Wharf multi-information processing and equipment cooperative scheduling system

Abstract

The invention provides a wharf multi-information processing and equipment collaborative scheduling system, and belongs to the technical field of intelligent port automation control, and the system comprises an equipment communication layer which is configured to receive heterogeneous data through a multi-protocol port; the middle layer is used for converting the heterogeneous data into a unified message format and dynamically routing through message middleware; the business processing layer adopts a distributed micro-service architecture and is used for calculating the position of the container and generating an equipment scheduling instruction; the data stream service cluster is used for realizing real-time data circulation by adopting a timestamp-driven streaming processing framework; the equipment control layer adopts an OPC service deployed in a main and standby mode and is directly connected with a quay crane, an AGV and a yard crane controller; the service registration and discovery module is used for managing micro-service node addresses and supporting dynamic capacity expansion of the micro-service node addresses; the service processing layer is in data synchronization with the equipment control layer through a distributed cache, and the equipment control layer is deployed independent of the service processing layer.

Description

Technical Field

[0001] This invention belongs to the field of intelligent port automation control technology, and in particular relates to a terminal multi-information processing and equipment collaborative scheduling system. Background Technology

[0002] Current automated terminal operations involve the exchange of information in various data formats, such as container operation plans, equipment control signals, and real-time monitoring data. These data sources are heterogeneous, and traditional system architectures face the following challenges in integration and processing: Traditional equipment control uses a centralized architecture, which leads to system bottlenecks and affects the ability of equipment to work together.

[0003] Traditional methods lack efficient and unified parsing and processing mechanisms for heterogeneous data sources.

[0004] The existing system may cause operational interruptions and affect the continuity of port operations when equipment fails, the system is upgraded, or a node fails.

[0005] Therefore, the present invention provides a multi-dimensional information processing and equipment collaborative scheduling system for wharves. Summary of the Invention

[0006] This invention provides a multi-source information processing and equipment collaborative scheduling system for wharves, which at least solves the problems of poor equipment collaboration capabilities, lack of efficient and unified parsing and processing of heterogeneous data sources, and potential operation interruptions when equipment fails, the system is upgraded, or nodes fail in the prior art.

[0007] This application provides a multi-source information processing and equipment collaborative scheduling system for a wharf, the system comprising: The device communication layer is configured to receive heterogeneous data through a multi-protocol port. The middle layer, including the route dispatcher and data transformation processor, is used to convert heterogeneous data into a unified message format and dynamically route it through the message middleware; The business processing layer adopts a distributed microservice architecture, including: A static computing service cluster is used to calculate container locations; A dynamic computing service cluster is used to generate device scheduling instructions; The data stream service cluster uses a timestamp-driven streaming processing framework to achieve real-time data flow. The equipment control layer uses an OPC service deployed in a primary / backup configuration, directly connecting to the controllers of the quay crane, AGV, and yard crane. The service registration and discovery module manages microservice node addresses and supports dynamic scaling of microservice node addresses. The business processing layer synchronizes data with the device control layer through a distributed cache, and the device control layer is deployed independently of the business processing layer.

[0008] Furthermore, the device communication layer includes: A multi-protocol adapter for handling HTTP / JSON, WebSocket, and OPC-UA protocols; The decryption module uses asymmetric encryption to decrypt data. The permission verification module verifies the binding status and blacklist based on the device serial number.

[0009] Furthermore, the data conversion processor of the intermediate layer performs: Convert the JSON job plan into a structured task object; Convert OPC device signals into standard control commands; Data can be distributed by configuring routing rules to either compute-intensive tasks to high-performance CPU nodes or memory-intensive tasks to large memory nodes.

[0010] Furthermore, the data stream service cluster employs a PID flow control algorithm:

[0011] In the formula, To account for the error between real-time data traffic and a set threshold, , , The parameters are adjustable to achieve peak shaving and valley filling of traffic flow.

[0012] Furthermore, the static computing service cluster includes: Intelligent location calculation microservices calculate the stacking location of containers; Interactive area computing microservices optimize cross-area operation paths; Each microservice is deployed in a containerized manner on an independent virtual machine, and load balancing is achieved through service registration and discovery.

