Methanol ship distributed gas supply monitoring method and system capable of collecting data in real time

Abstract

The invention provides a methanol ship distributed gas supply monitoring method and system capable of collecting data in real time, and relates to the technical field of gas supply monitoring. A sensor array is arranged, and a sensor array network is obtained; the method comprises the following steps: acquiring state parameters of a ship distributed gas supply system according to a preset acquisition period, and performing primary processing on state parameter acquisition data to obtain state primary processing data; state preliminary analysis is carried out, data transmission preliminary distribution is carried out according to state preliminary analysis data, data transmission preliminary distribution data is obtained, state preliminary transmission is carried out, and state preliminary transmission data is obtained; according to the method, distributed gas supply analysis is carried out to obtain distributed gas supply analysis data, a gas supply state abnormal node is determined according to the distributed gas supply analysis data, gas supply state regulation control analysis is carried out on the gas supply state abnormal node, and gas supply state regulation and control data is obtained. And safe and efficient operation of ships is ensured.

Description

Technical Field

[0001] This invention proposes a method and system for monitoring distributed gas supply to methanol ships with real-time data acquisition, which relates to the field of gas supply monitoring technology, specifically to the field of monitoring distributed gas supply to methanol ships with real-time data acquisition. Background Technology

[0002] With the advancement of green transformation in the shipping industry, methanol-powered ships have been widely adopted due to their environmental advantages. However, their distributed gas supply systems operate under complex conditions, requiring extremely high precision and timeliness in monitoring and control. Existing methanol ship gas supply monitoring technologies generally suffer from simplistic and insufficiently targeted control solutions, making it difficult to adapt to different anomalies and navigation conditions. Furthermore, traditional control methods lack effective utilization of historical operational data, and solution selection lacks multi-dimensional scientific evaluation, resulting in poor control effects, delayed responses, and low transparency in the control process. This hinders real-time intervention by staff and can easily lead to gas supply instability or even power system failure. Summary of the Invention

[0003] This invention provides a method and system for real-time data acquisition and distributed gas supply monitoring of methanol ships, to solve the above-mentioned problems:

[0004] This invention proposes a method and system for real-time data acquisition and distributed gas supply monitoring of methanol ships, the method comprising:

[0005] S1. Set up sensor arrays at key nodes of the methanol ship distributed gas supply system, calibrate the sensor arrays and connect them to the network to obtain the sensor array network;

[0006] S2. Collect the state parameters of the ship's distributed gas supply system through the sensor array network according to the preset collection period, obtain the state parameter collection data, perform preliminary processing on the state parameter collection data, and obtain preliminary state processing data.

[0007] S3. Perform preliminary state analysis on the preliminary state processing data to obtain preliminary state analysis data. Perform preliminary data transmission allocation based on the preliminary state analysis data to obtain preliminary data transmission allocation data. Perform preliminary state transmission based on the preliminary data transmission allocation data to obtain preliminary state transmission data.

[0008] S4. Perform distributed gas supply analysis on the initial status transmission data to obtain distributed gas supply analysis data. Based on the distributed gas supply analysis data, identify abnormal gas supply status nodes. Perform gas supply status regulation and control analysis on the abnormal gas supply status nodes to obtain gas supply status regulation data.

[0009] Further, S1 includes:

[0010] Obtain key node information of the methanol ship distributed gas supply system;

[0011] Node weight analysis is performed based on key node information to obtain node weight analysis data.

[0012] Sensor array information is determined based on node weight analysis data and key node distance data.

[0013] The sensor array information is calibrated and analyzed for communication to construct a sensor array network.

[0014] Furthermore, the calibration and communication analysis of the sensor array information to construct the sensor array network includes:

[0015] Perform independent sensor calibration and sensor linkage calibration on the sensor array information to obtain array calibration information;

[0016] Based on the array calibration information, sensor network connections are established to obtain sensor network connection data.

[0017] Sensor communication status analysis is performed based on sensor network connection data to obtain sensor communication status analysis data.

[0018] The sensor array network is obtained by address encoding and allocation of sensor array information based on sensor communication status analysis data.

[0019] Further, S2 includes:

[0020] Obtain preset collection cycle information, and determine the data collection time node based on the preset collection cycle information;

[0021] Based on the sensor array network, data is collected from the key nodes of the ship's distributed gas supply system at the data acquisition time nodes to obtain the state parameter data of the time nodes and key nodes.

[0022] The state parameter acquisition data is divided into state types to obtain state type acquisition data;

[0023] The data collected for different status types undergoes preliminary processing to obtain preliminary status data.

[0024] Furthermore, the preliminary processing of the state type collection data to obtain preliminary state processing data includes:

[0025] The state type acquisition data is subjected to time-series alignment processing to obtain time-series acquisition data;

[0026] The key nodes of the time series data are divided to obtain the time series key node type data.

[0027] Generate node type data collection logs based on time-series key node type data;

[0028] The node type data collection log is the preliminary status processing data.

[0029] Further, S3 includes:

[0030] The preliminary state processing data is compared with the preset state parameter threshold range to obtain the state data comparison result.

[0031] Based on the comparison results of the status data, anomaly determination of status data is performed to obtain status anomaly determination data;

[0032] The ratio of the preliminary state processing data corresponding to the state anomaly determination data to the preset state parameter threshold is obtained to obtain the state anomaly coefficient;

[0033] Generate state anomaly weight data based on the state anomaly coefficient;

[0034] Based on the state anomaly weight data, the transmission allocation priority is determined, and the initial data transmission allocation is performed according to the transmission allocation priority to obtain the initial state transmission data.

[0035] Further, the step of determining the transmission allocation priority based on the state anomaly weight data, performing preliminary data transmission allocation based on the transmission allocation priority, and obtaining preliminary state transmission data includes:

[0036] Sort the state anomaly weight data from largest to smallest to obtain the state anomaly weight sequence;

[0037] According to the state anomaly weight sequence, the corresponding state preliminary processing data is transmitted sequentially and exclusively to obtain the anomaly state preliminary transmission data.

