A bridge vibration response prediction method and system based on deep calculation

Abstract

The present invention relates to the technical field of data transmission, and is a method and system for predicting bridge vibration response based on deep calculation, comprising: collecting vibration data of a target bridge, obtaining internal vibration data and external excitation data, distributing base station connection instructions to a transmission base station set, obtaining a candidate base station set, performing complete data transmission base station calculation according to vibration data flow, obtaining the number of working base stations, judging whether the number of working base stations is greater than the number of candidate base stations, if greater, performing data flow transmission allocation, obtaining a complete base station set, if not greater, determining a task base station set, using the complete base station set or the task base station set, transmitting the internal vibration data and external excitation data to a response prediction platform, and using the response prediction platform to obtain predicted vibration data. The present invention can improve the transmission efficiency of vibration response data and the accuracy of bridge vibration response prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a method and system for predicting bridge vibration response based on deep computing. Background Art

[0002] By predicting the vibration response of the target bridge, the safety and stability of the target bridge can be ensured under various traffic and environmental conditions. However, since the target bridge is usually built in rivers and valleys, it is particularly important to transmit the vibration response data to a distant response prediction center after collecting the vibration response data of the target bridge.

[0003] At present, the transmission of vibration response data to the response prediction center is mainly achieved through wireless data transmission through transmission base stations. Although this method realizes the transmission of vibration response data, the transmission base stations are prone to problems such as transmission channel congestion and transmission information loss, which reduces the transmission efficiency of vibration response data and the accuracy of bridge vibration response prediction. Summary of the invention

[0004] The present invention provides a bridge vibration response prediction method and system based on deep calculation, the main purpose of which is to improve the transmission efficiency of vibration response data and the accuracy of bridge vibration response prediction.

[0005] To achieve the above-mentioned purpose, the present invention provides a bridge vibration response prediction method based on deep calculation, comprising: collecting vibration data of a pre-constructed target bridge to obtain internal vibration data and external excitation data, wherein the internal vibration data includes: a bridge vibration area set, a bridge vibration frequency set and a bridge vibration displacement set, and the external excitation data includes: a bridge real-time load, a bridge environmental wind speed and a bridge environmental magnitude; generating a base station connection instruction according to the internal vibration data and the external excitation data, distributing the base station connection instruction to a pre-constructed transmission base station set to obtain a response instruction set; determining the success of the base station connection according to the response instruction set and the transmission base station set to obtain a candidate base station set, wherein the number of candidate base stations in the candidate base station set is the same as the number of response instructions in the response instruction set; obtaining the vibration data flow of the internal vibration data and the external excitation data, and performing Complete data transmission base station calculation to obtain the number of working base stations; determine whether the number of working base stations is greater than the number of candidate base stations; if the number of working base stations is greater than the number of candidate base stations, perform data traffic transmission allocation on the candidate base station set to obtain an allocation speed set and an allocation traffic set, and based on the allocation speed set and the allocation traffic set, perform transmission task allocation on the candidate base station set to obtain a complete base station set; if the number of working base stations is not greater than the number of candidate base stations, determine the task base station in the candidate base station set according to the number of working base stations to obtain a task base station set; use the complete base station set or the task base station set to transmit the internal vibration data and the external excitation data to a pre-built response prediction platform, wherein the response prediction platform has a built-in deep learning algorithm; use the response prediction platform to predict the bridge vibration response of the target bridge to obtain predicted vibration data, and complete the bridge vibration response prediction based on deep calculation.

[0006] Optionally, the vibration data collection of the pre-constructed target bridge to obtain internal vibration data and external excitation data includes: demarcating important areas of the target bridge to obtain a bridge vibration area set, starting a plurality of pre-constructed frequency detectors and a plurality of displacement detectors in each bridge vibration area in the bridge vibration area set to obtain a measurement area set; sequentially extracting measurement areas from the measurement area set, using the plurality of frequency detectors and the plurality of displacement detectors to perform vibration response detection on the measurement areas to obtain an original vibration frequency group and an original vibration displacement group; using a preset min-max normalization method to perform vibration response detection on the original vibration frequency group and the original vibration displacement group, respectively. The data is normalized to obtain a normalized vibration frequency group and a normalized vibration displacement group; extreme value extraction and frequency extraction are performed on the normalized vibration frequency group and the normalized vibration displacement group to obtain a regional vibration frequency group and a regional vibration displacement group, wherein the regional vibration frequency group includes: maximum vibration frequency, minimum vibration frequency and mode vibration frequency, and the regional vibration displacement group includes: maximum vibration displacement, minimum vibration displacement and mode vibration displacement; the regional vibration frequency group and the regional vibration displacement group are summarized to obtain a bridge vibration frequency set and a bridge vibration displacement set; the real-time load of the current target bridge is measured to obtain the real-time load of the bridge, and the current bridge environment wind speed and bridge environment magnitude are obtained.

[0007] Optionally, the base station connection is successfully determined based on the response instruction set and the transmission base station set to obtain a candidate base station set, including: obtaining an acceptance frequency range and a rejection frequency range; extracting response instructions in the response instruction set in sequence, identifying the instruction signal frequency of the response instruction, and judging whether the instruction signal frequency is within the acceptance frequency range; if the instruction signal frequency is within the acceptance frequency range, recording the response instruction as an acceptance connection instruction, and identifying the candidate base station corresponding to the acceptance connection instruction in the transmission base station set; if the instruction signal frequency is not within the acceptance frequency range, judging whether the instruction signal frequency is within the rejection frequency range; if the instruction signal frequency is not within the rejection frequency range, recording the response instruction as an acceptance connection instruction. Recorded as an abnormal connection instruction, identifying the unresponsive base station corresponding to the abnormal connection instruction in the transmission base station set; identifying the number of connection requests of the unresponsive base station, and judging whether the number of connection requests is greater than the preset standard request number; if the number of connection requests is not greater than the standard request number, recording the unresponsive base station as a secondary request base station; summarizing the secondary request base stations to obtain a secondary request base station set, redistributing the base station connection instructions to each secondary request base station in the secondary request base station set to obtain a secondary response instruction set; using the secondary response instruction set to update the response instruction set, and returning to the step of extracting the response instructions in the response instruction set in sequence; summarizing the candidate base stations to obtain a candidate base station set.

[0008] Optionally, performing a complete data transmission base station calculation based on the vibration data flow to obtain the number of working base stations includes: obtaining a single transmission failure rate and a rated transmission flow of the candidate base station, and setting an overall success rate for transmitting the vibration data flow; identifying the number of transmission nodes between the candidate base station set and the response prediction platform; and calculating the number of working base stations based on the single transmission failure rate, the rated transmission flow, the overall success rate and the number of transmission nodes using the following formula:

[0009] ,in, Indicates the number of working base stations, Indicates the vibration data flow, represents the logarithmic function, The overall success rate, Indicates the rated transmission flow rate, represents the single transmission failure rate, k represents the number of transmission nodes, Indicates the round-up symbol.

[0010] Optionally, performing data traffic transmission allocation on the candidate base station set to obtain an allocation speed set and an allocation traffic set includes: setting a monitoring transmission duration, performing the following operations on each candidate base station in the candidate base station set: obtaining an overall signal round trip duration and a number of signal round trips of the candidate base station based on the monitoring transmission duration, and calculating a signal round trip duration according to the overall signal round trip duration, the number of signal round trips, the monitoring transmission duration, and a preset environmental disturbance factor, wherein the signal round trip duration is expressed as:

[0011] ,in, Indicates the round trip time of the signal. Indicates the monitoring transmission duration. represents the environmental disturbance factor, Indicates the overall signal round trip time, Indicates the number of signal round trips; counts the monitoring transmission flow of the candidate base station within the monitoring transmission duration; summarizes the signal round trip duration and the monitoring transmission flow respectively to obtain a signal round trip duration set and a monitoring transmission flow set; performs flow speed allocation according to the signal round trip duration set and the monitoring transmission flow set to obtain a distribution speed set; performs flow allocation on each candidate base station in the candidate base station set according to the distribution speed set to obtain a distribution flow set.

