Pavement maintenance planning method and system based on multi-source data

Abstract

The invention relates to the technical field of pavement maintenance planning, and provides a pavement maintenance planning method and system based on multi-source data, and the method comprises the steps: obtaining the historical road data, weather data and maintenance data of a pavement, and carrying out the data grouping based on the maintenance time point, substituting each group of data into a road surface damage correlation model to construct parameters in the model, so that the model can reflect the damage condition of the vehicle on the road on the road when the vehicle runs in different weather; real-time road data and weather data are substituted into the constructed pavement damage correlation model, the time point when the pavement needs to be maintained can be judged according to the accumulated maintenance parameters, pavement maintenance planning is carried out, and the disease condition of the pavement is accurately judged, so that the pavement maintenance execution efficiency is higher, and the pavement maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of pavement maintenance planning, and particularly to a pavement maintenance planning method and system based on multi-source data. Background Art

[0002] Existing research on the cause analysis of road diseases tends to be qualitative analysis, unable to give the proportional relationship of multiple causes, and the guiding significance for maintenance is not strong; the rutting and cracks that appear on the road surface will affect the vehicle driving, and for the existing pavement maintenance planning scheme, it is generally to carry out maintenance at a fixed cycle, but the damage situation of the road surface within the fixed cycle is unknown, and there may be situations where the damage degree is too large or too small during pavement maintenance, affecting the efficiency of pavement maintenance and increasing the maintenance cost. Summary of the Invention

[0003] The present invention provides a pavement maintenance planning method based on multi-source data, which is used to solve the problem of low efficiency caused by inaccurate pavement maintenance time planning in the prior art.

[0004] The first aspect of the present invention provides a pavement maintenance planning method based on multi-source data, including:

[0005] Obtain historical road data, historical weather data and historical maintenance data; group the historical road data and historical weather data based on adjacent maintenance time points in the historical maintenance data to obtain multiple groups of maintenance cycle data; dimensionless the historical maintenance data in each group of maintenance cycle data to obtain rutting maintenance parameters and crack maintenance parameters;

[0006] Construct a pavement damage association model according to the historical road data, historical weather data, rutting maintenance parameters and crack maintenance parameters. The pavement damage association model is specifically:

[0007] ;

[0008] Wherein, is the rutting maintenance parameter, is the crack maintenance parameter, is the number of days of the maintenance cycle, is the temperature influence coefficient at the average temperature on the mth day, is the average temperature on the mth day, is the average humidity on the mth day, is the humidity influence coefficient at the average humidity on the mth day, is the number of vehicles on the mth day, is the weight of the nth vehicle, is the rated load weight of the nth vehicle model, is the pavement damage constant, is the overloading damage index;

[0009] Obtain real-time weather data and real-time road data, substitute them into the road surface damage correlation model, accumulate the daily maintenance parameters, and issue a road surface maintenance instruction when the accumulated value of the maintenance parameters is greater than a preset threshold.

[0010] Optionally, the step of accumulating the daily maintenance parameters and issuing a road surface maintenance instruction when the accumulated value of the maintenance parameters is greater than a preset threshold is specifically:

[0011] Construct a maintenance parameter curve with the accumulated value of the daily maintenance parameters after accumulation, perform parameter prediction based on the maintenance parameter curve, identify the maintenance date when the accumulated value of the maintenance parameters is greater than the preset threshold, and issue a road surface maintenance instruction according to the maintenance date for road surface maintenance planning.

[0012] Optionally, after issuing the road surface maintenance instruction when the accumulated value of the maintenance parameters is greater than the preset threshold, it further includes:

[0013] Obtain the current actual maintenance data, compare the current actual maintenance data with the accumulated value of the maintenance parameters. If the difference exceeds the difference threshold, substitute the real-time weather data, real-time road data, and current actual maintenance data into the road surface damage correlation model for correction.