[0038] Set the preliminary processing data corresponding to the normal status determination data to ordinary priority, transmit it through the shared transmission channel, and obtain the preliminary transmission data of the normal status.

[0039] The initial state transmission process employs a data encryption transmission mechanism, adding CRC and 32 checksums to the data. The receiving end verifies the data integrity using the checksums. If the verification fails, a data retransmission mechanism is triggered to obtain the initial state transmission data.

[0040] Further, S4 includes:

[0041] Perform gas supply status correlation analysis on the initial status transmission data to obtain gas supply status correlation nodes;

[0042] Perform multi-node data correlation analysis on the gas supply status-related nodes to obtain multi-node gas supply correlation analysis data;

[0043] Analyze the changing trends of state parameters in the multi-node gas supply correlation analysis data to obtain gas supply change trend analysis data.

[0044] Identify abnormal gas supply nodes based on gas supply change trend analysis data;

[0045] Adjust the gas supply status of nodes with abnormal gas supply status to obtain gas supply status control data.

[0046] Furthermore, the step of adjusting the gas supply status of nodes with abnormal gas supply status to obtain gas supply status control data includes:

[0047] When analyzing the gas supply status regulation and control of abnormal nodes, multiple control schemes are generated by combining the historical operating data of the abnormal nodes with the current operating conditions.

[0048] By comparing the response speed, energy consumption, and safety of the control schemes, the optimal control scheme is selected as the gas supply status control data, and the abnormal node information and control scheme are pushed to the ship monitoring center.

[0049] Furthermore, the system includes:

[0050] The array setup module is used to set up sensor arrays at key nodes of the methanol ship distributed gas supply system, calibrate the sensor arrays and connect them to the network, and obtain the sensor array network.

[0051] The data acquisition and processing module is used to acquire the status parameters of the ship's distributed gas supply system through a sensor array network according to a preset acquisition cycle, obtain the status parameter acquisition data, perform preliminary processing on the status parameter acquisition data, and obtain preliminary status data.

[0052] The analysis and transmission module is used to perform preliminary state analysis on the preliminary state processing data, obtain preliminary state analysis data, perform preliminary data transmission allocation based on the preliminary state analysis data, obtain preliminary data transmission allocation data, and perform preliminary state transmission based on the preliminary data transmission allocation data to obtain preliminary state transmission data.

[0053] The status control module is used to perform distributed gas supply analysis on the initial status transmission data, obtain distributed gas supply analysis data, identify abnormal gas supply status nodes based on the distributed gas supply analysis data, perform gas supply status adjustment and control analysis on the abnormal gas supply status nodes, and obtain gas supply status control data.

[0054] The beneficial effects of this invention are as follows: This method effectively solves the technical problems of traditional methanol ship gas supply monitoring methods, such as incomplete monitoring coverage, delayed data processing, and untimely anomaly response. Through the deployment of a distributed sensor array, comprehensive perception of key areas of the gas supply system is achieved, avoiding the limitations of single-point monitoring; preset periodic data collection and preliminary data processing improve data reliability and standardization; data transmission priority allocation ensures priority transmission of abnormal data, shortening the time lag for anomaly response; distributed gas supply analysis and precise control enable accurate location and targeted handling of gas supply anomalies, improving the stability and safety of the gas supply system. Simultaneously, the automated operation of the entire monitoring process reduces the cost of manual intervention and improves monitoring efficiency. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of a distributed gas supply monitoring method for methanol ships that collects data in real time. Detailed Implementation

[0056] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0057] In one embodiment of the present invention, a method and system for real-time data acquisition and distributed gas supply monitoring of methanol ships is proposed, the method comprising:

[0058] S1. Set up sensor arrays at key nodes of the methanol ship distributed gas supply system, calibrate the sensor arrays and connect them to the network to obtain the sensor array network;

[0059] S2. Collect the state parameters of the ship's distributed gas supply system through the sensor array network according to the preset collection period, obtain the state parameter collection data, perform preliminary processing on the state parameter collection data, and obtain preliminary state processing data.

[0060] S3. Perform preliminary state analysis on the preliminary state processing data to obtain preliminary state analysis data. Perform preliminary data transmission allocation based on the preliminary state analysis data to obtain preliminary data transmission allocation data. Perform preliminary state transmission based on the preliminary data transmission allocation data to obtain preliminary state transmission data.

[0061] S4. Perform distributed gas supply analysis on the initial transmitted status data to obtain distributed gas supply analysis data. Based on the distributed gas supply analysis data, identify nodes with abnormal gas supply status. Perform gas supply status regulation and control analysis on these nodes to obtain gas supply status regulation data, such as... Figure 1 As shown.

[0062] The working principle and technical effects of the above technical solution are as follows: The core principle of this method is to construct a distributed monitoring system encompassing sensing, data acquisition, analysis, and control throughout the entire process, thereby achieving closed-loop management of the methanol ship gas supply system. By deploying sensor arrays at key nodes of the gas supply system and completing calibration and networking, a sensing network covering the core area is established. Gas supply status parameters are collected through the sensing network based on a preset cycle, and the data undergoes preliminary processing. The pre-processed data is then preliminarily analyzed, and transmission priorities are assigned based on the analysis results to ensure the timeliness of key data transmission. The transmitted data is then analyzed to assess the distributed operating status of the gas supply system, accurately locate abnormal nodes, and conduct control analysis to generate targeted control data, achieving dynamic control of the gas supply system. The entire process organically combines sensor sensing, data transmission, intelligent analysis, and control decision-making through the orderly flow and layered processing of data, forming a complete monitoring link.

