Bridge monitoring signal acquisition method and device based on meteorological data driving and storage medium

Abstract

The invention provides a bridge monitoring signal acquisition method and device based on meteorological data driving and a storage medium. The acquisition method comprises the following steps: acquiring surrounding meteorological data of various monitoring physical quantities of each sampling point in a sampling period; acquiring a plurality of peripheral meteorological data sequences of each sampling point in the first time window and the second time window; calculating the mahalanobis distance of the sampling points in the first time window and the second time window; acquiring meteorological data around the bridge in the first time window and the second time window; obtaining an influence function and a meteorological influence factor of the meteorological data around the bridge; acquiring meteorological data energy of each monitoring physical quantity in the first time window and the second time window; acquiring total meteorological data energy of the first time window and the second time window, and calculating a time window energy difference; and determining and adjusting the monitoring frequency of the meteorological monitoring sensor at each sampling point in the next sampling period. By adopting the acquisition method provided by the invention, the signal acquisition strategy can be optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge monitoring, and in particular to a bridge monitoring signal acquisition method, device and storage medium driven by meteorological data. Background Art

[0002] In recent years, extreme weather (such as heavy rain, typhoons, floods, etc.) has posed a serious threat to the safe operation of bridges. Frequent natural disasters have caused damage to bridges, road interruptions, and even casualties. In order to cope with these risks, important transportation facilities such as long-span bridges are usually equipped with monitoring systems to achieve all-weather safety monitoring. Due to high costs, complex technologies and high energy consumption, monitoring systems are not widely used in small and medium-span bridges. Many small and medium-span bridges have failed to install any monitoring equipment due to economic and maintenance considerations, which increases the hidden dangers of bridge structures in severe weather. This not only endangers the safety of the bridge itself, but also poses a potential threat to moving vehicles and passengers.

[0003] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art:

[0004] Existing bridge monitoring systems usually require high-frequency and high-precision monitoring around the clock to ensure that structural anomalies can be detected at any time. However, the disadvantages of this model are obvious: in normal or good weather, the monitoring demand is low, but the system still maintains high-frequency and high-precision monitoring, resulting in energy waste and excessive use of equipment. This high-power monitoring method means that frequent power supply and battery replacement are required; in harsh environmental conditions, the monitoring frequency and accuracy remain the same, and it is likely that relevant emergencies will not be detected in time, resulting in no timely warning.

[0005] Therefore, a bridge monitoring signal acquisition method, device and storage medium driven by meteorological data are needed to at least partially solve the above technical problems. Summary of the invention

[0006] In view of this, an embodiment of the present invention provides a bridge monitoring signal acquisition method, device and storage medium driven by meteorological data to at least solve one of the problems in the prior art.

[0007] In a first aspect, an embodiment of the present invention provides a bridge monitoring signal acquisition method driven by meteorological data, the acquisition method comprising:

[0008] Based on meteorological monitoring sensors set at multiple sampling points at different locations around the bridge, the surrounding meteorological data corresponding to various monitoring physical quantities at each sampling point within a set sampling period are obtained; wherein the monitoring physical quantities at each sampling point include temperature, humidity, wind speed and rainfall;

[0009] Separate the sampling period into a first time window and a second time window of equal length in sequence, and obtain multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window corresponding to the sampling period based on the surrounding meteorological data of the sampling point in the sampling period;

[0010] Perform standard normalization processing on multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window respectively to obtain the corresponding covariance matrix;

[0011] The bridge meteorological data in the first time window and the second time window corresponding to the same sampling period are called, and the Mahalanobis distance of each sampling point in the first time window and the second time window is calculated respectively by combining the multiple surrounding meteorological data sequences and the corresponding covariance matrix of each sampling point in the first time window and the second time window;

[0012] Based on the Mahalanobis distance of the sampling points in the first time window and the second time window, weights are assigned to the corresponding sampling points, and the average values ​​of the surrounding meteorological data corresponding to the same monitoring physical quantities of each sampling point in the first time window and the second time window are called, and the meteorological data around the bridge in the first time window and the second time window are obtained by weighted summation;

[0013] Based on the meteorological data around the bridge in the first time window and the second time window, the influence function of each monitored physical quantity is obtained, and the meteorological influence factor is further obtained;

[0014] Based on the multiple monitoring physical quantity frequency domain signals obtained by Fourier transforming the multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window, the frequency components of each monitoring physical quantity frequency domain signal corresponding to each sampling point in the first time window and the second time window are obtained, and the frequency components corresponding to each monitoring physical quantity frequency domain signal around the bridge in the first time window and the second time window and the meteorological data energy corresponding to each monitoring physical quantity are further obtained;

[0015] Obtain the total meteorological data energy of the first time window and the second time window respectively, and calculate the energy difference of the time windows;

[0016] The monitoring frequency of each meteorological monitoring sensor at all sampling points in the next sampling period is determined and adjusted based on the time window energy difference and the meteorological influencing factor.

[0017] In a second aspect, an embodiment of the present invention further provides a collection device, the collection device comprising:

[0018] A memory for storing computer executable instructions;

[0019] The processor is used to implement the collection method of the above technical solution when executing the computer executable instructions stored in the memory.

[0020] In a third aspect, an embodiment of the present invention further provides a storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the collection method of the above technical solution.

[0021] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.

