Distributed network architecture construction method and system for port environment monitoring

Abstract

Embodiments of the present application relate to the technical field of computer, and particularly relate to a distributed network architecture construction method and system for port environment monitoring, which divides multiple monitoring sub-areas according to the port area environment monitoring requirements, and deploys a set of heterogeneous monitoring nodes in each sub-area. Real-time running state sensing processing is performed on the node set to generate a target network topology structure. Based on the topology structure, routing protocol generation and data scheduling strategy formulation are performed to obtain an adaptive routing table and data transmission scheduling rule. Distributed collaborative optimization processing is performed on the nodes according to the table and rule to generate a network resource allocation scheme and node task load adjustment instruction, and finally the port area distributed network architecture is reconstructed through the scheme and instruction to improve the efficiency and accuracy of port environment monitoring.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method and system for constructing a distributed network architecture for port environmental monitoring. Background Technology

[0002] In the field of port environmental monitoring, existing environmental monitoring network architectures typically employ a uniform monitoring layout, failing to consider the varying environmental monitoring needs at different locations within the port area, resulting in inaccurate and incomplete monitoring results. The deployment of monitoring nodes often utilizes a single type of node, making it impossible to comprehensively monitor the complex and diverse environmental factors within the port. Furthermore, current technologies lack effective awareness of the real-time operational status of monitoring nodes and the quality of communication links. The fixed network topology makes it difficult to adapt to unexpected situations such as node failures and link interruptions, leading to poor reliability and stability of data transmission.

[0003] In terms of routing protocols and data scheduling, existing methods typically employ fixed routing strategies and uniform data scheduling rules, failing to adapt flexibly to the characteristics of different monitoring tasks. This results in high latency for real-time monitoring data transmission and high energy consumption for periodic monitoring tasks. Furthermore, existing network architectures struggle to achieve distributed collaborative optimization among nodes and cannot dynamically adjust based on node workload and network resource availability. This easily leads to problems such as uneven node load and wasted network resources, severely impacting the efficiency and quality of port environmental monitoring.

[0004] Therefore, how to effectively balance the task load and network resource allocation of each node, so that the entire network can operate more stably and efficiently and have the ability to self-adjust and optimize, is a technical challenge that needs to be overcome. Summary of the Invention

[0005] In order to at least overcome the above-mentioned shortcomings in the prior art, one of the objectives of the present invention is to provide a method and system for constructing a distributed network architecture for port environmental monitoring.

[0006] This invention provides a method for constructing a distributed network architecture for port environmental monitoring, comprising: dividing the port area into multiple monitoring sub-regions according to the environmental monitoring needs of the port area, and deploying a heterogeneous set of monitoring nodes containing different sensing types in each monitoring sub-region; performing real-time operational status sensing processing on the heterogeneous monitoring node set to obtain the current operational indicators of each heterogeneous monitoring node and the communication link quality characteristics of adjacent heterogeneous monitoring nodes, and generating a target network topology based on the current operational indicators and the communication link quality characteristics; performing routing protocol generation and data scheduling strategy formulation operations based on the target network topology to obtain an adaptive routing table including path priority sorting and data transmission scheduling rules based on monitoring task types; performing distributed collaborative optimization processing on the heterogeneous monitoring nodes in each monitoring sub-region according to the adaptive routing table and the data transmission scheduling rules to generate a network resource allocation scheme and node task load adjustment instructions, and reconstructing the distributed network architecture of the port area through the network resource allocation scheme and the node task load adjustment instructions.

[0007] This invention also provides a distributed network architecture construction system, including a processor, a memory and a bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the above-described distributed network architecture construction method for port environmental monitoring.

[0008] This invention also provides a computer-readable storage medium storing a program that, when executed by a processor, implements the above-described method for constructing a distributed network architecture for port environmental monitoring.

[0009] This invention achieves the effective construction and optimization of a distributed network architecture for port area environmental monitoring. First, by dividing the port area into monitoring sub-regions and deploying a heterogeneous set of monitoring nodes based on the port's environmental monitoring needs, it accurately covers the diverse monitoring requirements of different areas within the port. Nodes of different sensing types work collaboratively to collect environmental data comprehensively and meticulously, greatly improving the accuracy and comprehensiveness of monitoring. Second, real-time operational status sensing processing is performed on the heterogeneous monitoring node set to generate a target network topology. This ensures that the network topology can be dynamically adjusted according to the actual operational status of the nodes and link quality, making the network layout more rational and efficient, enhancing network stability and reliability, and reducing data transmission interruptions caused by node failures or poor link quality. Third, the adaptive routing table and data transmission scheduling rules generated based on the target network topology can flexibly arrange data transmission paths and times according to different monitoring task types. This ensures low-latency transmission for real-time environmental parameter acquisition tasks and achieves energy balance for periodic pollution diffusion tracking tasks, improving network resource utilization and data transmission efficiency. Finally, by reconstructing the network architecture through the network resource allocation scheme and node task load adjustment instructions generated by distributed collaborative optimization processing, the task load and network resource allocation of each node can be effectively balanced, making the operation of the entire network more stable and efficient. It also has the ability to self-adjust and optimize, adapting to the dynamic changes in the port environment and the continuous adjustment of monitoring tasks, and significantly improving the overall efficiency and quality of port environmental monitoring. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a method for constructing a distributed network architecture for port environmental monitoring, as provided in an embodiment of the present invention.

[0012] Figure 2 This is a block diagram illustrating a distributed network architecture construction system provided in an embodiment of the present invention.

[0013] icon:

[0014] 100-Distributed Network Architecture Construction System;

[0015] 101 - Processor; 102 - Memory; 103 - Bus. Detailed Implementation

[0016] Exemplary embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0017] To better understand the above technical solutions, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solutions of the present invention, rather than limitations on the technical solutions of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0018] Figure 1 The flowchart illustrates a method for constructing a distributed network architecture for port environmental monitoring according to an embodiment of the present invention, applied to a distributed network architecture construction system, including steps 110-140.

[0019] Step 110: Divide the port area into multiple monitoring sub-areas according to the environmental monitoring needs, and deploy a heterogeneous set of monitoring nodes containing different sensing types in each monitoring sub-area.

[0020] In this embodiment of the invention, the environmental monitoring needs of port areas are diverse, potentially including monitoring of air quality, water quality, noise, and other aspects. To achieve more precise monitoring, the port area needs to be divided into multiple monitoring sub-areas. For example, based on the port's functional zoning, the dock operation area, storage area, and waterway area can be divided into different monitoring sub-areas. A heterogeneous set of monitoring nodes is deployed within each monitoring sub-area, with these nodes possessing different sensing types. For instance, in the dock operation area, air quality monitoring nodes are deployed to monitor the concentration of dust and harmful gases in the air; noise monitoring nodes are deployed to monitor the noise level generated during operations; and water quality monitoring nodes are deployed to monitor the water quality of the waters near the dock. These different types of monitoring nodes collectively constitute a heterogeneous set of monitoring nodes to meet the environmental monitoring needs of that monitoring sub-area.

