WO2018001147A1 - 一种基于优化张紧弦模型的桥索监测方法及系统 - Google Patents

一种基于优化张紧弦模型的桥索监测方法及系统 Download PDF

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WO2018001147A1
WO2018001147A1 PCT/CN2017/089306 CN2017089306W WO2018001147A1 WO 2018001147 A1 WO2018001147 A1 WO 2018001147A1 CN 2017089306 W CN2017089306 W CN 2017089306W WO 2018001147 A1 WO2018001147 A1 WO 2018001147A1
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eigenfrequency
optimal
cable
acceleration sensor
newton
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French (fr)
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张光烈
詹少冬
陈猛
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深圳市智能机器人研究院
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
    • G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
    • G01L5/04—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes for measuring tension in flexible members, e.g. ropes, cables, wires, threads, belts or bands
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/30—Circuit design
    • G06F30/36—Circuit design at the analogue level
    • G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods

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  • the invention relates to the field of bridge monitoring, in particular to a bridge cable monitoring method and system based on an optimized tension string model.
  • Bridge cable is an important force component of bridge structure such as cable-stayed bridge and suspension bridge.
  • the cable force value of bridge cable is an important index to evaluate the state of bridge.
  • the measurement of bridge cable force value has become an important part of bridge cable monitoring system. .
  • the spectrum vibration method mainly uses the acceleration sensor to measure the bridge cable tension, obtains the vibration frequency of the bridge cable by obtaining the acceleration of the acceleration sensor under the environmental excitation, and finally uses the tension string model to derive the bridge cable tension.
  • the bending stiffness is a difficult parameter to measure, and the bending stiffness has a great influence on the high-order eigenfrequency.
  • the bridge cable obtained by the traditional tension string model is directly used. The pulling force will have a large error.
  • each acceleration sensor attached to the bridge cable collects the vibration signal of the bridge cable in the network coverage monitoring area through the wireless sensor network, and transmits the data to the upper computer monitoring center. Perform spectrum and cable force analysis.
  • the existing bridge vibration monitoring system based on frequency vibration method has the following defects or deficiencies:
  • the acceleration sensor needs to send all the collected signals to the upper computer monitoring center, which increases the power consumption of the sensor nodes; and the sampling frequency of the acceleration sensor is a manually selected fixed value, and the low sampling rate reduces the resolution of the collected signal. Affects the analysis of data, while the high sampling rate increases the power consumption of the sensor nodes, and cannot simultaneously analyze the analysis accuracy and power consumption.
  • the object of the present invention is to provide a bridge monitoring method based on an optimized tension string model which can reduce both the analysis accuracy and the power consumption.
  • Another object of the present invention is to provide a bridge monitoring system based on an optimized tension string model which has a small error and can simultaneously take into account analysis accuracy and power consumption.
  • a method for monitoring a bridge cable based on an optimized tension string model includes the following steps:
  • the residual function of Newton's Gaussian method is constructed by selecting the same cable as the measured bridge cable. Then the Jacobian matrix method is used to solve the residual function of Newton's Gaussian method. The optimal eigenfrequency of the residual function of Newton's Gaussian method is obtained. The order and the optimal bending stiffness of the steel cable, and finally the tension string model is optimized according to the result of the iterative solution;
  • the theoretical eigenfrequency is obtained according to the theoretical tensile force of the bridge cable and the optimized tension string model
  • the acceleration sensor node determines whether to send the collected data and adjust the sampling frequency of the acceleration sensor according to the ratio of the difference between the measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency to balance the power consumption and the analysis accuracy. .
  • the steel wire with the same specifications as the measured bridge cable is selected to construct the residual function of Newton's Gaussian method, and then the residual function of Newton's Gaussian method is solved iteratively by Jacobian matrix method, and the residual function of Newton's Gaussian method is obtained.
  • the eigenfrequency order and the optimal bending stiffness of the steel cable, and finally the step of optimizing the tensioned string model according to the result of the iterative solution which includes:
  • the steel cable with the same specifications as the measured bridge cable is selected for the tensile test, and the residual function of the Newton Gaussian method is constructed.
  • the expression of the residual function r of the Newton Gauss method is: Where k is the intrinsic frequency order, EI is the bending stiffness of the steel cable, m is the unit mass of the steel cable, L is the length between the two fixed test points of the steel cable during the tensile test, and f is the vibration spectrum of the steel cable The center frequency, N is the tensile force to which the cable is subjected;
  • the expression of the optimized tension chord model is : among them, The eigenfrequency corresponding to the optimal eigenfrequency order k o .
  • the step of the Gaussian residual function optimal eigenfrequency order and the optimal bending stiffness of the steel cable includes:
  • the Jacobian matrix of the residual function r is obtained from n residual functions r 1 , r 2 , . . . r n constructed by n different tensile tests, and the expression of the Jacobian matrix J r of the residual function r is:
  • n ⁇ 2 and n is an integer, with The partial derivative of k and EI for the residual function r n of the nth test;
  • the solution variable ⁇ is iterated until the difference between ⁇ s+1 and ⁇ s is less than the set threshold, and finally k and EI corresponding to ⁇ s+1 at the end of the iteration are optimal.
  • the vibration spectrum of the bridge is obtained according to the signal collected by the acceleration sensor node, and then the obtained vibration is obtained.
  • the step of filtering out the frequency that satisfies the optimal eigenfrequency order as the measured eigenfrequency in the dynamic spectrum includes:
  • a Hamming window is added to the collected signal to obtain an acquired signal after windowing
  • the frequency that accords with the optimal eigenfrequency order is selected from the obtained vibration spectrum as the measured eigenfrequency.
  • the acceleration sensor node determines whether to send the collected data and adjust the sampling frequency of the acceleration sensor according to the ratio of the difference between the measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency to equalize the power consumption.
  • the step of analyzing accuracy which includes:
  • the acceleration sensor node performs the operation of the corresponding event according to the ratio range in which the calculated ratio is located: if the calculated ratio belongs to the ratio range of the event 1, only the measured eigenfrequency is sent to the upper computer monitoring center; if the calculated ratio belongs to The ratio range of event 2 does not change the sampling frequency of the acceleration sensor and sends the data collected by the acceleration sensor to the upper computer monitoring center; if the calculated ratio belongs to the ratio range of event three, the sampling frequency of the acceleration sensor is increased and the acceleration is increased. The data collected by the sensor is sent to the upper computer monitoring center.
  • the event 1 has a ratio range of [0, 15%), the event 1 sets the sampling frequency of the acceleration sensor to 200 Hz, and the event 2 has a value range of [15%, 30%];
  • the value of event three ranges from (30%, 100%), and event three increases the sampling frequency of the acceleration sensor to 1000 Hz.
