Bridge safety intelligent monitoring method and system based on big data prediction

Abstract

The invention discloses a bridge safety intelligent monitoring method and system based on big data prediction, and the method comprises the steps: obtaining the data information of a bridge structure in real time, the data information comprises the stress at each position on the bridge, the vibration frequency at each position on the bridge, the temperature change value at each position on the bridge and the fatigue strength at each position on the bridge; setting a bridge structure health index evaluation model, and calculating a bridge structure health index of each position on the bridge according to the data information; and comparing the bridge structure health index of each position with a preset position health index threshold, or carrying out weighted average on the bridge structure health indexes of all positions, and comparing the bridge structure health indexes after weighted average with a preset comprehensive health index threshold, thereby judging the safety degree of the bridge.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge safety monitoring, and more specifically, relates to a bridge safety intelligent monitoring method and system based on big data prediction. Background Art

[0002] Bridge health refers to the comprehensive status of a bridge in terms of safety, durability, and applicability during its design life. Bridge health management is an important measure to ensure the safe operation of bridges, covering bridge inspection, monitoring, evaluation, maintenance, and reinforcement.

[0003] The Importance of Bridge Health

[0004] Safety: The health of bridges directly affects traffic safety. Timely detection and repair of potential problems can effectively prevent bridge accidents.

[0005] Economical: Through regular inspection and maintenance, the service life of the bridge can be extended, avoiding the high costs caused by major repairs or reconstruction.

[0006] Social benefits: Ensuring the normal operation of bridges and smooth traffic is crucial to the normal operation of the social economy.

[0007] However, there is no technical solution in the prior art that can perform all-round health monitoring of bridges. Summary of the invention

[0008] In order to solve the above technical problems, the present invention proposes a bridge safety intelligent monitoring method based on big data prediction, comprising:

[0009] Acquire data information of the bridge structure in real time, wherein the data information includes: stress at each position on the bridge, vibration frequency at each position on the bridge, temperature change value at each position on the bridge, and fatigue strength at each position on the bridge;

[0010] Setting a bridge structure health index evaluation model, and calculating the bridge structure health index at each position on the bridge according to the data information;

[0011] The bridge structure health index at each location is compared with the preset location health index threshold, or the bridge structure health index at all locations is weighted averaged and the weighted average bridge structure health index is compared with the preset comprehensive health index threshold to determine the safety of the bridge.

[0012] Furthermore, the bridge structure health index evaluation model includes:

[0013]

[0014] Where SHI(t) is the structural health index of the bridge at time t, N is the total number of stress collection locations, and α i (t) is the weight of the stress at the i-th position on the bridge at time t, β i (t) is the adjustment factor for the stress at the i-th position on the bridge at time t, X i (t) is the stress at the i-th position on the bridge at time t, X i，0 is the standard stress that the i-th position on the bridge can withstand at time t, γ j is the weight of the vibration frequency at the jth position on the bridge at time t, M is the total number of vibration frequency collection positions, δ j (t) is the first adjustment factor of the vibration frequency at the jth position on the bridge at time t, Y j (t) is the vibration frequency at the jth position on the bridge at time t, Y j，0 is the standard vibration frequency that the jth position on the bridge can withstand at time t, η j is the second adjustment factor of the vibration frequency at the jth position on the bridge at time t, K is the total number of locations where the temperature change values ​​are collected, μk is the weight of the temperature change value at the kth position on the bridge at time t, and Z k (t) is the temperature change at the kth position on the bridge at time t, Z k，0 is the standard temperature change value that the kth position on the bridge can withstand, ζ k is the adjustment factor of the temperature change value at the kth position on the bridge at time t, v is the overall adjustment factor of the temperature change value, L is the total number of fatigue strength collection positions, ξ l is the weight of the fatigue strength at the lth position on the bridge at time t, W l (t) is the fatigue strength at the lth position on the bridge at time t, W l，0 is the standard fatigue strength at the lth position on the bridge at time t, η l is the adjustment factor of the fatigue strength at the lth position on the bridge at time t, and ω is the overall adjustment factor of the fatigue strength.

