Spraying method based on multi-mode sensing synchronous seal paver

Abstract

The invention relates to the field of asphalt spreading, in particular to a spreading method based on a multi-mode sensing synchronous seal paver, which comprises the following steps: firstly, responding to the starting of the multi-mode sensing synchronous seal paver, acquiring the three-dimensional profile, longitudinal gradient and transverse curvature of a pavement, and acquiring the real-time speed and emulsion viscosity of the multi-mode sensing synchronous seal paver; inputting the three-dimensional contour, the longitudinal gradient and the transverse curvature into a preset three-dimensional relation equation, calculating to obtain a space pavement curvature, inputting the space pavement curvature, the real-time vehicle speed and the emulsion viscosity into a multivariable coupling model based on a PID algorithm, calculating to obtain the emulsified asphalt flow and the gravel spreading rate, and finally, calculating the pavement pavement curvature. And sending an adjusting instruction to a corresponding control system according to the calculation result. Compared with the prior art, the method disclosed by the invention is higher in spreading uniformity and dynamic adaptability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of asphalt spreading. More particularly, the present application relates to a spreading method based on a multi-modal sensor synchronous seal layer vehicle. BACKGROUND

[0002] In the field of contemporary highway maintenance, the exponential growth of traffic flow and the increasing proportion of heavy vehicles are accelerating the performance degradation of the functional layer of the road. Statistical data shows that about 67% of traffic accidents in wet weather are directly related to the lack of road skid resistance.

[0003] In the existing process, synchronous stone seal layer equipment is often used to spread emulsified asphalt and graded aggregate, and then double-roller compaction is performed to form a high-performance skid-resistant seal layer with skid resistance and waterproofness, thereby improving the durability of the road.

[0004] However, the above-mentioned road skid-resistant seal layer method relying on traditional stone seal layer vehicles for spreading has the following problems: (1) The spreading width, vehicle speed and spreading amount are mainly adjusted by the driver or technician based on visual observation and lagging adjustment, which cannot dynamically respond to complex road conditions such as curves and slopes, which may result in poor seal layer spreading line shape, uneven asphalt film thickness, and the occurrence of early particle loss and middle-late peeling on the road section after spreading is completed.

[0005] (2) The current stone seal layer spreading process has the problem of rigidification of the graded aggregate: the fixed particle size ratio of the premixed stone cannot be dynamically adjusted according to the characteristics of the construction road section (such as heavy vehicle lanes and curve radii) or real-time working conditions (such as temperature and humidity changes), and early damage may occur in stress concentration areas, resulting in spreading quality problems.

[0006] Therefore, the existing spreading method mainly has the problems of poor spreading uniformity and dynamic adaptability. SUMMARY

[0007] To solve the above-mentioned problems of poor spreading uniformity and dynamic adaptability, the present application discloses a spreading method based on a multi-modal sensor synchronous seal layer vehicle.

[0008] In a first aspect, the present application discloses a spreading method based on a multi-modal sensor synchronous seal layer vehicle, comprising: In response to the start of the multi-modal sensor synchronous seal layer vehicle, the three-dimensional profile, longitudinal slope and lateral curvature of the road surface are obtained, and the real-time vehicle speed and emulsion viscosity of the multi-modal sensor synchronous seal layer vehicle are obtained; The three-dimensional profile, longitudinal slope and lateral curvature are input into a preset three-dimensional relationship equation, and the spatial road curvature is calculated; The spatial road curvature, real-time vehicle speed and emulsion viscosity are input into a multi-variable coupling model based on a PID algorithm, and the emulsified asphalt flow and stone spreading rate are calculated; According to the emulsified asphalt flow, the emulsion-stone collaborative spraying system is sent a emulsion spray pressure adjustment instruction; according to the stone spraying rate, the emulsion-stone collaborative spraying system is sent a spraying width and unit spraying mass adjustment instruction; The multi-modal sensor synchronous sealing vehicle comprises an emulsion-stone collaborative spraying system.

[0009] Beneficial effects: The method is applied in the multi-modal sensor synchronous sealing vehicle, and depends on the three-dimensional contact equation. The method calculates more accurate multivariate coupling model input parameters (i.e. spatial road surface curvature) according to the three-dimensional profile, longitudinal slope and transverse curvature, realizes multi-dimensional output by using the multivariate coupling model, and finally drives the corresponding system to perform the emulsion spraying and stone spraying actions by using the multi-dimensional output results. Compared with the prior art, the method can adaptively adjust the emulsion spray pressure, spraying width and unit spraying mass according to the road conditions and vehicle speed, so as to improve the spraying uniformity and dynamic adaptability.

