Ship repair surface treatment control system based on big data

Abstract

The application relates to the technical field of real-time process control, in particular to a ship repair surface treatment control system based on big data, which comprises a transmission protocol configuration module, a surface state evaluation module, an operation parameter adjustment module and an energy consumption monitoring module.In the application, the communication load and response time data of multiple network nodes are collected in real time, the network state is monitored, the data transmission setting is adjusted, the dynamic response capability of the system to the network state is enhanced, the efficiency of data processing is improved, and the network delay is reduced, the operation information of the ship repair equipment and the real-time monitoring of the hull surface state index are realized, the real-time adjustment of the treatment equipment parameter configuration is realized, the efficiency and quality of the hull surface treatment are improved, the optimal operation mode of various treatment equipment is identified by combining the analysis of the energy consumption data, the energy consumption in the ship surface treatment process is reduced, the energy cost is reduced, and the economic benefit and environmental sustainability are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of real-time process control, and in particular to a ship repair surface treatment control system based on big data. BACKGROUND

[0002] The technical field of real-time process control focuses on monitoring and adjusting various parameters and conditions in continuous production processes, ensuring that the system responds to external changes and internal instructions in real time, and is applied to environments that require high precision and fast response, including chemical, pharmaceutical, energy production and ship repair industries, using integrated sensors, actuators and various computing technologies to collect and process data in real time, combining data analysis and decision support to automatically adjust operating parameters, optimize production efficiency and product quality, and achieve process automation and optimization.

[0003] Among them, the ship repair surface treatment control system is used to manage and optimize the process of surface treatment operations in the ship repair and maintenance process, aiming to realize the automation control of cleaning, polishing, painting and other operations in the ship repair process, improve efficiency, reduce labor demand and ensure treatment quality, by using real-time process control technology to monitor the working state of surface treatment equipment and adjust operating parameters in real time, improve the efficiency and safety of ship repair work, and prolong the service life and maintenance quality of the ship.

[0004] Traditional ship repair surface treatment control technology lacks sufficient flexibility and real-time performance to adapt to rapidly changing production conditions, and the operating parameters are preset and fixed throughout the production process, ignoring real-time changes in environmental conditions and material properties, resulting in poor treatment effect or resource waste, and energy management cannot fully utilize real-time data to optimize energy consumption, resulting in low energy efficiency, increasing production cost and environmental burden, lacking efficient data processing capability and self-adaptive adjustment mechanism, and performing poorly in the face of complex or non-standard operating conditions, increasing operating costs and affecting production efficiency. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art, and a ship repair surface treatment control system based on big data is proposed.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: the ship repair surface treatment control system based on big data comprises:

[0007] The transmission protocol configuration module is based on a multi-device interconnected network, and collects communication load and response time data of multiple network nodes in real time, including sensors and ship body treatment devices, monitors the network state of the interconnected network, analyzes the data transmission path and identifies the bottleneck node, adjusts the data transmission path, and generates a data transmission configuration;

[0008] Based on the data transmission configuration, the surface condition assessment module collects multiple surface condition index data of the target hull and the operating information of the processing equipment in real time, analyzes the surface condition of the target hull, assesses the processing quality, and generates processing effect monitoring information.

[0009] Based on the processing effect monitoring information, the operation parameter adjustment module analyzes the relationship between various ship repair equipment control parameters and ship surface condition indicators, calculates the correlation coefficient, and adjusts the operation parameters of various processing equipment in combination with the real-time ship condition to generate an equipment parameter configuration list.

[0010] Based on the equipment parameter configuration list, the energy consumption monitoring module monitors the energy consumption of the ship repair equipment in real time. By analyzing the energy efficiency performance under various operating modes, it adjusts the operating parameters of various equipment and generates energy-saving operating parameter configurations.

[0011] As a further aspect of the present invention, the step of obtaining the network status of the interconnected network specifically includes:

[0012] Based on a multi-device interconnection network, the communication load and response time data of multiple network nodes are collected in real time through sensors to generate a network operation dataset.

[0013] Based on the aforementioned network running dataset, using the formula:

[0014] ;

[0015] Calculate the network status scores of multiple nodes to obtain node status data;

[0016] in, For the first The status score of each node For the first The response time of each node. For the first The communication load of each node For the first The packet loss rate of each node. It is a weighting factor for response time. It is the weighting coefficient of the communication load. It is the weighting coefficient for packet loss rate. For the index number of the network node;

[0017] Based on the node status data, the network status of the interconnected network is evaluated by integrating the status data of multiple nodes.

[0018] As a further aspect of the present invention, the step of obtaining the data transmission configuration specifically includes:

[0019] Based on the network state of the interconnection network, topology analysis is performed on the network structure, data transmission paths are identified, and transmission path information is obtained.

[0020] Based on the transmission path information, the formula is used:

[0021] ;

[0022] The efficiency index of the computing node is calculated, and the network bottleneck node is identified.