[0015] Furthermore, the weight α of the stress at the i-th position on the bridge at time t is i (t) include:

[0016]

[0017] in, is the stress weight α at the i-th position i The first adjustment factor is is the stress weight α at the i-th position iThe second adjustment factor is is the stress weight α at the i-th position i The third adjustment factor, χ i is the amplitude of the periodic variation of stress at the i-th position on the bridge, ω i is the frequency of the periodic change of the stress at the i-th position on the bridge.

[0018] Furthermore, the weight γ of the vibration frequency at the jth position on the bridge at time t is j include:

[0019]

[0020] in, is the weight of the vibration frequency at the jth position γ j The first adjustment factor is is the weight of the vibration frequency at the jth position γ j The second adjustment factor is is the weight of the vibration frequency at the jth position γ j The third adjustment factor, χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

[0021] Furthermore, the first adjustment factor δ of the vibration frequency at the jth position on the bridge at time t is j (t) include:

[0022]

[0023] in, is the first adjustment factor δ of the vibration frequency at the jth position j The first adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The second adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The third adjustment factor for (t), χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

[0024] Furthermore, when the health index of the bridge structure at a certain location is greater than a preset location health index threshold, an alarm message is issued.

[0025] Furthermore, when the weighted average bridge structure health index is greater than a preset comprehensive health index threshold, an alarm message is issued.

[0026] The present invention also proposes a bridge safety intelligent monitoring system based on big data prediction, comprising:

[0027] A data acquisition module is used to acquire data information of the bridge structure in real time, wherein the data information includes: stress at each position on the bridge, vibration frequency at each position on the bridge, temperature change value at each position on the bridge, and fatigue strength at each position on the bridge;

[0028] A model setting module is used to set a bridge structure health index evaluation model and calculate the bridge structure health index at each position on the bridge according to the data information;

[0029] The safety monitoring module is used to compare the bridge structure health index at each location with the preset location health index threshold, or to weighted average the bridge structure health index at all locations, and compare the weighted average bridge structure health index with the preset comprehensive health index threshold, so as to judge the safety of the bridge.

[0030] Furthermore, the bridge structure health index evaluation model includes:

[0031]

[0032] Where SHI(t) is the structural health index of the bridge at time t, N is the total number of stress collection locations, and α i (t) is the weight of the stress at the i-th position on the bridge at time t, β i (t) is the adjustment factor for the stress at the i-th position on the bridge at time t, X i (t) is the stress at the i-th position on the bridge at time t, X i，0 is the standard stress that the i-th position on the bridge can withstand at time t, γ j is the weight of the vibration frequency at the jth position on the bridge at time t, M is the total number of vibration frequency collection positions, δ j (t) is the first adjustment factor of the vibration frequency at the jth position on the bridge at time t, Y j (t) is the vibration frequency at the jth position on the bridge at time t, Y j，0 is the standard vibration frequency that the jth position on the bridge can withstand at time t, η j is the second adjustment factor of the vibration frequency at the jth position on the bridge at time t, K is the total number of locations where the temperature change values ​​are collected, μk is the weight of the temperature change value at the kth position on the bridge at time t, and Zk (t) is the temperature change at the kth position on the bridge at time t, Z k，0 is the standard temperature change value that the kth position on the bridge can withstand, ζ k is the adjustment factor of the temperature change value at the kth position on the bridge at time t, v is the overall adjustment factor of the temperature change value, L is the total number of fatigue strength collection positions, ξ l is the weight of the fatigue strength at the lth position on the bridge at time t, W l (t) is the fatigue strength at the lth position on the bridge at time t, W l，0 is the standard fatigue strength at the lth position on the bridge at time t, η l is the adjustment factor of the fatigue strength at the lth position on the bridge at time t, and ω is the overall adjustment factor of the fatigue strength.

