A method and system for monitoring and fusing data of facilities of a cable-stayed bridge

Abstract

The present application relates to the technical field of data fusion, in particular to a cable-stayed bridge supporting facility state monitoring data fusion method and system. The method determines the basic correlation coefficient according to the correlation of the monitoring data of the internal sensor of the sensor; analyzes the oscillation performance of the sensor to determine the reference oscillation degree; combines the basic correlation coefficient of all sensor pairs and the reference oscillation degree in the historical period of the current section to determine the overall correlation performance of the current section; analyzes the basic correlation coefficient of the sensor pair containing the reference sensor, the overall correlation performance and the reference oscillation degree to determine the correlation collapse index of the reference sensor; combines the correlation collapse index of the sensor in the current section and the basic correlation coefficient of the sensor pair to determine the cooperative abnormal strength of the current section; and fuses the cooperative abnormal strength of all sections of the cable-stayed bridge to obtain the abnormal evaluation value of the cable-stayed bridge. The present application improves the data reliability and safety evaluation accuracy during the state monitoring of the cable-stayed bridge.

Description

Technical Field

[0001] This invention relates to the field of data fusion technology, specifically to a method and system for data fusion in monitoring the condition of cable-stayed bridge supporting facilities. Background Technology

[0002] In-service bridges are subjected to complex effects such as environmental erosion, dynamic load impact, and concrete shrinkage and creep over long periods. The structural response data collected by their health monitoring systems often exhibits a high degree of coupling between environmental effects, load effects, shrinkage and creep effects, and noise effects. For complex statically indeterminate structures such as multi-span continuous beam bridges and cable-stayed bridges, periodic changes in ambient temperature can trigger significant structural temperature effects, severely interfering with the effective identification of bridge damage information. Directly using coupled state data for the safety assessment of cable-stayed bridges is highly susceptible to erroneous conclusions due to data feature confusion, leading to errors. Currently, there is a lack of effective methods to handle data coupling and environmental interference, necessitating more accurate data fusion technologies. Summary of the Invention

[0003] To address the technical problem of potential errors when directly using coupled state data for safety assessment of cable-stayed bridges, the present invention aims to provide a method and system for fusing state monitoring data of cable-stayed bridge supporting facilities. The specific technical solution adopted is as follows:

[0004] In a first aspect, embodiments of the present invention provide a method for fusing data on the status monitoring of cable-stayed bridge supporting facilities, the method comprising:

[0005] Acquire monitoring data from various sensors on the cable-stayed bridge;

[0006] The basic correlation coefficient is determined based on the correlation between the monitoring data of the same sensor and the monitoring data of the internal sensors.

[0007] Analyze the oscillation performance of each sensor over historical periods to determine the baseline oscillation level; combine the baseline correlation coefficients of all sensor pairs in the historical period of the current section with the baseline oscillation level of the sensors within each sensor pair to determine the overall correlation performance of the current section.

[0008] When any one sensor is used as the reference sensor, the basic correlation coefficient, overall correlation performance, and reference oscillation degree of multiple sensor pairs including the reference sensor are analyzed to determine the correlation collapse index of the reference sensor; the correlation collapse index characterizes the degree of actual damage that may exist at the corresponding location of the sensor.

[0009] By combining the correlation collapse index of the sensors in the current section with the basic correlation coefficient of the sensor pair, the cooperative anomaly intensity of the current section is determined; by fusing the cooperative anomaly intensity of all sections of the cable-stayed bridge at the same moment, the anomaly assessment value of the cable-stayed bridge is obtained.

[0010] In a second aspect, a cable-stayed bridge supporting facility state monitoring data fusion system is provided, and the system comprises the following modules:

[0011] A monitoring module is configured to acquire monitoring data of each sensor on the cable-stayed bridge.

[0012] A basic correlation analysis module is configured to determine a basic correlation coefficient according to a correlation condition of the monitoring data of the sensors in the same sensor pair, wherein the sensors in the same sensor pair are located at symmetric positions.

[0013] An overall correlation analysis module is configured to analyze a shock performance condition of each sensor in a historical period to determine a reference shock degree, and to determine an overall correlation performance of a current section in combination with the basic correlation coefficients of all sensor pairs in the historical period of the current section and the reference shock degrees of the sensors in the sensor pairs.

[0014] A damage analysis module is configured to analyze the basic correlation coefficients, the overall correlation performances and the reference shock degrees of a plurality of sensor pairs containing a reference sensor when any one sensor is taken as the reference sensor to determine a correlation collapse index of the reference sensor, wherein the correlation collapse index represents a degree of possible actual damage at a corresponding position of the sensor.

