Coastal environment engineering safety intelligent monitoring and early warning system

Abstract

The invention provides an intelligent monitoring and early warning system for the safety of coastal environment engineering, and the system brings four environmental factors which affect the safety of coastal engineering into an integrated monitoring and evaluation system. The constructed multivariate factor dynamic evaluation system has the function of converting complex multi-source data into visual risk indexes and early warning levels. And meanwhile, the constructed dynamic weight updating mechanism can consider the difference of factor influence levels in different periods, so that the scientificity of a prediction and early warning result is improved, and quantifiable decision support is provided for operation and maintenance management of coastal engineering. According to the method, data acquisition and data analysis of the most threatening core indexes of the coastal engineering safety are expected to be realized, and meanwhile, a set of dynamically self-adaptive multi-element factor evaluation system with high interpretability is constructed to evaluate the comprehensive risk, so that the risk evaluation result is credible and traceable, a visual and reliable decision basis is provided for engineering management personnel, and the risk evaluation efficiency is improved. Assistance is provided for risk-based predictive maintenance.

Description

Technical Field

[0001] This invention belongs to the field of civil engineering safety monitoring and Internet of Things technology, and specifically relates to an intelligent monitoring and early warning system for coastal environmental engineering safety. Background Technology

[0002] Coastal environments, characterized by high salinity, high humidity, strong winds, and soft soil, pose a serious threat to the durability and safety of engineering structures. Currently, monitoring and evaluation of these environmental factors are often fragmented: existing systems mostly focus on single environmental impacts, such as total salt spray; wind speed and actual structural response; and monitoring and evaluation of settlement and soil salinity are often conducted independently. This isolated, single-factor evaluation cannot reveal the comprehensive risks arising from the coupled effects of multiple environmental factors.

[0003] Furthermore, existing multi-factor safety assessment methods largely rely on simple models with fixed weights or manual threshold judgments, failing to adapt to the dynamic characteristics of coastal environments. For example, during typhoon season, the weight of wind force should be significantly increased, but most existing systems lack this adaptive adjustment capability, leading to inaccurate risk assessments and delayed early warnings. Managers struggle to quickly obtain the overall safety status of projects, often only taking action after damage has manifested, resulting in high costs and limited effectiveness, hindering early warning capabilities. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring and early warning system for coastal environmental engineering safety.

[0005] To address the above problems, this invention provides an intelligent monitoring and early warning system for coastal environmental engineering safety, comprising:

[0006] The data acquisition and analysis module is used to acquire CEI, SSI, WEI, and DSI monitoring data;

[0007] The intelligent evaluation and early warning module is used to obtain the comprehensive risk index CRI based on CEI, SSI, WEI, and DSI monitoring data.

[0008] Furthermore, in the aforementioned system, the data acquisition and analysis module includes:

[0009] The Salt Spray Erosion Index (CEI) monitoring unit uses a conventional salt spray concentration monitor and IoT technology to acquire atmospheric chloride ion concentration and relative humidity (RH) within a certain time range and engineering area.

[0010] The soil salinity index (SSI) monitoring unit uses soil electrical conductivity (EC) sensors and pH sensors to acquire soil electrical conductivity (EC) and pH values ​​within a certain time range and engineering area based on Internet of Things (IoT) technology.

[0011] The Wind Impact Index (WEI) monitoring unit utilizes wind speed and direction instruments installed in reasonable parts of the project, as well as GNSS displacement monitoring stations and acceleration sensors installed at key locations of the project structure, to acquire wind speed (v) and acceleration (a) of the project structure within a certain time range and project area based on Internet of Things (IoT) technology.

[0012] The DSI (Digital Subsidence Index) monitoring unit uses InSAR satellite scanning technology or airborne and ground-based LiDAR technology to obtain the cumulative settlement S within a preset time range and engineering area. total and settling rate S rate .

[0013] Furthermore, in the above system, the intelligent evaluation and early warning module includes:

[0014] The data cleaning and storage unit is used to filter the monitoring data of the salt spray erosion index (CEI), soil salinization index (SSI), wind impact index (WEI), and basement settlement index (DSI) monitoring units to obtain the filtered data for each monitoring unit.

[0015] The single-factor index calculation unit is used to evaluate and calculate the filtered data of each monitoring unit after the data cleaning and storage unit has been processed, according to the constructed four-category index model of CEI, SSI, WEI and DSI, and obtain the scores of each single-factor index of CEI, SSI, WEI and DSI.

[0016] The multi-factor dynamic weight calculation unit is used to obtain the weights of each indicator, CEI, SSI, WEI, and DSI, based on the filtered data of each monitoring unit output by the data cleaning and storage unit.

