Vehicle load management method and system for bridge group cascade deduction application

Abstract

The application discloses a kind of vehicle load management and control method and system for bridge group cascade deduction application, method includes: completing bridge section division and arranging data acquisition equipment;The correlation of different bridge sections is calculated, and the bridge sections with correlation exceeding the set threshold are automatically classified into the same linkage section;According to the chain length of vehicle load data traceable, different bridge sections are divided into grades;And based on the correlation of bridge section, load matching is completed for the bridge section without direct load monitoring data by adjacent bridge load migration method;Based on the physical cascade order of bridge section and the correlation of bridge section, short-time traffic volume prediction is completed;Then deduce the load distribution change of downstream bridge;Further complete the normalization of bridge load and extract the worst load distribution in deduction prediction period;Bridge load state grade evaluation is completed and the corresponding management and control scheme is triggered.To provide guarantee for safe and efficient operation of bridge group.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of bridge load control, and more particularly relates to a vehicle load control method and system for bridge group cascade deduction application. BACKGROUND

[0002] In the traffic infrastructure system, bridges as key nodes bear the dynamic effect of vehicle load. With the increase of traffic flow and the complexity of load, the demand for coordinated control of bridge groups is increasingly urgent. Traditional bridge load analysis focuses on a single bridge, and the load data is processed in an isolated mode, ignoring the correlation between bridge sections in the bridge group. Vehicle traffic has fluidity and continuity, and the transfer of vehicle flow and load transmission between different bridge sections is related. Isolated analysis cannot capture the cascade effect of the bridge group, resulting in insufficient coordinated application of load data between sections.

[0003] At the same time, there are limitations in bridge section load monitoring. Due to the difficulty in equipment deployment, cost and other problems, some sections lack direct load monitoring data, making it difficult to completely restore the load distribution. Moreover, the load tracing ability of different sections is different, and the data integrity and continuity differ greatly, further increasing the difficulty of bridge group load analysis. In the operation of the bridge group, vehicle flow change and load distribution deduction are crucial to the safety control of the bridge. The existing method does not fully consider the correlation between sections and the physical cascade order, and the accuracy of vehicle flow prediction and load deduction is limited, which cannot accurately capture the dynamic changes of downstream bridge load.

[0004] In addition, there are various types of bridges, and different bridge types have different sensitivities to load response and distribution. The traditional load processing does not consider the differences in bridge type and lane function, and the load normalization lacks adaptability, making it difficult to extract the worst load distribution for safety evaluation. Therefore, it is urgent to build a vehicle load control method for bridge group cascade deduction, integrate section correlation characteristics, supplement load data, accurately deduce load distribution and adapt to bridge characteristics, and propose vehicle flow grading control strategy combined with the current limit load capacity of the bridge, to ensure the scientificity and effectiveness of bridge group load control, and effectively ensure the safe and efficient operation of the bridge group. SUMMARY

[0005] The present application aims to build a vehicle load control method for bridge group cascade deduction, integrate section correlation characteristics, supplement load data for unmonitored sections, accurately deduce vehicle flow and load distribution, adapt to bridge type characteristics, extract the worst load, and propose a vehicle load control scheme combined with the current limit load capacity of the bridge, to ensure the safe and efficient operation of the bridge group.

[0006] In view of the above defects or improvement needs of the prior art, as a first aspect of the present application, the present application provides a vehicle load control method for bridge group cascade deduction application, comprising:

[0007] S1. Complete the bridge section division and arrange the data collection equipment; calculate the correlation degree of vehicle passing through different bridge sections, and automatically classify the bridge sections with correlation degree exceeding the set threshold into the same linkage section;

[0008] S2. According to the chain length of vehicle load data, divide the different bridge sections into grades; and based on the correlation degree of the bridge section, complete the load matching of the bridge section without direct load monitoring data through the adjacent bridge load migration method;

[0009] S3. Based on the physical cascade order of the bridge section and the correlation degree of the bridge section, the short-term traffic volume of each bridge section is predicted by vehicle type and lane; combined with the predicted traffic volume data and the allocated vehicle load data, the running track of the vehicle is simulated and simulated, and the load distribution change of the downstream bridge is deduced;

[0010] S4. According to the bridge type, the load of different lanes is assigned a specific weight to complete the normalization of the bridge load; and the worst load distribution in the prediction period is extracted;

[0011] S5. Combined with the state of the bridge itself and the surrounding environment, the ultimate bearing capacity of each bridge is calculated, which is compared with the predicted load distribution to judge the load state level of the bridge and take corresponding vehicle load control strategy.

[0012] Further, the bridge section in S1 is a bridge area with similar characteristics divided according to road linearity, similarity of the environment and physical space continuity; at least one data collection equipment configured to extract data including vehicle driving track, traffic volume parameter and license plate information is provided in each direction within the bridge section.