[0013] Furthermore, the dynamic computing service cluster includes: AGV scheduling microservice generates paths based on real-time location; The bridge dispatch microservice manages command priority through an instruction queue. The core processing service uses historical data analysis to predict peak workloads and pre-allocate resources.

[0014] Furthermore, the system also includes a cross-service data synchronization module: Each service has a built-in listener and scheduled tasks; Data changes are recorded through the "Event Log Table"; Redis distributed locks are used to ensure data synchronization during cluster execution in sentinel mode; The failed node was taken over by the standby node before data processing was completed.

[0015] Furthermore, the system also includes an intelligent orchestration engine: Define microservice dependencies and execution order; Based on dynamic combination of microservice chains according to job workflow; The scheduling interface is provided to the outside world through the API gateway.

[0016] Furthermore, the high availability mechanism of the system includes: The microservice cluster adopts a master-slave node architecture, where the standby node automatically takes over when the master node fails. The equipment control layer adopts dual-machine hot standby, with a switching time of less than 50ms; Achieve uninterrupted upgrades through blue-green deployment.

[0017] Furthermore, the system employs a two-layer resource isolation system, comprising a physical layer and a virtual layer; The physical layer is used to distribute microservice clusters across multiple physical machines through resource pooling; The virtualization layer is used for each microservice to run in an independent Docker container and is orchestrated by Kubernetes to enable on-demand scaling of microservice instances, resource utilization monitoring, and automatic scheduling.

[0018] As can be seen from the above technical solutions, the present invention has the following advantages: In the multi-dimensional information processing and equipment collaborative scheduling system for wharves provided in this application, the business processing layer is divided into independent microservices such as static computing services and dynamic computing services, which support parallel processing of different equipment tasks.

[0019] The device communication layer enables plug-and-play functionality for heterogeneous devices through multi-protocol adaptation, and the middleware layer converts heterogeneous data into a unified message format and dynamically routes it.

[0020] The cross-service data synchronization module uses Redis distributed locks and event log tables to ensure that data from a failed node is taken over by a backup node. Blue-green deployment enables non-stop upgrades, and the system has zero downtime in the event of a failure when in sentinel mode. Attached Figure Description

[0021] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1A schematic diagram illustrating the dynamic expansion of microservice nodes in a multi-dimensional information processing and equipment collaborative scheduling system for a wharf. Detailed Implementation

[0023] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this patent, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this patent.

[0024] The various embodiments of this disclosure will be described more fully in the following detailed description of the multi-source information processing and equipment collaborative scheduling system at the port. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0025] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of the foregoing.

[0026] In various embodiments of this disclosure, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0027] The terms used in the various embodiments of this disclosure (such as "first," "second," etc.) may modify various components in the various embodiments, but do not limit the corresponding components. For example, the above terms do not limit the order and / or importance of the components. The above terms are only used for the purpose of distinguishing one component from others. For example, a first user device and a second user device refer to different user devices, although both are user devices. For example, a first component may be referred to as a second component without departing from the scope of the various embodiments of this disclosure, and similarly, a second component may also be referred to as a first component.

[0028] It should be noted that if a description is made of "connecting" one component to another, then the first component can be directly connected to the second component, and a third component can be "connected" between the first and second components. Conversely, when a component is "directly connected" to another component, it can be understood that there is no third component between the first and second components.

[0029] The term "user" as used in various embodiments of this disclosure may refer to a person using an electronic device, and may be a monitoring person, a testing person, or an operator.

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] This application provides a multi-source information processing and equipment collaborative scheduling system for wharves, which solves the current urgent technical problems of poor equipment collaboration capabilities, lack of efficient and unified parsing and processing of heterogeneous data sources, and potential operation interruptions when equipment fails, the system is upgraded, or nodes fail.

[0032] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0033] Figure 1 This is a system block diagram of a multi-source information processing and equipment collaborative scheduling system for a wharf, provided as an embodiment of this application. Figure 1 As shown in the embodiment of this application, a multi-dimensional information processing and equipment collaborative scheduling system for a wharf is provided. The system includes: an equipment communication layer, an intermediate layer, a business processing layer, a data flow service cluster, an equipment control layer, and a service registration and discovery module.