[0021] Step 120: Perform real-time operation status perception processing on the heterogeneous monitoring node set, obtain the current operation indicators of each heterogeneous monitoring node and the communication link quality characteristics of adjacent heterogeneous monitoring nodes, and generate the target network topology based on the current operation indicators and the communication link quality characteristics.

[0022] Next, to ensure the stable and efficient operation of the heterogeneous monitoring node set, real-time operational status awareness processing is required. This involves monitoring the hardware status of each heterogeneous monitoring node and the communication links between adjacent nodes to obtain relevant characteristic information. For example, for an air quality monitoring node, its current operational indicators may reflect whether the node is functioning normally, while the quality characteristics of the communication links between adjacent nodes affect the reliability of data transmission. Based on these obtained current operational indicators and communication link quality characteristics, a series of analyses and processes are performed to generate a target network topology that accurately characterizes the connection relationships and data transmission paths between the various heterogeneous monitoring nodes.

[0023] As one implementation, the step of performing real-time operational status awareness processing on the heterogeneous monitoring node set to obtain the current operational indicators of each heterogeneous monitoring node and the communication link quality characteristics of adjacent heterogeneous monitoring nodes, and generating a target network topology based on the current operational indicators and the communication link quality characteristics, includes:

[0024] Step 121: Perform hardware status detection operation on each heterogeneous monitoring node to obtain the node's remaining energy, data processing delay time, and the working stability of the sensing module as current operating indicators.

[0025] In this step, a hardware status detection operation is performed on each heterogeneous monitoring node. Taking a water quality monitoring node as an example, by detecting its hardware status, the remaining energy of the node can be obtained. The remaining energy reflects how long the node can continue to work. If the remaining energy is too low, it may be necessary to replace the battery or recharge it in time. Data processing latency refers to the time it takes for the node to receive data, process it, and output the result. The shorter this time, the higher the node's data processing efficiency. The operational stability of the sensing module is an indicator of whether the node's sensing function is normal. For example, the sensors of the water quality monitoring node may malfunction or have measurement errors for various reasons. By monitoring the operational stability of the sensing module, these problems can be detected and addressed in a timely manner. By obtaining these current operating indicators, a comprehensive understanding of the operating status of each heterogeneous monitoring node can be obtained.

[0026] Step 122: Perform signal quality monitoring on the communication link between adjacent heterogeneous monitoring nodes to obtain the link signal strength fluctuation range, data packet transmission error rate, and available bandwidth as communication link quality characteristics.

[0027] Next, signal quality monitoring is performed on the communication links between adjacent heterogeneous monitoring nodes. Taking the communication link between an air quality monitoring node and an adjacent noise monitoring node as an example, the range of signal strength fluctuation reflects the stability of the signal strength. If the signal strength fluctuation is too large, it may lead to data transmission interruption or errors. The bit error rate (BER) refers to the proportion of data packets that have errors during data transmission out of the total number of transmitted data packets. The higher the BER, the lower the reliability of data transmission. The available bandwidth of the link represents the bandwidth resources currently available for data transmission on the communication link. The larger the available bandwidth, the higher the data transmission rate that can be supported. By obtaining these communication link quality characteristics, the quality and reliability of communication between adjacent heterogeneous monitoring nodes can be evaluated.

[0028] Step 123: Construct a node operation status evaluation model. Input the current operation indicators and the communication link quality characteristics into the node operation status evaluation model for comprehensive evaluation, and generate an operation reliability score for each heterogeneous monitoring node and a transmission reliability score for each communication link.

[0029] Next, a node operational status evaluation model is constructed. This model can be an artificial intelligence model based on machine learning algorithms, receiving current operational metrics and communication link quality characteristics as input. Taking a simple neural network model as an example, features such as the remaining energy of each heterogeneous monitoring node, data processing latency, operational stability of the sensing module, and the fluctuation range of link signal strength between adjacent nodes, data packet transmission error rate, and available bandwidth are input into the model. The model performs a comprehensive evaluation by learning and analyzing these input features. Finally, the model outputs an operational reliability score for each heterogeneous monitoring node and a transmission reliability score for each communication link. A higher operational reliability score indicates a more stable and reliable operational status for the node; a higher transmission reliability score indicates better data transmission quality for the communication link.

[0030] Step 124: Construct an initial network topology candidate set based on the operational reliability score and the transmission reliability score. The initial network topology candidate set contains multiple topology candidate structures composed of nodes and links.

[0031] In this step, based on the operational reliability score of each heterogeneous monitoring node and the transmission reliability score of each communication link obtained previously, an initial network topology candidate set is constructed. For example, based on the operational reliability score of the nodes and the transmission reliability score of the links, different combinations of node connection methods and data transmission paths are generated. These combinations constitute multiple topology candidate structures, which together form the initial network topology candidate set. Each topology candidate structure represents a possible network topology form, where different node and link connection methods may result in different data transmission efficiency and reliability.

[0032] Step 125: Perform connectivity verification and redundancy analysis on each candidate topology structure in the initial network topology candidate set, and select the candidate topology structure whose connectivity meets the monitoring coverage requirements and whose redundancy meets the preset fault tolerance standard as the target network topology structure.

[0033] Then, connectivity verification and redundancy analysis are performed on each candidate topology in the initial network topology candidate set. Connectivity verification checks whether the nodes in the candidate topology can communicate normally and whether it can cover the entire monitoring sub-area. For example, in a candidate topology, it checks whether there is a situation where a monitoring node cannot transmit data with other nodes. If this situation exists, the connectivity of the candidate topology does not meet the requirements. Redundancy analysis assesses whether there are redundant links or nodes in the candidate topology, and whether these redundant parts can provide backup paths when nodes or links fail to ensure network reliability. The preset fault tolerance standard is set according to the actual needs of port environmental monitoring, such as requiring the network to still operate normally for a certain period of time when a node or link fails. The candidate topology with connectivity that meets the monitoring coverage requirements and redundancy that meets the preset fault tolerance standard is selected as the target network topology. This target network topology can ensure stable and reliable communication between heterogeneous monitoring nodes and has a certain degree of fault tolerance.

[0034] Step 130: Based on the target network topology, perform routing protocol generation and data scheduling strategy formulation operations to obtain an adaptive routing table containing path priority sorting and data transmission scheduling rules based on monitoring task type.

[0035] In this embodiment of the invention, based on the previously generated target network topology, routing protocol generation and data scheduling strategy formulation operations are performed. The routing protocol generation determines the data transmission path within the network, while the data scheduling strategy formulation rationally arranges the data transmission for different types of monitoring tasks. For example, for monitoring tasks with high real-time requirements, reliable and fast transmission paths should be prioritized; for periodic monitoring tasks, relatively low-energy transmission paths can be selected. Through these operations, an adaptive routing table containing path priority ranking and data transmission scheduling rules based on monitoring task types are obtained. The path priority ranking in the adaptive routing table can be adjusted according to the real-time state of the network to ensure the efficiency and reliability of data transmission; the data transmission scheduling rules provide specific scheduling schemes for data transmission according to different monitoring task types.

