Bridge state prediction method and related device

Abstract

The invention relates to the technical field of bridge maintenance, and provides a bridge state prediction method and a related device, and the method comprises the steps: obtaining first bridge state data; extracting second bridge state data from the first bridge state data; extracting third bridge state data from the first bridge state data; according to the third bridge state data, a Markov transition probability algorithm is adopted to calculate a bridge state transition probability; according to the second bridge state data and the bridge state transition probability, the state of the target bridge is predicted, a target bridge state prediction result is obtained, the possible degradation state of the bridge after the preset time can be provided for bridge maintenance personnel, then reference is provided for the maintenance personnel to arrange the maintenance period of the bridge, and the maintenance efficiency is improved. Maintenance personnel can maintain the bridge in time according to the bridge maintenance period, and the safety of the bridge is improved.

Description

Technical Field

[0001] The present application relates to the technical field of bridge maintenance, and in particular to a bridge state prediction method and related devices. Background Art

[0002] As an important transportation infrastructure, bridges carry important tasks such as transportation, personnel exchanges and material circulation. Their safety is directly related to the smooth operation of the social economy and the safety of life and property of the public. However, as time goes by, the bridge structure will degrade due to various factors such as natural environmental erosion, material aging, load accumulation, etc., such as cracks, steel corrosion, and support failure. If these degradation phenomena are not discovered and handled in time, they will seriously threaten the safety and durability of the bridge, and may even cause the bridge to collapse suddenly, causing major safety accidents.

[0003] In order to ensure the safe operation of bridges, it is crucial to carry out timely and effective maintenance of bridges that have degraded to a certain extent. The formulation of maintenance decisions depends largely on the accurate assessment of the current damage state of the bridge. However, in actual operation, due to the large number of bridges and their wide distribution, especially in some large regions or countries, comprehensive and real-time monitoring and evaluation of all bridges is not only technically difficult, but also costly and difficult to achieve.

[0004] At present, most of the bridge condition assessment methods on the market rely on engineers' experience or regular on-site inspections. Although this method can reflect the current condition of the bridge to a certain extent, it is often difficult to accurately predict the future degradation trend and possible dangerous state of the bridge due to its lack of scientificity and systematicness. In addition, due to the limitation of the inspection cycle, some bridges that do not currently show obvious signs of degradation but have higher potential risks may be neglected, resulting in delayed maintenance work and increased safety hazards. Summary of the invention

[0005] The embodiment of the present application provides a bridge status prediction method and related devices, which can calculate the bridge status transition probability of a bridge that may need maintenance based on the bridge status data in the same area, and use this to infer the status of the bridge after a preset time, so as to help bridge maintenance personnel effectively arrange the maintenance period of the bridge according to the bridge status, thereby maintaining the bridge in a timely manner and improving the safety of the bridge.

[0006] A first aspect of an embodiment of the present application provides a bridge state prediction method, the method comprising:

[0007] Acquire first bridge status data, where the first bridge status data is bridge status data corresponding to bridges in the same area;

[0008] Extracting second bridge status data from the first bridge status data, where the second bridge status data is bridge status data corresponding to the target bridge;

[0009] Extracting third bridge status data from the first bridge status data, wherein the third bridge status data is bridge status data corresponding to a bridge of the same type as the target bridge;

[0010] According to the third bridge state data, the bridge state transition probability is calculated by using the Markov transition probability algorithm;

[0011] The state of the target bridge is predicted according to the second bridge state data and the bridge state transition probability to obtain a target bridge state prediction result.

[0012] Further, according to the third bridge state data, a Markov transition probability algorithm is used to calculate the bridge state transition probability, including:

[0013] Extracting the number of times the bridge degrades from state i to state j from the third bridge state data to obtain bridge state degradation number data;

[0014] Extracting the number of times the bridge stays in state i from the third bridge state data to obtain bridge state stay number data;

[0015] The bridge state transition probability is calculated according to the bridge state degradation frequency data and the bridge state stay frequency data.

[0016] Furthermore, the calculation formula of the bridge state transition probability is specifically as follows:

[0017]

[0018] Among them, p ij (d) is the bridge state transition probability of the bridge degenerating from state i to state j, n ij (d) is the number of bridge state degradation data from state i to state j, n i (d) is the sum of the number of bridge state degradation data and the number of bridge state stay data when the bridge degenerates from state i to state j.

