Bridge safety detection method based on cloud platform

Abstract

The invention discloses a bridge safety detection method based on a cloud platform, relates to the technical field of bridge safety detection, and solves the technical problem that when different types of vehicles pass through different lanes on a bridge, different loads are caused to the bridge, so that the bridge cannot be accurately subjected to safety detection. Through the generated first early warning signal, the real-time traffic flow borne on the bridge is detected and early warned, which indicates that the existing real-time traffic flow borne on the bridge has an overflow phenomenon, easily affects the safety of the bridge and possibly causes potential risks to the safety of the bridge, so that related personnel can detect the bridge in time and early warn the safety condition of the bridge, and the safety of the bridge is improved. Therefore, related personnel can take corresponding measures in time, such as limiting vehicle passing, strengthening inspection and maintenance and the like, so as to ensure safe operation of the bridge.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge safety detection, and in particular to a bridge safety detection method based on a cloud platform. Background Art

[0002] The cloud-based bridge safety detection method is an application that integrates a variety of advanced technologies to monitor the health of bridges in real time and prevent potential safety hazards. The following are the problems that may be encountered in the actual application of this technology and their corresponding solutions. Select the corresponding monitoring module according to the actual situation and needs of the bridge;

[0003] However, when inspecting the safety of bridges, it is not taken into account that different lanes are usually set on the bridge. When different types of vehicles pass through different lanes on the bridge, the loads on the bridge will be different, which makes it impossible to accurately perform safety inspections on the bridge. Based on this, a bridge safety inspection method based on a cloud platform is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a bridge safety detection method based on a cloud platform, which solves the technical problem that when different types of vehicles pass through different lanes on the bridge, the loads on the bridge will be different, making it impossible to accurately perform safety detection on the bridge.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The bridge safety detection method based on the cloud platform includes the following steps:

[0007] Step 1: Obtain the traffic state value corresponding to each lane of the bridge and the maintenance time corresponding to each state value;

[0008] Step 2: Analyze the traffic state values ​​corresponding to each lane of the bridge and the maintenance time corresponding to each state value, and obtain the vehicle frequency value corresponding to each lane according to the analysis results;

[0009] Step 3: According to the number of vehicles of each type corresponding to each lane of the bridge under different traffic flow state values, the vehicle type value corresponding to each type of vehicle of each lane of the bridge is analyzed and obtained;

[0010] Step 4: Calculate the bearing value of the bridge according to the material deflection parameters, material stress parameters, material strength parameters, bridge design width and bridge design height of the bridge;

[0011] Step 5: According to the lane design width and lane design length of each lane of the bridge and the straight-line distance between the center point of each lane and the center point of the bridge, analyze and obtain the lane load value corresponding to each lane;

[0012] Step six: mark the traffic state value of each lane on the bridge with a holding time greater than 30 seconds as the calculated traffic state value of each lane, compare the calculated traffic state value of each lane with its corresponding vehicle frequency standard value, obtain and mark the lane corresponding to the calculated traffic state value greater than the corresponding vehicle frequency standard value as an overflow lane, obtain the real-time number of each type of vehicles corresponding to each lane of the overflow lane under the corresponding calculated traffic state value, and compare it with the vehicle type value of each type of vehicles corresponding to each overflow lane, obtain the total number of vehicles of each type in each overflow lane whose real-time number is greater than the corresponding vehicle type value, analyze the number, and generate the first warning signal according to the analysis result.

[0013] As a further solution of the present invention, the specific method of obtaining the vehicle frequency standard value corresponding to each lane of the bridge is:

[0014] S1: Randomly select one lane from all lanes of the bridge as the analysis lane;

[0015] S2: Obtain the traffic state value corresponding to each lane within the preset time T and the maintenance time corresponding to each state value, and mark them as CTj and WTj respectively;

[0016] Here, j refers to the number of traffic state values ​​corresponding to the lane being analyzed every day within the preset time T, j ≥ 1, T ≥ 1;

[0017] S3: By formula Calculate and obtain the traffic heat value CWT corresponding to each day of the analyzed lane within the preset time T, where j≥i≥1;

[0018] S4: Obtain the total maintenance time corresponding to each day of the analysis lane within the preset time T, and mark the ratio between the traffic flow heat value CWT corresponding to each day of the analysis lane within the preset time T and the total maintenance time as the vehicle frequency daily value PCT;