[0010] Preferably, the multivariate coupling model based on the PID algorithm comprises:

[0011] In the formula, is the emulsified asphalt flow, is a nonlinear function based on a multi-modal neural network, is the spatial road surface curvature, is the real-time vehicle speed, is the emulsion viscosity, is the proportional coefficient, is the flow error at time is the integral coefficient, is the integral of the flow error, is the differential coefficient, is the derivative of the flow error with respect to time .

[0012] Beneficial effects: The multivariate coupling model realizes effective coupling of the multi-modal neural network and the PID feedback regulation, has the characteristics of closed-loop control regulation, and also has the performance of multi-modal input analysis.

[0013] Preferably, the three-dimensional contact equation is specifically:

[0014] In the formula, is the spatial road surface curvature, is the profile influence coefficient, is the derivative symbol, is the three-dimensional profile of the road surface, ​a three-dimensional profile in the axial coordinate of the forward direction of the multi-modal sensor synchronous seal vehicle, a slope influence coefficient, a longitudinal slope, a curvature influence coefficient, a transverse curvature.

[0015] Beneficial effects: the three-dimensional contact equation can convert complex road conditions into standard measurement parameters, so that the multi-variable coupling model can obtain more accurate data input.

[0016] Preferably, after the start of the multi-modal sensor synchronous seal vehicle, the method of the present application further comprises: monitoring the temperature of the liquid tank for containing emulsified asphalt in the multi-modal sensor synchronous seal vehicle; judging whether the emulsion viscosity matches the set viscosity of the liquid tank temperature according to the preset emulsion viscosity-temperature curve correlation database, and if not, driving the magnetic heating stirrer built-in the liquid tank to adjust the temperature and stirring speed of the emulsified asphalt.

[0017] Preferably, the method of the present application further comprises: monitoring the pavement seal thickness, texture uniformity and asphalt coverage rate in response to the forward movement of the multi-modal sensor synchronous seal vehicle; inputting the pavement seal thickness, texture uniformity and asphalt coverage rate into the preset quality defect detection model to obtain a feedback compensation coefficient; inputting the feedback compensation coefficient into the multi-variable coupling model for coefficient adjustment.

[0018] Preferably, after the start of the multi-modal sensor synchronous seal vehicle, the method of the present application further comprises: monitoring the gravel inventory; delivering the gravel inventory to the display module in the multi-modal sensor synchronous seal vehicle for display.

[0019] In the second aspect, the present application discloses a multi-modal sensor synchronous seal vehicle for realizing the spraying method based on the multi-modal sensor synchronous seal vehicle as described in the first aspect, and the multi-modal sensor synchronous seal vehicle comprises a general control system and an emulsion-gravel collaborative spraying system. The general control system performs the following functions: in response to the start of the multi-modal sensor synchronous seal vehicle, obtaining the three-dimensional profile, longitudinal slope and transverse curvature of the road surface, and simultaneously obtaining the real-time vehicle speed and emulsion viscosity of the multi-modal sensor synchronous seal vehicle; inputting the three-dimensional profile, longitudinal slope and transverse curvature into the preset three-dimensional contact equation to calculate the spatial road curvature; The spatial road curvature, real-time vehicle speed and emulsion viscosity are input into a multivariate coupling model based on a PID algorithm, and the emulsified asphalt flow and the gravel spreading rate are calculated; According to the emulsified asphalt flow, an emulsion-gravel collaborative spreading system is sent a emulsion spray pressure adjustment instruction; according to the gravel spreading rate, the emulsion-gravel collaborative spreading system is sent a spreading width and unit spreading mass adjustment instruction.

[0020] Beneficial effects: The multi-modal sensing synchronous seal vehicle of the present application is provided with a spraying method based on the multi-modal sensing synchronous seal vehicle, has a multi-modal data analysis processing function, can adjust the emulsified asphalt flow and the gravel spreading rate in real time according to the road condition, vehicle speed and emulsion viscosity, solves the problems of poor spraying uniformity and dynamic adaptability in the prior art, and can improve the quality of the pavement anti-skid seal.