[0019] Further, the state of the target bridge is predicted according to the second bridge state data and the bridge state transition probability to obtain a target bridge state prediction result, including:

[0020] Calculate the bridge transfer matrix according to the bridge state transition probability;

[0021] extracting a current state vector of the target bridge from the second bridge state data;

[0022] Calculate the state vector of the target bridge degraded for m years according to the bridge transfer matrix and the current state vector of the target bridge;

[0023] According to the state vector of the target bridge degraded for m years, the state of the target bridge after m years is calculated to obtain a prediction result of the state of the target bridge.

[0024] Furthermore, the calculation formula of the state vector of the target bridge after m years of degradation is as follows:

[0025] π(m)=π(0)·P(d) m

[0026] Among them, π(m) is the state vector of the target bridge after m years of degradation, π(0) is the current state vector of the target bridge, p(d) is the transfer matrix of the target bridge of the same type, and m is the degradation time.

[0027] Furthermore, the calculation formula for the target bridge status after m years is as follows:

[0028] D r (m) = π(m)·S T

[0029] Among them, D r (m) is the target bridge state after m years, the state vector of the target bridge after m years of degradation, S is the state space, and T is the transposed matrix.

[0030] In this example, the first bridge status data is first obtained, and then the second bridge status data and the third bridge status data are extracted from the first bridge status data. By using the Markov transition probability algorithm for the third bridge status data, the bridge status transition probability is obtained. Finally, based on the second bridge status data and the bridge status transition probability, the target bridge status prediction result after a preset time can be calculated. This can provide bridge maintenance personnel with the possible degradation state of the bridge after a preset time, and then provide maintenance personnel with a reference for arranging the maintenance period of the bridge, so that maintenance personnel can perform timely maintenance on the bridge according to the bridge maintenance period, thereby improving the safety of the bridge.

[0031] A second aspect of an embodiment of the present application provides a bridge state prediction device, the device comprising:

[0032] A first acquisition unit is used to acquire first bridge status data, where the first bridge status data is bridge status data corresponding to bridges in the same area;

[0033] A first processing unit is used to extract second bridge status data from the first bridge status data, where the second bridge status data is bridge status data corresponding to a target bridge;

[0034] A second processing unit is used to extract third bridge status data from the first bridge status data, where the third bridge status data is bridge status data corresponding to a bridge of the same type as the target bridge;

[0035] A third processing unit is used to calculate the bridge state transition probability using a Markov transition probability algorithm according to the third bridge state data;

[0036] The fourth processing unit is used to predict the state of the target bridge according to the second bridge state data and the bridge state transition probability to obtain a target bridge state prediction result.

[0037] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device and a memory, wherein the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the step instructions of the bridge state prediction method in the first aspect of the embodiment of the present application.

[0038] The fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute part or all of the steps described in the bridge state prediction method in the first aspect of the embodiment of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic diagram of the overall flow of a bridge state prediction method is provided for an embodiment of the present application;

[0041] Figure 2 A schematic flow chart of the calculation steps of the bridge state transition probability of a bridge degenerating from state i to state j in a bridge state prediction method is provided for an embodiment of the present application;

[0042] Figure 3 A schematic diagram of a specific calculation process of a target bridge state prediction result in a bridge state prediction method is provided for an embodiment of the present application;

[0043] Figure 4 A structural schematic diagram of a bridge state prediction device is provided for an embodiment of the present application;

[0044] Figure 5 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0045] Reference numerals:

[0046] First acquisition unit-1, first processing unit-2, second processing unit-3, third processing unit-4, fourth processing unit-5. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0049] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0050] In order to better understand the bridge state prediction method provided by the embodiment of the present application, the following first briefly introduces the scenario of applying the bridge state prediction method. Since most of the bridge state assessment methods currently on the market rely on the experience judgment of engineers or regular on-site inspections. Although the above traditional experience judgment methods or regular inspection methods can reflect the current condition of the bridge to a certain extent, due to the lack of scientificity and systematicity, it is often difficult to accurately predict the future degradation trend of the bridge and the dangerous state that may be reached. Therefore, this embodiment provides a bridge state prediction method, which can be based on the big data of the same type of bridges in the past, and adopts the Markov transition probability algorithm method to deduce the system's bridge state estimation method, so as to provide bridge maintenance personnel with the possible degradation state of the bridge after a preset time, and then provide maintenance personnel with a reference for arranging the maintenance period of the bridge, so that maintenance personnel can timely maintain the bridge according to the bridge maintenance period, and improve the safety of the bridge.

