CN116629618B - Bridge disease diagnosis system based on Internet - Google Patents

Bridge disease diagnosis system based on Internet Download PDF

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CN116629618B
CN116629618B CN202310886071.5A CN202310886071A CN116629618B CN 116629618 B CN116629618 B CN 116629618B CN 202310886071 A CN202310886071 A CN 202310886071A CN 116629618 B CN116629618 B CN 116629618B
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bridge
disease
target bridge
diagnosis
target
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CN116629618A (en
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栾心国
苏磊
矫恒信
吴建新
刘全青
刘国飞
孟令强
邱锡荣
杨浩亮
李小花
耿靖玮
赵方华
刘洪图
韩静
聂军委
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Tiezheng Testing Technology Co ltd
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Tiezheng Testing Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00—Economic sectors
    • G16Y10/80—Homes; Buildings
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G16Y20/10—Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/20—Analytics; Diagnosis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/40—Maintenance of things
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/50—Safety; Security of things, users, data or systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention relates to the technical field of bridge diagnosis, which is used for solving the problems that in the existing bridge disease diagnosis mode, the use state of a bridge cannot be clearly judged and output, so that the bridge disease diagnosis period cannot be accurately set, the bridge disease diagnosis precision is low, and the bridge disease cannot be accurately evaluated, and particularly relates to an internet-based bridge disease diagnosis system which comprises a data acquisition unit, a cloud database, a bridge use state evaluation unit, a bridge disease diagnosis period setting unit, a bridge disease degree evaluation unit, a bridge disease diagnosis management and control unit and a display terminal. According to the invention, the use state of the bridge is defined, the disease diagnosis period of the bridge is set and analyzed, the disease degree of the bridge is analyzed based on the use state, the disease grade of the bridge is defined, the disease maintenance scheme is determined, the damage of the bridge is found and treated in time, and the service life and safety of the bridge are improved.

Description

Bridge disease diagnosis system based on Internet
Technical Field
The invention relates to the technical field of bridge diagnosis, in particular to an internet-based bridge disease diagnosis system.
Background
Bridge defects refer to damage, defects and changes of a bridge caused by various reasons in use, and the bridge defects not only affect the safety performance of the bridge, but also directly relate to the smoothness and the efficiency of driving.
Therefore, bridge disease diagnosis is an important link of bridge maintenance and repair maintenance, and aims to discover and evaluate the bridge disease condition in time, formulate a scientific and reasonable repair scheme and ensure the safety and normal use of the bridge.
However, in the existing bridge disease diagnosis method, clear judgment output cannot be performed on the use state of the bridge, so that accurate setting of the bridge diagnosis period cannot be achieved, the diagnosis precision of the bridge disease is low, accurate diagnosis of the diseases of the component members of the bridge is difficult to achieve, accurate assessment of the bridge disease cannot be achieved, further safety performance of the bridge cannot be guaranteed, and driving safety is seriously compromised.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
The invention aims to provide an internet-based bridge disease diagnosis system.
The aim of the invention can be achieved by the following technical scheme: bridge disease diagnosis system based on internet includes: the system comprises a data acquisition unit, a cloud database, a bridge use state evaluation unit, a bridge disease diagnosis period setting unit, a bridge disease degree evaluation unit, a bridge disease diagnosis management and control unit and a display terminal;
the data acquisition unit is used for acquiring basic data information, maintenance parameters and load state parameters of the target bridge, acquiring weather condition parameters and environment pollution parameters of the area where the target bridge is located, acquiring disease parameter information of various component members of the target bridge, and transmitting the various information to the cloud database for storage;
the cloud database is used for storing a base state judging table of the target bridge, storing a using load state judging table of the target bridge, storing a data judging table of the target bridge and storing a disease state judging table of the target bridge;
the bridge use state evaluation unit is used for monitoring basic data information and load state parameters of the target bridge, analyzing the basic state and the load state of the target bridge, obtaining a use state judgment grade of the target bridge, wherein the use state judgment grade comprises a secondary use feedback signal, a middle use feedback signal, a superior use feedback signal and a superior use feedback signal, and sending the secondary use feedback signal, the middle use feedback signal, the superior use feedback signal and the superior use feedback signal to the bridge disease diagnosis period setting unit;
the bridge disease diagnosis period setting unit is used for judging the grade of the use state of the received target bridge, setting and analyzing the disease diagnosis period of the target bridge, and sending the obtained disease diagnosis period of the target bridge to the bridge disease degree evaluation unit;
the bridge disease degree evaluation unit is used for analyzing the disease degree of the target bridge according to the corresponding diagnosis period set for the target bridge, and sending the obtained disease grade of each component of the target bridge to the bridge disease diagnosis management and control unit;
the bridge disease diagnosis and control unit is used for performing diagnosis and control operation on the received corresponding disease grades of all the component members of the target bridge, so that a primary disease diagnosis early warning signal, a secondary disease diagnosis early warning signal and a tertiary disease diagnosis early warning signal are obtained, and are sent to a display terminal to display and explain, and a primary maintenance scheme, a secondary maintenance scheme and a tertiary maintenance scheme are triggered at the same time.