[0063] This method effectively solves the technical problems of traditional methanol ship gas supply monitoring methods, such as incomplete monitoring coverage, delayed data processing, and untimely anomaly response. Through the deployment of a distributed sensor array, comprehensive perception of key areas of the gas supply system is achieved, avoiding the limitations of single-point monitoring. Pre-set periodic data collection and preliminary data processing improve data reliability and standardization. Prioritized data transmission ensures the priority transmission of abnormal data, shortening the time lag in anomaly response. Distributed gas supply analysis and precise control enable accurate location and targeted handling of gas supply anomalies, improving the stability and safety of the gas supply system. Simultaneously, the automated operation of the entire monitoring process reduces manual intervention costs, improves monitoring efficiency, and provides strong support for the safe and efficient operation of the methanol ship gas supply system.

[0064] In one embodiment of the present invention, S1 includes:

[0065] Obtain key node information of the methanol ship distributed gas supply system;

[0066] Node weight analysis is performed based on key node information to obtain node weight analysis data.

[0067] Sensor array information is determined based on node weight analysis data and key node distance data.

[0068] The sensor array information is calibrated and analyzed for communication to construct a sensor array network.

[0069] The key nodes include the gas supply pipeline inlet and outlet, valve opening and closing ends, methanol fuel tank outlet and power unit inlet. The sensor array includes pressure sensors, flow sensors, temperature sensors and methanol purity sensors.

[0070] This includes performing node weight analysis based on key node information to obtain node weight analysis data, including:

[0071] Acquire gas supply demand data and actual gas supply operation data for key nodes of the methanol ship distributed gas supply system;

[0072] Obtain the absolute value of the difference between the gas supply operation demand data and the actual gas supply operation data, and obtain the node gas supply operation coefficient;

[0073] Based on the gas supply operation coefficient of the nodes, node weights are set for key node information to obtain node weight analysis data.

[0074] Calculate the difference in gas supply operation coefficients for multiple key nodes to obtain gas supply difference data;

[0075] The gas supply difference data is compared with a preset gas supply difference threshold to obtain the node gas supply comparison result;

[0076] Obtain information on key nodes whose gas supply difference data exceeds a preset gas supply difference threshold, and identify them as abnormal nodes (compared to previous nodes).

[0077] The gas supply difference data is used to adjust the node weight analysis data of the abnormal nodes to obtain node weight adjustment data.

[0078] The node weight analysis data of abnormal nodes is updated based on the node weight adjustment data to obtain the updated node weight analysis data.

[0079] For example: First, clarify the gas supply requirements of each node, and then collect the actual operating performance of each node.

[0080] By comparing the gap between the demand and the actual performance of each node, the larger the gap, the higher the gas supply operation coefficient of that node.

[0081] Nodes are assigned weights based on their coefficients: nodes with higher coefficients (such as the air intake of a power unit) have higher weights.

[0082] Compare the gas supply operation coefficients of all nodes and calculate the differences between them.

[0083] Compare this difference with the preset standard value. Nodes with a difference exceeding the standard value are abnormal nodes (such as the coefficient difference between the air intake end of the power unit and other nodes exceeding the standard).

[0084] For this abnormal node, increase its weight (because it needs to be monitored more closely), while correspondingly decreasing the weights of other normal nodes to complete the weight update.

[0085] The working principle and technical effects of the above-mentioned technical solution are as follows: This method systematically analyzes the structure of the distributed gas supply system for methanol ships, accurately extracts information from core key nodes such as gas supply pipeline inlets and outlets and valve opening and closing ends, and clarifies the data positioning of each node in the gas supply process; it performs weight analysis on each node, and then analyzes the gas supply gap between multiple nodes to generate node weight analysis data, solving the technical problem that traditional gas supply systems struggle to detect key nodes where gas supply attenuation occurs, leading to inaccurate weighting that does not accurately reflect actual operating conditions; furthermore, by combining the spatial distance data between key nodes, the number, type, and installation location of sensors are comprehensively determined to form sensor array information; the sensor array information is calibrated and communication tested to ensure that the performance of each sensor meets the standards and communication is stable, ultimately constructing a reliable sensor array network covering key nodes. Specifically, dedicated sensors for pressure, flow, temperature, and purity are matched to meet the functional requirements of different key nodes, ensuring the relevance and comprehensiveness of the sensed data.

[0086] This method addresses the technical problems inherent in traditional sensor deployment, such as blind deployment, mismatch between sensor types and node requirements, and poor reliability of the sensing network. By acquiring key node information and performing weight analysis, it achieves precise sensor deployment, avoids resource waste, and ensures that core nodes are monitored at their core. Configuring sensor arrays based on node distance data improves the coverage rationality of the sensor network and reduces blind spots. Matching specific sensor types ensures the accuracy of status parameter acquisition for each node, improving the quality of the sensed data. Simultaneously, sensor array calibration and communication analysis ensure the communication stability and data transmission reliability of the sensing network, enhancing the overall sensing capability and operational stability of the monitoring system.

[0087] In one embodiment of the present invention, the calibration and communication analysis of sensor array information to construct a sensor array network includes:

[0088] Perform independent sensor calibration and sensor linkage calibration on the sensor array information to obtain array calibration information;

[0089] Based on the array calibration information, sensor network connections are established to obtain sensor network connection data.

[0090] Sensor communication status analysis is performed based on sensor network connection data to obtain sensor communication status analysis data.

[0091] The sensor array network is obtained by address encoding and allocation of sensor array information based on sensor communication status analysis data.

[0092] The calibration process uses standard pressure sources and standard flow sources for multi-point calibration. The network connection adopts a dual-mode networking of industrial Ethernet and LoRaWAN to ensure the anti-interference ability of the sensor array network and the stability of data transmission.