[0036] In a preferred embodiment, the step of performing routing protocol generation and data scheduling strategy formulation operations based on the target network topology to obtain an adaptive routing table containing path priority ranking and data transmission scheduling rules based on monitoring task types includes:

[0037] Step 131: Extract all transmission paths from source nodes to sink nodes in the target network topology, accumulate the link transmission reliability scores of each transmission path, and generate an overall path reliability index.

[0038] In this step, all transmission paths from source nodes to aggregation nodes are extracted from the target network topology. Source nodes are heterogeneous monitoring nodes that generate monitoring data, while aggregation nodes are nodes that receive and process this data. Taking a water quality monitoring node as the source node and a data processing center as the aggregation node as an example, all transmission paths from the water quality monitoring node to the data processing center are identified in the target network topology. Then, the link transmission reliability scores for each transmission path are accumulated. For example, if a transmission path has three communication links with transmission reliability scores A, B, and C, these three scores are added together to obtain the cumulative reliability value for that transmission path. This cumulative reliability value is then normalized to ensure its range matches the range of the link transmission reliability scores, resulting in an overall path reliability index that reflects the overall reliability of the path. A higher overall path reliability index indicates more reliable data transmission along that path.

[0039] As one implementation, the step involves extracting all transmission paths from the source node to the sink node in the target network topology, accumulating the link transmission reliability scores for each transmission path, and generating an overall path reliability index, including:

[0040] Step 1310: Using the sink node as the root node, traverse all nodes in the target network topology using a breadth-first search algorithm to generate all node paths from each source node to the sink node; for the communication links between adjacent heterogeneous monitoring nodes in each node path, extract the transmission reliability score from the quality characteristics of the communication link; sum the transmission reliability scores in the order of the paths to obtain the cumulative reliability value for each node path; normalize the cumulative reliability value to generate a path overall reliability index that reflects the overall reliability of the path, and the value range of the path overall reliability index is consistent with the value range of the transmission reliability score.

[0041] In this step, the target network topology is traversed using a breadth-first search algorithm, with the sink node as the root node. Breadth-first search is a graph traversal algorithm that starts from the root node and visits nodes layer by layer until all nodes have been traversed. This algorithm generates all node paths from each source node to the sink node. For example, in a target network topology containing multiple heterogeneous monitoring nodes, with the data processing center as the sink node, breadth-first search is used to find all node paths from source nodes such as air quality monitoring nodes and water quality monitoring nodes to the data processing center. For the communication links between adjacent heterogeneous monitoring nodes in each node path, a transmission reliability score is extracted from the communication link quality characteristics. Then, these transmission reliability scores are summed according to the path order to obtain the cumulative reliability value for each node path. To make the reliability indices of different paths comparable, the cumulative reliability value is normalized so that its value range is consistent with the value range of the transmission reliability score, ultimately generating an overall path reliability index reflecting the overall reliability of the path.

[0042] Step 132: Prioritize all transmission paths according to the overall reliability index of the path, mark the top X paths in the reliability index as priority paths, and mark the remaining paths as backup paths, forming an adaptive routing table containing priority paths and backup paths.

[0043] Next, all transmission paths are prioritized based on the overall path reliability index. For example, all transmission paths from the source node to the sink node are sorted from highest to lowest based on their overall path reliability index. A value X is set, and the top X paths in terms of reliability index are marked as priority paths. These priority paths have higher reliability and will be selected first during data transmission. The remaining paths are marked as backup paths, which provide alternative transmission solutions when priority paths fail or become congested. The priority paths and backup paths are compiled into a table, forming an adaptive routing table that includes path priority ranking. This adaptive routing table can adjust according to the real-time network status, and when the reliability of a priority path decreases, data can be switched to a backup path for transmission in a timely manner.

[0044] Step 133: Identify the monitoring task types that the heterogeneous monitoring node set needs to perform, including real-time environmental parameter acquisition tasks and periodic pollution diffusion tracking tasks.

[0045] This step identifies the types of monitoring tasks that the heterogeneous monitoring node set needs to perform. Real-time environmental parameter acquisition tasks refer to tasks that require real-time acquisition of environmental parameters, such as real-time monitoring of the concentration of harmful gases in air quality or real-time monitoring of water pH. Periodic pollution diffusion tracking tasks refer to tasks that track pollution diffusion at certain intervals, such as monitoring the diffusion of oil spills in port waters at regular intervals. By analyzing the functions and monitoring requirements of the heterogeneous monitoring nodes, the type of monitoring task that each node needs to perform is determined. For example, air quality monitoring nodes mainly perform real-time environmental parameter acquisition tasks, while monitoring nodes deployed in the waters surrounding the port may need to perform periodic pollution diffusion tracking tasks.

[0046] Step 134: For the real-time environmental parameter acquisition task, formulate transmission scheduling rules based on minimum latency, prioritize the allocation of target bandwidth links and shorten the data packet fragment length.

[0047] For real-time environmental parameter acquisition tasks, transmission scheduling rules based on minimum latency need to be formulated. Because real-time environmental parameter acquisition tasks have high requirements for the timeliness of data transmission, it is essential to minimize data transmission latency. First, prioritize the allocation of target bandwidth links. Target bandwidth links refer to links with larger available bandwidth and higher data transmission rates. For example, in the target network topology, links with available bandwidth greater than a preset bandwidth threshold are selected as target bandwidth links. Then, shorten the data packet fragment length. The shorter the data packet fragment length, the lower the data transmission latency. By prioritizing the allocation of target bandwidth links and shortening the data packet fragment length, it can be ensured that the data from the real-time environmental parameter acquisition task can be transmitted to the aggregation node quickly and accurately.

[0048] In a preferred embodiment, the step of formulating a transmission scheduling rule based on minimum latency for the real-time environmental parameter acquisition task, prioritizing the allocation of target bandwidth links and shortening data packet fragment length, includes:

[0049] Step 1341: Identify the data packet type corresponding to the real-time environmental parameter acquisition task. The data packet type includes a time-sensitive data field and a basic environmental parameter data field.

[0050] This step identifies the data packet type corresponding to the real-time environmental parameter acquisition task. Taking air quality monitoring as an example, the data packet type may include time-sensitive data fields and basic environmental parameter data fields. Time-sensitive data fields refer to data with very strict time requirements, such as the current concentration of harmful gases. This data needs to be transmitted to the aggregation node for analysis and processing in a timely manner. Basic environmental parameter data fields refer to relatively stable environmental parameter data, such as average humidity and average temperature of the air. By identifying the data packet type, different transmission strategies can be adopted for different types of data.

[0051] Step 1342: Extract the available bandwidth information of each link in the target network topology and filter out the target bandwidth links whose available bandwidth is greater than the preset bandwidth threshold.

[0052] Next, the available bandwidth information of each link in the target network topology is extracted. For example, the available bandwidth value is obtained by real-time monitoring of each communication link. The preset bandwidth threshold is set according to the transmission requirements of the real-time environmental parameter acquisition task, such as requiring the data transmission rate to be no less than a certain value. Links with available bandwidth greater than the preset bandwidth threshold are selected as target bandwidth links. These target bandwidth links can provide a higher data transmission rate, meeting the timeliness requirements of the real-time environmental parameter acquisition task for data transmission.