[0051] The bridge state prediction method is applied to the bridge state prediction device. Figure 1 A schematic diagram of the overall process of a bridge state prediction method is shown in FIG. Figure 1 As shown, including:

[0052] S1. Obtain first bridge status data, which is bridge status data corresponding to bridges in the same area. It should be noted that there are five levels of bridge status in the bridge status data, S = {1, 2, 3, 4, 5}, corresponding to Class 1, Class 2, Class 3, Class 4 and Class 5 bridges in the bridge technical condition level Dr, respectively.

[0053] In this example, the first bridge status data is obtained from the bridge status monitoring database, with the purpose of extracting the bridge status data of the area where the target bridge currently needs to be estimated is located, so as to provide a data basis for subsequent target bridge status analysis and processing.

[0054] S2. Extract second bridge status data from the first bridge status data, where the second bridge status data is bridge status data corresponding to a target bridge.

[0055] In this example, the second bridge status data is extracted from the first bridge status data to provide a data basis for subsequent Markov transition probability calculation.

[0056] S3. Extract third bridge status data from the first bridge status data, where the third bridge status data is bridge status data corresponding to a bridge of the same type as the target bridge.

[0057] Similar to step S2, in this example, bridge status data corresponding to bridges of the same type as the target bridge are extracted to provide a data basis for subsequent Markov transition probability calculation.

[0058] S4. Calculate the bridge state transition probability using the Markov transition probability algorithm based on the third bridge state data. Figure 2 A schematic flow chart of the calculation steps of the bridge state transition probability of a bridge degenerating from state i to state j in a bridge state prediction method is shown, specifically, including:

[0059] S401. Extract the number of times the bridge degrades from state i to state j from the third bridge state data to obtain bridge state degradation number data. For example, the bridge degrades from state 1 to state 2, the bridge degrades from state 2 to state 3, etc. It should be noted that, unless force majeure occurs, only the normal aging of the bridge is considered. The transfer of the bridge state is generally from one state to an adjacent state according to the indication in the historical data, and there will be no transition from state 1 directly degraded to state 3. Therefore, this embodiment mainly considers the normal bridge aging state transfer.

[0060] S402: Extract the number of times the bridge stays in state i from the third bridge state data to obtain bridge state stay number data.

[0061] S403, calculating the bridge state transition probability according to the bridge state degradation times data and the bridge state stay times data. The calculation formula of the bridge state transition probability is as follows:

[0062]

[0063] Among them, p ij (d) is the bridge state transition probability of the bridge degenerating from state i to state j, n ij (d) is the number of bridge state degradation data from state i to state j, n i (d) is the sum of the number of bridge state degradation data and the number of bridge state stay data when the bridge degenerates from state i to state j.

[0064] In this example, a method for calculating the probability of bridge state transition is provided. According to the bridge state degradation times data and the bridge state stay times data, the transition probability of the bridge degenerating from state i to state j can be calculated. Based on the bridge state data in the same area, the probability of the same type of bridge degenerating from state i to state j is obtained, which reflects the probability law of bridge state changing over time, thereby providing a basis for the subsequent Markov matrix establishment.

[0065] S5. Predict the state of the target bridge according to the second bridge state data and the bridge state transition probability to obtain a target bridge state prediction result. Figure 3A schematic diagram of a specific calculation process of a target bridge state prediction result in a bridge state prediction method is shown, specifically including:

[0066] S501. Calculate the bridge transfer matrix according to the bridge state transition probability. In this example, the calculation formula of the transfer matrix is:

[0067]

[0068] Among them, p 11 is the probability that the bridge condition value remains 1, p 22 is the probability that the bridge condition value remains 2, p 33 is the probability that the bridge condition value remains 3, p 44 is the probability that the bridge condition value remains 4.

[0069] The transfer matrix was determined, that is, the probability that the condition value of bridges of the same type in the same area remained unchanged and the condition value decayed to the next stage within a stable degradation stage was determined.

[0070] According to the calculation formula of the above transfer matrix, the transition probability of all bridge states of the same type degenerating from the current state to the next bridge state can be obtained.

[0071] S502: Extracting a current state vector of the target bridge from the second bridge state data.

[0072] S503, calculating the state vector of the target bridge degraded for m years according to the bridge transfer matrix and the current state vector of the target bridge. In this embodiment, the calculation formula of the state vector of the target bridge degraded for m years is as follows:

[0073] π(m)=π(0)·P(d) m

[0074] Among them, π(m) is the state vector of the target bridge after m years of degradation, π(0) is the current state vector of the target bridge, p(d) is the transfer matrix of the target bridge of the same type, and m is the degradation time. The state vector represents the state of the target bridge after m years.