[0093] The working principle and technical effects of the above-mentioned technical solution are as follows: This method improves the measurement accuracy and network anti-interference capability of the sensor array through layered calibration and dual-mode networking technology. A dual calibration strategy of independent calibration and linkage calibration is adopted. Each sensor is calibrated independently to ensure the measurement accuracy of a single sensor. Linkage calibration is then used to test the consistency of multiple sensors working together, avoiding the impact of individual sensor deviations on overall data accuracy, ultimately obtaining comprehensive array calibration information. Based on the array calibration information, a sensor network is built using a dual-mode networking approach of Industrial Ethernet and LoRaWAN. Industrial Ethernet ensures short-distance, high-bandwidth data transmission, while LoRaWAN ensures long-distance, low-power communication coverage, achieving complementarity between the two networks. A comprehensive analysis of the sensor network's communication status is performed, including testing indicators such as communication latency, signal strength, and data packet loss rate, to ensure network communication performance meets standards. Based on the communication status analysis results, each sensor is assigned a unique address code, enabling precise positioning and data traceability of each sensor node, ultimately constructing a stable and traceable sensor array network. The calibration process uses standard pressure sources and standard flow sources for multi-point calibration to further improve calibration accuracy.

[0094] This method effectively solves the technical problems of incomplete sensor calibration, weak network anti-interference capability, and lack of data traceability in traditional methods. The dual calibration strategy not only improves the measurement accuracy of individual sensors but also ensures the overall consistency of the sensor array, reducing monitoring errors caused by sensor deviations. The dual-mode networking method compensates for the communication deficiencies of a single network, improves the network's anti-interference capability and coverage, and adapts to the complex navigation environment of methanol-powered ships (such as metal structure obstruction and electromagnetic interference). Communication status analysis ensures that network communication performance meets standards, reducing data transmission latency and packet loss rate. Address coding allocation enables precise positioning of sensor nodes and data traceability. This method improves the measurement accuracy, communication stability, and anti-interference capability of sensor array networks.

[0095] In one embodiment of the present invention, S2 includes:

[0096] Obtain preset collection cycle information, and determine the data collection time node based on the preset collection cycle information;

[0097] Based on the sensor array network, data is collected from the key nodes of the ship's distributed gas supply system at the data acquisition time nodes to obtain the state parameter data of the time nodes and key nodes.

[0098] The state parameter acquisition data is divided into state types to obtain state type acquisition data;

[0099] The data collected for different status types undergoes preliminary processing to obtain preliminary status data.

[0100] The preset acquisition period is adjustable to 10 or 100 milliseconds, and the status parameter acquisition data includes gas supply pressure, instantaneous flow rate, medium temperature and methanol purity.

[0101] The working principle and technical effects of the above-mentioned technical solution are as follows: The working principle of this method is based on an adjustable high-frequency acquisition cycle and a standardized data processing flow to achieve real-time, accurate acquisition and standardized processing of gas supply status parameters. According to the operating conditions of the methanol ship gas supply system, an adjustable acquisition cycle of 10 or 100 milliseconds is preset, and specific data acquisition time nodes are determined based on this cycle to ensure that the acquisition frequency can capture the dynamic changes in the gas supply status. Through a sensor array network, core parameters such as gas supply pressure and instantaneous flow rate of each key node are synchronously acquired at preset time nodes to obtain status parameter acquisition data containing time node and key node information. The acquired data is then classified into status types according to parameter type.

[0102] This method solves the technical problems of fixed data acquisition cycles, weak anti-interference capabilities, and inconsistent data formats in traditional systems. The adjustable high-frequency acquisition cycle can adapt to the monitoring needs under different navigation conditions, ensuring real-time capture of dynamic changes in gas supply status, improving the flexibility and timeliness of data acquisition; enhancing the reliability and accuracy of acquired data; reducing the difficulty of subsequent data transmission and analysis; and improving data processing efficiency. Simultaneously, the classification of status types makes data processing more targeted, further improving the standardization of data processing. This method enhances the data processing capabilities and operational efficiency of the entire monitoring system.

[0103] In one embodiment of the present invention, the preliminary processing of the state type collection data to obtain preliminary state processing data includes:

[0104] The state type acquisition data is subjected to time-series alignment processing to obtain time-series acquisition data;

[0105] The key nodes of the time series data are divided to obtain the time series key node type data.

[0106] Generate node type data collection logs based on time-series key node type data;

[0107] The node type data collection log is the preliminary status processing data.

[0108] After generating node type data collection logs, an abnormal data tracing module is added. By using the timestamps, node addresses, and parameter change curves in the logs, the cause of abnormal data can be traced in reverse.

[0109] If multiple adjacent node parameters are abnormal at the same time, it is determined to be a pipeline coordination failure;

[0110] If the parameters of a single node fluctuate and there are no neighboring nodes associated with it, it is determined to be a fault of the sensor itself.

[0111] The working principle and technical effects of the above technical solution are as follows: The working principle of this method is to achieve time consistency and traceability of the collected data through time alignment and node division. Since the acquisition response speeds of different sensors vary, the state-type acquired data is time-aligned to calibrate the acquired data of each sensor with a unified time benchmark, ensuring that the acquired data of different key nodes and different types of parameters are synchronized in the time dimension, avoiding analysis errors caused by time differences, and obtaining time-series acquired data. Based on the address encoding information of key nodes, the time-series acquired data is divided into key nodes, clarifying the node affiliation of each data point, and obtaining time-series key node type data. Based on the time-series key node type data, a node type data acquisition log containing information such as acquisition time, node address, parameter type, and data value is generated. This log fully records the acquisition and processing process of each node and each parameter, and is ultimately used as preliminary state processing data, providing a basis for subsequent data traceability and analysis.

[0112] This method addresses the technical challenges of traditional data acquisition, such as time asynchrony, unclear data attribution, and lack of traceability. Time-series alignment ensures the consistency of data from different sensors, eliminating analytical biases caused by time differences and improving the accuracy of subsequent data analysis. Clarifying key nodes clearly defines the node attribution of data, ensuring precise correspondence between data and monitored objects, facilitating subsequent status analysis of specific nodes. The generation of data acquisition logs enables full-process traceability of acquired data, allowing for rapid identification of the source node and acquisition time of abnormal data when anomalies occur. Furthermore, the retention of log data provides data support for subsequent system optimization and fault review, enhancing the maintainability and reliability of the entire monitoring system.