  • a bridge monitoring system based on an optimized tension string model comprising the following modules:
  • the tension string model optimization module is used to select the steel cable of the same specification as the measured bridge cable to construct the residual function of Newton's Gaussian method, and then the Jacobian matrix method is used to iteratively solve the residual function of Newton's Gaussian method, and the Newton Gaussian method is obtained.
  • the theoretical eigenfrequency calculation module is configured to obtain a theoretical eigenfrequency according to the theoretical tension value of the bridge cable and the optimized tension string model;
  • the measured eigenfrequency acquisition module is configured to obtain a vibration spectrum of the bridge cable according to the signal collected by the acceleration sensor node, and then select a frequency that meets the optimal eigenfrequency order from the obtained vibration spectrum as the measured eigenfrequency;
  • the acceleration sensor node determining and adjusting module is configured to determine, according to the ratio of the difference between the measured eigenfrequency and the theoretical eigenfrequency and the theoretical eigenfrequency, whether the acceleration sensor node sends the collected data to the monitoring center of the upper computer and adjusts the sampling of the acceleration sensor. Frequency to balance power consumption and analysis accuracy.
  • the tension string model optimization module includes:
  • the building unit is configured to select a steel cable with the same specifications as the measured bridge cable on the tensile testing machine for the tensile test, and construct a residual function of the Newton Gauss method, and the expression of the residual function r of the Newton Gauss method is:
  • k is the intrinsic frequency order
  • EI is the bending stiffness of the steel cable
  • m is the unit mass of the steel cable
  • L is the length between the two fixed test points of the steel cable during the tensile test
  • f is the vibration spectrum of the steel cable
  • N is the tensile force to which the cable is subjected;
  • the optimized tension string The expression of the model is: among them, The eigenfrequency corresponding to the optimal eigenfrequency order k o .
  • the iterative unit includes:
  • J r J 1 , J 2 , J 3 , J 4 , J 5 , J 6 , J 7 , J 8 , J 10 , J 11 , J 11 , J 12 , J 13 , J 15 , J 16 , J 17 , J 17 , J 18 , J 20 , J 18 , J 20 , J 22
  • n ⁇ 2 and n is an integer, with The partial derivative of k and EI for the residual function r n of the nth test;
  • acceleration sensor node determining and adjusting module comprises:
  • a calculation unit for calculating a ratio of a difference between the measured eigenfrequency and the theoretical eigenfrequency to a theoretical eigenfrequency
  • the execution event determining unit is configured to perform an operation of the corresponding event according to the ratio range in which the calculated ratio is calculated: if the calculated ratio belongs to the ratio range of the event 1, only the measured eigenfrequency is sent to the upper computer monitoring center If the calculated ratio belongs to the ratio range of the event two, the sampling frequency of the acceleration sensor is not changed and the data collected by the acceleration sensor is sent to the monitoring center of the upper computer; if the calculated ratio belongs to the ratio range of the event three, the acceleration is increased. The sampling frequency of the sensor and the data collected by the acceleration sensor are sent to the upper computer monitoring center.
  • the beneficial effect of the method of the invention is that the Newton Gaussian method is used to optimize the traditional tension string model, and the optimal eigenfrequency order and the bending stiffness of the steel cable are solved, and the influence of bending stiffness is considered. And the bending stiffness can be accurately obtained, and the error is small; the acceleration sensor node determines whether to send the collected data and adjust the acceleration to the monitoring center of the upper machine according to the ratio of the difference between the measured eigenfrequency and the theoretical eigenfrequency and the theoretical eigenfrequency.
  • the sampling frequency of the sensor avoids the situation of continuously transmitting the sampling signal, and avoids the case of low analysis accuracy, and can simultaneously take into account the analysis accuracy and power consumption.
  • the beneficial effects of the system of the present invention are: Newton Gaussian method is used to optimize the traditional tension string model, and the optimal eigenfrequency order and the bending stiffness of the steel cable are solved, and the influence of bending stiffness is considered. And the bending stiffness can be accurately obtained, and the error is small; the acceleration sensor node determines whether to send the collected data and adjust the acceleration to the monitoring center of the upper machine according to the ratio of the difference between the measured eigenfrequency and the theoretical eigenfrequency and the theoretical eigenfrequency.
  • the sampling frequency of the sensor avoids the situation of continuously transmitting the sampling signal, and avoids the case of low analysis accuracy, and can simultaneously take into account the analysis accuracy and power consumption.
  • FIG. 1 is an overall flow chart of a method for monitoring a bridge cable based on an optimized tension string model according to the present invention
  • a method for monitoring a bridge cable based on an optimized tension string model includes the following steps:
  • the residual function of Newton's Gaussian method is constructed by selecting the same cable as the measured bridge cable. Then the Jacobian matrix method is used to solve the residual function of Newton's Gaussian method. The optimal eigenfrequency of the residual function of Newton's Gaussian method is obtained. The order and the optimal bending stiffness of the steel cable, and finally the tension string model is optimized according to the result of the iterative solution;
  • the theoretical eigenfrequency is obtained according to the theoretical tensile force of the bridge cable and the optimized tension string model
  • the acceleration sensor node determines whether to send the collected data and adjust the sampling frequency of the acceleration sensor according to the ratio of the difference between the measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency to balance the power consumption and the analysis accuracy. .
  • the steel wire of the same specification as the measured bridge cable is selected to construct the residual function of the Newton Gauss method, and then the residual function of the Newton Gauss method is solved iteratively by using the Jacobian matrix method to obtain the Newton Gaussian method.
  • the optimal eigenfrequency order of the residual function and the optimal bending stiffness of the steel cable, and finally the step of optimizing the tensioned string model according to the result of the iterative solution which includes:
  • the steel cable with the same specifications as the measured bridge cable is selected for the tensile test, and the residual function of the Newton Gaussian method is constructed.
  • the expression of the residual function r of the Newton Gauss method is: Where k is the intrinsic frequency order, EI is the bending stiffness of the steel cable, m is the unit mass of the steel cable, L is the length between the two fixed test points of the steel cable during the tensile test, and f is the vibration spectrum of the steel cable The center frequency, N is the tensile force to which the cable is subjected;
  • the expression of the optimized tension chord model is : among them, The eigenfrequency corresponding to the optimal eigenfrequency order k o .
