Highway pavement settlement on-line monitoring method and system

Abstract

The invention relates to the technical field of highway settlement monitoring, in particular to a highway pavement settlement online monitoring method and system. Comprising the steps of obtaining highway pavement parameters according to a preset feedback time node, and generating a pavement monitoring model; establishing a plurality of monitoring sub-areas according to the road surface monitoring model, and generating settlement of each monitoring sub-area according to a preset static load model and a dynamic load model; and generating a settlement evaluation value of each monitoring sub-region according to a preset correlation model, and judging whether an early warning instruction is generated or not. The settlement observation weight is calculated after the pavement settlement chromatogram under the dual action of static and dynamic loads is analyzed, so that the monitoring precision of the settlement amount of the highway pavement is improved, the settlement state of each monitoring sub-region is compared and analyzed by constructing a correlation model, the settlement amount is dynamically corrected in combination with historical parameters, and the monitoring precision of the settlement amount of the highway pavement is improved. Therefore, the prediction precision of the settlement risk of each monitored sub-region is improved, and the safety of the road surface is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of highway settlement monitoring, and in particular to an online monitoring method and system for highway pavement settlement. Background Art

[0002] In recent years, with the rapid development of urban construction, the over-exploitation of the surface soil, the drastic changes in the static load of reinforced concrete in buildings and the dynamic load formed by the transportation network have affected local ground subsidence to a certain extent. The ground subsidence phenomenon has a lasting and far-reaching impact on the ground. The causes of ground subsidence are relatively complex and difficult to prevent and control, which seriously affects road construction and traffic safety.

[0003] The road surface settlement is caused, which leads to vehicle jumping at the bridge head, road damage and other situations, causing heavy losses to people’s property. Therefore, a method is needed to detect and warn of road surface settlement in a timely manner. Summary of the invention

[0004] The purpose of this application is: to solve the above-mentioned technical problems, this application provides an online monitoring method and system for highway pavement settlement, aiming to improve the monitoring and early warning efficiency of highway pavement settlement and ensure the safety of the road surface.

[0005] In some embodiments of the present application, a layered monitoring and analysis method for pavement settlement observation is adopted, and the settlement observation weight is calculated after effectively analyzing the pavement settlement spectrum under the dual effects of static and dynamic loads, thereby improving the monitoring accuracy of highway pavement settlement, ensuring the early warning efficiency of pavement settlement risks, and improving the safety of monitoring.

[0006] In some embodiments of the present application, by establishing multiple monitoring sub-areas, the settlement state of the highway pavement is comprehensively monitored, and by constructing a correlation model, the settlement state of each monitoring sub-area is compared and analyzed, and the settlement amount is dynamically corrected in combination with historical parameters, thereby improving the prediction accuracy of the settlement risk of each monitoring sub-area and ensuring the safety of the road surface.

[0007] In some embodiments of the present application, a method for online monitoring of road pavement settlement is provided, comprising:

[0008] Obtain road pavement parameters according to preset feedback time nodes and generate a road pavement monitoring model;

[0009] Establish multiple monitoring sub-areas according to the pavement monitoring model, and generate the settlement of each monitoring sub-area according to the preset static load model and dynamic load model;

[0010] Generate the settlement evaluation value of each monitoring sub-area according to the preset correlation model, and determine whether to generate an early warning instruction.

[0011] In some embodiments of the present application, the road surface parameters include:

[0012] Pavement shape, pavement texture and pavement color.

[0013] In some embodiments of the present application, when constructing a static load model and a dynamic load model to generate the settlement of each monitoring sub-area, it includes:

[0014] Establish a monitoring sub-area sequence A, A = (a1, a2...an), where ai is the i-th monitoring sub-area; n is the number of monitoring sub-areas;

[0015] Set multiple static monitoring areas according to the static load model;

[0016]

[0017] f1 = α / β; f2 = α / θ;

[0018] Among them, K is the static monitoring area division parameter; R is the auxiliary data; cos f1 and sin f2 are the road slope values; n is the number of monitoring sub-areas;

[0019] Generate static settlement parameters for each static monitoring area;

[0020] Set up multiple dynamic monitoring areas according to the dynamic load model;

[0021]

[0022] f1=α / β;f2=α / θ;

[0023] Generate dynamic settlement parameters for each dynamic monitoring area;

[0024] Obtain all static settlement parameters and all dynamic settlement parameters, perform weighted processing, and generate settlement amounts for each monitoring sub-area based on the processing results;

[0025] A sedimentation amount series B of the current feedback time node is established, B=(b1, b2...bn), where bi is the sedimentation amount of the i-th monitoring sub-area at the current feedback time node.