[0014] The second aspect of the present application provides a road surface maintenance planning system based on multi-source data, including:

[0015] A data processing module, configured to obtain historical road data, historical weather data, and historical maintenance data; group the historical road data and historical weather data based on adjacent maintenance time points in the historical maintenance data to obtain multiple groups of maintenance cycle data; perform dimensionless processing on the historical maintenance data in each group of maintenance cycle data to obtain rut maintenance parameters and crack maintenance parameters;

[0016] A model construction module, configured to construct a road surface damage correlation model according to the historical road data, historical weather data, rut maintenance parameters, and crack maintenance parameters. The road surface damage correlation model is specifically:

[0017] ;

[0018] Wherein, is the rut maintenance parameter, is the crack maintenance parameter, is the number of days of the maintenance cycle, is the temperature influence coefficient at the average temperature on the mth day, is the average temperature on the mth day, is the average humidity on the mth day, is the humidity influence coefficient at the average humidity on the mth day, is the number of vehicles on the mth day, is the weight of the nth vehicle, is the rated load of the nth vehicle model, is the road damage constant, is the overloading damage index;

[0019] The maintenance planning module is used to obtain real-time weather data and real-time road data, substitute them into the road damage correlation model, accumulate the daily maintenance parameters, and issue a road maintenance instruction when the accumulated value of the maintenance parameters is greater than a preset threshold.

[0020] Optionally, in the maintenance planning module, accumulating the daily maintenance parameters and issuing a road maintenance instruction when the accumulated value of the maintenance parameters is greater than a preset threshold is specifically:

[0021] Construct a maintenance parameter curve with the accumulated value of the maintenance parameters after accumulating the daily maintenance parameters, perform parameter prediction based on the maintenance parameter curve, identify the maintenance date when the accumulated value of the maintenance parameters is greater than the preset threshold, and issue a road maintenance instruction for road maintenance planning based on the maintenance date.

[0022] Optionally, after the maintenance planning module issues a road maintenance instruction when the accumulated value of the maintenance parameters is greater than the preset threshold, it further includes:

[0023] Obtain the current actual maintenance data, compare the current actual maintenance data with the accumulated value of the maintenance parameters, and if the difference exceeds the difference threshold, substitute the real-time weather data, real-time road data, and current actual maintenance data into the road damage correlation model for correction.

[0024] A third aspect of the present application provides a road maintenance planning method device based on multi-source data, and the device includes a processor and a memory:

[0025] The memory is used to store program code and transmit the program code to the processor;

[0026] The processor is used to execute a road maintenance planning method according to any one of the first aspects of the present invention according to the instructions in the program code.

[0027] A fourth aspect of the present application provides a computer-readable storage medium, and the computer-readable storage medium is used to store program code, and the program code is used to execute a road maintenance planning method according to any one of the first aspects of the present invention.

[0028] As can be seen from the above technical solutions, the present invention has the following advantages: By obtaining historical road data, weather data, and maintenance data of the road surface, grouping the data based on the maintenance time points, and substituting each group of data into the road surface damage correlation model to construct the parameters in the model, the model can reflect the damage situation of vehicles on the road under different weather conditions; substituting real-time road data and weather data into the constructed road surface damage correlation model can determine the time points when the road surface needs to be maintained according to the accumulated maintenance parameters, conduct road surface maintenance planning, accurately judge the disease conditions existing on the road surface, make the maintenance execution efficiency of the road surface higher, and reduce the road surface maintenance cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0030] Figure 1 It is a flowchart of a road surface maintenance planning method based on multi-source data;

[0031] Figure 2 It is a structural diagram of a road surface maintenance planning system based on multi-source data. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] In order to make the invention objectives, features, and advantages of the present invention more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0033] The present invention provides a road surface maintenance planning method based on multi-source data, which is used to solve the problem of low efficiency caused by inaccurate road surface maintenance time planning in the prior art.

[0034] Please refer to Figure 1 , Figure 1 which is the first flowchart of a road surface maintenance planning method based on multi-source data provided by an embodiment of the present invention.