[0015] A monitoring data fusion module is configured to determine a cooperative abnormal strength of a current section in combination with the correlation collapse indexes of the sensors in the current section and the basic correlation coefficients of the sensor pairs, and to obtain an abnormal evaluation value of the cable-stayed bridge by fusing the cooperative abnormal strengths of all sections of the cable-stayed bridge at the same moment.

[0016] In a third aspect, an electronic device is provided, comprising a memory and a processor, the memory stores executable code, and the processor executes the executable code to implement the embodiments of each possible implementation of the first aspect.

[0017] In a fourth aspect, a computer program product is provided, which comprises computer program code, and when the computer program code is executed on a computer, the computer executes the method in the first aspect or any one of the possible implementation manners of the first aspect.

[0018] In a fifth aspect, a computer readable storage medium is provided, which stores a computer program, and when the computer program is executed in a computer, the computer executes the embodiments of each possible implementation of the first aspect.

[0019] The embodiments of the present application have at least the following beneficial effects:

[0020] The present application can effectively separate the environmental effect, load effect and noise effect in the monitoring data of the cable-stayed bridge by analyzing the monitoring data between different sensor pairs, so as to judge the degree of possible actual damage at the position corresponding to the sensor pair; through the correlation characteristics of the sensor and the data collaborative analysis, the real damage and abnormality of the structure can be accurately identified, and the misjudgment caused by data coupling can be avoided; the collaborative abnormal strength of all sections of the cable-stayed bridge is fused to realize the overall state evaluation of the bridge, the anti-noise interference ability is improved, the local noise and global abnormality can be distinguished; the abnormal positioning and hierarchical early warning can be completed synchronously, the data reliability and safety evaluation accuracy are improved, and accurate technical support is provided for bridge operation and maintenance. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0022] Figure 1 The method flow chart of a cable-stayed bridge supporting facility state monitoring data fusion method provided by an embodiment of the present application;

[0023] Figure 2 The system block diagram of a cable-stayed bridge supporting facility state monitoring data fusion system provided by an embodiment of the present application;

[0024] Figure 3 The structural schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purpose, the specific implementation, structure, features and effects of the cable-stayed bridge supporting facility state monitoring data fusion method and system according to the present application are described in detail as follows by combining the drawings and preferred embodiments.

[0026] In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0027] In the description of the embodiments of the present application, unless otherwise specified, " / " represents the meaning of or, for example, A / B can represent A or B: "and / or" in the text is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist together, and B exists alone, and in addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0028] Hereinafter, the terms "first", "second" are only for descriptive purposes, and cannot be understood as implying or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art of the technology to which the present application belongs.

[0030] The embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art can know that with the development of technology and the appearance of new scenes, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0031] The specific scheme of the method and system for monitoring and fusing the state of the supporting facilities of the cable-stayed bridge provided by the present application will be specifically described below with reference to the accompanying drawings.

[0032] Please refer to Figure 1 which shows the step flow chart of the method for monitoring and fusing the state of the supporting facilities of the cable-stayed bridge provided by an embodiment of the present application, which comprises the following steps:

[0033] Step S100, acquiring the monitoring data of each sensor on the cable-stayed bridge.

[0034] For the currently monitored cable-stayed bridge, for the key control section, for example: the main beam section: the midspan, 1 / 4 span, the anchorage zone, etc., and taking the bridge longitudinal axis as the symmetrical reference, the opposite sensors are defined, for example, the left web plate and the right web plate. The key control section is also the typical position that needs to be monitored.

[0035] At the same time, in addition to the symmetrical position, there are also cross-type sensors at the monitored section of each sensor, for example, the adjacent cable force corresponding to the left web plate longitudinal strain. These setting methods are all set according to the nearby sensor distribution network, and the distribution of the sensor will not be described here.

[0036] Each two sensors are divided into a sensor pair, that is, two sensors are included in the same sensor pair; wherein the positions of the same type sensors belonging to the same sensor pair are symmetric positions, and the positions of the cross type sensors belonging to the same sensor pair are symmetric positions or adjacent positions.

[0037] In the subsequent step, taking the sensor pair (i, j) as an example, in order to avoid ambiguity, first, it is explained that in the sensor pair (i, j), taking sensor i as a reference, for example, sensor i is a stress sensor, then the corresponding sensor j can be a same type sensor located at a symmetric position with sensor i, or a cross type sensor located at an adjacent position or a symmetric position with sensor i, for example, a deflection sensor, a cable force dynamic tester and the like. In the embodiment of the present application, the sensor at the adjacent position is the sensor with a distance from sensor i not more than a preset distance threshold, wherein the preset distance threshold is limited by the implementer according to the actual situation, and in the embodiment of the present application, the preset distance threshold is set to 1 meter.