[0026] In some embodiments of the present application, when generating the settlement evaluation value of each monitoring sub-area according to the preset correlation model, it includes:

[0027] According to the monitoring sub-area sequence A, set ai as the target monitoring sub-area in sequence;

[0028] Generate a correlation evaluation value between the target monitoring sub-area and each monitoring sub-area according to a preset correlation model;

[0029] Establish a sequence D of correlation evaluation values, D = (d1, d2…dn), where di is the correlation evaluation value between the i-th monitoring sub-region and the target monitoring sub-region;

[0030] Preset a first correlation evaluation value threshold D1;

[0031] If di < D1, set the i-th monitoring sub-region as the first-level associated sub-region of the target monitoring sub-region;

[0032] Obtain the settlement amounts of all the first-level associated sub-regions;

[0033] Generate a settlement evaluation value h of the target monitoring sub-region at the current feedback time node;

[0034] Generate the settlement evaluation values of each monitoring sub-region at the current feedback time node in sequence.

[0035] In some embodiments of the present application, when generating the settlement evaluation value h, it includes:

[0036] h = G * [e1 * Q1 * b + e2 * Q2 * (v - Δv) + e2 * Q2 * (b - Δb)];

[0037] Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; Q3 is a preset third fixed coefficient; b is the settlement amount of the target monitoring sub-region at the current feedback time node; v is the settlement rate of the target monitoring sub-region at the current feedback time node; Δb is the average settlement amount of all the first-level associated sub-regions of the target monitoring sub-region; Δv is the average settlement rate of all the first-level associated sub-regions of the target monitoring sub-region; G is a compensation coefficient set based on historical settlement parameters.

[0038] In some embodiments of the present application, when determining whether to generate a warning instruction, it includes:

[0039] Establish a sequence H of settlement evaluation values at the current feedback time node, H = (h1, h2…hn), where hi is the settlement evaluation value of the i-th monitoring sub-region at the current feedback time node;

[0040] Preset a settlement evaluation value threshold H1;

[0041] If hi > H1, generate a warning instruction for the i-th monitoring sub-region at the current feedback time node;

[0042] If hi < H1, no warning instruction is generated for the i-th monitoring sub-region at the current feedback time node.

[0043] In some embodiments of the present application, a highway pavement settlement online monitoring system is provided, including:

[0044] A monitoring unit for collecting road pavement parameters;

[0045] The central control unit is used to obtain road surface parameters according to preset feedback time nodes and generate a road surface monitoring model;

[0046] The central control unit comprises:

[0047] A first processing module is used to establish multiple monitoring sub-areas according to the road surface monitoring model, and generate the settlement amount of each monitoring sub-area according to a preset static load model and a dynamic load model;

[0048] The second processing module is used to generate a settlement evaluation value for each monitoring sub-area according to a preset correlation model;

[0049] The early warning module is used to determine whether to generate an early warning instruction based on the settlement evaluation value of each monitoring sub-area.

[0050] In some embodiments of the present application, the first processing module is further used to:

[0051] Establish a monitoring sub-area sequence A, A = (a1, a2...an), where ai is the i-th monitoring sub-area; n is the number of monitoring sub-areas;

[0052] Set multiple static monitoring areas according to the static load model;

[0053]

[0054] f1 = α / β; f2 = α / θ;

[0055] Among them, K is the static monitoring area division parameter; R is the auxiliary data; cos f1 and sin f2 are the road slope values; n is the number of monitoring sub-areas;

[0056] Generate static settlement parameters for each static monitoring area;

[0057] Set up multiple dynamic monitoring areas according to the dynamic load model;

[0058]

[0059] f1 = α / β; f2 = α / θ;

[0060] Generate dynamic settlement parameters for each dynamic monitoring area;

[0061] Obtain all static settlement parameters and all dynamic settlement parameters, perform weighted processing, and generate settlement amounts for each monitoring sub-area based on the processing results;

[0062] Establish a settlement amount sequence B for the current feedback time node, B = (b1, b2... bn), where bi is the settlement amount of the i-th monitoring sub-region at the current feedback time node.