[0022] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention. The components in the drawings are not drawn to scale, but are only for illustrating the principles of the present invention. In order to facilitate the illustration and description of some parts of the present invention, the corresponding parts in the drawings may be enlarged, that is, they may become larger relative to other components in the exemplary device actually manufactured according to the present invention. In the drawings:

[0024] Figure 1 is a flow chart of a bridge monitoring signal acquisition method driven by meteorological data according to an embodiment of the present invention;

[0025] Figure 2 Another flow chart of a bridge monitoring signal acquisition method based on meteorological data drive according to an embodiment of the present invention

[0026] Figure 3 is a schematic diagram of a collection device according to an embodiment of the present invention;

[0027] Figure 4 FIG. 4 is a schematic diagram of a collection system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0029] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0030] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0031] It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

[0032] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0033] First, refer to Figure 1 The following describes a bridge monitoring signal acquisition method 100 based on meteorological data drive according to an embodiment of the present application. Figure 1 As shown, the collection method 100 may include steps S110 to S190, which are specifically as follows:

[0034] In step S110, based on meteorological monitoring sensors set at multiple sampling points at different locations around the bridge, surrounding meteorological data corresponding to multiple monitored physical quantities at each sampling point within a set sampling period are obtained; wherein the monitored physical quantities at each sampling point include temperature, humidity, wind speed and rainfall.

[0035] In step S120, the sampling period is sequentially divided into a first time window and a second time window of equal length, and a plurality of surrounding meteorological data sequences of each sampling point in the first time window and the second time window corresponding to the sampling period are obtained based on the surrounding meteorological data of the sampling point in the sampling period.

[0036] In step S130, standard normalization processing is performed on the various surrounding meteorological data sequences of each sampling point in the first time window and the second time window to obtain the corresponding covariance matrix.

[0037] In step S140, the bridge meteorological data in the first time window and the second time window corresponding to the same sampling period are called, and the Mahalanobis distance of each sampling point in the first time window and the second time window is calculated respectively by combining multiple surrounding meteorological data sequences and corresponding covariance matrices of each sampling point in the first time window and the second time window.

[0038] In step S150, weights are assigned to corresponding sampling points based on the Mahalanobis distances of the sampling points in the first time window and the second time window, and the average values ​​of the surrounding meteorological data corresponding to the same monitored physical quantities of each sampling point in the first time window and the second time window are called, and the meteorological data around the bridge in the first time window and the second time window are obtained by weighted summation.

[0039] In step S160, the influence function of each monitored physical quantity is obtained based on the meteorological data around the bridge in the first time window and the second time window, and the meteorological influence factor is further obtained.

[0040] In step S170, based on the multiple monitoring physical quantity frequency domain signals obtained by Fourier transforming the multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window, the frequency components of each monitoring physical quantity frequency domain signal corresponding to each sampling point in the first time window and the second time window are obtained, and the frequency components corresponding to each monitoring physical quantity frequency domain signal around the bridge in the first time window and the second time window and the meteorological data energy corresponding to each monitoring physical quantity are further obtained.

[0041] In step S180, the total meteorological data energy of the first time window and the second time window are respectively obtained, and the energy difference of the time windows is calculated.

[0042] In step S190, the monitoring frequency of each meteorological monitoring sensor at all sampling points in the next sampling period is determined and adjusted based on the time window energy difference and the meteorological influencing factor.

[0043] In an embodiment of the present application, first, based on meteorological monitoring sensors at multiple sampling points at different positions around the bridge, surrounding meteorological data corresponding to multiple monitored physical quantities at each sampling point within a sampling period are obtained; the sampling period is sequentially divided into a first time window and a second time window of equal length, and multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window corresponding to the sampling period are obtained and standard normalized respectively to obtain the corresponding covariance matrix; then, the Mahalanobis distance of each sampling point in the first time window and the second time window is calculated respectively, and weights are assigned to corresponding sampling points based on the Mahalanobis distances of the sampling points in the first time window and the second time window, and the meteorological data around the bridge in the first time window and the second time window are obtained respectively through weighted summation; then, based on the Mahalanobis distances of the sampling points in the first time window and the second time window, the surrounding meteorological data of the bridge in the first time window and the second time window are calculated. The influence function of each monitored physical quantity is obtained by using meteorological data, and the meteorological influence factor is further obtained. Based on the frequency domain signals of multiple monitored physical quantities obtained by Fourier transforming the multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window, the frequency components of each monitored physical quantity frequency domain signal corresponding to each sampling point in the first time window and the second time window are obtained, and the frequency components corresponding to each monitored physical quantity frequency domain signal around the bridge in the first time window and the second time window and the meteorological data energy corresponding to each monitored physical quantity are obtained. The total meteorological data energy of the first time window and the second time window is further obtained respectively, and the time window energy difference is calculated. Finally, the monitoring frequency of each meteorological monitoring sensor at all sampling points in the next sampling period is determined and adjusted based on the time window energy difference and the meteorological influence factor.

[0044] From the description of the above process, it can be seen that according to the collection method 100 of the embodiment of the present application, the monitoring frequency can be dynamically adjusted according to the real-time weather conditions. When the energy difference in the time window is greater than zero (corresponding to relatively severe weather conditions), the monitoring frequency is increased, and when the energy difference in the time window is less than zero (corresponding to normal weather conditions), the monitoring frequency and energy consumption are reduced, thereby extending the service life of the equipment, so as to optimize energy consumption and resource utilization while ensuring effective monitoring of bridge safety under various weather conditions.