[0113] In one embodiment of the present invention, S3 includes:

[0114] The preliminary state processing data is compared with the preset state parameter threshold range to obtain the state data comparison result.

[0115] Based on the comparison results of the status data, anomaly determination of status data is performed to obtain status anomaly determination data;

[0116] The ratio of the preliminary state processing data corresponding to the state anomaly determination data to the preset state parameter threshold is obtained to obtain the state anomaly coefficient;

[0117] Generate state anomaly weight data based on the state anomaly coefficient;

[0118] Based on the state anomaly weight data, the transmission allocation priority is determined, and the initial data transmission allocation is performed according to the transmission allocation priority to obtain the initial state transmission data.

[0119] The working principle and technical effects of the above technical solution are as follows: The working principle of this method is based on the quantitative analysis of data anomaly degree to achieve differentiated priority allocation of data transmission and ensure the timeliness of key data transmission. The preliminary state processing data is compared one by one with the preset normal state parameter threshold range to determine whether each data is within the normal range, obtaining the state data comparison result; based on the comparison result, anomaly judgment is made to identify which data belongs to abnormal data, obtaining state anomaly judgment data; the ratio of abnormal data to the corresponding preset threshold is calculated to obtain the state anomaly coefficient, which quantifies the degree of data anomaly (the larger the coefficient, the more severe the anomaly); state anomaly weight data is generated based on the state anomaly coefficient, with a higher coefficient resulting in a higher weight; the priority of data transmission is determined based on the state anomaly weight data, with higher weight abnormal data receiving higher priority; then, preliminary data transmission is allocated according to the priority to ensure that high-priority data is transmitted first, ultimately obtaining the preliminary state transmission data.

[0120] This method addresses the technical problems of delayed transmission of critical abnormal data and wasted network resources caused by the equal transmission approach used in traditional data transmission. Through quantitative analysis of data anomaly severity, it achieves precise prioritization of transmission, allowing severely abnormal data to occupy network resources first for transmission, shortening transmission delays and buying time for subsequent anomaly response and control. Normal data is transmitted with lower priority, avoiding excessive network resource consumption and improving network resource utilization efficiency. Simultaneously, the anomaly detection and weighting analysis process further filters critical data, reducing the workload of subsequent data processing and improving the overall response efficiency and resource utilization efficiency of the monitoring system. This solution ensures the monitoring system's rapid detection and response to gas supply anomalies, enhancing the operational safety of the gas supply system.

[0121] In one embodiment of the present invention, the step of determining the transmission allocation priority based on the state anomaly weight data, performing preliminary data transmission allocation based on the transmission allocation priority, and obtaining preliminary state transmission data includes:

[0122] Sort the state anomaly weight data from largest to smallest to obtain the state anomaly weight sequence;

[0123] According to the state anomaly weight sequence, the corresponding state preliminary processing data is transmitted sequentially and exclusively to obtain the anomaly state preliminary transmission data.

[0124] Set the preliminary processing data corresponding to the normal status determination data to ordinary priority, transmit it through the shared transmission channel, and obtain the preliminary transmission data of the normal status.

[0125] The initial state transmission process employs a data encryption transmission mechanism, adding CRC and 32 checksums to the data. The receiving end verifies the data integrity using the checksums. If the verification fails, a data retransmission mechanism is triggered to obtain the initial state transmission data.

[0126] The working principle and technical effects of the above technical solution are as follows: This method ensures the timeliness, security, and integrity of data transmission through priority sorting, differentiated transmission channel allocation, and encryption verification. Abnormal status weight data are sorted from largest to smallest to form an abnormal status weight sequence, which intuitively reflects the urgency of each abnormal data point. Based on the weight sequence, the corresponding abnormal status preliminary processing data is transmitted sequentially using a dedicated transmission channel, ensuring that each high-priority data point receives independent transmission resources, avoiding delays caused by competition with other data, and obtaining preliminary abnormal status transmission data. Normal status judgment data is set to ordinary priority and transmitted in batches using a shared transmission channel, improving network resource utilization and obtaining normal status preliminary transmission data. Throughout the data transmission process, AES and 256 encryption standards are used to encrypt the data to prevent theft or tampering during transmission. Simultaneously, CRC and 32 checksums are added to each transmitted data point. The receiving end verifies the integrity of the data using the checksum; if verification fails, a retransmission mechanism is triggered (retransmissions do not exceed 3 times), ensuring complete and secure data transmission, ultimately obtaining the preliminary status transmission data.

[0127] This method effectively solves the technical problems of unclear priority, critical data delay, data transmission insecurity, and easy data loss in traditional data transmission. The allocation of differentiated transmission channels ensures the timeliness of high-priority abnormal data transmission and guarantees timely anomaly response; shared transmission of ordinary data improves network resource utilization and reduces network operating costs; AES and 256 encryption ensure data transmission security, preventing sensitive data leakage or tampering; CRC, 32 checksum, and retransmission mechanisms ensure data transmission integrity and reduce data loss rate.

[0128] In one embodiment of the present invention, S4 includes:

[0129] Perform gas supply status correlation analysis on the initial status transmission data to obtain gas supply status correlation nodes;

[0130] Perform multi-node data correlation analysis on the gas supply status-related nodes to obtain multi-node gas supply correlation analysis data;

[0131] Analyze the changing trends of state parameters in the multi-node gas supply correlation analysis data to obtain gas supply change trend analysis data.

[0132] Identify abnormal gas supply nodes based on gas supply change trend analysis data;

[0133] Adjust the gas supply status of nodes with abnormal gas supply status to obtain gas supply status control data.

[0134] This process involves performing gas supply status correlation analysis on the initial transmitted status data to identify gas supply status correlation nodes, and then performing multi-node data correlation analysis on these nodes to obtain multi-node gas supply correlation analysis data.

