Intelligent Control System for All-process Equipment in General Cargo Port

Abstract

The present invention relates to the field of port intelligent control technology, specifically an intelligent control system for all-process equipment in bulk cargo ports. The system includes: an intelligent identification and monitoring subsystem, an information analysis and processing subsystem, a port planning and scheduling subsystem, and a safety monitoring and early warning subsystem; multi-dimensional dynamic monitoring information during the port operation is obtained based on the intelligent identification and monitoring subsystem; a multi-level information processing mechanism is constructed in the information analysis and processing subsystem, and the multi-dimensional dynamic monitoring information is processed based on the mechanism to obtain multi-dimensional characteristic information during the port operation; the port planning and scheduling subsystem sets port scheduling constraint conditions based on the multi-dimensional characteristic information, and formulates an operation plan for bulk cargo in the port according to the constraint conditions and the multi-dimensional characteristic information; the safety monitoring and early warning subsystem visually displays and gives safety early warnings to the operation plan to achieve intelligent monitoring and control of the entire process of bulk cargo ports.

Description

Technical Field

[0001] The present invention relates to the technical field of port intelligent control, and specifically to an intelligent control system for all-process equipment in bulk cargo ports. Background Art

[0002] As an important hub connecting the inland and the ocean, the operation efficiency, safety standards and environmental protection level of bulk cargo ports are the current focus of attention. However, there are many deficiencies in the process control mode of traditional bulk cargo ports, especially in aspects such as data processing and utilization, and optimization of logistics facilities and equipment. At the data processing level, the business scope of bulk cargo ports is extensive and there are many management departments, resulting in problems such as chaos and inconsistency in the process of collecting, storing, processing and analyzing port data, which directly affects the effective integration and in-depth analysis of port data, and at the same time affects the effectiveness and accuracy of the intelligent control system in decision-making. Therefore, it is difficult to achieve scientific adjustment and control of the bulk cargo port process.

[0003] In addition, for the logistics facilities and equipment in bulk cargo ports, the existing port process control mode cannot adjust the port process plan or optimize the operation conditions based on the situation of bulk cargo, resulting in a mismatch between the actual operation plan of the port and the current situation of the equipment, making it impossible to fully guarantee the port safety operation standards. Therefore, it is difficult to substantially improve the operation efficiency of bulk cargo ports, and the safety performance also faces challenges. To address and solve the above challenges, it is urgent to innovate and optimize the intelligent control system for all-process equipment in bulk cargo ports. Summary of the Invention

[0004] In view of the deficiencies of existing methods and the requirements of practical applications, in order to solve problems such as the data processing of break-bulk ports, the adjustment of logistics plans, and the optimization of operating conditions, multiple intelligent subsystems, information processing mechanisms, and port constraint conditions are set to optimize the intelligent control system for the whole-process equipment of the port, which helps to improve the operating efficiency, enhance the safety, and optimize the resource allocation of the break-bulk port. On the one hand, the present invention provides an intelligent control system for the whole-process equipment of the break-bulk port. The above system includes: an intelligent identification and monitoring subsystem, an information analysis and processing subsystem, a port planning and scheduling subsystem, and a safety monitoring and early warning subsystem; obtaining multi-dimensional dynamic monitoring information during the port operation based on the intelligent identification and monitoring subsystem; constructing a multi-level information processing mechanism in the information analysis and processing subsystem, and processing the multi-dimensional dynamic monitoring information based on the multi-level information processing mechanism to obtain multi-dimensional characteristic information during the port operation; the port planning and scheduling subsystem sets port scheduling constraint conditions based on the multi-dimensional characteristic information, and formulates an operation plan for break-bulk goods in the port according to the port scheduling constraint conditions and the multi-dimensional characteristic information; the safety monitoring and early warning subsystem visually displays and gives safety warnings to the operation plan to realize the intelligent monitoring and control of the whole process of the break-bulk port. The present invention sets multiple intelligent subsystems, which not only helps to continuously improve the intelligent level of the break-bulk port, but also helps to improve the operating efficiency and management level of the port, and promotes the sustainable development of the break-bulk port.

[0005] Optionally, constructing a multi-level information processing mechanism in the information analysis and processing subsystem includes: obtaining time series information during the port operation based on the multi-dimensional dynamic monitoring information; constructing a multi-level information processing mechanism based on the time series information, and the multi-level information processing mechanism includes a hidden information analysis model and a time series-space convolutional network processing model. The multi-level information processing mechanism of the present invention can comprehensively and deeply analyze the port operation data, which helps to improve the integrity and accuracy of the port data, and further comprehensively understand the actual operation status and process implementation of the break-bulk port.

[0006] Optionally, the hidden information analysis model satisfies the following relationship:

[0007]

[0008] Wherein, represents the hidden information of the time series information of different virtual machines in the break-bulk port, represents the fusion node information corresponding to different virtual machines, represents the weight matrix of, represents the operator of the spatio-temporal information convolutional network model, Represents the spatial features in the time series information of different virtual machines, Represents the feature matrix corresponding to the monitoring nodes of different virtual machines, Represents The diagonal matrix of Represents The network bias of

[0009] The present invention uses the model to obtain the hidden information in the time series information, further captures the spatio-temporal correlation in the time series information, and makes the extracted hidden information more comprehensive and accurate.

[0010] Optionally, the time-series spatial convolution network processing model satisfies the following relationship:

[0011]

[0012] Wherein, Represents the output result of the time-series spatial convolution network processing model, Represents the model activation function, Represents the weight coefficient of the causal convolution layer, Represents the causal convolution kernel parameter, Represents the Time series information of the nth virtual machine, Represents the Hidden information in the time series information of the nth virtual machine, Represents the weight coefficient of the first dilated convolution layer, Represents the dilation coefficient of the first dilated convolution layer, Represents the weight coefficient of the second dilated convolution layer, Represents the dilation coefficient of the second dilated convolution layer, Represents the bias term of the time-series spatial convolution network. The time-series spatial convolution network processing model of the present invention can process and analyze the time series information from different virtual machines, promoting the effective integration and sharing of internal data in the port.

[0013] Optionally, processing the multi-dimensional dynamic monitoring information based on the multi-level information processing mechanism to obtain multi-dimensional feature information in the port operation process includes: analyzing the time series information by using the hidden information analysis model and obtaining the hidden information in different time series information; processing the hidden information and the time series information by the time-series spatial convolution network processing model to obtain multi-dimensional feature information in the port operation process. The present invention analyzes the multi-dimensional feature information, can realize the accurate judgment of the port operation state, helps the port management layer to formulate countermeasures, and optimize the resource allocation.

