Bridge dynamic checking coefficient calculation method, system and device based on space-time characteristics and storage medium

Abstract

The present application relates to the technical field of bridge, especially to a bridge dynamic calibration coefficient calculation method, system and device based on space-time characteristics and a storage medium, the method comprises: obtaining a theoretical displacement change value caused by temperature according to main beam temperature time series data; correcting the main beam displacement time series response data by using the theoretical displacement change value to obtain the corrected main beam displacement measured response value; obtaining dynamic load time series distribution data and time-varying influence line, and obtaining a theoretical displacement value based on real operating conditions and bridge time-varying state; obtaining a time series sequence of calibration coefficients according to the corrected main beam displacement measured response value and the theoretical displacement value; dividing the time series sequence of calibration coefficients into windows and extracting statistical characteristics to obtain a representative calibration coefficient corresponding to each window, and fusing the representative calibration coefficient corresponding to each window to obtain a final comprehensive representative calibration coefficient.

Description

Technical Field

[0001] This invention relates to the field of bridge technology, and in particular to a method, system, device, and storage medium for calculating bridge dynamic verification coefficients based on spatiotemporal characteristics. Background Technology

[0002] The current bridge verification coefficient calculation method is mainly the traditional static load test method. This method is the traditional standard means for the quality assessment of newly built bridges and the load-bearing capacity testing of existing bridges. The core logic is to obtain the verification coefficient by manually applying a controllable load. The specific steps are: (1) Load application: After closing the traffic, use heavy vehicles or heavy objects to apply static loads at designated locations on the bridge (such as mid-span or 1 / 4 span). The load level is usually set at 0.95-1.05 times the design live load; (2) Response acquisition: Install strain gauges, displacement gauges and other sensors at key sections of the main beam to collect the structural response (such as deflection and strain) under the load; (3) Theoretical calculation: Calculate the theoretical response value under the corresponding test load based on the initial finite element model of the bridge; Coefficient solution: Calculate the single-point coefficient according to "verification coefficient = measured response / theoretical response", and take the average of multiple load conditions as the final result.

[0003] The existing calculation method has the following disadvantages: (1) The data sample is scarce and the timeliness is poor: a single test can only obtain the coefficient values ​​of a few load conditions, which cannot reflect the dynamic changes of "load-response" in the long-term operation of the bridge; and the test cycle is as long as several days, with a cost of up to hundreds of thousands of yuan, and even more than one million yuan for large bridges, which is difficult to implement frequently; (2) It is detached from the actual operating conditions: the test load is a static concentrated load set by humans, which is significantly different from the load characteristics of "multiple vehicles passing through continuously and interacting dynamically" in actual operation, resulting in a large deviation between the calculation results and the real state; (3) The model is rigid and uncorrected: The theoretical calculation relies on the initial finite element model and does not consider the time-varying characteristics such as stiffness decay and bearing aging after bridge operation. The model error further amplifies the deviation of the verification coefficient; (4) The data processing is simple: It does not combine the dynamic correlation of time series data and only calculates the average ratio of a single or fixed period. It cannot capture the response change pattern of different periods, and it cannot achieve dynamic fusion of recent data with high weight and long-term data with low weight. As a result, the verification coefficient is difficult to reflect the real stress state of the bridge in the time series dimension and is prone to deviation due to abnormal value residue or time period information fragmentation.

[0004] Therefore, it is necessary to propose a method, system, device, and storage medium for calculating bridge dynamic verification coefficients based on spatiotemporal characteristics to solve or at least alleviate the above-mentioned defects. Summary of the Invention

[0005] The main objective of this invention is to provide a method, system, device, and storage medium for calculating bridge dynamic verification coefficients based on spatiotemporal characteristics, in order to solve the problems in the prior art.

[0006] To achieve the above objectives, the first aspect of the present invention provides a method for calculating the dynamic verification coefficient of a bridge based on spatiotemporal characteristics, comprising the following steps:

[0007] S1, acquire the main beam displacement time-series response data and the main beam temperature time-series data;

[0008] S2, obtain the theoretical displacement change value caused by temperature based on the main beam temperature time series data;

[0009] S3, use the theoretical displacement change value to correct the main beam displacement time-series response data to obtain the corrected measured main beam displacement response value;

[0010] S4, obtain dynamic load time-series distribution data and time-varying influence lines during actual operation, and obtain theoretical displacement values ​​based on actual operating conditions and the time-varying state of the bridge according to the dynamic load time-series distribution data and the time-varying influence lines.