[0013] Further, the ramp entrance and exit of the bridge section is provided with a data collection equipment configured to identify the number of real-time in-flow and out-flow vehicles.

[0014] Further, the calculation method of the correlation degree of vehicle passing through different bridge sections in S1 is:

[0015] Let any two bridge sections be and , the correlation degree focuses on the vehicle passing between them, which is as follows:

[0016] Calculate the historical traffic transfer intensity : the number of vehicles from section to section in the analysis period is , and the total number of vehicles leaving section per unit time is , then:

[0017] ,

[0018] wherein, is the statistical period, is the minimum value, reflects the proportion of vehicles passing from to ;

[0019] Calculate the traffic continuity coefficient : the number of vehicles passing through and then passing through is , the total number of vehicles passing through , is , , then:

[0020] ,

[0021] wherein, embodies the degree of continuity of vehicle passing between two sections;

[0022] and introduce auxiliary parameters and weight coefficients to construct the correlation degree weighted model as follows:

[0023] ,

[0024] wherein, is the spatial passing resistance parameter; is the linear passing fitness; are the weights of the historical traffic transfer intensity, traffic continuity coefficient, spatial passing resistance parameter and linear passing fitness respectively; the weights satisfy and ; .

[0025] Further, the specific calculation method of the auxiliary parameter is:

[0026] Calculate the spatial passing resistance parameter : let the distance between the two sections of the nearest bridge be , then:

[0027] ,

[0028] wherein, is the maximum effective distance, the closer the distance, the easier it is for vehicles to pass continuously;

[0029] Linear passing fitness : section , The linear eigenvectors are ,

[0030] ,

[0031] That is, the cosine similarity of vectors; the closer the value is to 1, the better the linearity fits continuous vehicle passage. .

[0032] Furthermore, the specific process of classifying different bridge sections according to the traceable chain length based on vehicle load data in S2 is as follows:

[0033] Let the bridge section be The total number of key monitoring nodes within the section is The total number of vehicles during the analysis period was The total travel time for each vehicle within the section is as follows: , ;

[0034] chain length To analyze the average number of nodes with traceable vehicle load data within a given time period, ;

[0035] Calculate data coverage ratio :

[0036] ,

[0037] in, , The passage time covered by the chain length of each vehicle;

[0038] when and At that time, the bridge section was classified as a Class A section;

[0039] when and At that time, the bridge section was classified as a Class B section;

[0040] when and At that time, the bridge section was classified as a Class C section.

[0041] Furthermore, the specific process of the adjacent bridge load transfer method in S2 is as follows:

[0042] Let the target section be , For Class C sections with no direct load data; the candidate source section set is: ,gather All elements in the text are Class A sections with complete load trajectories;

[0043] Compute all Associations with Sort in descending order; filter out high correlation sections according to the set filter threshold, denoted as , as the main source section; the rest are backups;

[0044] Set the load distribution of the main source section to ; calculate the weighted coefficient of :

[0045] ,

[0046] where is the correlation between the source section and the target section ; is the correlation between the source section and the target section ; is the total number of high correlation source sections filtered out; satisfies The higher the correlation, the greater the weight, and the higher the contribution of the load distribution;

[0047] Further, the target section load distribution is obtained by calculation:

[0048] ,

[0049] where is the target section load distribution to be restored.

[0050] Further, the specific process of normalizing the bridge load in S4 is as follows:

[0051] Let the bridge type be , containing a set of lanes , and the normalization process is as follows:

[0052] Let the measured load of lane be ; the lane function coefficient be ; the bridge type sensitivity coefficient be ; and the maximum lane load be ;

[0053] The comprehensive weight of the bridge type and lane function is calculated as :

[0054] , ​

[0055] wherein, is the functional coefficient of the th lane; is the total number of lanes contained by the bridge; satisfies ;

[0056] the normalized load of the th lane :

[0057] ,

[0058] wherein, is the minimum value for preventing the denominator from being 0;

[0059] the normalized load of the bridge as a whole:

[0060] ,

[0061] in the formula, is the normalized load of the bridge as a whole.