[0075] S504, according to the state vector of the target bridge degraded for m years, calculate the target bridge state after m years to obtain the target bridge state prediction result. In this embodiment, the calculation formula of the target bridge state after m years is as follows:

[0076] D r (m) = π(m)·S T

[0077] Among them, D r(m) is the target bridge state after m years, the state vector of the target bridge after m years of degradation, S is the state space, and T is the transposed matrix. T , we can get the target bridge status m years later, and thus get the target bridge status prediction result.

[0078] In this example, the first bridge status data is first obtained, and then the second bridge status data and the third bridge status data are extracted from the first bridge status data. By using the Markov transition probability algorithm for the third bridge status data, the bridge status transition probability is obtained. Finally, based on the second bridge status data and the bridge status transition probability, the target bridge status prediction result after a preset time can be calculated. This can provide bridge maintenance personnel with the possible degradation state of the bridge after a preset time, and then provide maintenance personnel with a reference for arranging the maintenance period of the bridge, so that maintenance personnel can perform timely maintenance on the bridge according to the bridge maintenance period, thereby improving the safety of the bridge.

[0079] In line with the above, see Figure 4 , Figure 4 The present invention provides a schematic diagram of a bridge state prediction device. Figure 4 As shown, the device comprises:

[0080] A first acquisition unit 1 is used to acquire first bridge status data, where the first bridge status data is bridge status data corresponding to bridges in the same area;

[0081] A first processing unit 2 is used to extract second bridge status data from the first bridge status data, where the second bridge status data is bridge status data corresponding to a target bridge;

[0082] A second processing unit 3 is used to extract third bridge status data from the first bridge status data, where the third bridge status data is bridge status data corresponding to a bridge of the same type as the target bridge;

[0083] A third processing unit 4 is used to calculate the bridge state transition probability using a Markov transition probability algorithm according to the third bridge state data;

[0084] The fourth processing unit 5 is used to predict the state of the target bridge according to the second bridge state data and the bridge state transition probability to obtain a target bridge state prediction result.

[0085] In a possible implementation, in the aspect of calculating the bridge state transition probability by using a Markov transition probability algorithm according to the third bridge state data, the second processing unit is used to:

[0086] Extracting the number of times the bridge degrades from state i to state j from the third bridge state data to obtain bridge state degradation number data;

[0087] Extracting the number of times the bridge stays in state i from the third bridge state data to obtain bridge state stay number data;

[0088] The bridge state transition probability is calculated according to the bridge state degradation frequency data and the bridge state stay frequency data.

[0089] In this example, the first bridge status data is obtained, and then the second bridge status data and the third bridge status data are extracted from the first bridge status data. The bridge status transition probability is obtained by using the Markov transition probability algorithm on the third bridge status data. Finally, the target bridge status prediction result after a preset time can be calculated based on the second bridge status data and the bridge status transition probability. This can provide bridge maintenance personnel with the possible degradation state of the bridge after a preset time, and then provide maintenance personnel with a reference for arranging the maintenance period of the bridge, so that maintenance personnel can perform timely maintenance on the bridge according to the bridge maintenance period, thereby improving the safety of the bridge.

[0090] For the above embodiments, please refer to Figure 5 , Figure 5 A schematic diagram of the structure of a terminal provided in an embodiment of the present application, as shown in the figure, includes a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for executing the following steps;

[0091] Acquire first bridge status data, where the first bridge status data is bridge status data corresponding to bridges in the same area;

[0092] Extracting second bridge status data from the first bridge status data, where the second bridge status data is bridge status data corresponding to the target bridge;

[0093] Extracting third bridge status data from the first bridge status data, wherein the third bridge status data is bridge status data corresponding to a bridge of the same type as the target bridge;

[0094] According to the third bridge state data, the bridge state transition probability is calculated by using the Markov transition probability algorithm;

[0095] The state of the target bridge is predicted according to the second bridge state data and the bridge state transition probability to obtain a target bridge state prediction result.

[0096] In this example, the first bridge status data is obtained, and then the second bridge status data and the third bridge status data are extracted from the first bridge status data. By using the Markov transition probability algorithm for the third bridge status data, the bridge status transition probability is obtained. Finally, based on the second bridge status data and the bridge status transition probability, the target bridge status prediction result after a preset time can be calculated. This can provide bridge maintenance personnel with the possible degradation state of the bridge after a preset time, and then provide maintenance personnel with a reference for arranging the maintenance period of the bridge, so that maintenance personnel can perform timely maintenance on the bridge according to the bridge maintenance period, thereby improving the safety of the bridge.

