Seawater cooling comprehensive analysis system based on multi-sensor monitoring

Abstract

The invention relates to the technical field of seawater cooling analysis, in particular to a seawater cooling comprehensive analysis system based on multi-sensor monitoring, which comprises a multi-source sensing array module for synchronously outputting a pipe wall temperature gradient, an axial vibration spectrum, an electrochemical noise signal and a real-time water quality parameter; the dirt-corrosion coupling module is used for calculating a dynamic dirt thermal resistance value based on the energy attenuation characteristics of the pipe wall temperature gradient and the axial vibration spectrum, analyzing the current peak density of the electrochemical noise signal, and generating a corrosion heat transfer influence factor in combination with the dynamic dirt thermal resistance value; the self-adaptive calibration engine module is used for compensating and calibrating the electrochemical noise signal according to the real-time water quality parameter and generating a multi-domain early warning threshold value; and the intelligent decision module is used for comparing the corrosion heat transfer influence factor with a multi-domain early warning threshold value and outputting an energy efficiency optimization instruction set. Quantitative indexes of corrosion heat transfer influence factors are formed, and the perception sensitivity and engineering interpretability of the corrosion-heat exchange degradation process are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seawater cooling analysis, and particularly relates to a seawater cooling comprehensive analysis system based on multi-sensor monitoring. BACKGROUND

[0002] In a seawater cooling system, the heat exchange pipe is easily affected by the coupling effect of fouling deposition and corrosion during long-term operation, resulting in a decrease in heat transfer efficiency, a shortening of equipment life, and an increase in operation risk. Existing monitoring methods usually rely on single parameter acquisition (such as temperature or flow), which is difficult to accurately capture fault signs under the interaction of multiple physical fields, especially in the working condition where corrosion and heat transfer degradation evolve synchronously, lacking systematic identification and dynamic control mechanism. In addition, the traditional system mainly relies on periodic cleaning and static threshold control, and cannot realize responsive intervention on the actual health status of different pipe sections, resulting in low resource allocation efficiency and high maintenance cost.

[0003] Especially in a complex seawater environment, multiple factors such as temperature gradient, vibration characteristics, water quality fluctuation, and electrochemical interference jointly act on the corrosion behavior and affect the change of thermal resistance. At present, there is still a lack of an intelligent analysis system that can integrate multi-source sensing information, dynamically analyze corrosion risk, and output cleaning, protection, and compensation strategies in parallel. How to build a multi-sensor array collaborative monitoring mechanism, realize quantitative modeling and early warning response of corrosion and heat transfer influencing factors, and intelligently generate an optimized instruction set according to the operating state has become a key technical difficulty to improve the operation reliability and energy efficiency of the seawater cooling system. SUMMARY

[0004] Based on the above purpose, the present application provides a seawater cooling comprehensive analysis system based on multi-sensor monitoring.

[0005] A seawater cooling comprehensive analysis system based on multi-sensor monitoring includes the following modules: Multi-source sensor array module: along the axial direction of the heat exchange pipe, an infrared temperature matrix, a vibration accelerometer, a microelectrode array, and a water quality multi-parameter probe are arranged to synchronously output the pipe wall temperature gradient, axial vibration spectrum, electrochemical noise signal, and real-time water quality parameter; Fouling-corrosion coupling module: based on the energy attenuation characteristics of the pipe wall temperature gradient and the axial vibration spectrum, the dynamic fouling thermal resistance value is calculated, the current peak density of the electrochemical noise signal is analyzed, and the corrosion and heat transfer influencing factor is generated in combination with the dynamic fouling thermal resistance value; Adaptive calibration engine module: the electrochemical noise signal is compensated and calibrated according to the real-time water quality parameter to generate a multi-domain early warning threshold; Intelligent decision module: the corrosion and heat transfer influencing factor is compared with the multi-domain early warning threshold, and an energy efficiency optimization instruction set is output, including cleaning priority, corrosion prevention level, and heat transfer compensation amount.