  • the Jacobian matrix of the residual function r is obtained from n residual functions r 1 , r 2 , . . . r n constructed by n different tensile tests, and the expression of the Jacobian matrix J r of the residual function r is:
  • n ⁇ 2 and n is an integer, with The partial derivative of k and EI for the residual function r n of the nth test;
  • the solution variable ⁇ is iterated until the difference between ⁇ s+1 and ⁇ s is less than the set threshold, and finally k and EI corresponding to ⁇ s+1 at the end of the iteration are optimal.
  • the vibration spectrum of the bridge cable is obtained according to the signal collected by the acceleration sensor node, and then the frequency corresponding to the optimal eigenfrequency order is selected from the obtained vibration spectrum as the measured eigenfrequency.
  • a Hamming window is added to the collected signal to obtain an acquired signal after windowing
  • the frequency that accords with the optimal eigenfrequency order is selected from the obtained vibration spectrum as the measured eigenfrequency.
  • the acceleration sensor node determines whether to send the collected data and adjust the sampling frequency of the acceleration sensor according to the ratio of the difference between the measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency. To equalize power consumption and analyze accuracy, this includes:
  • the acceleration sensor node performs the operation of the corresponding event according to the ratio range in which the calculated ratio is located: if the calculated ratio belongs to the ratio range of the event 1, only the measured eigenfrequency is sent to the upper computer monitoring center; if the calculated ratio belongs to The ratio range of event 2 does not change the sampling frequency of the acceleration sensor and sends the data collected by the acceleration sensor to the upper computer monitoring center; if the calculated ratio belongs to the ratio range of event three, the sampling frequency of the acceleration sensor is increased and the acceleration is increased. The data collected by the sensor is sent to the upper computer monitoring center.
  • the ratio of the event one is [0, 15%), the event one sets the sampling frequency of the acceleration sensor to 200 Hz, and the event two ranges from [15%, 30 %]; The value of the event three ranges from (30%, 100%), and the event three increases the sampling frequency of the acceleration sensor to 1000 Hz.
  • a bridge monitoring system based on an optimized tension string model includes the following modules:
  • the tension string model optimization module is used to select the steel cable of the same specification as the measured bridge cable to construct the residual function of Newton's Gaussian method, and then the Jacobian matrix method is used to iteratively solve the residual function of Newton's Gaussian method, and the Newton Gaussian method is obtained.
  • Theoretical eigenfrequency calculation module for finding the theory based on the theoretical tension value of the bridge cable and the optimized tension string model Eigenfrequency
  • the measured eigenfrequency acquisition module is configured to obtain a vibration spectrum of the bridge cable according to the signal collected by the acceleration sensor node, and then select a frequency that meets the optimal eigenfrequency order from the obtained vibration spectrum as the measured eigenfrequency;
  • the acceleration sensor node determining and adjusting module is configured to determine, according to the ratio of the difference between the measured eigenfrequency and the theoretical eigenfrequency and the theoretical eigenfrequency, whether the acceleration sensor node sends the collected data to the monitoring center of the upper computer and adjusts the sampling of the acceleration sensor. Frequency to balance power consumption and analysis accuracy.
  • the tension string model optimization module includes:
  • the building unit is configured to select a steel cable with the same specifications as the measured bridge cable on the tensile testing machine for the tensile test, and construct a residual function of the Newton Gauss method, and the expression of the residual function r of the Newton Gauss method is:
  • k is the intrinsic frequency order
  • EI is the bending stiffness of the steel cable
  • m is the unit mass of the steel cable
  • L is the length between the two fixed test points of the steel cable during the tensile test
  • f is the vibration spectrum of the steel cable
  • N is the tensile force to which the cable is subjected;
  • the optimized tension string The expression of the model is: among them, The eigenfrequency corresponding to the optimal eigenfrequency order k o .
  • the iterative unit includes:
  • J r J 1 , J 2 , J 3 , J 4 , J 5 , J 6 , J 7 , J 8 , J 10 , J 11 , J 11 , J 12 , J 13 , J 15 , J 16 , J 17 , J 17 , J 18 , J 20 , J 18 , J 20 , J 22
  • n ⁇ 2 and n is an integer, with The partial derivative of k and EI for the residual function r n of the nth test;
  • the acceleration sensor node determining and adjusting module includes:
  • a calculation unit for calculating a ratio of a difference between the measured eigenfrequency and the theoretical eigenfrequency to a theoretical eigenfrequency
  • the execution event determining unit is configured to perform an operation of the corresponding event according to the ratio range in which the calculated ratio is calculated: if the calculated ratio belongs to the ratio range of the event 1, only the measured eigenfrequency is sent to the upper computer monitoring center If the calculated ratio belongs to the ratio range of the event two, the sampling frequency of the acceleration sensor is not changed and the data collected by the acceleration sensor is sent to the monitoring center of the upper computer; if the calculated ratio belongs to the ratio range of the event three, the acceleration is increased. The sampling frequency of the sensor and the data collected by the acceleration sensor are sent to the upper computer monitoring center.
  • the present invention proposes a novel bridge monitoring method which takes into account the influence of bending stiffness on the conventional tensioned string model. Optimized, and intelligently adjust the sampling frequency of the sensor node and whether to send the collected data to the host computer.
  • the flow of the bridge monitoring algorithm of the present invention is shown in FIG. 2, and mainly includes the following processes:
  • k is the order of the eigenfrequency
  • EI is the bending stiffness of the steel cable
  • m is the unit mass of the cable
  • L is the length between the two test fixed points of the cable
  • f is the center frequency of the vibration spectrum
  • N is the tensile force that the cable is subjected to.
  • ⁇ (k, EI) is used as an unknown variable
  • (m, L, f, N) is a known variable.
  • T is the transpose of the matrix and J r is the Jacobian matrix of the residual function.
  • the main process of obtaining the measured eigenfrequency is as follows: in the sensor node, the Hamming window is added to the acquired signal; then the fast Fourier transform is used to obtain the vibration spectrum of the bridge; finally, the optimized tensioned string model in the vibration spectrum is extracted. The frequency of the optimal eigenfrequency order is used as the measured eigenfrequency.
  • the adjustment setting of whether the sensor sends the collected data or not and the sampling frequency takes the ratio between the difference between the measured eigenfrequency and the theoretical eigenfrequency and the theoretical eigenfrequency as the criterion for judgment.
  • the present invention determines three types of events according to the range in which the ratio is located, and specifies information sent by the sensor node:
  • Event 1 Only send the measured eigenfrequency to the upper computer monitoring center.
  • the sampling frequency of the acceleration sensor can be set according to actual needs (for example, set to 200 Hz).
  • Event 2 The sampling frequency of the sensor is not changed, the data collected by the acceleration sensor is sent to the monitoring center of the upper computer, and then further analysis is performed.