[0034] The device communication layer is configured to receive heterogeneous data through a multi-protocol port, including JSON format job plans, Websocket format monitoring data, and OPC protocol control signals; The middle layer includes a route dispatcher and a data transformation processor, which are used to convert heterogeneous data into a unified message format and dynamically route it through the message middleware (RabbitMQ); The business processing layer adopts a distributed microservice architecture, including: a static computing service cluster and a dynamic computing service cluster. The static computing service cluster is used to calculate the container location; the dynamic computing service cluster is used to generate equipment scheduling instructions. The data stream service cluster uses a timestamp-driven streaming processing framework (Workflow) to achieve real-time data flow. The equipment control layer uses OPC services deployed in a primary / backup configuration, directly connecting to the controllers of the quay crane, AGV, and yard crane. The service registration and discovery module manages microservice node addresses and supports dynamic expansion of microservice node addresses. The business processing layer synchronizes data with the device control layer through a distributed cache (Redis), and the device control layer is deployed independently of the business processing layer.

[0035] This invention solves the system integration challenges and resource scheduling inflexibility problems caused by protocol heterogeneity. It achieves plug-and-play functionality for heterogeneous devices (compatible with JSON / Websocket / OPC) through multi-protocol adaptation at the device communication layer. Improve resource utilization by 40%+ through dynamic routing in the intermediate layer (automatically assigning compute-intensive tasks to high-performance nodes). The business processing layer is microservice-based, enabling independent scaling (a single service failure does not affect the overall system). By deploying the device control layer independently, the control signals are not affected by fluctuations in the business layer. The actual measured device access efficiency is greatly improved and resource waste is greatly reduced.

[0036] This application provides another terminal multi-information processing and equipment collaborative scheduling system, the system including: equipment communication layer, middleware layer, business processing layer, data stream service cluster, equipment control layer, and service registration and discovery module.

[0037] The device communication layer is configured to receive heterogeneous data through a multi-protocol port, including JSON format job plans, Websocket format monitoring data, and OPC protocol control signals; The middle layer includes a route dispatcher and a data transformation processor, which are used to convert heterogeneous data into a unified message format and dynamically route it through the message middleware (RabbitMQ); The business processing layer adopts a distributed microservice architecture, including: a static computing service cluster and a dynamic computing service cluster. The static computing service cluster is used to calculate the container location; the dynamic computing service cluster is used to generate equipment scheduling instructions. The data stream service cluster uses a timestamp-driven streaming processing framework (Workflow) to achieve real-time data flow. The equipment control layer uses OPC services deployed in a primary / backup configuration, directly connecting to the controllers of the quay crane, AGV, and yard crane. The service registration and discovery module manages microservice node addresses and supports dynamic expansion of microservice node addresses. The business processing layer synchronizes data with the device control layer through a distributed cache (Redis), and the device control layer is deployed independently of the business processing layer.

[0038] The device communication layer includes: a multi-protocol adapter, a decryption module, and an authorization verification module.

[0039] The multi-protocol adapter is used to handle HTTP / JSON, WebSocket, and OPC-UA protocols; The HTTP / JSON adapter listens on port 17996 to receive dock operation plans. The Websocket adapter listens on port 27996 to receive real-time monitoring data from the device. The OPC-UA adapter listens on port 37996 and connects to the PLC controller.

[0040] The decryption module uses asymmetric encryption to decrypt data, specifically employing RSA-2048 asymmetric encryption for data transmission.

[0041] The permission verification module verifies the binding status and blacklist based on the device serial number. It verifies the device serial number registration status through Redis caching and filters out requests from abnormal devices in real time using a blacklist.

[0042] The device communication layer overcomes the risks of unauthorized device access and data leakage. The multi-protocol adapter eliminates protocol conversion delays (HTTP / JSON / Websocket / OPC-UA processing latency <10ms); the asymmetric encryption module (RSA-2048) ensures the confidentiality of data transmission; and the permission verification module intercepts blacklisted devices in real time based on the device serial number, improving security protection efficiency.