[0038] Therefore, the monitoring data of the obtained sensors need to be pre-processed for standardization, so as to eliminate the dimensional and data magnitude differences, that is, the monitoring data of the sensors used in the subsequent steps are all post-data after standardization.

[0039] In step S200, the basic correlation coefficient is determined according to the correlation of the monitoring data of the sensors in the same sensor pair; wherein the sensors in the same sensor pair are located at symmetric positions.

[0040] The monitoring data of the cable-stayed bridge has a significant stable correlation feature, for example, in the same bridge body section, the same type data such as longitudinal strain and transverse strain at the symmetric positions show strong correlation, and the correlation direction is positive or negative; there are also significant positive and negative correlation characteristics between different types of data. When analyzing across the section, in addition to the transverse strain, the same type data and different type data of longitudinal strain, deflection and cable force all show stable and high strength correlation characteristics. When the data is in a normal state, the correlation coefficient fluctuates slightly around a stable threshold; once abnormal data occurs, the correlation coefficient will change significantly.

[0041] However, directly using the original monitoring data of the sensors for bridge state evaluation is usually disturbed by noise, and cannot present the unique data correlation or similarity characteristics of the symmetric positions of the cable-stayed bridge.

[0042] The basic correlation model is constructed according to the correlation characteristics of the sensor pair under the same section to obtain a basic correlation coefficient. Specifically: according to the correlation of the monitoring data of the sensors in the same sensor pair, the basic correlation coefficient is determined, and more specifically: the monitoring data of each sensor is standardized, and the standardized monitoring data is taken as new monitoring data; the same sensor pair includes two sensors; wherein, the positions of the same type sensors belonging to the same sensor pair are symmetrical positions, and the positions of the cross-type sensors belonging to the same sensor pair are symmetrical positions or adjacent positions; the minimum monitoring data of the sensors in the same sensor pair is taken as the numerator, and the sum of the absolute values of the monitoring data of the two sensors in the sensor pair is taken as the denominator, and a preset multiple of the ratio formed by the numerator and the denominator is taken as the basic correlation coefficient of the sensor pair; the preset multiple is related to the number of sensors in the same sensor pair. In the embodiment of the application, the preset multiple is 2.

[0043] In some embodiments, the calculation formula of the basic correlation coefficient of the sensor pair (i, j) is:

[0044] ; wherein, is a minimum value function; is the monitoring value of sensor i at the tth moment; is the monitoring value of sensor j at the tth moment; is a preset small constant for preventing zero; is an absolute value function; in the embodiment of the application the value of can be set to 0.0001, and in other embodiments, the value of can also be 0.000001.

[0045] Wherein, the minimum value function is used to take the minimum value of the monitoring data between the two sensors in the same sensor pair, which is used to realize abnormal sensitive amplification, that is, when the data mutates, the corresponding molecule will decrease sharply, at this time, the closer the monitoring values of the sensor pair are, the closer the feature is to 1, indicating that the difference between the monitoring data corresponding to the sensor pair is smaller, that is, the monitoring data of the current sensor pair or the performance at the position is better or the sensor data reliability is higher.

[0046] Step S300, analyze the oscillation performance of each sensor in the historical period to determine the reference oscillation degree; combine the basic correlation coefficients of all sensor pairs in the historical period of the current section and the reference oscillation degrees of the sensors in the sensor pairs to determine the overall correlation performance of the current section.

[0047] When analyzing the correlation performance between a single sensor pair, fluctuations, differences and other situations may occur, and there is random noise interference, which needs to be further analyzed from the whole section level.​​

[0048] For any time in the monitoring data of the historical period, the sliding window range is set to 1 hour, the referenceable time range is obtained with the time as the center, the historical monitoring data in the same referenceable time range in the past week is selected as the reference range for any one sensor, for example, the current time is 16:00, the corresponding historical reference range is (15:30-16:30), and the historical monitoring data of all days in the past week is the monitoring data of (15:30-16:30) of the remaining days. In the embodiment of the application, the historical period is the past week corresponding to the current day, and in other embodiments, the size of the historical period can be adjusted.

[0049] Based on the historical data division mode mentioned above, the monitoring data of each time and the historical monitoring data in the referenceable time range of different days in the historical period are obtained for any time of any sensor, the mean value of the monitoring data in the referenceable time range of each day of the historical period is calculated, and further, the absolute difference between the monitoring data of the current time and the mean value of the monitoring data in the referenceable time range of different days in the historical period is calculated, and the absolute difference is normalized to obtain the single-range difference of the current time t ; wherein the absolute difference is denoted as the absolute value of the difference. It should be noted that the meaning of time t and the t-th time is the same.