[0053] Step 1343: For the time-sensitive data field, allocate the shortest path in the target bandwidth link for transmission, and set the data packet fragment length to a short fragment that is less than a preset fragment length threshold.

[0054] For time-sensitive data fields, the shortest path in the target bandwidth link is prioritized for transmission. The shortest path refers to the path with the shortest data transmission distance and the least transmission delay. For example, in the target bandwidth link, a path search algorithm is used to find the shortest path from the source node to the aggregation node. Simultaneously, the data packet fragment length is set to be shorter than a preset fragment length threshold. The preset fragment length threshold is set based on the characteristics of the real-time environmental parameter acquisition task; shorter fragments reduce data transmission delay and ensure that time-sensitive data fields are transmitted to the aggregation node in a timely manner.

[0055] Step 1344: For the basic environmental parameter data field, after ensuring that the time-sensitive data field has been transmitted, use the remaining target bandwidth link for transmission, and set the data packet fragment length to the standard fragment length that matches the link bandwidth.

[0056] For basic environmental parameter data fields, after ensuring the transmission of time-sensitive data fields is complete, use the remaining target bandwidth link for transmission. Because the time requirements for basic environmental parameter data fields are relatively low, they can be transmitted after the time-sensitive data fields have been transmitted. Setting the packet fragment length to a standard fragment length that matches the link bandwidth improves data transmission efficiency. For example, if the link bandwidth is large, the packet fragment length can be appropriately increased to fully utilize the link's bandwidth resources.

[0057] Step 1345: Generate minimum delay transmission scheduling rules for the real-time environmental parameter acquisition task based on the target bandwidth link allocation strategy and the data packet fragmentation length setting strategy.

[0058] Finally, based on the target bandwidth link allocation strategy and data packet fragmentation length setting strategy, a minimum latency transmission scheduling rule is generated for the real-time environmental parameter acquisition task. This rule clarifies the transmission path of different types of data fields in the target bandwidth link and the method for setting the data packet fragmentation length, which can ensure that the data transmission of the real-time environmental parameter acquisition task has the minimum latency and improve the timeliness and accuracy of data transmission.

[0059] Step 135: For the periodic pollution spread tracking task, formulate transmission scheduling rules based on energy consumption balance, distribute the use of different links and extend the data packet transmission time interval.

[0060] For periodic pollution spread tracking tasks, it is necessary to formulate transmission scheduling rules based on energy consumption balance. This is because periodic pollution spread tracking tasks have relatively lower requirements for the timeliness of data transmission and place greater emphasis on energy consumption balance. Distributing data across different links means that during data transmission, data is not concentrated on a single link but distributed across multiple links. This avoids excessive energy consumption due to overuse of a single link and also improves network reliability. Extending the data packet transmission interval means appropriately increasing the transmission interval between adjacent data packets while meeting the task cycle requirements. For example, if data packets are originally transmitted every 10 minutes, they can now be transmitted every 15 minutes. By distributing data packets across different links and extending the data packet transmission interval, the overall energy consumption of the network can be reduced, achieving a balanced distribution of energy.

[0061] Step 136: Integrate the minimum delay transmission scheduling rule and the energy consumption balance transmission scheduling rule into a data transmission scheduling rule based on the monitoring task type.

[0062] Next, the minimum latency transmission scheduling rule for real-time environmental parameter acquisition tasks and the energy-balanced transmission scheduling rule for periodic pollution spread tracking tasks are integrated into a data transmission scheduling rule based on monitoring task type. This rule provides different scheduling schemes for data transmission according to different monitoring task types. During actual data transmission, the appropriate transmission scheduling rule is automatically selected according to the type of monitoring task to ensure that data transmission can meet both real-time requirements and achieve energy balance.

[0063] Step 140: Based on the adaptive routing table and the data transmission scheduling rules, perform distributed collaborative optimization processing on the heterogeneous monitoring nodes of each monitoring sub-region to generate a network resource allocation scheme and node task load adjustment instructions. Then, reconstruct the distributed network architecture of the port area using the network resource allocation scheme and the node task load adjustment instructions.

[0064] In this embodiment of the invention, distributed collaborative optimization processing is performed on heterogeneous monitoring nodes in each monitoring sub-region based on an adaptive routing table and data transmission scheduling rules. Distributed collaborative optimization processing refers to the cooperation among heterogeneous monitoring nodes in each monitoring sub-region to jointly optimize the allocation of network resources and the task load of nodes. For example, if a node in a monitoring sub-region finds its load too high, it can communicate and cooperate with nodes in other monitoring sub-regions to transfer some of the task load to other nodes. Through this distributed collaborative optimization processing, a network resource allocation scheme and node task load adjustment instructions are generated. The network resource allocation scheme includes link bandwidth allocation ratios and node computing resource allocation ratios, and the node task load adjustment instructions include instructions to reduce the task load of nodes with excessive load and instructions to increase the task load of nodes with insufficient load. By sending these schemes and instructions to each heterogeneous monitoring node, a distributed network architecture reconstruction of the port area is performed, making the network operate more stably and efficiently.

[0065] In an alternative embodiment, the step of performing distributed collaborative optimization processing on heterogeneous monitoring nodes in each monitoring sub-region based on the adaptive routing table and the data transmission scheduling rules to generate a network resource allocation scheme and node task load adjustment instructions includes:

[0066] Step 141: Extract the priority path and backup path information from the adaptive routing table, and calculate the current load of each path. The current load includes the allocated bandwidth utilization and the node computing resource utilization.

[0067] In this step, priority and backup path information is extracted from the adaptive routing table. Priority and backup paths have already been marked and organized previously. The current load of each path is calculated, including allocated bandwidth utilization and node computing resource utilization. For example, for a priority path, the percentage of allocated bandwidth on that path relative to the total bandwidth, and the computing resource utilization of each node on that path, are calculated. By analyzing this information, the load status of each path can be understood, thereby achieving load balancing and resource allocation.

[0068] Step 142: Based on the transmission requirements of different monitoring task types in the data transmission scheduling rules, perform demand matching analysis on the current load of each path to identify overloaded paths and underloaded paths.

[0069] Next, based on the transmission requirements of different monitoring task types in the data transmission scheduling rules, a demand matching analysis was performed on the current load of each path. Different monitoring task types have different requirements for the bandwidth and computing resources of the path. For example, real-time environmental parameter acquisition tasks have higher requirements for bandwidth and computing resources, while periodic pollution diffusion tracking tasks have relatively lower requirements for bandwidth and computing resources. By matching the current load of each path with the transmission requirements of different monitoring task types, overloaded paths and underloaded paths were identified. Overloaded paths refer to paths whose current load exceeds the maximum load that the path can bear, and cannot meet the transmission requirements of some monitoring tasks; underloaded paths refer to paths with a lower current load, and there are still some resources that can be allocated to other monitoring tasks.

[0070] In one embodiment, the step of combining the transmission requirements of different monitoring task types in the data transmission scheduling rules to perform demand matching analysis on the current load of each path and identify overloaded and underloaded paths includes:

[0071] Step 1421: Analyze the transmission requirements of the real-time environmental parameter acquisition task and the periodic pollution diffusion tracking task in the data transmission scheduling rules. The transmission requirements of the real-time environmental parameter acquisition task include the maximum allowable delay time and the minimum required bandwidth. The transmission requirements of the periodic pollution diffusion tracking task include the maximum allowable energy consumption and the minimum required transmission success rate.