[0043] This application provides another terminal multi-information processing and equipment collaborative scheduling system, the system including: equipment communication layer, middleware layer, business processing layer, data stream service cluster, equipment control layer, and service registration and discovery module.

[0044] The device communication layer is configured to receive heterogeneous data through a multi-protocol port, including JSON format job plans, Websocket format monitoring data, and OPC protocol control signals; The middle layer includes a route dispatcher and a data transformation processor, which are used to convert heterogeneous data into a unified message format and dynamically route it through the message middleware (RabbitMQ); The business processing layer adopts a distributed microservice architecture, including: a static computing service cluster and a dynamic computing service cluster. The static computing service cluster is used to calculate the container location; the dynamic computing service cluster is used to generate equipment scheduling instructions. The data stream service cluster uses a timestamp-driven streaming processing framework (Workflow) to achieve real-time data flow. The equipment control layer uses OPC services deployed in a primary / backup configuration, directly connecting to the controllers of the quay crane, AGV, and yard crane. The service registration and discovery module manages microservice node addresses and supports dynamic expansion of microservice node addresses. The business processing layer synchronizes data with the device control layer through a distributed cache (Redis), and the device control layer is deployed independently of the business processing layer.

[0045] The device communication layer includes: a multi-protocol adapter, a decryption module, and an authorization verification module.

[0046] The multi-protocol adapter is used to handle HTTP / JSON, WebSocket, and OPC-UA protocols; The HTTP / JSON adapter listens on port 17996 to receive dock operation plans. The Websocket adapter listens on port 27996 to receive real-time monitoring data from the device. The OPC-UA adapter listens on port 37996 and connects to the PLC controller.

[0047] The decryption module uses asymmetric encryption to decrypt data, specifically employing RSA-2048 asymmetric encryption for data transmission.

[0048] The permission verification module verifies the binding status and blacklist based on the device serial number. It verifies the device serial number registration status through Redis caching and filters out requests from abnormal devices in real time using a blacklist.

[0049] The intermediate layer's data conversion processor performs: Convert the JSON job plan into a structured task object; Convert OPC device signals into standard control commands; Data is distributed by configuring routing rules to: compute-intensive tasks to high-performance CPU nodes and memory-intensive tasks to large memory nodes.

[0050] By setting up an intermediate layer, the bottleneck of resource mismatch in task scheduling is solved. The efficiency of JSON job plan parsing is effectively improved through structured transformation. Task queuing latency is reduced through dynamic routing rules (e.g., CPU-intensive → Xeon node, high memory → 128GB server). The error rate of device control commands is reduced through OPC signal standardization, thereby improving resource utilization.

[0051] In this embodiment, the device communication layer receives OPC device signals (such as the hoisting status of a quay crane), which are then decrypted by the decryption module (RSA-2048). The device serial number is then verified by the authorization verification module. The data conversion processor converts the OPC signals into standard instructions (JSON format), and the task type is identified by the routing distributor. If it is a container route computation (computation-intensive), it is routed to a static computing service node equipped with an Intel Xeon CPU; If it is AGV real-time scheduling (high memory consumption), it is routed to a dynamic computing service node with 128GB of memory.

[0052] This application provides another terminal multi-information processing and equipment collaborative scheduling system, the system including: equipment communication layer, middleware layer, business processing layer, data stream service cluster, equipment control layer, and service registration and discovery module.

[0053] The device communication layer is configured to receive heterogeneous data through a multi-protocol port, including JSON format job plans, Websocket format monitoring data, and OPC protocol control signals; The middle layer includes a route dispatcher and a data transformation processor, which are used to convert heterogeneous data into a unified message format and dynamically route it through the message middleware (RabbitMQ); The business processing layer adopts a distributed microservice architecture, including: a static computing service cluster and a dynamic computing service cluster. The static computing service cluster is used to calculate the container location; the dynamic computing service cluster is used to generate equipment scheduling instructions. The data stream service cluster uses a timestamp-driven streaming processing framework (Workflow) to achieve real-time data flow. The equipment control layer uses OPC services deployed in a primary / backup configuration, directly connecting to the controllers of the quay crane, AGV, and yard crane. The service registration and discovery module manages microservice node addresses and supports dynamic expansion of microservice node addresses. The business processing layer synchronizes data with the device control layer through a distributed cache (Redis), and the device control layer is deployed independently of the business processing layer.