[0019] That is, through the formula Calculate and obtain the daily vehicle frequency value PCT corresponding to the analyzed lane within the preset time T, j≥a≥1;

[0020] S5: obtaining the value PCb that satisfies the preset condition A1 in the vehicle frequency value PCT corresponding to the analysis lane every day within the preset time T, and analyzing it, thereby obtaining the vehicle frequency standard value BP1 corresponding to the analysis lane, wherein b is the corresponding number of values ​​in PCT that satisfy the preset condition A1;

[0021] Compare and analyze b with the preset value Y1, and then obtain the vehicle frequency standard value BP1 corresponding to the analysis lane;

[0022] S6: Repeat the above steps S1-S5 to obtain the vehicle frequency criterion BPv corresponding to each lane of the bridge. The vehicle frequency criterion BPv corresponding to each lane of the bridge can be used to represent the traffic heat conditions of different lanes, helping to analyze and monitor the traffic load conditions of each lane of the bridge, where v refers to the number of lanes on the bridge, v≥1.

[0023] As a further solution of the present invention, a specific method of obtaining the vehicle frequency standard value BP1 corresponding to the analysis lane is as follows:

[0024] When b ≥ Y1, the mean value PCp of PCT is used as the vehicle frequency standard value BP1 corresponding to the analysis lane, where PCp is the mean value of PCT;

[0025] When b < Y1, PCT is arranged in ascending order, and the median is taken as the vehicle frequency standard value BP1 corresponding to the analysis lane. When T is an odd number, BP1 = PC[(d+1) / 2] is taken as the vehicle frequency standard value BP1 corresponding to the analysis lane.

[0026] When T is an even number, [PC(d / 2)+PC(d / 2+1)] / 2=BP1 is used as the vehicle frequency standard value BP1 corresponding to the analysis lane, where T≥d≥1, and the preset condition A1 is: |PCT-PCp|×β1>Y2, Y2 is the preset value, β1 is the preset proportional coefficient, where β1=1.187.

[0027] As a further solution of the present invention, the vehicle type value corresponding to each type of vehicle in each lane of the bridge is obtained, and the specific analysis method is as follows:

[0028] S01: Select the same lane as that in step S from all lanes of the bridge as the analysis lane;

[0029] S02: randomly select one day from the preset time T as the target analysis day;

[0030] Obtain the number of vehicles of each type maintained in the analysis lane on the target analysis day, and mark them as Efe respectively. At the same time, mark the duration of the number of vehicles of each type maintained in the analysis lane on the target analysis day as Ffe, where e is the number of vehicles of each type maintained in the analysis lane on the target analysis day, f is the number of corresponding vehicles, e≥1, f≥1;

[0031] S03: Obtain the sum of the products of the maintained number Efe of each type of vehicle and the number duration Ffe corresponding thereto, and mark the ratio between the sum of the number duration Ffe and the number duration Ffe as the calculated value EFf1 corresponding to each type of vehicle in the analysis lane on the target analysis day;

[0032] S04: repeating steps S02-S03, calculating the corresponding values ​​EFfT of each type of vehicle in the analysis lane every day within the preset time T;

[0033] S05: obtaining the average of the calculated values ​​of the same type of vehicles in the analysis lane every day within the preset time T, and using it as the vehicle class value CDf corresponding to the corresponding type of vehicle in the analysis lane;

[0034] S06: Repeat steps S01-S05 to obtain the vehicle type value CDfv corresponding to each type of vehicle in each lane.

[0035] As a further solution of the present invention: the specific method of obtaining the bearing value of the bridge is:

[0036] The material characteristic parameters, material structure parameters, material strength parameters, bridge design width and bridge design length are marked as QA, QB, QC, QD and QE respectively, and the bearing value QCZ of the bridge is calculated by the formula (QA×β1+QB×β2+QC×β3)×(QD×QE)×β4=QCZ, where β1, β2, β3 and β4 are fixed calculation coefficients.

[0037] As a further solution of the present invention: the specific method of obtaining the lane load value corresponding to each lane is:

[0038] The lane design width and lane design length corresponding to each lane are marked as DAv and DBv respectively, and the straight-line distance between the center point of each lane and the center point of the bridge is marked as DCv. The lane load value DZv corresponding to each lane is calculated by the formula [(DAv×DBv)+DCv×β5 / (QD×QE)]×QCZ=DZv, where β5 is a fixed coefficient.