Preferably, the specific process of monitoring the basic data information of the target bridge and analyzing the basic state thereof is as follows:
monitoring the input use time, the use environment influence factor and the history maintenance factor in the basic data information of the target bridge, calibrating the basic data information into tsl, uef and hcf respectively, and carrying out calculation and analysis on three items of data according to a set data model:obtaining a basic state coefficient bas of the target bridge, wherein δ1, δ2 and δ3 are weight factor coefficients of the duration of use, the environmental impact factor of use and the historical maintenance factor respectively, and δ1, δ2 and δ3 are natural numbers greater than 0;
and comparing and matching the basic state coefficient of the target bridge with a basic state judging table of the target bridge stored in the cloud database, so as to obtain basic state grades of the target bridge, wherein each obtained basic state coefficient of the target bridge corresponds to one basic state grade, and the basic state grades comprise a secondary basic state, a medium basic state and a high basic state.
Preferably, the specific solution process using the environmental impact factor is as follows:
monitoring an average temperature change value, an average humidity, an average rainfall and an average wind speed in weather condition parameters of an area where a target bridge is located, calibrating the average temperature change value, the average humidity, the average rainfall and the average wind speed into wd, sd, rl and fs respectively, carrying out normalization analysis on the four weather condition parameters, and according to a set model: wev1 = λ1×wd+λ2×sd+λ3×rl+λ4×fs, whereby a first influence value wev1 of the target bridge is obtained, wherein λ1, λ2, λ3 and λ4 are normalization factors of an average temperature change value, an average humidity, an average rainfall and an average wind speed, respectively, and λ1, λ2, λ3 and λ4 are natural numbers greater than 0;
monitoring the atmospheric pollution value, the soil pollution value and the water pollution value in the environmental pollution parameters of the area where the target bridge is located, respectively calibrating the atmospheric pollution value, the soil pollution value and the water pollution value into apv, spv and wpv, carrying out normalization analysis on the three data, and according to a set model: wev2 =γ1× apv +γ2×spv+γ3×wpv, whereby a second influence value wev2 of the target bridge is obtained, wherein γ1, γ2 and γ3 are normalization factors of the air pollution value, the soil pollution value and the water pollution value, respectively, and γ1, γ2 and γ3 are natural numbers greater than 0;
the first influence value and the second influence value of the target bridge are added, thereby obtaining the usage environment influence factor uef of the target bridge, namely uef = wev1+ wev2.
Preferably, the specific solution process of the historical maintenance factor is as follows:
monitoring maintenance times, maintenance effective values and maintenance investment values in maintenance parameters of the target bridge in a unit history period, calibrating the maintenance effective values and the maintenance investment values as mf, mev and miv respectively, performing calculation and analysis on the maintenance effective values and the maintenance investment values, and according to a set model: hcf =ρ1×mf+ρ2×mev+ρ3χ miv, whereby the historical maintenance factor hcf of the target bridge is obtained, where ρ1, ρ2 and ρ3 are correction factor coefficients of the maintenance number, maintenance effective value and maintenance input value, respectively, and ρ1, ρ2 and ρ3 are natural numbers greater than 0.
Preferably, the monitoring and analyzing the load state parameters of the target bridge specifically includes the following steps:
the traffic load value, the using frequency value, the design load value and the actual load value in the load state parameters of the target bridge are obtained, and are respectively calibrated to tlv, ufv, dlv and alv, and four items of data are calculated and analyzed according to a set model:thereby obtaining a load state coefficient plv of the target bridge;
comparing and matching the load state coefficient of the target bridge with a using load state judging table stored in a cloud database, thereby obtaining the load level of the target bridge, wherein each obtained load state coefficient of the target bridge corresponds to one load level, and the load levels comprise a serious load level, a general load level and a low load level;
comprehensively analyzing the obtained basic state grade and load grade of the target bridge, and specifically:
establishing a set W according to the basic state level, calibrating a secondary basic state as an element a1, calibrating a medium basic state as an element a2, calibrating a superior basic state as an element a3, wherein the element a1 epsilon the set W, the element a2 epsilon the set W and the element a3 epsilon the set W;
establishing a set V according to the load level, calibrating a severe load level as an element b1, calibrating a general load level as an element b2, calibrating a low load level as an element b3, wherein the element b1 epsilon the set V, the element b2 epsilon the set V and the element b3 epsilon the set V;
the set W and V are processed in a union mode, if W U V= { a1, b1}, the use state of the target bridge is judged as a secondary use feedback signal, if W U V= { a1, b2}, { a1, b3}, { a2, b1}, { a3, b1}, the use state of the target bridge is judged as a medium use feedback signal, if W U V= { a3, b2}, { a2, b3}, { a2, b2}, or { a2, b2}, the use state of the target bridge is judged as a medium use feedback signal, and if W U V= { a3, b3}, the use state of the target bridge is judged as a high use feedback signal.