[0033] Furthermore, the weight α of the stress at the i-th position on the bridge at time t is i (t) include:

[0034]

[0035] in, is the stress weight α at the i-th position i The first adjustment factor is is the stress weight α at the i-th position i The second adjustment factor is is the stress weight α at the i-th position i The third adjustment factor, χ i is the amplitude of the periodic variation of stress at the i-th position on the bridge, ω i is the frequency of the periodic change of the stress at the i-th position on the bridge.

[0036] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0037] The present invention sets up a bridge structure health index evaluation model, and calculates the bridge structure health index of each position on the bridge according to the data information; compares the bridge structure health index of each position with a preset position health index threshold, or performs weighted average on the bridge structure health index of all positions, and compares the weighted average bridge structure health index with a preset comprehensive health index threshold, thereby judging the safety of the bridge. Through the above technical scheme, the present invention comprehensively considers the impact of multiple factors on the health of the bridge structure, thereby judging the health of the bridge and completing safety monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1is a flow chart of the method of embodiment 1 of the present invention;

[0039] Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0040] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0041] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0042] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0043] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0044] The display is used to show the interactive cross-section of each application.

[0045] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0046] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0047] Example 1

[0048] like Figure 1 As shown, the present invention proposes a bridge safety intelligent monitoring method based on big data prediction, comprising:

[0049] Step 101, acquiring data information of the bridge structure in real time, wherein the data information includes: stress at each position on the bridge, vibration frequency at each position on the bridge, temperature change value at each position on the bridge, and fatigue strength at each position on the bridge;

[0050] Step 102, setting a bridge structure health index evaluation model, and calculating the bridge structure health index at each position on the bridge according to the data information;

[0051] Specifically, the bridge structure health index evaluation model includes:

[0052]

[0053] Where SHI(t) is the structural health index of the bridge at time t, N is the total number of stress collection locations, and α i (t) is the weight of the stress at the i-th position on the bridge at time t, β i (t) is the adjustment factor for the stress at the i-th position on the bridge at time t, X i (t) is the stress at the i-th position on the bridge at time t, X i，0 is the standard stress that the i-th position on the bridge can withstand at time t, γ j is the weight of the vibration frequency at the jth position on the bridge at time t, M is the total number of vibration frequency collection positions, δ j (t) is the first adjustment factor of the vibration frequency at the jth position on the bridge at time t, Y j (t) is the vibration frequency at the jth position on the bridge at time t, Y j，0 is the standard vibration frequency that the j-th position on the bridge can withstand at time t, ηj is the second adjustment factor of the vibration frequency at the k-th position on the bridge at time t, K is the total number of temperature change value collection positions, μk is the weight of the temperature change value at the k-th position on the bridge at time t, and Z k (t) is the temperature change at the kth position on the bridge at time t, Z k，0 is the standard temperature change value that the kth position on the bridge can withstand, ζ k is the adjustment factor of the temperature change value at the kth position on the bridge at time t, v is the overall adjustment factor of the temperature change value, L is the total number of fatigue strength collection positions, ξ l is the weight of the fatigue strength at the lth position on the bridge at time t, W l (t) is the fatigue strength at the lth position on the bridge at time t, W l，0 is the standard fatigue strength at the lth position on the bridge at time t, η l is the adjustment factor of the fatigue strength at the lth position on the bridge at time t, and ω is the overall adjustment factor of the fatigue strength.

[0054] Specifically, the weight α of the stress at the i-th position on the bridge at time t is i (t) include:

[0055]

[0056] in, is the stress weight α at the i-th position i The first adjustment factor is is the stress weight α at the i-th position i The second adjustment factor is is the stress weight α at the i-th position i The third adjustment factor, χ i is the amplitude of the periodic variation of stress at the i-th position on the bridge, ω i is the frequency of the periodic change of the stress at the i-th position on the bridge.