[0012] Optionally, the obtaining of the overall signal round trip time and the number of signal round trips of the candidate base station based on the monitoring transmission time includes: setting the monitoring start time and the initial round trip number; determining the monitoring end time for the response prediction platform to complete the reception of the traffic signal based on the monitoring start time; calculating the single monitoring time at the monitoring end time and the monitoring start time, and calculating the cumulative monitoring time according to the single monitoring time, the preset initial monitoring time and the initial preparation time, wherein the single monitoring time and the cumulative monitoring time are respectively expressed as:

[0013] ,in, Indicates the duration of a single monitoring. Indicates the end time of monitoring. Indicates the monitoring start time. Indicates the cumulative monitoring time. Indicates the initial monitoring duration. Indicates the initial preparation time; determines whether the cumulative monitoring time is less than the monitoring transmission time; if the cumulative monitoring time is less than the monitoring transmission time, calculates the target round trip number according to the initial round trip number, wherein the target round trip number is expressed as:

[0014] ,in, Indicates the number of round trips to the target. represents the initial round trip number; based on the monitoring end time, determine the post-position start time of the candidate base station, calculate the transmission preparation time of the post-position start time and the monitoring end time, wherein the post-position start time is the time when the candidate base station performs data transmission for the first time after the monitoring end time, and the transmission preparation time is expressed as:

[0015] ,in, Indicates the transmission preparation time. Indicates the start time of the rear position; according to the single monitoring time and the preset initial round-trip time, the target round-trip time is calculated using the following formula:

[0016] ,in, Indicates the target round trip time. Represents the initial round-trip duration; uses the cumulative monitoring duration, the post-position start time, the target round-trip duration, the target round-trip number and the transmission preparation time to respectively update the initial monitoring duration, the monitoring start time, the initial round-trip duration, the initial round-trip number and the initial preparation time, and returns to the step of determining the monitoring end time when the response prediction platform completes the reception of the traffic signal based on the monitoring start time; if the cumulative monitoring duration is not less than the monitoring transmission duration, the target round-trip duration and the target round-trip number are respectively recorded as the overall signal round-trip duration and the signal round-trip number.

[0017] Optionally, performing flow rate allocation according to the signal round trip time set and the monitored transmission flow rate set to obtain the allocated speed set includes: calculating a total time factor and a total flow factor according to the signal round trip time set and the monitored transmission flow rate set using the following formula:

[0018] ,in, represents the total duration factor, Indicates the number of signal round trip times in the signal round trip time set, represents the round trip time of the i-th signal, represents the total flow factor, m represents the number of transmission flows monitored in the centralized monitoring transmission flow, represents the jth monitored transmission flow; extracting speed allocation base stations in the candidate base station set in turn, and identifying the transmission duration to be allocated and the transmission flow to be allocated in the signal round trip time set and the monitored transmission flow set according to the speed allocation base stations;

[0019] The allocation speed is calculated using the following formula according to the total duration factor, the total flow factor, the transmission duration to be allocated and the transmission flow to be allocated:

[0020] ,in, Indicates the distribution speed, Indicates the transmission time to be allocated. represents the transmission flow to be allocated; and the allocation speeds are summarized to obtain an allocation speed set.

[0021] Optionally, performing traffic allocation on each candidate base station in a candidate base station set according to the allocation speed set to obtain an allocation traffic set includes: sequentially extracting traffic allocation base stations from the candidate base station set, and identifying a weighted allocation speed in the allocation speed set according to the traffic allocation base stations;

[0022] According to the weight distribution speed and the distribution speed set, the distribution flow is calculated, wherein the distribution flow is expressed as:

[0023] ,in, Indicates the distribution flow, represents the weight distribution speed, Indicates the number of distribution speeds in the distribution speed set, represents the pth distribution speed, represents the vibration data flow; and the distribution flow is summarized to obtain a distribution flow set.

[0024] Optionally, determining the task base station in the candidate base station set according to the number of working base stations to obtain a task base station set includes: extracting candidate base stations in sequence from the candidate base station set, identifying the current number of transmission tasks of the candidate base stations, key-pairing the candidate base stations with the current number of transmission tasks to obtain a transmission task group; summarizing the transmission task groups to obtain a transmission task group set, numerically sorting the transmission task group set to obtain a transmission task sequence set; extracting target task sequences in sequence from the transmission task sequence set according to the number of working base stations to obtain a target task sequence set, wherein the number of target task sequences in the target task sequence set is the same as the number of working base stations; and extracting task base stations from the target task sequence set to obtain a task base station set.

[0025] To achieve the above-mentioned purpose, the present invention also provides a bridge vibration response prediction system based on deep calculation, including: a response instruction distribution module, used to collect vibration data of a pre-constructed target bridge, and obtain internal vibration data and external excitation data, wherein the internal vibration data includes: a bridge vibration area set, a bridge vibration frequency set and a bridge vibration displacement set, and the external excitation data includes: a bridge real-time load, a bridge environmental wind speed and a bridge environmental magnitude, a base station connection instruction is generated according to the internal vibration data and the external excitation data, and the base station connection instruction is distributed to a pre-constructed transmission base station set to obtain a response instruction set; a working base station calculation module is used to determine the success of the base station connection according to the response instruction set and the transmission base station set to obtain a candidate base station set, wherein the number of candidate base stations in the candidate base station set is the same as the number of response instructions in the response instruction set, and the vibration data flow of the internal vibration data and the external excitation data is obtained according to the response instruction set. Vibration data flow, complete data transmission base station calculation is performed to obtain the number of working base stations; a task base station acquisition module is used to determine whether the number of working base stations is greater than the number of candidate base stations. If the number of working base stations is greater than the number of candidate base stations, data flow transmission allocation is performed on the candidate base station set to obtain an allocation speed set and an allocation flow set. Based on the allocation speed set and the allocation flow set, transmission tasks are allocated to the candidate base station set to obtain a complete base station set. If the number of working base stations is not greater than the number of candidate base stations, the task base station is determined in the candidate base station set according to the number of working base stations to obtain a task base station set; a vibration response prediction module is used to use the complete base station set or the task base station set to transmit the internal vibration data and external excitation data to a pre-built response prediction platform, wherein the response prediction platform has a built-in deep learning algorithm, and the response prediction platform is used to predict the bridge vibration response of the target bridge to obtain predicted vibration data.

[0026] In order to solve the above problem, the present invention also provides an electronic device, which includes: a memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the above-mentioned bridge vibration response prediction method based on deep calculation.

[0027] In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned bridge vibration response prediction method based on deep calculation.