[0062] As a second aspect of the present application, the present application provides a vehicle load control system for bridge group cascading deduction application, comprising:

[0063] a bridge section division unit, used for completing bridge section division and arranging data acquisition equipment; calculating the correlation degree of vehicle passing of different bridge sections, and automatically classifying the bridge sections with correlation degree exceeding a set threshold into the same linkage section;

[0064] a section grade division and load matching unit, used for dividing the grades of different bridge sections according to the chain length of vehicle load data traceability; and based on the correlation degree of the bridge sections, completing load matching of the bridge sections without direct load monitoring data through the adjacent bridge load migration method;

[0065] a downstream load deduction unit, used for performing short-time vehicle flow prediction of each bridge section by vehicle type and lane based on the physical cascading sequence of the bridge sections and the correlation degree of the bridge sections; combining the predicted vehicle flow data and the allocated vehicle load data, simulating the running track of the vehicle, and deducing the change of the load distribution of the downstream bridge;

[0066] a load distribution extraction unit, used for completing normalization of the bridge load by assigning specific weights to the loads of different lanes according to the bridge type; and extracting the worst load distribution in the deduction prediction period;

[0067] a load control unit, used for calculating the ultimate bearing capacity of each bridge in combination with the state of the bridge itself and the surrounding environment, comparing it with the predicted load distribution, judging the load state grade of the bridge, and taking corresponding vehicle load control strategies.

[0068] As a third aspect of the present application, a computer readable storage medium is also provided, which stores a computer program for executing any step of the vehicle load control method for bridge group cascade deduction application by a processor.

[0069] Overall, compared with the prior art, the above technical solutions conceived by the present application can achieve the following beneficial effects:

[0070] 1. The vehicle load control method for bridge group cascade deduction application of the present application divides the bridge section and arranges the data acquisition equipment, calculates the correlation degree of vehicle passing through different bridge sections, and classifies the bridge sections with correlation degree exceeding the threshold value into the same linkage section. The data acquisition equipment accurately captures the vehicle passing information, providing basic data support for correlation degree calculation, focusing on the transfer strength and continuity of vehicles between sections, and accurately quantifying the correlation between sections. The division of the same linkage section breaks the limitation of independent analysis of a single bridge, provides a basis for collaborative analysis of the bridge group, enables subsequent load control to be based on the linkage characteristics of the bridge group, provides a prerequisite for cross-section load transfer and collaborative control, and realizes the preliminary integration of load analysis at the bridge group level.

[0071] 2. The vehicle load control method for bridge group cascade deduction application of the present application divides the bridge section level according to the traceable chain length of vehicle load data, and based on the section correlation degree, completes load matching for sections without direct load monitoring data by means of adjacent bridge load migration method. The chain length classification clearly defines the section load traceability, and the complete trajectory of A-level section and the data of B-level and C-level sections form a hierarchical complement. Adjacent bridge load migration selects high adaptation source sections by correlation degree, uses source section load data, combines structure correction and precision verification, and reasonably migrates the load to the target section. This process effectively supplements the load data of sections without monitoring, makes the load information of each section in the bridge group complete, provides comprehensive and accurate load input for bridge group cascade deduction, solves the problem of insufficient deduction accuracy caused by data loss, and improves the coverage and availability of load data in bridge group analysis.

[0072] 3.The vehicle load control method for bridge group cascade deduction application of the present application, through the short-time traffic flow prediction based on the physical cascade order and correlation degree of the bridge section, the vehicle trajectory is simulated by combining the predicted traffic flow and the allocated load data, and the load distribution change of the downstream bridge is deduced. The cascade order and correlation degree make the traffic flow prediction fit the actual traffic logic of the bridge group, and the traffic composition is refined by predicting by vehicle type and lane. The trajectory simulation converts the traffic flow into dynamic load distribution, accurately deduces the load change of the downstream, clearly presents the load transfer process in the bridge group. Then, according to the bridge type, specific weights are assigned to the load of different lanes to complete normalization, and the worst load distribution is extracted. Normalization makes the load data adapt to the structural characteristics of the bridge, and the worst distribution provides a key reference for bridge safety control, realizes the closed loop from load prediction, deduction to accurate control, and provides data and strategy support for the safe operation of the bridge group.

[0073] 4.The vehicle load control method for bridge group cascade deduction application of the present application, by comparing the normalized load prediction data with the self-limiting bearing capacity of the bridge, the bridge load state grade is evaluated, and the corresponding bridge vehicle load control strategy is proposed, which can reduce the influence of vehicle load on the bridge, ensure the stable and safe operation of the vehicle on the bridge, and also expand the emergency response application in specific scenarios, and reduce the bridge maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0074] Figure 1 The flowchart of the vehicle load control method for bridge group cascade deduction application of the embodiment of the present application;

[0075] Figure 2 The flowchart of the bridge section division and load matching method of the embodiment of the present application;

[0076] Figure 3 The flowchart of the bridge load state grade evaluation of the embodiment of the present application;

[0077] Figure 4 The schematic diagram of the bridge vehicle load grading control method of the embodiment of the present application;

[0078] Figure 5 The composition diagram of the vehicle load control system of the embodiment of the present application. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0080] Example 1

[0081] Reference is made to Figure 1 The embodiment 1 provides a vehicle load control method for bridge group cascade deduction application, comprising the following steps:

[0082] S1. Complete bridge section division and arrange data collection equipment; calculate the correlation degree of vehicle passing of different bridge sections, and automatically classify the bridge sections with correlation degree exceeding the set threshold into the same linkage section;

[0083] S2. Divide the different bridge sections into grades according to the traceable chain length of vehicle load data; and complete load matching of the bridge sections without direct load monitoring data through the adjacent bridge load migration method based on the correlation degree of the bridge sections;

[0084] S3. Perform short-time vehicle flow prediction of each bridge section based on the physical cascade sequence of the bridge section and the correlation degree of the bridge section; and simulate the running track of the vehicle in combination with the predicted vehicle flow data and the allocated vehicle load data, to deduce the load distribution change of the downstream bridge;

[0085] S4. Normalize the bridge load by assigning specific weights to the loads of different lanes according to the bridge type; and extract the worst load distribution in the prediction and deduction period;

[0086] S5. Calculate the ultimate bearing capacity of each bridge in combination with the state of the bridge itself and the surrounding environment, compare it with the predicted load distribution, judge the load state grade of the bridge, and take corresponding vehicle load control strategy.

[0087] The embodiment 1 further expands the above steps.

[0088] (1) Bridge section division

[0089] The division of bridge sections and the construction of linkage sections are the basic links of bridge group load control, and the core lies in providing a logical framework for subsequent load deduction through systematic data collection and correlation analysis.

[0090] In a preferred embodiment, the bridge section is a bridge area with similar characteristics divided according to road linearity, similarity of the environment and physical space continuity; at least one data collection equipment configured to extract data including vehicle driving track, traffic volume parameter and license plate information is arranged in each direction in the bridge section.

[0091] In a preferred embodiment, the ramp entrance and exit of the bridge section is provided with a data collection equipment configured to identify the number of real-time in-flow and out-flow vehicles.

[0092] The significance of this is that the similar feature section can ensure the consistency of the vehicle driving rules, providing a homogeneous data basis for the extraction of subsequent load distribution rules; comprehensive equipment arrangement can fully capture vehicle trajectory, flow, identity and other information, avoiding load analysis deviation caused by data missing, while ramp data can accurately reflect the dynamic changes of traffic flow, ensuring the authenticity of traffic flow prediction.

[0093] Secondly, the correlation degree of vehicle passing in different sections is calculated and the linkage section is divided. By analyzing the traffic transfer intensity and continuity, combining spatial distance and linear adaptability, the correlation degree of the section is quantified, and the high correlation degree section is classified as the linkage section. This process is to break the limitations of single bridge independent analysis, identify the section cluster of mutual influence of traffic flow in the bridge group through correlation degree, and upgrade the load analysis from isolated individuals to correlated whole; the construction of linkage section makes the load transfer across sections, traffic flow coordination and other characteristics visible, providing reasonable spatial and logical boundaries for subsequent adjacent bridge load migration and cascade deduction, ensuring that the load control can fit the actual linkage relationship of the bridge group.

[0094] In a preferred embodiment, the extraction basis of historical reference data is: considering that the vehicle flow characteristics are different in different time periods of weekdays and holidays, first, the vehicle flow information of each feature day should be divided into several groups of vehicle flow data of analysis period according to the specific analysis interval, and then the historical vehicle flow characteristics of each period of feature day are divided into different bridge sections.

[0095] In a preferred embodiment, the calculation method of the correlation degree of vehicle passing in different bridge sections is:

[0096] Let any two bridge sections be and , and the correlation degree focuses on the vehicle passing between them, which is as follows:

[0097] Calculate the historical traffic transfer intensity : the number of vehicles from section to section in the analysis period is , and the total number of vehicles leaving section per unit time is , then:

[0098] ,

[0099] Among them, is the statistical period, is the minimum value, , reflecting the proportion of vehicle passing from to ;

[0100] Calculate the traffic continuity coefficient : the number of vehicles passing through after is , the total number of vehicles passing through , is , , then:

[0101] ,

[0102] where, , the degree of continuity of vehicle passing between two sections;

[0103] and introduce auxiliary parameters and weight coefficients to construct the correlation degree weighted model as follows:

[0104] ,

[0105] where, is the spatial passing resistance parameter; is the linear passing fitness; are the weights of historical traffic transfer intensity, traffic continuity coefficient, spatial passing resistance parameter and linear passing fitness respectively; the weights satisfy and ; .

[0106] In a preferred embodiment, the specific calculation method of the auxiliary parameter is:

[0107] Calculate the spatial passing resistance parameter : let the distance between the two sections of the nearest bridge be , then:

[0108] ,

[0109] where, is the maximum effective distance, , the closer the distance, the easier the vehicle to pass continuously;

[0110] Linear passing fitness : the linear eigenvector of section , is ,

[0111] ,

[0112] , that is, the vector cosine similarity, the closer the value to 1, the more linearly adapted to vehicle continuous passing, .