[0057] Specifically, the weight γ of the vibration frequency at the jth position on the bridge at time t is j include:

[0058]

[0059] in, is the weight of the vibration frequency at the jth position γ j The first adjustment factor is is the weight of the vibration frequency at the jth position γ j The second adjustment factor is is the weight of the vibration frequency at the jth position γ j The third adjustment factor, χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

[0060] Specifically, the first adjustment factor δ of the vibration frequency at the jth position on the bridge at time t is j (t) include:

[0061]

[0062] in, is the first adjustment factor δ of the vibration frequency at the jth position j The first adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The second adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The third adjustment factor for (t), χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ωj is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

[0063] Step 103, comparing the bridge structure health index at each location with a preset location health index threshold, or taking a weighted average of the bridge structure health indexes at all locations, and comparing the weighted average bridge structure health index with a preset comprehensive health index threshold, so as to determine the safety of the bridge.

[0064] Specifically, when the health index of the bridge structure at a certain location is greater than a preset location health index threshold, an alarm message is issued.

[0065] Specifically, when the weighted average bridge structure health index is greater than a preset comprehensive health index threshold, an alarm message is issued.

[0066] Example 2

[0067] like Figure 2 As shown, the embodiment of the present invention also proposes a bridge safety intelligent monitoring system based on big data prediction, including:

[0068] A data acquisition module is used to acquire data information of the bridge structure in real time, wherein the data information includes: stress at each position on the bridge, vibration frequency at each position on the bridge, temperature change value at each position on the bridge, and fatigue strength at each position on the bridge;

[0069] A model setting module is used to set a bridge structure health index evaluation model and calculate the bridge structure health index at each position on the bridge according to the data information;

[0070] Specifically, the bridge structure health index evaluation model includes:

[0071]

[0072] Where SHI(t) is the structural health index of the bridge at time t, N is the total number of stress collection locations, and α i (t) is the weight of the stress at the i-th position on the bridge at time t, β i (t) is the adjustment factor for the stress at the i-th position on the bridge at time t, X i (t) is the stress at the i-th position on the bridge at time t, X i，0 is the standard stress that the i-th position on the bridge can withstand at time t, γ j is the weight of the vibration frequency at the jth position on the bridge at time t, M is the total number of vibration frequency collection positions, δ j (t) is the first adjustment factor of the vibration frequency at the jth position on the bridge at time t, Yj (t) is the vibration frequency at the jth position on the bridge at time t, Y j，0 is the standard vibration frequency that the jth position on the bridge can withstand at time t, η j is the second adjustment factor of the vibration frequency at the jth position on the bridge at time t, K is the total number of locations where the temperature change values ​​are collected, μ k is the weight of the temperature change value at the kth position on the bridge at time t, Z k (t) is the temperature change at the kth position on the bridge at time t, Z k，0 is the standard temperature change value that the kth position on the bridge can withstand, ζ k is the adjustment factor of the temperature change value at the kth position on the bridge at time t, v is the overall adjustment factor of the temperature change value, L is the total number of fatigue strength collection positions, ξ l is the weight of the fatigue strength at the lth position on the bridge at time t, W l (t) is the fatigue strength at the lth position on the bridge at time t, W l，0 is the standard fatigue strength at the lth position on the bridge at time t, η l is the adjustment factor of the fatigue strength at the lth position on the bridge at time t, and ω is the overall adjustment factor of the fatigue strength.

[0073] Specifically, the weight α of the stress at the i-th position on the bridge at time t is i (t) include:

[0074]

[0075] in, is the stress weight α at the i-th position i The first adjustment factor is is the stress weight α at the i-th position i The second adjustment factor is is the stress weight α at the i-th position i The third adjustment factor, χ i is the amplitude of the periodic variation of stress at the i-th position on the bridge, ω i is the frequency of the periodic change of the stress at the i-th position on the bridge.