[0028] In order to solve the problems described in the background technology, the present invention first collects vibration data of the target bridge to obtain internal vibration data and external excitation data, completes the collection of the internal vibration data and external excitation data of the bridge, and provides necessary raw data for subsequent bridge vibration response prediction. Then, base station connection instructions are generated according to the internal vibration data and external excitation data, and the base station connection instructions are distributed to the transmission base station set to obtain a response instruction set. By generating and distributing the base station connection instructions, it is ensured that the bridge vibration data can be effectively transmitted to the transmission base station. According to the response instruction set and the transmission base station set, the base station connection is successfully determined to obtain a candidate base station set, and it is ensured that the successfully connected transmission base station is a candidate base station, which reduces unnecessary information sending and improves the efficiency of data transmission, thereby improving the efficiency of subsequent vibration response prediction. By obtaining the vibration data flow of the internal vibration data and the external excitation data, the flow size of the vibration response data is understood. At the same time, according to the vibration data flow, a complete data transmission base station calculation is performed to obtain the number of working base stations. This step calculates how many transmission base stations are needed to transmit data, thereby improving the accuracy and efficiency of data transmission. It is judged whether the number of working base stations is greater than the number of candidate base stations. If the working base stations are If the number of working base stations is greater than the number of candidate base stations, data traffic transmission allocation is performed on the candidate base station set to obtain an allocation speed set and an allocation traffic set. By reasonably allocating traffic, congestion of the transmission path during data traffic transmission is avoided, the efficiency of data traffic transmission is improved, and the efficiency of vibration response prediction is thereby improved. Then, based on the allocation speed set and the allocation traffic set, transmission tasks are allocated to the candidate base station set to obtain a complete base station set. This step allocates tasks to each candidate base station so that they can cooperate with each other. When there are fewer working base stations, the integrity of vibration data can be retained to a great extent, thereby improving the accuracy of vibration response prediction. If the number of working base stations is not greater than the number of candidate base stations, task base stations are determined in the candidate base station set according to the number of working base stations to obtain a task base station set. This step achieves rapid task base station determination when there are enough candidate base stations, and also ensures that the task base station has fewer parallel tasks, thereby improving the efficiency of vibration data transmission. By using the complete base station set or the task base station set, the internal vibration data and the external excitation data are transmitted to the response prediction platform. Finally, the response prediction platform is used to predict the bridge vibration response of the target bridge to obtain predicted vibration data, thereby completing the bridge vibration response prediction based on deep calculation. Therefore, the present invention can improve the transmission efficiency of vibration response data and the accuracy of bridge vibration response prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic flow chart of a bridge vibration response prediction method based on deep calculation provided by an embodiment of the present invention; Figure 2 A functional module diagram of a bridge vibration response prediction system based on deep calculation provided by an embodiment of the present invention; Figure 3A schematic diagram of the structure of an electronic device for implementing the bridge vibration response prediction method based on deep calculation provided by an embodiment of the present invention.

[0030] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0031] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0032] The embodiment of the present application provides a bridge vibration response prediction method based on deep calculation. The execution subject of the bridge vibration response prediction method based on deep calculation includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the bridge vibration response prediction method based on deep calculation can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0033] Reference Figure 1 As shown, it is a flow chart of a bridge vibration response prediction method based on deep calculation provided by an embodiment of the present invention. In this embodiment, the bridge vibration response prediction method based on deep calculation includes: S1, collecting vibration data of a pre-built target bridge to obtain internal vibration data and external excitation data, wherein the internal vibration data includes: a bridge vibration region set, a bridge vibration frequency set and a bridge vibration displacement set, and the external excitation data includes: a bridge real-time load, a bridge environmental wind speed and a bridge environmental magnitude.

[0034] It can be understood that the target bridge refers to the bridge that needs to be predicted for vibration response, and the internal vibration data refers to the vibration data inside the target bridge, including: bridge vibration area, bridge vibration frequency and bridge vibration displacement, wherein each bridge vibration area includes multiple bridge vibration frequencies and multiple bridge vibration displacements. The external excitation data refers to the data of external vibration that causes the target bridge to vibrate, including: bridge real-time load, bridge environmental wind speed and bridge environmental earthquake magnitude.

[0035] It is understandable that the real-time bridge load refers to the real-time weight carried on the target bridge, which includes: the weight of the collection unit for measuring internal vibration data and the weight of the train running on the target bridge at the time of collection. The bridge environmental wind speed refers to the environmental wind speed around the target bridge. Since the target bridge is often built between rivers or between empty valleys, the wind force in the environment where the target bridge is located is strong, and the fast running of the train will also generate strong wind force, so it is necessary to consider the impact of the bridge environmental wind speed on the vibration of the target bridge. The bridge environmental magnitude refers to the earthquake level in the area where the target bridge is located. Although the human body has little perception of slight earthquakes, its impact on the target bridge is extremely significant.

[0036] In detail, the vibration data of the pre-constructed target bridge is collected to obtain the internal vibration data and the external excitation data, including: demarcating the important areas of the target bridge to obtain a bridge vibration area set, starting the pre-constructed multiple frequency detectors and multiple displacement detectors of each bridge vibration area in the bridge vibration area set to obtain a measurement area set; sequentially extracting measurement areas from the measurement area set, using the multiple frequency detectors and multiple displacement detectors to perform vibration response detection on the measurement areas to obtain an original vibration frequency group and an original vibration displacement group; using a preset min-max normalization method to respectively perform the vibration response detection on the original vibration frequency group and the original vibration displacement group. The data is normalized to obtain a normalized vibration frequency group and a normalized vibration displacement group; extreme value extraction and frequency extraction are performed on the normalized vibration frequency group and the normalized vibration displacement group to obtain a regional vibration frequency group and a regional vibration displacement group, wherein the regional vibration frequency group includes: maximum vibration frequency, minimum vibration frequency and mode vibration frequency, and the regional vibration displacement group includes: maximum vibration displacement, minimum vibration displacement and mode vibration displacement; the regional vibration frequency group and the regional vibration displacement group are summarized to obtain a bridge vibration frequency set and a bridge vibration displacement set; the real-time load of the current target bridge is measured to obtain the real-time load of the bridge, and the current bridge environment wind speed and bridge environment magnitude are obtained.

[0037] It can be understood that the bridge vibration area set refers to the important areas of the bridge after artificial division, wherein the important areas of the bridge may be: piers, abutments, main beams, secondary beams and bridge decks, etc. These important areas of the bridge are crucial for evaluating the overall structural stability and safety of the target bridge. The measurement area set refers to the vibration area of ​​the bridge after the detector is started, the original vibration frequency group refers to the combination of frequencies detected by multiple frequency detectors, and the original vibration displacement group refers to the combination of displacements detected by multiple displacement detectors.

[0038] It can be understood that the min-max normalization method refers to a method of scaling the data in a data set to a fixed numerical range according to the maximum value of the data set. For example, the data set {1, 2, 3}, the normalized data set obtained according to the min-max normalization method is: {0, 0.5, 1}. The normalized vibration frequency group and the normalized vibration displacement group refer to the original vibration frequency group and the original vibration displacement group with values ​​between 0 and 1, respectively. The maximum vibration frequency, the minimum vibration frequency and the mode vibration frequency refer to the maximum value, the minimum value and the value with the highest frequency in the normalized vibration frequency group, respectively, and the maximum vibration displacement, the minimum vibration displacement and the mode vibration displacement refer to the maximum value, the minimum value and the value with the highest frequency in the normalized vibration displacement group, respectively.

[0039] S2. Generate a base station connection instruction according to the internal vibration data and the external excitation data, distribute the base station connection instruction to a pre-built transmission base station set, and obtain a response instruction set.

[0040] It can be understood that the transmission base station set refers to a fixed radio station used for wireless data transmission, which can transmit internal vibration data and external excitation data to a more distant response prediction platform so that the response prediction platform can perform vibration response prediction on the target bridge. The response instruction set refers to the signal instructions returned by each transmission base station in the transmission base station set after receiving the base station connection instruction, and the response instruction contains information on whether the connection is made.

[0041] It should be explained that since the location of the target bridge is usually remote and there is no corresponding bridge control center around the target bridge, it is necessary to use a transmission base station set to transmit the collected internal vibration data and external excitation data to a more remote response prediction platform. When the collection of the internal vibration data and external excitation data of the target bridge is completed, a base station connection instruction will be issued. The base station connection instruction instructs the transmission base station to transmit the collected internal vibration data and external excitation data. After the transmission base station receives the base station connection instruction, it can choose to execute the command of the instruction, connect with the collection system of the target bridge, and start data transmission, or it can choose not to execute the base station connection instruction. For example, when the transmission base station has other data transmission tasks, it will not be able to transmit the data of the target bridge.

[0042] S3. Determine that the base station connection is successful according to the response instruction set to obtain a candidate base station set, wherein the number of candidate base stations in the candidate base station set is the same as the number of response instructions in the response instruction set.

[0043] It can be understood that the candidate base station set refers to a set of transmission base stations that return a response instruction with connection information.

