Bridge monitoring signal acquisition method, equipment and storage medium driven by meteorological data

Abstract

The present invention provides a bridge monitoring signal acquisition method, device, and storage medium driven by meteorological data. The acquisition method includes: obtaining surrounding meteorological data of multiple monitored physical quantities at each sampling point within a sampling period; obtaining multiple surrounding meteorological data sequences for each sampling point within a first time window and a second time window; calculating the Mahalanobis distance of the sampling points within the first and second time windows; obtaining meteorological data surrounding the bridge within the first and second time windows; obtaining the influence function and meteorological influence factor of the meteorological data surrounding the bridge; obtaining the meteorological data energy of each monitored physical quantity within the first and second time windows; obtaining the total meteorological data energy of the first and second time windows and calculating the time window energy difference; and determining and adjusting the monitoring frequency of the meteorological monitoring sensor at each sampling point within the next sampling period. The acquisition method provided by the present invention can optimize signal acquisition strategies.

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 installed with monitoring systems to achieve all-weather safety monitoring. Due to high costs, complex technologies and high energy consumption, monitoring systems have not been 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 factors, increasing 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 discovered that the prior art has at least the following problems:

[0004] Existing bridge monitoring systems typically require high-frequency, high-precision monitoring around the clock to ensure that structural anomalies can be detected at all times. However, this model has obvious drawbacks: in normal or good weather, monitoring demand is low, but the system still maintains high-frequency, high-precision monitoring, resulting in energy waste and excessive equipment use. This high-power monitoring method requires frequent power connections and battery replacements. In harsh environments, the monitoring frequency and accuracy remain the same, and it is likely that relevant emergencies will not be detected in a timely manner, resulting in the lack of timely warnings.

[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, embodiments of the present invention provide a bridge monitoring signal acquisition method, device, and storage medium driven by meteorological data to solve at least 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] Meteorological monitoring sensors are installed at multiple sampling points around the bridge to obtain surrounding meteorological data corresponding to various monitored physical quantities at each sampling point within a set sampling period. The monitored physical quantities at each sampling point include temperature, humidity, wind speed, and rainfall.

[0009] 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 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 the various surrounding meteorological data series 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 by combining the multiple surrounding meteorological data series and the corresponding covariance matrix of each sampling point in the first time window and the second time window respectively;

[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 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.

[0013] Obtaining 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 obtaining the meteorological influence factor;

[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 frequencies of the meteorological monitoring sensors at all sampling points in the next sampling period are 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 acquisition method of the above technical solution.

[0021] Additional advantages, objects, and features of the present invention will be set forth in part in the following description and will become apparent to those skilled in the art upon examination of the following or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained by the structures particularly pointed out in the description and drawings.

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

[0023] The drawings described herein are intended 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 merely for the purpose of illustrating the principles of the present invention. To facilitate the illustration and description of certain portions of the present invention, corresponding portions in the drawings may be exaggerated, that is, may be larger than other components in an exemplary device actually manufactured according to the present invention. In the drawings:

[0024] Figure 1 Flowchart 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 the bridge monitoring signal acquisition method based on meteorological data drive according to one 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. 1 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 solutions 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 exemplary 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, the accompanying drawings only show structures and / or processing steps closely related to the solutions according to the present invention, 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 existence of features, elements, steps or components, but does not exclude the existence 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 bridge monitoring signal acquisition method 100 driven by meteorological data according to an embodiment of the present application is described. 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 corresponding covariance matrices.

[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 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.

[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 respectively obtained by weighted summation.