[0076] Specifically, the weight γ of the vibration frequency at the jth position on the bridge at time t is j include:

[0077]

[0078] in, is the weight of the vibration frequency at the jth position γ j The first adjustment factor is is the weight of the vibration frequency at the jth position γ j The second adjustment factor is is the weight of the vibration frequency at the jth position γ j The third adjustment factor, χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

[0079] Specifically, the first adjustment factor δ of the vibration frequency at the tth position on the bridge at time t is j (t) include:

[0080]

[0081] in, is the first adjustment factor δ of the vibration frequency at the jth position j The first adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The second adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The third adjustment factor for (t), χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

[0082] The safety monitoring module is used to compare the bridge structure health index at each location with the preset location health index threshold, or to weighted average the bridge structure health index at all locations, and compare the weighted average bridge structure health index with the preset comprehensive health index threshold, so as to judge the safety of the bridge.

[0083] Specifically, when the health index of the bridge structure at a certain location is greater than a preset location health index threshold, an alarm message is issued.

[0084] Specifically, when the weighted average bridge structure health index is greater than a preset comprehensive health index threshold, an alarm message is issued.

[0085] Example 3

[0086] An embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the intelligent bridge safety monitoring method based on big data prediction.

[0087] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0088] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the method steps: Step 101, acquiring data information of the bridge structure in real time, wherein the data information includes: stress at each position on the bridge, vibration frequency at each position on the bridge, temperature change value at each position on the bridge, and fatigue strength at each position on the bridge;

[0089] Step 102, setting a bridge structure health index evaluation model, and calculating the bridge structure health index at each position on the bridge according to the data information;

[0090] Specifically, the bridge structure health index evaluation model includes:

[0091]

[0092] Where SHI(t) is the structural health index of the bridge at time t, N is the total number of stress collection locations, and α i (t) is the weight of the stress at the i-th position on the bridge at time t, β i (t) is the adjustment factor for the stress at the i-th position on the bridge at time t, X i (t) is the stress at the i-th position on the bridge at time t, X i，0 is the standard stress that the i-th position on the bridge can withstand at time t, γ j is the weight of the vibration frequency at the jth position on the bridge at time t, M is the total number of vibration frequency collection positions, δ j (t) is the first adjustment factor of the vibration frequency at the jth position on the bridge at time t, Y j (t) is the vibration frequency at the jth position on the bridge at time t, Y j，0 is the standard vibration frequency that the jth position on the bridge can withstand at time t, η j is the second adjustment factor of the vibration frequency at the jth position on the bridge at time t, K is the total number of locations where the temperature change values ​​are collected, μ k is the weight of the temperature change value at the kth position on the bridge at time t, Z k (t) is the temperature change at the kth position on the bridge at time t, Z k，0 is the standard temperature change value that the kth position on the bridge can withstand, ζ kis the adjustment factor of the temperature change value at the kth position on the bridge at time t, v is the overall adjustment factor of the temperature change value, L is the total number of fatigue strength collection positions, ξ l is the weight of the fatigue strength at the lth position on the bridge at time t, W l (t) is the fatigue strength at the lth position on the bridge at time t, W l，0 is the standard fatigue strength at the lth position on the bridge at time t, η l is the adjustment factor of the fatigue strength at the lth position on the bridge at time t, and ω is the overall adjustment factor of the fatigue strength.

[0093] Specifically, the weight α of the stress at the i-th position on the bridge at time t is i (t) include:

[0094]

[0095] in, is the stress weight α at the i-th position i The first adjustment factor is is the stress weight α at the i-th position i The second adjustment factor is is the stress weight α at the i-th position i The third adjustment factor, χ i is the amplitude of the periodic variation of stress at the i-th position on the bridge, ω i is the frequency of the periodic change of the stress at the i-th position on the bridge.

[0096] Specifically, the weight γ of the vibration frequency at the jth position on the bridge at time t is j include:

[0097]

[0098] in, is the weight of the vibration frequency at the jth position γ j The first adjustment factor is is the weight of the vibration frequency at the jth position γ j The second adjustment factor is is the weight of the vibration frequency at the jth position γ j The third adjustment factor, χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

[0099] Specifically, the first adjustment factor δ of the vibration frequency at the jth position on the bridge at time t isj (t) include:

[0100]

[0101] in, is the first adjustment factor δ of the vibration frequency at the jth position j The first adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The second adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The third adjustment factor for (t), χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

[0102] Step 103, comparing the bridge structure health index at each location with a preset location health index threshold, or taking a weighted average of the bridge structure health indexes at all locations, and comparing the weighted average bridge structure health index with a preset comprehensive health index threshold, so as to determine the safety of the bridge.