[0011] S5. Based on the measured response value of the corrected main beam displacement and the theoretical displacement value, obtain the time sequence of the verification coefficient;

[0012] S6, perform windowing and statistical feature extraction on the time series of the verification coefficients to obtain the representative verification coefficients corresponding to each window;

[0013] S7, based on the time decay weight, fuses the representative verification coefficients corresponding to each window to obtain the final comprehensive representative verification coefficient.

[0014] Preferably, step S2 includes the following steps:

[0015] S201, define the period of no vehicles passing through for Q consecutive minutes as the empty period, and calculate the average temperature value T0 of the empty period;

[0016] S202, based on finite element analysis, takes the temperature difference ΔT at different time points as input and outputs the theoretical displacement change ΔR caused by temperature. T The temperature difference at different time points ΔT=T t -T0 where T t T represents the temperature value at time point t, and T0 represents the average temperature value during the no-load period.

[0017] S203, the transfer function is obtained through polynomial fitting. Where a0, a1, a2, and a3 are fitting coefficients. , , This represents the temperature difference at different points in time.

[0018] Preferably, the step S4 of obtaining the time-varying influence line during actual operation includes the following steps:

[0019] S401, Obtain the time-series axle load of the vehicle crossing the bridge. Where i is the axis number;

[0020] S402, using formula Calculate the spatially distributed load field ,in, For the Dirac function, The sequential axle load for vehicles crossing the bridge. Let i be the position of the i-th axis at time t. Let i be the moment when the i-th axis enters the bridge. Where n is the vehicle speed and n is the number of axles;

[0021] S403, Obtain the initial influence line using the formula Calculation of the influence line correction model considering environmental factors and cumulative damage The influence line correction model The time-varying influence line during actual operation was obtained; among which, This is the initial influence line. This is the damage correction factor. This is the cumulative load effect; The time points are from time 0 to time t.

[0022] Preferably, in step S403, the damage correction coefficient The calculation includes the following steps:

[0023] S4031, Extract the cumulative effect of historical vehicle loads. In the formula, Let the total weight of the i-th vehicle be . The effective time is T, where T is the total number of vehicles that have crossed the bridge by time t.

[0024] S4032, obtains structural stiffness attenuation rate through modal recognition. In the formula, K(t) represents the initial stiffness, and K(t) represents the measured stiffness at time t.

[0025] S4033, using formula Calculate the damage correction factor N is the total number of time points;

[0026] In the formula, The initial influence line is β, which is the damage evolution index. This is the cumulative effect of historical vehicle loads. The deviation between the measured influence line and the initial influence line. This is the measured influence line. This is the initial damage correction factor.

[0027] Preferably, after step S5, the following step is further included:

[0028] S501, using formula Calculate the local density ρ(t) for each data point, where N(t) is the time neighbor window at time t. Let N(t) be the time points, and σ be the standard deviation of the sequence. The verification coefficient is the value at time point t.

[0029] S502, using formula Calculate the distance threshold δ(t) for each data point, where, The verification coefficient is the value at time point t.

[0030] S503 sets an adaptive threshold and removes isolated data points based on the local density of each data point, the distance threshold of each data point, and the adaptive threshold.

[0031] Preferably, in step S503, let , ,in, Average density;

[0032] Let ρ(t) < And δ(t)> The data points are isolated and are removed.

[0033] A second aspect of the present invention provides a bridge dynamic verification coefficient calculation system based on spatiotemporal characteristics, the system comprising:

[0034] The acquisition module is used to acquire the displacement time-series response data and temperature time-series data of the main beam.

[0035] The first calculation module is used to obtain the theoretical displacement change value caused by temperature based on the temperature time series data of the main beam;

[0036] The second calculation module is used to correct the main beam displacement time-series response data using the theoretical displacement change value to obtain the corrected measured main beam displacement response value.

[0037] The third calculation module is used to obtain dynamic load time-series distribution data and time-varying influence lines during actual operation, and to obtain theoretical displacement values ​​based on actual operating conditions and the time-varying state of the bridge based on the dynamic load time-series distribution data and the time-varying influence lines.