  • Event 3 Increase the sampling frequency of the acceleration sensor (such as changing the sampling frequency of the sensor to 1000 Hz) and send the data collected by the acceleration sensor to the upper computer monitoring center for further analysis.
  • the three types of events identified by different bridges are also in different ratio ranges.
  • the ratio of event one can be set to less than 15%
  • the ratio of event two is in the range of 15% to 30%
  • the ratio of event three is greater than 30%.
  • the bridge state of event one is good
  • the bridge of event two is good.
  • the state is general, and the state of the bridge of the event one is abnormal. Therefore, the present invention can also analyze the abnormal condition of the bridge cable according to the ratio range of the ratio of the three events and the ratio calculated in real time.
  • the present invention has the following advantages:
  • the tensioned string model optimized by Gauss-Newton method can increase the reliability of cable force identification with less error.
  • the acceleration sensor sends the acquisition information or not and the sampling frequency is adjusted according to the relationship between the measured and theoretical eigenfrequency, thereby avoiding the continuous transmission of the sampling signal, and can effectively reduce the state of the bridge while continuously monitoring.
  • the power consumption of the sensor node is balanced with efficiency and analysis accuracy.

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Abstract

一种基于优化张紧弦模型的桥索监测方法及系统，方法包括：选取与实测桥索相同规格的钢缆来构建牛顿高斯法的残余函数，然后对牛顿高斯法的残余函数进行迭代求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度，最后根据迭代求解的结果优化张紧弦模型；根据桥索的理论拉力值以及优化后的张紧弦模型求得理论本征频率；根据加速度传感器节点采集的信号得出桥索的振动频谱，然后从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率；加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率。该桥索监测方法的误差小，能同时兼顾分析准确度和功耗，可广泛应用于桥梁监测领域。

Description

一种基于优化张紧弦模型的桥索监测方法及系统 技术领域
本发明涉及桥梁监测领域，尤其是一种基于优化张紧弦模型的桥索监测方法及系统。
背景技术
桥索是斜拉桥和悬索桥等桥梁结构的重要受力构件，桥索的索力值是评估桥梁状态的重要指标，桥索索力值的测定也成了桥索监控系统中的一个重要组成部分。目前，已有不少的桥索索力值测定方法，其中具有简易性和非破坏性优点的频谱振动法，得到了较广泛的应用。频谱振动法主要采用了加速度传感器来对桥索拉力进行测量，通过获取加速度传感器在环境激励下的加速度来得到桥索的振动频率，最后利用张紧弦模型来得出桥索拉力。在作为频谱振动法理论依据的张紧弦模型中，抗弯刚度是一个难以测量的参数，而且抗弯刚度对高阶本征频率的影响大，直接采用传统的张紧弦模型得到的桥索拉力将会出现较大的误差。
在现有基于频率振动法的桥索监测系统中，附在桥索上的各个加速度传感器通过无线传感器网络对网络覆盖监测区域内的桥索进行振动信号采集，并将数据传送至上位机监控中心进行频谱和索力分析。然而，现有基于频率振动法的桥索监测系统存在着以下缺陷或不足：
(1)仍直接采用传统的张紧弦模型来得出桥索的拉力，忽略了抗弯刚度的影响，误差较大；
(2)加速度传感器需要将采集的全部信号发送至上位机监控中心，增加了传感器节点的功耗；而且加速度传感器的采样频率为人工选择的固定值，低采样率会降低采集信号的分辨率最终影响到数据的分析，而高采样率则会增加传感器节点的功耗，无法同时兼顾分析准确度和功耗。
发明内容
为解决上述技术问题，本发明的目的在于：提供一种误差小，能同时兼顾分析准确度和功耗的，基于优化张紧弦模型的桥索监测方法。
本发明的另一目的在于：提供一种误差小，能同时兼顾分析准确度和功耗的，基于优化张紧弦模型的桥索监测系统。
本发明所采取的技术方案是：
一种基于优化张紧弦模型的桥索监测方法，包括以下步骤：
选取与实测桥索相同规格的钢缆来构建牛顿高斯法的残余函数，然后采用雅可比矩阵法对牛顿高斯法的残余函数进行迭代求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度，最后根据迭代求解的结果优化张紧弦模型；
根据桥索的理论拉力值以及优化后的张紧弦模型求得理论本征频率；
根据加速度传感器节点采集的信号得出桥索的振动频谱，然后从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率；
加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，以均衡功耗和分析准确度。
进一步，所述选取与实测桥索相同规格的钢缆来构建牛顿高斯法的残余函数，然后采用雅可比矩阵法对牛顿高斯法的残余函数进行迭代求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度，最后根据迭代求解的结果优化张紧弦模型这一步骤，其包括：
在拉力试验机上选取与实测桥索相同规格的钢缆进行拉力试验，构建出牛顿高斯法的残 余函数，所述牛顿高斯法的残余函数r的表达式为：
Figure PCTCN2017089306-appb-000001
其中，k为本征频率阶数，EI为钢缆的抗弯刚度，m为钢缆的单位质量，L为拉力试验时钢缆两个固定测试点之间的长度，f为钢缆振动频谱的中心频率，N为钢缆受到的拉力；