[0039] As a further solution of the present invention: the specific method of generating the first warning signal is:

[0040] The real-time traffic state value of each lane on the bridge is obtained, and the corresponding holding time of the traffic state value is obtained. The traffic state value with a holding time greater than 30 seconds is extracted as the calculated traffic state value JSv of each lane, and the number of vehicles of each type in each lane under the corresponding calculated traffic state value is obtained, and they are marked as BHfv respectively. The calculated traffic state value JSv of each lane is analyzed and compared with the corresponding vehicle frequency criterion BPv respectively, and the lane corresponding to the calculated traffic state value JSv greater than the corresponding vehicle frequency criterion BPv is obtained, and it is marked as the overflow lane;

[0041] Obtain the real-time number of each type of vehicle corresponding to each lane of the overflow lane under the corresponding calculated traffic state value, and mark them as BHfk respectively, and compare them with the vehicle type value CDfk of each type of vehicle corresponding to each overflow lane, and obtain the real-time number of each type of vehicle in each overflow lane BHfk is greater than the total number of the corresponding vehicle type value CDfk of each type of vehicle, and mark it as M, where k is the corresponding overflow lane number, v≥k≥1, when k×β6+M×β7>Y1, the first warning signal is generated, otherwise no processing is performed.

[0042] As a further solution of the present invention: when the first warning signal is generated, the real-time number BHfk of each type of vehicle in each overflow lane is obtained and analyzed to obtain the real-time number LSf of each type of vehicle on the bridge. The sum of the products of the real-time number and the corresponding preset load coefficient HZf is marked as the real-time load QSZ of the bridge. When the real-time load QSZ of the bridge is greater than the bearing value QCZ of the bridge, an overload signal is generated, where f≥x≥1, otherwise no processing is performed.

[0043] As a further solution of the present invention: when an overload signal is generated, the number of vehicles of each type corresponding to each overflow lane is obtained and marked as MDfk respectively;

[0044] The sum of the products of the number MDfk of each type of vehicle corresponding to each overflow lane and the load coefficient HZf corresponding to each type of vehicle is marked as the real-time lane load value RZk corresponding to each overflow lane. The real-time lane load value RZk corresponding to each overflow lane is compared and analyzed with the corresponding lane load value DZk, and the overflow lane whose real-time lane load value RZk is greater than the corresponding lane load value DZk is marked and output.

[0045] Beneficial effects of the present invention:

[0046] (1) The present invention obtains the vehicle frequency value by obtaining the traffic state value and the maintenance time of each lane of the bridge, obtains the vehicle type value of each type of vehicle according to the maintenance number of each type of vehicle corresponding to each lane of the bridge under different traffic state values, calculates the bearing value of the bridge, calculates the lane load value of each lane according to the design parameters of the bridge, compares the calculated traffic state value and the vehicle frequency standard value of each lane, marks the lane greater than the vehicle frequency standard value as an overflow lane, and obtains the real-time number of each type of vehicle in the overflow lane, compares it with the vehicle type value, and generates a first warning signal. Through the generated first warning signal, the real-time carrying traffic volume on the bridge is detected and warned, indicating that the existing real-time carrying traffic volume on the bridge has an overflow phenomenon, which is easy to affect the safety of the bridge and may cause potential risks to the safety of the bridge, so that relevant personnel can timely detect the bridge and warn the safety status of the bridge, so that relevant personnel can timely take corresponding measures, such as restricting vehicle passage, strengthening inspection and maintenance, etc., to ensure the safe operation of the bridge;

[0047] (2) When the first warning signal is generated, the present invention obtains the real-time number of various types of vehicles on the bridge, calculates the real-time load of the bridge, and compares it with the load value. If the real-time load is greater than the load value, an overload signal is generated. The overload signal is used to warn that the existing real-time load on the bridge is overflowing, so that relevant personnel can take corresponding measures in time to restrict vehicles from entering the bridge to reduce the load and ensure the safety of the bridge.