[0014] Optionally, the port planning and scheduling subsystem sets port scheduling constraint conditions based on the multi-dimensional feature information, including: setting port scheduling constraint conditions based on the multi-dimensional feature information and the operation requirements of break-bulk ports. The port scheduling constraint conditions include: break-bulk location constraint conditions, port vehicle load constraint conditions, and port vehicle volume constraint conditions. The present invention sets break-bulk port constraint conditions. Based on reasonable load and volume, the wear and damage degree of port equipment can be reduced, the service life of port equipment can be further extended, and the maintenance cost can be reduced.

[0015] Optionally, the port planning and scheduling subsystem setting port scheduling constraint conditions based on the multi-dimensional feature information further includes: analyzing the initial centroid position coordinates of the break-bulk in the port based on the break-bulk location constraint conditions in the port scheduling constraint conditions; restricting the transport weight of the break-bulk in the port according to the port vehicle load constraint conditions in the port scheduling constraint conditions; and adjusting the carrying volume of the break-bulk in the port according to the port vehicle volume constraint conditions in the port scheduling constraint conditions. The present invention collects and analyzes the multi-dimensional feature information of the port through the port planning and scheduling subsystem, sets scheduling constraint conditions, and conducts cargo stacking and transportation planning accordingly, reflecting the development direction of data-driven decision-making.

[0016] Optionally, the break-bulk location constraint conditions satisfy the following relationship:

[0017]

[0018] Wherein, represents the initial centroid position coordinates of the break-bulk in the port, represents the centroid coordinates of the break-bulk after being loaded into the vehicle, respectively represent the length, width, and height in the X-axis, Y-axis, and Z-axis directions after the cargo is loaded, respectively represent the length, width, and height of the vehicle loaded with the break-bulk in the port, represents the allowable offset coefficient of the centroid of the break-bulk in the X-axis direction, represents the allowable offset coefficient of the centroid of the break-bulk in the Y-axis direction, represents the allowable offset coefficient of the centroid of the break-bulk in the Z-axis direction. The present invention sets break-bulk location constraint conditions. Based on this, the stacking position of the cargo can be accurately planned to ensure that the cargo does not exceed the carrying range of the vehicle during the loading process, thereby improving the port loading efficiency.

[0019] Optionally, the port vehicle load constraint conditions satisfy the following relationship:

[0020]

[0021] Wherein, Indicates the weight of the general cargo loaded into the vehicle at the port, Indicates the safety overload factor for loading the vehicle, Indicates the recorded weight of the general cargo loaded into the vehicle at the port;

[0022] The volume constraint condition of the port vehicle satisfies the following relationship:

[0023]

[0024] Wherein, Indicates the statistical volume of the general cargo loaded into the vehicle at the port, Indicates the proportion index of the invalid space of the general cargo at the port, Indicates the effective volume of the general cargo loaded into the vehicle at the port.

[0025] The present invention calculates the vehicle load and volume of the port through mathematical functions, which helps the port to reasonably allocate vehicles according to the weight and volume of the goods, avoids phenomena such as resource waste and empty running, helps to optimize resource allocation, and reduces the port transportation cost.

[0026] Optionally, the safety monitoring and warning subsystem visually displays and gives safety warnings to the operation plan, so as to realize the intelligent monitoring and control of the whole process of the general cargo port, including: adding a visualization device and a port safety warning device to the safety monitoring and warning subsystem; the safety monitoring and warning subsystem visually displays the whole process of the operation plan through the visualization device; the safety monitoring and warning subsystem gives intelligent safety warnings to the whole process of the operation plan through the port safety warning device; the safety monitoring and warning subsystem realizes the intelligent monitoring and control of the whole process of the general cargo port based on the visual display results and intelligent safety warning information. The safety monitoring and warning subsystem of the present invention can improve the transparency and visualization of port operations, enhance port safety and emergency response capabilities, and promote port intelligence and sustainable development, jointly promoting the intelligent development and management level of the port. Description of the Drawings

[0027] Figure 1 Is the flow chart of the intelligent control system for the whole process equipment of the general cargo port of the present invention;

[0028] Figure 2 Is the schematic diagram of the multi-level information processing mechanism in the intelligent control system for the whole process equipment of the general cargo port of the present invention;

[0029] Figure 3 Is the structure diagram of the intelligent control system for the whole process equipment of the general cargo port of the present invention. Detailed Embodiments

[0030] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustrative purposes and are not intended to limit the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to those of ordinary skill in the art that the present invention does not necessarily require these specific details. In other instances, well-known circuits, software, or methods have not been described in detail to avoid obscuring the present invention.

[0031] Throughout the specification, references to "one embodiment", "an embodiment", "an example", or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Thus, the phrases "in one embodiment", "in an embodiment", "an example", or "an example" appearing throughout the specification do not necessarily all refer to the same embodiment or example. Additionally, the particular features, structures, or characteristics may be combined in any suitable combination and / or sub-combination in one or more embodiments or examples. Further, those of ordinary skill in the art will understand that the diagrams provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0032] Please refer to Figure 1 , in order to solve the problems of bulk cargo ports in aspects such as data processing, logistics plan adjustment, and optimization of control conditions, the present invention introduces multiple intelligent subsystems, information processing mechanisms, and port constraint conditions, which helps to achieve the sharing and in-depth analysis of bulk cargo port information, the effective improvement of operational efficiency, the significant enhancement of safety, and the optimization of resource allocation, thereby comprehensively improving the operational efficiency and safety management level of bulk cargo ports. The present invention provides an intelligent control system for all-process equipment of bulk cargo ports, and the above system includes the following steps:

[0033] An intelligent identification and monitoring subsystem, an information analysis and processing subsystem, a port plan and scheduling subsystem, and a safety monitoring and early warning subsystem are set up in the intelligent control system for all-process equipment of bulk cargo ports.

[0034] S1. Based on the above-mentioned identification and monitoring subsystem, multi-dimensional dynamic monitoring information during the port operation is obtained, and its specific implementation steps and related content are as follows:

[0035] In this embodiment, the identification and monitoring subsystem is mainly used to obtain multi-dimensional real-time dynamic information of port operations. The above-mentioned subsystem integrates advanced monitoring and identification technologies, including but not limited to RFID technology, high-definition cameras, and various sensors, to intelligently identify and real-time monitor the goods, vehicles, operating equipment, and staff in the bulk cargo port, and can continuously monitor the core status of various port operations, mainly including the location of goods, the real-time operating status of loading and unloading equipment, the progress of different processes, and the work conditions of port staff.