[0038] The fourth calculation module is used to obtain the time sequence of the verification coefficients based on the measured response value of the corrected main beam displacement and the theoretical displacement value.

[0039] The fifth calculation module is used to divide the time series of the verification coefficients into windows and extract statistical features to obtain the representative verification coefficients corresponding to each window;

[0040] The sixth calculation module is used to fuse the representative verification coefficients corresponding to each window based on the time decay weight to obtain the final comprehensive representative verification coefficient.

[0041] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0042] One or more processors;

[0043] A storage device for storing one or more programs, which, when executed by one or more processors, enable the electronic device to implement a method for calculating bridge dynamic verification coefficients based on spatiotemporal characteristics.

[0044] A fourth aspect of the present invention provides a storage medium having a computer program stored thereon, which, when executed by a computer processor, causes the computer to perform a method for calculating bridge dynamic verification coefficients based on spatiotemporal characteristics.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] This application relies on the time-series response data during bridge operation, eliminating the need for time-consuming and costly load tests. It can acquire massive amounts of continuous data, overcoming the limitations of a single test with "a few working conditions" and significantly reducing test costs. It also enables high-frequency and routine updates to the bridge's dynamic verification coefficients. Based on the load time-series distribution of "multiple vehicles passing continuously and interacting dynamically" in actual operation, this application replaces manually set static concentrated loads. The calculation scenario is highly consistent with the bridge's actual stress environment, reducing the result deviation caused by differences in working conditions from the source. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0048] Figure 1 This is a flowchart of a bridge dynamic verification coefficient calculation method based on spatiotemporal characteristics in one embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the module structure of a bridge dynamic verification coefficient calculation system based on spatiotemporal characteristics in one embodiment of the present invention.

[0050] Explanation of reference numerals in the attached figures:

[0051] 10. Acquisition Module; 20. First Calculation Module; 30. Second Calculation Module; 40. Third Calculation Module; 50. Fourth Calculation Module; 60. Fifth Calculation Module; 70. Sixth Calculation Module.

[0052] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] In this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0056] Example 1:

[0057] Please see the appendix Figure 1 The present invention provides a method for calculating the dynamic verification coefficient of a bridge based on spatiotemporal characteristics, comprising the following steps:

[0058] S1, acquire the main beam displacement time-series response data and the main beam temperature time-series data;

[0059] S2, obtain the theoretical displacement change value caused by temperature based on the main beam temperature time series data;

[0060] S3, the theoretical displacement change value is used to correct the time-series response data of the main beam displacement, resulting in the corrected measured response value of the main beam displacement; the formula is used. Calculate the measured response value of the main beam displacement ,in, The time-series response value of the main beam displacement. To correct the measured time-series response data of the main beam displacement, This represents the theoretical displacement change caused by temperature at time point t.

[0061] S4, obtain dynamic load time-series distribution data and time-varying influence lines during actual operation, and obtain theoretical displacement values ​​based on actual operating conditions and the time-varying state of the bridge according to the dynamic load time-series distribution data and the time-varying influence lines.

[0062] S5, based on the corrected measured response value of the main beam displacement and the theoretical displacement value, obtain the time sequence of the verification coefficients; use the formula Calculate the verification coefficient at each time point t. ,in, The theoretical response value at time point t is obtained; and the time series sequence of the verification coefficients is obtained. Where N is the total number of time points;

[0063] S6, the time series sequence of the verification coefficients is divided into windows and statistical features are extracted to obtain the representative verification coefficients corresponding to each window; the formula η is used. 95% (w)=inf{x|P(η(t)≤x|t∈w)≥0.95} Calculate the 95th percentile value of each window as a statistical feature, and its validation coefficient is η. 95% (w);

[0064] S7, based on the time decay weight, fuses the representative verification coefficients corresponding to each window to obtain the final comprehensive representative verification coefficient; using the formula... Calculate the representative verification coefficient Where M is the total number of windows, The attenuation coefficient is determined by technical personnel through experimentation and table lookup, based on the principle of giving higher weight to recent data and lower weight to older data.