[0006] Further, the multi-source sensor array module comprises: Sensor array arrangement: A multi-type sensor array is arranged equidistantly along the axial direction of the heat exchange tube, including an infrared temperature matrix array, a vibration accelerometer array, a microelectrode array, and a water quality multi-parameter probe; Multi-source data processing and synchronous output: Based on the arranged multi-type sensors, temperature distribution, vibration response, electrochemical noise, and water quality state along the axial direction of the heat exchange tube are obtained, respectively, the temperature gradient of the tube wall is calculated in sequence to evaluate the change of the fouling thermal resistance, the vibration spectral density is extracted to analyze the structural attenuation characteristics, the current peak density is identified to represent the corrosion activity, and the state vector is constructed by integrating various water quality parameters.

[0007] Further, the multi-source data processing and synchronous output comprises: Tube wall temperature gradient calculation: According to the temperature data collected by the infrared temperature matrix array, the temperature change rate at each axial position is calculated; Axial vibration spectral density extraction: Fourier transform is performed on the vibration acceleration signal to extract its frequency energy distribution; Current peak density calculation: The maximum current value of the electrochemical noise signal within a specified time window is extracted and standardized as the peak density per unit time; Water quality state vector construction: The real-time water quality information of pH, salinity, conductivity, and turbidity collected by the multi-parameter probe at each monitoring position is integrated to construct a multi-dimensional state vector.

[0008] Further, the fouling-corrosion coupling module comprises: Dynamic fouling thermal resistance and current peak density calculation: Based on the temperature gradient of the heat exchange tube wall and the axial vibration spectral density, the energy attenuation characteristics are extracted, the dynamic fouling thermal resistance values at different positions are estimated, the electrochemical noise signal is analyzed, the maximum amplitude of the current signal within a specified time window is extracted, and the current peak density is calculated; Corrosion heat transfer influence factor generation: The obtained dynamic fouling thermal resistance values and current peak density are coupled to model and output the corrosion heat transfer influence factor.

[0009] Further, the dynamic fouling thermal resistance and current peak density calculation comprises: Dynamic fouling thermal resistance value calculation: Based on the obtained axial temperature gradient and vibration spectral density, the energy attenuation factor is extracted, the thermal resistance change process is estimated, and the dynamic fouling thermal resistance value is calculated; Current peak density analysis: The maximum instantaneous current amplitude of each axial position within a specified time window is extracted from the electrochemical noise signal to obtain the current peak density.

[0010] Further, the corrosion heat transfer influence factor generation comprises: Parameter mapping and modeling preparation: Obtain the dynamic fouling thermal resistance value and the current peak density as input parameters, complete data standardization and unit unification; Corrosion heat transfer impact factor calculation: Based on the input parameters, a coupling relationship model for evaluating the influence degree of corrosion on heat transfer performance is established, and a corrosion heat transfer impact factor is generated.

[0011] Further, the adaptive calibration engine module comprises: Current signal calibration compensation: According to the real-time water quality state vector of each position, the electrochemical noise current peak density of the position is adaptively compensated, and the calibrated density value is output; Multi-domain early warning threshold generation: Combined with the calibrated current peak density, dynamic fouling thermal resistance value and historical operation sample distribution of each monitoring position, a statistical threshold model is used to generate a multi-domain joint early warning threshold.

[0012] Further, the intelligent decision module comprises: Corrosion risk level determination: The corrosion heat transfer impact factor is compared with the corresponding early warning threshold, and the risk level is determined according to the exceeding degree as high level, medium level or low level; Energy efficiency optimization instruction set generation: According to the risk level, the system outputs the cleaning priority, the corrosion prevention level and the heat transfer compensation amount.

[0013] Further, the corrosion risk level determination comprises: Comparison and determination input preparation: The corrosion heat transfer impact factor of each axial position and the corresponding multi-domain joint early warning threshold are called to establish a dynamic risk response framework corresponding to the position; Corrosion risk level division: According to the comparison relationship of the input parameters, the corrosion risk level is calculated.