将(m,L,f,N)作为已知变量，β＝(k,EI)作为待求解变量，采用雅可比矩阵迭代法对牛顿高斯法的残余函数进行求解，得出牛顿高斯法的残余函数最优的本征频率阶数ko以及最优的钢缆抗弯刚度EIo；
将最优的本征频率阶数ko以及最优的钢缆抗弯刚度EIo代入张紧弦模型，生成优化后的张紧弦模型，所述优化后的张紧弦模型的表达式为：
Figure PCTCN2017089306-appb-000002
其中，
Figure PCTCN2017089306-appb-000003
为最优的本征频率阶数ko对应的本征频率。
进一步，所述将(m,L,f,N)作为已知变量，β＝(k,EI)作为待求解变量，采用雅可比矩阵迭代法对牛顿高斯法的残余函数进行求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度这一步骤，其包括：
根据n次不同拉力试验构建的n个残余函数r1,r2,...rn得到残余函数r的雅可比矩阵，所述残余函数r的雅可比矩阵Jr的表达式为：
Figure PCTCN2017089306-appb-000004
其中，n≥2且n为整数，
Figure PCTCN2017089306-appb-000005
和
Figure PCTCN2017089306-appb-000006
为第n次试验的残余函数rn分别对k和EI的偏导数；
根据得到的雅可比矩阵Jr对待求解变量β进行迭代，直至βs+1和βs的差值小于设定阈值为止，最终以迭代结束时βs+1对应的k和EI作为最优的本征频率阶数以及最优的钢缆抗弯刚度，其中，对待求解变量β进行迭代的公式为：
Figure PCTCN2017089306-appb-000007
式中，s为迭代次数，且0≤s≤n，迭代初始值β0＝(1,EI0)，EI0为钢缆抗弯刚度的最小估计值。
进一步，所述根据加速度传感器节点采集的信号得出桥索的振动频谱，然后从得到的振 动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率这一步骤，其包括：
在加速度传感器节点中，对采集的信号添加海明窗口，得到加窗后的采集信号；
对加窗后的采集信号进行快速傅里叶变换，得到桥索的振动频谱；
从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率。
进一步，所述加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，以均衡功耗和分析准确度这一步骤，其包括：
计算实测本征频率和理论本征频率的差值与理论本征频率的比值；
加速度传感器节点根据计算出的比值所处的比值范围执行相应事件的操作：若计算出的比值属于事件一的比值范围，则只发送实测本征频率给上位机监控中心；若计算出的比值属于事件二的比值范围，则不改变加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心；若计算出的比值属于事件三的比值范围，则提高加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心。
进一步，所述事件一的比值范围为[0，15％)，所述事件一将加速度传感器的采样频率设为200Hz；所述事件二的取值范围为[15％，30％]；所述事件三的取值范围为(30％，100％]，所述事件三将加速度传感器的采样频率增加至1000Hz。
本发明所采取的另一技术方案是：
一种基于优化张紧弦模型的桥索监测系统，包括以下模块：
张紧弦模型优化模块，用于选取与实测桥索相同规格的钢缆来构建牛顿高斯法的残余函数，然后采用雅可比矩阵法对牛顿高斯法的残余函数进行迭代求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度，最后根据迭代求解的结果优化张紧弦模型；
理论本征频率计算模块，用于根据桥索的理论拉力值以及优化后的张紧弦模型求得理论本征频率；
实测本征频率获取模块，用于根据加速度传感器节点采集的信号得出桥索的振动频谱，然后从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率；
加速度传感器节点确定与调整模块，用于加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，以均衡功耗和分析准确度。
进一步，所述张紧弦模型优化模块包括：
构建单元，用于在拉力试验机上选取与实测桥索相同规格的钢缆进行拉力试验，构建出牛顿高斯法的残余函数，所述牛顿高斯法的残余函数r的表达式为：
Figure PCTCN2017089306-appb-000008
其中，k为本征频率阶数，EI为钢缆的抗弯刚度，m为钢缆的单位质量，L为拉力试验时钢缆两个固定测试点之间的长度，f为钢缆振动频谱的中心频率，N为钢缆受到的拉力；
迭代单元，用于将(m,L,f,N)作为已知变量，β＝(k,EI)作为待求解变量，采用雅可比矩阵迭代法对牛顿高斯法的残余函数进行求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度；
代入单元，用于将最优的本征频率阶数ko以及最优的钢缆抗弯刚度EIo代入张紧弦模型，生成优化后的张紧弦模型，所述优化后的张紧弦模型的表达式为：
Figure PCTCN2017089306-appb-000009
其中，
Figure PCTCN2017089306-appb-000010
为最优的本征频率阶数ko对应的本征频率。
进一步，所述迭代单元包括：
雅可比矩阵获取子单元，用于根据n次不同拉力试验构建的n个残余函数r1,r2,...rn得到残余函数r的雅可比矩阵，所述残余函数r的雅可比矩阵Jr的表达式为：
Figure PCTCN2017089306-appb-000011
其中，n≥2且n为整数，
Figure PCTCN2017089306-appb-000012
和
Figure PCTCN2017089306-appb-000013
为第n次试验的残余函数rn分别对k和EI的偏导数；
迭代子单元，用于根据得到的雅可比矩阵Jr对待求解变量β进行迭代，直至βs+1和βs的差值小于设定阈值为止，最终以迭代结束时βs+1对应的k和EI作为最优的本征频率阶数以及最优的钢缆抗弯刚度，其中，对待求解变量β进行迭代的公式为：
Figure PCTCN2017089306-appb-000014
式中，s为迭代次数，且0≤s≤n，迭代初始值β0＝(1,EI0)，EI0为钢缆抗弯刚度的最小估计值。
进一步，所述加速度传感器节点确定与调整模块包括：
计算单元，用于计算实测本征频率和理论本征频率的差值与理论本征频率的比值；
执行事件确定单元，用于加速度传感器节点根据计算出的比值所处的比值范围执行相应事件的操作：若计算出的比值属于事件一的比值范围，则只发送实测本征频率给上位机监控中心；若计算出的比值属于事件二的比值范围，则不改变加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心；若计算出的比值属于事件三的比值范围，则提高加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心。
本发明的方法的有益效果是：采用了牛顿高斯法来对传统的张紧弦模型进行了优化，求解出最优的本征频率阶数和钢缆抗弯刚度，考虑了抗弯刚度的影响且能准确得出抗弯刚度，误差较小；加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，既避免了持续发送采样信号的情况，又避免了分析准确度低的情况，能同时兼顾分析准确度和功耗。