Preferably, the specific solving process of the traffic load value is as follows:
acquiring speed values and load values of all vehicles passing through the target bridge in unit time, and calibrating the speed values and the load values as sv respectively k And lod k And performing calculation analysis on the two items of data according to a set model:the traffic load value tlv of the target bridge is thus obtained, where k represents a data set of all vehicles passing through the bridge per unit time, and k=1, 2,3 … … m, μ1 and μ2 are the normalization factors of the speed value and the load value, respectively, and μ1 and μ2 are natural numbers greater than 0.
Preferably, the setting analysis is performed on the disease diagnosis period of the target bridge, and the specific analysis process is as follows:
according to the generated secondary usage feedback signal, the disease diagnosis period of the target bridge is set to be divided into z1 diagnosis nodes, namely z1 diagnosis operations are executed in the t1 diagnosis period;
according to the generated intermediate-level use feedback signals, the disease diagnosis period of the target bridge is set to be divided into z2 diagnosis nodes by the t1 diagnosis period, namely z2 diagnosis operations are executed in the t1 diagnosis period;
setting a disease diagnosis period of the target bridge to be divided into z3 diagnosis nodes according to the generated intermediate level upper using feedback signals, namely executing z3 diagnosis operations in the t1 diagnosis period;
according to the generated priority, a feedback signal is used, the disease diagnosis period of the target bridge is set to be divided into z4 diagnosis nodes, namely z4 diagnosis operations are executed in the t1 diagnosis period;
wherein z4 is less than z3 and less than z2 is less than z1.
Preferably, the disease degree of the target bridge is analyzed, and the specific analysis process is as follows:
according to the set corresponding diagnosis period, bridge disease conditions under each diagnosis node in the corresponding diagnosis period are obtained;
dividing the target bridge according to the component members, and obtaining crack values, corrosion values and deformation values in the disease parameter information of each type of component members of the target bridge;
performing assignment calibration on various damage parameters of various types of component members of the target bridge, and specifically:
comparing and matching the parameters of each disease of each type of component of the target bridge with a data judging table stored in a cloud database, thereby obtaining score assignment of each disease parameter of each type of component of the target bridge, wherein each disease parameter data item of each corresponding type of component of the target bridge has a score assignment corresponding to the score assignment;
the score assignment of each disease parameter of each type of component member of the target bridge is summed up, and the formula is used for: total disease score = assigned score of crack value damage parameter item + assigned score of corrosion value damage parameter item + assigned score of deformation value damage parameter item, obtain total disease score of corresponding component member of the target bridge;
and performing comparison matching analysis on the total disease scores of all the component parts of the target bridge and a disease state judging table stored in a cloud database, so as to obtain the disease grade of all the component parts of the target bridge, wherein each total disease score of all the component parts of the target bridge is provided with a disease grade corresponding to the disease grade, and the disease grade comprises a high disease grade, a general disease grade and a low disease grade.
Preferably, the diagnosis and control operation is performed on the received corresponding disease grade of each component of the target bridge, and the specific operation steps are as follows:
the method comprises the steps of calling the positions of the component members of the target bridge calibrated to be high in disease level, generating a first-level disease diagnosis early warning signal, sending the first-level disease diagnosis early warning signal to a display terminal for display description, and simultaneously making a first-level maintenance scheme, wherein the first-level maintenance scheme comprises the following maintenance methods: and (3) repairing the comprehensive diseases, replacing corresponding component members, and maintaining the time: in the pt1 period, maintenance cost: cot1;
the method comprises the steps of calling the positions of the component members of the target bridge calibrated to be in a general disease grade, generating a secondary disease diagnosis early warning signal, sending the secondary disease diagnosis early warning signal to a display terminal for display description, and simultaneously making a secondary maintenance scheme, wherein the secondary maintenance scheme comprises the following maintenance methods: repairing local diseases, and maintaining time: in the pt2 period, maintenance cost: cot2;
the method comprises the steps of calling the positions of the component members of the target bridge calibrated to be low in disease level, generating three-level disease diagnosis early warning signals, sending the three-level disease diagnosis early warning signals to a display terminal for display description, and simultaneously making three-level maintenance schemes, wherein the three-level maintenance schemes comprise a maintenance method: repairing local diseases, and maintaining time: in pt3 period, maintenance cost: cot3;
wherein pt1 > pt2 > pt3, cot1 > cot2 > cot3.