[0035] S100, obtain historical road data, historical weather data, and historical maintenance data; group the historical road data and historical weather data based on adjacent maintenance time points in the historical maintenance data to obtain multiple groups of maintenance cycle data; dimensionlessize the historical maintenance data in each group of maintenance cycle data to obtain rut maintenance parameters and crack maintenance parameters;

[0036] It should be noted that the historical road data includes the passing time of vehicles in the section and vehicle passing information. In this embodiment, the maintained road surface is mainly the highway. The section passed by the vehicle can be determined by the time points when the vehicle enters and exits the toll station on the highway, as well as the passing time; and the vehicle will be weighed at the toll station, and the vehicle type will be recorded, such as vehicle types like small passenger cars, medium-sized passenger cars, large passenger cars, medium-sized freight trucks, large heavy-duty trucks, etc., so as to obtain the passing date and time, vehicle type, and weight of each vehicle passing through the current data processing section; after obtaining the historical road data, the historical weather data of each date can be obtained from the meteorological bureau according to the date, including the change situation of humidity and the change situation of temperature. These two kinds of data have the greatest impact on the damage of the road surface caused by vehicle imprinting. The average value can be used for the historical weather data of each day, that is, the average temperature and average humidity of each day; the historical road surface maintenance plan generally uses empirical values to set fixed maintenance cycles, such as performing maintenance once every fixed number of days. The historical maintenance data will record the time points of each maintenance, as well as the specific road surface damage situation of each maintenance. The time interval between two adjacent maintenance time points is a maintenance cycle. The historical road data, historical weather data, and the maintenance data of the next time point corresponding to the date within this maintenance cycle are related. Therefore, the data can be divided into multiple groups of maintenance cycle data. Each group of maintenance cycle data is the historical road data, historical weather data, and the historical maintenance data of a later time point corresponding to the adjacent maintenance time points;

[0037] The specific road surface damage situation of each maintenance includes the type and quantity of road surface diseases. This embodiment mainly focuses on rut maintenance and crack maintenance. For the historical maintenance data, the areas and depths of each rut are multiplied and then accumulated, and after dimensionlessization, rut maintenance parameters are obtained. The depths and lengths of the cracks are multiplied and then accumulated, and after dimensionlessization, crack maintenance parameters are obtained. These two maintenance parameters reflect the damage of vehicles to the road surface in this group of maintenance cycle data.

[0038] S200, construct a road surface damage correlation model according to the historical road data, historical weather data, rut maintenance parameters, and crack maintenance parameters. The road surface damage correlation model is specifically:

[0039] ;

[0040] Among them, is the rut maintenance parameter, is the crack maintenance parameter, is the number of days of the maintenance cycle, is the temperature influence coefficient at the average temperature on the mth day, is the average temperature on the mth day, is the average humidity on the mth day, is the humidity influence coefficient under the average humidity on the mth day, is the number of vehicles on the mth day, is the weight of the nth vehicle, is the rated load of the nth vehicle model, is the pavement damage constant, is the overload damage index;

[0041] It should be noted that the rutting maintenance parameters and crack maintenance parameters reflect the damage to the road surface caused by vehicles in the maintenance cycle data, and the damage to the road surface is caused by all vehicles passing through in the cycle; changes in vehicle load will cause changes in the pressure on the road surface, and overloaded vehicles will cause the road surface to bear pressure beyond expectations, thereby aggravating the wear and damage of the road surface. The increase in the weight of overloaded vehicles will increase geometrically with the road surface damage. For example, a truck that exceeds the limit by 10% will increase the road surface damage by 40%, and the damage caused by a vehicle that is overloaded by 2 times in one trip is equivalent to the cumulative damage of a non-overloaded vehicle 16 times. Therefore, in the constructed road surface damage association model, the damage caused by the vehicle weight exceeding the corresponding nuclear load weight of its model should be exponential. The road surface damage constant is the normal wear and tear that a non-overloaded vehicle will cause to the road surface. The higher the vehicle overload ratio in the model, the more severe the road surface damage caused.