[0135] Based on the initial transmitted status data, nodes with abnormal status and nodes with normal status are identified.

[0136] Connect the nodes with abnormal states to obtain the combination of nodes with abnormal states;

[0137] Connect the nodes in normal status to obtain a combination of associated nodes in normal status;

[0138] The association information of abnormal state associated nodes is compared with that of normal state associated nodes of Chint to obtain association comparison information.

[0139] The correlation comparison information is the multi-node gas supply correlation analysis data.

[0140] Assume that the key nodes of the methanol ship distributed gas supply system include: ① methanol fuel tank outlet, ② gas supply pipeline inlet, ③ pipeline valve opening and closing end, ④ gas supply pipeline outlet, and ⑤ power unit gas inlet, and each node is connected in sequence through pipelines.

[0141] After acquiring initial data on the status of each node through a sensor array, correlation patterns are then identified by combining this data with pipeline connection relationships.

[0142] The fuel tank outlet ① is directly connected to the pipeline inlet ②, and changes in the outlet flow rate will directly affect the inlet pressure;

[0143] The valve opening and closing end ③ is located between the pipeline inlet ② and outlet ④. The valve opening will simultaneously affect the upstream and downstream pressure and flow.

[0144] Pipeline outlet ④ is connected to the air inlet of the power unit ⑤, and the outlet pressure directly determines the air intake stability at the inlet. Based on this, the associated nodes for the air supply status are determined as: ①-②-③-④-⑤ (full-link associated node group).

[0145] Based on the preliminary transmitted data, the sensor data shows that the pressure / flow of nodes ② (pipeline inlet), ③ (valve), and ④ (pipeline outlet) all exceed the preset threshold, and are therefore identified as nodes with abnormal status; while the parameters of nodes ① (fuel tank) and ⑤ (power unit) are within the normal range, and are therefore identified as nodes with normal status.

[0146] Construct abnormally associated node combinations: ②-③-④ (connected in the order of pipeline connection); normally associated node combinations: ①-⑤ (the input and output ends of the abnormal combination, respectively).

[0147] Comparison of related information revealed that in the normal combination, ① (fuel tank) output flow was stable, but in the abnormal combination, ② (inlet) pressure dropped sharply, ③ (valve) flow was abnormally low, and ④ (outlet) pressure was insufficient; and although ⑤ (air inlet) of the normal combination was currently normal, signs of impending pressure fluctuations had already appeared. The above comparative information constitutes the multi-node gas supply correlation analysis data.

[0148] In the abnormal combination, ② (inlet) pressure dropped rapidly from the normal threshold, with a decrease of 30% within 30 seconds, and the trend was still downward; ③ (valve) flow rate decreased synchronously with the pressure, and ④ (outlet) pressure followed the decrease 10 seconds later. Combining trend analysis and correlation patterns, valve opening and closing end ③ was determined to be the core abnormal node (it is speculated that valve jamming caused insufficient opening, which in turn triggered abnormalities in upstream and downstream cascading parameters).

[0149] For valve opening / closing end ③, based on its historical operating data (including records of slight jamming) and current operating conditions (uniform ship speed and stable load), two control schemes were generated: ① increase valve driving pressure; ② activate the backup valve. After comparison, scheme ① (faster response speed and lower energy consumption) was selected as the gas supply status control data, and the abnormal node information and control scheme were pushed to the ship monitoring center.

[0150] The working principle and technical effects of the above technical solution are as follows: This method performs gas supply status correlation analysis on the preliminary transmission data, combines the pipeline connection relationship and fluid transmission characteristics of the methanol ship gas supply system, and explores the correlation law of state parameters between different nodes (such as the effect of pipeline inlet pressure change on outlet flow) to identify gas supply status correlation nodes with correlation; performs multi-node data correlation analysis on the transmission data of these correlation nodes, compares the parameter changes of each correlation node, and judges whether there is a coordinated anomaly (such as a sudden drop in inlet pressure and an abnormal decrease in outlet flow) to obtain multi-node gas supply correlation analysis data; performs state parameter change trend analysis on the multi-node gas supply correlation analysis data, and predicts the direction and rate of parameter change by fitting parameter change curves to obtain gas supply change trend analysis data; combines the correlation analysis results and trend analysis data to comprehensively judge the operating status of the gas supply system, accurately locate the core node causing the gas supply anomaly, and complete the determination of the gas supply status anomaly node; for the anomaly node, further analyze its anomaly cause, formulate corresponding node gas supply status adjustment strategy, and generate gas supply status control data.

[0151] This method addresses the technical problems of inaccurate anomaly localization and inability to identify coordinated anomalies caused by single-node analysis in traditional monitoring methods. Multi-node correlation analysis overcomes the limitations of single-point analysis, enabling the identification of coordinated anomalies between different nodes and improving the comprehensiveness of anomaly judgment. Parameter change trend analysis enables anomaly prediction, providing the possibility for early intervention and control, and reducing the risk of anomaly escalation. Based on a comprehensive judgment of correlation and trend analysis, precise localization of anomaly nodes is achieved, avoiding blind investigation and improving problem-solving efficiency. Simultaneously, targeted node adjustment strategies ensure the accuracy of control, enabling rapid restoration of normal gas supply to anomaly nodes, improving the stability and reliability of the gas supply system, and reducing the risk of power system failure due to gas supply anomalies.

[0152] In one embodiment of the present invention, the step of adjusting the gas supply status of nodes with abnormal gas supply status to obtain gas supply status control data includes:

[0153] When analyzing the gas supply status regulation and control of abnormal nodes, multiple control schemes are generated by combining the historical operating data of the abnormal nodes with the current operating conditions.

[0154] By comparing the response speed, energy consumption, and safety of the control schemes, the optimal control scheme is selected as the gas supply status control data, and the abnormal node information and control scheme are pushed to the ship monitoring center.