[0045] Among them, Figure 1 In the figure, steps S110 to S190 are shown to be arranged to be performed sequentially, which is only an example. It is understandable that the order of some steps may not be limited. For example, step S170 may be performed before step S120, or both may be performed in parallel and simultaneously.

[0046] The contents of the above steps of the acquisition method 100 according to an embodiment of the present application will be described in detail below.

[0047] In the embodiment of the present application, in step S110, based on the meteorological monitoring sensors at multiple sampling points at different locations around the bridge, the surrounding meteorological data corresponding to the various monitoring physical quantities at each sampling point within the set sampling period are obtained. The monitoring physical quantities at each sampling point include temperature, humidity, wind speed and rainfall.

[0048] The meteorological monitoring sensors at each sampling point include a temperature sensor, a humidity sensor, a wind speed sensor and a rainfall sensor to monitor the temperature, humidity, wind speed and rainfall data in real time. The sampling period can be set to a suitable duration such as one hour, two hours, or six hours, which can be reasonably set according to actual needs and is not strictly limited.

[0049] Specifically, for ease of understanding and description, taking a cable-stayed bridge as an example, it is assumed that 12 sampling points are pre-set at different locations around the bridge, and each sampling point is equipped with a temperature sensor, a humidity sensor, a wind speed sensor and a rainfall sensor.

[0050] In a sampling period, such as one hour, each sensor at each sampling point acquires a total of 30 monitoring data of the corresponding monitored physical quantity at a sampling interval Δt (e.g., 2 minutes). For example, the temperature sensor, humidity sensor, wind speed sensor, and rainfall sensor at sampling point No. 6 acquire 30 temperature, humidity, wind speed, and rainfall data respectively.

[0051] In an embodiment of the present application, in step S120, the sampling period is sequentially divided into a first time window and a second time window of equal length, and multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window corresponding to the sampling period are obtained based on the surrounding meteorological data of the sampling point in the sampling period.

[0052] Specifically, taking the sampling period of one hour as an example, the sampling period is divided into the first half hour and the second half hour. The 30 temperature, humidity, wind speed and rainfall data obtained at sampling point 6 in chronological order are also divided into two parts of multiple surrounding meteorological data sequences, denoted as xu = (T uv ,H uv ,V uv ,R uv ).

[0053] Where xu represents the surrounding meteorological data sequence corresponding to the u-th sampling point in the first time window or the second time window. uv ,H uv ,V uv ,R uvare the vth corresponding meteorological data of the temperature, humidity, wind speed and rainfall data sequence in the surrounding meteorological data sequence of the uth sampling point. Taking the above example, the value of v is 1, 2, ..., 15.

[0054] In an embodiment of the present application, in step S130, standard normalization processing is performed on multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window to obtain the corresponding covariance matrix S.

[0055] Since standard normalization processing and covariance matrix calculation are both very mature existing technologies, the embodiments of the present application will not be described in detail here.

[0056] In an embodiment of the present application, step S140 calls the bridge meteorological data in the first time window and the second time window corresponding to the same sampling period, combines multiple surrounding meteorological data sequences and corresponding covariance matrices of each sampling point in the first time window and the second time window, and calculates the Mahalanobis distance of each sampling point in the first time window and the second time window, respectively.

[0057] Specifically, before performing step S140, it is necessary to first obtain bridge meteorological data in a first time window and a second time window corresponding to the same sampling period.

[0058] For example, based on the meteorological monitoring sensors at multiple sampling points set at different key positions of the bridge, meteorological data corresponding to multiple monitoring physical quantities at each sampling point in the first time window and the second time window corresponding to the sampling period can be obtained. Among them, the meteorological monitoring sensors at each sampling point at different key positions of the bridge also include a temperature sensor, a humidity sensor, a wind speed sensor and a rainfall sensor. The key position of the bridge is defined as the main bearing structure, including the main beam, piers, abutments, cables and stays.

[0059] The meteorological data corresponding to the same monitored physical quantity of all sampling points are averaged to obtain the bridge meteorological data y within the first time window and the second time window;

[0060] y=(T 桥梁 ,H 桥梁 ,V 桥梁 ,R 桥梁 )

[0061] T 桥梁 ,H 桥梁 ,V 桥梁 ,R 桥梁 They are the temperature, humidity, wind speed and rainfall data in the bridge meteorological data y within the first time window or the second time window respectively.

[0062] For example, 10 sampling points are arranged at different key positions of the bridge. Taking the sampling period as one hour and the sampling interval of the sensor as 2 minutes, each monitoring sensor at each sampling point obtains 15 temperature, humidity, wind speed and rainfall data in the first time window and the second time window, that is, a total of 150 temperature, humidity, wind speed and rainfall data are obtained in the first time window and the second time window. Then, the average value of the 150 temperature, humidity, wind speed and rainfall data is taken as the bridge meteorological data y in the first time window and the second time window.

[0063] As can be seen from the above, a variety of surrounding meteorological data sequences xu=(T uv ,H uv ,V uv ,R uv ), based on which, the vector Xu=[T uv ,H uv ,V uv ,R uv ].

[0064] Next, calculate the Mahalanobis distance D of the u-th sampling point in the first time window and the second time window M (u),

[0065] D M (u)=√(Xu-y) T S -1 (Xu-y)

[0066] Among them, (Xu-y) T is the transposed matrix.