The invention has the beneficial effects that:
according to the invention, the judgment and analysis of the basic state and the load state of the target bridge are realized by adopting a data model calculation and data matching mode, so that the basic state grade and the load grade of the target bridge are defined, the comprehensive judgment of the use state of the target bridge is realized by adopting a set calibration and union analysis mode, and a powerful data support is provided for realizing the setting of the disease diagnosis period of the target bridge;
according to the use state judgment grade of the target bridge, the disease diagnosis period of the target bridge is set and analyzed, the disease diagnosis period of the target bridge is determined, the disease parameter information of various component members of the target bridge is obtained based on the disease diagnosis period, the disease degree of the target bridge is analyzed, the disease grade of the target bridge is determined by means of data assignment calibration, summation analysis and data comparison, the maintenance method, the maintenance time and the maintenance cost of the target bridge disease are determined by means of classification analysis and maintenance scheme making, unnecessary waste and loss are avoided, the service life and the safety of the bridge are improved, the diseases of the bridge are found and treated in time, and the service life of the bridge can be prolonged.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a system block diagram of the present invention;
fig. 2 is a flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention is an internet-based bridge disease diagnosis system, comprising: the system comprises a data acquisition unit, a cloud database, a bridge use state evaluation unit, a bridge disease diagnosis period setting unit, a bridge disease degree evaluation unit, a bridge disease diagnosis management and control unit and a display terminal.
The bridge disease diagnosis control system comprises a data acquisition unit, a bridge use state evaluation unit, a bridge disease diagnosis period setting unit, a bridge disease degree evaluation unit and a bridge disease diagnosis control unit, wherein the data acquisition unit, the bridge use state evaluation unit, the bridge disease diagnosis period setting unit, the bridge disease degree evaluation unit and the bridge disease diagnosis control unit are respectively connected with a cloud database.
The data acquisition unit is used for acquiring basic data information, maintenance parameters and load state parameters of the target bridge, acquiring weather condition parameters and environment pollution parameters of the area where the target bridge is located, acquiring lesion parameter information of various types of constituent members of the target bridge, and sending the various types of information to the cloud database for storage.
The cloud database is also used for storing a base state judging table of the target bridge, storing a using load state judging table of the target bridge, storing a data judging table of the target bridge and storing a disease state judging table of the target bridge.
The bridge use state evaluation unit is used for monitoring basic data information of the target bridge, so that the basic state of the target bridge is analyzed, and specifically:
monitoring the input use time, the use environment influence factor and the history maintenance factor in the basic data information of the target bridge, calibrating the basic data information into tsl, uef and hcf respectively, and carrying out calculation and analysis on three items of data according to a set data model:the basic state coefficient bas of the target bridge is obtained, wherein delta 1, delta 2 and delta 3 are weight factor coefficients of the time length of being put into use, the environmental impact factor and the history maintenance factor respectively, delta 1, delta 2 and delta 3 are natural numbers larger than 0, and the weight factor coefficients are used for balancing the duty ratio weight of each item of data in formula calculation, so that the accuracy of a calculation result is promoted;
it should be noted that, the specific solving process using the environmental impact factor is as follows:
monitoring an average temperature change value, an average humidity, an average rainfall and an average wind speed in weather condition parameters of an area where a target bridge is located, calibrating the average temperature change value, the average humidity, the average rainfall and the average wind speed into wd, sd, rl and fs respectively, carrying out normalization analysis on the four weather condition parameters, and according to a set model: wev1 = λ1×wd+λ2×sd+λ3×rl+λ4×fs, whereby a first influence value wev1 of the target bridge is obtained, wherein λ1, λ2, λ3, and λ4 are normalization factors of an average temperature change value, an average humidity, an average rainfall, and an average wind speed, respectively, and each of λ1, λ2, λ3, and λ4 is a natural number greater than 0, the normalization factors being used to represent coefficients for converting each item of data into a dimensionless form;
it should be noted that the average temperature change value refers to an average data value of the degree of change of the ambient temperature to which the target bridge is subjected in a unit time, the average rainfall refers to an average rainfall in a region where the target bridge is located in a unit time, the average humidity refers to an average humidity of an environment in a region where the target bridge is located in a unit time, and the average wind speed refers to an average wind speed in a region where the target bridge is located in a unit time, wherein the unit time is generally expressed as one year;
monitoring the atmospheric pollution value, the soil pollution value and the water pollution value in the environmental pollution parameters of the area where the target bridge is located, respectively calibrating the atmospheric pollution value, the soil pollution value and the water pollution value into apv, spv and wpv, carrying out normalization analysis on the three data, and according to a set model: wev2 =γ1× apv +γ2×spv+γ3×wpv, whereby a second influence value wev2 of the target bridge is obtained, wherein γ1, γ2 and γ3 are normalization factors of the air pollution value, the soil pollution value and the water pollution value, respectively, and γ1, γ2 and γ3 are natural numbers greater than 0;