[0021] Preferably, it further comprises an emulsion constant-temperature storage system, which comprises a magnetic heating stirrer and a liquid tank for containing emulsified asphalt; the magnetic heating stirrer is used for temperature adjustment and stirring of the emulsified asphalt.

[0022] The beneficial effects of the present application are: (1) Compared with the prior art, the method of the present application can self-adaptively adjust the emulsion spray pressure, spreading width and unit spreading mass according to the road condition and vehicle speed, thereby improving the spraying uniformity and dynamic adaptability.

[0023] (2) Compared with the prior art, the three-dimensional contact equation of the method of the present application can convert complex road conditions into standard measurement parameters, so that the multivariate coupling model can obtain more accurate data input.

[0024] (3) Compared with the prior art, the multi-modal sensing synchronous seal vehicle of the present application has a multi-modal data analysis processing function, and can adjust the emulsified asphalt flow and the gravel spreading rate in real time according to the road condition, vehicle speed and emulsion viscosity. BRIEF DESCRIPTION OF DRAWINGS

[0025] The above and other objects, features and advantages of the exemplary embodiments of the present application will be more apparent from the following detailed description read in conjunction with the accompanying drawings, in which several embodiments of the present application are shown by way of example, and wherein like reference numerals refer to like elements throughout. In the drawings: Figure 1 is a flow chart of the spraying method based on the multi-modal sensing synchronous seal vehicle in the first embodiment of the present application; Figure 2 is a structural schematic diagram of the multi-modal sensing synchronous seal vehicle in the second embodiment of the present application; Figure 3 is a structural schematic diagram of the magnetic heating stirrer in the second embodiment of the present application; Figure 4 This is a schematic diagram of the emulsion constant temperature storage system in Embodiment 2 of the present invention; Figure 5 This is a schematic diagram of the structure of the crushed stone storage system and the emulsion-crushed stone co-spreading system in Embodiment 2 of the present invention.

[0026] Explanation of reference numerals in the attached figures: 1. Central Control System; 11. Road Surface Alignment Recognition Imager; 12. HMI Display Control Room; 13. Radar Speed ​​Meter; 14. Sealing Coat Texture Scanner; 15. Viscosity-Temperature Control Platform; 2. Emulsion Constant Temperature Storage System; 21. Magnetic Heating Stirrer; 211. Drive Unit; 212. Coating Heating Wire; 213. Infrared Temperature Measurement Module; 22. Liquid Tank; 221. Exhaust Pipe; 222. Delivery Pipe; 223. Extraction Pump; 224. Return Pipe; 225. Metal Welding Position; 226. Flow compensation controller; 227, output pipe; 228, flow meter; 3, crushed stone storage system; 31, silo; 32, vibrating motor; 33, screen; 4, emulsion-crushed stone co-spreading system; 41, emulsion spraying device; 411, pressure gauge; 412, pneumatic telescopic rod; 413, nozzle; 414, spraying arm lifter; 415, emulsion inlet; 42, crushed stone spreading device; 421, spiral distributor; 422, spiral gap solenoid valve; 423, distributor pneumatic telescopic rod. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] This embodiment discloses a spraying method based on a multimodal sensing synchronous sealing vehicle, which is used to solve the problems of poor spraying uniformity and dynamic adaptability in the prior art.

[0029] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0030] Example 1 like Figure 1 As shown, this embodiment discloses a spraying method based on a multimodal sensing synchronous sealing vehicle, including: S10: In response to the start-up of the multimodal sensing synchronous seal car, acquire the three-dimensional profile, longitudinal slope and lateral curvature of the road surface, and at the same time acquire the real-time vehicle speed and emulsion viscosity of the multimodal sensing synchronous seal car.

[0031] In this embodiment, based on binocular vision sensing technology, a road surface linear identification imager arranged on the front side of the multi-modal sensing synchronous seal layer vehicle is used to obtain the three-dimensional profile, longitudinal slope and transverse curvature of the road surface. A radar speedometer mounted on the top of the vehicle head of the multi-modal sensing synchronous seal layer vehicle is used to obtain the real-time vehicle speed. The emulsion viscosity can be periodically sampled and tested.

[0032] S20: input the three-dimensional profile, longitudinal slope and transverse curvature into the preset three-dimensional contact equation, and calculate the spatial road surface curvature.