[0023] Wherein, represents the data transmission capacity of the first node, represents the current data transmission demand of the first node, represents the node set directly connected to the node , represents the data transmission time of the node to the node , represents the efficiency index of the node , represents the index of the network node currently being evaluated, represents one of the other nodes directly connected to the node ;

[0024] According to the network bottleneck node, the data transmission path of the interconnection network is adjusted, and a data transmission configuration is generated.

[0025] As a further scheme of the present application, the surface state of the target ship body is obtained by:

[0026] Based on the data transmission configuration, real-time acquisition of multiple surface state index data of the target ship body and operation information of the equipment, including humidity and temperature data of the ship body surface, cleanliness and flatness indexes of the ship body surface after rust removal, thickness and uniformity indexes of the coating, and operation power index of the processing equipment, to generate a surface state data set;

[0027] Based on the surface state data set, the roughness and rust residue of the ship body surface after rust removal are detected and analyzed, the cleanliness and flatness of the ship body surface after rust removal are evaluated, and a rust removal quality analysis result is generated.

[0028] Based on the rust removal quality analysis result, the thickness and uniformity indexes of the coating are extracted, and the formula:

[0029] ;

[0030] The quality index of the coating is calculated, and the surface state of the target ship body is obtained by combining the humidity, temperature and rust removal quality information of the ship body surface.

[0031] wherein, is the total number of measurement points, is the index of the measurement point, is the coating thickness of the measurement point, is the average value of the coating thickness of all measurement points, is the uncoated area of the measurement point, is the quality index of the coating.

[0032] As a further scheme of the present application, the step of obtaining the treatment effect monitoring information is specifically:

[0033] Based on the surface state of the target ship body, a plurality of ship body surface state data are extracted, including the temperature, humidity, rust removal effect and spraying effect of the ship body surface, using the formula:

[0034] ;

[0035] The treatment quality index is calculated;

[0036] wherein, represents the measured value of the evaluation index, represents the preset threshold value of the evaluation index, represents the weight of the evaluation index, is the total number of evaluation indexes, indicates the treatment quality ratio, represents the index number of the treatment quality evaluation index;

[0037] According to the treatment quality index, the treatment quality of the ship body surface is evaluated by comparing the surface treatment state of the ship body with the preset quality standard, and the treatment effect monitoring information is generated.

[0038] As a further scheme of the present application, the step of obtaining the correlation coefficient is specifically:

[0039] Based on the treatment effect monitoring information, a plurality of ship repair equipment operation information data are extracted, including the humidity setting of the dehumidifier, the temperature setting of the heater and the operation speed of the rust removal robot, and the equipment operation parameter data is obtained;

[0040] According to the equipment operation parameter data, the data is preprocessed, including removing outliers and standardizing data, to obtain the operation parameter preprocessing record;

[0041] According to the operation parameter preprocessing record, the formula is used:

[0042] ;

[0043] Calculate the correlation between various equipment operating parameters and corresponding processing indicators to obtain the correlation coefficient;

[0044] Representing the The control parameter values ​​of the ship repair equipment at each data point. Representing the The hull surface condition index values ​​for each data point It is all The average value It is all The average value It is the index of the data point currently being processed. This represents the correlation coefficient.

[0045] As a further aspect of the present invention, the step of obtaining the device parameter configuration list specifically includes:

[0046] Based on the correlation coefficient and combined with the surface condition indicators of the hull, the current operating parameters of various ship repair equipment are analyzed, the processing equipment for control parameters that need to be adjusted is identified, and the adjustment requirement analysis results are generated.

[0047] Based on the results of the adjustment requirements analysis, using the formula:

[0048] ;

[0049] Calculate the control parameters of multiple devices and generate the adjustment parameter calculation results;

[0050] These are the adjusted equipment parameter values. The current device parameter values, This is the proportional control coefficient. The integral control coefficient, These are the differential control coefficients. For at a certain point in time The control error, To represent a point in time, For a tiny time increment, This represents the differentiation operation. For error In time The differential;

[0051] Based on the calculation results of the adjustment parameters, the control parameter adjustment values ​​of multiple devices, including dehumidifiers, heaters, rust removal robots, water pumps, and coolers, are combined to generate a device parameter configuration list.

[0052] As a further aspect of the present invention, the step of obtaining the energy-saving operation parameter configuration specifically includes:

[0053] Based on the equipment parameter configuration list, by extracting and analyzing the operating information of various ship processing equipment, the energy consumption of ship repair equipment in various operating modes is identified, and equipment energy consumption dataset is obtained.

[0054] Based on the aforementioned device energy consumption dataset, the formula is used:

[0055] ;

[0056] Calculate the highest energy efficiency ratio of the calculation equipment and identify the optimal operating mode of the target equipment;

[0057] in, In the pattern The output below In the pattern The amount of energy consumed. It is the highest energy efficiency ratio. Used to select the maximum value from a set of given values. An index for different operating modes of the target device;

[0058] Based on the optimal operating mode, and considering energy consumption and actual operational needs, the actual operating parameters of various devices are adjusted to generate energy-saving operating parameter configurations.