[0036] In addition, the identification and monitoring subsystem also has the function of process task monitoring and display. It can comprehensively monitor and display the detailed information of various tasks in the port. The above information includes but not limited to task plans, operation processes, mechanical usage conditions, cargo stacking arrangements, and personnel configurations. In order to further improve the actual use experience of the intelligent control system, the system also designs an intuitive and easy-to-use information query interface. This interface can real-time update the full-process task status of the bulk cargo port to ensure that relevant port personnel can quickly obtain the latest dynamics of different tasks in the bulk cargo port. At the same time, the interface design fully considers user interactivity to ensure that different port managers can quickly query and obtain relevant information of the bulk cargo port according to their actual needs.

[0037] Furthermore, the acquisition method of the multi-dimensional dynamic monitoring information of the port in this embodiment is only an optional condition of the present invention. In one or some other embodiments, the acquisition method of the multi-dimensional dynamic monitoring information can be adjusted according to the actual situation of the bulk cargo port and the data monitoring target. Different bulk cargo ports have different operation modes and business requirements. Adjusting the acquisition method of the monitoring information is beneficial to meeting the actual needs of each port and ensuring the effectiveness and practicability of the monitoring system.

[0038] S2. A hierarchical information processing mechanism is constructed in the above information analysis and processing subsystem, and the multi-dimensional dynamic monitoring information is processed based on the multi-level information processing mechanism to obtain multi-dimensional characteristic information in the process of port operation. The specific implementation content is as follows:

[0039] First, time series information in the process of port operation is obtained based on the multi-dimensional dynamic monitoring information.

[0040] In this embodiment, combining the data structure of the bulk cargo port with time series data helps to comprehensively and accurately analyze and represent the subsequent time series information. There are multiple virtual machines deployed in the above-mentioned intelligent identification and monitoring subsystem. As a software simulation environment running on physical hardware, the virtual machines can completely simulate the various functions of the intelligent control system, including but not limited to core components such as a central processing unit (CPU), internal memory, hard disk drive, and network interface card (NIC).

[0041] Each virtual machine interacts efficiently with the underlying physical hardware through an advanced hypervisor, thus constructing multiple independent and secure isolated operating system environments on a single physical server. Under this architecture, a multi-dimensional dynamic monitoring information generated by different virtual machines is formed within the intelligent control system. Further, the time series data set in the intelligent control system is analyzed by combining the data acquisition time point and the preset monitoring time interval, and is marked as T.

[0042] Specifically, for different virtual machines in the time series data set, their unique time series data are separately recorded and marked as , and this time series data contains different timing information, where represents a specific data point or time interval segment in the time series. The above timing information not only reflects the dynamic changes during the operation of the virtual machine, but also is closely related to the overall operation status of the port system and the real-time update of the target task data.

[0043] The time series data set of different virtual machines in the intelligent control system is T, and the time series of the nth virtual machine is , and the corresponding time series information of different virtual machines is .

[0044] Among them, the time series of different virtual machines need to satisfy the following relationship:

[0045]

[0046] Among them, represents the time series corresponding to different virtual machines, represents the first time interval in the time series, represents the second time interval in the time series, represents the th time interval in the time series.

[0047] Furthermore, the time series information of different virtual machines satisfies the following relationship:

[0048]

[0049] Among them, represents the time series information of different virtual machines, represents the timing information of the time interval, represents the timing information of the time interval, represents the timing information of the time interval.

[0050] Based on the time series information of different virtual machines, the time series information of all virtual machines in the break-bulk port system can be obtained, that is, the time series information during the port operation process is obtained.

[0051] In this embodiment, the preset intelligent control system has n virtual machines:

[0052]

[0053] Among them, represents the time series information set of all virtual machines in the break-bulk port system, represents the time series information of the first virtual machine, represents the time series information of the second virtual machine, represents the time series information of the nth virtual machine.

[0054] During the operation of the break-bulk port, through multi-dimensional dynamic monitoring technology, the change information of each process operation parameter can be captured in real time, such as cargo throughput, arrival and departure time, port yard utilization rate, etc. Processing and analyzing the multi-dimensional dynamic monitoring information and converting it into corresponding time series number information is beneficial to reflecting the operation situation of the break-bulk port at different time points and the process implementation status.

[0055] In order to accurately obtain the time series information of the break-bulk port, multiple virtual machines are deployed in the break-bulk port system. Each virtual machine is responsible for obtaining data from specific monitoring sources or subsystems, and performing preliminary processing and storage on it. By integrating the time series data on different virtual machines, a comprehensive time series information set during the entire port operation process can be obtained, thereby improving the data collection efficiency and enabling faster acquisition of the comprehensive time series information of the entire port process.

[0056] Then, based on the above time series information, a multi-level information processing mechanism is constructed. In the embodiment, the multi-level information processing mechanism mainly includes a hidden information analysis model and a time-sequence and space convolutional network processing model. For the structural situation of the above multi-level information processing mechanism, please refer to Figure 2 .

[0057] Due to the complex interaction relationships among different virtual machines in the intelligent control system for the whole process equipment of break-bulk ports at the network level topology, the performance indicators of their operating states also show a high degree of dynamism. The above-mentioned dynamism is not only reflected inside a single virtual machine, such as the real-time fluctuations of indicators like CPU usage rate, memory occupancy rate, disk I / O, etc., but also reflected in the mutual influence among virtual machines due to factors such as resource competition and communication delay. Therefore, in this embodiment, a multi-level information processing mechanism is constructed based on time series information to analyze the multi-dimensional dynamic monitoring information in the port operation process.

[0058] An optionally implemented example sets a hidden information analysis model in the multi-level information processing mechanism.

[0059] The above hidden information analysis model satisfies the following relationship:

[0060]

[0061] Among them, represents the hidden information of the time series information of different virtual machines in the break-bulk port, represents the fusion node information corresponding to different virtual machines, represents the weight matrix of represents the operator of the spatio-temporal information convolutional network model, represents the spatial features in the time series information of different virtual machines, represents the feature matrix corresponding to the monitoring nodes of different virtual machines, represents the diagonal matrix of represents the network bias of

[0062] In the intelligent control system for the whole process equipment of break-bulk ports, the hidden information of the time series information of different virtual machines refers to the information that is not directly revealed on the surface and needs to be mined and analyzed through specific methods or models. The above information is related to the operating states, performance manifestations, and mutual interaction relationships of the system virtual machines.