[0067] In an embodiment of the present application, in step S150, weights are assigned to corresponding sampling points based on the Mahalanobis distances of the sampling points in the first time window and the second time window, and the average values ​​of the surrounding meteorological data corresponding to the same monitored physical quantities of each sampling point in the first time window and the second time window are called, and the meteorological data around the bridge in the first time window and the second time window are obtained by weighted summation.

[0068] Specifically, it can be seen from the above that a plurality of surrounding meteorological data sequences xu=(T uv ,H uv ,V uv ,R uv), for example, sampling point 6 obtains 15 temperature, humidity, wind speed and rainfall data in the first time window and the second time window corresponding to the sampling period of one hour, and takes the average value of the 15 temperature, humidity, wind speed and rainfall data to obtain the average value of the temperature, humidity, wind speed and rainfall data of sampling point 6 in the first time window and the second time window, and the same is true for other sampling points. Combined with the Mahalanobis distance D corresponding to each sampling point in the first time window and the second time window obtained in the previous article M (u), obtain the meteorological data D around the bridge in the first time window and the second time window respectively through weighted summation weather ,

[0069] D weather ={T,H,V,R}.

[0070] Among them, a weight is assigned to each sampling point according to the calculated Mahalanobis distance. The weight is inversely proportional to the Mahalanobis distance, and a weight is assigned to each sampling point.

[0071] In an embodiment of the present application, in step S160, the influence function of each monitored physical quantity is obtained based on the meteorological data around the bridge in the first time window and the second time window, and the meteorological influence factor is further obtained.

[0072] Specifically, the influence functions of each monitored physical quantity, including the temperature influence function f(T), the humidity influence function f(H), the wind speed influence function f(V) and the rainfall influence function f(R), specifically refer to:

[0073]

[0074] Among them, T opt is the optimal temperature of the bridge structure, which can be set to 20°C. max For extreme wind speed, it can be set to 100km / h. max For extreme rainfall, it can be set to 200 mm / hour.

[0075] Next, obtain the meteorological impact factor W, which specifically refers to:

[0076] W=α1·f(T)+α2·f(H)+α3·f(V)+α4·f(R)

[0077] Among them, α1, α2, α3, and α4 are weight coefficients.

[0078] In an embodiment of the present application, in step S170, based on the multiple monitoring physical quantity frequency domain signals obtained by Fourier transforming the multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window, the frequency components of each monitoring physical quantity frequency domain signal corresponding to each sampling point in the first time window and the second time window are obtained, and the frequency components corresponding to each monitoring physical quantity frequency domain signal around the bridge in the first time window and the second time window and the meteorological data energy corresponding to each monitoring physical quantity are further obtained.

[0079] Specifically, by performing Fourier transform on multiple surrounding meteorological data sequences of the sampling point, sudden meteorological changes can be identified. Among them, performing Fourier transform on multiple surrounding meteorological data sequences to obtain multiple monitored physical quantity frequency domain signals is a conventional mathematical processing method and is not described in detail here. The embodiment of the present application is based on multiple monitored physical quantity frequency domain signals to obtain the required data.

[0080] First, the frequency component F(u,v) of the frequency domain signal of each monitored physical quantity corresponding to each sampling point in the first time window and the second time window is obtained.

[0081] F(u,v)=D(u,v)·e -i·2πvu / N

[0082] Where D(u,v) is T uv ,H uv ,V uv ,R uv N is the number of meteorological data corresponding to the corresponding type of monitored physical quantity obtained by the sampling point in the first time window or the second time window. 2 =-1.

[0083] Obtain the frequency component F(v) corresponding to the frequency domain signal of each monitored physical quantity around the bridge in the first time window and the second time window,

[0084]

[0085] M is the number of sampling points.

[0086] Obtain the meteorological data energy corresponding to each monitored physical quantity in the first time window and the second time window,

[0087] E=∑ v |F(v)| 2 .

[0088] For ease of description, take temperature data as an example. Assume that there are 3 sampling points, and the frequency is 15 minutes per hour to collect data. Then each sampling point will have 4 sample data. For each sampling point, N = 4. The value of v corresponds to the sequence number when the temperature data is sampled, that is, it is also from 1 to 4. T (u, v) is the vth frequency component of the temperature frequency domain signal at the uth sampling point. e is the base of the natural logarithm. T (v) is the vth frequency component of the temperature frequency domain signal around the bridge.

[0089] Explain as follows: For sampling point 1, assume that: D T (1, v) = (20, 21, 19, 20) (measured values ​​at 4 15-minute intervals); sampling point 2: D T (2, v) = (22, 23, 18, 20); sampling point 3: D T (3, v) = (24, 25, 23, 21).

[0090] For each sampling point, we calculate F when v = 1, 2, 3, 4 respectively T (u, v). Taking sampling point 1 as an example, for sampling point 1: F T (1, 1) = 20*e -i·2π1*1 / N ; F T (1, 2) = 21*e -i·2π2*1 / N ; F T (1, 3) = 19*e -i·2π3*1 / N ; F T (1, 4) = 20*e -i·2π4*1 / N The same goes for other sampling points.

[0091] Then,

[0092] Further, the energy of the temperature data in the first time window and the second time window is obtained,

[0093] E T =∑ v |F T (v)| 2 .

[0094] For humidity data energy E H , wind speed data energy E V and rainfall data energy E R , and so on, which will not be described in detail here.