[0065] In this embodiment, relying on the time-series response data during bridge operation, there is no need to conduct time-consuming and costly load tests. It can obtain massive amounts of continuous data, which not only breaks through the limitation of "a few working conditions" in a single test, but also significantly reduces the test cost and enables high-frequency and routine updates of the bridge's dynamic verification coefficients.

[0066] This embodiment is based on the load time distribution of "multiple vehicles passing continuously and interacting dynamically" in actual operation, replacing the manually set static concentrated load. The calculation scenario is highly consistent with the actual stress environment of the bridge, reducing the result deviation caused by the difference in working conditions from the source.

[0067] This embodiment divides the verification coefficients according to the original time point of the monitoring system (rather than by vehicle), perfectly matching the dynamic load time series distribution data and the time-varying influence line, solving the problem of time alignment between vehicles and responses; by correcting the displacement response through temperature time series data, and by incorporating post-operational changes such as stiffness decay and support aging into the time-varying influence line, it breaks the rigid limitations of the initial finite element model, dynamically corrects the calculation results, and reduces the impact of model errors.

[0068] Finally, this embodiment uses sliding window quantiles and exponential weighting to retain the dynamic characteristics of time series data while highlighting the representativeness of recent responses, which is more in line with the time-varying laws of bridge performance than traditional statistical methods.

[0069] In a preferred embodiment, step S2 includes the following steps:

[0070] S201, define a period of time during which no vehicles pass through for Q consecutive minutes as an empty period, and calculate the average temperature value T0 of the empty period; generally speaking, a period of time during which no vehicles pass through for 5 minutes can be defined as an empty period.

[0071] S202, based on finite element analysis, takes the temperature difference ΔT at different time points as input and outputs the theoretical displacement change ΔR caused by temperature. T The temperature difference at different time points ΔT=T t -T0; where T t T represents the temperature value at time point t, and T0 represents the average temperature value during the no-load period.

[0072] S203, the transfer function is obtained through polynomial fitting. Where a0, a1, a2, and a3 are fitting coefficients. , , This represents the temperature difference at different time points. It should be noted that this formula can calculate down to the sub-millimeter level; generally, reaching the sub-millimeter level is sufficient. A cubic polynomial fitting method is used to achieve refined removal of temperature interference, improving the correction accuracy by >15% compared to a linear model.

[0073] This embodiment directly eliminates the interference of vehicle load on displacement response during the no-load period, and calculates the temperature difference based on the average temperature of this period, ensuring that the temperature difference data only reflects pure temperature changes, providing an interference-free and reliable reference for temperature displacement correction. The theoretical calculation process fully considers the temperature response characteristics of the bridge, avoiding the adaptation deviation of general models, and making the quantification of temperature interference more consistent with the actual state of the bridge.

[0074] In a preferred embodiment, the step S4 of obtaining the time-varying influence line during actual operation includes the following steps:

[0075] S401, obtain the time-series axle load of vehicles crossing the bridge through the WIM system. Where i is the axis number;

[0076] S402, using formula Calculate the spatially distributed load field ,in, For the Dirac function, The sequential axle load for vehicles crossing the bridge. Let i be the position of the i-th axis at time t. Let i be the moment when the i-th axis enters the bridge. Where n is the vehicle speed and n is the number of axles;

[0077] S403, Obtain the initial influence line using the formula Calculation of the influence line correction model considering environmental factors and cumulative damage The influence line correction model The time-varying influence line during actual operation was obtained; among which, This is the initial influence line. This is the damage correction factor. This is the cumulative load effect; The time points are from time 0 to time t.

[0078] In a preferred embodiment, in step S403, the damage correction coefficient The calculation includes the following steps:

[0079] S4031, Extract the cumulative effect of historical vehicle loads. In the formula, Let the total weight of the i-th vehicle be . The effective time is T, where T is the total number of vehicles that have crossed the bridge by time t.

[0080] S4032, obtains structural stiffness attenuation rate through modal recognition. In the formula, K(t) represents the initial stiffness, and K(t) represents the measured stiffness at time t.

[0081] S4033, using formula Calculate the damage correction factor N is the total number of time points;

[0082] In the formula, The initial influence line is β, which is the damage evolution index. This is the cumulative effect of historical vehicle loads. The deviation between the measured influence line and the initial influence line. This is the measured influence line. This is the initial damage correction factor.