[0063] In the break-bulk port system, the fusion node information corresponding to different virtual machines refers to the port information involved and included when each virtual machine acts as an information source or a data processing node and interacts and integrates with each other through specific fusion structures and methods during the data fusion or information fusion process.

[0064] In the spatio-temporal information convolutional network model, an operator refers to a function or module that performs specific calculations or operations. The above-mentioned operator can be used to process spatio-temporal data, such as video frames, time series data, or other data with spatial and temporal dimensions, in order to capture the temporal distribution characteristics of multi-dimensional dynamic monitoring information.

[0065] In the spatio-temporal information convolutional network model, the weight matrix of an operator refers to the parameter matrix used to perform operations such as convolution, pooling, and fully connected. The above weight matrix is learned by the network during training, and it determines how the network extracts hidden information features from the input data.

[0066] The spatial features in the time series information of different virtual machines refer to the features such as the time interval, location shape associated with the virtual machine during operation, and the spatial relationship with adjacent virtual machines or resources. In the time series information, the above interaction features can be manifested as changes in parameters such as the communication frequency between virtual machines, the transmission speed of data packets, and network latency.

[0067] The feature matrix corresponding to different virtual machine monitoring nodes, in the technical field of virtualization technology and system monitoring, can be used to describe and record the data structure of the status, performance, and other related attributes of each virtual machine monitoring node. This feature matrix can be a two-dimensional array or table, where each row represents a virtual machine monitoring node, and each column represents a specific port feature or process attribute.

[0068] A diagonal matrix is a matrix in which all elements outside the main diagonal are zero. When referring to the diagonal matrix of the feature matrix corresponding to different virtual machine monitoring nodes, if some elements or performance indicators in the feature matrix are equal on all virtual machine monitoring nodes, then these equal elements can form a scalar matrix, which is also a special diagonal matrix, where the elements on the main diagonal are all equal, and other elements are all 0. In fact, a special case is considered, that is, some specific time information in the feature matrix is extracted to form a diagonal matrix.

[0069] In the spatio-temporal information convolutional network model, the network bias of an operator is a parameter corresponding to the weight matrix, which is used to adjust the output of the hidden information analysis model to help the model better fit the training data. Thus, the hidden information of the time series information of different virtual machines in the break-bulk port can be accurately obtained.

[0070] In an optional embodiment, before inputting the time series information of different virtual machines into the hidden information analysis model, it is first necessary to fuse and preprocess the relevant information, including integrating the feature matrix, adjacency matrix, and undirected information graph of each monitoring node to form an input set containing the complete topological structure and time series data. The above steps are crucial for the subsequent extraction of hidden information, further ensuring that the model can capture both the dynamic characteristics of the time series and the network relationships between virtual machines.

[0071] The hidden information analysis model receives the preprocessed input set, that is, the time series information of different virtual machines and the corresponding topological information. The model analyzes and mines the input information through algorithms and calculations, and then identifies the hidden association information between different levels in the time series information. The above information can be manifested as the mutual influence between virtual machines, the imbalance of resource allocation, or the chain reaction caused by certain specific events, etc. Finally, the hidden information analysis model will output the hidden information in the time series information of different virtual machines.

[0072] In an optional embodiment, a time-series - spatial convolution network processing model is set up in the multi-level information processing mechanism.

[0073] In this embodiment, the time-series - spatial convolution network processing model is responsible for further integrating and analyzing the features extracted by the hidden information analysis model. This model combines the advantages of the time series analysis model and the spatial convolution network, can capture both the time dependence and spatial correlation of the virtual machine running state, and obtains multi-dimensional feature information in the port operation process by optimizing the network structure and parameter settings.

[0074] In the embodiment, the structure of the convolutional neural network (CNN) is improved to make it suitable for processing time series data, and then the time-series - spatial convolution network model of the present invention is constructed. The above time-series - spatial convolution network model has a unique structural design and can efficiently process the dynamic monitoring information in the break-bulk port system. The model cleverly combines causal convolution and dilated convolution to achieve in-depth analysis of the port process information and dynamic monitoring information.

[0075] In the time-series - spatial convolution network model, a causal convolution layer is first set up. Its main function is to ensure that when the model processes time series information, it can strictly follow the time order, that is, only use the information of the current and previous time points, avoiding the leakage of future port dynamic monitoring information, which helps to more accurately simulate and predict the actual changes and task running status in the port process.

[0076] Subsequently, the temporal-spatial convolutional network model includes two dilated convolutional layers, namely the first dilated convolutional layer and the second dilated convolutional layer in the embodiment. The above-mentioned dilated convolution effectively expands the processing volume of the convolution by inserting additional intervals between the convolutional kernel elements. Thus, while keeping the size of the convolutional kernel unchanged, the model's capture range of port process information and monitoring information is increased. The above design enables the model to capture more port process details, including but not limited to the mutual influence and dependency relationships between system virtual machines, the interaction and overlap relationships of multi-dimensional monitoring information, and the distribution of temporal information, etc.

[0077] Finally, through the output layer in the temporal-spatial convolutional network model, the processed port time series and spatial series feature information are extracted. The above information not only includes the basic data in the port process but also the hidden information and mutual dependency relationships in the time series information, providing information support for the subsequent process management and equipment regulation scheme optimization of break-bulk ports.

[0078] Compared with the temporal recurrent neural network, the temporal-spatial convolutional network model can process all the basic data and hidden information in the time series in parallel, greatly improving the computational efficiency. When the model processes large-scale port process data and multi-dimensional monitoring information, it can obtain more comprehensive and accurate information analysis results, providing a decision-making basis for the operation of break-bulk ports and the control of equipment throughout the process.

[0079] In this embodiment, the operation process and related content of the multi-level information processing mechanism are as follows:

[0080] Input the time series information set into the hidden information analysis model, and the input information satisfies the following relationship:

[0081]

[0082] Among them, represents the time series information set of all virtual machines in the break-bulk port system, represents the time series information of the first virtual machine, represents the time series information of the second virtual machine, represents the th virtual machine's time series information.