it should be further noted that the atmospheric pollution value refers to a data value of a sum of concentrations of various types of polluted gases in an environment where the target bridge is located, the polluted gases include atmospheric pollutants such as sulfur dioxide, nitrogen oxides, ozone, PM2.5, PM10 and the like, the water pollution value refers to a data value of a sum of water pollution indexes of the water environment where the target bridge is located, and the soil pollution value refers to a data value of a sum of contents of heavy metal elements and organic matters in a soil environment where the target bridge is located;
adding the first influence value and the second influence value of the target bridge, thereby obtaining a use environment influence factor uef of the target bridge, namely uef = wev1+ wev2;
the specific solving process of the historical maintenance factor is as follows:
monitoring maintenance times, maintenance effective values and maintenance investment values in maintenance parameters of the target bridge in a unit history period, calibrating the maintenance effective values and the maintenance investment values as mf, mev and miv respectively, performing calculation and analysis on the maintenance effective values and the maintenance investment values, and according to a set model: hcf =ρ1×mf+ρ2×mev+ρ3χ miv, thereby obtaining a historical maintenance factor hcf of the target bridge, wherein ρ1, ρ2 and ρ3 are correction factor coefficients of maintenance times, maintenance effective values and maintenance input values, respectively, and ρ1, ρ2 and ρ3 are natural numbers greater than 0, and the correction factor coefficients are used for correcting deviations of various parameters occurring in the formula calculation process, so that more accurate parameter data are calculated;
the curing effective value refers to the ratio of the actual curing completion amount of the target bridge to the expected curing completion amount in the unit history period, and the curing input value refers to the ratio of the actual curing expense of the target bridge to the rated curing expense in the unit history period;
comparing and matching the basic state coefficient of the target bridge with a basic state judging table of the target bridge stored in the cloud database, thereby obtaining basic state grades of the target bridge, wherein each obtained basic state coefficient of the target bridge corresponds to one basic state grade, and the basic state grades comprise a secondary basic state, a medium basic state and a high basic state;
the bridge use state evaluation unit is further used for monitoring load state parameters of the target bridge, so that the load state of the target bridge is analyzed, and specifically:
the traffic load value, the using frequency value, the design load value and the actual load value in the load state parameters of the target bridge are obtained, and are respectively calibrated to tlv, ufv, dlv and alv, and four items of data are calculated and analyzed according to a set model:the load state coefficients plv of the target bridge are obtained, wherein ω1, ω2, ω3 and ω4 are weight factor coefficients of a traffic load value, a use frequency value, a design load value and an actual load value respectively, and ω1, ω2, ω3 and ω4 are natural numbers larger than 0;
it should be noted that, the specific solving process of the traffic load value is as follows:
acquiring speed values and load values of all vehicles passing through the target bridge in unit time, and calibrating the speed values and the load values as sv respectively k And lod k And performing calculation analysis on the two items of data according to a set model:obtaining a traffic load value tlv of the target bridge, wherein k represents a data set of all vehicles passing through the bridge in unit time, k=1, 2,3 … … m, mu 1 and mu 2 are normalization factors of a speed value and a load value respectively, and mu 1 and mu 2 are natural numbers larger than 0;
the use frequency value refers to the occupation ratio of the use time of the target bridge in unit time;
comparing and matching the load state coefficient of the target bridge with a using load state judging table stored in a cloud database, thereby obtaining the load level of the target bridge, wherein each obtained load state coefficient of the target bridge corresponds to one load level, and the load levels comprise a serious load level, a general load level and a low load level;
thus obtaining the basic state grade and the load grade of the target bridge;
comprehensively analyzing the obtained basic state grade and load grade of the target bridge, and specifically:
establishing a set W according to the basic state level, calibrating a secondary basic state as an element a1, calibrating a medium basic state as an element a2, calibrating a superior basic state as an element a3, wherein the element a1 epsilon the set W, the element a2 epsilon the set W and the element a3 epsilon the set W;
establishing a set V according to the load level, calibrating a severe load level as an element b1, calibrating a general load level as an element b2, calibrating a low load level as an element b3, wherein the element b1 epsilon the set V, the element b2 epsilon the set V and the element b3 epsilon the set V;
the method comprises the steps of performing union processing on a set W and V, judging the use state of a target bridge as a secondary use feedback signal if W U V= { a1, b1}, judging the use state of the target bridge as a middle-level use feedback signal if W U V= { a1, b2}, { a1, b3}, { a2, b1}, or { a3, b1}, judging the use state of the target bridge as a middle-level upper use feedback signal if W U V= { a3, b2}, { a2, b3}, or { a2, b2}, and judging the use state of the target bridge as a high-level use feedback signal if W U V= { a3, b3 };
the use state judgment grade of the target bridge is obtained, and sent to the bridge disease diagnosis period setting unit.