[0017] The risk index calculation and early warning unit is used to calculate the comprehensive risk index based on the adaptive weights of each individual factor and the scores of each individual factor index output by the individual factor index calculation unit.

[0018] Furthermore, in the above system, the single-factor index calculation unit is used to construct the CEI index weighted function calculation model CEI=α cl f(Cl - )+α rh h(RH) where the chloride ion concentration function f(Cl) - )=min(100,50([Cl - ] / Thresh Cl Thresh Cl The chloride ion concentration increases exponentially after exceeding the threshold; the relative humidity effect function is: h(RH) = 50(1 + tanh((RH-65) / 10));

[0019] Construct a weighted function calculation model for the SSI index: SSI = β ec m(EC)+β ph n(pH), where the corrected conductivity function is constructed from a logarithmic function m(EC)=100 / (1+exp((EC-5) / 2)), and the pH function is constructed from two logarithmic functions considering acidic and alkaline environments respectively, n(pH)=50[1 / (1+exp(-(2pH-11)))+1 / (1+exp(-(2pH-17)))];

[0020] Construct a weighted function calculation model for the WEI index: WEI = γ v q(v / v ref )+γ a r(a / a safe ), where v ref For the design reference wind speed, a safe To establish an acceleration safety threshold, the wind speed function is constructed from a growth function; the higher the wind speed, the higher the risk q(v / v). ref )=100(1-1 / (1+(v / v ref ) 3 Similarly, the structural acceleration response function r(a / a) safe )=100(1-1 / (1+(a / a safe ) 2 ));

[0021] Construct a weighted function calculation model for the DSI index: DSI = δ total u(S total )+δ rate v(S rate ), where the cumulative settlement function u(S) total )=100(1-1 / (1+(S total / t) 0.7 ), where t is the allowable settlement; the settlement rate function is v(S) rate )=100(1-exp(-0.8max(0,S rate -0.05) / 2).

[0022] Furthermore, in the above system, the single-factor index calculation unit is used to access the data cleaning and storage unit to obtain the filtered data of each monitoring unit, analyze the filtered data, and use the combined weighting method to update the initial weights of the weighting function calculation model of CEI, SSI, WEI and DSI indicators.

[0023] Furthermore, in the above system, the combined weighting method is a coefficient of variation method, including:

[0024] Calculate the coefficient of variation (CV) for each sub-indicator. j =σ j / μ j , where σ j μ represents the variance of index j. j This represents the mean of index j;

[0025] Calculate the weight ω of the corresponding indicator j =CV j / ∑CV;

[0026] The updated weights are combined with the initial weights to obtain the combined weights:

[0027] Combined weight = λ × initial weight + (1-λ) × updated weight, where λ is the update coefficient.

[0028] Furthermore, in the aforementioned system, the multi-factor dynamic weight calculation unit is used to calculate the filtered data from each monitoring unit output by the data cleaning and storage unit according to the formula. Normalize to the [0,1] interval to obtain the standardized data for each single-factor index;

[0029] Based on the standardized data of each single-factor index, the characteristic weight of index j at time i is calculated. Then through the formula Obtain the information entropy E of each indicator. j Where k = 1 / ln(m), to ensure 0 ≤ E j ≤1, where m is the number of time points;

[0030] Introducing an exponential decay factor w i =exp(-λ(mi)), using w i For p ij Perform weighting and recalculate the weighted information entropy E' j Where λ is the attenuation coefficient and i is the time index;

[0031] Through formula g j =1-E' j Calculate the difference coefficient g j Then for g j Normalization yields ω j This refers to the adaptive weights α, β, γ, and δ of the CEI, SSI, WEI, and DSI indicators within the current calculation period.

[0032] Furthermore, in the above system, the formula for the comprehensive risk index is CRI=αCEI+βSSI+γWEI+δDSI.

[0033] Furthermore, in the aforementioned system, the risk index calculation and early warning unit is used to determine the status as safe when the comprehensive risk index (CRI) is 0-25, and to continuously monitor the situation; to determine the impact as slight when the comprehensive risk index (CRI) is 26-50, and to predict that the situation may worsen, and to recommend arranging an inspection; to determine the threat as significant when the comprehensive risk index (CRI) is 51-75, or when the core factor (WEI) or DSI enters a high-risk state, and to predict that the situation will continue to worsen, and to recommend immediate inspection and measures; and to determine the state as dangerous when the comprehensive risk index (CRI) is 76-100, or when the core factor reaches an extremely high risk.