[0042] High temperature will significantly reduce the stiffness of asphalt materials, resulting in a weakening of their anti-rutting ability. The dynamic stability of asphalt mixture is inversely proportional to temperature. The higher the temperature, the lower the dynamic stability, which increases the probability of pavement rutting disease; while low temperature will make asphalt materials brittle, easily leading to pavement cracks and fissures. In seasonally frozen areas, rapid temperature changes will cause temperature stress in cement concrete pavements, which will damage the pavement structure and cause cracks; damp pavement will have an adverse effect on the adhesion of asphalt, resulting in loose paving, and a large amount of moisture will affect the quality of the finished asphalt and cause cracks; the impact of temperature and humidity on pavement damage can be obtained based on the preset relationship function of the impact of temperature and humidity on asphalt. The daily average temperature and average humidity are respectively substituted into the preset temperature influence coefficient function and humidity influence coefficient function. The pavement damage association model can reflect the cumulative damage to the pavement caused by the weight of each vehicle passing under the daily temperature and humidity, and the model can be further modified based on multiple groups of maintenance cycle data.

[0043] S300 obtains real-time weather data and real-time road data, substitutes them into the road surface damage correlation model, accumulates the daily maintenance parameters, and issues a road surface maintenance instruction when the accumulated value of the maintenance parameters is greater than a preset threshold.

[0044] It should be noted that the real-time weather data and real-time road data need to be obtained after the nearest maintenance time point. Substitute the daily data into the road surface damage correlation model constructed in the previous step, and sum the rut maintenance parameters and the crack maintenance parameters to obtain the accumulated value of the maintenance parameters. This accumulated value of the maintenance parameters reflects the damage to the road surface caused by the vehicle weight on the road surface from the last road surface maintenance to the current date and time under the influence of temperature and humidity. When the accumulated value of the maintenance parameters is greater than the preset threshold, it indicates that the road surface damage has reached the level that affects driving, and the next road surface maintenance is required, that is, a road surface maintenance instruction is issued. The preset threshold can be set according to the actual use requirements of the road surface.

[0045] In this embodiment, by obtaining the historical road data, weather data, and maintenance data of the road surface, grouping the data based on the maintenance time point, and substituting each group of data into the road surface damage correlation model to construct the parameters in the model, the model can reflect the damage to the road surface caused by vehicles driving on this road under different weather conditions; substituting the real-time road data and weather data into the constructed road surface damage correlation model can determine the time point when the road surface needs to be maintained according to the accumulated maintenance parameters, plan the road surface maintenance, accurately judge the disease conditions existing on the road surface, improve the execution efficiency of the road surface maintenance, and reduce the road surface maintenance cost.

[0046] The above is the detailed description of the first embodiment of a road surface maintenance planning method based on multi-source data provided by this application. The following is the detailed description of the second embodiment of a road surface maintenance planning method based on multi-source data provided by this application.

[0047] In this embodiment, a road surface maintenance planning method based on multi-source data is further provided. In the previous step S300, the step of accumulating the daily maintenance parameters and issuing a road surface maintenance instruction when the accumulated value of the maintenance parameters is greater than a preset threshold is specifically as follows: constructing a maintenance parameter curve with the accumulated value of the maintenance parameters after accumulating the daily maintenance parameters, performing parameter prediction based on the maintenance parameter curve, identifying the maintenance date when the accumulated value of the maintenance parameters is greater than the preset threshold, and issuing a road surface maintenance instruction based on the maintenance date for road surface maintenance planning.

[0048] It should be noted that the maintenance of the road surface needs to be planned in advance. Therefore, after the daily maintenance parameters change, it can reflect the trend of vehicles passing through the current road surface to predict the future road surface damage trend. By fitting the accumulated value of the daily accumulated maintenance parameters into a maintenance parameter curve and predicting the future accumulated value of the maintenance parameters based on this curve, the date when the accumulated value of the maintenance parameters will be greater than the preset threshold is identified as the maintenance date for road surface maintenance planning, and the date for road surface maintenance is set in advance.

[0049] Further, in the foregoing step S300, after issuing the road surface maintenance instruction when the accumulated value of the maintenance parameters is greater than the preset threshold, it further includes: obtaining the current actual maintenance data, comparing the current actual maintenance data with the accumulated value of the maintenance parameters, and if the difference exceeds the difference threshold, substituting the real-time weather data, real-time road data, and current actual maintenance data into the road surface damage correlation model for correction.

