locking, container slot confirmation and container slot update;

[0071] like Figure 1 As shown in the figure, the entire port management is divided into the planning stage and the real-time scheduling stage. In the planning stage, the intelligent berth plan is first carried out. After the berth is determined, the port management system will carry out the intelligent stowage plan according to the berth information, and finally the port management system will carry out the intelligent storage plan according to the situation of the intelligent stowage plan, as well as the type, quantity, storage requirements and other factors of the goods; in the real-time scheduling stage, the port management system will automatically send out the boxes according to the plan. After the cargo boxes are taken out, the system will carry out intelligent location selection and select the best storage location according to the real-time transportation demand, inventory status and other factors;

[0072] Relying on the above-mentioned intelligent positioning, we can further realize intelligent real-time bridge crane operation planning, intelligent horizontal transportation machinery scheduling and intelligent yard bridge scheduling.

[0073] Step 6: Collect port operation information, and obtain cargo location information through the port operation information query; input the loading and unloading time of the corresponding cargo according to the cargo location information, and complete the cargo loading and unloading reservation;

[0074] Collecting environmental parameter information, the environmental parameter information includes temperature information and humidity information, and outputting an alarm signal if the environmental parameter information has an abnormal value;

[0075] Real-time image information is collected, and port monitoring is performed based on the real-time image information.

[0076] In practical applications, the method provided in this embodiment processes massive data based on the BP neural network algorithm and establishes a decision model. Through the intelligent berth plan, intelligent loading plan and intelligent stockpiling plan based on the BP neural network algorithm, it can predict traffic congestion and improve resource utilization. This embodiment adopts a data-driven intelligent algorithm in intelligent stockpiling, and provides stockpiling services for port cargo through the BRP strategy and the BSS-BRP strategy to improve the scientific nature of stockpiling and loading and unloading efficiency. The intelligent stockpiling plan uses the entry unloading sequence and the pickup retrieval sequence to provide a basis for stockpiling planning and site selection. The intelligent stockpiling plan can automatically arrange the stockpiling plan, reduce the on-site time of external container trucks and the conflict between mechanical operations in the yard, improve loading and unloading efficiency, and actively arrange pre-turning and transfer merging during operation gaps to prepare for subsequent operations.

[0077] The intelligent stockpiling provided in this embodiment supports automatic site selection, stockpiling distribution and conflict detection, supports different business rules and scenarios, and can assist manual work in quickly specifying a large stockpiling plan, thereby increasing the yard utilization rate by 10% and reducing the box dispatch and turnover rate by 5%.

[0078] This embodiment provides a port management system based on a digital intelligent algorithm. The specific execution process is as follows: Figure 4 and Figure 6 As shown, first, various types of sensors deployed at different key locations in the port are used to collect and store the original data, which includes cargo information, ship information, vehicle information and environmental parameter information. Then, an artificial intelligence algorithm is used to analyze the original data, extract target information, and generate a real-time data analysis report based on the target information. Based on the real-time data analysis report, a preset decision model is used to optimize the port operation process, generate corresponding operation strategies, and obtain corresponding execution results. Finally, the decision model is optimized according to the execution results, and the optimized decision model is used to reallocate port resources and generate new operation strategies.

[0079] like Figure 7 As shown, the port management system includes: data collection module, intelligent decision-making module, real-time scheduling module, cargo query module, loading and unloading reservation module, real-time alarm module and video monitoring module;

[0080] The data collection module uses deployed sensors to collect cargo information, ship information, and vehicle information and performs pre-processing;

[0081] The intelligent decision-making module is used to perform intelligent berth planning according to the pre-processed ship information to obtain berth information, and then perform intelligent loading planning according to the pre-processed cargo information, the pre-processed vehicle information and the berth information to obtain loading information; collect port storage information, and perform intelligent storage planning in combination with the loading information to obtain storage information;

[0082] A real-time scheduling module is used to perform intelligent site selection according to the berth information, stowage information and storage information, obtain site selection parameters and site selection tasks, and automatically send containers according to the site selection parameters and site selection tasks to realize real-time port scheduling;

[0083] A cargo query module is used to collect port operation information and obtain cargo location information through querying the port operation information;

[0084] 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 to complete the cargo loading and unloading reservation.

[0085] The above-mentioned cargo query module and loading and unloading reservation module are deployed in mobile applications, which display the port operation status in real time and provide online services. Port staff can query the cargo location and make reservations for loading and unloading services based on the mobile application.