The bridge disease diagnosis period setting unit is used for judging the grade of the use state of the received target bridge, and accordingly, setting and analyzing the disease diagnosis period of the target bridge, wherein the specific analysis process is as follows:
according to the generated secondary usage feedback signal, the disease diagnosis period of the target bridge is set to be divided into z1 diagnosis nodes, namely z1 diagnosis operations are executed in the t1 diagnosis period;
according to the generated intermediate-level use feedback signals, the disease diagnosis period of the target bridge is set to be divided into z2 diagnosis nodes by the t1 diagnosis period, namely z2 diagnosis operations are executed in the t1 diagnosis period;
setting a disease diagnosis period of the target bridge to be divided into z3 diagnosis nodes according to the generated intermediate level upper using feedback signals, namely executing z3 diagnosis operations in the t1 diagnosis period;
according to the generated priority, a feedback signal is used, the disease diagnosis period of the target bridge is set to be divided into z4 diagnosis nodes, namely z4 diagnosis operations are executed in the t1 diagnosis period;
wherein z4 < z3 < z2 < z1, and the setting of the specific values of z1, z2, z3 and z4 is specifically set by the person skilled in the art in the specific case;
and sending the obtained disease diagnosis period of the target bridge to a bridge disease degree evaluation unit.
The bridge disease degree evaluation unit is used for setting a corresponding diagnosis period for the target bridge, so that the disease degree of the target bridge is analyzed, and the specific analysis process is as follows:
according to the set corresponding diagnosis period, bridge disease conditions under each diagnosis node in the corresponding diagnosis period are obtained;
dividing the target bridge according to the component members to obtain crack values, corrosion values and deformation values in the disease parameter information of each type of component members of the target bridge, wherein the component members of the target bridge comprise: bridge deck, bridge pier, abutment, beam, abutment, support and guardrail;
it should be noted that the crack value refers to the sum of the total lengths of all cracks occurring in the various types of component members, the corrosion value refers to the sum of the areas of all corrosion points occurring in the various types of component members, and the deformation value refers to the data value of the displacement, angle and other variation of the various types of component members caused by the external load;
performing assignment calibration on various damage parameters of various types of component members of the target bridge, and specifically:
comparing and matching the parameters of each damage of each type of component of the target bridge with a data judging table stored in a cloud database, thereby obtaining score assignment of each damage parameter of each type of component of the target bridge, and recording the score assignment as bs ij Each disease parameter data item of the corresponding type of the target bridge has a score assignment corresponding to the disease parameter data item, wherein i is represented as a set of each component of the target bridge, and j is represented as a set of each disease parameter data item of each type of component of the target bridge;
the score assignment of each disease parameter of each type of component member of the target bridge is summed up, and the formula is used for: total disease score = assigned score of crack value damage parameter item + assigned score of corrosion value damage parameter item + assigned score of deformation value damage parameter item, total disease score of corresponding component of the target bridge is obtained and is recorded as sum ij ；
Performing comparison matching analysis on the total disease scores of all the component parts of the target bridge and a disease state judging table stored in a cloud database, so as to obtain disease grades of all the component parts of the target bridge, wherein each total disease score of all the component parts of the target bridge has a disease grade corresponding to the disease grade, and the disease grades comprise a high disease grade, a general disease grade and a low disease grade;
and sending the obtained disease grades of all the component members of the target bridge to a bridge disease diagnosis and management and control unit.
The bridge disease diagnosis and control unit is used for performing diagnosis and control operation on the received corresponding disease grades of all the component members of the target bridge, and comprises the following specific operation steps:
the method comprises the steps of calling the positions of the component members of the target bridge calibrated to be high in disease level, generating a first-level disease diagnosis early warning signal, sending the first-level disease diagnosis early warning signal to a display terminal for display description, and simultaneously making a first-level maintenance scheme, wherein the first-level maintenance scheme comprises the following maintenance methods: and (3) repairing the comprehensive diseases, replacing corresponding component members, and maintaining the time: in the pt1 period, maintenance cost: cot1;
the method comprises the steps of calling the positions of the component members of the target bridge calibrated to be in a general disease grade, generating a secondary disease diagnosis early warning signal, sending the secondary disease diagnosis early warning signal to a display terminal for display description, and simultaneously making a secondary maintenance scheme, wherein the secondary maintenance scheme comprises the following maintenance methods: repairing local diseases, and maintaining time: in the pt2 period, maintenance cost: cot2;
the method comprises the steps of calling the positions of the component members of the target bridge calibrated to be low in disease level, generating three-level disease diagnosis early warning signals, sending the three-level disease diagnosis early warning signals to a display terminal for display description, and simultaneously making three-level maintenance schemes, wherein the three-level maintenance schemes comprise a maintenance method: repairing local diseases, and maintaining time: in pt3 period, maintenance cost: cot3;
wherein pt1 > pt2 > pt3, cot1 > cot2 > cot3.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.