[0034] Compared with existing technologies, this invention proposes an intelligent monitoring and early warning system for coastal environmental engineering safety. This system integrates four major environmental factors affecting coastal engineering safety into a unified monitoring and evaluation system. The constructed multi-factor dynamic evaluation system can transform complex multi-source data into intuitive risk indices and early warning levels. Simultaneously, the constructed dynamic weight update mechanism can consider the differences in the influence levels of factors within different periods, improving the scientific nature of the prediction and early warning results. This provides quantifiable decision support for the operation and maintenance management of coastal engineering projects and offers a certain degree of protection for engineering safety in the coastal environment. The aim of this invention is to achieve data collection and analysis of the core indicators most threatening to coastal engineering safety, while constructing a highly interpretable and dynamically adaptive multi-factor evaluation system to assess comprehensive risks. This makes the risk assessment results credible and traceable, providing engineering managers with intuitive and reliable decision-making basis and supporting risk-based predictive maintenance. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of an intelligent monitoring and early warning system for coastal environmental engineering safety according to an embodiment of the present invention;

[0036] Figure 2 This is a flowchart of a multi-factor dynamic weight calculation unit according to an embodiment of the present invention. Detailed Implementation

[0037] The present invention will now be described in further detail with reference to the accompanying drawings.

[0038] In a typical configuration of this application, the terminal, the device of the service network, and the trusted party all include one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0039] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0040] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include non-transitory computer-readable media, such as modulated data signals and carrier waves.

[0041] like Figure 1 and Figure 2 As shown, the present invention provides an intelligent monitoring and early warning system for coastal environmental engineering safety, comprising: a data acquisition module and an intelligent evaluation and early warning module.

[0042] Step 1, Data Acquisition and Analysis Module, used to acquire CEI, SSI, WEI, and DSI monitoring data;

[0043] The data acquisition and analysis module consists of four monitoring units: CEI (Chloride Erosion Index), SSI (Soil Salinization Index), WEI (Wind Effect Index), and DSI (Differential Settlement Index). These four units represent four different damage modes: chemical erosion, chemical and physical erosion, physical impact, and long-term deformation. They are interconnected and mutually causal. For example, typhoons (WEI) may bring heavy rainfall that affects the groundwater level, thus exacerbating subsidence (DSI), while the salt spray (CEI) it brings can accelerate structural corrosion. The coordinated operation of these four monitoring units helps to comprehensively and accurately assess the overall safety and health status of coastal environmental engineering projects.

[0044] The Salt Spray Erosion Index (CEI) monitoring unit utilizes a conventional salt spray concentration monitor and, based on Internet of Things (IoT) technology, acquires atmospheric chloride ion concentration and relative humidity (RH) within a certain time range and engineering area.

[0045] The soil salinity index (SSI) monitoring unit utilizes soil electrical conductivity (EC) sensors and pH sensors to acquire soil electrical conductivity (EC) and pH values ​​within a certain time range and engineering area based on Internet of Things (IoT) technology.

[0046] The Wind Impact Index (WEI) monitoring unit utilizes wind speed and direction instruments installed in reasonable sections of the project, as well as GNSS displacement monitoring stations and acceleration sensors installed at key locations on the project structure, to acquire wind speed v and acceleration a of the project structure within a certain time range and project area based on Internet of Things (IoT) technology.

[0047] The foundation settlement index (DSI) monitoring unit utilizes InSAR satellite scanning technology or airborne / ground-based LiDAR technology to scan and obtain the cumulative settlement S within a certain time range and engineering area. total and settling rate S rate .

[0048] Step 2, the intelligent evaluation and early warning module, is used to obtain the comprehensive risk index CRI based on CEI, SSI, WEI, and DSI monitoring data.

[0049] The intelligent evaluation and early warning module consists of a data cleaning and storage unit, a single-factor index calculation unit, a multi-factor dynamic weight calculation unit, and a risk index calculation and early warning unit.

[0050] Step 21, data cleaning and storage unit, which is used to filter the monitoring data of the above four monitoring units to obtain filtered data for each monitoring unit.

[0051] Step 22, Single Factor Index Calculation Unit: The single factor index calculation unit evaluates and calculates the filtered data of each monitoring unit after the data cleaning and storage unit has processed the data, according to the constructed four index models of CEI, SSI, WEI, and DSI, to obtain the scores of each single factor index of CEI, SSI, WEI, and DSI.

[0052] Here, based on the filtered data of each monitoring unit, the corresponding weighting function of each monitoring unit is used to calculate the score of each single factor index.