[0059] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0060] In this invention, by collecting real-time communication load and response time data from multiple network nodes, monitoring network status and adjusting data transmission settings, the system's dynamic response capability to network conditions is enhanced, data processing efficiency is improved, and network latency is reduced. Real-time monitoring of ship repair equipment operation information and hull surface condition indicators enables real-time adjustment of processing equipment parameter configurations, improving the efficiency and quality of hull surface treatment. Combined with the analysis of energy consumption data, the optimal operating mode of various processing equipment is identified, reducing energy consumption during ship surface treatment, reducing energy costs, and enhancing economic benefits and environmental sustainability. Attached Figure Description

[0061] Figure 1 This is a system flowchart of the present invention;

[0062] Figure 2 This is a flowchart illustrating the process of evaluating the network status of an interconnected network according to the present invention;

[0063] Figure 3 A flowchart for setting up data transmission configuration for this invention;

[0064] Figure 4 Flow chart for evaluating surface state of target ship body of the present application;

[0065] Figure 5 Flow chart for obtaining processing effect monitoring information of the present application;

[0066] Figure 6 Flow chart for calculating correlation coefficient of the present application;

[0067] Figure 7 Flow chart for obtaining equipment parameter configuration list of the present application;

[0068] Figure 8 Flow chart for obtaining energy-saving operation parameter configuration of the present application. DETAILED DESCRIPTION

[0069] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0070] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0071] Please refer to Figure 1 The ship surface treatment control system based on big data comprises:

[0072] The transmission protocol configuration module monitors the network state of the interconnected network by collecting the communication load and response time data of a plurality of network nodes in real time, including sensors and ship treatment equipment, analyzes the data transmission path and identifies the bottleneck nodes, adjusts the data transmission path, and generates a data transmission configuration based on the interconnected network.

[0073] The surface state evaluation module collects a plurality of surface state index data of the target ship body and operation information of the treatment equipment in real time based on the data transmission configuration, analyzes the surface state of the target ship body, evaluates the treatment quality, and generates processing effect monitoring information.

[0074] The operation parameter adjustment module analyzes the relationship between various ship repair equipment control parameters and ship surface condition indicators based on the processing effect monitoring information, calculates the correlation coefficient, and adjusts the operation parameters of various processing equipment in combination with the real-time ship condition to generate an equipment parameter configuration list.

[0075] The energy consumption monitoring module monitors the energy consumption of ship repair equipment in real time based on the equipment parameter configuration list. By analyzing the energy efficiency performance under various operating modes, it adjusts the operating parameters of various equipment and generates energy-saving operating parameter configurations.

[0076] The data transmission configuration specifically includes sensor data transmission configuration, device link status information, and communication path configuration. The processing effect monitoring information includes hull surface status data, coating uniformity index information, and rust removal effect analysis results. The equipment parameter configuration list specifically includes dehumidifier parameter settings, heater temperature parameters, and rust removal robot control parameters. The energy-saving operation parameter configuration includes energy consumption monitoring data, energy efficiency performance analysis results, and adjusted energy utilization rate.

[0077] Please see Figure 2 The specific steps for obtaining the network status of the Internet are as follows:

[0078] Based on a multi-device interconnection network, the communication load and response time data of multiple network nodes are collected in real time through sensors to generate a network operation dataset.

[0079] The system collects communication load data from multiple network nodes, including network switches, routers, and terminal devices. This includes the number and size of data packets sent and received. It also collects response time data for each node, which is the statistical data of the time required from sending a data packet to receiving an acknowledgment. The target data is uploaded and recorded in the central monitoring system in real time, forming a dataset that includes timestamps, node identifiers, load information, and response times. The dataset is stored in the network operation database for subsequent analysis. The process ensures the integrity and timeliness of the data, providing basic data support for network status assessment.

[0080] Based on the network-run dataset, using the formula:

[0081] ;

[0082] Calculate the network status scores of multiple nodes to obtain node status data;

[0083] in, For the first The status score of each node For the first The response time of each node. For the first The communication load of each node For the first The packet loss rate of each node. It is a weighting factor for response time. It is the weighting coefficient of the communication load. It is the weighting coefficient for packet loss rate. For the index number of the network node;

[0084] formula:

[0085] ;

[0086] Parameter meanings and acquisition methods:

[0087] : Represents the first The response time of each node is measured in real time by the network monitoring system;

[0088] : Represents the first The communication load of each node is measured using network traffic analysis tools;

[0089] : Represents the first The packet loss rate of each node is obtained in real time through network quality monitoring software;

[0090] : These are weighting factors for multiple indicators, which adjust the influence of response time, communication load, and packet loss rate on the status score, and are adjusted according to network conditions and performance requirements;

[0091] Calculation example:

[0092] Configure network nodes The monitoring value is ms, Mbps, The weighting factors are respectively , , Calculate network state score :

[0093] ;

[0094] The result 0.0153 represents the node. The comprehensive network status index reflects the performance of the node under the current network conditions. The calculation method is used to monitor the network status in real time and optimize network operation settings.