[0048] (3) When an overload signal is generated, the present invention obtains the number of each type of vehicle in each overflow lane, calculates the real-time lane load value of each overflow lane, and compares it with the lane load value. The overflow lane with a load value greater than the lane load value is marked and output, which is beneficial for relevant personnel to take targeted maintenance measures for the corresponding lane, such as restricting vehicle traffic, strengthening inspections and maintenance, etc., to ensure the safe operation of the bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] Figure 1 It is a schematic diagram of the framework structure of the method for generating the first warning signal of the present invention;

[0051] Figure 2 It is a schematic diagram of the framework structure of the method for generating an overload signal according to the present invention;

[0052] Figure 3 It is a schematic diagram of the framework structure of the method for marking an overflow lane with an overload value according to the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Embodiment 1

[0055] See also Figure 1 As shown, the present invention is a bridge safety detection method based on a cloud platform, comprising the following steps:

[0056] Step 1: Obtain the historical traffic flow data of each lane of the bridge. These data include the traffic flow state values ​​corresponding to each lane of the bridge and the maintenance time corresponding to each state value. The historical traffic flow data of each lane is monitored and recorded by various sensors, monitoring equipment or traffic management systems;

[0057] Step 2: Analyze the historical traffic flow data corresponding to each lane of the bridge, and obtain the vehicle frequency value corresponding to each lane according to the analysis results. The specific method of obtaining the vehicle frequency value corresponding to each lane of the bridge is:

[0058] S1: Randomly select one lane from all lanes of the bridge as the analysis lane;

[0059] S2: Obtain the traffic state value corresponding to each lane within the preset time T and the maintenance time corresponding to each state value, and mark them as CTj and WTj respectively;

[0060] Here, j refers to the number of traffic state values ​​corresponding to the lane being analyzed every day within the preset time T, j ≥ 1, and the preset time refers to the period of 270 days from the day the data is obtained. It should be noted that the data on the day the data is obtained is not included, T = 270;

[0061] S3: By formula Calculate and obtain the traffic heat value CWT corresponding to each day of the analyzed lane within the preset time T, where j≥i≥1;

[0062] S4: Obtain the total maintenance time corresponding to each day of the analysis lane within the preset time T, and mark the ratio between the traffic flow heat value CWT corresponding to each day of the analysis lane within the preset time T and the total maintenance time as the vehicle frequency daily value PCT;

[0063] That is, through the formula Calculate and obtain the daily vehicle frequency value PCT corresponding to the analyzed lane within the preset time T, j≥a≥1;

[0064] S5: obtaining the value PCb that satisfies the preset condition A1 in the vehicle frequency value PCT corresponding to the analysis lane every day within the preset time T, and analyzing it, thereby obtaining the vehicle frequency standard value BP1 corresponding to the analysis lane, wherein b is the corresponding number of values ​​in PCT that satisfy the preset condition A1;

[0065] Compare b with the preset value Y1. When b≥Y1, the mean PCp of PCT is used as the vehicle frequency standard value BP1 corresponding to the analysis lane, where PCp is the mean of PCT, PCp=(PC1+PC2+……+CPc) / c, where T≥c≥1, and the specific value of the preset value Y1 is determined by relevant personnel according to actual needs;

[0066] When b < Y1, PCT is arranged in ascending order, and the median is taken as the vehicle frequency standard value BP1 corresponding to the analysis lane. When T is an odd number, BP1 = PC[(d+1) / 2] is taken as the vehicle frequency standard value BP1 corresponding to the analysis lane.

[0067] When T is an even number, [PC(d / 2)+PC(d / 2+1)] / 2=BP1 is used as the vehicle frequency standard value BP1 corresponding to the analysis lane; where T≥d≥1, the preset condition A1 is: |PCT-PCp|×β1>Y2, Y2 is the preset value, β1 is the preset proportional coefficient, and the specific value can be formulated by relevant personnel according to actual needs and application scenarios, where β1=1.187;

[0068] S6: Repeat the above steps S1-S5 to obtain the vehicle frequency quasi-value BPv corresponding to each lane of the bridge. The vehicle frequency quasi-value BPv corresponding to each lane of the bridge can be used to represent the traffic heat of different lanes, which helps to analyze and monitor the traffic load of each lane of the bridge, where v refers to the number of lanes on the bridge, and v≥1;

[0069] Step 3: Based on the historical traffic flow data of each lane of the bridge, analyze the number of vehicles of each type corresponding to each lane of the bridge under different traffic state values, and then obtain the vehicle type value corresponding to each type of vehicle in each lane of the bridge. The specific analysis method is as follows:

[0070] S01: Select the same lane as that in step S from all lanes of the bridge as the analysis lane;

[0071] S02: randomly select one day from the preset time T as the target analysis day;

[0072] Obtain the number of vehicles of each type maintained in the analysis lane on the target analysis day, and mark them as Efe respectively. At the same time, mark the duration of the number of vehicles of each type maintained in the analysis lane on the target analysis day as Ffe, where e is the number of vehicles of each type maintained in the analysis lane on the target analysis day, f is the number of corresponding vehicles, e≥1, f≥1;

[0073] S03: Obtain the sum of the products of the maintained number Efe of each type of vehicle and the number duration Ffe corresponding thereto, and mark the ratio between the sum of the number duration Ffe and the number duration Ffe as the calculated value EFf1 corresponding to each type of vehicle in the analysis lane on the target analysis day;

[0074] S04: repeating steps S02-S03, calculating the corresponding values ​​EFfT of each type of vehicle in the analysis lane every day within the preset time T;

[0075] S05: obtaining the average of the calculated values ​​of the same type of vehicles in the analysis lane every day within the preset time T, and using it as the vehicle class value CDf corresponding to the corresponding type of vehicle in the analysis lane;

[0076] S06: Repeat steps S01-S05 to obtain the vehicle type value CDfv corresponding to each type of vehicle in each lane;

[0077] By combining various sensors, monitoring equipment or traffic management systems with vehicle identification and classification technologies, the number of vehicles of various types corresponding to different traffic flow status values ​​in each lane on the bridge can be determined based on vehicle dynamic data. Vehicle types include small cars, trucks, buses, etc. The distribution of different types of vehicles in each lane can be analyzed. The above technologies are existing and mature, so they will not be elaborated here.

[0078] Step 4: Calculate the bearing value of the bridge according to the design parameters of the bridge. The design parameters of the bridge include material deflection parameters, material stress parameters, material strength parameters, bridge design width and bridge design height. The design parameters of the bridge can be obtained through materials such as bridge design documents;

[0079] The material characteristic parameters, material structure parameters, material strength parameters, bridge design width and bridge design length are marked as QA, QB, QC, QD and QE respectively;

[0080] The bearing value QCZ of the bridge is calculated by the formula (QA×β1+QB×β2+QC×β3)×(QD×QE)×β4=QCZ;

[0081] Among them, β1, β2, β3 and β4 are fixed calculation coefficients, and the specific values ​​are set by relevant staff according to actual needs;

[0082] Step 5: According to the design parameters corresponding to each lane of the bridge, analyze and obtain the lane load value corresponding to each lane. The design parameters corresponding to each lane include the lane design width, lane design length and the straight-line distance between the center point of each lane and the center point of the bridge. The specific method is:

[0083] The lane design width and lane design length corresponding to each lane are marked as DAv and DBv respectively, and the straight-line distance between the center point of each lane and the center point of the bridge is marked as DCv;

[0084] The lane load value DZv corresponding to each lane is calculated by the formula [(DAv×DBv)+DCv×β5 / (QD×QE)]×QCZ=DZv, where β5 is a fixed coefficient, and the specific value is set by relevant staff according to actual needs;

[0085] Step 6: Obtain the real-time traffic state value of each lane on the bridge, obtain the real-time traffic state value of each lane on the bridge, and obtain the corresponding holding time of its traffic state value, extract the traffic state value with a holding time greater than 30 seconds as the calculated traffic state value JSv of each lane, obtain the number of vehicles of each type in each lane under the corresponding calculated traffic state value, and mark them as BHfv respectively;

[0086] The calculated traffic flow state value JSv of each lane is analyzed and compared with its corresponding vehicle frequency criterion value BPv, and the lane corresponding to which the calculated traffic flow state value JSv is greater than the corresponding vehicle frequency criterion value BPv is obtained and marked as an overflow lane;

[0087] Obtain the real-time number of each type of vehicle corresponding to each lane of the overflow lane under the corresponding calculated traffic flow state value, and mark them as BHfk respectively, and compare them with the vehicle type value CDfk of each type of vehicle corresponding to each overflow lane, and obtain the total number of each type of vehicle in each overflow lane whose real-time number BHfk is greater than the corresponding vehicle type value CDfk of each type of vehicle, and mark it as M, where k is the corresponding overflow lane number, v≥k≥1;