[0053] Step 221, construct the CEI index weighting function calculation model: CEI = α cl f(Cl - )+α rh h(RH) where the chloride ion concentration function f(Cl) - )=min(100,50([Cl - ] / Thresh Cl ThreshCl The chloride ion concentration increases exponentially after exceeding the threshold. The relative humidity effect function is: h(RH) = 50(1 + tanh((RH-65) / 10)). When the humidity exceeds the critical value of 65%, the corrosion effect is significantly enhanced. The initial weight α of this weighting function was determined by combining theoretical literature on electrochemical corrosion and expert questionnaires (AHP method). cl =0.7, α rh =0.3.

[0054] Step 222: Construct the SSI index weighting function calculation model: SSI = β ec m(EC)+β ph n(pH), where the corrected conductivity function is constructed from a logarithmic function m(EC) = 100 / (1 + exp((EC-5) / 2)), and the pH function is constructed from two logarithmic functions considering acidic and alkaline environments respectively, n(pH) = 50[1 / (1 + exp(-(2pH-11))) + 1 / (1 + exp(-(2pH-17)))]. The initial weight β of this weighting function is determined by combining electrochemical corrosion theory literature and expert questionnaire survey method (AHP method). ec =0.6, β ph =0.4.

[0055] Step 223: Construct the WEI index weighting function calculation model: WEI = γ v q(v / v ref )+γ a r(a / a safe ), where v ref For the design reference wind speed, a safe To establish an acceleration safety threshold, the wind speed function is constructed from a growth function; the higher the wind speed, the higher the risk q(v / v). ref )=100(1-1 / (1+(v / v ref ) 3 Similarly, the structural acceleration response function r(a / a) safe )=100(1-1 / (1+(a / a safe ) 2 Combining structural dynamics theory with the Expert Hierarchy Process (AHP) method, the initial weight γ of this weighting function is... v =0.5, γ a =0.5.

[0056] Step 224: Construct the DSI index weighting function calculation model: DSI = δ total u(S total )+δ rate v(S rate ), where the cumulative settlement function u(S) total)=100(1-1 / (1+(S total / t) 0.7 ), where t is the allowable settlement; the settlement rate function is v(S) rate )=100(1-exp(-0.8max(0,S rate -0.05) / 2), combining the standard and expert questionnaire survey method (AHP method), the initial weight of this weighting function is δ. total =0.5, δ rate =0.5.

[0057] Preferably, the data cleaning and storage unit can be accessed periodically to obtain the filtered data of each monitoring unit, and the filtered data can be analyzed. The initial weights of the four index models, namely CEI, SSI, WEI, and DSI, can be updated using a combined weighting method.

[0058] The combined weighting method consists of two parts: original weights and the coefficient of variation method for updating. The coefficient of variation method update is specifically as follows:

[0059] Calculate the coefficient of variation (CV) for each sub-indicator. j =σ j / μ j , where σ j μ represents the variance of index j. j This represents the mean of index j;

[0060] Calculate the weight ω of the corresponding indicator j =CV j / ∑CV;

[0061] The updated weights are combined with the initial weights to obtain the combined weights, specifically:

[0062] Combined weight = λ × initial weight + (1-λ) × updated weight, where λ is the update coefficient.

[0063] Step 23, Multi-factor dynamic weight calculation unit, is used to obtain the weights of each indicator CEI, SSI, WEI, and DSI based on the filtered data of each monitoring unit output by the data cleaning and storage unit.

[0064] The multi-factor dynamic weight calculation unit comprises four parts: data standardization and weight calculation, information entropy calculation, introduction of time decay factor, and dynamic weight calculation.

[0065] The data cleaning and storage unit outputs filtered data from each monitoring unit at multiple time points (such as the past 24 hours), including multi-time point monitoring data corresponding to CEI, SSI, WEI, and DSI.

[0066] Step 231, Data Standardization: The filtered data (CEI, SSI, WEI, DSI) from each monitoring unit output by the data cleaning and storage unit, i.e., multi-time-point data, are processed according to the formula... Normalize to the [0,1] interval to eliminate dimensional differences and obtain standardized data for each single factor index;

[0067] Here, we assume there are m time points (e.g., the past 24 hours) and n evaluation indicators (CEI, SSI, WEI, DSI).

[0068] Data standardization normalizes the raw data of each indicator to the [0,1] interval, eliminating the influence of units of measurement. Specifically:

[0069]

[0070] Step 232, Calculate feature weight and information entropy: Based on the standardized data of each single-factor index, calculate the feature weight of single-factor index j at time i. Then through the formula Obtain the information entropy E of each indicator. j ;

[0071] Here, the specific characteristic weight of the single-factor index j at time point i is as follows:

[0072]

[0073] Information entropy E for each indicator j Specifically:

[0074]

[0075] Where k = 1 / ln(m), to ensure 0 ≤ E j ≤1, where m is the number of time points.