[0050] The monitoring data of the historical time of each day in the historical period is compared with the monitoring data of the current time , and the mean value of all historical monitoring data is obtained, and the overall monitoring difference of the single monitoring data of each day is obtained. More specifically: the difference between the monitoring data of the historical time of each day in the historical period and the mean value of all historical monitoring data is calculated, and the overall monitoring difference of the single monitoring data is obtained.

[0051] Further, the overall monitoring difference and the single-range difference of the single monitoring data of each day are combined to determine the reference oscillation degree of the monitoring data of the sensor, specifically: the square of the product value of the overall monitoring difference and the single-range difference of the single monitoring data of each day is calculated as the initial oscillation degree of each day in the history; the arithmetic square root of the mean value of the initial oscillation degree of each day in the historical period is calculated, and the result value is normalized to obtain the reference oscillation degree corresponding to the monitoring data of the current time of the sensor.

[0052] In some embodiments, the t-th time is the current time, and the calculation formula of the reference oscillation degree of the sensor i corresponding to the current time is:​

[0053] ; wherein norm is a normalization function; N is the number of historical days in the historical period; is the monitoring data of the sensor i on the nth day of the historical period at the tth time point; is the mean value of all historical monitoring data in the historical period; is the single-range difference at the tth time point.

[0054] The overall monitoring difference is taken as the weight, and the weight of the data with a larger amplitude is enlarged in the calculation. The larger the amplitude, the higher the current bridge load or the more severe the corresponding weather, so further attention needs to be paid to such data.

[0055] The higher the reference oscillation performance, the stronger the data abnormal fluctuation or deviation from the mean value of the sensor in the past history range, and the lower the data quality itself.

[0056] For the historical data recorded in the historical database, the monitoring data of each sensor in the past historical period is selected, the reference oscillation degree of each sensor pair in the historical period is calculated, and then the single-section analysis of the current section is performed. The reference oscillation degree of the sensor in the sensor pair and the overall correlation performance of the current section are determined.

[0057] More specifically, the mean value of the basic correlation coefficient of each sensor pair in the historical period is obtained as the correlation coefficient reference value; the difference between the basic correlation coefficient of each sensor pair and the correlation coefficient reference value is calculated as the correlation oscillation value of each sensor pair; the correlation oscillation value and the reference oscillation degree are combined to determine the oscillation difference degree of each sensor pair; and the mean value of the oscillation difference degrees of all sensor pairs in the current section is negatively correlated to obtain the overall correlation performance of the current section.

[0058] In some embodiments, the overall correlation performance of the current section The calculation formula is:

[0059] ; wherein f is a set of sensor pairs in the current section; is the basic correlation coefficient of the sensor pair (i, j); is the correlation coefficient reference value corresponding to the current section; is the mean value of the reference oscillation degrees of the sensor i and the sensor j in the sensor pair (i, j); is the oscillation difference degree of the sensor pair (i, j); is the mean value of the oscillation difference degrees of all sensor pairs in the current section.

[0060] In the correlation performance calculation of the current section as a whole, the sensor pairs that pay attention to the benchmark oscillation performance are realized by calculating the difference between the basic correlation coefficient of each sensor itself and the average of the basic correlation coefficient in the historical period and expanding after squaring.

[0061] By constructing a correlation network of multiple sensor pairs in the section of the cable-stayed bridge, when a real damage or abnormal performance occurs, the correlation coefficients of multiple sensor pairs will simultaneously mutate, and noise only affects the local.

[0062] The closer the overall correlation performance obtained at this time is to 1, the smaller the random noise interference on the current section at the current time, and the more likely the current section is in a stable state, the smaller the corresponding oscillation difference, and the smaller the corresponding benchmark oscillation degree, at this time, the credibility of the data quality of the overall section and the possibility of data abnormality due to actual local damage are relatively low, and relatively lower sensitivity will be given below.

[0063] Step S400, when any one sensor is taken as a benchmark sensor, the basic correlation coefficient of a plurality of sensor pairs containing the benchmark sensor, the overall correlation performance and the benchmark oscillation degree are analyzed, and the correlation collapse index of the benchmark sensor is determined; the correlation collapse index represents the degree of possible actual damage at the position corresponding to the sensor.