[0063] In some embodiments of the present application, the second processing module is further configured to:

[0064] Set ai as the target monitoring sub-region in sequence according to the monitoring sub-region sequence A;

[0065] Generate a correlation degree evaluation value between the target monitoring sub-region and each monitoring sub-region according to a preset correlation model;

[0066] Establish a correlation degree evaluation value sequence D, D = (d1, d2... dn), where di is the correlation degree evaluation value between the i-th monitoring sub-region and the target monitoring sub-region;

[0067] Preset a first correlation degree evaluation value threshold D1;

[0068] If di < D1, set the i-th monitoring sub-region as the first-level associated sub-region of the target monitoring sub-region;

[0069] Obtain the settlement amounts of all the first-level associated sub-regions;

[0070] Generate a settlement evaluation value h of the target monitoring sub-region at the current feedback time node;

[0071] h = G * [e1 * Q1 * b + e2 * Q2 * (v - Δv) + e2 * Q2 * (b - Δb)];

[0072] Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; Q3 is a preset third fixed coefficient; b is the settlement amount of the target monitoring sub-region at the current feedback time node; v is the settlement rate of the target monitoring sub-region at the current feedback time node; Δb is the average settlement amount of all the first-level associated sub-regions of the target monitoring sub-region; Δv is the average settlement rate of all the first-level associated sub-regions of the target monitoring sub-region; G is a compensation coefficient set based on historical settlement parameters;

[0073] Generate the settlement evaluation values of each monitoring sub-region at the current feedback time node in sequence.

[0074] In some embodiments of the present application, the warning module is further configured to:

[0075] Establish a settlement evaluation value sequence H for the current feedback time node, H = (h1, h2... hn), where hi is the settlement evaluation value of the i-th monitoring sub-region at the current feedback time node;

[0076] Preset the settlement evaluation value threshold H1;

[0077] If hi>H1, the i-th monitoring sub-area generates a warning instruction at the current feedback time node;

[0078] If hi

[0079] Compared with the prior art, the method and system for online monitoring of road pavement settlement in the embodiment of the present application have the following beneficial effects:

[0080] A layered monitoring and analysis method for pavement settlement observation is adopted. After effectively analyzing the pavement settlement spectrum under the dual effects of static and dynamic loads, the settlement observation weight is calculated, thereby improving the monitoring accuracy of highway pavement settlement, ensuring the early warning efficiency of pavement settlement risks, and improving the safety of monitoring.

[0081] By establishing multiple monitoring sub-areas, the settlement status of the highway pavement is comprehensively monitored. By building a correlation model, the settlement status of each monitoring sub-area is compared and analyzed, and the settlement amount is dynamically corrected in combination with historical parameters, thereby improving the prediction accuracy of the settlement risk of each monitoring sub-area and ensuring the safety of the road surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is a flow chart of a method for online monitoring of highway pavement settlement in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0083] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0084] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0085] The terms "first", "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more. ​

[0086] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0087] like Figure 1 As shown, a method for online monitoring of road pavement settlement according to a preferred embodiment of the present application includes:

[0088] S101: Obtaining road surface parameters according to preset feedback time nodes and generating a road surface monitoring model;

[0089] S102: establishing a plurality of monitoring sub-areas according to the road surface monitoring model, and generating the settlement amount of each monitoring sub-area according to a preset static load model and a dynamic load model;

[0090] S103: Generate a settlement evaluation value for each monitoring sub-area according to a preset correlation model, and determine whether to generate an early warning instruction.

[0091] Specifically, the road surface parameters include:

[0092] Pavement shape, pavement texture and pavement color.

[0093] Specifically, the road surface information, including the shape, texture, and color of the road surface, is obtained by scanning the road surface with a laser scanner, and then entered into the chromatographic analysis platform and converted into a digital image for chromatographic analysis. Relevant information about the road surface settlement condition is obtained by extracting specific color, shape, and other parameters from the image. The chromatographic analysis results are used as input data to establish a road surface model and multiple monitoring sub-areas.