[0072] This step involves analyzing the data transmission scheduling rules for real-time environmental parameter acquisition and periodic pollution spread tracing tasks. The real-time environmental parameter acquisition task transmission requirements include the maximum allowable latency and minimum required bandwidth; for example, the data transmission latency must not exceed 10 seconds, and the minimum required bandwidth must be no less than 10 Mbps. The periodic pollution spread tracing task transmission requirements include the maximum allowable energy consumption and the minimum required transmission success rate; for example, the maximum energy consumption within a task cycle must not exceed 100 joules, and the minimum required transmission success rate must be no less than 95%. By analyzing these transmission requirements, the specific path requirements for different monitoring task types can be clarified.

[0073] Step 1422: For the current load of each path, extract the allocated bandwidth utilization and node computing resource utilization, and calculate the remaining available bandwidth and remaining computing resources.

[0074] Next, the current load of each path is analyzed to extract the allocated bandwidth utilization and node computing resource utilization. For example, for a path with a total bandwidth of 100Mbps and an allocated bandwidth of 60Mbps, the allocated bandwidth utilization is 60%. Node computing resource utilization is calculated using a similar method. Based on the allocated bandwidth utilization and node computing resource utilization, the remaining available bandwidth and remaining computing resources are calculated. The remaining available bandwidth equals the total bandwidth minus the allocated bandwidth, and the remaining computing resources equals the total computing resources minus the used computing resources. By calculating these remaining resources, it is possible to understand how much resource each path can still provide for new monitoring tasks.

[0075] Step 1423: Compare the remaining available bandwidth with the minimum bandwidth required for the real-time environmental parameter acquisition task, match the remaining computing resources with the maximum allowable latency of the real-time environmental parameter acquisition task, and identify overloaded paths that cannot meet the requirements of the real-time task.

[0076] Next, the remaining available bandwidth is compared with the minimum bandwidth required for the real-time environmental parameter acquisition task. If the remaining available bandwidth is less than the minimum bandwidth required for the real-time environmental parameter acquisition task, it indicates that the path cannot meet the bandwidth requirements of the real-time task. Simultaneously, the remaining computing resources are matched with the maximum allowable latency time for the real-time environmental parameter acquisition task. If the remaining computing resources cannot complete the data processing and transmission of the real-time task within the maximum allowable latency time, it indicates that the path cannot meet the computing resource requirements of the real-time task. Through this comparison and matching, overloaded paths that cannot meet the requirements of real-time tasks are identified.

[0077] Step 1424: Perform correlation analysis between the remaining available bandwidth and the maximum allowable energy consumption of the periodic pollution diffusion tracking task, match the remaining computing resources with the minimum required transmission success rate of the periodic pollution diffusion tracking task, and identify the underloaded path that matches the multi-period task.

[0078] For periodic pollution spread tracing tasks, a correlation analysis is performed between remaining available bandwidth and maximum allowable energy consumption. If the energy consumption of the remaining available bandwidth, while meeting the transmission requirements of the periodic pollution spread tracing task, does not exceed the maximum allowable energy consumption, then the path meets the energy consumption requirements of the periodic pollution spread tracing task. Simultaneously, remaining computing resources are matched with the minimum required transmission success rate of the periodic pollution spread tracing task. If the remaining computing resources can guarantee that the transmission success rate of the periodic pollution spread tracing task is not lower than the minimum required transmission success rate, then the path meets the computing resource requirements of the periodic pollution spread tracing task. Through this correlation analysis and matching, underloaded paths suitable for multi-periodic tasks are identified.

[0079] Step 1425: Based on the comparison and matching results, generate demand matching analysis results containing overloaded path identifiers and underloaded path identifiers.

[0080] Finally, based on the previous comparison and matching results, a demand matching analysis result containing overloaded path identifiers and underloaded path identifiers is generated. This result clarifies which paths are overloaded and which are underloaded, providing an important basis for subsequent load balancing and resource allocation.

[0081] Step 143: For the overloaded path, select an alternative path from the backup paths that has a partially overlapping link with the overloaded path, and migrate the transmission requirements of some monitoring tasks to the alternative path.

[0082] For overloaded paths, alternative paths with partially overlapping links are selected from the backup paths. These partially overlapping links can utilize the existing network topology, reducing the complexity of network adjustments. For example, if both an overloaded path and a backup path pass through a certain node or link segment, then the backup path can serve as the alternative path. The transmission requirements of some monitoring tasks are migrated to the alternative path; for instance, periodic pollution spread tracking tasks originally transmitted on the overloaded path are moved there to alleviate the burden on the overloaded path and achieve load balancing.

[0083] Step 144: For the underloaded path, allocate part of the transmission demand of the overloaded path in other monitoring sub-regions to the underloaded path to achieve cross-sub-region load balancing.

[0084] For underloaded paths, some of the transmission demands from overloaded paths in other monitoring sub-regions are allocated to the underloaded path. Load imbalances may exist between different monitoring sub-regions. By allocating load across sub-regions, the resources of underloaded paths can be fully utilized, achieving load balancing across the entire port area. For example, if one path in one monitoring sub-region is overloaded while another path in another is underloaded, some real-time environmental parameter acquisition tasks from the overloaded path can be allocated to the underloaded path, making the load more balanced across paths.

[0085] Step 145: Based on the load migration and allocation results, generate a network resource allocation scheme that includes the link bandwidth allocation ratio and the node computing resource allocation ratio.

[0086] Based on the preceding load migration and allocation results, a network resource allocation scheme is generated. This scheme includes link bandwidth allocation ratios and node computing resource allocation ratios. For example, for a path that was originally overloaded, after migrating some monitoring tasks to an alternative path, the link bandwidth allocation ratio and node computing resource allocation ratio for that path are readjusted to ensure reasonable resource utilization. Simultaneously, for underloaded paths, the link bandwidth allocation ratio and node computing resource allocation ratio are increased accordingly based on the allocated monitoring tasks. By generating a network resource allocation scheme, optimized allocation of network resources can be achieved.

[0087] Step 146: Calculate the current task load of each heterogeneous monitoring node. The current task load includes the frequency of sensing task execution and the amount of data processing tasks. Compare the current task load with a preset load balancing threshold. Generate a task load reduction instruction for nodes whose load exceeds the threshold and a task load increase instruction for nodes whose load is below the threshold, thus forming a node task load adjustment instruction.

[0088] Next, the current task load of each heterogeneous monitoring node is calculated. The current task load includes the frequency of sensing task execution and the amount of data processing tasks. For example, for an air quality monitoring node, the number of times it executes sensing tasks over a period of time is counted as the sensing task execution frequency, and the number and volume of air quality data packets it processes are counted as the data processing task volume. The preset load balancing threshold is set based on the actual needs of port environmental monitoring, including upper and lower limits for the comprehensive load index. The current task load is compared with the preset load balancing threshold. For nodes with loads exceeding the threshold, a task load reduction instruction is generated, such as reducing the node's sensing task execution frequency and lowering the data processing task volume. For nodes with loads below the threshold, a task load increase instruction is generated, such as increasing the node's sensing task execution frequency and increasing the data processing task volume. The task load reduction and increase instructions are then linked and integrated according to node identifiers to form node task load adjustment instructions.