[0044] In detail, the base station connection is successfully determined based on the response instruction set and the transmission base station set to obtain a candidate base station set, including: obtaining an acceptance frequency range and a rejection frequency range; extracting response instructions in the response instruction set in turn, identifying the instruction signal frequency of the response instruction, and judging whether the instruction signal frequency is within the acceptance frequency range; if the instruction signal frequency is within the acceptance frequency range, recording the response instruction as an acceptance connection instruction, and identifying the candidate base station corresponding to the acceptance connection instruction in the transmission base station set; if the instruction signal frequency is not within the acceptance frequency range, judging whether the instruction signal frequency is within the rejection frequency range; if the instruction signal frequency is not within the rejection frequency range, recording the response instruction as an acceptance connection instruction. Recorded as an abnormal connection instruction, identifying the unresponsive base station corresponding to the abnormal connection instruction in the transmission base station set; identifying the number of connection requests of the unresponsive base station, and judging whether the number of connection requests is greater than the preset standard request number; if the number of connection requests is not greater than the standard request number, recording the unresponsive base station as a secondary request base station; summarizing the secondary request base stations to obtain a secondary request base station set, redistributing the base station connection instructions to each secondary request base station in the secondary request base station set to obtain a secondary response instruction set; using the secondary response instruction set to update the response instruction set, and returning to the step of extracting the response instructions in the response instruction set in sequence; summarizing the candidate base stations to obtain a candidate base station set.

[0045] It can be understood that the acceptance frequency range refers to a signal frequency range that is preset manually and contains information about accepting the connection. If the signal frequency of the response instruction returned by the transmission base station is within the acceptance frequency range, it means that the transmission base station has accepted the transmission task. The rejection frequency range refers to a signal frequency range that is preset manually and contains information about rejecting the connection. The unresponsive base station refers to a transmission base station whose returned response signal frequency cannot be accurately identified, that is, it is impossible to determine whether the transmission base station receives the data transmission based on the response instruction returned by the transmission base station.

[0046] It can be understood that the number of connection requests refers to the number of times base station connection instructions have been sent to unresponsive base stations, and the standard number of requests refers to a constant number of connection requests that is artificially set.

[0047] It needs to be explained that since the transmission base station will be affected by environmental interference in the process of returning the response instruction, the originally clear response instruction will become blurred, making it impossible to judge whether the transmission base station can perform the data transmission task based on the response instruction. Therefore, it is necessary to send a base station connection instruction to the transmission base station again.

[0048] S4. Obtain the vibration data flow of the internal vibration data and the external excitation data, and perform complete data transmission base station calculation according to the vibration data flow to obtain the number of working base stations.

[0049] It can be understood that the vibration data flow refers to the storage capacity of internal vibration data and external excitation data, and the number of working base stations refers to the number of transmission base stations required to complete the data transmission task.

[0050] In detail, the calculation of complete data transmission base stations is performed according to the vibration data flow to obtain the number of working base stations, including: obtaining the single transmission failure rate and rated transmission flow of the candidate base station, and setting the overall success rate of transmitting the vibration data flow; identifying the number of transmission nodes between the candidate base station set and the response prediction platform; and calculating the number of working base stations according to the single transmission failure rate, rated transmission flow, overall success rate and number of transmission nodes using the following formula:

[0051] ,in, Indicates the number of working base stations, Indicates the vibration data flow, represents the logarithmic function, The overall success rate, Indicates the rated transmission flow rate, represents the single transmission failure rate, k represents the number of transmission nodes, Indicates the round-up symbol.

[0052] It is clear that the single transmission failure rate refers to the transmission failure rate when a single transmission base station performs traffic transmission, wherein the reasons for the transmission failure may be: failure of the transmission base station itself, environmental interference of the transmission path, etc. The single transmission failure rate is obtained by preliminary experiments or by querying relevant work logs. The rated transmission flow refers to the maximum transmission flow set by the candidate base station for the one-time transmission of internal vibration data and external excitation data. When the total data flow of internal vibration data and external excitation data, that is, the vibration data flow, exceeds the rated transmission flow, the candidate base station will only be able to transmit part of the vibration data flow at a time.

[0053] It should be explained that the overall success rate refers to the probability that the internal vibration data and external excitation data are successfully transmitted to the response prediction platform. It should be noted that the overall success rate is directly set by humans and is not calculated, for example: 98%. The number of transmission nodes refers to the number of transmission nodes in the process of the candidate base station set transmitting traffic to the response prediction platform, where transmission nodes include: routers, signal relay points, etc.

[0054] S5. Determine whether the number of working base stations is greater than the number of candidate base stations.

[0055] It is clear that since there may be a difference between the number of working base stations calculated based on the vibration data flow and the actual number of candidate base stations, it is necessary to judge the size of the two and allocate the transmission flow according to the actual situation.

[0056] If the number of working base stations is greater than the number of candidate base stations, execute S6, allocate data traffic transmission to the candidate base station set to obtain an allocation speed set and an allocation traffic set, and allocate transmission tasks to the candidate base station set based on the allocation speed set and the allocation traffic set to obtain a complete base station set.

[0057] It is understandable that when the number of working base stations is greater than the number of candidate base stations, it means that the signal transmission channel corresponding to the transmission base station at this time is relatively crowded, and the vibration data traffic cannot be transmitted at one time, so it is necessary to allocate the transmission speed to continuously transmit the vibration data traffic. The allocated speed set refers to the set of traffic transmission speeds when each candidate base station in the candidate base station set transmits traffic, and the allocated traffic set refers to the set of traffic sizes that need to be transmitted when each candidate base station transmits traffic. The complete base station set refers to the candidate base station set after speed allocation and traffic allocation.

[0058] In detail, the data flow transmission allocation for the candidate base station set to obtain the allocation speed set and the allocation flow set includes: setting the monitoring transmission duration, performing the following operations on each candidate base station in the candidate base station set: based on the monitoring transmission duration, obtaining the overall signal round-trip duration and the number of signal round-trip times of the candidate base station, and calculating the signal round-trip duration according to the overall signal round-trip duration, the number of signal round-trip times, the monitoring transmission duration and the preset environmental disturbance factor, wherein the signal round-trip duration is expressed as:

[0059] ,in, Indicates the round trip time of the signal. Indicates the monitoring transmission duration. represents the environmental disturbance factor, Indicates the overall signal round trip time, Indicates the number of signal round trips; counts the monitoring transmission flow of the candidate base station within the monitoring transmission duration; summarizes the signal round trip duration and the monitoring transmission flow respectively to obtain a signal round trip duration set and a monitoring transmission flow set; performs flow speed allocation according to the signal round trip duration set and the monitoring transmission flow set to obtain a distribution speed set; performs flow allocation on each candidate base station in the candidate base station set according to the distribution speed set to obtain a distribution flow set.

[0060] It can be understood that the monitoring transmission duration refers to the artificially set duration for monitoring the candidate base station set, and the monitoring transmission duration is set to evaluate the data transmission duration and data transmission flow of the candidate base station during the data transmission process. The overall signal round-trip duration refers to the duration of signal transmission between the candidate base station and the response prediction platform within the monitoring transmission duration. The number of signal round trips refers to the number of times the signal travels back and forth between the candidate base station and the response prediction platform within the monitoring transmission duration. The environmental disturbance factor refers to a constant artificially set by the environment for interfering with data transmission. Due to the interference of the environment with data transmission, the duration of each signal transmission between the candidate base station and the response prediction platform is not the same, so it is necessary to introduce an environmental disturbance factor to compensate for the interference of the environment with data transmission. The monitoring transmission flow refers to the total flow of data transmission completed by the candidate base station within the monitoring transmission duration.