[0094] Specifically, when generating the settlement amount of each monitoring sub-area, it includes:

[0095] Establish a monitoring sub-area sequence A, A = (a1, a2...an), where ai is the i-th monitoring sub-area; n is the number of monitoring sub-areas;

[0096] Set multiple static monitoring areas according to the static load model;

[0097]

[0098] f1 = α / β; f2 = α / θ;

[0099] Wherein, K is a static monitoring area division parameter; R is auxiliary data; cos f1 and sin f2 are road surface slope values; n is the number of monitoring sub-areas;

[0100] Generate static settlement parameters for each static monitoring area;

[0101] Set multiple dynamic monitoring areas according to the dynamic load model;

[0102]

[0103] f1 = α / β; f2 = α / θ;

[0104] Generate dynamic settlement parameters for each dynamic monitoring area;

[0105] Obtain all static settlement parameters and all dynamic settlement parameters, perform weighted processing, and generate the settlement amount of each monitoring sub-area according to the processing result;

[0106] Establish a settlement amount sequence B at the current feedback time node, B = (b1, b2... bn), where bi is the settlement amount of the i-th monitoring sub-area at the current feedback time node.

[0107] Specifically, according to vehicle load, nearby buildings, soil properties, environmental humidity and road surface structure characteristics. Through the superposition factor data, perform enhanced calculation processing to generate the weight values of static settlement parameters and dynamic settlement parameters. Through weighted processing, reduce the monitoring error, improve the monitoring accuracy of the highway road surface settlement state and the calculation accuracy of the settlement amount of each monitoring sub-area.

[0108] In the preferred embodiment of the present application, when generating the settlement evaluation value of each monitoring sub-area according to the preset correlation model, it includes:

[0109] Set ai as the target monitoring sub-area in sequence according to the monitoring sub-area sequence A;

[0110] Generate the correlation degree evaluation value between the target monitoring sub-area and each monitoring sub-area according to the preset correlation model;

[0111] Establish a correlation degree evaluation value sequence D, D = (d1, d2... dn), where di is the correlation degree evaluation value between the i-th monitoring sub-area and the target monitoring sub-area;

[0112] Preset the first correlation degree evaluation value threshold D1;

[0113] If di < D1, set the i-th monitoring sub-area as the first-level associated sub-area of the target monitoring sub-area;

[0114] Obtain the settlement amounts of all first-level associated sub-areas;

[0115] Generate the settlement evaluation value h of the target monitoring sub-area at the current feedback time node;

[0116] When generating the settlement evaluation value h, it includes:

[0117] h=G*[e1*Q1*b+e2*Q2*(v-Δv)+e2*Q2*(b-Δb)];

[0118] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; b is the settlement amount of the target monitoring sub-area at the current feedback time node; v is the settlement rate of the target monitoring sub-area at the current feedback time node; Δb is the average settlement amount of all first-level associated sub-areas of the target monitoring sub-area; Δv is the average settlement rate of all first-level associated sub-areas of the target monitoring sub-area; G is the compensation coefficient set based on historical settlement parameters.

[0119] Generate the settlement evaluation value of each monitoring sub-area at the current feedback time node in turn.

[0120] Specifically, similarity processing is performed based on the load parameters of each monitoring sub-area between the current feedback time node and the previous feedback time node, the pavement parameters between different monitoring sub-areas, the soil structure and other parameters. The higher the similarity, the higher the correlation evaluation value between the two monitoring sub-areas.

[0121] Specifically, the greater the difference between the settlement parameters of the target monitoring sub-area and the settlement parameters of the corresponding first-level associated sub-area, the higher the possibility that an abnormality exists in the target monitoring sub-area.

[0122] Specifically, each evaluation index is normalized according to the preset first fixed coefficient, the second fixed coefficient and the third fixed coefficient, so that each evaluation index is within the same value range.

[0123] Specifically, when determining whether to generate an early warning instruction, it includes:

[0124] Establish a sedimentation evaluation value sequence H at the current feedback time node, H = (h1, h2…hn), where hi is the sedimentation evaluation value of the i-th monitoring sub-area at the current feedback time node;

[0125] Preset the settlement evaluation value threshold H1;

[0126] If hi>H1, the i-th monitoring sub-area generates a warning instruction at the current feedback time node;

[0127] If hi

[0128] Specifically, the larger the settlement evaluation value, the greater the possibility that the settlement state of the corresponding monitoring sub-area is abnormal. When the settlement evaluation value exceeds the settlement evaluation value threshold, it means that there is a settlement risk in the corresponding monitoring sub-area, and timely maintenance is required to avoid driving problems and ensure the safety of the road surface.