[0095] In an embodiment of the present application, in step S180, the total meteorological data energy of the first time window and the second time window are obtained respectively, and the energy difference of the time windows is calculated.

[0096] Specifically, the total meteorological data energy of the first time window and the second time window is obtained respectively, and the time window energy difference ΔE is calculated.

[0097] E w =β1·E T +β2·E H +β3·E V +β4·E R

[0098] ΔE=E W1 -E W2

[0099] Among them, E T 、E H 、E V and E R are the energy of temperature, humidity, wind speed and rainfall data in the first time window or the second time window respectively, and E is E T 、E H 、E V and E R Any one of them. β1, β1, β1 and β1 are weight coefficients respectively. E W1 、E W2 are the total meteorological data energy of the first time window and the second time window, E W YesE W1 、E W2 Any one of .

[0100] Among them, when ΔE is greater than zero, it means that the weather conditions during the sampling period are relatively bad and the monitoring frequency needs to be increased. When ΔE is less than zero, it means that the weather conditions during the sampling period are normal.

[0101] In an embodiment of the present application, in step S190, the monitoring frequency of each meteorological monitoring sensor at all sampling points in the next sampling period is determined and adjusted based on the time window energy difference and the meteorological influencing factor.

[0102] Specifically, determine and adjust the monitoring frequency of each meteorological monitoring sensor at all sampling points in the next sampling period

[0103] Where f0 is the basic monitoring frequency. ΔE baseline is the maximum energy difference in the historical time window. γ is the adjustment coefficient, for example, the value is 5.

[0104] Monitoring is performed at the re-determined monitoring frequency f, so that the monitoring frequency can be increased when the weather conditions are severe to capture more detailed changes, and the monitoring frequency can be reduced in normal weather conditions to reduce sensor energy consumption.

[0105] Based on the above description, according to the collection method of the embodiment of the present application, by intelligently adjusting the monitoring frequency, resource utilization is optimized under different weather conditions, a balance between safety and efficiency is achieved, and it has wide practicality and promotion value.

[0106] For common bridges, in order to monitor the bridge status in real time, in addition to monitoring meteorological data, structural monitoring sensors are generally arranged in different locations. For example, high-precision sensors are installed on the pier foundation and main beam of the bridge to monitor a variety of structural parameters.

[0107] The sensors at each monitoring point at different parts of the bridge in the embodiment of the present application can obtain structural state data of multiple different structural state physical quantities at each monitoring point. The structural state physical quantity of each monitoring point includes at least two of strain, acceleration, displacement and inclination. Correspondingly, the sensors at each monitoring point include at least two of strain sensors, acceleration sensors, displacement sensors, and inclinometers. The sensors at each monitoring point can be multiple sensors with a single monitoring function, or they can be all-in-one sensors that can measure multiple physical quantities.

[0108] In order to achieve dynamic adjustment of structural monitoring sensors, refer to Figure 2 The embodiment of the present application also provides a bridge monitoring signal collection method 200 driven by meteorological data. The collection method 200 includes arrangement steps S110 to S180, and steps S210 to S230. Among them, steps S110 to S180 are the same as the relevant steps in the collection method 100, and will not be repeated here. Only steps S210 to S230 will be described in detail below.

[0109] In an embodiment of the present application, in step S210, based on the structural monitoring sensors at multiple monitoring points at different positions of the bridge structure, the structural state data corresponding to the multiple structural state physical quantities of each monitoring point within a set sampling period are obtained. The structural state physical quantity of each monitoring point includes at least two of strain, acceleration, displacement and inclination.

[0110] In the embodiment of the present application, in step S220, standard normalization processing is performed on the various structural state data of each monitoring point, and the structural state influence factor of each monitoring point is calculated.

[0111] Specifically, standard normalization processing is performed on various structural state data of the monitoring points. Since this is a mature and conventional prior art, it will not be described in detail here.

[0112] After the data is normalized, the weights are evenly distributed to all sensors at each monitoring point, and the structural state influence factor SSI of each monitoring point is calculated using these evenly distributed weights.

[0113]

[0114] Among them, S g It refers to the maximum value of the corresponding structural state data obtained by the g-th structural monitoring sensor at the h-th monitoring point of the bridge after standard normalization. Q is the corresponding number of structural monitoring sensors at the h-th monitoring point.

[0115] In an embodiment of the present application, in step S230, the monitoring accuracy of each structural monitoring sensor at each monitoring point in the next sampling period is determined and adjusted based on the structural state influencing factor of the monitoring point and the time window energy difference.

[0116] Among them, determining and adjusting the monitoring accuracy P of each structural monitoring sensor at each monitoring point in the next sampling period specifically refers to:

[0117] P=P fg ·(1+η·sgn(ΔE)·|ΔE|·SSI)

[0118] Among them, P fg is the basic monitoring accuracy of the g-th structural monitoring sensor at the h-th monitoring point, η is the adjustment coefficient, sgn(ΔE) is the sign function, and takes the value 1 when the time window energy difference ΔE is positive, and takes the value -1 when it is negative.

[0119] Through the acquisition method 200 of the embodiment of the present application, the monitoring accuracy is dynamically adjusted according to the real-time weather conditions. When the energy difference in the time window is greater than zero (corresponding to relatively bad weather conditions), the monitoring accuracy is improved. When the energy difference in the time window is less than zero (corresponding to normal weather conditions), the monitoring accuracy and energy consumption are reduced, and the service life of the sensor is extended. Multi-source data fusion technology ensures robustness and efficiency in complex environments, and is suitable for monitoring large-span bridges, small and medium-span bridges, pavements, slopes and tunnels.