[0083] Subsequently, the hidden information analysis model outputs the hidden information set, and the hidden information set satisfies the following relationship:

[0084]

[0085] Among them represents the hidden information set in the time series information of all virtual machines in the break-bulk port system, Represents the hidden information in the time series information of the first virtual machine, Represents the hidden information in the time series information of the second virtual machine, Represents the Hidden information in the time series information of the

[0086] Then, use the temporal-spatial convolutional network processing model to obtain the output results of different virtual machines. Combining all the output results can obtain the multi-dimensional feature information in the port operation process. The above temporal-spatial convolutional network processing model satisfies the following relationship:

[0087]

[0088] Among them, Represents the output result of the temporal-spatial convolutional network processing model, Represents the model activation function, Represents the weight coefficient of the causal convolutional layer, Represents the causal convolution kernel parameter, Represents the Time series information of the Represents the Hidden information in the time series information of the Represents the weight coefficient of the first dilation convolutional layer, Represents the dilation coefficient of the first dilation convolutional layer, Represents the weight coefficient of the second dilation convolutional layer, Represents the dilation coefficient of the second dilation convolutional layer, Represents the bias term of the temporal-spatial convolutional network.

[0089] The activation function in the model refers to a non-linear mapping in the neural network, which converts the input information into the corresponding output result. The introduction of non-linear factors can increase the expression ability of the temporal-spatial convolutional network processing model.

[0090] The weight coefficient of the causal convolutional layer refers to the coefficient for weighted summation of the input information in the causal convolutional layer. The above weight coefficient is learned during the training process of the convolutional neural network, and it determines the contribution degree of each input information to the output result. Causal convolution is a special convolution method mainly used to process sequence data. In causal convolution, for the value at time t in the previous layer, it only depends on the value at time t and its previous values in the next layer. The above convolution method ensures that the output result only depends on the input sequence and is a causal output result that conforms to the time sequence.

[0091] The causal convolution kernel parameters refer to the specific values that can be used to define the characteristics of the convolution kernel in the causal convolution operation. The above parameters determine how the causal convolution kernel weights and sums the input information, thereby affecting the relevant characteristics of the output result. In this embodiment, each element in the convolution kernel corresponds to a weight coefficient. The above weight coefficients are obtained through learning during the training process and can be used to capture the features in the input information. In causal convolution, the update of the weight coefficients needs to follow the causality constraint, that is, it only depends on the current and previous data.

[0092] The weight coefficients of different dilated convolution layers refer to the weight coefficients used by the respective convolution kernels in the convolution layers with different dilation rates. Dilated convolution is a special convolution operation that increases the receptive field by inserting gaps between the convolution kernel elements while keeping the actual size of the convolution kernel unchanged. The above operation enables the time-series - spatial convolution network processing model to capture a larger range of port process monitoring information and time-series information without increasing the computational complexity.

[0093] The dilation coefficients of different dilated convolution layers (Dilation Rate) refer to the different numbers of gaps used in each layer with dilated convolution operation in the convolutional neural network. This parameter determines the gap size between the convolution kernel elements, thereby affecting the receptive field and feature information extraction ability of the convolution layer.

[0094] Among them, dilated convolution (also known as atrous convolution or inflated convolution) is to inject holes into the standard convolution kernel to increase the receptive field of the processing model. Compared with the original normal convolution operation, dilated convolution has an additional parameter, namely the dilation coefficient. The above dilation coefficient defines the spacing of each value when the convolution kernel processes data, that is, it determines the distance between adjacent valid elements (non-zero elements) in the convolution kernel.

[0095] The bias term in the time-series - spatial convolution network refers to the additional parameter added to each output channel corresponding to the convolution kernel. This bias term translates or offsets the input information, thereby adjusting the output range of the processing model. In the time-series - spatial convolution network, the bias term can more flexibly fit the data and further improve the expression ability of the model.

[0096] Finally, use the hidden information analysis model to analyze the time-series information and obtain the hidden information in different time-series information; and process the above hidden information and time-series information through the time-series - spatial convolution network processing model to obtain the multi-dimensional feature information during the port operation process.

[0097] In the intelligent analysis process of the full-process operation management of a port, a comprehensive multi-level information processing mechanism is adopted. First, the above mechanism mines the hidden information in the port time series information through a hidden information analysis model, and the above hidden information contains the key details and potential rules in the port operation process.

[0098] Immediately afterwards, the time-series spatial convolutional network processing model is used to receive the feature input from the hidden information analysis model, and in combination with the original time series information and dynamic monitoring information, deeply processes and analyzes the port information. The above steps aim to comprehensively capture the multi-dimensional feature information in the port operation process, including but not limited to various aspects such as logistics efficiency, equipment status, and personnel scheduling.

[0099] In addition, each model in the multi-level information processing mechanism is not isolated, but cooperates with each other and jointly plays a role to obtain multi-dimensional feature information. Among them, the hidden information analysis model provides rich feature inputs for the time-series spatial convolutional network processing model, and the time-series spatial convolutional network processing model, through its powerful processing ability, converts the relevant features into multi-dimensional feature information in the port operation process, which is beneficial to the accurate prediction, intelligent control, and fault warning of the full-process equipment operation status of the bulk cargo port. This collaborative relationship enables the multi-level information processing mechanism to more efficiently process the complex and changeable monitoring data and operation status information in the port system.

[0100] By efficiently processing the complex and changeable port operation status information in the port system through the multi-level information processing mechanism, the accurate prediction and fault warning of the operation status of each device or component in the port operation management system are realized. Based on this, not only the intelligent level of port operation management is improved, but also more comprehensive and accurate data support is provided for decision-makers, which helps the bulk cargo port to operate and manage more efficiently and safely.

[0101] Furthermore, the way of obtaining the port multi-dimensional feature information in this embodiment is only an optional condition of the present invention. In one or some other embodiments, the way of obtaining the port multi-dimensional feature information can be adjusted according to the actual situation of the port monitoring data and the intelligent control requirements of the equipment. Different ports have differences in operation scale, equipment configuration, business model, etc. Adjusting the way of obtaining the feature information according to the actual situation enables the present invention to better adapt to the actual needs of various ports and ensures the accuracy and practicality of the analysis results.