Claims (5)

1. Bridge disease diagnosis system based on internet, characterized by comprising:
the data acquisition unit is used for acquiring basic data information, maintenance parameters and load state parameters of the target bridge, acquiring weather condition parameters and environment pollution parameters of the area where the target bridge is located, acquiring lesion parameter information of various types of component members of the target bridge, and transmitting the various types of information to the cloud database for storage;
the cloud database is used for storing a base state judging table of the target bridge, storing a using load state judging table of the target bridge, storing a data judging table of the target bridge and storing a disease state judging table of the target bridge;
the bridge use state evaluation unit is used for monitoring basic data information and load state parameters of the target bridge, and specifically:
monitoring the input use time, the use environment influence factor and the history maintenance factor in the basic data information of the target bridge, and calculating and analyzing the three items of data to obtain the basic state coefficient of the target bridge;
comparing and matching the basic state coefficient of the target bridge with a basic state judging table of the target bridge stored in the cloud database, thereby obtaining basic state grades of the target bridge, wherein each obtained basic state coefficient of the target bridge corresponds to one basic state grade, and the basic state grades comprise a secondary basic state, a medium basic state and a high basic state;
the basic state and the load state of the target bridge are analyzed, and the specific process is as follows:
acquiring traffic load values, using frequency values, design load values and actual load values in load state parameters of a target bridge, and performing calculation and analysis on four items of data to obtain a load state coefficient of the target bridge;
comparing and matching the load state coefficient of the target bridge with a using load state judging table stored in a cloud database, thereby obtaining the load level of the target bridge, wherein each obtained load state coefficient of the target bridge corresponds to one load level, and the load levels comprise a serious load level, a general load level and a low load level;
comprehensively analyzing the obtained basic state grade and load grade of the target bridge, and specifically:
establishing a set W according to the basic state level, calibrating a secondary basic state as an element a1, calibrating a medium basic state as an element a2, calibrating a superior basic state as an element a3, wherein the element a1 epsilon the set W, the element a2 epsilon the set W and the element a3 epsilon the set W;
establishing a set V according to the load level, calibrating a severe load level as an element b1, calibrating a general load level as an element b2, calibrating a low load level as an element b3, wherein the element b1 epsilon the set V, the element b2 epsilon the set V and the element b3 epsilon the set V;
the set W and V are processed in a union way, if W U V= { a1, b1}, the use state of the target bridge is judged as a secondary use feedback signal, if W U V= { a1, b2}, { a1, b3}, { a2, b1}, { a3, b1}, the use state of the target bridge is judged as a medium use feedback signal, if W U V= { a3, b2}, { a2, b3}, { a2, b2}, or { a2, b2}, the use state of the target bridge is judged as a medium use feedback signal, if W U V= { a3, b3}, the use state of the target bridge is judged as a high use feedback signal, and the use state of the target bridge is sent to a bridge disease diagnosis period setting unit;
the bridge disease diagnosis period setting unit is used for judging the grade of the use state of the received target bridge, and accordingly setting and analyzing the disease diagnosis period of the target bridge, and the specific process is as follows:
according to the generated secondary usage feedback signal, the disease diagnosis period of the target bridge is set to be divided into z1 diagnosis nodes, namely z1 diagnosis operations are executed in the t1 diagnosis period;
according to the generated intermediate-level use feedback signals, the disease diagnosis period of the target bridge is set to be divided into z2 diagnosis nodes by the t1 diagnosis period, namely z2 diagnosis operations are executed in the t1 diagnosis period;
setting a disease diagnosis period of the target bridge to be divided into z3 diagnosis nodes according to the generated intermediate level upper using feedback signals, namely executing z3 diagnosis operations in the t1 diagnosis period;
according to the generated priority, a feedback signal is used, the disease diagnosis period of the target bridge is set to be divided into z4 diagnosis nodes, namely z4 diagnosis operations are executed in the t1 diagnosis period; wherein z4 is more than 3 and z2 is more than 1; the obtained disease diagnosis period of the target bridge is sent to a bridge disease degree evaluation unit;
the bridge disease degree evaluation unit is used for setting a corresponding diagnosis period for the target bridge, so that the disease degree of the target bridge is analyzed, and the specific analysis process is as follows:
according to the set corresponding diagnosis period, bridge disease conditions under each diagnosis node in the corresponding diagnosis period are obtained;
dividing the target bridge according to the component members, and obtaining crack values, corrosion values and deformation values in the disease parameter information of each type of component members of the target bridge;
performing assignment calibration on various damage parameters of various types of component members of the target bridge, and specifically:
comparing and matching the parameters of each disease of each type of component of the target bridge with a data judging table stored in a cloud database, thereby obtaining score assignment of each disease parameter of each type of component of the target bridge, wherein each disease parameter data item of each corresponding type of component of the target bridge has a score assignment corresponding to the score assignment;