[0061] In detail, the overall signal round trip time and the number of signal round trips of the candidate base station are obtained based on the monitoring transmission time, including: setting the monitoring start time and the initial round trip number; based on the monitoring start time, determining the monitoring end time for the response prediction platform to complete the flow signal reception; calculating the single monitoring time at the monitoring end time and the monitoring start time, and calculating the cumulative monitoring time according to the single monitoring time, the preset initial monitoring time and the initial preparation time, wherein the single monitoring time and the cumulative monitoring time are respectively expressed as:

[0062] ,in, Indicates the duration of a single monitoring. Indicates the end time of monitoring. Indicates the monitoring start time. Indicates the cumulative monitoring time. Indicates the initial monitoring duration. Indicates the initial preparation time; determines whether the cumulative monitoring time is less than the monitoring transmission time; if the cumulative monitoring time is less than the monitoring transmission time, calculates the target round trip number according to the initial round trip number, wherein the target round trip number is expressed as:

[0063] ,in, Indicates the number of round trips to the target. represents the initial round trip number; based on the monitoring end time, determine the post-position start time of the candidate base station, calculate the transmission preparation time of the post-position start time and the monitoring end time, wherein the post-position start time is the time when the candidate base station performs data transmission for the first time after the monitoring end time, and the transmission preparation time is expressed as:

[0064] ,in, Indicates the transmission preparation time. Indicates the start time of the rear position; according to the single monitoring time and the preset initial round-trip time, the target round-trip time is calculated using the following formula:

[0065] ,in, Indicates the target round trip time. Represents the initial round-trip duration; uses the cumulative monitoring duration, the post-position start time, the target round-trip duration, the target round-trip number and the transmission preparation time to respectively update the initial monitoring duration, the monitoring start time, the initial round-trip duration, the initial round-trip number and the initial preparation time, and returns to the step of determining the monitoring end time when the response prediction platform completes the reception of the traffic signal based on the monitoring start time; if the cumulative monitoring duration is not less than the monitoring transmission duration, the target round-trip duration and the target round-trip number are respectively recorded as the overall signal round-trip duration and the signal round-trip number.

[0066] It can be understood that the monitoring start time refers to the time when the transmission information monitoring of the candidate base station starts, and the monitoring start time is set manually. The initial round-trip number refers to the number of times the current transmission signal travels back and forth between the candidate base station and the response prediction center. When the transmission information monitoring starts, the initial round-trip number is 0. The monitoring end time refers to the time after the monitoring start time when the response prediction platform receives the transmission information for the first time and returns the received information. The monitoring end time can be obtained by querying the relevant work log. The single monitoring duration refers to the time difference between the monitoring start time and the monitoring end time. The initial monitoring duration refers to the currently accumulated monitoring duration. The initial preparation duration refers to the time difference between the monitoring end time of the current transmission stage and the monitoring start time of the next transmission stage. When the information transmission monitoring is performed for the first time, the initial monitoring duration and the initial preparation duration are both 0.

[0067] It should be explained that the cumulative monitoring duration refers to the currently accumulated monitoring duration. Unlike the initial monitoring duration, the cumulative monitoring duration is the sum of the initial monitoring duration, the single monitoring duration and the initial preparation duration. The target round trip number refers to the number of times the signal has traveled back and forth between the candidate base station and the response prediction center. The post-position start time refers to the time when the candidate base station performs data transmission for the first time after the monitoring end time. The transmission preparation duration refers to the time difference from the completion of one data transmission to the start of the next signal transmission. The initial round trip duration refers to the total transmission duration of the signal during the transmission process before the current transmission stage. The initial round trip duration does not include the transmission preparation duration. The target round trip duration refers to the total transmission duration of the current signal during the transmission process.

[0068] For example, Xiao Zhang is a maintenance worker of a target bridge. One day, Xiao Zhang sets the monitoring start time of a candidate base station to 8:00 and the monitoring transmission time to 30 minutes. The initial round trip times, initial monitoring time, initial preparation time and initial round trip time are all 0 at the beginning of monitoring. Xiao Zhang searches the work log of the candidate base station at 8:00 and finds that the time when the response prediction platform receives the transmission information and returns the receiving information to the candidate base station is 8:10, that is, the monitoring end time is 8:10. Then the single monitoring time and the cumulative monitoring time are 10min=8:10-8:00 and 10min=10min+0min+0min respectively. Because 10min is less than 30min, that is, the cumulative monitoring time If the target round trip time is less than the monitoring transmission time, the target round trip time is calculated as: 1=0+1. Then Xiao Zhang inquires in the work log of the candidate base station that after 8:10, the candidate base station starts sending signals to the response prediction center again at 8:15, that is, the post-position start time is 8:15. The transmission preparation time is calculated as: 5min=8:15-8:10, and the target round trip time is: 10min=10min+0. Finally, the initial monitoring time, monitoring start time, initial round trip time, initial round trip times and initial preparation time are updated to: 10min, 8:15, 10min, 1 time and 5min respectively, and data transmission monitoring is performed again until the cumulative monitoring time is not less than the monitoring transmission time.

[0069] In detail, the flow rate allocation is performed according to the signal round trip time set and the monitored transmission flow rate set to obtain the allocation speed set, including: calculating the total time factor and the total flow factor according to the signal round trip time set and the monitored transmission flow rate set using the following formula:

[0070] ,in, represents the total duration factor, Indicates the number of signal round trip times in the signal round trip time set, represents the round trip time of the i-th signal, represents the total flow factor, m represents the number of transmission flows monitored in the centralized monitoring transmission flow, represents the jth monitored transmission flow; extracting speed allocation base stations in sequence from the candidate base station set, and identifying the transmission duration to be allocated and the transmission flow to be allocated in the signal round trip duration set and the monitored transmission flow set according to the speed allocation base stations; calculating the allocation speed according to the total duration factor, the total flow factor, the transmission duration to be allocated and the transmission flow to be allocated using the following formula:

[0071] ,in, Indicates the distribution speed, Indicates the transmission time to be allocated. represents the transmission flow to be allocated; and the allocation speeds are summarized to obtain an allocation speed set.

[0072] It can be understood that the speed allocation base station refers to the candidate base station in the candidate base station set that will perform transmission speed allocation, the total time factor refers to a natural number used to represent the weight of the signal round-trip time in the subsequent calculation of the allocation speed, the total flow factor refers to a natural number used to represent the weight of the monitored transmission flow in the subsequent calculation of the allocation speed, the transmission time to be allocated refers to the signal round-trip time corresponding to the speed allocation base station in the signal round-trip time set, and the transmission flow to be allocated refers to the monitored transmission flow corresponding to the speed allocation base station in the monitored transmission flow set.

[0073] In detail, the method of allocating traffic to each candidate base station in the candidate base station set according to the allocation speed set to obtain the allocation traffic set includes: extracting traffic allocation base stations in the candidate base station set in sequence, identifying weighted allocation speeds in the allocation speed set according to the traffic allocation base stations; and calculating the allocation traffic according to the weighted allocation speed and the allocation speed set, wherein the allocation traffic is expressed as:

[0074] ,in, Indicates the distribution flow, represents the weight distribution speed, Indicates the number of distribution speeds in the distribution speed set, represents the pth distribution speed, represents the vibration data flow; and the distribution flow is summarized to obtain a distribution flow set.

[0075] It can be understood that the traffic allocation base station refers to a candidate base station in the candidate base station set that will perform transmission traffic allocation, and the weight allocation speed refers to an allocation speed in the allocation speed set that corresponds to the traffic allocation base station.

[0076] If the number of working base stations is not greater than the number of candidate base stations, S7 is executed to determine a task base station from the candidate base station set according to the number of working base stations to obtain a task base station set.

[0077] It can be understood that the task base station refers to the candidate base station in the candidate base station set that needs to perform the vibration data traffic transmission task. Since the number of working base stations is not greater than the number of candidate base stations, there is no problem of transmission channel congestion in this vibration data traffic transmission, and it is necessary to select a base station based on the current processing task status of each candidate base station in the candidate base station set to determine the task base station that will perform the vibration data traffic transmission.