[0102] S3. The above port planning and scheduling subsystem sets port scheduling constraint conditions based on the multi-dimensional feature information, and further formulates an operation plan scheme for the bulk cargo in the port according to the above port scheduling constraint conditions and multi-dimensional feature information. The specific implementation content is as follows:

[0103] Based on multi-dimensional feature information and the operation requirements of break-bulk ports, port scheduling constraint conditions are set. In this embodiment, the port scheduling constraint conditions mainly include: break-bulk location constraint conditions, port vehicle load constraint conditions, and port vehicle volume constraint conditions.

[0104] Based on multi-dimensional feature information and the complex and changeable operation requirements of break-bulk ports, a series of port scheduling constraint conditions are set to ensure the normal operation of the break-bulk port process and the scientific and efficient control of the scheduling plan. In this embodiment, the above constraint conditions not only cover the basic operation management elements but also go deep into the details of the actual operation level.

[0105] To ensure that the break-bulk port implements the operation process according to the optimal strategy, relevant information conditions for break-bulk goods and carrying vehicles are preset in this embodiment. The above conditions aim to ensure the safety and efficiency of the loading process of the break-bulk port through precise mathematical expressions.

[0106] First, an initial center-of-gravity position coordinate is defined , which represents the position of the center of gravity of the break-bulk goods in the port before being loaded. When the break-bulk goods are loaded into the vehicle, its center of gravity will change, and the new center-of-gravity position is represented by . The above changes play an important role in evaluating the stability and safety of the loaded goods.

[0107] In addition, the dimensions of the goods along the X-axis, Y-axis, and Z-axis directions after loading are defined, which are represented by respectively. These dimension data help to understand the actual occupied space of the goods in the vehicle, thus avoiding space waste or overloading during the loading process.

[0108] At the same time, the specific parameters of the carrying vehicle c are also considered, including but not limited to its length , width and height . These parameters are the key to determining the loading capacity of the vehicle. In addition to the dimension parameters, the load capacity and effective volume of the vehicle are also concerned. They respectively limit the maximum weight and maximum volume that the vehicle can load, which are important information to ensure the safety and compliance of the loading process in the break-bulk port.

[0109] Among them, the break-bulk location constraint conditions require that the specific storage location of each batch of break-bulk goods must be accurately mastered during scheduling in order to formulate the optimal handling and loading plan, reducing unnecessary handling costs and time waste.

[0110] The above break-bulk location constraint conditions satisfy the following relationship:

[0111]

[0112] Among them, represents the initial center-of-gravity position coordinates of the port's break-bulk cargo, represents the center-of-gravity coordinates of the port's break-bulk cargo after being loaded into the vehicle, respectively represent the length, width, and height in the X-axis, Y-axis, and Z-axis directions after the cargo is loaded, respectively represent the length, width, and height of the vehicle loaded with the port's break-bulk cargo, represents the allowable offset coefficient in the X-axis direction of the center of gravity of the port's break-bulk cargo, represents the allowable offset coefficient in the Y-axis direction of the center of gravity of the port's break-bulk cargo, represents the allowable offset coefficient in the Z-axis direction of the center of gravity of the port's break-bulk cargo.

[0113] When loading port cargo onto a carrying vehicle, it is necessary to ensure that the size, weight, and volume of the cargo do not exceed the maximum values allowed by the vehicle. Specifically, the length, width, and height of the cargo should be strictly controlled within the corresponding dimensions of the vehicle to avoid loading spills or vehicle overloading. At the same time, the total weight of the cargo should not exceed the load capacity of the vehicle to ensure safety and stability during transportation. In addition, the total volume of the cargo should also be less than or equal to the effective volume of the vehicle to prevent overloading from causing unreasonable use of vehicle space or safety hazards.

[0114] The load constraints for port vehicles need to consider the safety and operating efficiency of port transport vehicles. In the embodiments, a clear maximum load capacity is set for each type of vehicle. During the optimization and scheduling of port processes, it is necessary to ensure that the total weight of the allocated cargo is controlled within the load capacity of the vehicle to prevent safety hazards and reduced transport efficiency caused by overloading.

[0115] The above load constraints for port vehicles satisfy the following relationship:

[0116]

[0117] Among them, represents the weight of the port's break-bulk cargo loaded into the vehicle, represents the safety overload coefficient of the loading vehicle, represents the recorded weight of the vehicle loaded with the port's break-bulk cargo;

[0118] In addition to load restrictions, reasonable settings and planning have also been carried out for the volume constraints of port vehicles. During the port operation process and the process of adjusting the plan, it is necessary to reasonably allocate transport vehicles according to the volume size and shape characteristics of the cargo to ensure that the cargo can be fully and compactly loaded in the vehicle, thereby improving transport efficiency and reducing transport costs.

[0119] The above port vehicle volume constraint conditions satisfy the following relationship:

[0120]

[0121] Among them, represents the statistical volume of break-bulk cargo loaded into the vehicle at the port, represents the proportion index of the ineffective space of break-bulk cargo at the port, represents the effective volume of the vehicle loaded with break-bulk cargo at the port.

[0122] In addition, according to the actual situation and specific requirements of port operation, these constraint conditions can be further expanded and improved, such as considering the cargo handling time limit, transportation route optimization, port congestion situation, etc., so as to comprehensively improve the high efficiency of break-bulk cargo port equipment, as well as the intelligence and refinement level of the port scheduling system.

[0123] In an alternative embodiment, based on the break-bulk cargo position constraint condition in the port scheduling constraint conditions, the initial centroid position coordinates of the break-bulk cargo at the port are analyzed; the transportation weight of the break-bulk cargo at the port is restricted according to the port vehicle load constraint condition in the port scheduling constraint conditions; the carrying volume of the break-bulk cargo at the port is adjusted according to the port vehicle volume constraint condition in the port scheduling constraint conditions, and then the loading process and operation process of the break-bulk cargo at the port are carefully planned.

[0124] First, based on the break-bulk cargo position constraint condition in the port scheduling constraint conditions, the initial centroid position coordinates of the break-bulk cargo at the port are analyzed. This step aims to determine the specific position of the cargo in the port, so as to efficiently and accurately transport it to the designated position during the subsequent loading process, and at the same time ensure the stability and safety of the loading process.

[0125] Secondly, according to the port vehicle load constraint condition in the port scheduling constraint conditions, the transportation weight of the break-bulk cargo at the port is restricted. During the loading process of the port cargo, it is always ensured that the total weight of the allocated cargo does not exceed the load capacity of the carrying vehicle, so as to prevent potential safety hazards and reduced transportation efficiency caused by overloading.