[0113] In addition, in specific embodiments, the strategy of different load monitoring for different segment configurations is matched. The segment with perfect equipment is matched by license plate associated trajectory and load, and the segment with only limited equipment is matched by statistical law. The significance lies in that no matter the equipment condition, the correspondence of load data and vehicle passing information can be ensured, the data gap is filled, the complete input for load analysis of the whole bridge group is provided, and the subsequent deduction and control based on comprehensive and reliable load information is ensured.

[0114] (2) Segment level division and load matching

[0115] The division of segment level by the length of chain traceable by vehicle load data and the ratio of data coverage is essentially to quantify and calibrate the integrity and reliability of load data of each segment. The A-level segment has high chain length and data coverage ratio, representing strong traceability and good continuity of load data, which can be used as a reliable source of load migration; the B-level segment has medium data integrity, which can be used as an auxiliary reference; the C-level segment needs to rely on external supplement due to data loss.

[0116] Please refer to Figure 2 In a preferred embodiment, the specific process of dividing the levels of different bridge segments according to the length of the chain traceable by vehicle load data is as follows:

[0117] Let the bridge segment be , the total number of key monitoring nodes in the segment be , the total number of vehicles in the analysis period be , and the total duration of each vehicle passing through the segment in the analysis period be , ;

[0118] The length of the chain is the average number of nodes that can trace the load data of each vehicle in the analysis period, ;

[0119] The data coverage ratio is calculated as follows:

[0120] ,

[0121] wherein , is the passing time covered by the chain length of each vehicle;

[0122] When and , the bridge segment is divided into an A-level segment;

[0123] When and , the bridge segment is divided into a B-level segment;

[0124] When and , the bridge section is divided into a C-level section.

[0125] Based on the correlation degree of the section, the load migration of the adjacent bridge is carried out. The core is to use the correlation of the traffic characteristics in the bridge group to make up for the data missing. High correlation degree means that the vehicle traffic law is similar between sections, and the load distribution characteristics have commonality. Based on this, the main source section is selected and weighted fusion is carried out, so that the C-level section without direct monitoring data can obtain the load distribution that fits its actual situation. The significance of this approach is to break through the data limitations of a single section, and to realize the cross-section supplement of load data through the inherent traffic correlation characteristics of the bridge group, which not only ensures the integrity of the load data, but also makes the supplemented data consistent with the actual load characteristics of the target section due to the quantitative constraint of the correlation degree, providing continuous and reliable load input for the cascade deduction of the bridge group, and avoiding the failure of the overall analysis due to data faults.

[0126] Please refer to Figure 2 , in the preferred embodiment, the specific process of the adjacent bridge load migration method is:

[0127] Let the target section be , a C-level section without direct load data; the candidate source section set is , the elements in the set are all A-level sections with complete load trajectories;

[0128] Calculate the correlation between all and , and sort them in descending order; according to the set screening threshold, select high correlation degree sections, denoted as , as the main source section; the rest are backups;

[0129] Let the load distribution of the main source section be ; calculate the weighted coefficient of :

[0130] ,

[0131] Among them, is the correlation degree of the source section and the target section ; is the correlation degree of the source section and the target section ; is the total number of high correlation degree source sections selected; satisfies The higher the correlation degree, the greater the weight, and the higher the contribution of the load distribution;

[0132] Further, the target section load distribution is obtained by calculation:

[0133] ,

[0134] wherein, is the target section The load distribution needs to be restored.

[0135] (3) Downstream load deduction

[0136] The physical cascade order and correlation degree of the bridge section are the core support for traffic flow prediction and load deduction. The physical cascade order clearly defines the upstream and downstream path logic of vehicle traffic, allowing vehicle flow to pass between sections with a traceable spatial context. Based on this, the topological relationship of vehicle flow transmission is constructed to ensure that the prediction model can follow the actual traffic order and capture the impact of upstream sections on downstream sections. The correlation degree quantifies the interaction strength between sections, which is converted into a spatial weight in the model, allowing the vehicle flow fluctuations in highly correlated sections to be accurately included in the prediction, avoiding the neglect of implicit cross-section effects.

[0137] Short-term traffic flow prediction by vehicle type and lane strictly focuses on the lane function and vehicle type traffic rules within the section. Because the lane function within the section is stable, the vehicle type traffic characteristics of the same lane in different periods are continuous. Therefore, based on historical traffic flow data collected by monitoring videos and other facilities, a lane-specific and vehicle type-specific traffic flow prediction sub-model can be trained to make the prediction results fit the traffic characteristics within the section. At the same time, considering the physical connection characteristics of the section, the prediction range is extended from a single section to multiple cascaded sections, forming a continuous vehicle flow prediction chain.