[0033] Specifically, the above-mentioned three-dimensional contact equation is:

[0034] In the formula, the spatial road surface curvature, is the profile influence coefficient, is the derivative symbol, is the three-dimensional profile of the road surface, is the axis coordinate of the multi-modal sensing synchronous seal layer vehicle in the forward direction in the three-dimensional profile, is the slope influence coefficient, is the longitudinal slope, is the curvature influence coefficient, is the transverse curvature.

[0035] Through the above algorithm design, the effective quantization of complex road conditions including straight sections, turns and ups and downs is realized. The spatial road surface curvature calculated by the above-mentioned three-dimensional contact equation can provide effective data support for subsequent model technology. The above-mentioned 、 and are influence coefficients of dynamic adjustment, which can be matched and adjusted according to the road condition type according to the preset road condition coefficient configuration table. In simple road conditions (not involving ups and downs), the spatial road surface curvature is approximately equal to the transverse curvature.

[0036] S30: input the spatial road surface curvature, real-time vehicle speed and emulsion viscosity into the multi-variable coupling model based on the PID algorithm, and calculate the emulsified asphalt flow and gravel spreading rate.

[0037] In this embodiment, the multi-variable coupling model is a multi-modal input and multi-modal data model combining multi-modal neural network and PID. The multi-modal neural network architecture can use Transformer. Before using the multi-variable coupling model, the model needs to be trained with sample parameters to adapt to the application environment of the method of the present application.

[0038] Specifically, after pre-training, for the output of emulsified asphalt flow, the above-mentioned multi-variable coupling model includes:

[0039] wherein, is the emulsion asphalt flow rate, is a nonlinear function based on a multi-modal neural network (pre-training process is completed to construct), is the spatial pavement curvature, is the real-time vehicle speed, is the emulsion viscosity, is a proportional coefficient, is the flow rate error at time is the flow rate error at time is an integral coefficient, is the integral of the flow rate error, is a differential coefficient, is the derivative of the flow rate error with respect to time is the derivative of the flow rate error with respect to time

[0040] Similarly, the above gravel spreading rate can also be calculated in a similar manner, but the nonlinear function of the gravel spreading rate will be adjusted in parameters according to the actual training results, and the PID control coefficients and deviation definitions will also be adaptively adjusted.

[0041] Through the above algorithm design, the method of the embodiment realizes effective coupling of AI technology and traditional PID control technology, and can realize multi-dimensional data synchronous output on the basis of meeting the closed-loop control characteristics.

[0042] S40: According to the emulsion asphalt flow rate, an emulsion spraying pressure adjustment instruction is issued to the emulsion-gravel collaborative spreading system.

[0043] S50: According to the gravel spreading rate, a spreading width and unit spreading mass adjustment instruction is issued to the emulsion-gravel collaborative spreading system.

[0044] It should be noted that the multi-modal sensor synchronous sealing layer vehicle includes an emulsion-gravel collaborative spreading system. The above steps S40-S50 belong to the device control layer, and in the embodiment, the emulsion-gravel collaborative spreading system is issued an emulsion spraying pressure adjustment instruction by the main control system, so that the emulsion-gravel collaborative spreading system adjusts the emulsion spraying flow rate of the emulsion spreading device. The emulsion-gravel collaborative spreading system is issued a spreading width and unit spreading mass adjustment instruction by the main control system, so that the emulsion-gravel collaborative spreading system adjusts the spreading width and unit spreading mass of the gravel spreading device.

[0045] Through the above steps S10-S50, the method of the embodiment can adaptively adjust the emulsion spraying pressure, the spreading width and the unit spreading mass according to the road conditions and the vehicle speed, thereby solving the technical problems of poor spreading uniformity and dynamic adaptability in the prior art.

[0046] Further, in order to ensure that the emulsion maintains flow during spraying and prevent pipeline blockage, after step S10, the method of the embodiment further comprises: Monitoring the temperature of the liquid bin for containing emulsified asphalt in the multi-modal sensor synchronous seal vehicle.

[0047] According to the preset emulsion viscosity-temperature curve correlation database, it is judged whether the emulsion viscosity matches the set viscosity of the liquid bin temperature. If not, the built-in magnetic heating stirrer in the liquid bin is driven to adjust the temperature and stirring speed of the emulsified asphalt.