[0126] Finally, according to the port vehicle volume constraint condition in the port scheduling constraint conditions, the carrying volume of the break-bulk cargo at the port is flexibly adjusted. Mainly according to the volume size and shape characteristics of the break-bulk cargo, as well as the volume limit of the carrying vehicle, the port transport vehicles are reasonably allocated and the loading plan is optimized, so as to ensure that the cargo can be fully and compactly loaded in the vehicle, thereby improving the transportation efficiency and reducing the transportation cost.

[0127] Through careful planning and flexible adjustment, on the premise of ensuring safety and compliance, the efficient, accurate and intelligent management of the loading process of break-bulk cargo at the port is realized, further ensuring the actual application effect of the intelligent control system for the whole process equipment of the break-bulk cargo port.

[0128] Furthermore, the method for obtaining the port operation plan and the regulation steps in this embodiment are only an optional condition of the present invention. In one or some other embodiments, the method for obtaining the port operation plan and the regulation steps can be optimized according to the safety requirements of the bulk cargo port and the intelligent control conditions of the port equipment, which helps to realize the real-time monitoring and intelligent scheduling of the port equipment, so as to more accurately grasp the cargo loading and unloading progress, vehicle operation status and port resource utilization situation, and further provide strong support for the continuous optimization of the port operation plan.

[0129] S4. The safety monitoring and early warning subsystem visually displays and gives safety early warnings to the above operation plan to achieve the intelligent monitoring and control of the whole process of the bulk cargo port. The specific implementation content is as follows:

[0130] The intelligent control system for the whole process equipment of the bulk cargo port further includes configuring a visualization device and a port safety early warning device in the safety monitoring and early warning subsystem.

[0131] The visualization device can capture and display the whole process situation and operation status of the bulk cargo port in real time. Through image processing and display technology, port operation data, multi-dimensional feature information and operation plan can be converted into intuitive and easy-to-understand graphics, schematic diagrams or animations, enabling port management personnel to quickly master the process situation and operation status of the bulk cargo port, including but not limited to the cargo loading and unloading progress, vehicle scheduling situation, and equipment operation status, etc. The above real-time visualization display method not only improves the operation transparency of the bulk cargo port, but also provides a decision-making basis for management personnel, enabling them to make more accurate and efficient equipment regulation and decision-making adjustments according to the actual situation.

[0132] The visualization device also has the function of pushing and notifying internal port messages, which can fully cover all aspects of task management, including but not limited to key nodes such as task issuance, change, start, suspension and fault notification, etc., making the bulk cargo port messages presented in the form of instant communication to ensure that information can be quickly conveyed to relevant personnel. At the same time, the past message records of the port can be consulted. The message overview interface clearly distinguishes the read and unread status of the messages, which is convenient for quickly mastering the message processing situation. In order to more intuitively display the message content, the system arranges it in a table form. For specific details, see Table 1, which makes each message clear at a glance, further improving the efficiency and accuracy of message management, and thus enhancing the overall performance and user satisfaction of the intelligent control system for the whole process equipment of the bulk cargo port.

[0133] Table 1 Message Table of Bulk Cargo Port

[0134]

[0135] The port safety warning device integrates a variety of sensors and data analysis algorithms, and can real-time monitor the fluctuations and change trends of various safety parameters during the port operation process, such as the stability of goods, the load and speed of transport vehicles, and the wear condition of port equipment, etc. Once the relevant parameters exceed the preset safety range, the warning device will be immediately activated, and send warning messages to relevant personnel by means of sound, light or text message, etc., to remind port managers to take measures in time to prevent potential safety risks from turning into actual accidents.

[0136] In summary, the safety monitoring and warning subsystem in the intelligent control system of the whole-process equipment of the bulk cargo port, by configuring the visualization device and the port safety warning device, realizes the comprehensive monitoring and intelligent warning of the port operation status and equipment operation status, and provides technical guarantee for the safe and efficient operation of the bulk cargo port.

[0137] The above-mentioned safety monitoring and warning subsystem visually displays the whole process of the operation plan through the visualization device; conducts intelligent safety warning on the whole process of the operation plan through the port safety warning device; based on the visualization display results and intelligent safety warning information, the safety monitoring and warning subsystem can realize the intelligent monitoring and control of the whole process of the bulk cargo port.

[0138] First of all, the subsystem uses the visualization device to present the whole process of the operation plan of the bulk cargo port in an intuitive and vivid way, enabling port managers to clearly understand the port operation status at a glance. Whether it is the real-time task situation, the cabin status, the bill of lading information, or the operation status of key modules such as the yard, gantry crane, horizontal transportation, and clamp truck, etc., can be displayed in detail on the visualization interface.

[0139] At the same time, the port safety warning device can conduct intelligent safety warning on the whole process of the operation plan. Through real-time monitoring and data comparison and analysis, the warning device can timely detect and warn potential safety risks, including but not limited to equipment failures, abnormal task executions, and incorrect process sequences, etc., so as to ensure the safety and stability of the bulk cargo port operation.

[0140] On this basis, the intelligent control system of the whole-process equipment of the bulk cargo port further strengthens the monitoring ability of the system. It can not only monitor the overall task execution situation of the port, but also conduct operation status statistics and real-time status monitoring for different types of equipment such as gantry cranes, horizontal transport vehicles, and clamp trucks, etc. In addition, the task status monitoring function enables managers to real-time track the executed and to-be-executed operation tasks, including but not limited to task basic information, status information, mechanical information, stack position information, etc., so as to accurately control the whole process and the execution process of the bulk cargo port task execution.

[0141] When an abnormal situation occurs, the system will immediately issue safety warnings and early warning signals by using the abnormal alarm monitoring function, and send warning signals to port management personnel through page reminders, wireless communication, telephone reminders, etc. At the same time, the abnormal alarm statistics function can also provide abnormal alarm analysis and real-time monitoring of various indicators according to grouping methods such as cycle and type, providing decision-making support for port management personnel.

[0142] In summary, through a series of functions such as visual display, intelligent safety early warning, real-time monitoring, task status monitoring, and abnormal alarm monitoring, the safety monitoring and early warning subsystem realizes the intelligent monitoring and control of the entire process of break-bulk ports. The system of the present invention not only improves the safety and efficiency of port operations, but also lays a solid foundation for the sustainable development of the port.