[0076] Step 233, Weighted update of information entropy: Introduce an exponential decay factor w i =exp(-λ(mi)) (reinforcing recent data weights), using w i For p ij Perform weighting and recalculate the weighted information entropy E' j ;

[0077] To better focus on the impact of recent data, an exponential decay factor is introduced here, specifically:

[0078] w i =exp(-λ(mi));

[0079] Where λ is the attenuation coefficient and i is the time index.

[0080] Using wi For p ij Perform weighting and recalculate the weighted information entropy E' j ;

[0081] Step 234, calculate the difference coefficient and normalization weight: using formula g j =1-E' j Calculate the difference coefficient g j Then for g j Normalization yields ω j This refers to the adaptive weights α, β, γ, and δ of CEI, SSI, WEI, and DSI within the current calculation period.

[0082] Here, the dynamic weight calculation first calculates the difference coefficient, specifically:

[0083] g j =1-E j ', g j The larger the value, the stronger the evaluation effect of indicator j.

[0084] The normalized final weights are as follows:

[0085]

[0086] ω j This refers to the adaptive weights α, β, γ, and δ of CEI, SSI, WEI, and DSI within the current calculation period.

[0087] Step 24, Risk Index Calculation and Early Warning Unit: Based on the adaptive weights of each single factor and the scores of each single factor index output by the single factor index calculation unit, calculate the comprehensive risk index CRI = αCEI + βSSI + γWEI + δDSI, and construct a graded early warning system: Blue (Level IV, Attention): CRI 0-25, safe status, continuous monitoring; Yellow (Level III, Warning): CRI 26-50, slight impact, predicted to worsen, inspection recommended; Orange (Level II, Warning): CRI 51-75, or core factor (WEI / DSI) enters high risk, significant threat, predicted to continue to worsen, immediate inspection and measures recommended; Red (Level I, Emergency): CRI 76-100, or core factor reaches extremely high risk, dangerous state, predicted to cause damage, emergency evacuation and intervention recommended.

[0088] Specifically, see the attached document below. Figure 1 and Figure 2 Taking conventional engineering application scenarios as an example, this invention provides a detailed description of an intelligent monitoring and early warning system for coastal environmental engineering safety.

[0089] First, for the data acquisition and analysis module, based on the actual project situation, conventional salt spray concentration monitors, soil electrical conductivity (EC) sensors, and pH sensors are deployed near the project site using reasonable sampling methods. Anemometers are installed in reasonable sections of the project, GNSS displacement monitoring stations and acceleration sensors are installed at key locations on the project structure, and InSAR corner reflectors or laser LiDAR target spheres are installed at the top of the project.

[0090] Utilizing IoT technology, the system's Salt Spray Erosion Index (CEI) monitoring unit connects to data from a salt spray concentration monitor to obtain atmospheric chloride ion concentration and relative humidity (RH) within a specific time range and engineering area. The system's Soil Salinization Index (SSI) monitoring unit connects to data from soil electrical conductivity (EC) and pH sensors to obtain soil electrical conductivity (EC) and pH values ​​within a specific time range and engineering area. The system's Wind Impact Index (WEI) monitoring unit connects to anemometers, GNSS displacement monitoring stations, and acceleration sensors to obtain wind speed (v) and structural acceleration (a) within a specific time range and engineering area. The system's Foundation Settlement Index (DSI) monitoring unit connects to InSAR or LiDAR data to obtain cumulative settlement (S) within a specific time range and engineering area. total and settling rate S rate .

[0091] For the intelligent evaluation and early warning module, refer to Figure 1 As shown, after the above data is acquired, it is promptly transmitted to the data cleaning and storage unit. At the same time, the monitoring data of the above four monitoring units are filtered. In addition, data for a preset period is stored for the single-factor index and multi-factor evaluation units to update the weights.

[0092] The data then enters the single-factor index calculation unit, which evaluates and calculates the data after it has been processed by the data cleaning and storage unit according to the constructed four-category index model to obtain the single-factor index score.

[0093] This unit includes the calculation of CEI = α cl f(Cl - )+α rh h(RH) where the chloride ion concentration function f(Cl) - )=min(100,50([Cl - ] / Thresh Cl Thresh Cl The chloride ion concentration increases exponentially after exceeding the threshold. The relative humidity effect function is: h(RH) = 50(1 + tanh((RH-65) / 10)). When the humidity exceeds the critical value of 65%, the corrosion effect is significantly enhanced. The initial weight α of this weighting function was determined by combining theoretical literature on electrochemical corrosion and expert questionnaires (AHP method). cl=0.7, α rh =0.3.