[0014] Further, the energy efficiency optimization instruction set generation comprises: Cleaning priority generation: Based on the obtained corrosion risk level, the cleaning priority of the corresponding position is output; Corrosion prevention level evaluation: Based on the current peak density compensated by the real-time water quality parameter, the corresponding corrosion prevention level is calculated; Heat transfer compensation amount calculation: In order to cope with the decline of heat transfer capacity caused by corrosion, the required heat transfer compensation amount is calculated by combining the current unit area heat flux density, the ratio of the corrosion heat transfer impact factor and the multi-domain joint early warning threshold.

[0015] The beneficial effects of the present application are: The application provides a seawater cooling comprehensive analysis system based on multi-sensing monitoring, breaks through the limitation of traditional single-point and static detection means, innovatively constructs a multi-source sensing array module, realizes synchronous collection and fusion expression of temperature gradient, vibration spectrum, electrochemical noise and water quality parameters, correlates and models dynamic dirt heat resistance and current peak density through a dirt-corrosion coupling model for the first time, forms a quantitative index of a corrosion heat transfer influencing factor, improves the sensing sensitivity and engineering interpretability of a corrosion-heat exchange degradation process, further compensates electrochemical noise dynamically by using water quality offset through a self-adaptive calibration engine module, makes the early warning threshold have environmental adaptability, and effectively reduces the false alarm rate and regulation lag phenomenon.

[0016] According to the comparison result of the corrosion heat transfer influencing factor and the multi-domain threshold value, the application constructs a multi-dimensional energy efficiency optimization instruction set, covers three key regulation quantities of cleaning priority, corrosion prevention grade and heat transfer compensation amount, realizes full-process closed-loop control from monitoring, diagnosis to strategy output, compared with a traditional periodical maintenance mode, the application can intervene accurately according to a risk grade, improves resource utilization efficiency, reduces system energy consumption and failure rate, has high engineering practical value and popularization prospect, provides an integrated, adaptive and intelligent health management scheme for large seawater cooling equipment, and has significant application advantages in high corrosion and high heat exchange load scenes. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only illustrate the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0018] Fig. 1 It is a system module diagram of the embodiment of the application. Fig. 2 It is an instruction optimization diagram of the embodiment of the application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the application more clear, the following will further illustrate the application in detail by combining specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] like Figs. 1-2 As shown, a comprehensive seawater cooling analysis system based on multi-sensor monitoring includes the following modules: Multi-source sensor array module: An infrared temperature matrix, vibration accelerometer, microelectrode array, and multi-parameter water quality probe are arranged along the heat exchange tube axis to synchronously output the tube wall temperature gradient, axial vibration spectrum, electrochemical noise signal, and real-time water quality parameters; The multi-source sensor array module includes: Sensor array layout: Multiple sensor arrays, including infrared temperature matrix arrays, vibration accelerometer arrays, microelectrode arrays, and multi-parameter water quality probes, are equidistantly arranged along the axial direction of the heat exchange tubes, as follows: (1) Infrared temperature matrix array ,in, Indicates the first The circumferential points are located in the axial position. The temperature value, ranging from 10 to 90 degrees Celsius, is crucial for the surface temperature of the heat exchange tubes during normal operation of the seawater cooling system. Temperatures below 10 degrees Celsius may cause condensation, while temperatures above 90 degrees Celsius may exacerbate corrosion or lead to failure. These are the axial spatial coordinates of the heat exchange tube, ranging from 0 to 5. When deploying sensors, the entire heat exchange section must be completely covered to obtain continuous monitoring data. It is an infrared temperature matrix array; (2) Vibration accelerometer array ,in, Indicates the location The instantaneous vibration acceleration at the point is measured in the range of 0-5. Under normal water flow or structural vibration, the acceleration should be maintained at a low level. An acceleration exceeding 5 may indicate structural loosening or water hammer effect. It is a vibration accelerometer array; (3) Microelectrode array , collect the electrochemical noise current signals at different positions; (4) Water quality multi-parameter probe , collect the pH, salinity, conductivity, turbidity parameters in the water body, index the water quality parameter type; Multi-source data processing and synchronous output: Based on the laid multi-type sensors, the temperature distribution along the axial direction of the heat exchange pipe, vibration response, electrochemical noise and water quality state are obtained respectively, the pipe wall temperature gradient is calculated in turn to evaluate the change of fouling thermal resistance, the vibration spectral density is extracted to analyze the structural attenuation characteristics, the current peak density is identified to represent the corrosion activity, and the state vector is constructed by integrating various water quality parameters, realizing the synchronous output and comprehensive representation of multi-source data; Multi-source data processing and synchronous output includes: Pipe wall temperature gradient calculation: According to the temperature data collected by the infrared temperature matrix array, the temperature change rate at each axial position is calculated, which is used to reflect the degree of thermal resistance change caused by fouling accumulation, and is represented as: ; Among them, represents the pipe wall temperature gradient at the axial position , reflects the thermal resistance change caused by fouling accumulation, the value range is 0-30, high temperature gradient usually corresponds to more serious fouling or local thermal resistance, which needs to be alarmed or energy compensation, is the axial coordinate; Axial vibration spectral density extraction: Fourier transform is performed on the vibration acceleration signal to extract its frequency energy distribution, so as to identify the local vibration attenuation characteristics of the structure, which is represented as: ; Among them, is the Fourier transform, represents the vibration power spectral density at position at frequency , the value range is , which can distinguish normal hydrodynamic disturbance from abnormal mechanical vibration, and improve the diagnostic sensitivity, is the frequency representing the frequency domain component of the vibration spectrum, the value range is 1-500, which is convenient for identifying abnormal working conditions; Current peak density calculation: The maximum current value of the electrochemical noise signal in the specified time window is extracted and standardized as the peak density per unit time, which is used to represent the corrosion activity level, which is represented as: ; Among them, represents the maximum noise current density peak value per unit time, which measures the local corrosion activity, the value range is 0.01-1, and the corrosion active area is usually accompanied by high-frequency high-amplitude current noise, which exceeds 0.5 as a warning indicator, is the position is the time is the electrochemical noise current, the value range is 0.1-10, in the development process of local corrosion or pitting corrosion, the current will suddenly rise, the microelectrode can sensitively capture the signal in the range, is the current peak analysis time window length, the value range is 1-10, small time window helps to capture transient corrosion behavior, setting to several seconds helps to balance sensitivity and calculation cost; Water quality state vector construction: integrate the real-time water quality information of pH, salinity, conductivity, turbidity collected by the multi-parameter probe at each monitoring position, construct a multi-dimensional state vector to express the current water quality characteristics, which is expressed as: ; Wherein, represents the multi-parameter water quality state vector at position , vector expression is convenient for unified input to the multi-parameter decision model to realize multi-dimensional coupling analysis and joint early warning, is the value of the th water quality parameter at position and time , i.e. pH, salinity, conductivity and turbidity, the parameter value range of different water quality is different, the pH parameter value range is 6.5-8.5, the salinity parameter value range is 30-40, the conductivity parameter value range is 40-55, and the turbidity parameter value range is 0-50, which covers the normal operation condition, and the abnormal overrun usually indicates pollution, corrosion or water quality disorder.