[0129] It can be understood that in the above embodiment, by establishing multiple monitoring sub-areas, the settlement state of the highway pavement is comprehensively monitored, and by constructing a correlation model, the settlement state of each monitoring sub-area is compared and analyzed, and the settlement amount is dynamically corrected in combination with historical parameters, thereby improving the prediction accuracy of the settlement risk of each monitoring sub-area and ensuring the safety of the road surface.

[0130] Based on another preferred embodiment of a method for online monitoring of road pavement settlement in any of the above preferred embodiments, this preferred embodiment provides an online monitoring system for road pavement settlement, including:

[0131] A monitoring unit for collecting road pavement parameters;

[0132] The central control unit is used to obtain road surface parameters according to preset feedback time nodes and generate a road surface monitoring model;

[0133] The central control unit includes:

[0134] A first processing module is used to establish multiple monitoring sub-areas according to the road surface monitoring model, and generate the settlement amount of each monitoring sub-area according to a preset static load model and a dynamic load model;

[0135] The second processing module is used to generate a settlement evaluation value for each monitoring sub-area according to a preset correlation model;

[0136] The early warning module is used to determine whether to generate an early warning instruction based on the settlement evaluation value of each monitoring sub-area.

[0137] Specifically, the first processing module is also used for:

[0138] Establish a monitoring sub-area sequence A, A = (a1, a2...an), where ai is the i-th monitoring sub-area; n is the number of monitoring sub-areas;

[0139] Set multiple static monitoring areas according to the static load model;

[0140]

[0141] f1 = α / β; f2 = α / θ;

[0142] ​Wherein, K is the static monitoring area division parameter; R is the auxiliary data; cos f1 and sin f2 are the road surface slope values; n is the number of monitoring sub-areas;

[0143] Generate the static settlement parameters of each static monitoring area;

[0144] Set multiple dynamic monitoring areas according to the dynamic load model;

[0145]

[0146] f1 = α / β; f2 = α / θ;

[0147] Generate the dynamic settlement parameters of each dynamic monitoring area;

[0148] Obtain all the static settlement parameters and all the dynamic settlement parameters, perform weighted processing, and generate the settlement amounts of each monitoring sub-area according to the processing results;

[0149] Establish a settlement amount sequence B at the current feedback time node, B = (b1, b2... bn), where bi is the settlement amount of the i-th monitoring sub-area at the current feedback time node.

[0150] In the preferred embodiment of the present application, the second processing module is further used for:

[0151] Set ai as the target monitoring sub-area in sequence according to the monitoring sub-area sequence A;

[0152] Generate the correlation degree evaluation value between the target monitoring sub-area and each monitoring sub-area according to the preset correlation model;

[0153] Establish a correlation degree evaluation value sequence D, D = (d1, d2... dn), where di is the correlation degree evaluation value between the i-th monitoring sub-area and the target monitoring sub-area;

[0154] Preset the first correlation degree evaluation value threshold D1;

[0155] If di < D1, set the i-th monitoring sub-area as the first-level associated sub-area of the target monitoring sub-area;

[0156] Obtain the settlement amounts of all the first-level associated sub-areas;

[0157] Generate the settlement evaluation value h of the target monitoring sub-area at the current feedback time node;

[0158] h = G * [e1 * Q1 * b + e2 * Q2 * (v - Δv) + e2 * Q2 * (b - Δb)];

[0159] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; b is the settlement amount of the target monitoring sub-area at the current feedback time node; v is the settlement rate of the target monitoring sub-area at the current feedback time node; Δb is the average settlement amount of all first-level associated sub-areas of the target monitoring sub-area; Δv is the average settlement rate of all first-level associated sub-areas of the target monitoring sub-area; G is the compensation coefficient set based on historical settlement parameters;

[0160] Generate the settlement evaluation value of each monitoring sub-area at the current feedback time node in turn.