[0094] Calculate SSI = β ec m(EC)+β ph n(pH), where the corrected conductivity function is constructed from a logarithmic function m(EC)=100 / (1+exp((EC-5) / 2)), and the pH function is constructed from two logarithmic functions considering acidic and alkaline environments respectively, n(pH)=50[1 / (1+exp(-(2pH-11)))+1 / (1+exp(-(2pH-17)))]. The initial weight β of this weighting function is determined by combining electrochemical corrosion theory literature and expert questionnaire survey (AHP method). ec =0.6, β ph =0.4.

[0095] Calculate WEI = γ v q(v / v ref )+γ a r(a / a safe ), where v ref For the design reference wind speed, a safe To establish an acceleration safety threshold, the wind speed function is constructed from a growth function; the higher the wind speed, the higher the risk q(v / v). ref )=100(1-1 / (1+(v / v ref ) 3 Similarly, the structural acceleration response function r(a / a) safe )=100(1-1 / (1+(a / a safe ) 2 The initial weights γ of the weighting function were determined by combining structural dynamics theory with an expert questionnaire survey (AHP method). v =0.5, γ a =0.5.

[0096] Calculate DSI = δ total u(S total )+δ rate v(S rate ), where the cumulative settlement function u(S) total )=100(1-1 / (1+(S total / t) 0.7 ), where t is the allowable settlement; the settlement rate function is v(S) rate )=100(1-exp(-0.8max(0,S rate -0.05) / 2), combined with the standard and expert questionnaire survey (AHP method), the initial weight of this weighting function is δ. total =0.5, δ rate =0.5.

[0097] The above initial weights are based on... Figure 1 It is necessary to periodically access the storage data unit and analyze the data for updates, using a combined weighting method.

[0098] The combined weighting method consists of two parts: original weights and the coefficient of variation method for updating. The coefficient of variation method update is specifically as follows:

[0099] This includes calculating the coefficient of variation (CV) for each sub-indicator. j =σ j / μ j , where σ j μ represents the variance of index j. j This represents the mean of index j.

[0100] Calculate the weight ω of the corresponding indicator j =CV j / ∑CV;

[0101] The updated weights are combined with the initial weights to obtain the combined weights, specifically:

[0102] Combined weight = λ × initial weight + (1-λ) × updated weight, where λ is the update coefficient.

[0103] The data then enters the multi-factor dynamic weight calculation unit, referring to... Figure 2 The multi-factor dynamic weight calculation unit comprises four parts: data standardization and weight calculation, information entropy calculation, introduction of time decay factor, and dynamic weight calculation. Assume there are m time points (e.g., the past 24 hours) and n evaluation indicators (CEI, SSI, WEI, DSI).

[0104] The data calculation process in this unit is as follows:

[0105] Data standardization will normalize the raw data of each indicator to the [0,1] interval, eliminating the influence of dimensions:

[0106]

[0107] The specific calculation of feature weight is as follows:

[0108]

[0109] Calculate the information entropy E for each indicator. j :

[0110]

[0111] Where k = 1 / ln(m), to ensure 0 ≤ E j ≤1.

[0112] Calculate the exponential decay factor:

[0113] w i =exp(-λ(mi));

[0114] Where λ is the attenuation coefficient and i is the time index.

[0115] Using w i For p ij Perform weighting and recalculate the weighted information entropy E' j :

[0116] Calculate the coefficient of difference:

[0117] g j =1-E' j g j The larger the value, the stronger the evaluation effect of indicator j.

[0118] Calculate the normalized final weights:

[0119]

[0120] ω j This refers to the adaptive weights α, β, γ, and δ of CEI, SSI, WEI, and DSI within the current calculation period.

[0121] Within the multi-factor dynamic weight calculation unit, the adaptive index weights are automatically updated periodically according to a preset cycle following the steps above.

[0122] The obtained weights and the results of single-factor index calculations are entered into the risk index calculation and early warning unit. The risk index CRI = αCEI + βSSI + γWEI + δDSI is calculated, and relevant early warnings are issued according to the constructed hierarchical early warning system. Blue (Level IV, Attention): CRI 0-25, safe status, continuous monitoring; Yellow (Level III, Warning): CRI 26-50, slight impact, predicted to worsen, inspection recommended; Orange (Level II, Warning): CRI 51-75, or core factors (WEI / DSI) enter high risk, significant threat, predicted to continue to worsen, immediate inspection and measures recommended; Red (Level I, Emergency): CRI 76-100, or core factors reach extremely high risk, dangerous state, predicted to cause damage, emergency evacuation and intervention recommended. Early warning information will be pushed to management personnel through various channels such as the platform interface, SMS, and App.