[0088] When k×β6+M×β7>Y1, the first warning signal is generated, otherwise no processing is performed;

[0089] By obtaining the traffic state value and maintenance time of each lane of the bridge, analyzing the vehicle frequency value, obtaining the vehicle type value of each type of vehicle according to the maintained number of each type of vehicles corresponding to each lane of the bridge under different traffic state values, and calculating the bearing value of the bridge, the lane load value of each lane is calculated according to the design parameters of the bridge, and the calculated traffic state value and the vehicle frequency standard value of each lane are compared. The lanes greater than the vehicle frequency standard value are marked as overflow lanes, and the real-time number of each type of vehicles in the overflow lane is obtained and compared with the vehicle type value to generate a first warning signal. Through the generated first warning signal, the real-time carrying traffic volume on the bridge is detected and warned, indicating that the existing real-time carrying traffic volume on the bridge has an overflow phenomenon, which is easy to affect the safety of the bridge and may cause potential risks to the safety of the bridge, so that relevant personnel can timely detect the bridge and warn the safety status of the bridge, so that relevant personnel can take corresponding measures in time, such as restricting vehicle traffic, strengthening inspections and maintenance, etc., to ensure the safe operation of the bridge.

[0090] Embodiment 2

[0091] As a second embodiment of the present invention, please refer to Figure 2 As shown, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from that of the first embodiment only in that,

[0092] When the first warning signal is generated, the real-time number of each type of vehicle BHfk in each overflow lane is obtained and analyzed to obtain the real-time number LSf corresponding to each type of vehicle on the bridge, and the sum of the products of each type of vehicle and the corresponding preset load coefficient HZf is marked as the real-time bridge load QSZ;

[0093] When the real-time bridge load QSZ is greater than the bridge's bearing value QCZ, an overload signal is generated;

[0094] That is, through the formula Calculate and obtain the real-time bridge load QSZ, where f≥x≥1, HZf is the preset load factor corresponding to each type of vehicle, and the specific value is set by relevant staff according to actual needs;

[0095] When the first warning signal is generated, the real-time number of various vehicles on the bridge is obtained, the real-time load of the bridge is calculated, and compared with the bearing value. If the real-time load is greater than the bearing value, an overload signal is generated. The overload signal is used to remind that the existing real-time load on the bridge is overflowing, so that relevant personnel can take corresponding measures in time to restrict vehicles from entering the bridge to reduce the load and ensure the safety of the bridge.

[0096] Embodiment 3

[0097] As the third embodiment of the present invention, please refer to Figure 3 As shown, when the present application is implemented, compared with the first embodiment and the second embodiment, the difference of the embodiment is that in the present embodiment, when the overload signal is generated, the number of each type of vehicle corresponding to each overflow lane is obtained and marked as MDfk respectively;

[0098] The sum of the products of the number of vehicles of each type MDfk corresponding to each overflow lane and the load coefficient HZf corresponding to each type of vehicle is marked as the lane real-time load value RZk corresponding to each overflow lane;

[0099] Compare and analyze the lane real-time load value RZk corresponding to each overflow lane and its corresponding lane load value DZk, and mark and output the overflow lane where the lane real-time load value RZk is greater than the corresponding lane load value DZk;

[0100] When an overload signal is generated, the number of each type of vehicle in each overflow lane is obtained, the real-time lane load value of each overflow lane is calculated and compared with the lane load value, and the overflow lanes that are greater than the lane load value are marked and output, which is beneficial for relevant personnel to carry out targeted maintenance measures on the corresponding lanes, such as restricting vehicle traffic, strengthening inspections and maintenance, etc., to ensure the safe operation of the bridge.

[0101] Embodiment 4

[0102] As the fourth embodiment of the present invention, when the present application is specifically implemented, compared with the first, second and third embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second, third and fourth embodiments.