[0161] Specifically, the early warning module is also used to:

[0162] Establish a sedimentation evaluation value sequence H at the current feedback time node, H = (h1, h2...hn), where hi is the sedimentation evaluation value of the i-th monitoring sub-area at the current feedback time node;

[0163] Preset the settlement evaluation value threshold H1;

[0164] If hi>H1, the i-th monitoring sub-area generates a warning instruction at the current feedback time node;

[0165] If hi

[0166] According to the first concept of the present application, a layered monitoring and analysis method for pavement settlement observation is adopted, and the settlement observation weight is calculated after effectively analyzing the pavement settlement spectrum under the dual effects of static and dynamic loads, thereby improving the monitoring accuracy of highway pavement settlement, ensuring the early warning efficiency of pavement settlement risks, and improving the safety of monitoring.

[0167] According to the second concept of the present application, a plurality of monitoring sub-areas are established to comprehensively monitor the settlement state of the highway pavement, and a correlation model is constructed to compare and analyze the settlement state of each monitoring sub-area, and the settlement amount is dynamically corrected in combination with historical parameters, thereby improving the prediction accuracy of the settlement risk of each monitoring sub-area and ensuring the safety of the road surface.

[0168] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.​

Claims

1. A method for online monitoring of road pavement settlement, characterized in that: Including: Obtain highway pavement parameters according to a preset feedback time node and generate a pavement monitoring model; Establish multiple monitoring sub-areas based on the pavement monitoring model, and generate the settlement amounts of each monitoring sub-area according to a preset static load model and dynamic load model; Generate a settlement evaluation value for each monitoring sub-area according to a preset correlation model, and determine whether to generate a warning instruction.

2. The method for online monitoring of road pavement settlement according to claim 1, characterized in that: The highway ground parameters include: Pavement shape, pavement texture and pavement color.

3. The method for online monitoring of road pavement settlement according to claim 2, characterized in that: When generating the settlement amounts of each monitoring sub-area by constructing a state load model and a dynamic load model, it includes: Establish a sequence A of monitoring sub-areas, A = (a1, a2... an), where ai is the i-th monitoring sub-area; n is the number of monitoring sub-areas; Set multiple static monitoring areas according to the static load model; f1 = α / β; f2 = α / θ; Where K is a static monitoring area division parameter; R is auxiliary data; cos f1 and sin f2 are pavement slope values; n is the number of monitoring sub-areas; Generate static settlement parameters for each static monitoring area; Set multiple dynamic monitoring areas according to the dynamic load model; f1 = α / β; f2 = α / θ; Generate dynamic settlement parameters for each dynamic monitoring area; Obtain all static settlement parameters and all dynamic settlement parameters, perform weighted processing, and generate the settlement amounts of each monitoring sub-area according to the processing results; Establish a sequence B of settlement amounts at the current feedback time node, B = (b1, b2... bn), where bi is the settlement amount of the i-th monitoring sub-area at the current feedback time node.

4. The method for online monitoring of road pavement settlement according to claim 1, characterized in that: When generating a settlement evaluation value for each monitoring sub-area according to a preset correlation model, it includes: Set ai as the target monitoring sub-area in sequence according to the sequence A of monitoring sub-areas; Generate a correlation evaluation value between the target monitoring sub-area and each monitoring sub-area according to a preset correlation model; Establish a sequence D of correlation evaluation values, D = (d1, d2... dn), where di is the correlation evaluation value between the i-th monitoring sub-area and the target monitoring sub-area; Preset a first correlation evaluation value threshold D1; If di < D1, set the i-th monitoring sub-area as a first-level associated sub-area of the target monitoring sub-area; Obtain the settlement amounts of all first-level associated sub-areas; Generate a settlement evaluation value h of the target monitoring sub-area at the current feedback time node; Generate the settlement evaluation values of each monitoring sub-area at the current feedback time node in sequence.

5. The method for online monitoring of road pavement settlement according to claim 4, characterized in that: When generating the settlement evaluation value h, it includes: h = G * [e1 * Q1 * b + e2 * Q2 * (v - Δv) + e2 * Q2 * (b - Δb)]; Where e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; Q3 is a preset third fixed coefficient; b is the settlement amount of the target monitoring sub-area at the current feedback time node; v is the settlement rate of the target monitoring sub-area at the current feedback time node; Δb is the average settlement amount of all first-level associated sub-areas of the target monitoring sub-area; Δv is the average settlement rate of all first-level associated sub-areas of the target monitoring sub-area; G is a compensation coefficient set based on historical settlement parameters.