[0048] It needs to be explained that the above-mentioned emulsion viscosity-temperature curve correlation database represents the corresponding relationship between emulsion viscosity and liquid bin temperature, and is stored in the emulsion viscosity-temperature curve correlation database in the form of a data table. After sampling the emulsion, if the emulsion viscosity matches the set viscosity of the liquid bin temperature, the temperature control condition and stirring speed are maintained. If the emulsion viscosity does not match the set viscosity of the liquid bin temperature, there are mainly two cases. One is that the emulsion is too thick, which requires increasing the stirring speed and increasing the temperature of the liquid bin; and the other is that the emulsion is too thin, which requires reducing the stirring speed and reducing the temperature of the liquid bin.

[0049] Further, in order to improve the spraying quality, the method of the embodiment provides a multi-closed loop adjustment scheme. After step S50, the method of the embodiment further comprises: In response to the advance of the multi-modal sensor synchronous seal vehicle, the road seal thickness, texture uniformity and asphalt coverage are monitored; The road seal thickness, texture uniformity and asphalt coverage are input into the preset quality defect detection model to obtain a feedback compensation coefficient; The feedback compensation coefficient is input into the multi-variable coupling model for coefficient adjustment.

[0050] In the embodiment, the above-mentioned road seal thickness, texture uniformity and asphalt coverage can be obtained by the seal texture scanner arranged at the rear side of the multi-modal sensor synchronous seal vehicle. After obtaining the above-mentioned data, the data is input into the preset quality defect detection model for data analysis to obtain a defect analysis result. Based on the expert model, a feedback compensation coefficient is given, and the feedback compensation coefficient is used to weight correct the coefficients in the multi-variable coupling model.

[0051] Further, after step S10, the method of the embodiment further comprises: Monitoring the gravel inventory.

[0052] The gravel inventory is transmitted to the display module in the multi-modal sensor synchronous seal vehicle for display.

[0053] Specifically, the technical effect of displaying the aggregate inventory in the screen display module is realized by installing a weight sensor in the bin for accommodating the aggregate and connecting the signal output line to the screen display module.

[0054] Embodiment two As Figure 2 shown, the embodiment discloses a multi-modal sensor synchronous seal layer vehicle for realizing the multi-modal sensor synchronous seal layer vehicle-based spraying method described in embodiment one. The multi-modal sensor synchronous seal layer vehicle includes a general control system 1, an emulsion constant-temperature storage system 2, an aggregate storage system 3, and an emulsion-aggregate collaborative spraying system 4.

[0055] The general control system 1 is installed at the front end of the cab, the emulsion constant-temperature storage system 2 and the aggregate storage system 3 are arranged in sequence at the rear of the cab, and the emulsion-aggregate collaborative spraying system 4 is hung at the tail of the vehicle, forming a three-level modular structure integrating sensing, storage, and operation functions.

[0056] Specifically, when the multi-modal sensor synchronous seal layer vehicle is working, the general control system 1 performs the following functions: The three-dimensional profile, longitudinal slope, and transverse curvature are input into a preset three-dimensional relationship equation, and the spatial road curvature is calculated.

[0057] The spatial road curvature, real-time vehicle speed, and emulsion viscosity are input into a multivariate coupling model based on the PID algorithm, and the emulsified asphalt flow and aggregate spraying rate are calculated.

[0058] According to the emulsified asphalt flow, the emulsion-aggregate collaborative spraying system 4 is sent a emulsion ejection pressure adjustment instruction.

[0059] According to the aggregate spraying rate, the emulsion-aggregate collaborative spraying system 4 is sent a spraying width and unit spraying mass adjustment instruction.

[0060] More specifically, the general control system 1 includes a road line identification imager 11, an HMI screen display control room 12 (i.e., a screen display module), a radar speedometer 13, a seal texture scanner 14, and a viscosity-temperature control platform 15. The road line identification imager 11 is located at the front end of the vehicle cab, the HMI screen display control room 12 is installed inside the cab, the radar speedometer 13 is arranged on the top of the cab, the seal texture scanner 14 is hung at the end of the tail, and the viscosity-temperature control platform 15 is placed on the top of the liquid bin 22.

[0061] The road surface alignment identification imager 11 adopts a binocular vision sensing technology, synchronously acquires road surface three-dimensional profile, longitudinal slope and transverse curvature data and transmits them to the general control system 1. The radar speedometer 13 inputs real-time vehicle speed data to the general control system 1. The seal coat texture scanner 14 detects seal coat thickness, texture uniformity and asphalt coverage rate in real time, identifies construction quality defects, and feeds data back to the general control system 1. The viscosity-temperature control platform 15 has a built-in emulsion viscosity-temperature curve correlation database. The HMI screen display control room 12 integrates multi-modal fusion sensing technology, connects various sensing units, can display construction parameters such as vehicle speed, spreading width, crushed stone spreading amount, coverage rate, texture uniformity, alignment offset value and emulsion spraying flow, and can support manual interactive control.