[0123] This invention proposes an intelligent monitoring and early warning system for coastal environmental engineering safety. It integrates four major environmental factors affecting coastal engineering safety into a unified monitoring and evaluation system. The constructed multi-factor dynamic evaluation system can transform complex multi-source data into intuitive risk indices and early warning levels. Simultaneously, the constructed dynamic weight update mechanism can consider the differences in the influence levels of factors within different periods, improving the scientific nature of the prediction and early warning results. This provides quantifiable decision support for the operation and maintenance management of coastal engineering projects and offers a certain degree of protection for engineering safety in the coastal environment. The aim of this invention is to achieve data collection and analysis of the core indicators most threatening to coastal engineering safety, while constructing a highly interpretable and dynamically adaptive multi-factor evaluation system to assess comprehensive risks. This makes the risk assessment results credible and traceable, providing engineering managers with intuitive and reliable decision-making basis and supporting risk-based predictive maintenance.

[0124] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0125] It should be noted that the present invention can be implemented in software and / or a combination of software and hardware, for example, using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In one embodiment, the software program of the present invention can be executed by a processor to implement the steps or functions described above. Similarly, the software program of the present invention (including associated data structures) can be stored in a computer-readable recording medium, such as RAM memory, a magnetic or optical drive, a floppy disk, or similar devices. Furthermore, some steps or functions of the present invention can be implemented in hardware, for example, as circuitry that works with a processor to perform the various steps or functions.

[0126] Furthermore, a portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. The program instructions invoking the methods of the invention may be stored in a fixed or removable recording medium, and / or transmitted via a data stream in a broadcast or other signal-carrying medium, and / or stored in the working memory of a computer device operating according to the program instructions. Here, an embodiment of the invention includes an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein, when the computer program instructions are executed by the processor, the apparatus is triggered to operate the methods and / or technical solutions based on the foregoing embodiments of the invention.

[0127] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in the apparatus claims may also be implemented by a single unit or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

Claims

1. An intelligent monitoring and early warning system for coastal environmental engineering safety, characterized in that, include: The data acquisition and analysis module is used to acquire CEI, SSI, WEI, and DSI monitoring data; The intelligent evaluation and early warning module is used to obtain the comprehensive risk index CRI based on CEI, SSI, WEI, and DSI monitoring data.

2. The intelligent monitoring and early warning system for coastal environmental engineering safety as described in claim 1, characterized in that, The data acquisition and analysis module includes: The Salt Spray Erosion Index (CEI) monitoring unit uses a conventional salt spray concentration monitor and IoT technology to acquire atmospheric chloride ion concentration and relative humidity (RH) within a certain time range and engineering area. The soil salinity index (SSI) monitoring unit uses soil electrical conductivity (EC) sensors and pH sensors to acquire soil electrical conductivity (EC) and pH values ​​within a certain time range and engineering area based on Internet of Things (IoT) technology. The Wind Impact Index (WEI) monitoring unit utilizes wind speed and direction instruments installed in reasonable parts of the project, as well as GNSS displacement monitoring stations and acceleration sensors installed at key locations of the project structure, to acquire wind speed (v) and acceleration (a) of the project structure within a certain time range and project area based on Internet of Things (IoT) technology. The DSI (Digital Subsidence Index) monitoring unit uses InSAR satellite scanning technology or airborne and ground-based LiDAR technology to obtain the cumulative settlement S within a preset time range and engineering area. total and settling rate S rate .

3. The intelligent monitoring and early warning system for coastal environmental engineering safety as described in claim 2, characterized in that, The intelligent evaluation and early warning module includes: The data cleaning and storage unit is used to filter the monitoring data of the salt spray erosion index (CEI), soil salinization index (SSI), wind impact index (WEI), and basement settlement index (DSI) monitoring units to obtain the filtered data for each monitoring unit. The single-factor index calculation unit is used to evaluate and calculate the filtered data of each monitoring unit after the data cleaning and storage unit has been processed, according to the constructed four-category index model of CEI, SSI, WEI and DSI, and obtain the scores of each single-factor index of CEI, SSI, WEI and DSI. The multi-factor dynamic weight calculation unit is used to obtain the weights of each indicator, CEI, SSI, WEI, and DSI, based on the filtered data of each monitoring unit output by the data cleaning and storage unit. The risk index calculation and early warning unit is used to calculate the comprehensive risk index based on the adaptive weights of each individual factor and the scores of each individual factor index output by the individual factor index calculation unit.