[0078] In detail, the method of determining a task base station in the candidate base station set according to the number of working base stations to obtain a task base station set includes: extracting candidate base stations in sequence from the candidate base station set, identifying the current number of transmission tasks of the candidate base stations, and key-pairing the candidate base stations with the current number of transmission tasks to obtain a transmission task group; summarizing the transmission task groups to obtain a transmission task group set, and numerically sorting the transmission task group set to obtain a transmission task sequence set; according to the number of working base stations, sequentially extracting target task sequences in the transmission task sequence set to obtain a target task sequence set, wherein the number of target task sequences in the target task sequence set is the same as the number of working base stations; and extracting task base stations from the target task sequence set to obtain a task base station set.

[0079] It can be understood that the current number of transmission tasks refers to the number of signal transmission tasks currently being performed by the candidate base stations, the transmission task sequence set refers to a set of transmission task groups sorted from small to large according to the current number of transmission tasks, and the target task sequence refers to the transmission task sequence whose sequence number is before the number of working base stations in the transmission task sequence set.

[0080] S8. Using the complete base station set or the task base station set, the internal vibration data and the external excitation data are transmitted to a pre-built response prediction platform, wherein the response prediction platform has a built-in deep learning algorithm.

[0081] It can be understood that the response prediction platform refers to a control center that collects and analyzes data on the target bridge. The response prediction platform will simultaneously connect to multiple target bridges or other buildings that require data processing and analysis. The deep learning algorithm refers to an algorithm that can perform data analysis and prediction based on the collected target bridge big data, such as: convolutional neural network, etc.

[0082] S9. Utilizing the response prediction platform, predicting the bridge vibration response of the target bridge, obtaining predicted vibration data, and completing the bridge vibration response prediction based on deep calculation.

[0083] It should be explained that the predicted vibration data includes: a predicted vibration area set, a predicted vibration frequency set and a predicted vibration displacement set, wherein the predicted vibration area set corresponds one-to-one to the bridge vibration area set, the predicted vibration frequency set includes: predicted maximum frequency, predicted minimum frequency and predicted multi-digit frequency, which correspond one-to-one to the maximum vibration frequency, minimum vibration frequency and multi-digit vibration frequency, and the predicted vibration displacement set includes: predicted maximum displacement, predicted minimum displacement and predicted multi-digit displacement, which correspond one-to-one to the maximum vibration displacement, minimum vibration displacement and multi-digit vibration displacement.

[0084] In order to solve the problems described in the background technology, the present invention first collects vibration data of the target bridge to obtain internal vibration data and external excitation data, completes the collection of the internal vibration data and external excitation data of the bridge, and provides necessary raw data for subsequent bridge vibration response prediction. Then, base station connection instructions are generated according to the internal vibration data and external excitation data, and the base station connection instructions are distributed to the transmission base station set to obtain a response instruction set. By generating and distributing the base station connection instructions, it is ensured that the bridge vibration data can be effectively transmitted to the transmission base station. According to the response instruction set and the transmission base station set, the base station connection is successfully determined to obtain a candidate base station set, and it is ensured that the successfully connected transmission base station is a candidate base station, which reduces unnecessary information sending and improves the efficiency of data transmission, thereby improving the efficiency of subsequent vibration response prediction. By obtaining the vibration data flow of the internal vibration data and the external excitation data, the flow size of the vibration response data is understood. At the same time, according to the vibration data flow, a complete data transmission base station calculation is performed to obtain the number of working base stations. This step calculates how many transmission base stations are needed to transmit data, thereby improving the accuracy and efficiency of data transmission. It is judged whether the number of working base stations is greater than the number of candidate base stations. If the working base stations are If the number of working base stations is greater than the number of candidate base stations, data traffic transmission allocation is performed on the candidate base station set to obtain an allocation speed set and an allocation traffic set. By reasonably allocating traffic, congestion of the transmission path during data traffic transmission is avoided, the efficiency of data traffic transmission is improved, and the efficiency of vibration response prediction is thereby improved. Then, based on the allocation speed set and the allocation traffic set, transmission tasks are allocated to the candidate base station set to obtain a complete base station set. This step allocates tasks to each candidate base station so that they can cooperate with each other. When there are fewer working base stations, the integrity of vibration data can be retained to a great extent, thereby improving the accuracy of vibration response prediction. If the number of working base stations is not greater than the number of candidate base stations, task base stations are determined in the candidate base station set according to the number of working base stations to obtain a task base station set. This step achieves rapid task base station determination when there are enough candidate base stations, and also ensures that the task base station has fewer parallel tasks, thereby improving the efficiency of vibration data transmission. By using the complete base station set or the task base station set, the internal vibration data and the external excitation data are transmitted to the response prediction platform. Finally, the response prediction platform is used to predict the bridge vibration response of the target bridge to obtain predicted vibration data, thereby completing the bridge vibration response prediction based on deep calculation. Therefore, the present invention can improve the transmission efficiency of vibration response data and the accuracy of bridge vibration response prediction.

[0085] like Figure 2 , which is a functional module diagram of a bridge vibration response prediction system based on deep calculation provided by an embodiment of the present invention.

[0086] The bridge vibration response prediction system 100 based on deep calculation of the present invention can be installed in an electronic device. According to the functions implemented, the bridge vibration response prediction system 100 based on deep calculation can include a response instruction distribution module 101, a working base station calculation module 102, a task base station acquisition module 103 and a vibration response prediction module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0087] The response instruction distribution module 101 is used to collect vibration data of a pre-constructed target bridge to obtain internal vibration data and external excitation data, wherein the internal vibration data includes: a bridge vibration area set, a bridge vibration frequency set and a bridge vibration displacement set, and the external excitation data includes: a bridge real-time load, a bridge environmental wind speed and a bridge environmental magnitude, and generate a base station connection instruction according to the internal vibration data and the external excitation data, and distribute the base station connection instruction to a pre-constructed transmission base station set to obtain a response instruction set; the working base station calculation module 102 is used to determine the success of the base station connection according to the response instruction set and the transmission base station set to obtain a candidate base station set, wherein the number of candidate base stations in the candidate base station set is the same as the number of response instructions in the response instruction set, and obtain the vibration data flow of the internal vibration data and the external excitation data, and perform complete data transmission base station calculation according to the vibration data flow. The task base station acquisition module 103 is used to determine whether the number of working base stations is greater than the number of candidate base stations. If the number of working base stations is greater than the number of candidate base stations, data traffic transmission is allocated to the candidate base station set to obtain an allocation speed set and an allocation traffic set. Based on the allocation speed set and the allocation traffic set, transmission tasks are allocated to the candidate base station set to obtain a complete base station set. If the number of working base stations is not greater than the number of candidate base stations, a task base station is determined in the candidate base station set according to the number of working base stations to obtain a task base station set. The vibration response prediction module 104 is used to use the complete base station set or the task base station set to transmit the internal vibration data and the external excitation data to a pre-built response prediction platform, wherein the response prediction platform has a built-in deep learning algorithm, and the response prediction platform is used to predict the bridge vibration response of the target bridge to obtain predicted vibration data.

[0088] In detail, each module in the bridge vibration response prediction system 100 based on depth calculation in the embodiment of the present invention is used in the same manner as described above. Figure 1 The bridge vibration response prediction method based on deep calculation described in the invention has the same technical means and can produce the same technical effect, so it will not be repeated here.

[0089] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a bridge vibration response prediction method based on deep calculation provided by an embodiment of the present invention.

[0090] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a bridge vibration response prediction method program based on deep calculation.

[0091] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the bridge vibration response prediction method program based on deep calculation, but also can be used to temporarily store data that has been output or is to be output.

[0092] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (such as a bridge vibration response prediction method program based on deep calculation, etc.) stored in the memory 11, and calling data stored in the memory 11.