[0050] It should be noted that when the road surface maintenance instruction is issued to perform road surface maintenance, the current actual maintenance data can be obtained. When the difference between the actual maintenance data and the accumulated value of the maintenance parameters is greater than the difference threshold, it indicates that the time for issuing the planned road surface maintenance instruction may be too late or too early, which is not conducive to the efficient planned road surface maintenance. Therefore, the data needs to be substituted into the road surface damage correlation model again as in the foregoing step S200 to correct the parameters and improve the accuracy of the road surface maintenance planning.

[0051] The above is a detailed description of a road surface maintenance planning method based on multi-source data provided by the first aspect of the present application. The following is a detailed description of an embodiment of a road surface maintenance planning system based on multi-source data provided by the second aspect of the present application.

[0052] Please refer to Figure 2 , Figure 2 which is a structural diagram of a road surface maintenance planning system based on multi-source data. This embodiment provides a road surface maintenance planning system based on multi-source data, including:

[0053] A data processing module 10, configured to obtain historical road data, historical weather data, and historical maintenance data; group the historical road data and historical weather data based on adjacent maintenance time points in the historical maintenance data to obtain multiple groups of maintenance cycle data; and dimensionless the historical maintenance data in each group of maintenance cycle data to obtain rut maintenance parameters and crack maintenance parameters.

[0054] A model construction module 20, configured to construct a road surface damage correlation model according to the historical road data, historical weather data, rut maintenance parameters, and crack maintenance parameters. The road surface damage correlation model is specifically:

[0055] ;

[0056] Among them, is the rut maintenance parameter, is the crack maintenance parameter, is the number of days of the maintenance period, is the temperature influence coefficient at the average temperature on the m-th day, is the average temperature on the m-th day, is the average humidity on the m-th day, is the humidity influence coefficient at the average humidity on the m-th day, is the number of vehicles on the m-th day, is the weight of the n-th vehicle, is the rated load weight of the n-th vehicle model, is the road surface damage constant, is the overload damage index;

[0057] The maintenance planning module 30 is used to obtain real-time weather data and real-time road data, substitute them into the road surface damage correlation model, accumulate the daily maintenance parameters, and issue a road surface maintenance instruction when the accumulated value of the maintenance parameters is greater than a preset threshold.

[0058] Further, in the maintenance planning module 30, when the accumulated value of the daily maintenance parameters is greater than a preset threshold, a road surface maintenance instruction is issued, specifically:

[0059] Construct a maintenance parameter curve with the accumulated value of the daily maintenance parameters, perform parameter prediction based on the maintenance parameter curve, identify the maintenance date when the accumulated value of the maintenance parameters is greater than the preset threshold, and issue a road surface maintenance instruction for road surface maintenance planning based on the maintenance date.

[0060] Further, after the maintenance planning module 30 issues a road surface maintenance instruction when the accumulated value of the maintenance parameters is greater than the preset threshold, it further includes:

[0061] Obtain the current actual maintenance data, compare the current actual maintenance data with the accumulated value of the maintenance parameters. If the difference exceeds the difference threshold, substitute the real-time weather data, real-time road data, and current actual maintenance data into the road surface damage correlation model for correction.

[0062] The third aspect of this application also provides a device for a road surface maintenance planning method based on multi-source data, including a processor and a memory: among them, the memory is used to store program code and transmit the program code to the processor; the processor is used to execute the above-mentioned road surface maintenance planning method based on multi-source data according to the instructions in the program code.

[0063] A fourth aspect of the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium is used to store program code, and the program code is used to execute the above-mentioned pavement maintenance planning method based on multi-source data.

[0064] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0065] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0066] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0067] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0068] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0069] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A road maintenance planning method based on multi-source data, characterized in that include: Obtain historical road data, historical weather data, and historical maintenance data; The historical road data and historical weather data are grouped based on adjacent maintenance time points in the historical maintenance data to obtain multiple groups of maintenance cycle data; the historical maintenance data in each group of maintenance cycle data is dimensionless to obtain rutting maintenance parameters and crack maintenance parameters; A pavement damage association model is constructed based on historical road data, historical weather data, rutting maintenance parameters and crack maintenance parameters. The pavement damage association model is specifically as follows: ； in, is the rutting maintenance parameter, is the crack maintenance parameter, is the number of days of the maintenance cycle, is the temperature influence coefficient at the average temperature on the mth day, is the average temperature on the mth day, is the average humidity on the mth day, is the humidity influence coefficient under the average humidity on the mth day, is the number of vehicles on the mth day, is the weight of the nth vehicle, is the rated load of the nth vehicle model, is the pavement damage constant, is the overload damage index; Real-time weather data and real-time road data are obtained and substituted into the pavement damage association model. Daily maintenance parameters are accumulated and a pavement maintenance instruction is issued when the accumulated value of the maintenance parameter is greater than the preset threshold.