[0138] Vehicle trajectory simulation and downstream load deduction also proceed in sections. The simulation boundary is set according to the physical parameters of the section (such as length and slope), and the trajectory connection of vehicles entering one section from another completely follows the spatial connection relationship of the section, ensuring that the trajectory simulation conforms to the actual traffic path. The trajectory of the vehicle can be obtained from the vehicle trajectory information collected by the camera equipment installed on the bridge, and the parameters of the car-following and lane-changing model can be adjusted to make the simulation of vehicle trajectory more accurate. During load deduction, the vehicle load is applied to the corresponding section according to the load distribution law of each vehicle type, and the load transfer and distribution in the downstream section are calculated through the structural connection relationship between sections, so that the deduction results can reflect the load accumulation effect caused by the cascade of sections, and the load change of the downstream bridge is associated with the vehicle flow dynamics of the upstream section.

[0139] (4) Load distribution extraction

[0140] After the load distribution of the downstream bridge is deduced, specific weights are given to the loads of different lanes in combination with the bridge type to realize load normalization, and the worst load distribution in the prediction period is extracted. In this process, the bridge type determines the influence weight of the load of different lanes, and the difference in lane function affects the specific weight allocation. In this way, the normalized load data can not only reflect the sensitivity of the bridge structure to the load, but also reflect the actual role of different lanes in load transfer, so that the load data has cross-bridge type comparability and provides a unified standard for subsequent bearing capacity assessment.

[0141] In a preferred embodiment, the specific process of normalizing the bridge load is as follows:

[0142] Let the bridge type be , containing a lane set , and the normalization process is as follows:

[0143] Let the lane be , the measured load be , the lane function coefficient be , the bridge type sensitivity coefficient be , and the maximum lane load be .

[0144] The comprehensive weight integrating the bridge type and the lane function is calculated as :

[0145] ,

[0146] wherein is the function coefficient of the th lane; is the total number of lanes included in the bridge; and .

[0147] The normalized load of the lane is :

[0148] ,

[0149] wherein is a small value to prevent the denominator from being 0;

[0150] The overall normalized load of the bridge is :

[0151] ,

[0152] wherein is the overall normalized load of the bridge.

[0153] After the load normalization operation is completed, the worst load distribution in the predicted period of time is extracted from the derived prediction, which can intuitively present the maximum load challenge that the bridge may face in the prediction period, and provide key basis for subsequent comparison with the ultimate bearing capacity of the bridge.

[0154] (5) Load control

[0155] Please refer to Figure 3 In the preferred embodiment, a bridge ultimate bearing capacity evaluation method and a bridge vehicle load control method are also introduced; the former method mainly involves two aspects, namely: current bridge state identification and bridge bearing capacity judgment. The bridge state identification method uses the structural state perceived by the bridge health monitoring sensor, the bridge inspection report, environmental monitoring (prediction) and other data to determine the current state of the bridge itself and the environment; while the bridge bearing capacity evaluation method is to periodically modify the finite element model parameters of the bridge based on the bridge state and environmental state data, and then obtain the current ultimate bearing capacity of the bridge through finite element analysis model, the specific value is .

[0156] When the ultimate bearing capacity of the bridge is determined, it can provide data support for the latter method.

[0157] As for the latter method, please refer to Figure 4 By comparing the current bridge load limit capacity with the worst bridge load situation derived, the current bridge load state level is divided into four levels. The first level of bridge load state determination condition is: And the absolute value of the difference between the two should be greater than or equal to a certain threshold The threshold can be determined based on long-term monitoring results of bridge traffic volume and vehicle load, as well as the opinions of bridge maintenance experts; the second level of bridge load state determination condition is: And | The third level of bridge load state determination condition is: And | The fourth level of bridge load state determination condition is: And | .

[0158] After the bridge load state level in the future period of time is determined, the bridge load state level can be marked with red, orange, yellow and blue colors in the GIS display interface of the vehicle load control system according to the level of state.

[0159] Please refer to Figure 4 Combined with the actual traffic operation, appropriate vehicle load control measures are taken, and the specific scheme is as follows:

[0160] Primary bridge load state: the bridge can bear the subsequent traffic load and has a large margin, without the need for traffic control.

[0161] Secondary bridge load state: the subsequent traffic load is about to reach the bridge load limit value, so real-time monitoring of road traffic flow is needed, through various information release channels, using methods such as controlling the speed, distance, and number of vehicles in a formation of heavy vehicles to reduce their dynamic superposition effect on the bridge.