[0064] When noise occurs, it will usually only affect a single point, and the correlation collapse caused thereby has locality, that is, it will destroy all the correlation pairs corresponding to all the sensor pairs it participates in, such as strain-strain and strain-cable force, and a real damage will destroy all the correlation paths through the damage point, therefore, based on the contents in the previous steps S100-S300, here, the correlation collapse analysis of the sensor i at the tth time is needed, that is, the proportion of the set of sensor pairs with correlation collapse is counted, and individual abnormal analysis of the current sensor is also performed.

[0065] When any one sensor is taken as a benchmark sensor, the sensitivity coefficient of the benchmark sensor is determined in combination with the overall correlation performance of the current section and the benchmark oscillation degree of the benchmark sensor; wherein, the sensitivity coefficient is obtained by: performing negative correlation mapping on the overall correlation performance of the current section, taking the product value of the mapped result value and the benchmark oscillation degree of the benchmark sensor as the sensitivity coefficient of the benchmark sensor. In the embodiment of the application, the difference between the constant 1 and the overall correlation performance of the current section is taken as the result value after negative correlation mapping.

[0066] The reference oscillation degree is derived from the reference performance of the sensor itself in a historical period. The higher the reference oscillation degree, the higher the sensitivity of the sensor itself to abnormality. The overall correlation performance reflects the sensitivity of the actual local damage of the current sensor.

[0067] The basic correlation coefficient of each sensor pair containing the reference sensor is negatively mapped to obtain an initial correlation collapse index. The mean of the initial correlation collapse indexes of all sensor pairs containing the reference sensor is weighted by using the sensitivity coefficient as the weight to obtain the correlation collapse index of the reference sensor.

[0068] In some embodiments, taking sensor i as the reference sensor, the calculation formula of the corresponding correlation collapse index at the t-th moment is: ; wherein, The sensitivity coefficient of sensor i at the t-th moment; The number of sensor pairs containing the reference sensor; The set of sensor pairs containing the reference sensor; The basic correlation coefficient of the sensor pair (i, j) containing the reference sensor; The initial correlation collapse index of the sensor pair (i, j) containing the reference sensor.

[0069] The smaller the basic correlation coefficient, the more serious the correlation collapse shown by the current reference sensor. The larger the correlation collapse index, the more likely that the current reference sensor has actual damage rather than abnormality caused by noise.

[0070] Step S500, combining the correlation collapse index of the sensor in the current section and the basic correlation coefficient of the sensor pair, the cooperative abnormality strength of the current section is determined; and the cooperative abnormality strengths of all sections of the cable-stayed bridge at the same moment are fused to obtain the abnormality evaluation value of the cable-stayed bridge.

[0071] Since actual damage can trigger cooperative abnormality of the sensor pair, all section information is integrated to distinguish local noise from global abnormality.

[0072] First, still taking any section as the current section to be analyzed; the maximum value of the correlation collapse index of the sensor in the current section is obtained; the average value of the basic correlation coefficient of all sensor pairs in the current section is calculated; and the difference between the maximum value of the correlation collapse index and the average value of the basic correlation coefficient is taken as the cooperative abnormality strength of the current section.

[0073] In some embodiments, the calculation formula of the cooperative abnormality strength is as follows:

[0074] ; wherein, is the maximum value of the correlation collapse index of the sensors in the current section; is the number of all sensor pairs in the current section; is the set of all sensor pairs in the current section; is the base correlation coefficient corresponding to the sensor pair (i, j).

[0075] The first half of the formula for calculating the cooperative anomaly strength is used to capture the most serious single-point correlation collapse, and the second half is used to represent the health degree between the sensor pairs of the current section as a whole.

[0076] The role of the second half is that compared with the correlation collapse index of the single sensor above, the robustness is higher, for example, if there is a single-point collapse but the overall health, it is usually represented as a local noise, at this time the cooperative anomaly strength obtained is very small, and when there is a single-point collapse and the overall correlation collapses, it usually represents that there is a real damage at the current section, at this time the cooperative anomaly strength obtained is higher.

[0077] After obtaining the cooperative anomaly strengths of the multiple sections of the cable-stayed bridge under analysis at the same time, the cooperative anomaly strengths of all sections of the cable-stayed bridge at the same time are fused to obtain an anomaly evaluation value of the cable-stayed bridge, specifically: the cooperative anomaly strengths of all sections of the cable-stayed bridge at the same time are multiplied, and the result value is taken as the anomaly evaluation value of the cable-stayed bridge.