[0120] refer to Figure 3 The embodiment of the present application also provides a collection device 300 for implementing the collection method 100 according to the embodiment of the present application. The collection device 300 includes a processor 310 and a memory 320. The collection device 300 may include one or more processors 310 and one or more memories 320. The memory 320 stores an executable program run by the processor 310, and when the executable program is run by the processor 310, the processor 310 executes the collection method 100 according to the embodiment of the present application described above.

[0121] The processor 310 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities.

[0122] The memory 320 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 310 may run the program instructions to implement the client functions (implemented by the processor) in the embodiments of the present application described herein and / or other desired functions. Various applications and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the application, etc.

[0123] The acquisition device 300 may also include an input device and an output device, and these components are interconnected through a bus system and / or other forms of connection mechanisms. It should be noted that Figure 3 The components and structures of the collection device 300 shown are merely exemplary and non-limiting. The collection device 300 may also have other components and structures as required.

[0124] The input device may be a device used by a user to input instructions, and may include one or more of a keyboard, a mouse, a microphone, a touch screen, etc. In addition, the input device may also be any interface for receiving information.

[0125] The output device may output various information (eg, images or sounds) to the outside (eg, a user), and may include one or more of a display, a speaker, etc. In addition, the output device may also be any other device with an output function.

[0126] Exemplarily, the example acquisition device 300 for implementing the acquisition method 100 according to the embodiment of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (ultra-mobile personal computers, UMPCs), handheld computers, netbooks, personal digital assistants (personal digital assistants, PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (augmented reality, AR), virtual reality (virtual reality, VR) devices, smart home devices, car computers and other electronic devices, and the embodiments of the present application do not impose any restrictions on this.

[0127] Those skilled in the art can understand the specific operation of the acquisition device 300 for implementing the acquisition method 100 according to the embodiment of the present application in combination with the contents described above. For the sake of brevity, the specific details are not repeated here, and only some main operations of the processor 310 are described.

[0128] In one embodiment of the present application, when the executable program is executed by the processor 310, the processor 310 executes the following steps: based on the meteorological monitoring sensors of multiple sampling points set at different positions around the bridge, obtain the surrounding meteorological data corresponding to the multiple monitoring physical quantities of each sampling point within the set sampling period; wherein the monitoring physical quantities of each sampling point include temperature, humidity, wind speed and rainfall; divide the sampling period into a first time window and a second time window of equal length in sequence, and obtain the multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window corresponding to the sampling period based on the surrounding meteorological data of the sampling point in the sampling period; perform standard normalization processing on the multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window respectively to obtain the corresponding covariance matrix; call the bridge meteorological data in the first time window and the second time window corresponding to the same sampling period, and calculate the Mahalanobis distance of each sampling point in the first time window and the second time window respectively based on the multiple surrounding meteorological data sequences and the corresponding covariance matrix of each sampling point in the first time window and the second time window; The Mahalanobis distance of the sampling points in the time window assigns weights to the corresponding sampling points, and calls the average values ​​of the surrounding meteorological data corresponding to the same monitoring physical quantity of each sampling point in the first time window and the second time window, and obtains the meteorological data around the bridge in the first time window and the second time window respectively through weighted summation; based on the meteorological data around the bridge in the first time window and the second time window, the influence function of each monitoring physical quantity is obtained, and the meteorological influence factor is further obtained; based on the frequency domain signals of multiple monitoring physical quantities obtained by Fourier transforming the multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window, the frequency components corresponding to each monitoring physical quantity frequency domain signal of each sampling point in the first time window and the second time window are obtained, and the frequency components corresponding to each monitoring physical quantity frequency domain signal around the bridge in the first time window and the second time window and the meteorological data energy corresponding to each monitoring physical quantity are further obtained; the total meteorological data energy of the first time window and the second time window are obtained separately, and the time window energy difference is calculated; based on the time window energy difference and the meteorological influence factor, the monitoring frequency of each meteorological monitoring sensor of all sampling points in the next sampling period is determined and adjusted.

[0129] The above exemplary illustrates the collection method 100 according to the embodiment of the present application. Figure 4A collection system 400 provided in another aspect of an embodiment of the present application is described.

[0130] Reference Figure 4 The example acquisition system 400 for implementing the acquisition method of the embodiment of the present application is described. The acquisition system 400 may include a sampling point meteorological data acquisition module 410, a data sequence acquisition module 420, a covariance matrix acquisition module 430, a Mahalanobis distance calculation module 440, a bridge meteorological data acquisition module 450, an influence factor acquisition module 460, a data energy acquisition module 470, an energy difference calculation module 480 and a monitoring frequency adjustment module 490. Among them:

[0131] The sampling point meteorological data acquisition module 410 is used to: based on the meteorological monitoring sensors set at multiple sampling points at different positions around the bridge, obtain the surrounding meteorological data corresponding to various monitoring physical quantities of each sampling point within a set sampling period; wherein the monitoring physical quantities of each sampling point include temperature, humidity, wind speed and rainfall.