[0093] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0094] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0095] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0096] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0097] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0098] The bridge vibration response prediction method program based on deep calculation stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: collecting vibration data of a pre-constructed target bridge to obtain internal vibration data and external excitation data, wherein the internal vibration data includes: a bridge vibration area set, a bridge vibration frequency set and a bridge vibration displacement set, and the external excitation data includes: a bridge real-time load, a bridge environmental wind speed and a bridge environmental magnitude; generating a base station connection instruction according to the internal vibration data and the external excitation data, distributing the base station connection instruction to a pre-constructed transmission base station set to obtain a response instruction set; determining the success of the base station connection according to the response instruction set and the transmission base station set to obtain a candidate base station set, wherein the number of candidate base stations in the candidate base station set is the same as the number of response instructions in the response instruction set; obtaining the vibration data flow of the internal vibration data and the external excitation data , according to the vibration data flow, complete data transmission base station calculation is performed to obtain the number of working base stations; whether the number of working base stations is greater than the number of candidate base stations is determined; if the number of working base stations is greater than the number of candidate base stations, data flow transmission allocation is performed on the candidate base station set to obtain an allocation speed set and an allocation flow set, and based on the allocation speed set and the allocation flow set, transmission tasks are allocated to the candidate base station set to obtain a complete base station set; if the number of working base stations is not greater than the number of candidate base stations, task base stations are determined in the candidate base station set according to the number of working base stations to obtain a task base station set; using the complete base station set or the task base station set, the internal vibration data and the external excitation data are transmitted to a pre-built response prediction platform, wherein the response prediction platform has a built-in deep learning algorithm; using the response prediction platform, bridge vibration response prediction is performed on the target bridge to obtain predicted vibration data, and bridge vibration response prediction based on deep calculation is completed.

[0099] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0100] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0101] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor of an electronic device, it can achieve: collecting vibration data of a pre-constructed target bridge to obtain internal vibration data and external excitation data, wherein the internal vibration data includes: a bridge vibration area set, a bridge vibration frequency set and a bridge vibration displacement set, and the external excitation data includes: a bridge real-time load, a bridge environmental wind speed and a bridge environmental magnitude; generating a base station connection instruction according to the internal vibration data and the external excitation data, distributing the base station connection instruction to a pre-constructed transmission base station set to obtain a response instruction set; determining the success of the base station connection according to the response instruction set and the transmission base station set to obtain a candidate base station set, wherein the number of candidate base stations in the candidate base station set is the same as the number of response instructions in the response instruction set; obtaining the vibration data flow of the internal vibration data and the external excitation data, According to the vibration data flow, complete data transmission base stations are calculated to obtain the number of working base stations; it is determined whether the number of working base stations is greater than the number of candidate base stations; if the number of working base stations is greater than the number of candidate base stations, data flow transmission is allocated to the candidate base station set to obtain an allocation speed set and an allocation flow set, and based on the allocation speed set and the allocation flow set, transmission tasks are allocated to the candidate base station set to obtain a complete base station set; if the number of working base stations is not greater than the number of candidate base stations, task base stations are determined in the candidate base station set according to the number of working base stations to obtain a task base station set; using the complete base station set or the task base station set, the internal vibration data and the external excitation data are transmitted to a pre-built response prediction platform, wherein the response prediction platform has a built-in deep learning algorithm; using the response prediction platform, bridge vibration response prediction is performed on the target bridge to obtain predicted vibration data, and bridge vibration response prediction based on deep calculation is completed.

[0102] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0103] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0105] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A bridge vibration response prediction method based on deep calculation, characterized in that: The method includes: collecting vibration data of a pre-constructed target bridge to obtain internal vibration data and external excitation data, wherein the internal vibration data includes: a bridge vibration area set, a bridge vibration frequency set and a bridge vibration displacement set, and the external excitation data includes: a bridge real-time load, a bridge environmental wind speed and a bridge environmental magnitude; generating a base station connection instruction according to the internal vibration data and the external excitation data, distributing the base station connection instruction to a pre-constructed transmission base station set to obtain a response instruction set; determining a successful base station connection according to the response instruction set and the transmission base station set to obtain a candidate base station set, wherein the number of candidate base stations in the candidate base station set is the same as the number of response instructions in the response instruction set; obtaining the vibration data flow of the internal vibration data and the external excitation data, performing a complete data transmission base station calculation according to the vibration data flow, and obtaining a working number of base stations; judging whether the number of working base stations is greater than the number of candidate base stations; if the number of working base stations is greater than the number of candidate base stations, allocating data traffic transmission to the candidate base station set to obtain an allocation speed set and an allocation traffic set, and allocating transmission tasks to the candidate base station set based on the allocation speed set and the allocation traffic set to obtain a complete base station set; if the number of working base stations is not greater than the number of candidate base stations, determining a task base station in the candidate base station set according to the number of working base stations to obtain a task base station set; using the complete base station set or the task base station set, transmitting the internal vibration data and the external excitation data to a pre-built response prediction platform, wherein the response prediction platform has a built-in deep learning algorithm; using the response prediction platform, predicting the bridge vibration response of the target bridge to obtain predicted vibration data, and completing the bridge vibration response prediction based on deep calculation.

2. The bridge vibration response prediction method based on deep calculation according to claim 1, characterized in that: The vibration data of the pre-constructed target bridge is collected to obtain internal vibration data and external excitation data, including: demarcating important areas of the target bridge to obtain a bridge vibration area set, starting a plurality of pre-constructed frequency detectors and a plurality of displacement detectors in each bridge vibration area in the bridge vibration area set to obtain a measurement area set; sequentially extracting measurement areas from the measurement area set, using the plurality of frequency detectors and the plurality of displacement detectors to perform vibration response detection on the measurement areas to obtain an original vibration frequency group and an original vibration displacement group; using a preset min-max normalization method to perform data analysis on the original vibration frequency group and the original vibration displacement group, respectively. Normalization operation is performed to obtain a normalized vibration frequency group and a normalized vibration displacement group; extreme value extraction and frequency extraction are performed on the normalized vibration frequency group and the normalized vibration displacement group to obtain a regional vibration frequency group and a regional vibration displacement group, wherein the regional vibration frequency group includes: maximum vibration frequency, minimum vibration frequency and mode vibration frequency, and the regional vibration displacement group includes: maximum vibration displacement, minimum vibration displacement and mode vibration displacement; the regional vibration frequency group and the regional vibration displacement group are respectively summarized to obtain a bridge vibration frequency set and a bridge vibration displacement set; the real-time load of the current target bridge is measured to obtain the real-time load of the bridge, and the current bridge environment wind speed and bridge environment magnitude are obtained.

3. The bridge vibration response prediction method based on deep calculation as claimed in claim 2, characterized in that: The method of determining the success of base station connection according to the response instruction set and the transmission base station set to obtain a candidate base station set includes: obtaining an acceptance frequency range and a rejection frequency range; extracting response instructions in the response instruction set in turn, identifying the instruction signal frequency of the response instruction, and judging whether the instruction signal frequency is within the acceptance frequency range; if the instruction signal frequency is within the acceptance frequency range, recording the response instruction as an acceptance connection instruction, and identifying the candidate base station corresponding to the acceptance connection instruction in the transmission base station set; if the instruction signal frequency is not within the acceptance frequency range, judging whether the instruction signal frequency is within the rejection frequency range; if the instruction signal frequency is not within the rejection frequency range, recording the response instruction as Abnormal connection instruction, identifying the unresponsive base station corresponding to the abnormal connection instruction in the transmission base station set; identifying the number of connection requests of the unresponsive base station, and judging whether the number of connection requests is greater than a preset standard request number; if the number of connection requests is not greater than the standard request number, recording the unresponsive base station as a secondary request base station; summarizing the secondary request base stations to obtain a secondary request base station set, redistributing base station connection instructions to each secondary request base station in the secondary request base station set to obtain a secondary response instruction set; updating the response instruction set using the secondary response instruction set, and returning to the step of extracting response instructions in the response instruction set in sequence; summarizing the candidate base stations to obtain a candidate base station set.

4. The bridge vibration response prediction method based on deep calculation as claimed in claim 3, characterized in that: The method of performing complete data transmission base station calculation according to the vibration data flow to obtain the number of working base stations includes: obtaining a single transmission failure rate and a rated transmission flow of the candidate base station, and setting an overall success rate for transmitting the vibration data flow; identifying the number of transmission nodes between the candidate base station set and the response prediction platform; and calculating the number of working base stations according to the single transmission failure rate, the rated transmission flow, the overall success rate and the number of transmission nodes using the following formula: ,in, Indicates the number of working base stations, Indicates the vibration data flow, represents the logarithmic function, The overall success rate, Indicates the rated transmission flow rate, represents the single transmission failure rate, k represents the number of transmission nodes, Indicates the round-up symbol.