[0103] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0104] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A bridge safety detection method based on a cloud platform, characterized in that: The following steps are involved: Step 1: Obtain the traffic state value corresponding to each lane of the bridge and the maintenance time corresponding to each state value; Step 2: Analyze the traffic state values ​​corresponding to each lane of the bridge and the maintenance time corresponding to each state value, and obtain the vehicle frequency value corresponding to each lane according to the analysis results; Step 3: According to the number of vehicles of each type corresponding to each lane of the bridge under different traffic flow state values, the vehicle type value corresponding to each type of vehicle of each lane of the bridge is analyzed and obtained; Step 4: Calculate the bearing value of the bridge according to the material deflection parameters, material stress parameters, material strength parameters, bridge design width and bridge design height of the bridge; Step 5: According to the lane design width and lane design length of each lane of the bridge and the straight-line distance between the center point of each lane and the center point of the bridge, analyze and obtain the lane load value corresponding to each lane; Step six: mark the traffic state value of each lane on the bridge with a holding time greater than 30 seconds as the calculated traffic state value of each lane, compare the calculated traffic state value of each lane with its corresponding vehicle frequency standard value, obtain and mark the lane corresponding to the calculated traffic state value greater than the corresponding vehicle frequency standard value as an overflow lane, obtain the real-time number of each type of vehicles corresponding to each lane of the overflow lane under the corresponding calculated traffic state value, and compare it with the vehicle type value of each type of vehicles corresponding to each overflow lane, obtain the total number of vehicles of each type in each overflow lane whose real-time number is greater than the corresponding vehicle type value, analyze the number, and generate the first warning signal according to the analysis result.

2. The bridge safety detection method based on the cloud platform according to claim 1 is characterized in that: The specific method for obtaining the vehicle frequency standard value corresponding to each lane of the bridge is: S1: Randomly select one lane from all lanes of the bridge as the analysis lane; S2: Obtain the traffic state value corresponding to each lane within the preset time T and the maintenance time corresponding to each state value, and mark them as CTj and WTj respectively; Here, j refers to the number of traffic state values ​​corresponding to the lane being analyzed every day within the preset time T, j ≥ 1, T ≥ 1; S3: By formula Calculate and obtain the traffic heat value CWT corresponding to each day of the analyzed lane within the preset time T, where j≥i≥1; S4: Obtain the total maintenance time corresponding to each day of the analysis lane within the preset time T, and mark the ratio between the traffic flow heat value CWT corresponding to each day of the analysis lane within the preset time T and the total maintenance time as the vehicle frequency daily value PCT; That is, through the formula Calculate and obtain the daily vehicle frequency value PCT corresponding to the analyzed lane within the preset time T, j≥a≥1; S5: obtaining the value PCb that satisfies the preset condition A1 in the vehicle frequency value PCT corresponding to the analysis lane every day within the preset time T, and analyzing it, thereby obtaining the vehicle frequency standard value BP1 corresponding to the analysis lane, wherein b is the corresponding number of values ​​in PCT that satisfy the preset condition A1; Compare and analyze b with the preset value Y1, and then obtain the vehicle frequency standard value BP1 corresponding to the analysis lane; S6: Repeat the above steps S1-S5 to obtain the vehicle frequency standard value BPv corresponding to each lane of the bridge, through the vehicle frequency standard value BPv corresponding to each lane of the bridge, where v refers to the number of lanes on the bridge, v≥1.

3. The bridge safety detection method based on cloud platform according to claim 2 is characterized in that: The specific method of obtaining the vehicle frequency standard value BP1 corresponding to the analysis lane is: When b ≥ Y1, the mean value PCp of PCT is used as the vehicle frequency standard value BP1 corresponding to the analysis lane, where PCp is the mean value of PCT; When b < Y1, PCT is arranged in ascending order, and the median is taken as the vehicle frequency standard value BP1 corresponding to the analysis lane. When T is an odd number, BP1 = PC[(d+1) / 2] is taken as the vehicle frequency standard value BP1 corresponding to the analysis lane. When T is an even number, [PC(d / 2)+PC(d / 2+1)] / 2=BP1 is used as the vehicle frequency standard value BP1 corresponding to the analysis lane, where T≥d≥1, and the preset condition A1 is: |PCT-PCp|×β1>Y2, Y2 is the preset value, β1 is the preset proportional coefficient, where β1=1.

187.