[0086] A real-time alarm module is used to collect environmental parameter information, including temperature information and humidity information, and output an alarm signal if an abnormal value appears in the environmental parameter information;

[0087] The video monitoring module is used to collect real-time image information and perform port monitoring based on the real-time image information.

[0088] In the port management system provided in this embodiment, the transmitted data is encrypted and protected, and different levels of authority control are set to ensure the security of port information.

[0089] This embodiment uses advanced data processing and decision support technology, so the port management system can respond to various changes in the port in real time and dynamically adjust the operation strategy, thereby maximizing the port's operational efficiency. Secondly, since all operations are completed automatically, the possibility of human error is greatly reduced and the service quality is improved. Finally, since all data is collected and processed in real time, accurate real-time reports can be provided to management to help them make better strategic decisions.

[0090] The port management system based on digital intelligent algorithms provided in this embodiment can efficiently process massive amounts of data, optimize the cargo loading and unloading process, improve the scheduling efficiency of ships, realize accurate data monitoring, intelligent analysis, optimized decision-making and real-time feedback, thereby improving port operating efficiency, reducing costs, and providing a better service experience.

[0091] The division of modules in the embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0092] In this embodiment, a computer device is also provided, which includes a processor and a memory, wherein the memory is used to store a computer program (in this embodiment, the computer program includes a computing component and an iterative component, which can perform model calculation and model update), the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a method for intelligent collaborative operation of a thermal power plant.

[0093] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of 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 code.

[0094] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A port management method based on digital intelligent algorithm, It is characterized in that The following steps are involved: Collect cargo information, ship information and vehicle information and perform pre-processing; Performing intelligent berth planning according to the preprocessed ship information to obtain berth information, and then performing intelligent loading planning according to the preprocessed cargo information, the preprocessed vehicle information and the berth information to obtain loading information; Collecting port storage information, combining the loading information with the port storage information to perform intelligent storage planning to obtain storage information; Intelligent location selection is performed according to the berth information, stowage information and storage information, location selection parameters and location selection tasks are obtained, and automatic container dispatch is performed according to the location selection parameters and location selection tasks to realize real-time port scheduling.

2. According to the port management method based on digital intelligent algorithm as described in claim 1, It is 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, input the loading and unloading time of the corresponding cargo to complete the cargo loading and unloading reservation.

3. According to the port management method based on digital intelligent algorithm as described in claim 1, It is characterized in that The following steps are also included: Collecting environmental parameter information, the environmental parameter information includes 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. According to the port management method based on digital intelligent algorithm as described in claim 1, It is characterized in that The intelligent berth plan, intelligent loading plan and intelligent storage plan are implemented based on the BP neural network model; The berth information, stowage information and storage information are intelligently selected through a 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 differential calculation based on the predicted values ​​and actual values ​​to obtain port throughput prediction residual data; According to the predicted value of the port throughput, an ARIMA model is established after ARIMA stationarity judgment is performed; According to the port throughput prediction residual data, cargo information, ship information and vehicle information, and after being optimized by the ARIMA model, a BP neural network model is constructed.

5. According to the port management method based on digital intelligent algorithm as described in claim 1, It is characterized in that The step of automatically sending containers according to the location selection parameters and location selection tasks to realize real-time port scheduling specifically includes: Selecting a slot according to the slot selection parameters and the slot selection task, updating historical status information corresponding to the slot, and obtaining current status information corresponding to the slot; Automatically dispatch containers according to 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, stack transfer 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 updating.

6. According to the port management method based on digital intelligent algorithm as described in claim 1, It 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 through the BRP strategy to obtain an initial storage position, and then the initial storage position is optimized according to the pickup retrieval sequence through the BSS-BRP strategy to obtain an optimized storage position.

7. A port management system based on digital intelligent algorithm, It is characterized in that include Data collection 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 according to the preprocessed ship information to obtain berth information, and then perform intelligent loading planning according to the preprocessed cargo information, the preprocessed vehicle information and the berth information to obtain loading information; Collecting port storage information, combining the loading information with the port storage information to perform intelligent storage planning to obtain storage information; The real-time scheduling module is used to perform intelligent site selection according to the berth information, loading information and storage information, obtain site selection parameters and site selection tasks, and automatically send containers according to the site selection parameters and site selection tasks to realize real-time port scheduling.

8. According to the port management system based on digital intelligent algorithm as described in claim 7, It is 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 to complete the cargo loading and unloading reservation.

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

10. An electronic device, It is characterized in that The method comprises a memory, a processor and a computer program stored in the memory and executable in the processor. 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 6 are implemented.

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