[0022] Fouling-corrosion coupling module: based on the energy attenuation characteristics of the temperature gradient of the pipe wall and the axial vibration spectrum, the dynamic fouling thermal resistance value is calculated, the current peak density of the electrochemical noise signal is analyzed, and the corrosion heat transfer influence factor is generated combined with the dynamic fouling thermal resistance value; The fouling-corrosion coupling module includes: Dynamic fouling thermal resistance and current peak density calculation: based on the temperature gradient and axial vibration spectrum density of the heat exchange pipe wall, the energy attenuation characteristics are extracted, the dynamic fouling thermal resistance values at different positions are estimated to reflect the influence of fouling accumulation on heat exchange performance, the electrochemical noise signal is analyzed, the maximum amplitude of the current signal is extracted within the set time window, the current peak density is calculated as a quantitative index of corrosion activity; Dynamic fouling thermal resistance and current peak density calculation includes: Dynamic fouling thermal resistance value calculation: based on the obtained axial temperature gradient and vibration spectrum density , the energy attenuation factor is extracted, the thermal resistance change process is estimated, the dynamic fouling thermal resistance value is calculated, which is expressed as: ; wherein, is the dynamic fouling thermal resistance value at axial position , used to reflect the change of thermal resistance due to fouling deposition on the heat exchange tube, is the temperature gradient of the tube wall at axial position , with a value range of 0-30, reflecting the degree of temperature distribution change along the tube direction, and high temperature gradient usually indicates that the thermal resistance rises significantly or the heat transfer is uneven, is the heat flux density per unit area at axial position , with a value range of , to ensure effective heat transfer while preventing local overheating or tube wall damage, is the vibration coupling coefficient, used to adjust the degree of influence of vibration on thermal resistance change, with a value range of 0.01-0.5, used to characterize the structural coupling or working condition difference of different tube sections, and a small value indicates that the vibration has a weak effect, and a large value indicates that the vibration has a significant effect on the change of thermal resistance, is the vibration power spectral density, with an integral frequency range of , which covers the low-frequency fluid disturbance, high-frequency tube resonance and equipment conduction vibration in most heat exchange systems, facilitating unified modeling analysis; Current peak density analysis: Extract the maximum instantaneous current amplitude of each axial position within a set time window from the electrochemical noise signal to obtain the current peak density , expressed as: ; wherein, is the current peak density at position , representing the maximum electrochemical noise current amplitude per unit time, is the time window length for peak analysis, is the electrochemical noise current value at axial position at time ; Corrosion heat transfer influence factor generation: coupling modeling of the obtained dynamic fouling thermal resistance value and current peak density, outputting the corrosion heat transfer influence factor, used to comprehensively evaluate the attenuation degree of the corrosion process on the heat transfer performance, and providing criteria for subsequent energy efficiency regulation; Corrosion heat transfer influence factor generation includes: Parameter mapping and modeling preparation: taking the obtained dynamic fouling thermal resistance value and the current peak density as input parameters, completing data standardization and unit unification, and providing basic input conditions for subsequent construction of coupling relationship model; Corrosion heat transfer impact factor calculation: based on the input parameters, a coupling relationship model is established for evaluating the degree of influence of corrosion on heat transfer performance, and a corrosion heat transfer impact factor is generated , which is used to characterize the degree of heat transfer performance degradation caused by corrosion, and is expressed as: ; wherein, is the corrosion heat transfer impact factor at the axial position , which is used to characterize the degree of attenuation of heat transfer performance by corrosion behavior, is the dynamic fouling resistance value at the axial position , which measures the heat transfer resistance caused by fouling deposition, and reflects the influence of fouling growth on heat transfer performance, is an empirical proportionality coefficient, with a value range of 0.1-5, which is used to adjust the weight of corrosion intensity in the coupling model. Different equipment has different sensitivity to corrosion. By adjusting the model response intensity through the empirical coefficient, individualized regulation and control of the equipment can be realized. Smaller values are used for low sensitivity areas, and larger values are used for high risk sections.