[0103] Specifically, when the health index of the bridge structure at a certain location is greater than a preset location health index threshold, an alarm message is issued.

[0104] Specifically, when the weighted average bridge structure health index is greater than a preset comprehensive health index threshold, an alarm message is issued.

[0105] Example 4

[0106] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute a bridge safety intelligent monitoring method based on big data prediction.

[0107] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0108] Among them, the storage medium can be used to store software programs and modules, such as a bridge safety intelligent monitoring method based on big data prediction in an embodiment of the present invention, corresponding program instructions / modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned bridge safety intelligent monitoring method based on big data prediction. The storage medium may include high-speed random storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0109] The processor can call the information and application program stored in the storage medium through the transmission system to execute the method steps: Step 101, real-time acquisition of data information of the bridge structure, wherein the data information includes: stress at each position on the bridge, vibration frequency at each position on the bridge, temperature change value at each position on the bridge, and fatigue strength at each position on the bridge;

[0110] Step 102, setting a bridge structure health index evaluation model, and calculating the bridge structure health index at each position on the bridge according to the data information;

[0111] Specifically, the bridge structure health index evaluation model includes:

[0112]

[0113] Where SHI(t) is the structural health index of the bridge at time t, N is the total number of stress collection locations, and α i (t) is the weight of the stress at the i-th position on the bridge at time t, β i (t) is the adjustment factor for the stress at the i-th position on the bridge at time t, X i (t) is the stress at the i-th position on the bridge at time t, X i，0 is the standard stress that the i-th position on the bridge can withstand at time t, γ j is the weight of the vibration frequency at the jth position on the bridge at time t, M is the total number of vibration frequency collection positions, δ j (t) is the first adjustment factor of the vibration frequency at the jth position on the bridge at time t, Y j (t) is the vibration frequency at the jth position on the bridge at time t, Y j，0 is the standard vibration frequency that the jth position on the bridge can withstand at time t, η jis the second adjustment factor of the vibration frequency at the jth position on the bridge at time t, K is the total number of locations where the temperature change values ​​are collected, μ k is the weight of the temperature change value at the kth position on the bridge at time t, Z k (t) is the temperature change at the kth position on the bridge at time t, Z k，0 is the standard temperature change value that the kth position on the bridge can withstand, ζ k is the adjustment factor of the temperature change value at the kth position on the bridge at time t, v is the overall adjustment factor of the temperature change value, L is the total number of fatigue strength collection positions, ξ l is the weight of the fatigue strength at the lth position on the bridge at time t, W l (t) is the fatigue strength at the lth position on the bridge at time t, W l，0 is the standard fatigue strength at the lth position on the bridge at time t, η l is the adjustment factor of the fatigue strength at the lth position on the bridge at time t, and ω is the overall adjustment factor of the fatigue strength.

[0114] Specifically, the weight α of the stress at the i-th position on the bridge at time t is i (t) include:

[0115]

[0116] in, is the stress weight α at the i-th position i The first adjustment factor is is the stress weight α at the i-th position i The second adjustment factor is is the stress weight α at the i-th position i The third adjustment factor, χ i is the amplitude of the periodic variation of stress at the i-th position on the bridge, ω i is the frequency of the periodic change of the stress at the i-th position on the bridge.

[0117] Specifically, the weight γ of the vibration frequency at the jth position on the bridge at time t is j include:

[0118]

[0119] in, is the weight of the vibration frequency at the jth position γ j The first adjustment factor is is the weight of the vibration frequency at the jth position γ j The second adjustment factor is is the weight of the vibration frequency at the jth position γ j The third adjustment factor, χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

[0120] Specifically, the first adjustment factor δ of the vibration frequency at the jth position on the bridge at time t is j (t) include:

[0121]

[0122] in, is the first adjustment factor δ of the vibration frequency at the jth position j The first adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The second adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The third adjustment factor for (t), χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

[0123] Step 103, comparing the bridge structure health index at each location with a preset location health index threshold, or taking a weighted average of the bridge structure health indexes at all locations, and comparing the weighted average bridge structure health index with a preset comprehensive health index threshold, so as to determine the safety of the bridge.