[0132] The data sequence acquisition module 420 is used to: divide the sampling period into a first time window and a second time window of equal length in sequence, and obtain multiple surrounding meteorological data sequences for each sampling point in the first time window and the second time window corresponding to the sampling period based on the surrounding meteorological data of the sampling point in the sampling period.

[0133] The covariance matrix acquisition module 430 is used to perform standard normalization processing on the various surrounding meteorological data sequences of each sampling point in the first time window and the second time window to obtain the corresponding covariance matrix.

[0134] The Mahalanobis distance calculation module 440 is used to: call the bridge meteorological data in the first time window and the second time window corresponding to the same sampling period, combine the multiple surrounding meteorological data sequences and the corresponding covariance matrix of each sampling point in the first time window and the second time window, and calculate the Mahalanobis distance of each sampling point in the first time window and the second time window respectively.

[0135] The bridge meteorological data acquisition module 450 is used to: assign weights to corresponding sampling points based on the Mahalanobis distances of the sampling points in the first time window and the second time window, and call the average values ​​of the surrounding meteorological data corresponding to the same monitored physical quantities of each sampling point in the first time window and the second time window, and obtain the meteorological data around the bridge in the first time window and the second time window respectively through weighted summation.

[0136] The influence factor acquisition module 460 is used to obtain the influence function of each monitored physical quantity based on the meteorological data around the bridge in the first time window and the second time window, and further obtain the meteorological influence factor.

[0137] The data energy acquisition module 470 is used to obtain the frequency components of each monitoring physical quantity frequency domain signal corresponding to each sampling point in the first time window and the second time window based on the multiple monitoring physical quantity frequency domain signals obtained by Fourier transforming the multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window, and further obtain the frequency components corresponding to each monitoring physical quantity frequency domain signal around the bridge in the first time window and the second time window and the meteorological data energy corresponding to each monitoring physical quantity.

[0138] The energy difference calculation module 480 is used to respectively obtain the total meteorological data energy of the first time window and the second time window, and calculate the energy difference of the time windows.

[0139] The monitoring frequency adjustment module 490 is used to determine and adjust the monitoring frequency of each meteorological monitoring sensor at all sampling points in the next sampling period based on the time window energy difference and the meteorological influencing factor.

[0140] The acquisition system 400 proposed in the embodiment of the present invention has the function of dynamically adjusting the monitoring frequency, optimizing energy consumption and resource utilization, so as to ensure effective monitoring of the safety of the bridge under various weather conditions.

[0141] In addition, according to an embodiment of the present application, the present application further provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it is used to execute the corresponding steps of the acquisition method 100 or the acquisition method 200 of the embodiment of the present application. The storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0142] In addition, according to an embodiment of the present application, the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the acquisition method 100 or 200 of the embodiment of the present application.

[0143] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present application to this. Those of ordinary skill in the art may make various changes and modifications therein without departing from the scope and spirit of the present application. All these changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0144] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0146] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0147] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

[0148] The above is only a specific implementation or description of a specific implementation of the present application, and the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A bridge monitoring signal acquisition method based on meteorological data drive, characterized in that: The collection method comprises: Based on meteorological monitoring sensors set at multiple sampling points at different locations around the bridge, the surrounding meteorological data corresponding to various monitoring physical quantities at each sampling point within a set sampling period are obtained; wherein the monitoring physical quantities at each sampling point include temperature, humidity, wind speed and rainfall; Sequentially divide the sampling period into a first time window and a second time window of equal length, and obtain a plurality of surrounding meteorological data sequences for each sampling point in the first time window and the second time window; Perform standard normalization processing on multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window respectively to obtain the corresponding covariance matrix; Call the bridge meteorological data in the first time window and the second time window corresponding to the same sampling period, combine the multiple surrounding meteorological data sequences and the corresponding covariance matrix of each sampling point in the first time window and the second time window, and calculate the Mahalanobis distance of each sampling point in the first time window and the second time window respectively; Based on the Mahalanobis distance of the sampling points in the first time window and the second time window, weights are assigned to the corresponding sampling points, and the average values ​​of the surrounding meteorological data corresponding to the same monitoring physical quantities of each sampling point in the first time window and the second time window are called, and the meteorological data around the bridge in the first time window and the second time window are obtained by weighted summation; Based on the meteorological data around the bridge in the first time window and the second time window, the influence function of each monitored physical quantity is obtained, and the meteorological influence factor is further obtained; Based on the multiple monitoring physical quantity frequency domain signals obtained by Fourier transforming the multiple surrounding meteorological data sequences of each sampling point in the first time window and the second time window, the frequency components of each monitoring physical quantity frequency domain signal corresponding to each sampling point in the first time window and the second time window are obtained, and the frequency components corresponding to each monitoring physical quantity frequency domain signal around the bridge in the first time window and the second time window and the meteorological data energy corresponding to each monitoring physical quantity are further obtained; Obtain the total meteorological data energy of the first time window and the second time window respectively, and calculate the energy difference of the time windows; The monitoring frequency of each meteorological monitoring sensor at all sampling points in the next sampling period is determined and adjusted based on the time window energy difference and the meteorological influencing factor.

2. The collection method according to claim 1, characterized in that: The collection method also includes: Based on structural monitoring sensors set at multiple monitoring points at different positions of the bridge structure, structural state data corresponding to multiple structural state physical quantities at each monitoring point within a set sampling period are obtained; wherein the structural state physical quantity of each monitoring point includes at least two of strain, acceleration, displacement and inclination; Perform standard normalization on the various structural state data of each monitoring point and calculate the structural state influencing factor of each monitoring point; Based on the structural state influencing factors of the monitoring points and the time window energy difference, the monitoring accuracy of each structural monitoring sensor at each monitoring point in the next sampling period is determined and adjusted.