[0083] The final result is: .

[0084] In a preferred embodiment, after step S5, the following step is further included:

[0085] S501, using formula Calculate the local density ρ(t) for each data point, where N(t) is the time neighbor window at time t. Let N(t) be the time points, and σ be the standard deviation of the sequence. The verification coefficient is the value at time point t.

[0086] S502, using formula Calculate the distance threshold δ(t) for each data point, where, The verification coefficient is the value at time point t.

[0087] S503 sets an adaptive threshold and removes isolated data points based on the local density of each data point, the distance threshold of each data point, and the adaptive threshold.

[0088] In this embodiment, outlier adaptive identification is achieved based on DBSCAN density clustering, thereby eliminating isolated data points and avoiding the subjectivity of manually setting thresholds.

[0089] In a preferred embodiment, in step S503, let , ,in, Average density;

[0090] Let ρ(t) < And δ(t)> The data points are isolated and are removed.

[0091] Example 2

[0092] Please refer to Figure 2 A bridge dynamic verification coefficient calculation system based on spatiotemporal characteristics, the system comprising:

[0093] Module 10 is used to acquire the displacement time-series response data and temperature time-series data of the main beam.

[0094] The first calculation module 20 is used to obtain the theoretical displacement change value caused by temperature based on the temperature time series data of the main beam;

[0095] The second calculation module 30 is used to correct the main beam displacement time-series response data using the theoretical displacement change value to obtain the corrected measured main beam displacement response value.

[0096] The third calculation module 40 is used to acquire dynamic load time-series distribution data and time-varying influence lines during actual operation, and to obtain theoretical displacement values ​​based on actual operating conditions and the time-varying state of the bridge according to the dynamic load time-series distribution data and the time-varying influence lines.

[0097] The fourth calculation module 50 is used to obtain the time sequence of the verification coefficients based on the measured response value of the corrected main beam displacement and the theoretical displacement value.

[0098] The fifth calculation module 60 is used to perform windowing and statistical feature extraction on the time series of the verification coefficients to obtain the representative verification coefficients corresponding to each window;

[0099] The sixth calculation module 70 is used to fuse the representative verification coefficients corresponding to each window based on the time decay weight to obtain the final comprehensive representative verification coefficient.

[0100] Example 3

[0101] An electronic device, the electronic device comprising:

[0102] One or more processors;

[0103] A storage device is provided for storing one or more programs, which, when executed by one or more processors, enable the electronic device to implement the bridge dynamic verification coefficient calculation method based on spatiotemporal characteristics as described in Embodiment 1.

[0104] Example 4

[0105] A storage medium storing a computer program, which, when executed by a computer's processor, causes the computer to perform the bridge dynamic verification coefficient calculation method based on spatiotemporal characteristics as described in Embodiment 1.

[0106] The above are merely preferred embodiments of the present invention and do not limit the scope of protection of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention’s specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for calculating dynamic verification coefficients of bridges based on spatiotemporal characteristics, characterized in that, Includes the following steps: S1, acquire the main beam displacement time-series response data and the main beam temperature time-series data; S2, obtain the theoretical displacement change value caused by temperature based on the main beam temperature time series data; S3, use the theoretical displacement change value to correct the main beam displacement time-series response data to obtain the corrected measured main beam displacement response value; S4, obtain dynamic load time-series distribution data and time-varying influence lines during actual operation, and obtain theoretical displacement values ​​based on actual operating conditions and the time-varying state of the bridge according to the dynamic load time-series distribution data and the time-varying influence lines. S5. Based on the measured response value of the corrected main beam displacement and the theoretical displacement value, obtain the time sequence of the verification coefficient; S6, perform windowing and statistical feature extraction on the time series of the verification coefficients to obtain the representative verification coefficients corresponding to each window; S7, based on the time decay weight, fuses the representative verification coefficients corresponding to each window to obtain the final comprehensive representative verification coefficient.

2. The method for calculating bridge dynamic verification coefficients based on spatiotemporal characteristics according to claim 1, characterized in that, Step S2 includes the following steps: S201, define the period of no vehicles passing through for Q consecutive minutes as the empty period, and calculate the average temperature value T0 of the empty period; S202, based on finite element analysis, takes the temperature difference ΔT at different time points as input and outputs the theoretical displacement change ΔR caused by temperature. T The temperature difference at different time points ΔT=T t -T0; where T t T represents the temperature value at time point t, and T0 represents the average temperature value during the no-load period. S203, the transfer function is obtained through polynomial fitting. Where a0, a1, a2, and a3 are fitting coefficients. , , This represents the temperature difference at different points in time.