5. The bridge vibration response prediction method based on deep calculation according to claim 4, characterized in that: The data flow transmission allocation for the candidate base station set to obtain an allocation speed set and an allocation flow set includes: setting a monitoring transmission duration, performing the following operations on each candidate base station in the candidate base station set: obtaining the overall signal round trip duration and the number of signal round trips of the candidate base station based on the monitoring transmission duration, and calculating the signal round trip duration according to the overall signal round trip duration, the number of signal round trips, the monitoring transmission duration and a preset environmental disturbance factor, wherein the signal round trip duration is expressed as: ,in, Indicates the round trip time of the signal. Indicates the monitoring transmission duration. represents the environmental disturbance factor, Indicates the overall signal round trip time, Indicates the number of signal round trips; counts the monitoring transmission flow of the candidate base station within the monitoring transmission duration; summarizes the signal round trip duration and the monitoring transmission flow respectively to obtain a signal round trip duration set and a monitoring transmission flow set; performs flow speed allocation according to the signal round trip duration set and the monitoring transmission flow set to obtain a distribution speed set; performs flow allocation on each candidate base station in the candidate base station set according to the distribution speed set to obtain a distribution flow set.

6. The bridge vibration response prediction method based on deep calculation according to claim 5, characterized in that: The method of obtaining the overall signal round trip time and the number of signal round trips of the candidate base station based on the monitoring transmission time includes: setting the monitoring start time and the initial round trip number; determining the monitoring end time of the response prediction platform completing the flow signal reception based on the monitoring start time; calculating the single monitoring time at the monitoring end time and the monitoring start time, and calculating the cumulative monitoring time according to the single monitoring time, the preset initial monitoring time and the initial preparation time, wherein the single monitoring time and the cumulative monitoring time are respectively expressed as: ,in, Indicates the duration of a single monitoring. Indicates the end time of monitoring. Indicates the monitoring start time. Indicates the cumulative monitoring time. Indicates the initial monitoring duration. Indicates the initial preparation time; determines whether the cumulative monitoring time is less than the monitoring transmission time; if the cumulative monitoring time is less than the monitoring transmission time, calculates the target round trip number according to the initial round trip number, wherein the target round trip number is expressed as: ,in, Indicates the number of round trips to the target. represents the initial round trip number; based on the monitoring end time, determine the post-position start time of the candidate base station, calculate the transmission preparation time of the post-position start time and the monitoring end time, wherein the post-position start time is the time when the candidate base station performs data transmission for the first time after the monitoring end time, and the transmission preparation time is expressed as: ,in, Indicates the transmission preparation time. Indicates the start time of the rear position; according to the single monitoring time and the preset initial round-trip time, the target round-trip time is calculated using the following formula: ,in, Indicates the target round trip time. Represents the initial round-trip duration; uses the cumulative monitoring duration, the post-position start time, the target round-trip duration, the target round-trip number and the transmission preparation time to respectively update the initial monitoring duration, the monitoring start time, the initial round-trip duration, the initial round-trip number and the initial preparation time, and returns to the step of determining the monitoring end time when the response prediction platform completes the reception of the traffic signal based on the monitoring start time; if the cumulative monitoring duration is not less than the monitoring transmission duration, the target round-trip duration and the target round-trip number are respectively recorded as the overall signal round-trip duration and the signal round-trip number.

7. The bridge vibration response prediction method based on deep calculation according to claim 6, characterized in that: The method of performing flow rate allocation according to the signal round trip time set and the monitored transmission flow rate set to obtain the allocated flow rate set includes: calculating the total time factor and the total flow factor according to the signal round trip time set and the monitored transmission flow rate set using the following formula: ,in, represents the total duration factor, Indicates the number of signal round trip times in the signal round trip time set, represents the round trip time of the i-th signal, represents the total flow factor, m represents the number of transmission flows monitored in the centralized monitoring transmission flow, represents the jth monitored transmission flow; extracting speed allocation base stations in the candidate base station set in turn, and identifying the transmission duration to be allocated and the transmission flow to be allocated in the signal round trip time set and the monitored transmission flow set according to the speed allocation base stations; The allocation speed is calculated using the following formula according to the total duration factor, the total flow factor, the transmission duration to be allocated and the transmission flow to be allocated: ,in, Indicates the distribution speed, Indicates the transmission time to be allocated. represents the transmission flow to be allocated; and the allocation speeds are summarized to obtain an allocation speed set.

8. The bridge vibration response prediction method based on deep calculation according to claim 7, characterized in that: The method of allocating traffic to each candidate base station in the candidate base station set according to the allocation speed set to obtain an allocation traffic set includes: extracting traffic allocation base stations in the candidate base station set in sequence, identifying weighted allocation speeds in the allocation speed set according to the traffic allocation base stations; and calculating the allocation traffic according to the weighted allocation speed and the allocation speed set, wherein the allocation traffic is expressed as: ,in, Indicates the distribution flow, represents the weight distribution speed, Indicates the number of distribution speeds in the distribution speed set, represents the pth distribution speed, represents the vibration data flow; and the distribution flow is summarized to obtain a distribution flow set.

9. The bridge vibration response prediction method based on deep calculation according to claim 8, characterized in that: The method of determining a task base station in the candidate base station set according to the number of working base stations to obtain a task base station set includes: extracting candidate base stations in the candidate base station set in sequence, identifying the current number of transmission tasks of the candidate base stations, key-pairing the candidate base stations with the current number of transmission tasks to obtain a transmission task group; summarizing the transmission task groups to obtain a transmission task group set, numerically sorting the transmission task group set to obtain a transmission task sequence set; extracting target task sequences in sequence in the transmission task sequence set according to the number of working base stations to obtain a target task sequence set, wherein the number of target task sequences in the target task sequence set is the same as the number of working base stations; and extracting task base stations from the target task sequence set to obtain a task base station set.

10. A bridge vibration response prediction system based on deep calculation, characterized in that: The system includes: a response instruction distribution module, which is used to collect vibration data of a pre-constructed target bridge to obtain internal vibration data and external excitation data, wherein the internal vibration data includes: a bridge vibration area set, a bridge vibration frequency set and a bridge vibration displacement set, and the external excitation data includes: a bridge real-time load, a bridge environmental wind speed and a bridge environmental magnitude, and a base station connection instruction is generated according to the internal vibration data and the external excitation data, and the base station connection instruction is distributed to a pre-constructed transmission base station set to obtain a response instruction set; a working base station calculation module, which is used to determine the success of the base station connection according to the response instruction set and the transmission base station set to obtain a candidate base station set, wherein the number of candidate base stations in the candidate base station set is the same as the number of response instructions in the response instruction set, and the vibration data flow of the internal vibration data and the external excitation data is obtained, and complete data transmission is performed according to the vibration data flow. Base station calculation is used to obtain the number of working base stations; a task base station acquisition module is used to determine whether the number of working base stations is greater than the number of candidate base stations. If the number of working base stations is greater than the number of candidate base stations, data traffic transmission is allocated to the candidate base station set to obtain an allocation speed set and an allocation traffic set. Based on the allocation speed set and the allocation traffic set, transmission tasks are allocated to the candidate base station set to obtain a complete base station set. If the number of working base stations is not greater than the number of candidate base stations, task base stations are determined in the candidate base station set according to the number of working base stations to obtain a task base station set; a vibration response prediction module is used to use the complete base station set or the task base station set to transmit the internal vibration data and external excitation data to a pre-built response prediction platform, wherein the response prediction platform has a built-in deep learning algorithm, and the response prediction platform is used to predict the bridge vibration response of the target bridge to obtain predicted vibration data.

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