[0155] The working principle and technical effect of the above technical solution are as follows: The working principle of this method is based on a comprehensive consideration of historical data and current working conditions. Through comparison and screening of multiple solutions, the optimal decision for the control of abnormal nodes is achieved. Once the abnormal gas supply node is identified, its historical operational data (including historical anomaly records, control schemes, and their effects) is retrieved. Combined with the current ship's navigation conditions (such as speed, load, and sea state), the specific causes of the anomaly are analyzed (e.g., valve jamming, pipeline blockage, insufficient pressure). Based on the causes and operating conditions, multiple targeted control schemes are generated (e.g., adjusting valve opening, increasing pump power, clearing pipelines). An evaluation index system for the control schemes is established, quantifying each scheme from three core dimensions: response speed (time to return to normal after control), energy consumption (energy consumption during control), and safety (impact of control on other systems). By comparing the evaluation results of each scheme, the scheme with the fastest response speed, lowest energy consumption, and highest safety is selected as the optimal control scheme and used as the gas supply status control data. Simultaneously, information such as the location of the abnormal node, anomaly type, and control scheme is pushed to the ship's monitoring center, facilitating real-time monitoring and manual intervention (if necessary).

[0156] This method addresses the technical problems of traditional control schemes, such as their singularity, lack of specificity, and poor control effectiveness. By combining historical data with current operating conditions, multiple control schemes are generated, ensuring their relevance and comprehensiveness, adaptable to various anomaly causes and operating conditions. Multi-dimensional scheme evaluation and selection guarantee the scientific validity of the optimal control scheme, achieving optimal control effects and improving the efficiency of restoring normal operation from abnormal nodes. Control information is pushed to the monitoring center, realizing transparency in the control process, facilitating real-time monitoring and intervention by staff, and reducing control risks. Simultaneously, the adoption of the optimal control scheme reduces energy consumption during the control process, improves the energy efficiency of the gas supply system, reduces the impact of control on other systems, and enhances the stability and safety of the entire ship's power system. This solution achieves precise and efficient control of gas supply anomalies, further improving the closed-loop management capabilities of the monitoring system.

[0157] According to one embodiment of the present invention, the system includes:

[0158] The array setup module is used to set up sensor arrays at key nodes of the methanol ship distributed gas supply system, calibrate the sensor arrays and connect them to the network, and obtain the sensor array network.

[0159] The data acquisition and processing module is used to acquire the status parameters of the ship's distributed gas supply system through a sensor array network according to a preset acquisition cycle, obtain the status parameter acquisition data, perform preliminary processing on the status parameter acquisition data, and obtain preliminary status data.

[0160] The analysis and transmission module is used to perform preliminary state analysis on the preliminary state processing data, obtain preliminary state analysis data, perform preliminary data transmission allocation based on the preliminary state analysis data, obtain preliminary data transmission allocation data, and perform preliminary state transmission based on the preliminary data transmission allocation data to obtain preliminary state transmission data.

[0161] The status control module is used to perform distributed gas supply analysis on the initial status transmission data, obtain distributed gas supply analysis data, identify abnormal gas supply status nodes based on the distributed gas supply analysis data, perform gas supply status adjustment and control analysis on the abnormal gas supply status nodes, and obtain gas supply status control data.

[0162] The working principle and technical effects of the above-mentioned technical solution are as follows: This system establishes a sensing network covering the core area by deploying sensor arrays at key nodes of the gas supply system and completing calibration and networking, providing a hardware foundation for subsequent data acquisition; gas supply status parameters are collected through the sensing network at preset intervals, and after preliminary processing to remove noise and unify the format, the validity and standardization of the data are ensured; preliminary analysis is performed on the pre-processed data, and transmission priorities are assigned based on the analysis results to ensure the timeliness of key data transmission; the distributed operation status of the gas supply system is analyzed after transmission, abnormal nodes are accurately located and control analysis is carried out to generate targeted control data, thereby realizing dynamic control of the gas supply system. The entire process organically combines sensor perception, data transmission, intelligent analysis, and control decision-making through the orderly flow and layer-by-layer processing of data, forming a complete monitoring link.

[0163] This system effectively solves the technical problems of traditional methanol ship gas supply monitoring methods, such as incomplete monitoring coverage, delayed data processing, and untimely anomaly response. Through the deployment of a distributed sensor array, it achieves comprehensive perception of key areas of the gas supply system, avoiding the limitations of single-point monitoring. Pre-set periodic data collection and preliminary data processing improve data reliability and standardization, providing a high-quality data foundation for subsequent analysis. Prioritized data transmission ensures the priority transmission of abnormal data, shortening the time lag in anomaly response. Distributed gas supply analysis and precise control enable accurate location and targeted handling of gas supply anomalies, improving the stability and safety of the gas supply system. Simultaneously, the automated operation of the entire monitoring process reduces the cost of manual intervention and improves monitoring efficiency.

[0164] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for real-time data acquisition and monitoring of distributed gas supply for methanol ships, characterized in that, The method includes: S1. Set up sensor arrays at key nodes of the methanol ship distributed gas supply system, calibrate the sensor arrays and connect them to the network to obtain the sensor array network; S2. Collect the state parameters of the ship's distributed gas supply system through the sensor array network according to the preset collection period, obtain the state parameter collection data, perform preliminary processing on the state parameter collection data, and obtain preliminary state processing data. S3. Perform preliminary state analysis on the preliminary state processing data to obtain preliminary state analysis data. Perform preliminary data transmission allocation based on the preliminary state analysis data to obtain preliminary data transmission allocation data. Perform preliminary state transmission based on the preliminary data transmission allocation data to obtain preliminary state transmission data. S4. Perform distributed gas supply analysis on the initial status transmission data to obtain distributed gas supply analysis data. Based on the distributed gas supply analysis data, identify abnormal gas supply status nodes. Perform gas supply status regulation and control analysis on the abnormal gas supply status nodes to obtain gas supply status regulation data.