[0095] Based on node status data, the network status of the interconnected network is evaluated by integrating the status data of multiple nodes;

[0096] Considering the communication load and response time of each node, using statistical analysis and network traffic analysis, the average load and average response time of each node are calculated, and compared with historical data to identify abnormal growth and decline trends. Target analysis helps evaluate the status of the entire Internet, including identifying potential bottleneck nodes and traffic congestion points. Based on the results of target analysis, network configuration is adjusted or hardware is upgraded to optimize network performance and ensure efficient data transmission and stable network operation. The evaluation results will form a network status report, clearly indicating the current network operating conditions and recommended improvement measures.

[0097] Please refer to Figure 3 , the data transmission configuration acquisition step is specifically:

[0098] Based on the network status of the Internet, the network structure is analyzed for topology, and the transmission path information is obtained.

[0099] Using network topology discovery tools and techniques such as SNMP protocol and network mapping software, each node and connection line in the network is plotted and identified. Through the target tool, the connection relationship and communication status between each network node are collected and sorted, and a network topology diagram is drawn, showing the mutual connection relationship of all network devices such as routers, switches and other network devices and their communication efficiency. Using traffic analysis tools such as NetFlow analyzer, the data flow between nodes is monitored, and high-flow data transmission paths are identified. Analysis reveals the structural layout of the network, and key data transmission paths are identified, obtaining transmission path information to provide a basis for subsequent network optimization and bottleneck resolution.

[0100] Based on the transmission path information, the formula is:

[0101] ;

[0102] Calculate the efficiency index of the node to identify the network bottleneck node.

[0103] Where, represents the data transmission capacity of the th node, represents the current data transmission demand of the th node, represents the set of nodes directly connected to node , represents the data transmission time from node to node , represents the efficiency index of node , represents the index of the network node currently being evaluated, represents the set of nodes directly connected to node One of the other nodes directly connected to the node, used to represent the interaction with the node during the calculation,

[0104] Formula:

[0105] ;

[0106] Parameter meaning and acquisition method:

[0107] : The data transmission capacity of the first node, obtained through the technical specifications of network equipment and historical performance test data;

[0108] : The current data transmission demand of the first node, collected through real-time network monitoring tools;

[0109] : The data transmission time from node to node , obtained through real-time network delay testing;

[0110] : The set of nodes directly connected to node , representing all adjacent nodes that exchange data directly with node ;

[0111] Calculation example:

[0112] Set the data transmission capacity of node to Mbps, the current data transmission demand to Mbps, node is connected to three nodes , and the respective data transmission times are ms, ms, ms, calculate the network status score :

[0113] ;

[0114] The result 1.05 represents the efficiency score of node under the current network state. The calculation process is used to evaluate the performance of each node in the network, helping to identify and optimize network bottlenecks.

[0115] According to the network bottleneck node, adjust the data transmission path of the interconnected network, generate data transmission configuration;

[0116] Reconfigure the routing protocol settings in the network, such as OSPF or BGP, using network management tools and routing optimization software, such as Cisco's routing optimization tools, bypass congested areas, record the node positions, routing paths before and after adjustment, and expected data flow distribution during the reconfiguration process, target adjustment based on network traffic analysis results and network performance monitoring reports, through real-time monitoring of adjustment effect, ensure that each adjustment can significantly reduce the impact of bottlenecks, enhance the overall data transmission efficiency of the network, the generated data transmission configuration lists the update status and improvement measures of all important paths, ensure the continuous operation and efficient data processing of the network.

[0117] Please refer to Figure 4 The surface state acquisition step of the target ship body is specifically:

[0118] Based on the data transmission configuration, real-time acquisition of multiple surface state index data of the target ship body and operation information of the equipment, including humidity and temperature data of the ship body surface, cleanliness and flatness indicators of the ship body surface after rust removal, thickness and uniformity indicators of the coating, and operation power indicators of the processing equipment, to generate a surface state data set;

[0119] Through the network-connected sensors and monitoring equipment, real-time acquisition of humidity sensor readings and temperature sensor data of the target ship body surface, real-time operation power and operating state information of the processing equipment, use of surface detection equipment such as laser scanners to collect cleanliness and flatness data of the ship body surface after rust removal, and use of coating thickness meters to obtain coating thickness and uniformity data, the target data is subjected to preliminary formatting processing and verification, and is integrated into a surface state data set, which includes time stamp, equipment identification and various surface state indicators, providing basic information for subsequent surface quality analysis and equipment performance evaluation.