[0097] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that in order to realize the above functions, the terminal includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0098] The embodiment of the present application can divide the terminal into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0099] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any bridge state prediction method recorded in the above method embodiments.

[0100] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any bridge state prediction method recorded in the above method embodiments.

[0101] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0102] In the above embodiments, 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.

[0103] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as 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, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0104] 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.

[0105] In addition, the functional units in the various embodiments of the application 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 units may be implemented in the form of hardware or in the form of software program modules.

[0106] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially 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 memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0107] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0108] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A bridge state prediction method, characterized in that: include: Acquire first bridge status data, where the first bridge status data is bridge status data corresponding to bridges in the same area; Extracting second bridge status data from the first bridge status data, where the second bridge status data is bridge status data corresponding to the target bridge; Extracting third bridge status data from the first bridge status data, wherein the third bridge status data is bridge status data corresponding to a bridge of the same type as the target bridge; According to the third bridge state data, the bridge state transition probability is calculated by using the Markov transition probability algorithm; The state of the target bridge is predicted according to the second bridge state data and the bridge state transition probability to obtain a target bridge state prediction result.

2. The bridge state prediction method according to claim 1, characterized in that: According to the third bridge state data, the bridge state transition probability is calculated using the Markov transition probability algorithm, including: Extracting the number of times the bridge degrades from state i to state j from the third bridge state data to obtain bridge state degradation number data; Extracting the number of times the bridge stays in state i from the third bridge state data to obtain bridge state stay number data; The bridge state transition probability is calculated according to the bridge state degradation frequency data and the bridge state stay frequency data.

3. The bridge state prediction method according to claim 2, characterized in that: The calculation formula of the bridge state transition probability is specifically as follows: Among them, p ij (d) is the bridge state transition probability of the bridge degenerating from state i to state j, n ij (d) is the number of bridge state degradation data from state i to state j, n i (d) is the sum of the number of bridge state degradation data and the number of bridge state stay data when the bridge degenerates from state i to state j.

4. The bridge state prediction method according to claim 1, characterized in that: According to the second bridge state data and the bridge state transition probability, the state of the target bridge is predicted to obtain a target bridge state prediction result, including: Calculate the bridge transfer matrix according to the bridge state transition probability; extracting a current state vector of the target bridge from the second bridge state data; Calculate the state vector of the target bridge degraded for m years according to the bridge transfer matrix and the current state vector of the target bridge; According to the state vector of the target bridge degraded for m years, the state of the target bridge after m years is calculated to obtain a prediction result of the state of the target bridge.

5. The bridge state prediction method according to claim 4, characterized in that: The calculation formula of the state vector of the target bridge degraded for m years is as follows: π(m)=π(0)·P(d) m Among them, π(m) is the state vector of the target bridge after m years of degradation, π(0) is the current state vector of the target bridge, p(d) is the transfer matrix of the target bridge of the same type, and m is the degradation time.

6. The bridge state prediction method according to claim 4, characterized in that: The calculation formula for the target bridge status after m years is as follows: D r (m)=π(m)·S T Among them, D r (m) is the target bridge state after m years, the state vector of the target bridge after m years of degradation, S is the state space, and T is the transposed matrix.

7. A bridge state prediction device, characterized in that: include: A first acquisition unit is used to acquire first bridge status data, where the first bridge status data is bridge status data corresponding to bridges in the same area; A first processing unit is used to extract second bridge status data from the first bridge status data, where the second bridge status data is bridge status data corresponding to a target bridge; A second processing unit is used to extract third bridge status data from the first bridge status data, where the third bridge status data is bridge status data corresponding to a bridge of the same type as the target bridge; A third processing unit is used to calculate the bridge state transition probability using a Markov transition probability algorithm according to the third bridge state data; The fourth processing unit is used to predict the state of the target bridge according to the second bridge state data and the bridge state transition probability to obtain a target bridge state prediction result.

8. The bridge state prediction device according to claim 7, characterized in that: In the aspect of calculating the bridge state transition probability by using a Markov transition probability algorithm according to the third bridge state data, the second processing unit is used for: Extracting the number of times the bridge degrades from state i to state j from the third bridge state data to obtain bridge state degradation number data; Extracting the number of times the bridge stays in state i from the third bridge state data to obtain bridge state stay number data; The bridge state transition probability is calculated according to the bridge state degradation frequency data and the bridge state stay frequency data.

9. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the bridge state prediction method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the bridge state prediction method according to any one of claims 1 to 6.

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