[0023] Adaptive calibration engine module: compensate and calibrate the electrochemical noise signal according to the real-time water quality parameters, and generate multi-domain early warning threshold; The adaptive calibration engine module comprises: Current signal calibration compensation: according to the real-time water quality state vector , the electrochemical noise current peak density at each position is adaptively compensated, and the calibrated density value is output, and the compensation model is expressed as: ; wherein, is the calibrated current peak density at the axial position , which is adjusted on the basis of the original current peak value, taking into account the amplification effect of water quality deviation, and usually does not exceed twice the original range, in order to avoid overfitting or distortion, represents the upper limit of the number of water quality parameters involved in the current peak density compensation process, i.e. the total number of water quality factors considered in the compensation model, with a value range of 2-6. More than 2 core parameters (such as pH and conductivity) can effectively correct the current peak density; when more than 6 parameters are used, the marginal gain decreases, and the model complexity increases significantly, which may introduce redundancy or overfitting risk. Within the range of 2 to 6, the sensitivity of the model to water quality changes can be ensured, and the balance between real-time performance and stability of the system can be achieved, is the original current peak density at the axial position , is the The normalized offset value of the water quality parameter, i.e. the percentage change relative to the reference value, ranges from 0 to 1 , reflecting the driving effect of water quality fluctuation on corrosion, and an offset less than is physically unreasonable, and an offset higher than may have triggered a system alarm, , is the sensitivity weight of the first class water quality factor, indicating the degree of influence of the water quality parameter on the corrosion current, and ranges from 0 to 1 , with a larger value indicating that the parameter is more sensitive to corrosion behavior, is the reference baseline value of the first class water quality parameter, which is usually the safe value at the initial stage of equipment operation or the recommended value by design, and the value range of different parameters is different. The reference baseline value of the pH parameter is fixed at 7.5, the reference baseline value of the salinity is fixed at 35, the reference baseline value of the conductivity is fixed at 50, and the reference baseline value of the turbidity is fixed at 10. As a compensation reference, it is used to evaluate whether the current water quality deviates from the normal state, facilitating unified normalization modeling; Multi-domain early warning threshold generation: combined with the calibrated current peak density , dynamic fouling thermal resistance value and historical operation sample distribution, a statistical threshold model is used to generate a multi-domain joint early warning threshold , expressed as: ; wherein, is the corrosion heat transfer early warning threshold at the axial position , used for dynamically identifying high-risk areas, is the mean value of the corrosion heat transfer influencing factor in the historical data, ranging from 0 to 1 , representing the average level of normal corrosion heat exchange coupling behavior, serving as a dynamic monitoring baseline, is the corresponding standard deviation, ranging from 0 to 1 , indicating the fluctuation amplitude of the influencing factor, with a larger value indicating more unstable corrosion influence, which is an important basis for risk level assessment, is the confidence factor, used to adjust the alarm sensitivity, ranging from 1.5 to 3, and is set according to the actual early warning sensitivity requirement, usually set to 2 (about 95% confidence interval), and 3 is the extreme abnormal alarm value.