[0120] Based on the surface state data set, detect and analyze the roughness and rust residue of the ship body surface after rust removal, evaluate the cleanliness and flatness of the ship body surface after rust removal, and generate a rust removal quality analysis result;

[0121] Use data analysis software and image processing tools to detect and analyze the roughness and rust residue of the ship body surface after rust removal, use image recognition algorithms to process ship body surface images obtained from high-resolution surface scanners, automatically calculate the area proportion of different gray scale regions in the image to estimate the surface roughness, and compare the rust residue with the samples in the rust detection standard image library to quantify the rust residue, after algorithm verification and multiple repeated measurements, form a comprehensive evaluation result about the cleanliness and flatness of the ship body surface after rust removal, the result lists all measurement indicators and analysis data, providing clear ship body surface treatment quality feedback for the maintenance team.

[0122] Based on the rust removal quality analysis results, the thickness and uniformity indicators of the coating are extracted, and the coating quality index is calculated through the formula:

[0123] ;

[0124] The surface state of the target ship is obtained by combining the humidity, temperature and rust removal quality information of the ship surface.

[0125] Wherein, is the total number of measurement points, is the index of the measurement point, is the coating thickness of the th measurement point, is the average value of the coating thickness of all measurement points, is the uncovered area of the th measurement point, is the quality index of the coating.

[0126] Formula:

[0127] ;

[0128] Parameter meaning and acquisition method:

[0129] : Total number of measurement points;

[0130] : Index of measurement point;

[0131] : Coating thickness of the th measurement point, measured by sensor;

[0132] : Average value of the coating thickness of all measurement points;

[0133] : Uncovered area of the th measurement point;

[0134] : Quality index of the coating;

[0135] Calculation example:

[0136] Set , coating thickness measurement value is 0.5mm, 0.6mm, 0.55mm, 0.58mm, 0.57mm, and the uncovered area is 0.02mm², 0.01mm², 0.015mm², 0.013mm², 0.017mm², respectively. Substitute the set values into the formula for calculation:

[0137] Calculate the average thickness :

[0138] ;

[0139] calculate :

[0140] ;

[0141] ;

[0142] The calculated result of 95.03 represents the overall quality score of the coating. The result is used to evaluate the coating quality of the hull surface, quantify the surface treatment status, and ensure the transparency and repeatability of the evaluation.

[0143] Please see Figure 5 The specific steps for obtaining monitoring information on treatment effects are as follows:

[0144] Based on the surface condition of the target hull, various hull surface condition data are extracted, including hull surface temperature, humidity, rust removal effect, and coating effect, using the following formula:

[0145] ;

[0146] Calculate the processing quality index;

[0147] in, Representing the The measured values ​​of the evaluation indicators include coating uniformity, rust removal effect, surface temperature, and surface humidity. Representing the Preset thresholds for each evaluation indicator, Representing the The weight of each evaluation indicator, The total number of evaluation indicators, Indicates the processing quality ratio. The index number representing the quality assessment indicators;

[0148] formula:

[0149] ;

[0150] Parameter meanings and acquisition methods:

[0151] Processing quality ratio;

[0152] : No. The actual measured values ​​at each measurement point include coating uniformity, rust removal effect, surface temperature, and surface humidity;

[0153] : preset threshold value of the first evaluation index;

[0154] : weight of the first evaluation index;

[0155] : total number of evaluation indexes;

[0156] Calculation example:

[0157] Set the coating uniformity score 0.95, the rust removal effect score 0.9, the surface temperature score 0.85, and the surface humidity score 0.8, respectively, and the corresponding preset threshold values are 1.0, 1.0, 0.9, and 0.85, respectively, and the weights are 0.3, 0.3, 0.2, and 0.2, respectively. Substitute the set values into the calculation:

[0158] ;

[0159] ;

[0160] ;

[0161] The calculation of 0.233 indicates that the processing quality is highly consistent with the preset threshold value. The analysis result is used to provide a data basis for improving the surface treatment process and ensure that the processing quality meets the preset standard.

[0162] According to the processing quality index, the surface treatment state of the ship body is compared with the preset quality standard to evaluate the processing quality of the ship body surface and generate processing effect monitoring information;

[0163] From the surface state data set, the humidity, temperature, cleanliness, flatness, coating thickness, and uniformity of the ship body surface are extracted. The target data is processed by a surface analysis software to quantify the surface features, including edge detection and texture analysis algorithms, to analyze the microstructure and appearance features of the surface. In the processing quality evaluation stage, the measured data is compared and analyzed with the preset quality standard, which includes the minimum acceptable cleanliness, maximum allowable roughness, and coating thickness range of the ship body surface. During the process, the deviation between the actual data and the standard is calculated using a predefined evaluation model to determine the correlation and influence between each index and the quality standard. According to the analysis result, processing effect monitoring information is generated, which describes whether each aspect of the ship body surface treatment meets the preset standard and the location that needs to be reprocessed.