[0078] As a preferred embodiment of the present application, after the anomaly evaluation value of the cable-stayed bridge under analysis is determined, it further includes:

[0079] normalizing the anomaly evaluation value of the cable-stayed bridge;

[0080] when the value of the normalized anomaly evaluation value is greater than a preset first warning threshold, performing shutdown maintenance and red light alarm;

[0081] when the value of the normalized anomaly evaluation value is less than or equal to the preset first warning threshold and greater than or equal to a preset second warning threshold, performing enhanced inspection and yellow light warning;

[0082] when the value of the normalized anomaly evaluation value is less than the preset second warning threshold, performing routine detection and maintaining green light monitoring; wherein the preset first warning threshold is greater than the preset second warning threshold. In the embodiment of the present application, the value of the preset first warning threshold is 0.4, and the value of the preset second warning threshold is 0.25, and in other embodiments, the values of the two warning thresholds can also be adjusted by the implementer according to the actual situation.

[0083] Please refer to Figure 2 , Figure 2 The embodiment of the present application provides a cable-stayed bridge supporting facility state monitoring data fusion system, the system comprises:

[0084] The monitoring module is used for acquiring monitoring data of each sensor on the cable-stayed bridge.

[0085] The basic correlation analysis module is used for determining a basic correlation coefficient according to a correlation condition of the monitoring data of the sensors in the same sensor pair, wherein the sensors in the same sensor pair are located at symmetric positions.

[0086] The overall correlation analysis module is used for analyzing a shock performance condition of each sensor in a historical period to determine a reference shock degree; and the basic correlation coefficients of all sensor pairs in the historical period of the current section and the reference shock degrees of the sensors in the sensor pairs are combined to determine an overall correlation performance of the current section.

[0087] The damage analysis module is used for analyzing the basic correlation coefficients, the overall correlation performances and the reference shock degrees of the multiple sensor pairs containing the reference sensor to determine a correlation collapse index of the reference sensor when any one sensor is taken as the reference sensor; the correlation collapse index represents a degree of possible actual damage at a corresponding position of the sensor.

[0088] The monitoring data fusion module is used for combining the correlation collapse indexes of the sensors in the current section and the basic correlation coefficients of the sensor pairs to determine a cooperative abnormal strength of the current section; and the cooperative abnormal strengths of all sections of the cable-stayed bridge at the same moment are fused to obtain an abnormal evaluation value of the cable-stayed bridge.

[0089] Optionally, the transmission medium can be a wired link, such as but not limited to a coaxial cable, an optical fiber, a digital subscriber line, etc., or a wireless link, such as but not limited to a wireless fidelity (WIFI), a Bluetooth, a mobile device network, etc.

[0090] It should be noted that the apparatus provided in the above embodiment is only used as an example for the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the above described functions.

[0091] Figure 3 is a structural schematic diagram of a computer device provided by the embodiment of the present application. For example, Figure 3As shown, the computer device 600 comprises a memory 610, a processor 620 and a computer program 630 stored in the memory 610 and running on the processor 620, wherein the processor 620 executes the computer program 630, so that the computer device can execute any cable-stayed bridge supporting facility state monitoring data fusion method introduced above.

[0092] In addition, the embodiment of the present application also protects a device, which can comprise a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute the cable-stayed bridge supporting facility state monitoring data fusion method provided by the embodiment of the present application.

[0093] The embodiment of the present application can divide the device into functional modules according to the above method examples, for example, corresponding to each functional module, or two or more functions can be integrated in one processing module, and the integrated module can be realized in the form of hardware. It should be noted that the division of modules in the embodiment is illustrative, and is only a logical function division, and another division mode can be used in actual implementation.

[0094] In the case of dividing each module corresponding to each function, the device can further comprise a signal uploading module, a determination module and an adjustment module, etc. It should be noted that all related contents of each step involved in the above method embodiment can be referred to the function description of the corresponding functional module, which will not be repeated here.

[0095] It should be understood that the device provided by the embodiment of the present application is used to execute the above cable-stayed bridge supporting facility state monitoring data fusion method, so as to achieve the same effect as the above implementation method.

[0096] In the case of using integrated units, the device can comprise a processing module and a storage module. When the device is applied to a device, the processing module can be used to control and manage the action of the device. The storage module can be used to support the device to execute mutual program code, etc. The processing module can be a processor or a controller, which can realize or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure of the present application. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of digital signal processing (Digital Signal Processing, DSP) and microprocessors, etc., and the storage module can be a memory.

[0097] In addition, the device provided by the embodiment of the present application can be a chip, an assembly or a module, the chip can include a processor and a memory connected to each other, and the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the cable-stayed bridge auxiliary facility state monitoring data fusion method provided by the above embodiment.