4. The cloud platform-based bridge safety detection method according to claim 3 is characterized in that: The vehicle class values ​​corresponding to each type of vehicle in each lane of the bridge are obtained. The specific analysis method is as follows: S01: Select the same lane as that in step S from all lanes of the bridge as the analysis lane; S02: randomly select one day from the preset time T as the target analysis day; Obtain the number of vehicles of each type maintained in the analysis lane on the target analysis day, and mark them as Efe respectively. At the same time, mark the duration of the number of vehicles of each type maintained in the analysis lane on the target analysis day as Ffe, where e is the number of vehicles of each type maintained in the analysis lane on the target analysis day, f is the number of corresponding vehicles, e≥1, f≥1; S03: Obtain the sum of the products of the maintained number Efe of each type of vehicle and the number duration Ffe corresponding thereto, and mark the ratio between the sum of the number duration Ffe and the number duration Ffe as the calculated value EFf1 corresponding to each type of vehicle in the analysis lane on the target analysis day; S04: repeating steps S02-S03, calculating the corresponding values ​​EFfT of each type of vehicle in the analysis lane every day within the preset time T; S05: obtaining the average of the calculated values ​​of the same type of vehicles in the analysis lane every day within the preset time T, and using it as the vehicle class value CDf corresponding to the corresponding type of vehicle in the analysis lane; S06: Repeat steps S01-S05 to obtain the vehicle type value CDfv corresponding to each type of vehicle in each lane.

5. The cloud platform-based bridge safety detection method according to claim 4 is characterized in that: The specific method to obtain the bearing value of the bridge is: The material characteristic parameters, material structure parameters, material strength parameters, bridge design width and bridge design length are marked as QA, QB, QC, QD and QE respectively, and the bearing value QCZ of the bridge is calculated by the formula (QA×β1+QB×β2+QC×β3)×(QD×QE)×β4=QCZ, where β1, β2, β3 and β4 are fixed calculation coefficients.

6. The bridge safety detection method based on cloud platform according to claim 5 is characterized in that: The specific method for obtaining the lane load value corresponding to each lane is: The lane design width and lane design length corresponding to each lane are marked as DAv and DBv respectively, and the straight-line distance between the center point of each lane and the center point of the bridge is marked as DCv. The lane load value DZv corresponding to each lane is calculated by the formula [(DAv×DBv)+DCv×β5 / (QD×QE)]×QCZ=DZv, where β5 is a fixed coefficient.

7. The cloud platform-based bridge safety detection method according to claim 6 is characterized in that: The specific method of generating the first warning signal is: The real-time traffic state value of each lane on the bridge is obtained, and the corresponding holding time of the traffic state value is obtained. The traffic state value with a holding time greater than 30 seconds is extracted as the calculated traffic state value JSv of each lane, and the number of vehicles of each type in each lane under the corresponding calculated traffic state value is obtained, and they are marked as BHfv respectively. The calculated traffic state value JSv of each lane is analyzed and compared with the corresponding vehicle frequency criterion BPv respectively, and the lane corresponding to the calculated traffic state value JSv greater than the corresponding vehicle frequency criterion BPv is obtained, and it is marked as the overflow lane; Obtain the real-time number of each type of vehicle corresponding to each lane of the overflow lane under the corresponding calculated traffic state value, and mark them as BHfk respectively, and compare them with the vehicle type value CDfk of each type of vehicle corresponding to each overflow lane, and obtain the real-time number of each type of vehicle in each overflow lane BHfk is greater than the total number of the corresponding vehicle type value CDfk of each type of vehicle, and mark it as M, where k is the corresponding overflow lane number, v≥k≥1, when k×β6+M×β7>Y1, the first warning signal is generated, otherwise no processing is performed.

8. The cloud platform-based bridge safety detection method according to claim 7 is characterized in that: When the first warning signal is generated, the real-time number BHfk of each type of vehicle in each overflow lane is obtained and analyzed to obtain the real-time number LSf of each type of vehicle on the bridge. The sum of the products of the real-time number and the corresponding preset load coefficient HZf is marked as the real-time load QSZ of the bridge. When the real-time load QSZ of the bridge is greater than the bearing value QCZ of the bridge, an overload signal is generated, where f≥x≥1, otherwise no processing is performed.

9. The cloud platform-based bridge safety detection method according to claim 8, characterized in that: When an overload signal is generated, the number of vehicles of each type corresponding to each overflow lane is obtained and marked as MDfk respectively; The sum of the products of the number MDfk of each type of vehicle corresponding to each overflow lane and the load coefficient HZf corresponding to each type of vehicle is marked as the real-time lane load value RZk corresponding to each overflow lane. The real-time lane load value RZk corresponding to each overflow lane is compared and analyzed with the corresponding lane load value DZk, and the overflow lane whose real-time lane load value RZk is greater than the corresponding lane load value DZk is marked and output.

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