[0039] In step S160, influence functions of various monitored physical quantities are obtained based on meteorological data around the bridge in the first time window and the second time window, and meteorological influence factors are 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 of 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 within the first time window and the second time window corresponding to the sampling period are obtained and standard normalized respectively to obtain corresponding covariance matrices; then, the Mahalanobis distance of each sampling point within 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 within the first time window and the second time window, and meteorological data around the bridge within 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 within the first time window and the second time window, meteorological data around the bridge within the first time window and the second time window are calculated. The influence function of each monitored physical quantity is obtained based on the 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 are 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 the collection method 100 according to the embodiment of the present application can dynamically adjust the monitoring frequency according to the real-time weather conditions, and has the ability to dynamically adjust the monitoring frequency. 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 Steps S110 to S190 are shown as being performed sequentially, which is merely an example. It is understood that some steps may be performed in any order. For example, step S170 may be performed before step S120, or both may be performed 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, step S110 uses meteorological monitoring sensors installed at multiple sampling points at different locations around the bridge to obtain surrounding meteorological data corresponding to various monitored physical quantities at each sampling point within a set sampling period. The monitored physical quantities at each sampling point include temperature, humidity, wind speed, and rainfall.

[0048] Each sampling point's meteorological monitoring sensors include a temperature sensor, a humidity sensor, a wind speed sensor, and a rainfall sensor to monitor temperature, humidity, wind speed, and rainfall data in real time. The sampling period can be one, two, or six hours, depending on 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] During a sampling period, such as an hour, each sensor at each sampling point acquires a total of 30 monitoring data points for 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 6 each acquire 30 data points for temperature, humidity, wind speed, and rainfall, 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 based on the surrounding meteorological data of the sampling point within the sampling period, 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.

[0052] Specifically, taking the sampling period of one hour as an example, it 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 the surrounding meteorological data sequence, 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 in the temperature, humidity, wind speed and rainfall data series of the surrounding meteorological data series 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 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 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 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 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 within a first time window and a second time window corresponding to the same sampling period.

[0058] For example, meteorological monitoring sensors installed at multiple sampling points at key locations on a bridge can be used to obtain meteorological data corresponding to multiple monitored physical quantities at each sampling point within the first and second time windows corresponding to the sampling period. The meteorological monitoring sensors at each sampling point at key locations on the bridge also include a temperature sensor, a humidity sensor, a wind speed sensor, and a rainfall sensor. Key locations on a bridge are defined as the primary load-bearing structure, including the main beam, piers, abutments, suspension cables, and stay cables.

[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 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 placed at different key locations on a bridge. Still assuming a one-hour sampling period and a two-minute sampling interval, each monitoring sensor at each sampling point acquires 15 data points for temperature, humidity, wind speed, and rainfall within the first and second time windows, respectively. This results in a total of 150 data points for temperature, humidity, wind speed, and rainfall within each of the first and second time windows. The average of these 150 data points for temperature, humidity, wind speed, and rainfall is then taken as the bridge meteorological data y for each of the first and second time windows.

[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 distance 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 respectively by weighted summation.

[0068] Specifically, as can be seen from the above, 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. The average value of the 15 temperature, humidity, wind speed and rainfall data is taken 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. 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 above, 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] Here, 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 , influence functions of various monitored physical quantities are obtained based on meteorological data around the bridge in the first time window and the second time window, and meteorological influence factors are further obtained.

[0072] Specifically, the influence functions of each monitored physical quantity include 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), which 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, specifically:

[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, sudden meteorological changes are identified by performing a Fourier transform on multiple sequences of surrounding meteorological data at the sampling point. The Fourier transform of multiple sequences of surrounding meteorological data to obtain multiple frequency domain signals of monitored physical quantities is a conventional mathematical processing method and will not be described in detail here. The embodiments of the present application are based on multiple frequency domain signals of monitored physical quantities to obtain the required data.

[0080] First, obtain 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.

[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 at 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, let's take temperature data as an example. Assume there are three sampling points, and the frequency of collecting data is once every 15 minutes within an hour. 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 also ranges 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. F 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 at four 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 applies to other sampling points.

[0091] Then,

[0092] Furthermore, 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 are 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 It's E 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, it takes a value of 5.