本发明的系统的有益效果是：采用了牛顿高斯法来对传统的张紧弦模型进行了优化，求解出最优的本征频率阶数和钢缆抗弯刚度，考虑了抗弯刚度的影响且能准确得出抗弯刚度，误差较小；加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，既避免了持续发送采样信号的情况，又避免了分析准确度低的情况，能同时兼顾分析准确度和功耗。
附图说明
图1为本发明一种基于优化张紧弦模型的桥索监测方法的整体流程图；
图2为实施例一的算法流程图。
具体实施方式
参照图1，一种基于优化张紧弦模型的桥索监测方法，包括以下步骤：
选取与实测桥索相同规格的钢缆来构建牛顿高斯法的残余函数，然后采用雅可比矩阵法对牛顿高斯法的残余函数进行迭代求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度，最后根据迭代求解的结果优化张紧弦模型；
根据桥索的理论拉力值以及优化后的张紧弦模型求得理论本征频率；
根据加速度传感器节点采集的信号得出桥索的振动频谱，然后从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率；
加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，以均衡功耗和分析准确度。
进一步作为优选的实施方式，所述选取与实测桥索相同规格的钢缆来构建牛顿高斯法的残余函数，然后采用雅可比矩阵法对牛顿高斯法的残余函数进行迭代求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度，最后根据迭代求解的结果优化张紧弦模型这一步骤，其包括：
在拉力试验机上选取与实测桥索相同规格的钢缆进行拉力试验，构建出牛顿高斯法的残余函数，所述牛顿高斯法的残余函数r的表达式为：
Figure PCTCN2017089306-appb-000015
其中，k为本征频率阶数，EI为钢缆的抗弯刚度，m为钢缆的单位质量，L为拉力试验时钢缆两个固定测试点之间的长度，f为钢缆振动频谱的中心频率，N为钢缆受到的拉力；
将(m,L,f,N)作为已知变量，β＝(k,EI)作为待求解变量，采用雅可比矩阵迭代法对牛顿高斯法的残余函数进行求解，得出牛顿高斯法的残余函数最优的本征频率阶数ko以及最优的钢缆抗弯刚度EIo；
将最优的本征频率阶数ko以及最优的钢缆抗弯刚度EIo代入张紧弦模型，生成优化后的张紧弦模型，所述优化后的张紧弦模型的表达式为：
Figure PCTCN2017089306-appb-000016
其中，
Figure PCTCN2017089306-appb-000017
为最优的本征频率阶数ko对应的本征频率。
进一步作为优选的实施方式，所述将(m,L,f,N)作为已知变量，β＝(k,EI)作为待求解变量，采用雅可比矩阵迭代法对牛顿高斯法的残余函数进行求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度这一步骤，其包括：
根据n次不同拉力试验构建的n个残余函数r1,r2,...rn得到残余函数r的雅可比矩阵，所述残余函数r的雅可比矩阵Jr的表达式为：
Figure PCTCN2017089306-appb-000018
其中，n≥2且n为整数，
Figure PCTCN2017089306-appb-000019
和
Figure PCTCN2017089306-appb-000020
为第n次试验的残余函数rn分别对k和EI的偏导数；
根据得到的雅可比矩阵Jr对待求解变量β进行迭代，直至βs+1和βs的差值小于设定阈值为止，最终以迭代结束时βs+1对应的k和EI作为最优的本征频率阶数以及最优的钢缆抗弯刚度，其中，对待求解变量β进行迭代的公式为：
Figure PCTCN2017089306-appb-000021
式中，s为迭代次数，且0≤s≤n，迭代初始值β0＝(1,EI0)，EI0为钢缆抗弯刚度的最小估计值。
进一步作为优选的实施方式，所述根据加速度传感器节点采集的信号得出桥索的振动频谱，然后从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率这一步骤，其包括：
在加速度传感器节点中，对采集的信号添加海明窗口，得到加窗后的采集信号；
对加窗后的采集信号进行快速傅里叶变换，得到桥索的振动频谱；
从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率。
进一步作为优选的实施方式，所述加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，以均衡功耗和分析准确度这一步骤，其包括：
计算实测本征频率和理论本征频率的差值与理论本征频率的比值；
加速度传感器节点根据计算出的比值所处的比值范围执行相应事件的操作：若计算出的比值属于事件一的比值范围，则只发送实测本征频率给上位机监控中心；若计算出的比值属于事件二的比值范围，则不改变加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心；若计算出的比值属于事件三的比值范围，则提高加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心。
进一步作为优选的实施方式，所述事件一的比值范围为[0，15％)，所述事件一将加速度传感器的采样频率设为200Hz；所述事件二的取值范围为[15％，30％]；所述事件三的取值范围为(30％，100％]，所述事件三将加速度传感器的采样频率增加至1000Hz。
参照图1，一种基于优化张紧弦模型的桥索监测系统，包括以下模块：
张紧弦模型优化模块，用于选取与实测桥索相同规格的钢缆来构建牛顿高斯法的残余函数，然后采用雅可比矩阵法对牛顿高斯法的残余函数进行迭代求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度，最后根据迭代求解的结果优化张紧弦模型；
理论本征频率计算模块，用于根据桥索的理论拉力值以及优化后的张紧弦模型求得理论 本征频率；
实测本征频率获取模块，用于根据加速度传感器节点采集的信号得出桥索的振动频谱，然后从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率；
加速度传感器节点确定与调整模块，用于加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，以均衡功耗和分析准确度。
进一步作为优选的实施方式，所述张紧弦模型优化模块包括：
构建单元，用于在拉力试验机上选取与实测桥索相同规格的钢缆进行拉力试验，构建出牛顿高斯法的残余函数，所述牛顿高斯法的残余函数r的表达式为：
Figure PCTCN2017089306-appb-000022
其中，k为本征频率阶数，EI为钢缆的抗弯刚度，m为钢缆的单位质量，L为拉力试验时钢缆两个固定测试点之间的长度，f为钢缆振动频谱的中心频率，N为钢缆受到的拉力；
迭代单元，用于将(m,L,f,N)作为已知变量，β＝(k,EI)作为待求解变量，采用雅可比矩阵迭代法对牛顿高斯法的残余函数进行求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度；
代入单元，用于将最优的本征频率阶数ko以及最优的钢缆抗弯刚度EIo代入张紧弦模型，生成优化后的张紧弦模型，所述优化后的张紧弦模型的表达式为：
Figure PCTCN2017089306-appb-000023
其中，
Figure PCTCN2017089306-appb-000024
为最优的本征频率阶数ko对应的本征频率。
进一步作为优选的实施方式，所述迭代单元包括：
雅可比矩阵获取子单元，用于根据n次不同拉力试验构建的n个残余函数r1,r2,...rn得到残余函数r的雅可比矩阵，所述残余函数r的雅可比矩阵Jr的表达式为：