[0098] The embodiment of the present application also provides a computer readable storage medium, which stores computer program codes, and when the computer program codes run on a computer, the computer program codes make the computer execute the related method steps to realize the cable-stayed bridge auxiliary facility state monitoring data fusion method provided by the above embodiment.

[0099] The embodiment of the present application also provides a computer program product, and when the computer program product runs on a computer, the computer program product makes the computer execute the related steps to realize the cable-stayed bridge auxiliary facility state monitoring data fusion method provided by the above embodiment.

[0100] The device, the computer readable storage medium, the computer program product or the chip provided by the embodiment of the present application are used to execute the corresponding method provided above, so the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method provided above, and will not be repeated here. Through the description of the above implementation mode, the person skilled in the art can understand that, for the convenience and brevity of description, only the above functional module division is taken as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to the needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways.

[0101] The device embodiment described above is only schematic, for example, the division of the module or unit is only a logical function division, and actual implementation can have another division mode, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed each other can be through some interface, indirect coupling or communication connection between the devices or units, and can be electrical, mechanical or other forms.

[0102] It should also be noted that, as used in this document, the terms "comprises" or "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0103] It should be noted that the above-mentioned order of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0104] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0105] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for fusing data on the condition monitoring of cable-stayed bridge supporting facilities, characterized in that, The method includes the following steps: Acquire monitoring data from various sensors on the cable-stayed bridge; The basic correlation coefficient is determined based on the correlation between the monitoring data of the same sensor and the monitoring data of the internal sensors. Analyze the oscillation performance of each sensor over historical periods to determine the baseline oscillation level; combine the baseline correlation coefficients of all sensor pairs in the historical period of the current cross-section with the baseline oscillation level of the sensors within each sensor pair to determine the overall correlation performance of the current cross-section. When any one sensor is used as the reference sensor, the basic correlation coefficient, overall correlation performance, and reference oscillation degree of multiple sensor pairs including the reference sensor are analyzed to determine the correlation collapse index of the reference sensor; the correlation collapse index characterizes the degree of actual damage that may exist at the corresponding location of the sensor. By combining the correlation collapse index of the sensors in the current section with the basic correlation coefficient of the sensor pairs, the cooperative anomaly intensity of the current section is determined; by fusing the cooperative anomaly intensity of all sections of the cable-stayed bridge at the same moment, the anomaly assessment value of the cable-stayed bridge is obtained. The method for obtaining the cooperative anomaly strength is as follows: obtain the maximum value of the correlation collapse index of the sensors in the current section; calculate the average value of the basic correlation coefficients of all sensor pairs in the current section; and take the difference between the maximum value of the correlation collapse index and the average value of the basic correlation coefficients as the cooperative anomaly strength of the current section.

2. The method for fusing data on the status monitoring of cable-stayed bridge supporting facilities according to claim 1, characterized in that, The determination of the basic correlation coefficient based on the correlation of monitoring data from the same sensor to internal sensors includes: The monitoring data of each sensor is standardized, and the standardized monitoring data is used as the new monitoring data. The same sensor pair includes two sensors. Among them, the positions of the same type of sensors in the same sensor pair are symmetrical, and the positions of cross-type sensors in the same sensor pair are symmetrical or adjacent. The minimum monitoring data of the sensors in the same sensor pair is used as the numerator, and the sum of the absolute values ​​of the monitoring data of the two sensors in the sensor pair is used as the denominator. The preset multiple of the ratio formed by the numerator and the denominator is used as the basic correlation coefficient of the sensor pair; the preset multiple corresponds to the number of sensors in the same sensor pair.

3. The method for fusing data on the status monitoring of cable-stayed bridge supporting facilities according to claim 1, characterized in that, The analysis of the oscillation performance of each sensor over historical periods to determine the baseline oscillation level includes: Calculate the absolute difference between the mean of historical monitoring data within a reference time range for different days in the current period and the historical period, and normalize the absolute difference to obtain the single-range difference; By comparing the monitoring data of the same historical moment as the current moment on each day in the historical period with the mean of all historical monitoring data, the overall monitoring difference of a single monitoring data point on each day can be obtained; By combining overall monitoring differences and single-range differences, the baseline oscillation level of the sensor's monitoring data is determined.