[0104] Monitoring is performed at the re-determined monitoring frequency f, so that the monitoring frequency can be increased when weather conditions are severe to capture more detailed changes, and the monitoring frequency can be reduced under 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, and a balance between safety and efficiency is achieved, it has wide practicality and promotion value.

[0106] For common bridges, in addition to monitoring meteorological data, structural monitoring sensors are typically deployed at various locations to monitor their condition in real time. For example, high-precision sensors are installed on the pier foundations and main beams of a bridge to monitor various structural parameters.

[0107] In the embodiments of the present application, the sensors at each monitoring point in different parts of the bridge can obtain structural state data for multiple different structural state physical quantities at each monitoring point. The structural state physical quantities at each monitoring point include at least two of strain, acceleration, displacement, and inclination. Accordingly, the sensors at each monitoring point include at least two of a strain sensor, an acceleration sensor, a displacement sensor, and an inclinometer. The sensors at each monitoring point can be multiple sensors with a single monitoring function, or they can be all-in-one sensors capable of measuring multiple physical quantities.

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

[0109] In an embodiment of the present application, in step S210, structural state data corresponding to a plurality of structural state physical quantities at each monitoring point within a set sampling period is obtained based on structural monitoring sensors installed at multiple monitoring points at different locations on the bridge structure. The structural state physical quantities at each monitoring point include 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 impact 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 existing technology, 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 impact 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 normalized structural status data obtained by the g-th structural monitoring sensor at the h-th monitoring point of the bridge. Q is the 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] The determination and adjustment of 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, and sgn(ΔE) is the sign function, which takes the value 1 when the time window energy difference ΔE is positive and takes the value -1 when it is negative.

[0119] The acquisition method 200 of the embodiment of the present application dynamically adjusts monitoring accuracy based on real-time weather conditions. When the energy difference in the time window is greater than zero (corresponding to severe weather conditions), monitoring accuracy is improved. When the energy difference in the time window is less than zero (corresponding to normal weather conditions), monitoring accuracy and energy consumption are reduced, thereby extending the sensor's service life. 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, road surfaces, slopes, and tunnels.

[0120] refer to Figure 3 The present invention also provides a collection device 300 for implementing the collection method 100 according to the embodiment of the present invention. 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 executed by the processor 310. When the executable program is executed by the processor 310, the processor 310 executes the collection method 100 according to the embodiment of the present invention described above.

[0121] The processor 310 may be a central processing unit (CPU) or other 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, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), a hard disk, a 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 and / or other desired functions in the embodiments of the present application described herein (implemented by the processor). 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.

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

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

[0127] Those skilled in the art can understand the specific operations 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 meteorological monitoring sensors set at multiple sampling points at different positions around the bridge, obtain surrounding meteorological data corresponding to multiple 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; 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 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 within 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 is used to assign weights to the corresponding sampling points, and the average values ​​of the surrounding meteorological data corresponding to the same monitored physical quantity for 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; 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; based on the multiple monitored 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 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 further obtained; the total meteorological data energy of the first time window and the second time window are obtained, 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 shows the collection method 100 according to the embodiment of the present application. Figure 4The acquisition system 400 provided in another aspect of an embodiment of the present application is described.

[0130] Reference Figure 4 The following describes an example acquisition system 400 for implementing the acquisition method according to an embodiment of the present application. 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.

[0131] The sampling point meteorological data acquisition module 410 is used to obtain the surrounding meteorological data corresponding to various monitored physical quantities at each sampling point within a set sampling period based on meteorological monitoring sensors set at multiple sampling points at different locations around the bridge; the monitored physical quantities at 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 distance of the sampling points in the first time window and the second time window, and call the average value of the surrounding meteorological data corresponding to the same monitored physical quantity 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 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.