[0024] Intelligent decision-making module: compares the corrosion heat transfer influencing factor with the multi-domain early warning threshold, and outputs an energy efficiency optimization instruction set, including cleaning priority, corrosion prevention level, and heat transfer compensation amount; The intelligent decision-making module includes: Corrosion risk level determination: The corrosion heat transfer influencing factor is compared with the corresponding warning threshold, and the risk level is determined as high, medium or low based on the degree of exceedance. Corrosion risk level determination includes: Comparison and judgment input preparation: calling each axial position Corrosion heat transfer influencing factors With the corresponding multi-domain joint early warning threshold As the input basis for determining the corrosion risk level, a dynamic risk response framework corresponding to the location is established; Corrosion risk level classification: Calculate the corrosion risk level based on the comparison of input parameters. , is represented as: ; Among them, risk level It is divided into three levels: 1 for low risk, 2 for medium risk, and 3 for high risk. This is used for subsequent energy efficiency optimization command intensity control. 1.2 is the threshold amplification factor, meaning that when the corrosion heat transfer influence factor... Exceeding the current threshold When the value exceeds 20%, the area is considered to have entered a high-risk level. A value of 1.0 will cause the system to frequently alarm when there are slight fluctuations, which is prone to "false alarms". Values ​​above 1.5 may miss the early signs of increased corrosion. 1.2 balances the sensitivity and stability of early warning, covers significant deviations outside the general data fluctuation range, and has a strong tolerance identification capability. Energy efficiency optimization instruction set generation: Based on the risk level, the system outputs cleaning priority, corrosion protection level and heat transfer compensation amount to guide cleaning scheduling, protection strategies and thermal regulation, thereby improving heat exchange efficiency and operational safety. Energy efficiency optimization instruction set generation includes: Cleaning priority generation: based on the obtained corrosion risk level Output the cleaning priority at the corresponding position. , is represented as: ; in, This indicates the cleaning priority for the corresponding location, categorized as 1 (low), 2 (medium), and 3 (high). It reflects the urgency of cleaning operations and the priority of resource allocation for each location. Higher values ​​indicate more severe corrosion and significant dirt accumulation at that location, requiring priority cleaning. Axial position The corrosion risk level is used to directly drive the response level of cleaning, maintenance, etc., corresponding to different levels of corrosion threat. Corrosion resistance rating assessment: based on peak current density after real-time water quality parameter compensation. Calculate the corresponding corrosion resistance level. , is expressed as: ; wherein, is the corrosion level of the axial position , used to guide the intensity setting of the corrosion protection measures, is an empirical proportionality coefficient, with a value range of 0.1-5, adjusting the model sensitivity, taking into account the differences in corrosion characteristics under different system environments, and the greater the value indicates that the system is more sensitive to current changes, , indicating rounding up, the corrosion level is generally set to 1-5, corresponding to different intensity protection measures such as cathodic protection, electrode repair or corrosion coating enhancement, is the current peak density after water quality compensation, with a value range of 0.01-2, the compensated current value reflects the actual state of corrosion intensity, which is an important basis for evaluating the protection level; Heat compensation amount calculation: To cope with the decline in heat exchange capacity caused by corrosion, combined with the current unit area heat flux density and the corrosion heat transfer influence factor and the multi-domain joint early warning threshold ratio, the required heat compensation amount , is expressed as: ; wherein, is the heat compensation amount of the axial position , indicating the heat flow intensity required to compensate for the decline in heat exchange performance caused by corrosion, which is used to guide the heat exchange system to adjust the flow rate, temperature difference or running time, to realize dynamic correction of energy efficiency. In seawater heat exchange systems, corrosion and fouling may cause heat loss, and the compensation amount is calculated based on the thermal resistance ratio, with a range matching the conventional operation adjustment capacity, is the multi-domain joint early warning threshold, with a value range of , used as a baseline to determine whether the corrosion impact is abnormal.