4. The intelligent monitoring and early warning system for coastal environmental engineering safety as described in claim 3, characterized in that, The single-factor index calculation unit is used to construct the CEI index weighting function calculation model CEI=α cl f(Cl - )+α rh h(RH) where the chloride ion concentration function f(Cl) - )=min(100,50([Cl - ] / Thresh Cl Thresh Cl The chloride ion concentration increases exponentially after exceeding the threshold; the relative humidity effect function is: h(RH) = 50(1 + tanh((RH-65) / 10)); Construct a weighted function calculation model for the SSI index: SSI = β ec m(EC)+β ph n(pH), where the corrected conductivity function is constructed from a logarithmic function m(EC)=100 / (1+exp((EC-5) / 2)), and the pH function is constructed from two logarithmic functions considering acidic and alkaline environments respectively, n(pH)=50[1 / (1+exp(-(2pH-11)))+1 / (1+exp(-(2pH-17)))]; Construct a weighted function calculation model for the WEI index: WEI = γ v q(v / v ref )+γ a r(a / a safe ), where v ref For the design reference wind speed, a safe To establish an acceleration safety threshold, the wind speed function is constructed from a growth function; the higher the wind speed, the higher the risk q(v / v). ref )=100(1-1 / (1+(v / v ref ) 3 Similarly, the structural acceleration response function r(a / a) safe )=100(1-1 / (1+(a / a safe ) 2 )); Construct a weighted function calculation model for the DSI index: DSI = δ total u(S total )+δ rate v(S rate ), where the cumulative settlement function u(S) total )=100(1-1 / (1+(S total / t) 0.7 ), where t is the allowable settlement; the settlement rate function is v(S) rate )=100(1-exp(-0.8max(0,S rate -0.05) / 2).

5. The intelligent monitoring and early warning system for coastal environmental engineering safety as described in claim 4, characterized in that, The single-factor index calculation unit is used to access the data cleaning and storage unit to obtain the filtered data of each monitoring unit, analyze the filtered data, and use the combined weighting method to update the initial weights of the weighting function calculation model of CEI, SSI, WEI and DSI indicators.

6. The intelligent monitoring and early warning system for coastal environmental engineering safety as described in claim 5, characterized in that, The combined weighting method, which is the coefficient of variation method, includes: Calculate the coefficient of variation (CV) for each sub-indicator. j =σ j / μ j , where σ j μ represents the variance of index j. j This represents the mean of index j; Calculate the weight ω of the corresponding indicator j =CV j / ∑CV; The updated weights are combined with the initial weights to obtain the combined weights: Combined weight = λ × initial weight + (1-λ) × updated weight, where λ is the update coefficient.

7. The intelligent monitoring and early warning system for coastal environmental engineering safety as described in claim 3, characterized in that, The multi-factor dynamic weight calculation unit is used to calculate the filtered data from each monitoring unit output by the data cleaning and storage unit according to the formula. Normalize to the [0,1] interval to obtain the standardized data for each single-factor index; Based on the standardized data of each single-factor index, the characteristic weight of index j at time i is calculated. Then through the formula Obtain the information entropy E of each indicator. j Where k = 1 / ln(m), to ensure 0 ≤ E j ≤1, where m is the number of time points; Introducing an exponential decay factor w i =exp(-λ(mi)), using w i For p ij Perform weighting and recalculate the weighted information entropy E' j Where λ is the attenuation coefficient and i is the time index; Through formula g j =1-E j 'Calculate the coefficient of difference g' j Then for g j Normalization yields ω j This refers to the adaptive weights α, β, γ, and δ of the CEI, SSI, WEI, and DSI indicators within the current calculation period.

8. The intelligent monitoring and early warning system for coastal environmental engineering safety as described in claim 7, characterized in that, The formula for the comprehensive risk index is CRI = αCEI + βSSI + γWEI + δDSI.

9. The intelligent monitoring and early warning system for coastal environmental engineering safety as described in claim 1, characterized in that, The risk index calculation and early warning unit is used as follows: when the comprehensive risk index (CRI) is 0-25, the status is judged as safe and continuous monitoring is carried out; when the comprehensive risk index (CRI) is 26-50, it is judged as a minor impact, and the situation is predicted to worsen, so inspection is recommended; when the comprehensive risk index (CRI) is 51-75, or the core factor (WEI) or DSI enters a high-risk state, it is judged as a significant threat, and the situation is predicted to worsen, so immediate inspection and measures are recommended; when the comprehensive risk index (CRI) is 76-100, or the core factor reaches an extremely high risk, it is judged as a dangerous state.

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