[0054] The device communication layer includes: a multi-protocol adapter, a decryption module, and an authorization verification module.

[0055] The multi-protocol adapter is used to handle HTTP / JSON, WebSocket, and OPC-UA protocols; The HTTP / JSON adapter listens on port 17996 to receive dock operation plans. The Websocket adapter listens on port 27996 to receive real-time monitoring data from the device. The OPC-UA adapter listens on port 37996 and connects to the PLC controller.

[0056] The decryption module uses asymmetric encryption to decrypt data, specifically employing RSA-2048 asymmetric encryption for data transmission.

[0057] The permission verification module verifies the binding status and blacklist based on the device serial number. It verifies the device serial number registration status through Redis caching and filters out requests from abnormal devices in real time using a blacklist.

[0058] The intermediate layer's data conversion processor performs: Convert the JSON job plan into a structured task object; Convert OPC device signals into standard control commands; Data is distributed by configuring routing rules to: compute-intensive tasks to high-performance CPU nodes and memory-intensive tasks to large memory nodes.

[0059] The data stream service cluster uses a PID flow control algorithm:

[0060] In the formula, To account for the error between real-time data traffic and a set threshold, , , The parameters are adjustable to achieve peak shaving and valley filling of traffic flow.

[0061] In this embodiment, the PID flow control algorithm reduces flow fluctuations, and the streaming data processing latency is stabilized at 50ms±5ms. The peak shaving and valley filling mechanism enables the system to operate without packet loss under tens of thousands of concurrent users, which greatly improves throughput compared to traditional threshold flow limiting schemes.

[0062] This application provides another terminal multi-information processing and equipment collaborative scheduling system, the system including: equipment communication layer, middleware layer, business processing layer, data stream service cluster, equipment control layer, and service registration and discovery module.

[0063] The device communication layer is configured to receive heterogeneous data through a multi-protocol port, including JSON format job plans, Websocket format monitoring data, and OPC protocol control signals; The middle layer includes a route dispatcher and a data transformation processor, which are used to convert heterogeneous data into a unified message format and dynamically route it through the message middleware (RabbitMQ); The business processing layer adopts a distributed microservice architecture, including: a static computing service cluster and a dynamic computing service cluster. The static computing service cluster is used to calculate the container location; the dynamic computing service cluster is used to generate equipment scheduling instructions. The data stream service cluster uses a timestamp-driven streaming processing framework (Workflow) to achieve real-time data flow. The equipment control layer uses OPC services deployed in a primary / backup configuration, directly connecting to the controllers of the quay crane, AGV, and yard crane. The service registration and discovery module manages microservice node addresses and supports dynamic expansion of microservice node addresses. The business processing layer synchronizes data with the device control layer through a distributed cache (Redis), and the device control layer is deployed independently of the business processing layer.

[0064] The device communication layer includes: a multi-protocol adapter, a decryption module, and an authorization verification module.

[0065] The multi-protocol adapter is used to handle HTTP / JSON, WebSocket, and OPC-UA protocols; The HTTP / JSON adapter listens on port 17996 to receive dock operation plans. The Websocket adapter listens on port 27996 to receive real-time monitoring data from the device. The OPC-UA adapter listens on port 37996 and connects to the PLC controller.

[0066] The decryption module uses asymmetric encryption to decrypt data, specifically employing RSA-2048 asymmetric encryption for data transmission.

[0067] The permission verification module verifies the binding status and blacklist based on the device serial number. It verifies the device serial number registration status through Redis caching and filters out requests from abnormal devices in real time using a blacklist.

[0068] The intermediate layer's data conversion processor performs: Convert the JSON job plan into a structured task object; Convert OPC device signals into standard control commands; Data is distributed by configuring routing rules to: compute-intensive tasks to high-performance CPU nodes and memory-intensive tasks to large memory nodes.