Figure PCTCN2017089306-appb-000025
其中，n≥2且n为整数，
Figure PCTCN2017089306-appb-000026
和
Figure PCTCN2017089306-appb-000027
为第n次试验的残余函数rn分别对k和EI的偏导数；
迭代子单元，用于根据得到的雅可比矩阵Jr对待求解变量β进行迭代，直至βs+1和βs的差值小于设定阈值为止，最终以迭代结束时βs+1对应的k和EI作为最优的本征频率 阶数以及最优的钢缆抗弯刚度，其中，对待求解变量β进行迭代的公式为：
Figure PCTCN2017089306-appb-000028
式中，s为迭代次数，且0≤s≤n，迭代初始值β0＝(1,EI0)，EI0为钢缆抗弯刚度的最小估计值。
进一步作为优选的实施方式，所述加速度传感器节点确定与调整模块包括：
计算单元，用于计算实测本征频率和理论本征频率的差值与理论本征频率的比值；
执行事件确定单元，用于加速度传感器节点根据计算出的比值所处的比值范围执行相应事件的操作：若计算出的比值属于事件一的比值范围，则只发送实测本征频率给上位机监控中心；若计算出的比值属于事件二的比值范围，则不改变加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心；若计算出的比值属于事件三的比值范围，则提高加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心。
下面结合说明书附图和具体实施例对本发明作进一步解释和说明。
实施例一
参照图2，本发明的第一实施例：
针对现有技术误差大，不能同时兼顾分析准确度和功耗的问题，本发明提出了一种全新的桥索监测方法，该方法考虑了抗弯刚度的影响来对传统的张紧弦模型进行了优化，并可智能调整传感器节点的采样频率和是否向上位机发送采集数据。本发明的桥索监测算法的流程如图2所示，主要包括如下过程：
(一)提取理论本征频率。
提取理论本征频率的过程可进一步细分为：
(1)在拉力试验机上进行与实测桥索相同规格的钢缆的拉力试验，构造牛顿高斯法的残余函数r：
Figure PCTCN2017089306-appb-000029
其中，k为本征频率的阶数，EI为钢缆的抗弯刚度，m为钢缆的单位质量，L为钢缆两个测试固定点之间的长度，f为振动频谱的中心频率，N为钢缆受到的拉力。在这里，β＝(k,EI)作为未知变量，(m,L,f,N)作为已知变量。
(2)通过n次不同的拉力试验构造出n个残余函数(n>＝2)，然后从一个估计的最小β(即初始的变量组β0＝(1,EI0)，其由一阶本征频率阶数和钢缆的抗弯刚度估计值组成)开始不断地进行迭代，迭代的公式如下：
Figure PCTCN2017089306-appb-000030
Figure PCTCN2017089306-appb-000031
其中，T为矩阵的转置，Jr为残余函数的雅可比矩阵。当βs+1和βs的差值小于某个阈值时，迭代结束，此时即可根据迭代结束时βs+1对应的k和EI获得最优的频率阶数ko和钢缆的抗弯刚度EIo。
(3)将最优的本征频率阶数以及最优的钢缆抗弯刚度代入张紧弦模型，生成优化后的张紧弦模型，即钢缆受到的拉力可表达成：
Figure PCTCN2017089306-appb-000032
其中，
Figure PCTCN2017089306-appb-000033
为最优的本征频率阶数ko对应的本征频率，EIo为最优的钢缆抗弯刚度，m为钢缆的单位质量，L为钢缆固定的长度。由于钢缆的规格与桥索的规格一致，因此，此步骤得到的优化后的张紧弦模型即为桥索优化后的张紧弦模型。
(4)根据桥索的理论拉力值和优化后的张紧弦模型求得桥索的理论本征频率。
(二)获取实测本征频率。
获取实测本征频率的主要过程如下：在传感器节点中，对采集信号添加海明窗口；随后作快速傅里叶变换得到桥索的振动频谱；最后提取振动频谱中符合优化后的张紧弦模型中最优本征频率阶数的频率，作为实测本征频率。
(三)传感器节点事件选择。
传感器发送采集数据与否和采样频率的调整设置会以实测本征频率和理论本征频率的差值与理论本征频率之间的比值作为判断的标准。本发明根据该比值所处的范围来确定三类事件，指定传感器节点发送的信息：
事件一：只发送实测本征频率至上位机监控中心。此时，加速度传感器的采样频率可根据实际需要进行设置(如设为200Hz)。
事件二：不更改传感器的采样频率，发送加速度传感器采集的数据至上位机监控中心，然后做进一步的分析。
事件三：提高加速度传感器的采样频率(如更改传感器的采样频率至1000Hz)并将加速度传感器采集的数据发送给上位机监控中心，做进一步的分析。
不同桥索所确定的三类事件其所处的比值范围也不同。例如，可设置事件一的比值小于15％，事件二的比值在15％到30％的范围内，事件三的比值大于30％，此时事件一的桥索状态为良好，事件二的桥索状态为一般，事件一的桥索状态为异常，故本发明还可根据这三个事件的比值取值范围以及实时计算出的比值对桥索进行异常情况分析。
与现有技术相比，本发明具有以下优点：
第一，与传统的张紧弦模型对比，利用高斯牛顿法优化后的张紧弦模模型可以增加索力识别的可靠性，误差更小。
第二，加速度传感器发送采集信息与否和采样频率都会根据实测和理论本征频率的关系进行调整，从而避免了持续发送采样信号的情况，并能在保证持续监测桥索状态的同时有效地降低了传感器节点的功耗，同时兼顾了效率和分析准确度。
以上是对本发明的较佳实施进行了具体说明，但本发明并不限于所述实施例，熟悉本领域的技术人员在不违背本发明精神的前提下还可做作出种种的等同变形或替换，这些等同的变形或替换均包含在本申请权利要求所限定的范围内。

Claims (10)

  1. 一种基于优化张紧弦模型的桥索监测方法，其特征在于：包括以下步骤：
    选取与实测桥索相同规格的钢缆来构建牛顿高斯法的残余函数，然后采用雅可比矩阵法对牛顿高斯法的残余函数进行迭代求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度，最后根据迭代求解的结果优化张紧弦模型；
    根据桥索的理论拉力值以及优化后的张紧弦模型求得理论本征频率；
    根据加速度传感器节点采集的信号得出桥索的振动频谱，然后从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率；
    加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，以均衡功耗和分析准确度。
  2. 根据权利要求1所述的一种基于优化张紧弦模型的桥索监测方法，其特征在于：所述选取与实测桥索相同规格的钢缆来构建牛顿高斯法的残余函数，然后采用雅可比矩阵法对牛顿高斯法的残余函数进行迭代求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度，最后根据迭代求解的结果优化张紧弦模型这一步骤，其包括：
    在拉力试验机上选取与实测桥索相同规格的钢缆进行拉力试验，构建出牛顿高斯法的残余函数，所述牛顿高斯法的残余函数r的表达式为：
    Figure PCTCN2017089306-appb-100001
    其中，k为本征频率阶数，EI为钢缆的抗弯刚度，m为钢缆的单位质量，L为拉力试验时钢缆两个固定测试点之间的长度，f为钢缆振动频谱的中心频率，N为钢缆受到的拉力；
    将(m,L,f,N)作为已知变量，β＝(k,EI)作为待求解变量，采用雅可比矩阵迭代法对牛顿高斯法的残余函数进行求解，得出牛顿高斯法的残余函数最优的本征频率阶数ko以及最优的钢缆抗弯刚度EIo；
    将最优的本征频率阶数ko以及最优的钢缆抗弯刚度EIo代入张紧弦模型，生成优化后的张紧弦模型，所述优化后的张紧弦模型的表达式为：
    Figure PCTCN2017089306-appb-100002