[0025] It should be understood by those skilled in the art that the discussion of any of the above embodiments is merely exemplary and is not intended to suggest that the scope of the present application is limited to these examples; under the idea of the present application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the present application as described above. In order to be brief, they are not provided in detail.

Claims

1. A comprehensive analysis system for seawater cooling based on multi-sensing monitoring, characterized in that, The method comprises the following modules: Multi-source sensor array module: along the heat exchange pipe axis, infrared temperature matrix, vibration accelerometer, micro electrode array, water quality multi-parameter probe are arranged, and pipe wall temperature gradient, axial vibration spectrum, electrochemical noise signal and real-time water quality parameter are synchronously output; Fouling-corrosion coupling module: based on the energy attenuation characteristics of the pipe wall temperature gradient and the axial vibration spectrum, the dynamic fouling thermal resistance value is calculated, the current peak density of the electrochemical noise signal is analyzed, and the corrosion heat transfer influence factor is generated by combining the dynamic fouling thermal resistance value; Adaptive calibration engine module: the electrochemical noise signal is compensated and calibrated according to the real-time water quality parameter, and a multi-domain early warning threshold is generated; Intelligent decision module: the corrosion heat transfer influence factor is compared with the multi-domain early warning threshold, and an energy efficiency optimization instruction set is output, and the instruction dimension includes cleaning priority, corrosion prevention level and heat transfer compensation amount.

2. The seawater cooling comprehensive analysis system based on multi-sensor monitoring according to claim 1, characterized in that, The multi-source sensor array module comprises: Sensor array arrangement: a plurality of types of sensor arrays are arranged equidistantly along the heat exchange pipe axis, including an infrared temperature matrix array, a vibration accelerometer array, a micro electrode array and a water quality multi-parameter probe; Multi-source data processing and synchronous output: based on the arranged multi-type sensors, temperature distribution, vibration response, electrochemical noise and water quality state along the heat exchange pipe axis are acquired respectively, pipe wall temperature gradient is calculated in turn to evaluate fouling thermal resistance change, vibration spectrum density is extracted to analyze structure attenuation characteristics, current peak value density is identified to represent corrosion activity, and state vectors are integrated from various water quality parameters.

3. The seawater cooling comprehensive analysis system based on multi-sensor monitoring according to claim 2, characterized in that, The multi-source data processing and synchronous output comprises: Pipe wall temperature gradient calculation: according to temperature data acquired by the infrared temperature matrix array, the temperature change rate of each axial position is calculated; Axial vibration spectrum density extraction: Fourier transform is performed on the vibration acceleration signal to extract the frequency energy distribution thereof; Current peak density calculation: the maximum current value of the electrochemical noise signal in a specified time window is extracted, and the peak density per unit time is standardized; Water quality state vector construction: pH, salinity, conductivity and turbidity real-time water quality information acquired by the multi-parameter probe at each monitoring position is integrated to construct a multi-dimensional state vector.