2. A pavement maintenance planning method based on multi-source data according to claim 1, characterized in that: The daily maintenance parameters are accumulated, and when the accumulated value of the maintenance parameters is greater than a preset threshold, a road maintenance instruction is issued, specifically: The maintenance parameter cumulative value after accumulating the daily maintenance parameters is used to construct a maintenance parameter curve, and parameter prediction is performed based on the maintenance parameter curve. The maintenance date when the maintenance parameter cumulative value is greater than the preset threshold is identified, and the pavement maintenance plan is carried out based on the maintenance date to issue a pavement maintenance instruction.

3. The pavement maintenance planning method based on multi-source data according to claim 1 is characterized in that: After issuing the road maintenance instruction when the accumulated value of the maintenance parameter is greater than the preset threshold, the method further includes: The current actual maintenance data is obtained and compared with the accumulated value of the maintenance parameters. If the difference exceeds the difference threshold, the real-time weather data, real-time road data and the current actual maintenance data are substituted into the pavement damage association model for correction.

4. A road maintenance planning system based on multi-source data, characterized in that: include: Data processing module, used to obtain historical road data, historical weather data and historical maintenance data; The historical road data and historical weather data are grouped based on adjacent maintenance time points in the historical maintenance data to obtain multiple groups of maintenance cycle data; the historical maintenance data in each group of maintenance cycle data is dimensionless to obtain rutting maintenance parameters and crack maintenance parameters; The model building module is used to build a pavement damage association model based on historical road data, historical weather data, rutting maintenance parameters and crack maintenance parameters. The pavement damage association model is specifically: ； in, is the rutting maintenance parameter, is the crack maintenance parameter, is the number of days of the maintenance cycle, is the temperature influence coefficient at the average temperature on the mth day, is the average temperature on the mth day, is the average humidity on the mth day, is the humidity influence coefficient under the average humidity on the mth day, is the number of vehicles on the mth day, is the weight of the nth vehicle, is the rated load of the nth vehicle model, is the pavement damage constant, is the overload damage index; The maintenance planning module is used to obtain real-time weather data and real-time road data, and substitute them into the pavement damage association model to accumulate daily maintenance parameters. When the accumulated value of the maintenance parameters is greater than the preset threshold, a pavement maintenance instruction is issued.

5. The road maintenance planning system based on multi-source data according to claim 4 is characterized in that: In the maintenance planning module, daily maintenance parameters are accumulated, and when the accumulated value of the maintenance parameters is greater than a preset threshold, a road maintenance instruction is issued, specifically: The maintenance parameter cumulative value after accumulating the daily maintenance parameters is used to construct a maintenance parameter curve, and parameter prediction is performed based on the maintenance parameter curve. The maintenance date when the maintenance parameter cumulative value is greater than the preset threshold is identified, and the pavement maintenance plan is carried out based on the maintenance date to issue a pavement maintenance instruction.

6. The road maintenance planning system based on multi-source data according to claim 4 is characterized in that: In the maintenance planning module, after issuing a road maintenance instruction when the accumulated value of the maintenance parameter is greater than a preset threshold, the module further includes: The current actual maintenance data is obtained and compared with the accumulated value of the maintenance parameters. If the difference exceeds the difference threshold, the real-time weather data, real-time road data and the current actual maintenance data are substituted into the pavement damage association model for correction.

7. A road maintenance planning device based on multi-source data, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute a pavement maintenance planning method based on multi-source data as described in any one of claims 1-3 according to the instructions in the program code.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the pavement maintenance planning method based on multi-source data as described in any one of claims 1-3.

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