[0164] Please refer to Figure 6 , the correlation coefficient acquisition step is as follows:

[0165] ​​​​Based on the processing effect monitoring information, the operation information data of various ship repair equipment is extracted, including the humidity setting of the dehumidifier, the temperature setting of the heater, and the operation speed of the derusting robot, and the equipment operation parameter data is obtained;

[0166] The setting parameters of multiple ship repair processing equipment are collected, such as the humidity setting value of the dehumidifier, the temperature setting value of the heater, and the operation speed of the derusting robot, including accessing the embedded system or control panel of the equipment, exporting the data record of the current operation parameter, containing the timestamp and the corresponding parameter value, verifying the validity and real-time of the target parameter by comparing with the technical standard provided by the equipment manufacturer, ensuring that the collected data accurately reflects the actual operation state of the equipment, and the target operation parameter data will be transmitted to the central data processing center to provide original input for data analysis and equipment performance evaluation.

[0167] According to the equipment operation parameter data, the data is preprocessed, including removing outliers and standardizing data, to obtain the operation parameter preprocessing record;

[0168] Outlier removal is achieved by setting parameter threshold, data outside the normal operation range will be marked and excluded, standardization processing is completed by converting parameter data of different equipment into unified scale, so that data from different equipment can be compared in the same analysis model, using Z-score or Min-Max standardization method, after completing the target processing, the operation parameter preprocessing record is generated, which lists the original value, processed value and data adjustment information of each data, ensuring the transparency and traceability of the data processing process.

[0169] According to the operation parameter preprocessing record, the formula is:

[0170] ;

[0171] The correlation between the operation parameters of multiple equipment and the corresponding processing indicators is calculated, and the correlation coefficient is obtained;

[0172] represent the ship repair equipment control parameter value of the first data point, represent the ship body surface state index value of the first data point, is the average value of all values, is the average value of all values, is the current processing data point index, indicates the correlation coefficient; The formula is:

[0173]

[0174] ​​;

[0175] Parameter meaning and acquisition method:

[0176] : the set device workload parameter of the first data point;

[0177] : the hull surface index data corresponding to the first data point;

[0178] and : the average value of and respectively;

[0179] Calculation example:

[0180] Set the workload parameter of the dehumidifier , the corresponding hull surface humidity , the set value is substituted into the calculation:

[0181] ;

[0182] ;

[0183] ;

[0184] ;

[0185] ;

[0186] ;

[0187] Result The correlation degree between the set workload parameter and the measured surface humidity is shown, and the analysis process is used to help optimize the control parameter setting of the device to ensure the accuracy of the control of various processing indicators in the hull processing process.

[0188] Please refer to Figure 7 , the acquisition steps of the device parameter configuration list are as follows:

[0189] Based on the correlation coefficient, combined with the surface state index of the hull, the current operation parameters of various ship repair devices are analyzed, the processing devices of the control parameters that need to be adjusted are identified, and the adjustment requirement analysis result is generated;

[0190] ​​Data aggregation and correlation analysis are performed using statistical software, including calculating the correlation coefficient between the hull surface state index and the equipment operation parameters using SPSS or R software. The target correlation coefficient helps identify which equipment parameters have significant correlation with the surface treatment effect, indicating equipment parameters that have a greater impact on the effect. Through MATLAB or Python scripts, in-depth data mining is performed to determine which specific parameters need to be adjusted to optimize surface treatment quality, including generating a series of data models, comparing simulated processing results under different parameter settings, identifying optimal parameters, generating adjustment requirement analysis results, and listing multiple equipment control parameters that need to be adjusted.

[0191] According to the adjustment requirement analysis results, the control parameters of multiple devices are calculated by the formula:

[0192] ;

[0193] The adjustment parameter calculation results are generated;

[0194] is the adjusted equipment parameter value, is the current equipment parameter value, is the proportional control coefficient, is the integral control coefficient, is the differential control coefficient, is the control error at time point is the representative time point, is a small time increment, denotes the differential operation, is the error at time is the differential of the error at time

[0195] The formula is:

[0196] ;

[0197] Parameter meaning and acquisition method:

[0198] : adjusted equipment control parameter value;

[0199] : current equipment control parameter value;

[0200] : proportional control coefficient;

[0201] : integral control coefficient;

[0202] : differential control coefficient;

[0203] : error value at time ;

[0204] : integral of error ;

[0205] : derivative of error, indicating the rate of change of error ;

[0206] Calculation example:

[0207] Set the target time of the device operation parameter as , , , , , , , and substitute the set value into the calculation:

[0208] ;

[0209] The calculation result 53.2 indicates that under the current control strategy, the device operation parameter should be adjusted to 53.2 for meeting the target ship body processing indicators. The process ensures that the device operation is dynamically adjusted according to the real-time surface processing requirements to achieve the optimal processing effect.

[0210] Based on the adjustment parameter calculation result, the control parameter adjustment values of multiple devices are integrated, including dehumidifiers, heaters, derusting robots, water pumps, and coolers, to generate a device parameter configuration list.