[0069] The data stream service cluster uses a PID flow control algorithm:

[0070] In the formula, To account for the error between real-time data traffic and a set threshold, , , The parameters are adjustable to achieve peak shaving and valley filling of traffic flow.

[0071] The static computing service cluster includes: intelligent computing microservices and interactive area computing microservices; The intelligent location calculation microservice calculates the container stacking location; input container attributes (size / weight / cargo type) and ship loading plan, and predict the optimal stacking location based on reinforcement learning model to meet the requirements of centralized stacking of containers in the same voyage and the shortest loading route, and output three-dimensional coordinates (zone-shell-column-layer).

[0072] Interactive area computing microservices optimize cross-area operation paths.

[0073] Each microservice is deployed in a containerized manner on an independent virtual machine, and load balancing is achieved through service registration and discovery. The Consul service registry manages all microservice endpoints, performs health checks every minute, and automatically takes down any abnormal nodes.

[0074] The static computing service cluster optimizes the efficiency of container stacking and route planning. The intelligent computing microservice improves the utilization rate of yard space through reinforcement learning model. The interactive area computing microservice reduces the length of cross-area operation paths. Containerized deployment and service discovery reduce load balancing response latency and greatly compress the time consumption of single container positioning calculation.

[0075] The dynamic computing service cluster includes: AGV scheduling microservice, yard crane dispatching microservice, and core processing service.

[0076] AGV scheduling microservice generates paths based on real-time location; The bridge dispatch microservice manages command priority through a command queue (Redis ZSet); path generation: The system collects AGV location data in real time (GPS+UWB positioning), generates obstacle avoidance paths using algorithms, inserts emergency commands into the head of the RedisZSet queue, sorts ordinary commands by timestamp, and retrieves and issues commands to the OPC server every 200ms.

[0077] The core processing service uses historical data analysis to predict peak workloads and pre-allocate resources.

[0078] In this embodiment, the intelligent positioning microservice calculates the optimal stacking coordinates of the containers and stores the results in a Redis cache; the AGV scheduling microservice subscribes to real-time location data and combines it with a historical operation peak prediction model (LSTM neural network) to generate obstacle avoidance path instructions; the yard crane dispatching microservice manages instruction priorities (Score = task urgency + equipment load) through a Redis ZSet to ensure zero-latency response for high-priority tasks.

[0079] The system also includes a cross-service data synchronization module: each service has built-in listeners and scheduled tasks; data changes are recorded through an "event log table"; Redis distributed locks are used to ensure cluster data synchronization in sentinel mode; and failed nodes are taken over by standby nodes to process unfinished data.

[0080] The system also includes an intelligent orchestration engine: used to define microservice dependencies and execution order; dynamically combine microservice chains based on job processes; and provide scheduling interfaces to the outside world through an API gateway.

[0081] The high availability mechanism of the system includes: The microservice cluster adopts a master-slave node architecture, where the standby node automatically takes over when the master node fails. The equipment control layer adopts dual-machine hot standby, with a switching time of less than 50ms; Achieve uninterrupted upgrades through blue-green deployment.

[0082] The system employs a two-layer resource isolation system, including a physical layer and a virtual layer. The physical layer is used to distribute microservice clusters across multiple physical machines through resource pooling; The virtualization layer is used for each microservice to run in an independent Docker container and is orchestrated by Kubernetes to enable on-demand scaling of microservice instances, resource utilization monitoring, and automatic scheduling.

[0083] In this embodiment, each microservice listens for Redis keyspace events. When a change in the "event log table" is detected, it preempts the synchronization permission through a distributed lock (RedLock). After the master node fails, the Consul service discovery module switches the request to the backup node within 50ms. The physical layer includes a microservice cluster deployed across 3 Dell R750 servers. Each microservice in the virtual layer runs in an independent Docker container, and Kubernetes automatically scales up instances when CPU utilization is >80%.

[0084] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0085] Any changes, modifications, substitutions, and variations made to the embodiments without departing from the principles and spirit of the present invention still fall within the protection scope of the present invention.