3. The method for calculating bridge dynamic verification coefficients based on spatiotemporal characteristics according to claim 1, characterized in that, The step S4 of obtaining the time-varying influence line in the actual operation process includes the following steps: S401, Obtain the time-series axle load of the vehicle crossing the bridge. Where i is the axis number; S402, using formula Calculate the spatially distributed load field ,in, For the Dirac function, The sequential axle load for vehicles crossing the bridge. Let i be the position of the i-th axis at time t. Let i be the moment when the i-th axis enters the bridge. Where n is the vehicle speed and n is the number of axles; S403, Obtain the initial influence line using the formula Calculation of the influence line correction model considering environmental factors and cumulative damage The influence line correction model The time-varying influence line during actual operation was obtained; among which, This is the initial influence line. This is the damage correction factor. This is the cumulative load effect; The time points are from time 0 to time t.

4. The method for calculating bridge dynamic verification coefficients based on spatiotemporal characteristics according to claim 3, characterized in that, In step S403, the damage correction coefficient The calculation includes the following steps: S4031, Extract the cumulative effect of historical vehicle loads. In the formula, Let the total weight of the i-th vehicle be . The effective time is T, where T is the total number of vehicles that have crossed the bridge by time t. S4032, obtains structural stiffness attenuation rate through modal recognition. In the formula, K(t) represents the initial stiffness, and K(t) represents the measured stiffness at time t. S4033, using formula Calculate the damage correction factor N is the total number of time points; In the formula, The initial influence line is β, which is the damage evolution index. This is the cumulative effect of historical vehicle loads. The deviation between the measured influence line and the initial influence line. This is the measured influence line. This is the initial damage correction factor.

5. The method for calculating bridge dynamic verification coefficients based on spatiotemporal characteristics according to claim 1, characterized in that, Following step S5, the following steps are also included: S501, using formula Calculate the local density ρ(t) for each data point, where N(t) is the time neighbor window at time t. Let N(t) be the time points, and σ be the standard deviation of the sequence. The verification coefficient is the value at time point t. S502, using formula Calculate the distance threshold δ(t) for each data point, where, The verification coefficient is the value at time point t. S503 sets an adaptive threshold and removes isolated data points based on the local density of each data point, the distance threshold of each data point, and the adaptive threshold.

6. The method for calculating bridge dynamic verification coefficients based on spatiotemporal characteristics according to claim 5, characterized in that, In step S503, let , ,in, Average density; Let ρ(t) < And δ(t)> The data points are isolated and are removed.

7. A bridge dynamic verification coefficient calculation system based on spatiotemporal characteristics, characterized in that, The system includes: The acquisition module is used to acquire the displacement time-series response data and temperature time-series data of the main beam. The first calculation module is used to obtain the theoretical displacement change value caused by temperature based on the temperature time series data of the main beam; The second calculation module is used to correct the main beam displacement time-series response data using the theoretical displacement change value to obtain the corrected measured main beam displacement response value. The third calculation module is used to obtain dynamic load time-series distribution data and time-varying influence lines during actual operation, and to obtain theoretical displacement values ​​based on actual operating conditions and the time-varying state of the bridge based on the dynamic load time-series distribution data and the time-varying influence lines. The fourth calculation module is used to obtain the time sequence of the verification coefficients based on the measured response value of the corrected main beam displacement and the theoretical displacement value. The fifth calculation module is used to divide the time series of the verification coefficients into windows and extract statistical features to obtain the representative verification coefficients corresponding to each window; The sixth calculation module is used to fuse the representative verification coefficients corresponding to each window based on the time decay weight to obtain the final comprehensive representative verification coefficient.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the bridge dynamic verification coefficient calculation method based on spatiotemporal characteristics as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, It stores a computer program, which, when executed by the computer's processor, causes the computer to perform the bridge dynamic verification coefficient calculation method based on spatiotemporal characteristics as described in any one of claims 1 to 6.

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