[0211] According to the adjustment requirement analysis result, the control parameters of multiple devices are set, and the parameter adjustment is implemented using the control system. During the parameter adjustment process, the reactions of each device are monitored to confirm the actual effect of the new parameters, ensuring that the adjusted device operation meets the predetermined surface processing requirements. After each parameter adjustment, system performance testing is performed, and the operation data before and after the device adjustment, such as energy consumption, speed, and output quality, are recorded to verify the effectiveness of the adjustment. After completing all device parameter adjustments, a device parameter configuration list is generated, which details the parameter settings of all devices, providing guidance and records for the daily operation and maintenance of the devices.

[0212] Please refer to Figure 8 , the steps for obtaining energy-saving operation parameter configuration are as follows:

[0213] Based on the device parameter configuration list, the running information of multiple ship processing devices is extracted and analyzed to identify the energy consumption of the ship repair equipment under multiple operating modes, and the device energy consumption dataset is obtained.

[0214] The access device monitoring system collects energy use data of multiple devices in standard, enhanced and energy-saving modes, including power consumption and fuel usage, target data is recorded in real time through an automated data collection system and aggregated into a central data warehouse, during analysis, data analysis platforms such as Tableau or Power BI are used to visualize the collected data, quickly identify energy consumption peaks and troughs in each operating mode, calculate the energy use efficiency of each device, and determine the energy efficiency performance of each device.

[0215] Based on the device energy consumption dataset, the formula is:

[0216] ;

[0217] Calculate the highest energy efficiency ratio of the device, identify the optimal operating mode of the target device;

[0218] Wherein, is the output in mode , is the amount of energy consumed in mode , is the highest energy efficiency ratio, used to identify the optimal operating mode, is used to select the maximum value from a given set of values, is the index of the different operating modes of the target device;

[0219] Formula:

[0220] ;

[0221] Parameter meaning and acquisition method:

[0222] : Output in mode , reflecting the amount of material processed per unit time;

[0223] : Energy consumption in mode , recorded in real time by the device's energy monitoring system;

[0224] Calculation example:

[0225] Suppose the target processing device has two operating modes, in mode 1, , kWh, in mode 2, , kWh, substitute the given values into the formula for calculation:

[0226] ;

[0227] ;

[0228] ;

[0229] The calculation result shows that mode 1 can handle more materials under unit energy consumption, and is identified as the optimal mode, reflecting the energy efficiency performance of the target device in multiple operating modes, and the formula is used to ensure the maximum energy efficiency of the device operation and guide the operation and maintenance decision of the device.

[0230] According to the optimal operating mode, the actual operating parameters of the multiple devices are adjusted considering the energy consumption and actual operation requirements, and the energy-saving operating parameter configuration is generated;

[0231] By comparing and analyzing the energy consumption data and processing effect of the device under different modes, the operating mode with the highest energy efficiency ratio and meeting the processing quality standard is selected, and for the operating mode with high energy consumption but not obvious effect, the parameters are adjusted, such as reducing the operating power of the dehumidifier, adjusting the temperature setting of the heater or changing the operating speed of the derusting robot, the target adjustment is realized through the control system software, after each adjustment, the system will run the test program to verify the effect of the adjustment, to ensure that each adjustment can bring the reduction of energy consumption and the improvement of efficiency, through data analysis and actual test, the energy-saving operating parameter configuration is generated, the operating parameters, energy consumption data and expected energy-saving effect before and after adjustment of each device are recorded, for subsequent operation and maintenance reference.