    其中，
    Figure PCTCN2017089306-appb-100003
    为最优的本征频率阶数ko对应的本征频率。
  3. 根据权利要求2所述的一种基于优化张紧弦模型的桥索监测方法，其特征在于：所述将(m,L,f,N)作为已知变量，β＝(k,EI)作为待求解变量，采用雅可比矩阵迭代法对牛顿高斯法的残余函数进行求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度这一步骤，其包括：
    根据n次不同拉力试验构建的n个残余函数r1,r2,...rn得到残余函数r的雅可比矩阵，所述残余函数r的雅可比矩阵Jr的表达式为：
    Figure PCTCN2017089306-appb-100004
    其中，n≥2且n为整数，
    Figure PCTCN2017089306-appb-100005
    和
    Figure PCTCN2017089306-appb-100006
    为第n次试验的残余函数rn分别对k和EI的偏导数；
    根据得到的雅可比矩阵Jr对待求解变量β进行迭代，直至βs+1和βs的差值小于设定阈值为止，最终以迭代结束时βs+1对应的k和EI作为最优的本征频率阶数以及最优的钢缆抗弯刚度，其中，对待求解变量β进行迭代的公式为：
    Figure PCTCN2017089306-appb-100007
    式中，s为迭代次数，且0≤s≤n，迭代初始值β0＝(1,EI0)，EI0为钢缆抗弯刚度的最小估计值。
  4. 根据权利要求1所述的一种基于优化张紧弦模型的桥索监测方法，其特征在于：所述根据加速度传感器节点采集的信号得出桥索的振动频谱，然后从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率这一步骤，其包括：
    在加速度传感器节点中，对采集的信号添加海明窗口，得到加窗后的采集信号；
    对加窗后的采集信号进行快速傅里叶变换，得到桥索的振动频谱；
    从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率。
  5. 根据权利要求1-4任一项所述的一种基于优化张紧弦模型的桥索监测方法，其特征在于：所述加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，以均衡功耗和分析准确度这一步骤，其包括：
    计算实测本征频率和理论本征频率的差值与理论本征频率的比值；
    加速度传感器节点根据计算出的比值所处的比值范围执行相应事件的操作：若计算出的比值属于事件一的比值范围，则只发送实测本征频率给上位机监控中心；若计算出的比值属于事件二的比值范围，则不改变加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心；若计算出的比值属于事件三的比值范围，则提高加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心。
  6. 根据权利要求5所述的一种基于优化张紧弦模型的桥索监测方法，其特征在于：所述事件一的比值范围为[0，15％)，所述事件一将加速度传感器的采样频率设为200Hz；所述事件二的取值范围为[15％，30％]；所述事件三的取值范围为(30％，100％]，所述事件三将加速度传感器的采样频率增加至1000Hz。
  7. 一种基于优化张紧弦模型的桥索监测系统，其特征在于：包括以下模块：
    张紧弦模型优化模块，用于选取与实测桥索相同规格的钢缆来构建牛顿高斯法的残余函数，然后采用雅可比矩阵法对牛顿高斯法的残余函数进行迭代求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度，最后根据迭代求解的结果优化张紧弦模 型；
    理论本征频率计算模块，用于根据桥索的理论拉力值以及优化后的张紧弦模型求得理论本征频率；
    实测本征频率获取模块，用于根据加速度传感器节点采集的信号得出桥索的振动频谱，然后从得到的振动频谱中筛选出符合最优的本征频率阶数的频率作为实测本征频率；
    加速度传感器节点确定与调整模块，用于加速度传感器节点根据实测本征频率和理论本征频率的差值与理论本征频率的比值大小确定是否向上位机监控中心发送采集数据和调整加速度传感器的采样频率，以均衡功耗和分析准确度。
  8. 根据权利要求7所述的一种基于优化张紧弦模型的桥索监测系统，其特征在于：所述张紧弦模型优化模块包括：
    构建单元，用于在拉力试验机上选取与实测桥索相同规格的钢缆进行拉力试验，构建出牛顿高斯法的残余函数，所述牛顿高斯法的残余函数r的表达式为：
    Figure PCTCN2017089306-appb-100008
    其中，k为本征频率阶数，EI为钢缆的抗弯刚度，m为钢缆的单位质量，L为拉力试验时钢缆两个固定测试点之间的长度，f为钢缆振动频谱的中心频率，N为钢缆受到的拉力；
    迭代单元，用于将(m,L,f,N)作为已知变量，β＝(k,EI)作为待求解变量，采用雅可比矩阵迭代法对牛顿高斯法的残余函数进行求解，得出牛顿高斯法的残余函数最优的本征频率阶数以及最优的钢缆抗弯刚度；
    代入单元，用于将最优的本征频率阶数ko以及最优的钢缆抗弯刚度EIo代入张紧弦模型，生成优化后的张紧弦模型，所述优化后的张紧弦模型的表达式为：
    Figure PCTCN2017089306-appb-100009
    其中，
    Figure PCTCN2017089306-appb-100010
    为最优的本征频率阶数ko对应的本征频率。
  9. 根据权利要求8所述的一种基于优化张紧弦模型的桥索监测系统，其特征在于：所述迭代单元包括：
    雅可比矩阵获取子单元，用于根据n次不同拉力试验构建的n个残余函数r1,r2,...rn得到残余函数r的雅可比矩阵，所述残余函数r的雅可比矩阵Jr的表达式为：
    Figure PCTCN2017089306-appb-100011
    其中，n≥2且n为整数，
    Figure PCTCN2017089306-appb-100012
    和
    Figure PCTCN2017089306-appb-100013
    为第n次试验的残余函数rn分别对k和EI的偏导数；
    迭代子单元，用于根据得到的雅可比矩阵Jr对待求解变量β进行迭代，直至βs+1和βs的差值小于设定阈值为止，最终以迭代结束时βs+1对应的k和EI作为最优的本征频率阶数以及最优的钢缆抗弯刚度，其中，对待求解变量β进行迭代的公式为：
    Figure PCTCN2017089306-appb-100014
    式中，s为迭代次数，且0≤s≤n，迭代初始值β0＝(1,EI0)，EI0为钢缆抗弯刚度的最小估计值。
  10. 根据权利要求7、8或9所述的一种基于优化张紧弦模型的桥索监测系统，其特征在于：所述加速度传感器节点确定与调整模块包括：
    计算单元，用于计算实测本征频率和理论本征频率的差值与理论本征频率的比值；
    执行事件确定单元，用于加速度传感器节点根据计算出的比值所处的比值范围执行相应事件的操作：若计算出的比值属于事件一的比值范围，则只发送实测本征频率给上位机监控中心；若计算出的比值属于事件二的比值范围，则不改变加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心；若计算出的比值属于事件三的比值范围，则提高加速度传感器的采样频率并将加速度传感器采集的数据发送给上位机监控中心。
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