[0089] In one embodiment, the current task load of each heterogeneous monitoring node is statistically analyzed. The current task load includes the frequency of sensing task execution and the amount of data processing tasks. The current task load is compared with a preset load balancing threshold. For nodes with loads exceeding the threshold, a task load reduction instruction is generated; for nodes with loads below the threshold, a task load increase instruction is generated, forming a node task load adjustment instruction. This includes:

[0090] Step 1460: For each heterogeneous monitoring node, count the number of times its sensing tasks are executed within a preset time window as the sensing task execution frequency, and count the number of data packets and the amount of data processed as the data processing task volume; input the sensing task execution frequency and the data processing task volume into the load comprehensive calculation model to generate a comprehensive load index reflecting the overall load level of the node; obtain a preset load balancing threshold, which includes an upper limit and a lower limit of the comprehensive load index; for nodes whose comprehensive load index exceeds the upper limit, generate a task volume reduction instruction to reduce the sensing task execution frequency and the data processing task volume; for nodes whose comprehensive load index is below the lower limit, generate a task volume increase instruction to increase the sensing task execution frequency and the data processing task volume; associate and integrate the task volume reduction instruction and the task volume increase instruction according to the node identifier to form a node task load adjustment instruction.

[0091] For each heterogeneous monitoring node, the number of times its sensing tasks are executed is counted within a preset time window. For example, the number of samples taken by an air quality monitoring node within one hour is counted as the sensing task execution frequency. Simultaneously, the number of data packets and the amount of data processed by the node are counted as the data processing workload. The sensing task execution frequency and data processing workload are input into a comprehensive load calculation model. This model can be an artificial intelligence model based on machine learning algorithms. Through learning and analyzing input features, it generates a comprehensive load index reflecting the overall load level of the nodes. A preset load balancing threshold is obtained, which includes an upper and lower limit for the comprehensive load index. For nodes whose comprehensive load index exceeds the upper limit, a workload reduction instruction is generated to decrease the sensing task execution frequency and reduce the data processing workload, thereby reducing the node's load. For nodes whose comprehensive load index is below the lower limit, a workload increase instruction is generated to increase the sensing task execution frequency and increase the data processing workload, thereby improving node utilization. Finally, the workload reduction and workload increase instructions are associated and integrated according to node identifiers to form a node task load adjustment instruction.

[0092] In a standalone embodiment, the distributed network architecture reconstruction of the port area through the network resource allocation scheme and the node task load adjustment instructions includes:

[0093] Step 1471: Send the link bandwidth allocation ratio and node computing resource allocation ratio in the network resource allocation scheme to the heterogeneous monitoring nodes in each monitoring sub-region. Each node adjusts its own bandwidth usage strategy and computing resource scheduling strategy according to the received allocation ratio.

[0094] In this step, the link bandwidth allocation ratio and node computing resource allocation ratio from the network resource allocation scheme are sent to the heterogeneous monitoring nodes in each monitoring sub-region. For example, the allocation ratio information is sent to each node wirelessly. Each node adjusts its bandwidth usage strategy and computing resource scheduling strategy according to the received allocation ratio. If a node receives an increased link bandwidth allocation ratio, it can increase its data transmission rate and improve data processing efficiency. If a node receives a decreased node computing resource allocation ratio, it needs to optimize its computing resource usage and reduce unnecessary computing tasks.

[0095] Step 1472: Send the node task load adjustment instruction to the corresponding heterogeneous monitoring node. Nodes with load exceeding the threshold reduce the sensing frequency and data processing volume according to the task reduction instruction, while nodes with load below the threshold increase the sensing frequency and data processing volume according to the task increase instruction.

[0096] Next, the node task load adjustment command is sent to the corresponding heterogeneous monitoring nodes. The task load reduction and task load increase commands are accurately sent to the appropriate nodes using node identifiers. Nodes with loads exceeding the threshold reduce their sensing frequency and data processing volume according to the task load reduction command. For example, if a water quality monitoring node has a load exceeding the threshold, it reduces the number of samples, decreasing the number of data packets and the amount of data processed. Nodes with loads below the threshold increase their sensing frequency and data processing volume according to the task load increase command. For example, if an air quality monitoring node has a load below the threshold, it increases the number of samples, improving data processing efficiency.

[0097] Step 1473: During the adjustment process, monitor the operating indicators of each node and the communication link quality characteristics in real time, verify whether the node load after adjustment is close to the load balancing threshold, and verify whether the bandwidth utilization rate of the adjusted link meets the transmission requirements of different monitoring task types.

[0098] During the adjustment process, the operational metrics of each node and the communication link quality characteristics are monitored in real time. For example, operational metrics such as node energy reserves, data processing latency, and the stability of sensing modules are monitored, as well as communication link quality characteristics such as signal strength fluctuation range, data packet transmission error rate, and available bandwidth. The adjusted node load is verified to be close to the load balancing threshold by comparing the adjusted comprehensive load metrics with the upper and lower limits of the load balancing threshold to determine if the node load is within a reasonable range. The adjusted link bandwidth utilization is also verified to meet the transmission requirements of different monitoring task types. For example, it is checked whether the bandwidth requirements of real-time environmental parameter acquisition tasks are met, and whether the energy consumption and transmission success rate of periodic pollution diffusion tracking tasks meet the requirements.

[0099] As a preferred embodiment, the step of real-time monitoring of the operating indicators of each node and the communication link quality characteristics during the adjustment process, verifying whether the adjusted node load approaches the load balancing threshold, and verifying whether the adjusted link bandwidth utilization meets the transmission requirements of different monitoring task types, includes:

[0100] Step 14730: For each heterogeneous monitoring node after adjustment, reacquire its sensing task execution frequency and data processing task volume, calculate the comprehensive load index and compare it with the upper and lower limits of the load balancing threshold to determine whether the comprehensive load index is within the preset range between the upper and lower limits; for each communication link after adjustment, reacquire the link signal strength fluctuation range, data packet transmission error rate and available link bandwidth, calculate the link transmission reliability score and compare it with the minimum required bandwidth of the real-time environmental parameter acquisition task and the maximum allowable energy consumption of the periodic pollution diffusion tracking task to determine whether the link transmission reliability score meets the transmission requirements of the corresponding task type; integrate the preset range judgment result of the comprehensive load index and the requirement satisfaction judgment result of the link transmission reliability score to generate an adjustment effect verification report containing node load verification results and link requirement verification results; the adjustment effect verification report is used to determine the effectiveness of the network resource allocation scheme and the node task load adjustment instruction.