[0162] Tertiary bridge load state: the subsequent traffic load slightly exceeds the bridge load limit value, so traffic control of heavy vehicles upstream of the bridge area is needed through methods such as variable information board display, on-site police inspection, ETC voice warning, navigation software prompt, and electric barrier blocking, to timely guide vehicles approaching the bridge to leave the front bridge section, and if necessary, to timely notify the bridge management and maintenance department to carry out bridge maintenance work.

[0163] Quaternary bridge load state: the subsequent traffic load exceeds the bridge load limit value significantly, so the upstream entrance of the bridge needs to be completely closed, and methods such as setting up reflective cone cylinders and variable information board prompts are used to guide vehicles approaching the bridge to leave at the nearest exit. At the same time, the bridge management and maintenance department is notified to carry out bridge maintenance work.

[0164] Example 2

[0165] Please refer to Figure 5 The present embodiment 2 provides a vehicle load control system for bridge group cascading deduction application, comprising:

[0166] A bridge section division unit is used to complete bridge section division and arrange data collection equipment; the correlation degree of vehicle passing through different bridge sections is calculated, and bridge sections with a correlation degree exceeding a set threshold are automatically classified into the same linkage section;

[0167] A section level division and load matching unit is used to divide the levels of different bridge sections according to the chain length of vehicle load data traceability; and based on the correlation degree of the bridge section, the load of the bridge section without direct load monitoring data is matched through the adjacent bridge load migration method;

[0168] A downstream load deduction unit is used to perform short-term traffic flow prediction of each bridge section by vehicle type and lane based on the physical cascading sequence of the bridge section and the correlation degree of the bridge section; the running track of the vehicle is simulated combined with the predicted traffic flow data and the allocated vehicle load data, and the downstream bridge load distribution change is deduced;

[0169] The load distribution extraction unit is configured to normalize the bridge load by assigning specific weights to the loads of different lanes according to the type of the bridge, and extract the worst load distribution in the predicted time period;

[0170] The load control unit is configured to calculate the limit bearing capacity of each bridge in combination with the state of the bridge itself and the surrounding environment, compare the limit bearing capacity with the predicted load distribution, judge the load state grade of the bridge, and take a corresponding vehicle load control strategy.

[0171] Embodiment 3

[0172] The embodiment 3 also provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement any step of a vehicle load control method for bridge group cascading deduction application.

[0173] The computer readable storage medium can include a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0174] For the computer readable storage medium provided in the present application, refer to the above method embodiments, and the present application will not be repeated here.

[0175] Those skilled in the art can easily understand that the above description is only a preferred embodiment of the present application, and is not used to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A vehicle load management method for bridge group cascade deduction application, characterized in that, The application relates to a vehicle load control method for a bridge, which comprises the following steps: S1. completing bridge section division and arranging data collection equipment; calculating the correlation degree of vehicle passing of different bridge sections, and automatically classifying bridge sections with a correlation degree exceeding a set threshold into the same linkage section; S2. classifying different bridge sections according to the chain length of traceable vehicle load data; and based on the correlation degree of the bridge sections, completing load matching of bridge sections without direct load monitoring data through an adjacent bridge load migration method; S3. performing short-time vehicle flow prediction of each bridge section based on the physical cascade sequence of the bridge sections and the correlation degree of the bridge sections; combining the predicted vehicle flow data and the allocated vehicle load data, simulating the running track of the vehicle, and deducing the load distribution change of the downstream bridge; S4. normalizing the bridge load according to the specific weight of the load of different lanes of the bridge type; and extracting the worst load distribution in the deduced prediction period; S5. calculating the ultimate bearing capacity of each bridge according to the state of the bridge itself and the surrounding environment, comparing the ultimate bearing capacity with the predicted load distribution, judging the load state grade of the bridge, and taking corresponding vehicle load control strategies.

2. The vehicle load control method for bridge group level cascade deduction application according to claim 1, characterized in that, The bridge section in S1 is a bridge area with similar characteristics divided according to road linearity, similarity of the environment and physical space continuity; at least one data collection equipment configured to extract data including vehicle driving track, traffic volume parameter and license plate information is arranged in each direction in the bridge section.

3. The vehicle load control method for bridge group level cascade deduction application according to claim 2, characterized in that, The ramp entrance and exit of the bridge section are provided with data collection equipment configured to identify the number of real-time in-and-out vehicles.