Claims

1. A multi-source information processing and equipment collaborative scheduling system for a wharf, characterized in that, The system includes: The device communication layer is configured to receive heterogeneous data through a multi-protocol port. The middle layer, including the route dispatcher and data transformation processor, is used to convert heterogeneous data into a unified message format and dynamically route it through the message middleware; The business processing layer adopts a distributed microservice architecture, including: A static computing service cluster is used to calculate container locations; A dynamic computing service cluster is used to generate device scheduling instructions; The data stream service cluster uses a timestamp-driven streaming processing framework to achieve real-time data flow. The equipment control layer uses an OPC service deployed in a primary / backup mode, which directly connects to the controllers of the quay crane, AGV, and yard crane. The service registration and discovery module manages microservice node addresses and supports dynamic scaling of microservice node addresses. The business processing layer synchronizes data with the device control layer through a distributed cache, and the device control layer is deployed independently of the business processing layer.

2. The terminal multi-information processing and equipment collaborative scheduling system as described in claim 1, characterized in that, The device communication layer includes: A multi-protocol adapter for handling HTTP / JSON, WebSocket, and OPC-UA protocols; The decryption module uses asymmetric encryption to decrypt data. The permission verification module verifies the binding status and blacklist based on the device serial number.

3. The terminal multi-information processing and equipment collaborative scheduling system as described in claim 2, characterized in that, The intermediate layer's data conversion processor performs: Convert the JSON job plan into a structured task object; Convert OPC device signals into standard control commands; Data can be distributed by configuring routing rules to either compute-intensive tasks to high-performance CPU nodes or memory-intensive tasks to large memory nodes.

4. The terminal multi-information processing and equipment collaborative scheduling system as described in claim 3, characterized in that, The data stream service cluster uses a PID flow control algorithm: In the formula, To account for the error between real-time data traffic and a set threshold, , , The parameters are adjustable to achieve peak shaving and valley filling of traffic flow.

5. The terminal multi-information processing and equipment collaborative scheduling system as described in claim 4, characterized in that, The static computing service cluster includes: Intelligent location calculation microservices calculate the stacking location of containers; Interactive area computing microservices optimize cross-area operation paths; Each microservice is deployed in a containerized manner on an independent virtual machine, and load balancing is achieved through service registration and discovery.

6. The terminal multi-information processing and equipment collaborative scheduling system as described in claim 5, characterized in that, The dynamic computing service cluster includes: AGV scheduling microservice generates paths based on real-time location; The bridge dispatch microservice manages command priority through an instruction queue. The core processing service uses historical data analysis to predict peak workloads and pre-allocate resources.

7. The terminal multi-information processing and equipment collaborative scheduling system as described in claim 6, characterized in that, The system also includes a cross-service data synchronization module: Each service has a built-in listener and scheduled tasks; Data changes are recorded through the "Event Log Table"; Redis distributed locks are used to ensure data synchronization during cluster execution in sentinel mode; The failed node was taken over by the standby node before data processing was completed.

8. The terminal multi-information processing and equipment collaborative scheduling system as described in claim 7, characterized in that, The system also includes an intelligent orchestration engine: Define microservice dependencies and execution order; Based on dynamic combination of microservice chains according to job workflow; The scheduling interface is provided to the outside world through the API gateway.

9. The terminal multi-information processing and equipment collaborative scheduling system as described in claim 8, characterized in that, The high availability mechanism of the system includes: The microservice cluster adopts a master-slave node architecture, where the standby node automatically takes over when the master node fails. The equipment control layer adopts dual-machine hot standby, with a switching time of less than 50ms; Achieve uninterrupted upgrades through blue-green deployment.

10. The terminal multi-information processing and equipment collaborative scheduling system as described in claim 9, characterized in that, The system employs a two-layer resource isolation system, including a physical layer and a virtual layer. The physical layer is used to distribute microservice clusters across multiple physical machines through resource pooling; The virtualization layer is used for each microservice to run in an independent Docker container and is orchestrated by Kubernetes to enable on-demand scaling of microservice instances, resource utilization monitoring, and automatic scheduling.

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