[0139] The monitoring frequency adjustment module 490 is configured 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, and ensuring 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. When the computer program is executed by a processor, it is used to perform 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 illustrative and are not intended to limit the scope of the present application. Various changes and modifications may be made therein by those skilled in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0144] Those skilled 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 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 merely illustrative. For example, the division of the units described is merely a logical function division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another device, or ignoring or not performing some features.

[0146] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0147] It should be noted that the above embodiments illustrate rather than limit the present application, and that a person skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. 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 description is merely a specific embodiment or illustration of a specific embodiment of the present application, and the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. The scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A bridge monitoring signal acquisition method based on meteorological data, characterized in that: The collection method includes: Based on the 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 the set sampling period are obtained; 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 the various surrounding meteorological data series 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 series 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 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. Based on the meteorological data around the bridge in the first and second time windows, the influence function of each monitored physical quantity is obtained, and the meteorological influence factor is further obtained. , specifically including: in, The meteorological data around the bridge are The influence function of each monitored physical quantity, is the optimal temperature of the bridge structure, For extreme wind speeds, For extreme rainfall, is the weight coefficient; 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 ; 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 , in, As the basic monitoring frequency, is the maximum energy difference in the historical time window, is the adjustment coefficient.

2. The collection method according to claim 1, characterized in that: The collection method further comprises: Based on structural monitoring sensors installed at multiple monitoring points at different locations on 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 quantities at each monitoring point include 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 impact 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 impact factor of each monitoring point , specifically: in, The bridge The monitoring point The maximum value of the corresponding structural state data obtained by the structural monitoring sensors after standard normalization; For the The corresponding number of structural monitoring sensors at each monitoring point; Determine and adjust the monitoring accuracy of each structural monitoring sensor at each monitoring point in the next sampling period , specifically: in, It is The monitoring point The basic monitoring accuracy of each structural monitoring sensor is is the adjustment coefficient, is a sign function, when the time window energy difference It takes the value 1 when it is positive and -1 when it is negative.

4. The collection method according to claim 1, characterized in that: The method also includes obtaining bridge meteorological data within 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 locations on the bridge, meteorological data corresponding to multiple monitored physical quantities at each sampling point within 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 in the first time window and the second time window. ; Bridge meteorological data in the first time window or the second time window respectively Temperature, humidity, wind speed and rainfall data in.

5. The collection method according to claim 1, characterized in that: Calculating the Mahalanobis distance of each sampling point in the first time window and the second time window , specifically: in, Indicates the first time window or the second time window. The vector composed of the surrounding meteorological data sequence corresponding to the sampling points, They are The first part of the temperature, humidity, wind speed and rainfall data series of the surrounding meteorological data series of the sampling point The corresponding meteorological data, is the transposed matrix, is the covariance matrix, It is the bridge meteorological data within the first time window or the second time window.

6. The collection method according to claim 5, characterized in that: The frequency components of the frequency domain signals of each monitored physical quantity corresponding to each sampling point in the first time window and the second time window are obtained. , specifically: in, yes Any one of is the number of meteorological data corresponding to the corresponding type of monitored physical quantity obtained at the sampling point in the first time window or the second time window, ; The frequency components corresponding to the frequency domain signals of each monitored physical quantity around the bridge in the first time window and the second time window are obtained. , specifically: is the number of sampling points; The obtaining of meteorological data energy corresponding to each monitored physical quantity within the first time window and the second time window specifically refers to: The total meteorological data energy of the first time window and the second time window is obtained respectively, and the energy difference of the time windows is calculated. , specifically: in, and are the energy of temperature, humidity, wind speed and rainfall data in the first time window or the second time window respectively, yes and Any one of and are weight coefficients respectively; 、 are the total meteorological data energy of the first time window and the second time window, yes Any one of .

7. A bridge monitoring signal acquisition device driven by meteorological data, characterized in that: The acquisition equipment includes: a memory for storing computer-executable instructions; The processor is configured to implement the collection method according to any one of claims 1 to 6 when executing the computer executable instructions stored in the memory.

8. 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 6.

Images