[0062] As shown in Figures 2-4 , the emulsion constant-temperature storage system 2 includes a magnetic heating stirrer 21 and a liquid tank 22. The magnetic heating stirrer 21 includes a driving unit 211, a coating heating wire 212 and an infrared temperature measurement module 213. The driving unit 211 is in communication connection with the liquid tank viscosity-temperature control platform 15. The liquid tank 22 includes an exhaust pipe 221, a conveying pipe 222, a suction pump 223, a return pipe 224, a metal welding site 225, a flow compensation controller 226 (controlled by the main control system), an output pipe 227 and a flowmeter 228. The exhaust pipe 221 is located at the top of the liquid tank 22, the conveying pipe 222 is connected to the suction pump 223, the outlet of the suction pump 223 is connected to the flowmeter 228, the other end is connected to the flow compensation controller 226, the right side of the flow compensation controller 226 is connected by the metal welding site 225, the right side is installed with the return pipe 224, and the suction pump 223 and the flow compensation controller 226 are connected with the output pipe 227.

[0063] Through the structural design of the emulsion constant-temperature storage system 2, the constant-temperature control, viscosity control and flow output adjustment of the emulsion can be supported.

[0064] As shown in Figure 2 and Figure 5 , the crushed stone storage system 3 includes a stock bin 31, a vibration motor 32 and a screen 33, wherein the stock bin 31 is used for storing crushed stones, the vibration motor 32 is used for driving the screen 33 to vibrate, and the screen 33 is used for screening crushed stones with appropriate particle size, and the screened crushed stones are sent to the crushed stone spreading device 42.

[0065] As shown in Figure 1 and Figure 5 , the emulsion-crushed stone collaborative spreading system 4 includes an emulsion spreading device 41 and a crushed stone spreading device 42, and the crushed stone spreading device 42 is located behind the emulsion spreading device 41. The emulsion spreading device includes a pressure gauge 411, a pneumatic telescopic rod 412, a nozzle 413, a spraying arm lifter 414 and an emulsion input port 415, and the crushed stone spreading device 42 includes a spiral distributor 421, a spiral gap electromagnetic valve 422 and a distribution pneumatic telescopic rod 412.

[0066] The emulsion spraying device 41 drives the nozzle 413 to spread laterally by the pneumatic telescopic rod 412, and the width adjustment range is 1.5-4 m. The spraying arm lifter 414 is lifted by hydraulic control, and the height from the ground of the nozzle 413 is adjusted adaptively based on the control algorithm described in embodiment one. The pressure gauge 411 is used to monitor the spraying pressure in real time, and the emulsified asphalt flow is dynamically adjusted by the control algorithm described in embodiment one. The spiral distributor 421 in the gravel spraying device 42 is driven by a variable frequency motor, and the spraying width is adjusted by the distribution pneumatic telescopic rod 412. The spiral gap solenoid valve 422 is used to control the spiral gap to match the spraying of gravel of different particle sizes.

[0067] In the description of the present specification, the meaning of "a plurality of" is at least two, such as two, three or more, etc., unless otherwise explicitly specifically limited.

[0068] Although the present specification has shown and described several embodiments of the present application, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, changes and substitutions can be made by those skilled in the art without departing from the idea and spirit of the present application. It should be understood that various alternatives to the embodiments of the present application described herein can be employed in practicing the present application.

Claims

1. A spraying method based on a multimodal sensing synchronous sealing vehicle, characterized in that, The method comprises: in response to the start of the multi-modal sensor synchronous seal layer vehicle, acquiring the three-dimensional profile, longitudinal slope and lateral curvature of the road surface, and simultaneously acquiring the real-time vehicle speed and emulsion viscosity of the multi-modal sensor synchronous seal layer vehicle; inputting the three-dimensional profile, longitudinal slope and lateral curvature into a preset three-dimensional correlation equation to calculate the spatial road surface curvature; inputting the spatial road surface curvature, real-time vehicle speed and emulsion viscosity into a multi-variable coupling model based on a PID algorithm to calculate the emulsified asphalt flow and gravel spreading rate; according to the emulsified asphalt flow, issuing an emulsion spray pressure adjustment instruction to the emulsion-gravel collaborative spreading system (4); according to the gravel spreading rate, issuing a spreading width and unit spreading mass adjustment instruction to the emulsion-gravel collaborative spreading system (4); wherein the multi-modal sensor synchronous seal layer vehicle comprises the emulsion-gravel collaborative spreading system (4).