2. The method for monitoring distributed gas supply to methanol ships with real-time data acquisition according to claim 1, characterized in that, S1 includes: Obtain key node information of the methanol ship distributed gas supply system; Node weight analysis is performed based on key node information to obtain node weight analysis data. Sensor array information is determined based on node weight analysis data and key node distance data. The sensor array information is calibrated and analyzed for communication to construct a sensor array network.

3. The method for monitoring distributed gas supply to methanol ships with real-time data acquisition according to claim 2, characterized in that, The calibration and communication analysis of sensor array information, and the construction of a sensor array network, include: Perform independent sensor calibration and sensor linkage calibration on the sensor array information to obtain array calibration information; Based on the array calibration information, sensor network connections are established to obtain sensor network connection data. Sensor communication status analysis is performed based on sensor network connection data to obtain sensor communication status analysis data. The sensor array network is obtained by address encoding and allocation of sensor array information based on sensor communication status analysis data.

4. The method for real-time data acquisition and distributed gas supply monitoring of methanol ships according to claim 1, characterized in that, S2 includes: Obtain preset collection cycle information, and determine the data collection time node based on the preset collection cycle information; Based on the sensor array network, data is collected from the key nodes of the ship's distributed gas supply system at the data acquisition time nodes to obtain the state parameter data of the time nodes and key nodes. The state parameter acquisition data is divided into state types to obtain state type acquisition data; The data collected for different status types undergoes preliminary processing to obtain preliminary status data.

5. The method for monitoring distributed gas supply to methanol ships with real-time data acquisition according to claim 4, characterized in that, The preliminary processing of the state type collection data to obtain preliminary state processing data includes: The state type acquisition data is subjected to time-series alignment processing to obtain time-series acquisition data; The key nodes of the time series data are divided to obtain the time series key node type data. Generate node type data collection logs based on time-series key node type data; The node type data collection log is the preliminary status processing data.

6. The method for monitoring distributed gas supply to methanol ships with real-time data acquisition according to claim 1, characterized in that, S3 includes: The preliminary state processing data is compared with the preset state parameter threshold range to obtain the state data comparison result. Based on the comparison results of the status data, anomaly determination of status data is performed to obtain status anomaly determination data; The ratio of the preliminary state processing data corresponding to the state anomaly determination data to the preset state parameter threshold is obtained to obtain the state anomaly coefficient; Generate state anomaly weight data based on the state anomaly coefficient; Based on the state anomaly weight data, the transmission allocation priority is determined, and the initial data transmission allocation is performed according to the transmission allocation priority to obtain the initial state transmission data.

7. The method for real-time data acquisition and distributed gas supply monitoring of methanol ships according to claim 6, characterized in that, The step of determining the transmission allocation priority based on the state anomaly weight data, performing preliminary data transmission allocation based on the transmission allocation priority, and obtaining preliminary state transmission data includes: Sort the state anomaly weight data from largest to smallest to obtain the state anomaly weight sequence; According to the state anomaly weight sequence, the corresponding state preliminary processing data is transmitted sequentially and exclusively to obtain the anomaly state preliminary transmission data. Set the preliminary processing data corresponding to the normal status determination data to ordinary priority, transmit it through the shared transmission channel, and obtain the preliminary transmission data of the normal status. The initial state transmission process employs a data encryption transmission mechanism, adding CRC and 32 checksums to the data. The receiving end verifies the data integrity using the checksums. If the verification fails, a data retransmission mechanism is triggered to obtain the initial state transmission data.

8. The method for real-time data acquisition and distributed gas supply monitoring of methanol ships according to claim 1, characterized in that, S4 includes: Perform gas supply status correlation analysis on the initial status transmission data to obtain gas supply status correlation nodes; Perform multi-node data correlation analysis on the gas supply status-related nodes to obtain multi-node gas supply correlation analysis data; Analyze the changing trends of state parameters in the multi-node gas supply correlation analysis data to obtain gas supply change trend analysis data. Identify abnormal gas supply nodes based on gas supply change trend analysis data; Adjust the gas supply status of nodes with abnormal gas supply status to obtain gas supply status control data.

9. The method for monitoring distributed gas supply to methanol ships with real-time data acquisition according to claim 8, characterized in that, The step of adjusting the gas supply status of nodes with abnormal gas supply status to obtain gas supply status control data includes: When analyzing the gas supply status regulation and control of abnormal nodes, multiple control schemes are generated by combining the historical operating data of the abnormal nodes with the current operating conditions. By comparing the response speed, energy consumption, and safety of the control schemes, the optimal control scheme is selected as the gas supply status control data, and the abnormal node information and control scheme are pushed to the ship monitoring center.

10. A method and system for real-time data acquisition and monitoring of distributed gas supply for methanol ships, characterized in that, The system includes: The array setup module is used to set up sensor arrays at key nodes of the methanol ship distributed gas supply system, calibrate the sensor arrays and connect them to the network, and obtain the sensor array network. The data acquisition and processing module is used to acquire the status parameters of the ship's distributed gas supply system through a sensor array network according to a preset acquisition cycle, obtain the status parameter acquisition data, perform preliminary processing on the status parameter acquisition data, and obtain preliminary status data. The analysis and transmission module is used to perform preliminary state analysis on the preliminary state processing data, obtain preliminary state analysis data, perform preliminary data transmission allocation based on the preliminary state analysis data, obtain preliminary data transmission allocation data, and perform preliminary state transmission based on the preliminary data transmission allocation data to obtain preliminary state transmission data. The status control module is used to perform distributed gas supply analysis on the initial status transmission data, obtain distributed gas supply analysis data, identify abnormal gas supply status nodes based on the distributed gas supply analysis data, perform gas supply status adjustment and control analysis on the abnormal gas supply status nodes, and obtain gas supply status control data.

Images