[0143] Please refer to Figure 3 , in an optional embodiment, the present invention also provides an intelligent control system for the entire process equipment of break-bulk ports. The above system includes an intelligent identification and monitoring subsystem, an information analysis and processing subsystem, a port plan and scheduling subsystem, and a safety monitoring and early warning subsystem. The above intelligent identification and monitoring subsystem, information analysis and processing subsystem, port plan and scheduling subsystem, and safety monitoring and early warning subsystem are interconnected to implement the specific steps of the relevant embodiments of the intelligent control system for the entire process equipment of break-bulk ports provided by the present invention. The intelligent control system for the entire process equipment of break-bulk ports of the present invention has a complete structure, is objective and stable, and improves the overall applicability and practical application ability of the present invention.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered by the scope of the claims and the specification of the present invention.

Claims

1. An intelligent control system for all-process equipment in a break-bulk port, characterized in that, The intelligent control system for the whole process equipment of the general cargo port includes: an intelligent identification and monitoring subsystem, an information analysis and processing subsystem, a port planning and scheduling subsystem, and a safety monitoring and early warning subsystem; Based on the intelligent identification and monitoring subsystem, multi-dimensional dynamic monitoring information during the port operation is obtained; A multi-level information processing mechanism is constructed in the information analysis and processing subsystem, and the multi-dimensional dynamic monitoring information is processed based on the multi-level information processing mechanism to obtain multi-dimensional characteristic information during the port operation; The port planning and scheduling subsystem sets port scheduling constraint conditions based on the multi-dimensional characteristic information, and formulates an operation plan for general cargo in the port according to the port scheduling constraint conditions and the multi-dimensional characteristic information; The safety monitoring and early warning subsystem conducts visual display and safety early warning on the operation plan to achieve intelligent monitoring and control of the whole process of the general cargo port; The construction of the multi-level information processing mechanism in the information analysis and processing subsystem includes: Obtaining time series information during the port operation based on the multi-dimensional dynamic monitoring information; Constructing a multi-level information processing mechanism based on the time series information, and the multi-level information processing mechanism includes a hidden information analysis model and a time series-spatial convolution network processing model; The hidden information analysis model satisfies the following relationship: ， Among them, represents the hidden information of the time series information of different virtual machines in the break-bulk port, represents the fusion node information corresponding to different virtual machines, represents the weight matrix of, represents the operator of the spatio-temporal information convolutional network model, represents the spatial features in the time series information of different virtual machines, represents the feature matrix corresponding to the monitoring nodes of different virtual machines, represents the diagonal matrix of, represents the network bias of; The time series-spatial convolution network processing model satisfies the following relationship: ， Among them, represents the output result of the spatio-temporal convolutional network processing model, represents the model activation function, represents the weight coefficient of the causal convolutional layer, represents the causal convolution kernel parameter, represents the time series information of the th virtual machine, represents the hidden information in the time series information of the th virtual machine, represents the weight coefficient of the first dilated convolutional layer, represents the dilation coefficient of the first dilated convolutional layer, represents the weight coefficient of the second dilated convolutional layer, represents the bias term of the spatio-temporal convolutional network; The port planning and scheduling subsystem setting port scheduling constraint conditions based on the multi-dimensional characteristic information includes: Setting port scheduling constraint conditions based on the multi-dimensional characteristic information and the operation requirements of the general cargo port, and the port scheduling constraint conditions include: general cargo position constraint conditions, port vehicle load constraint conditions, and port vehicle volume constraint conditions; The general cargo position constraint conditions satisfy the following relationship: ， Among them, represents the initial center of gravity position coordinates of the general bulk cargo at the port, represents the center of gravity coordinates of the general bulk cargo at the port after being loaded into the vehicle, respectively represent the length, width and height in the X-axis, Y-axis and Z-axis directions after the cargo is loaded, respectively represent the length, width and height of the vehicle loaded with the general bulk cargo at the port, represents the allowable offset coefficient in the X-axis direction of the center of gravity of the general bulk cargo at the port, represents the allowable offset coefficient in the Y-axis direction of the center of gravity of the general bulk cargo at the port, represents the allowable offset coefficient in the Z-axis direction of the center of gravity of the general bulk cargo at the port.

2. The intelligent control system for the whole process equipment of break-bulk ports according to claim 1, characterized in that The processing of the multi-dimensional dynamic monitoring information based on the multi-level information processing mechanism to obtain multi-dimensional characteristic information during the port operation includes: Analyzing the time series information using the hidden information analysis model and obtaining the hidden information in different time series information; The time series-spatial convolution network processing model processes the hidden information and the time series information to obtain multi-dimensional characteristic information during the port operation.

3. The intelligent control system for the whole-process equipment of break-bulk ports according to claim 1, wherein, The port planning and scheduling subsystem setting port scheduling constraint conditions based on the multi-dimensional characteristic information further includes: Analyzing the initial center of gravity position coordinates of the general cargo in the port based on the general cargo position constraint conditions in the port scheduling constraint conditions; Restricting the transportation weight of the general cargo in the port according to the port vehicle load constraint conditions in the port scheduling constraint conditions; Adjusting the carrying volume of the general cargo in the port according to the port vehicle volume constraint conditions in the port scheduling constraint conditions.

4. The intelligent control system for the whole-process equipment of break-bulk ports according to claim 1, wherein, The port vehicle load constraint conditions satisfy the following relationship: ， Among them, represents the weight of the break-bulk cargo loaded into the vehicle at the port, represents the safety overload factor for loading the vehicle, represents the recorded weight of the break-bulk cargo loaded onto the vehicle at the port; The port vehicle volume constraint conditions satisfy the following relationship: ， Among them, represents the statistical volume of break bulk cargo loaded into vehicles at the port, represents the proportion index of invalid space of break bulk cargo at the port, represents the effective volume of break bulk cargo loaded into vehicles at the port.

5. The intelligent control system for all-process equipment of break-bulk ports according to claim 1, wherein, The safety monitoring and early warning subsystem conducting visual display and safety early warning on the operation plan to achieve intelligent monitoring and control of the whole process of the general cargo port includes: Add a visualization device and a port safety warning device to the safety monitoring and warning subsystem; The safety monitoring and warning subsystem visually displays the entire process of the operation plan through the visualization device; The safety monitoring and warning subsystem conducts intelligent safety warnings on the entire process of the operation plan through the port safety warning device; The safety monitoring and warning subsystem realizes the intelligent monitoring and control of the entire process of the bulk cargo port based on the visualization display results and intelligent safety warning information.

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