[0124] Specifically, when the health index of the bridge structure at a certain location is greater than a preset location health index threshold, an alarm message is issued.

[0125] Specifically, when the weighted average bridge structure health index is greater than a preset comprehensive health index threshold, an alarm message is issued.

[0126] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0127] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0128] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0129] The units described as separate components may or may not be physically separated, and the components shown as units 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or 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 software functional units.

[0131] If the integrated unit 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. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0132] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A bridge safety intelligent monitoring method based on big data prediction, characterized in that: include: Acquire data information of the bridge structure in real time, wherein the data information includes: stress at each position on the bridge, vibration frequency at each position on the bridge, temperature change value at each position on the bridge, and fatigue strength at each position on the bridge; Setting a bridge structure health index evaluation model, and calculating the bridge structure health index at each position on the bridge according to the data information; The bridge structure health index at each location is compared with the preset location health index threshold, or the bridge structure health index at all locations is weighted averaged and the weighted average bridge structure health index is compared with the preset comprehensive health index threshold to determine the safety of the bridge.

2. The intelligent bridge safety monitoring method based on big data prediction according to claim 1 is characterized in that: The bridge structure health index evaluation model includes: Where SHI(t) is the structural health index of the bridge at time t, N is the total number of stress collection locations, and α i (t) is the weight of the stress at the i-th position on the bridge at time t, β i (t) is the adjustment factor for the stress at the i-th position on the bridge at time t, X i (t) is the stress at the i-th position on the bridge at time t, X i，0 is the standard stress that the i-th position on the bridge can withstand at time t, γ j is the weight of the vibration frequency at the jth position on the bridge at time t, M is the total number of vibration frequency collection positions, δ j (t) is the first adjustment factor of the vibration frequency at the jth position on the bridge at time t, Y j (t) is the vibration frequency at the jth position on the bridge at time t, Y j，0 is the standard vibration frequency that the jth position on the bridge can withstand at time t, η j is the second adjustment factor of the vibration frequency at the jth position on the bridge at time t, K is the total number of locations where the temperature change values ​​are collected, μ k is the weight of the temperature change value at the kth position on the bridge at time t, Z k (t) is the temperature change at the kth position on the bridge at time t, Z k，0 is the standard temperature change value that the kth position on the bridge can withstand, ξ k is the adjustment factor of the temperature change value at the kth position on the bridge at time t, ν is the overall adjustment factor of the temperature change value, L is the total number of fatigue strength collection positions, ξ l is the weight of the fatigue strength at the lth position on the bridge at time t, W l (t) is the fatigue strength at the lth position on the bridge at time t, W l，0 is the standard fatigue strength at the lth position on the bridge at time t, η l is the adjustment factor of the fatigue strength at the lth position on the bridge at time t, and ω is the overall adjustment factor of the fatigue strength.

3. The intelligent bridge safety monitoring method based on big data prediction according to claim 2 is characterized in that: The weight α of the stress at the i-th position on the bridge at time t i (t) include: in, is the stress weight α at the i-th position i The first adjustment factor is is the stress weight α at the i-th position i The second adjustment factor is is the stress weight α at the i-th position i The third adjustment factor, χ i is the amplitude of the periodic variation of stress at the i-th position on the bridge, ω i is the frequency of the periodic change of the stress at the i-th position on the bridge.