4. The vehicle load control method for bridge group level cascade deduction application according to claim 1, characterized in that, The calculation method of the correlation degree of vehicle passing of different bridge sections in S1 is as follows: Let any two bridge sections be and , the degree of association focuses on the vehicle traffic between them, specifically as follows: Computing historical traffic shift intensity : the number of vehicles that travel from the section to the section in the analysis period is , and the total number of vehicles that travel out of the section per unit time is , then: ， wherein, is a statistical period, is a minimum value, reflects the proportion of vehicles passing from to ; Calculate the traffic continuity coefficient : by after passing the number of vehicles is , by , the total number of vehicles is , then: ， wherein , embodying the degree of continuity of the vehicle's passage between the two sections; And the introduction of auxiliary parameters and weight coefficient to construct the correlation degree The weighted model is as follows: ， wherein, is a spatial traffic resistance parameter; is a linear traffic fitness; are respectively weights of the historical traffic shift intensity, the traffic continuity coefficient, the spatial traffic resistance parameter and the linear traffic fitness; the weights satisfy and ; .

5. The vehicle load control method for bridge group level cascade deduction application according to claim 4, characterized in that, The specific calculation method of the auxiliary parameter is as follows: Computing spatial access resistance parameters : Let the two-zone nearest bridge spacing be : Then: ， wherein, is the maximum effective distance, The closer the distance, the more continuous the vehicle passes. Linear passage adaptation : segment , The linear feature vector for , ， i.e. vector cosine similarity, the closer the value to 1, the more linear the vehicle's continuous passage, .

6. The vehicle load control method for bridge group level cascade deduction application according to claim 1, characterized in that, The specific process of classifying different bridge sections according to the chain length of traceable vehicle load data in S2 is as follows: Let the bridge section be , the total number of key monitoring nodes in the section be , the total number of vehicles in the analysis period be , and the total duration of each vehicle passing through the section be , ; Chain length To analyze the average number of nodes that can trace each vehicle load data within the period, ; Computing data coverage ratio : ， wherein, , is the duration of the passage covered by the length of the chain of vehicles; When and the bridge section is divided into a class A section; When and the bridge section is divided into a B-class section; When and the bridge section is classified as a C-section.

7. The vehicle load control method for bridge group level cascade deduction application according to claim 6, characterized in that, The specific process of the adjacent bridge load migration method in S2 is as follows: Target section is , Class C section with no direct load data; The candidate source segment set is , the elements in the set are all A-level segments with complete payload traces; Calculate all and The connection Sort in descending order; filter according to the set filtering threshold. A highly correlated segment is denoted as... One section serves as the primary source section; the rest are reserved. Let the main source section The load distribution is ; Calculate the weighted coefficient of : ， wherein, is the source section is the association degree of the source section with the target section; is the source section is the association degree of the source section with the target section; is the total number of the screened high-association-degree source sections; satisfies The higher the association degree is, the greater the weight is, and the higher the contribution degree of the load distribution is. Then, the target section load distribution is obtained through calculation: ， wherein target section The load distribution needs to be restored.

8. The vehicle load control method for bridge group level cascade deduction application according to claim 1, characterized in that, The specific process of the normalization of the bridge load in S4 is as follows: Let the bridge type be , containing a set of lanes , the normalization procedure is as follows: Set lane Actual load is ; The lane function coefficient is ; the bridge type sensitive coefficient is ; the lane load maximum value ; The comprehensive weight of the fusion bridge type and the lane function is calculated : ， wherein, is the functional coefficient of the i-th lane; is the functional coefficient of the i-th lane; is the total number of lanes included in the bridge; satisfies ; Lane of the normalized load : ， wherein to prevent a minimum value of the denominator being zero; Bridge overall normalized load: ， In the formula, is the overall normalization load for the bridge.

9. A vehicle load management system for bridge cluster level cascading inference application, characterized in that, The application relates to a vehicle load control method for a bridge, which comprises the following steps: a bridge section division unit for completing bridge section division and arranging data collection equipment; calculating the correlation degree of vehicle passing of different bridge sections, and automatically classifying bridge sections with a correlation degree exceeding a set threshold into the same linkage section; a section grade division and load matching unit for classifying different bridge sections according to the chain length of traceable vehicle load data; and based on the correlation degree of the bridge sections, completing load matching of bridge sections without direct load monitoring data through an adjacent bridge load migration method; a downstream load deduction unit for performing short-time vehicle flow prediction of each bridge section based on the physical cascade sequence of the bridge sections and the correlation degree of the bridge sections; combining the predicted vehicle flow data and the allocated vehicle load data, simulating the running track of the vehicle, and deducing the load distribution change of the downstream bridge; a load distribution extraction unit for normalizing the bridge load according to the specific weight of the load of different lanes of the bridge type; and extracting the worst load distribution in the deduced prediction period; The load control unit is used for calculating the limit bearing capacity of each bridge in combination with the state of the bridge itself and the surrounding environment, comparing the limit bearing capacity with the predicted load distribution, judging the load state grade of the bridge, and taking corresponding vehicle load control strategies.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by a processor to implement the vehicle load control method for bridge group cascade deduction application according to any one of claims 1-8.

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