[0101] For each heterogeneous monitoring node after adjustment, its sensing task execution frequency and data processing workload are re-acquired. For example, after a period of adjustment, the number of samplings and the number of data packets processed by the air quality monitoring nodes are counted. This data is input into the load comprehensive calculation model to calculate the comprehensive load index. The comprehensive load index is compared with the upper and lower limits of the load balancing threshold to determine whether the comprehensive load index is within the preset range between the upper and lower limits. If the comprehensive load index is within the preset range, it indicates that the node load is approaching the load balancing threshold. For each communication link after adjustment, the link signal strength fluctuation range, data packet transmission error rate, and available bandwidth are re-acquired. The link transmission reliability score is calculated based on this information. The link transmission reliability score is compared with the minimum required bandwidth for real-time environmental parameter acquisition tasks and the maximum allowable energy consumption for periodic pollution diffusion tracking tasks to determine whether the link transmission reliability score meets the transmission requirements of the corresponding task types. The preset range judgment result of the comprehensive load index and the requirement satisfaction judgment result of the link transmission reliability score are integrated to generate an adjustment effect verification report containing node load verification results and link requirement verification results. This report is used to determine the effectiveness of the network resource allocation scheme and node task load adjustment instructions.

[0102] Step 1474: If the verification passes, confirm the validity of the network resource allocation scheme and the node task load adjustment instruction, and complete the initial reconstruction of the distributed network architecture; if the verification fails, extract the abnormal operation indicators and link quality characteristics that occur during the adjustment process, and feed the abnormal information back to the target network topology generation step and the routing protocol generation step, triggering the re-evaluation and adjustment of the network topology and routing strategy, forming an iterative optimization distributed network architecture reconstruction process.

[0103] Finally, if the adjustment effect verification report shows that the verification is successful, it indicates that the network resource allocation scheme and node task load adjustment instructions are effective, completing the initial reconstruction of the distributed network architecture. At this point, the distributed network architecture in the port area is more stable and efficient, better meeting the needs of environmental monitoring. If the verification fails, abnormal operating indicators and link quality characteristics that occur during the adjustment process are extracted. For example, the remaining energy of a node is too low, or the data packet transmission error rate of a link is too high. This abnormal information is fed back to the target network topology generation step and the routing protocol generation step, triggering a re-evaluation and adjustment of the network topology and routing strategy. Through continuous iterative optimization, the distributed network architecture can adapt to changes in the port environment and the needs of monitoring tasks, improving network reliability and performance.

[0104] In summary, this invention effectively constructs and optimizes a distributed network architecture for port area environmental monitoring. First, by dividing the port area into monitoring sub-regions and deploying a heterogeneous set of monitoring nodes based on the port's environmental monitoring needs, it accurately covers the diverse monitoring requirements of different areas within the port. Nodes of different sensing types work collaboratively to collect environmental data comprehensively and meticulously, significantly improving the accuracy and comprehensiveness of monitoring. Second, by performing real-time operational status sensing processing on the heterogeneous monitoring node set and generating a target network topology, the network topology is ensured to dynamically adjust according to the actual operational status of nodes and link quality, making the network layout more rational and efficient, enhancing network stability and reliability, and reducing data transmission interruptions caused by node failures or poor link quality. Third, the adaptive routing table and data transmission scheduling rules generated based on the target network topology can flexibly arrange data transmission paths and times according to different monitoring task types, ensuring low-latency transmission for real-time environmental parameter acquisition tasks and achieving energy balance for periodic pollution diffusion tracking tasks, thereby improving network resource utilization and data transmission efficiency. Finally, by reconstructing the network architecture through the network resource allocation scheme and node task load adjustment instructions generated by distributed collaborative optimization processing, the task load and network resource allocation of each node can be effectively balanced, making the operation of the entire network more stable and efficient. It also has the ability to self-adjust and optimize, adapting to the dynamic changes in the port environment and the continuous adjustment of monitoring tasks, and significantly improving the overall efficiency and quality of port environmental monitoring.

[0105] This invention provides a computer-readable storage medium storing a program that, when executed by a processor, implements the method for constructing a distributed network architecture for port environmental monitoring.

[0106] This invention provides a processor for running a program, wherein the program executes the method for constructing a distributed network architecture for port environmental monitoring.

[0107] In embodiments of the present invention, such as Figure 2 As shown, the distributed network architecture construction system 100 includes at least one processor 101, and at least one memory 102 and bus 103 connected to the processor 101; wherein, the processor 101 and the memory 102 communicate with each other through the bus 103; the processor 101 is used to call program instructions in the memory 102 to execute the above-mentioned distributed network architecture construction method for port environmental monitoring.

[0108] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, distributed network architecture construction systems (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] In a typical configuration, a distributed network architecture building system includes one or more processors (CPUs), memory, and a bus. The distributed network architecture building system may also include input / output interfaces, network interfaces, etc.

[0110] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM, and memory includes at least one memory chip. Memory is an example of computer-readable media.

[0111] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage. Computer-readable storage media or any other non-transferable media can be used to store information that can be accessed by systems built on a distributed network architecture. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0112] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or computer-readable storage medium that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or computer-readable storage medium. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or computer-readable storage medium that includes that element.

[0113] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for constructing a distributed network architecture for port environment monitoring, characterized in that, The method comprises the following steps: According to the environmental monitoring needs of the port area, multiple monitoring sub-areas are divided, and a set of heterogeneous monitoring nodes containing different sensing types are deployed in each monitoring sub-area; Real-time running state sensing processing is performed on the set of heterogeneous monitoring nodes to obtain the current running index of each heterogeneous monitoring node and the communication link quality characteristics of adjacent heterogeneous monitoring nodes, and a target network topology is generated based on the current running index and the communication link quality characteristics; Based on the target network topology, routing protocol generation and data scheduling strategy formulation operations are performed to obtain an adaptive routing table containing path priority ranking and data transmission scheduling rules based on monitoring task types, specifically including: In the target network topology, all transmission paths from source nodes to sink nodes are extracted, and the link transmission reliability scores of each transmission path are accumulated to generate a path overall reliability index; According to the path overall reliability index, all transmission paths are prioritized, the paths with the top X reliability index are marked as priority paths, and the remaining paths are marked as backup paths to form an adaptive routing table containing priority paths and backup paths; Identify the monitoring task types that the set of heterogeneous monitoring nodes need to perform, including real-time environmental parameter acquisition tasks and periodic pollution diffusion tracking tasks; For the real-time environmental parameter acquisition task, formulate a minimum delay-based transmission scheduling rule, preferentially allocate target bandwidth links and shorten data packet fragmentation length; For the periodic pollution diffusion tracking task, formulate an energy consumption balancing-based transmission scheduling rule, and disperse the use of different links and prolong the data packet transmission time interval; Integrate the minimum delay transmission scheduling rule and the energy consumption balancing transmission scheduling rule into the data transmission scheduling rule based on the monitoring task type; According to the adaptive routing table and the data transmission scheduling rule, distributed collaborative optimization processing is performed on the heterogeneous monitoring nodes of each monitoring sub-area to generate a network resource allocation scheme and node task load adjustment instructions, and the distributed network architecture reconstruction of the port area is performed through the network resource allocation scheme and the node task load adjustment instructions.