4. The intelligent bridge safety monitoring method based on big data prediction according to claim 2 is characterized in that: The weight γ of the vibration frequency at the jth position on the bridge at time t j include: in, is the weight of the vibration frequency at the jth position γ j The first adjustment factor is is the weight of the vibration frequency at the jth position γ j The second adjustment factor is is the weight of the vibration frequency at the jth position γ j The third adjustment factor, x j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

5. The intelligent bridge safety monitoring method based on big data prediction according to claim 2 is characterized in that: The first adjustment factor δ of the vibration frequency at the jth position on the bridge at time t j (t) include: in, is the first adjustment factor δ of the vibration frequency at the jth position j The first adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The second adjustment factor for (t), is the first adjustment factor δ of the vibration frequency at the jth position j The third adjustment factor for (t), χ j is the amplitude of the periodic variation of the vibration frequency at the jth position on the bridge, ω j is the frequency of the periodic change of the vibration frequency at the jth position on the bridge.

6. The intelligent bridge safety monitoring method based on big data prediction according to claim 1 is characterized in that: When the bridge structure health index at a certain location is greater than the preset location health index threshold, an alarm message is issued.

7. The intelligent bridge safety monitoring method based on big data prediction according to claim 1 is characterized in that: When the weighted average bridge structure health index is greater than the preset comprehensive health index threshold, an alarm message is issued.

8. A bridge safety intelligent monitoring system based on big data prediction, characterized in that: include: A data acquisition module is used to acquire data information of the bridge structure in real time, wherein the data information includes: stress at each position on the bridge, vibration frequency at each position on the bridge, temperature change value at each position on the bridge, and fatigue strength at each position on the bridge; A model setting module is used to set a bridge structure health index evaluation model and calculate the bridge structure health index at each position on the bridge according to the data information; The safety monitoring module is used to compare the bridge structure health index at each location with the preset location health index threshold, or to weighted average the bridge structure health index at all locations, and compare the weighted average bridge structure health index with the preset comprehensive health index threshold, so as to judge the safety of the bridge.

9. The intelligent bridge safety monitoring system based on big data prediction according to claim 8, characterized in that: The bridge structure health index evaluation model includes: Where SHI(t) is the structural health index of the bridge at time t, N is the total number of stress collection locations, and α i (t) is the weight of the stress at the i-th position on the bridge at time t, β i (t) is the adjustment factor for the stress at the i-th position on the bridge at time t, X i (t) is the stress at the i-th position on the bridge at time t, X i，0 is the standard stress that the i-th position on the bridge can withstand at time t, γ j is the weight of the vibration frequency at the jth position on the bridge at time t, M is the total number of vibration frequency collection positions, δ j (t) is the first adjustment factor of the vibration frequency at the jth position on the bridge at time t, Y j (t) is the vibration frequency at the jth position on the bridge at time t, Y j，0 is the standard vibration frequency that the jth position on the bridge can withstand at time t, η j is the second adjustment factor of the vibration frequency at the jth position on the bridge at time t, K is the total number of locations where the temperature change values ​​are collected, μ k is the weight of the temperature change value at the kth position on the bridge at time t, Z k (t) is the temperature change at the kth position on the bridge at time t, Z k，0 is the standard temperature change value that the kth position on the bridge can withstand, ζ k is the adjustment factor of the temperature change value at the kth position on the bridge at time t, ν is the overall adjustment factor of the temperature change value, L is the total number of fatigue strength collection positions, ξ l is the weight of the fatigue strength at the lth position on the bridge at time t, W l (t) is the fatigue strength at the lth position on the bridge at time t, W l，0 is the standard fatigue strength at the lth position on the bridge at time t, η l is the adjustment factor of the fatigue strength at the lth position on the bridge at time t, and ω is the overall adjustment factor of the fatigue strength.

10. The intelligent bridge safety monitoring system based on big data prediction according to claim 9, characterized in that: The weight α of the stress at the i-th position on the bridge at time t i (t) include: in, is the stress weight α at the i-th position i The first adjustment factor is is the stress weight α at the i-th position i The second adjustment factor is is the stress weight α at the i-th position i The third adjustment factor, χ i is the amplitude of the periodic variation of stress at the i-th position on the bridge, ω i is the frequency of the periodic change of the stress at the i-th position on the bridge.

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