the score assignment of each disease parameter of each type of component member of the target bridge is summed up, and the formula is used for: total disease score = assigned score of crack value damage parameter item + assigned score of corrosion value damage parameter item + assigned score of deformation value damage parameter item, obtain total disease score of corresponding component member of the target bridge;
performing comparison matching analysis on the total disease scores of all the component parts of the target bridge and a disease state judging table stored in a cloud database, so as to obtain disease grades of all the component parts of the target bridge, wherein each total disease score of all the component parts of the target bridge has a disease grade corresponding to the disease grade, and the disease grades comprise a high disease grade, a general disease grade and a low disease grade;
the obtained disease grades of all the component members of the target bridge are sent to a bridge disease diagnosis and control unit;
the bridge disease diagnosis and control unit is used for performing diagnosis and control operation on the received corresponding disease grades of all the component members of the target bridge, so that a primary disease diagnosis early warning signal, a secondary disease diagnosis early warning signal and a tertiary disease diagnosis early warning signal are obtained, and are sent to a display terminal to display and explain, and a primary maintenance scheme, a secondary maintenance scheme and a tertiary maintenance scheme are triggered at the same time.
2. The internet-based bridge disease diagnosis system according to claim 1, wherein a specific solving process using an environmental impact factor is as follows:
monitoring an average temperature change value, average humidity, average rainfall and average wind speed in weather condition parameters of an area where a target bridge is located, and carrying out normalization analysis on the four weather condition parameters, so as to obtain a first influence value of the target bridge;
monitoring the atmospheric pollution value, the soil pollution value and the water pollution value in the environmental pollution parameters of the area where the target bridge is located, and carrying out normalization analysis on the three data to obtain a second influence value of the target bridge;
and adding the first influence value and the second influence value of the target bridge, thereby obtaining the use environment influence factor of the target bridge.
3. The internet-based bridge disease diagnosis system according to claim 1, wherein the specific solving process of the history maintenance factor is as follows:
monitoring maintenance times, maintenance effective values and maintenance investment values in maintenance parameters of the target bridge in a unit historical period, and performing calculation and analysis on the maintenance effective values and the maintenance investment values, thereby obtaining historical maintenance factors of the target bridge.
4. The internet-based bridge disease diagnosis system according to claim 1, wherein the specific solving process of the traffic load value is as follows:
acquiring speed values and load values of all vehicles passing through the target bridge in unit time, and calibrating the speed values and the load values as sv respectively k And lod k And performing calculation analysis on the two items of data according to a set model:the traffic load value tlv of the target bridge is thus obtained, where k represents the data of all vehicles passing through the bridge per unit timeAggregate, and k=1, 2,3 … … m, μ1 and μ2 are normalized factors of speed and load values, respectively, and μ1 and μ2 are both natural greater than 0.
5. The bridge defect diagnosis system based on the internet according to claim 1, wherein the diagnosis and control operation is performed on the received corresponding defect levels of each component of the target bridge, and the specific operation steps are as follows:
the method comprises the steps of calling the positions of the component members of the target bridge calibrated to be high in disease level, generating a first-level disease diagnosis early warning signal, sending the first-level disease diagnosis early warning signal to a display terminal for display description, and simultaneously making a first-level maintenance scheme, wherein the first-level maintenance scheme comprises the following maintenance methods: and (3) repairing the comprehensive diseases, replacing corresponding component members, and maintaining the time: in the pt1 period, maintenance cost: cot1;
the method comprises the steps of calling the positions of the component members of the target bridge calibrated to be in a general disease grade, generating a secondary disease diagnosis early warning signal, sending the secondary disease diagnosis early warning signal to a display terminal for display description, and simultaneously making a secondary maintenance scheme, wherein the secondary maintenance scheme comprises the following maintenance methods: repairing local diseases, and maintaining time: in the pt2 period, maintenance cost: cot2;
the method comprises the steps of calling the positions of the component members of the target bridge calibrated to be low in disease level, generating three-level disease diagnosis early warning signals, sending the three-level disease diagnosis early warning signals to a display terminal for display description, and simultaneously making three-level maintenance schemes, wherein the three-level maintenance schemes comprise a maintenance method: repairing local diseases, and maintaining time: in pt3 period, maintenance cost: cot3;
wherein pt1 > pt2 > pt3, cot1 > cot2 > cot3.
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