4. The seawater cooling comprehensive analysis system based on multi-sensor monitoring according to claim 3, characterized in that, The fouling-corrosion coupling module comprises: Dynamic fouling thermal resistance and current peak density calculation: based on the temperature gradient of the heat exchange pipe wall and the axial vibration spectrum density, energy attenuation characteristics are extracted, the dynamic fouling thermal resistance value at different positions is estimated, the electrochemical noise signal is analyzed, the maximum amplitude of the current signal in a set time window is extracted, and the current peak density is calculated; Corrosion heat transfer influence factor generation: the obtained dynamic fouling thermal resistance value and the current peak density are coupled to model and output the corrosion heat transfer influence factor.

5. The seawater cooling comprehensive analysis system based on multi-sensor monitoring according to claim 4, characterized in that, The dynamic fouling thermal resistance and current peak density calculation comprises: Dynamic fouling thermal resistance value calculation: based on the obtained axial temperature gradient and vibration spectrum density, energy attenuation factors are extracted, the thermal resistance change process is estimated, and the dynamic fouling thermal resistance value is calculated; Current peak density analysis: the maximum instantaneous current amplitude of each axial position in a set time window is extracted from the electrochemical noise signal to obtain the current peak density.

6. The seawater cooling comprehensive analysis system based on multi-sensing monitoring according to claim 4, characterized in that, The corrosion heat transfer influence factor generation comprises: Parameter mapping and modeling preparation: The obtained dynamic fouling thermal resistance value and current peak density are taken as input parameters to complete data standardization and unit unification; Corrosion heat transfer impact factor calculation: Based on the input parameters, a coupling relationship model for evaluating the influence degree of corrosion on heat transfer performance is established to generate the corrosion heat transfer impact factor.

7. The sea water cooling comprehensive analysis system based on multi-sensing monitoring according to claim 6, characterized in that, The adaptive calibration engine module comprises: Current signal calibration compensation: According to the real-time water quality state vector of each position, the electrochemical noise current peak density of the position is adaptively compensated, and the calibrated density value is output; Multi-domain early warning threshold generation: Combined with the calibrated current peak density, dynamic fouling thermal resistance value and historical operation sample distribution of each monitoring position, a statistical threshold model is used to generate a multi-domain joint early warning threshold.

8. The seawater cooling comprehensive analysis system based on multi-sensing monitoring according to claim 7, characterized in that, The intelligent decision module comprises: Corrosion risk level determination: The corrosion heat transfer impact factor is compared with the corresponding early warning threshold, and the risk level is determined according to the exceeding degree as high, medium or low; Energy efficiency optimization instruction set generation: According to the risk level, the system outputs the cleaning priority, corrosion prevention level and heat transfer compensation amount.

9. The sea water cooling comprehensive analysis system based on multi-sensing monitoring according to claim 8, characterized in that, The corrosion risk level determination comprises: Comparison and determination input preparation: The corrosion heat transfer impact factor of each axial position and the corresponding multi-domain joint early warning threshold are called to establish the dynamic risk response framework corresponding to the position; Corrosion risk level division: According to the comparison relationship of the input parameters, the corrosion risk level is calculated.

10. The seawater cooling comprehensive analysis system based on multi-sensing monitoring according to claim 8, characterized in that, The energy efficiency optimization instruction set generation comprises: Cleaning priority generation: Based on the obtained corrosion risk level, the cleaning priority of the corresponding position is output; Corrosion prevention level evaluation: Based on the current peak density compensated by the real-time water quality parameter, the corresponding corrosion prevention level is calculated; Heat transfer compensation amount calculation: In order to cope with the decline of heat transfer capacity caused by corrosion, the required heat transfer compensation amount is calculated by combining the current unit area heat flux density, the ratio of corrosion heat transfer impact factor and multi-domain joint early warning threshold.

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