6. The method for online monitoring of road pavement settlement according to claim 4, characterized in that: When determining whether to generate a warning instruction, it includes: Establish a settlement evaluation value sequence H at the current feedback time node, H = (h1, h2…hn), where hi is the settlement evaluation value of the i-th monitoring sub-region at the current feedback time node; Preset a settlement evaluation value threshold H1; If hi > H1, a warning instruction is generated for the i-th monitoring sub-region at the current feedback time node; If hi < H1, no warning instruction is generated for the i-th monitoring sub-region at the current feedback time node.

7. A highway pavement settlement online monitoring system, using the highway pavement settlement online monitoring method according to any one of claims 1 to 6, characterized in that: It includes: A monitoring unit for collecting highway pavement parameters; A central control unit for obtaining highway pavement parameters according to a preset feedback time node and generating a pavement monitoring model; The central control unit includes: A first processing module for establishing multiple monitoring sub-regions according to the pavement monitoring model and generating the settlement amounts of each monitoring sub-region according to a preset static load model and dynamic load model; A second processing module for generating the settlement evaluation value of each monitoring sub-region according to a preset correlation model; A warning module for determining whether to generate a warning instruction according to the settlement evaluation values of each monitoring sub-region.

8. The highway pavement settlement online monitoring system according to claim 7, characterized in that: The first processing module is also used for: Establish a monitoring sub-region sequence A, A = (a1, a2…an), where ai is the i-th monitoring sub-region; n is the number of monitoring sub-regions; Set multiple static monitoring regions according to the static load model; f1 = α / β; f2 = α / θ; Where, K is a static monitoring region division parameter; R is auxiliary data; cos f1 and sin f2 are pavement slope values; n is the number of monitoring sub-regions; Generate the static settlement parameters of each static monitoring region; Set multiple dynamic monitoring regions according to the dynamic load model; f1 = α / β; f2 = α / θ; Generate the dynamic settlement parameters of each dynamic monitoring region; Obtain all the static settlement parameters and all the dynamic settlement parameters, perform weighted processing, and generate the settlement amounts of each monitoring sub-region according to the processing results; Establish a settlement amount sequence B at the current feedback time node, B = (b1, b2…bn), where bi is the settlement amount of the i-th monitoring sub-region at the current feedback time node.

9. The highway pavement settlement online monitoring system according to claim 8, characterized in that: The second processing module is also used for: Set ai as the target monitoring sub-region in sequence according to the monitoring sub-region sequence A; Generate the correlation degree evaluation value between the target monitoring sub-region and each monitoring sub-region according to a preset correlation model; Establish a correlation degree evaluation value sequence D, D = (d1, d2…dn), where di is the correlation degree evaluation value between the i-th monitoring sub-region and the target monitoring sub-region; Preset a first correlation degree evaluation value threshold D1; If di < D1, set the i-th monitoring sub-region as the first-level associated sub-region of the target monitoring sub-region; Obtain the settlement amounts of all the first-level associated sub-regions; Generate the settlement evaluation value h of the target monitoring sub-region at the current feedback time node; h = G * [e1 * Q1 * b + e2 * Q2 * (v - Δv) + e2 * Q2 * (b - Δb)]; Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; Q3 is a preset third fixed coefficient; b is the settlement amount of the target monitoring sub-region at the current feedback time node; v is the settlement rate of the target monitoring sub-region at the current feedback time node; Δb is the average settlement amount of all the first-level associated sub-regions of the target monitoring sub-region; Δv is the average settlement rate of all the first-level associated sub-regions of the target monitoring sub-region; G is a compensation coefficient set based on historical settlement parameters; Generate the settlement evaluation values of each monitoring sub-region at the current feedback time node in sequence.

10. The highway pavement settlement online monitoring system according to claim 9, characterized in that: The early warning module is further configured to: Establish a sequence H of settlement evaluation values at the current feedback time node, H = (h1, h2…hn), where hi is the settlement evaluation value of the i-th monitoring sub-region at the current feedback time node; Preset a settlement evaluation value threshold H1; If hi > H1, an early warning instruction is generated for the i-th monitoring sub-region at the current feedback time node; If hi < H1, no early warning instruction is generated for the i-th monitoring sub-region at the current feedback time node.

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