2. The method of claim 1, wherein, The real-time running state sensing processing on the set of heterogeneous monitoring nodes to obtain the current running index of each heterogeneous monitoring node and the communication link quality characteristics of adjacent heterogeneous monitoring nodes, and the generation of the target network topology based on the current running index and the communication link quality characteristics, comprises: Performing a hardware state detection operation on each heterogeneous monitoring node to obtain the node energy remaining amount, data processing delay time and sensing module working stability as the current running index; Performing a signal quality monitoring operation on the communication link between adjacent heterogeneous monitoring nodes to obtain the link signal strength fluctuation range, data packet transmission error rate and link available bandwidth as the communication link quality characteristics; Building a node running state evaluation model, inputting the current running index and the communication link quality characteristics into the node running state evaluation model for comprehensive evaluation to generate the running reliability score of each heterogeneous monitoring node and the transmission reliability score of each communication link; constructing an initial network topology candidate set based on the operation reliability score and the transmission reliability score, the initial network topology candidate set containing a plurality of topology candidate structures composed of nodes and links; performing connectivity verification and redundancy analysis on each topology candidate structure in the initial network topology candidate set, and selecting a topology candidate structure that meets the monitoring coverage requirement in connectivity and meets the preset fault tolerance standard in redundancy as a target network topology structure.

3. The method of claim 1, wherein, extracting all transmission paths from source nodes to sink nodes in the target network topology structure, and performing cumulative calculation on the link transmission reliability scores of each transmission path to generate a path overall reliability indicator, including: taking the sink node as a root node, traversing all nodes in the target network topology structure using a breadth-first search algorithm, and generating all node paths from each source node to the sink node; extracting the transmission reliability score in the communication link quality feature between adjacent heterogeneous monitoring nodes in each node path; cumulatively summing the transmission reliability scores in path order to obtain a reliability cumulative value of each node path; performing normalization processing on the reliability cumulative value to generate a path overall reliability indicator reflecting the overall reliability degree of the path, and the value range of the path overall reliability indicator is consistent with the value range of the transmission reliability score.

4. The method of claim 1, wherein, formulating a minimum delay-based transmission scheduling rule for the real-time environmental parameter collection task, preferentially allocating target bandwidth links and shortening data packet fragmentation length, including: identifying the data packet type corresponding to the real-time environmental parameter collection task, the data packet type containing a time-sensitive data field and a basic environmental parameter data field; extracting the available bandwidth information of each link in the target network topology structure, and screening out target bandwidth links with available bandwidth greater than a preset bandwidth threshold; allocating the shortest path in the target bandwidth link to the time-sensitive data field for transmission, and setting the data packet fragmentation length to a short fragmentation less than a preset fragmentation length threshold; for the basic environmental parameter data field, after ensuring the transmission of the time-sensitive data field, using the remaining target bandwidth link for transmission, and setting the data packet fragmentation length to a standard fragmentation length matching the link bandwidth; generating a minimum delay transmission scheduling rule for the real-time environmental parameter collection task according to the target bandwidth link allocation strategy and the data packet fragmentation length setting strategy.

5. The method of claim 1, wherein, performing distributed collaborative optimization processing on the heterogeneous monitoring nodes of each monitoring sub-region according to the adaptive routing table and the data transmission scheduling rule, generating a network resource allocation scheme and a node task load adjustment instruction, including: extracting the priority path and backup path information in the adaptive routing table, and counting the current load of each path, the current load including the allocated bandwidth occupancy rate and the node computing resource occupancy rate; combining the transmission requirements of different monitoring task types in the data transmission scheduling rule, performing demand matching analysis on the current load of each path, and identifying paths with excessive load and paths with insufficient load; For the load excess path, an alternative path with partially overlapped links is selected from the standby path to migrate part of the monitoring task transmission demand to the alternative path; For the load deficient path, part of the transmission demand of the load excess path in other monitoring sub-areas is allocated to the load deficient path to achieve load balancing across sub-areas; According to the load migration and allocation results, a network resource allocation scheme including link bandwidth allocation ratio and node computing resource allocation ratio is generated; The current task load of each heterogeneous monitoring node is counted, which includes the sensing task execution frequency and the data processing task amount, and the current task load is compared with a preset load balancing threshold, and a task amount reduction instruction is generated for the node whose load exceeds the threshold, and a task amount increase instruction is generated for the node whose load is lower than the threshold, to form a node task load adjustment instruction.

6. The method of claim 5, wherein, The current load of each path is demand matching analyzed in combination with the transmission demand of different monitoring tasks in the data transmission scheduling rule, and the load excess path and the load deficient path are identified, including: The real-time environmental parameter acquisition task transmission demand and the periodic pollution diffusion tracking task transmission demand in the data transmission scheduling rule are parsed, the real-time environmental parameter acquisition task transmission demand includes the maximum allowed delay time and the minimum required bandwidth, and the periodic pollution diffusion tracking task transmission demand includes the maximum allowed energy consumption and the minimum required transmission success rate; For the current load of each path, the allocated bandwidth occupancy and node computing resource occupancy are extracted, and the remaining available bandwidth and remaining computing resource are calculated; The remaining available bandwidth is compared with the minimum required bandwidth of the real-time environmental parameter acquisition task, and the remaining computing resource is matched with the maximum allowed delay time of the real-time environmental parameter acquisition task to identify the load excess path that cannot meet the real-time task demand; The remaining available bandwidth is associated with the maximum allowed energy consumption of the periodic pollution diffusion tracking task, and the remaining computing resource is matched with the minimum required transmission success rate of the periodic pollution diffusion tracking task to identify the load deficient path that matches the multi-periodic task; According to the comparison result and the matching result, a demand matching analysis result including the load excess path identifier and the load deficient path identifier is generated.

7. The method of claim 5, wherein, The current task load of each heterogeneous monitoring node is counted, which includes the sensing task execution frequency and the data processing task amount, and the current task load is compared with a preset load balancing threshold, and a task amount reduction instruction is generated for the node whose load exceeds the threshold, and a task amount increase instruction is generated for the node whose load is lower than the threshold, to form a node task load adjustment instruction, including: For each heterogeneous monitoring node, the number of sensing task executions in a preset time window is counted as the sensing task execution frequency, and the number of processed data packets and the data amount are counted as the data processing task amount; The sensing task execution frequency and the data processing task amount are input into a load comprehensive calculation model to generate a comprehensive load index reflecting the overall load level of the node; Obtaining a preset load balancing threshold, the load balancing threshold including an upper limit value and a lower limit value of the comprehensive load index; For the node whose comprehensive load index exceeds the upper limit value, generating a task amount reduction instruction for reducing the execution frequency of the sensing task and reducing the data processing task amount; For the node whose comprehensive load index is lower than the lower limit value, generating a task amount increase instruction for increasing the execution frequency of the sensing task and increasing the data processing task amount; Associating and integrating the task amount reduction instruction and the task amount increase instruction according to the node identifier to form a node task load adjustment instruction.

8. A distributed network architecture building system, characterized by The method comprises a processor, a memory and a bus connected with the processor; wherein the processor and the memory complete mutual communication through the bus; the processor is used for calling program instructions in the memory to execute the distributed network architecture construction method for port environment monitoring in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, A program is stored thereon, and the program is executed by a processor to implement the distributed network architecture construction method for port environment monitoring in any one of claims 1-7.

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