3. The collection method according to claim 2, characterized in that: The calculation of the structural state influence factor SSI of each monitoring point specifically refers to: Among them, S g It refers to the maximum value of the corresponding structural state data obtained by the g-th structural monitoring sensor at the h-th monitoring point of the bridge after standard normalization; Q is the corresponding number of structural monitoring sensors at the h-th monitoring point; Determining and adjusting the monitoring accuracy P of each structural monitoring sensor at each monitoring point in the next sampling period specifically refers to: P=P fg ·(1+η·sgn(ΔE)·|ΔE|·SSI) Among them, P fg is the basic monitoring accuracy of the g-th structural monitoring sensor at the h-th monitoring point, η is the adjustment coefficient, sgn(ΔE) is the sign function, and takes the value 1 when the time window energy difference ΔE is positive, and takes the value -1 when it is negative.

4. The collection method according to claim 1, characterized in that: The method also includes obtaining bridge meteorological data in the first time window and the second time window corresponding to the sampling period, specifically including: Based on meteorological monitoring sensors set at multiple sampling points at different key positions of the bridge, meteorological data corresponding to multiple monitoring physical quantities at each sampling point in a first time window and a second time window corresponding to a sampling period are obtained; The meteorological data corresponding to the same monitored physical quantity of all sampling points are averaged to obtain the bridge meteorological data y within the first time window and the second time window; y=(T 桥梁 ,H 桥梁 ,V 桥梁 ,R 桥梁 ) T 桥梁 ,H 桥梁 ,V 桥梁 ,R 桥梁 They are the temperature, humidity, wind speed and rainfall data in the bridge meteorological data y within the first time window or the second time window respectively.

5. The collection method according to claim 1, characterized in that: The Mahalanobis distance D of each sampling point in the first time window and the second time window is calculated. M (u) specifically refers to: Xu=[T uv ,H uv ,V uv ,R uv ] Where Xu represents the vector composed of the surrounding meteorological data sequence corresponding to the u-th sampling point in the first time window or the second time window, T uv ,H uv ,V uv ,R uv are the vth corresponding meteorological data of the temperature, humidity, wind speed and rainfall data sequence in the surrounding meteorological data sequence of the uth sampling point, respectively, (Xu-y) T is the transposed matrix, S is the covariance matrix, and y is the bridge meteorological data in the first time window or the second time window.

6. The collection method according to claim 1, characterized in that: The influence function of each monitored physical quantity is obtained based on the meteorological data around the bridge in the first time window and the second time window, specifically refers to: D weather ={T,H,V,R} Among them, f(T), f(H), f(V), and f(R) are the meteorological data D around the bridge. weather The influence function of each monitored physical quantity, T opt is the optimal temperature of the bridge structure, V max is the extreme wind speed, R max for extreme rainfall; The obtaining of the meteorological influence factor W specifically refers to: W=α1·f(T)+α2·f(H)+α3·f(V)+α4·f(R) Among them, α1, α2, α3, and α4 are weight coefficients.

7. The collection method according to claim 1, characterized in that: The obtaining of the frequency component F(u,v) of the frequency domain signal of each monitored physical quantity corresponding to each sampling point in the first time window and the second time window specifically refers to: F(u,v)=D(u,v)·e -i·2πvu / N Where D(u,v) is T uv ,H uv ,V uv ,R uv Any one of them, N is the number of meteorological data corresponding to the corresponding type of monitored physical quantity obtained by the sampling point in the first time window or the second time window, i 2 = -1; The obtaining of the frequency components F(v) corresponding to the frequency domain signals of each monitored physical quantity around the bridge in the first time window and the second time window specifically refers to: M is the number of sampling points; The obtaining of meteorological data energy corresponding to each monitored physical quantity in the first time window and the second time window specifically refers to: The obtaining of the total meteorological data energy of the first time window and the second time window respectively and the calculation of the time window energy difference ΔE specifically refers to: E W =β1·E T +β2·E H +β3·E V +β4·E R ΔE=E W1 -E W2 Among them, E T 、E H 、E V and E R are the energy of temperature, humidity, wind speed and rainfall data in the first time window or the second time window respectively, and E is E T 、E H 、E V and E R Any one of them, β1, β1, β1 and β1 are weight coefficients respectively; E W1 、E W2 are the total meteorological data energy of the first time window and the second time window, E W YesE W1 、E W2 Any one of .

8. The collection method according to claim 1, characterized in that: The determining and adjusting the monitoring frequency f of each meteorological monitoring sensor at each sampling point in the next sampling period based on the time window energy difference and the meteorological influencing factor specifically refers to: Among them, f0 is the basic monitoring frequency, ΔE baseline is the maximum energy difference in the historical time window, and γ is the adjustment coefficient.

9. A bridge monitoring signal acquisition device driven by meteorological data, characterized in that: The acquisition equipment comprises: A memory for storing computer executable instructions; The processor is used to implement the collection method described in any one of claims 1 to 8 when executing the computer executable instructions stored in the memory.

10. A storage medium storing computer instructions, wherein: The computer instructions are used to enable the computer to execute the collection method according to any one of claims 1 to 8.

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