4. The method for fusing data on the status monitoring of cable-stayed bridge supporting facilities according to claim 1, characterized in that, The overall correlation performance of the current cross-section is determined by combining the basic correlation coefficients of all sensor pairs within the historical time period of the current cross-section and the reference oscillation degree of the sensors within each sensor pair, including: The mean value of the basic correlation coefficient for each sensor pair over the historical period is obtained as the baseline value of the correlation coefficient; The difference between the basic correlation coefficient and the baseline correlation coefficient value for each sensor pair is calculated and used as the correlation oscillation value for each sensor pair. By combining the associated oscillation value and the baseline oscillation degree, the oscillation difference of each sensor pair is determined; By performing a negative correlation mapping on the mean oscillation difference of all sensor pairs within the current cross section, the overall correlation performance of the current cross section can be obtained.

5. The method for fusing data on the status monitoring of cable-stayed bridge supporting facilities according to claim 1, characterized in that, When any one sensor is used as the reference sensor, the basic correlation coefficients, overall correlation performance, and reference oscillation degree of multiple sensor pairs including the reference sensor are analyzed to determine the correlation collapse index of the reference sensor, including: Based on the overall correlation performance of the current cross section and the reference oscillation degree of the reference sensor, the sensitivity coefficient of the reference sensor is determined; The basic correlation coefficients of each sensor pair containing the benchmark sensor are negatively correlated to obtain the initial correlation collapse index. The mean of the initial correlation collapse indices of all sensor pairs containing the benchmark sensor is weighted using the sensitivity coefficient as the weight to obtain the correlation collapse index of the benchmark sensor.

6. The method for fusing data on the status monitoring of cable-stayed bridge supporting facilities according to claim 5, characterized in that, The determination of the sensitivity coefficient of the reference sensor, based on the overall correlation performance of the current cross-section and the reference oscillation degree of the reference sensor, includes: A negative correlation mapping is performed on the overall correlation performance of the current section. The product of the negative correlation mapping result and the reference oscillation degree of the reference sensor is used as the sensitivity coefficient of the reference sensor.

7. The method for fusing data on the status monitoring of cable-stayed bridge supporting facilities according to claim 1, characterized in that, The coordinated anomaly strength of all sections of the integrated cable-stayed bridge at the same moment is used to obtain the anomaly assessment value of the cable-stayed bridge, including: Multiply the coordinated anomaly strengths of all sections on the cable-stayed bridge at the same moment, and use the result as the anomaly assessment value of the cable-stayed bridge.

8. The method for fusing data on the status monitoring of cable-stayed bridge supporting facilities according to claim 1, characterized in that, After obtaining the anomaly assessment value of the cable-stayed bridge by measuring the coordinated anomaly strength of all sections of the integrated cable-stayed bridge at the same moment, the method further includes: The abnormal assessment values ​​of cable-stayed bridges are normalized. When the normalized abnormal assessment value is greater than the preset first warning threshold, the system will be shut down for maintenance. When the normalized abnormal assessment value is less than or equal to the preset first warning threshold and greater than or equal to the preset second warning threshold, enhanced inspection is carried out. When the normalized anomaly assessment value is less than the preset second warning threshold, a routine detection is performed; wherein the preset first warning threshold is greater than the preset second warning threshold.

9. A data fusion system for monitoring the condition of cable-stayed bridge supporting facilities, characterized in that, The system includes the following modules: The monitoring module is used to acquire monitoring data from various sensors on the cable-stayed bridge. The basic correlation analysis module is used to determine the basic correlation coefficient based on the correlation of monitoring data of sensors within the same sensor pair; wherein the sensors within the same sensor pair are located in symmetrical positions. The overall correlation analysis module is used to analyze the oscillation performance of each sensor in historical time periods and determine the baseline oscillation level; combined with the basic correlation coefficients of all sensor pairs in the historical time period of the current section and the baseline oscillation level of the sensors within the sensor pair, the overall correlation performance of the current section is determined. The damage analysis module is used to analyze the basic correlation coefficient, overall correlation performance, and baseline oscillation degree of multiple sensor pairs including the reference sensor when any one sensor is used as the reference sensor, and to determine the correlation collapse index of the reference sensor; the correlation collapse index characterizes the degree of actual damage that may exist at the corresponding location of the sensor. The monitoring data fusion module is used to determine the cooperative anomaly intensity of the current cross section by combining the correlation collapse index of the sensors in the current cross section with the basic correlation coefficient of the sensor pairs; it fuses the cooperative anomaly intensity of all cross sections of the cable-stayed bridge at the same moment to obtain the anomaly assessment value of the cable-stayed bridge; the cooperative anomaly intensity is obtained by: obtaining the maximum value of the correlation collapse index of the sensors in the current cross section; calculating the average value of the basic correlation coefficient of all sensor pairs in the current cross section; and taking the difference between the maximum value of the correlation collapse index and the average value of the basic correlation coefficient as the cooperative anomaly intensity of the current cross section.

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