[0232] The above is only the preferred embodiment of the present application, and does not limit the present application in other forms, any skilled person in the art can use the disclosed technical content to make changes or modifications as equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A ship repair surface treatment control system based on big data, characterized by, The system comprises: The transmission protocol configuration module monitors the network state of the interconnection network by collecting the communication load and response time data of multiple network nodes in real time based on the multi-device interconnection network, including sensors and ship body processing devices, analyzes the data transmission path and identifies bottleneck nodes, adjusts the data transmission path, and generates a data transmission configuration; The surface state evaluation module collects multiple surface state indicator data of the target ship body and operation information of the processing device in real time based on the data transmission configuration, analyzes the surface state of the target ship body, evaluates the processing quality, and generates processing effect monitoring information; The surface state of the target ship body is obtained by: Based on the data transmission configuration, real-time collection of multiple surface state indicator data of the target ship body and operation information of the device, including humidity and temperature data of the ship body surface, cleanliness and flatness indicators of the ship body surface after rust removal, thickness and uniformity indicators of the coating, and operation power indicators of the processing device, to generate a surface state data set; Based on the surface state data set, detect and analyze the roughness and rust residue of the ship body surface after rust removal, evaluate the cleanliness and flatness of the ship body surface after rust removal, and generate a rust removal quality analysis result; Based on the rust removal quality analysis result, extract the thickness and uniformity indicators of the coating, and calculate the quality index of the coating by the formula: ； Calculate the quality index of the coating, and combine the humidity, temperature and rust removal quality information of the ship body surface to obtain the surface state of the target ship body; wherein, is the total number of measurement points, is the index of the measurement point, is the coating thickness of the measurement point, is the average value of the coating thickness of all measurement points, is the uncoated area of the measurement point, is the quality index of the coating; The processing effect monitoring information is obtained by: Based on the surface state of the target ship body, extract multiple ship body surface state data, including temperature, humidity, rust removal effect, and spraying effect, and use the formula: ； Calculate the processing quality index; wherein, the measured value of the first evaluation index, the measured value of the first evaluation index, the preset threshold value of the first evaluation index, the preset threshold value of the first evaluation index, the weight of the first evaluation index, the weight of the first evaluation index, the total number of evaluation indexes, the processing quality ratio, the index number of the processing quality evaluation index; According to the processing quality index, compare the surface treatment state of the ship body with the preset quality standard to evaluate the processing quality of the ship body surface, and generate processing effect monitoring information; The running parameter adjustment module analyzes the relationship between the control parameters of multiple ship repair equipment and the ship body surface state indicators based on the processing effect monitoring information, calculates the correlation coefficient, combines the real-time ship body state, adjusts the operation parameters of multiple processing devices, and generates a device parameter configuration list; The correlation coefficient is obtained by: Based on the processing effect monitoring information, extract the operation information data of multiple ship repair equipment, including the humidity setting of the dehumidifier, the temperature setting of the heater, and the operation speed of the rust removal robot, and obtain the equipment operation parameter data; According to the equipment operation parameter data, pre-process the data, including removing outliers and standardizing data, to obtain operation parameter pre-processing records; According to the operation parameter pre-processing records, use the formula: ； Calculate the correlation between multiple device operation parameters and corresponding processing indicators to obtain the correlation coefficient; representing a ship repair equipment control parameter value for the first data point, representing a hull surface condition indicator value for the first data point, is the average of all values, is the average of all values, is the index of the currently processed data point, denotes the correlation coefficient; The device parameter configuration list is obtained by: Based on the correlation coefficient, combine the surface state indicators of the ship body, analyze the current operation parameters of multiple ship repair equipment, identify the processing equipment whose control parameters need to be adjusted, and generate an adjustment demand analysis result; According to the adjustment demand analysis result, calculate the control parameters of multiple devices by the formula: ； Generate adjustment parameter calculation results; These are the adjusted equipment parameter values. The current device parameter values, This is the proportional control coefficient. The integral control coefficient, The differential control coefficient, For at a certain point in time The control error, To represent a point in time, For a tiny time increment, This represents the differentiation operation. For error In time The differential; Based on the adjustment parameter calculation result, the control parameter adjustment values of multiple devices are synthesized, including dehumidifiers, heaters, rust removal robots, water pumps, coolers, to generate a device parameter configuration list; The energy consumption monitoring module monitors the energy consumption of the ship repair device in real time based on the device parameter configuration list, adjusts the operating parameters of multiple devices by analyzing the energy efficiency performance under multiple operating modes, and generates energy-saving operating parameter configurations.

2. The big data based ship repair surface treatment control system of claim 1, wherein, The network state acquisition step of the interconnection network is specifically: Based on the multi-device interconnection network, the communication load and response time data of multiple network nodes are collected in real time through sensors to generate a network operation data set; Based on the network operation data set, the network state score of multiple nodes is calculated through the formula: ； to obtain node state data; wherein, is a state score of the th node, is a response time of the th node, is a communication load of the th node, is a packet loss rate of the th node, is a weight coefficient of the response time, is a weight coefficient of the communication load, is a weight coefficient of the packet loss rate, is an index number of the network node; Based on the node state data, the network state of the interconnection network is evaluated by synthesizing the state data of multiple nodes.

3. The big data based ship repair surface treatment control system of claim 2, wherein, The data transmission configuration acquisition step is specifically: Based on the network state of the interconnection network, the network structure is topologically analyzed to identify data transmission paths and obtain transmission path information; Based on the transmission path information, the efficiency index of the node is calculated using the formula: ； to identify the network bottleneck node; in, Representing the Data transmission capability of each node Representing the The current data transmission requirements of each node Representatives and nodes The set of directly connected nodes Representative node To the node Data transmission time, Representative node Efficiency indicators The index representing the network node currently being evaluated. Representatives and nodes One of the other directly connected nodes; According to the network bottleneck node, the data transmission path of the interconnection network is adjusted to generate a data transmission configuration.

4. The big data based ship repair surface treatment control system of claim 1, wherein, The energy-saving operating parameter configuration acquisition step is specifically: Based on the device parameter configuration list, the operating information of multiple ship treatment devices is extracted and analyzed to identify the energy consumption of the ship repair device under multiple operating modes, and a device energy consumption data set is obtained; Based on the device energy consumption data set, the highest energy efficiency ratio of the device is calculated using the formula: ； to identify the optimal operating mode of the target device; wherein, is the output in mode , is the amount of energy consumed in mode , is the highest energy efficiency ratio, is used to select the maximum value from a given set of values, is the index of the different operating modes of the target device; According to the optimal operating mode, the actual operating parameters of multiple devices are adjusted considering energy consumption and actual job requirements to generate energy-saving operating parameter configurations.

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