2. The method of synchronizing the sealing layer vehicle based on the multi-modal sensor according to claim 1, wherein, The multi-variable coupling model based on the PID algorithm comprises: wherein is the emulsion asphalt flow rate, is a nonlinear function based on a multi-modal neural network, is the spatial road curvature, is the real-time vehicle speed, is the emulsion viscosity, is a proportional coefficient, is the flow error at time , is an integral coefficient, is the integral of the flow error, is a derivative coefficient, is the derivative of the flow error with respect to time .

3. The method for synchronizing sealing layer vehicle based on multi-modal sensor according to claim 2, characterized in that, The three-dimensional correlation equation is specifically: wherein is the spatial road curvature, is the profile influence coefficient, is the derivative sign, is the three-dimensional profile of the road, is the axis coordinate of the multi-modal sensor synchronous seal car in the forward direction of the profile, is the slope influence coefficient, is the longitudinal slope, is the curvature influence coefficient, is the transverse curvature.

4. The method of synchronizing the sealing layer vehicle based on the multi-modal sensor according to claim 1, wherein, After responding to the start of the multi-modal sensor synchronous seal layer vehicle, the method further comprises: monitoring the temperature of the liquid tank in the multi-modal sensor synchronous seal layer vehicle for containing emulsified asphalt; according to a preset emulsion viscosity-temperature curve correlation database, determining whether the emulsion viscosity matches the set viscosity of the liquid tank temperature, and if not, driving the magnetic heating stirrer (21) built-in the liquid tank (22) to adjust the temperature and stirring speed of the emulsified asphalt.

5. The method for synchronizing the sealing layer vehicle based on the multi-modal sensor according to claim 1, wherein, The method comprises: in response to the advance of the multi-modal sensor synchronous seal layer vehicle, monitoring the road surface seal layer thickness, texture uniformity and asphalt coverage rate; inputting the road surface seal layer thickness, texture uniformity and asphalt coverage rate into a preset quality defect detection model to obtain a feedback compensation coefficient; inputting the feedback compensation coefficient into the multi-variable coupling model for coefficient adjustment.

6. The method of synchronizing the sealing layer vehicle based on the multi-modal sensor according to claim 1, wherein, After responding to the start of the multi-modal sensor synchronous seal layer vehicle, the method further comprises: monitoring the gravel inventory; sending the gravel inventory to a display module in the multi-modal sensor synchronous seal layer vehicle for display.

7. A multi-modal sensor synchronization seal coat vehicle, characterized by, The method for implementing the multi-modal sensor synchronous seal layer vehicle of any one of claims 1-6 comprises a total control system (1) and an emulsion-gravel collaborative spreading system (4); wherein the total control system (1) performs the following functions: in response to the start of the multi-modal sensor synchronous seal layer vehicle, acquiring the three-dimensional profile, longitudinal slope and lateral curvature of the road surface, and simultaneously acquiring the real-time vehicle speed and emulsion viscosity of the multi-modal sensor synchronous seal layer vehicle; inputting the three-dimensional profile, longitudinal slope and lateral curvature into a preset three-dimensional correlation equation to calculate the spatial road surface curvature; inputting the spatial road surface curvature, real-time vehicle speed and emulsion viscosity into a multi-variable coupling model based on a PID algorithm to calculate the emulsified asphalt flow and gravel spreading rate; According to the emulsified asphalt flow, an emulsion spray pressure adjustment instruction is sent to an emulsion-stone collaborative spraying system (4); and according to the stone spraying rate, a spraying width and unit spraying mass adjustment instruction is sent to the emulsion-stone collaborative spraying system (4).

8. The multi-modal sensor synchronization slurry truck of claim 7, wherein, The emulsion constant-temperature storage system (2) comprises a magnetic heating stirrer (21) and a liquid tank (22) for containing the emulsified asphalt; and the magnetic heating stirrer (21) is used for temperature adjustment and stirring of the emulsified asphalt.

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