Highway cost intelligent evaluation method and system based on large model, and medium

Abstract

The invention provides a road cost intelligent evaluation method and system based on a large model, and a medium. The method comprises the following steps: obtaining geographic morphology information, environmental geographic information and construction index information of a preset road construction area, inquiring to obtain survey feature data of mountain texture and address structure, processing to obtain mountain geological strength indexes and corresponding mountain geological strength grades, inquiring to obtain a road surface construction standard data set according to the information, and obtaining a road surface construction standard data set according to the road surface construction standard data set. According to the geographic morphology information, the environmental geographic information and the construction index information, generating a regional highway landform distribution feature portrait and extracting highway facility construction quantity data, and then processing by combining highway facility layout unit price data, a material strength coefficient and a safety risk reinforcement coefficient to obtain regional highway facility price estimation data of a preset highway construction region; therefore, mountain foundation strength evaluation and highway facility cost calculation are carried out on the landform information of the highway construction area through the large model, and an intelligent evaluation technology for the complex terrain highway cost is realized.

Description

Technical Field

[0001] The present application relates to the technical field of highway construction, and in particular to a method, system and medium for intelligent evaluation of highway construction cost based on a large model. Background Art

[0002] As one of the important infrastructures, highways play an important role in the country's economic development. Highway construction is a complex and costly project, especially for special terrain areas and mountainous gully areas. Because it includes various facilities such as tunnels, culverts, bridges, lighting and greening, the estimated cost is complex and difficult. Traditional highway cost assessment methods mostly rely on manual experience, which is inefficient and has large deviations. Therefore, how to accurately assess the cost of highway construction projects in complex mountainous gully terrain, and at the same time combine the consideration of the stability of mountain bridge and tunnel structures and roadbed geological structures to obtain a comprehensive and accurate highway facility cost estimation method, has become a vacant technology at present.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a method, system and medium for intelligent assessment of highway construction costs based on a large model. The large model can be used to evaluate the terrain and geomorphic information of the highway construction area to conduct mountain foundation strength assessment and highway facility cost calculation, thereby realizing intelligent assessment technology for highway construction costs in complex terrains.

[0005] This application also provides a highway cost intelligent evaluation method based on a large model, comprising the following steps: Obtain the geographical features and environmental geographical information of the preset highway construction area and the construction index information of the target highway, and obtain the mountain texture survey feature data and geological structure survey feature data based on the environmental geographical information query; Performing model processing according to the mountain texture survey characteristic data and the geological structure survey characteristic data to obtain the mountain geological intensity index and the corresponding mountain geological intensity grade of the preset highway construction area; According to the environmental geographic information combined with the construction index information and the mountain geological strength grade query, an adapted road construction standard data set is obtained, including highway facility layout unit price data, material strength coefficient and safety risk reinforcement coefficient; According to the mountain massif survey characteristic data and the geological structure survey characteristic data, respectively, a culvert and tunnel stability factor and a roadbed strength factor are obtained; Generate a regional highway landform distribution feature image based on the geographic morphology information, environmental geographic information and construction index information, and extract highway landform feature data and highway facility construction volume data; The highway facility construction quantity data and the corresponding highway facility layout unit price data are processed in combination with the material strength coefficient and the safety risk reinforcement coefficient to obtain the regional highway facility valuation data of the preset highway construction area.

[0006] Optionally, in the large model-based intelligent highway cost assessment method described in the present application, the acquisition of geographical morphological information and environmental geographical information of the preset highway construction area and construction index information of the target highway to obtain mountain texture survey feature data and geological structure survey feature data according to the environmental geographical information query includes: Obtain geographical features and environmental geographical information of the preset highway construction area, and obtain construction index information of the target highway, including path distribution index information, special load-bearing level and functional administrative level; The geographical information includes mountain shape information, gully distribution information and river distribution information, and the environmental geographical information includes regional location mark information, mountain texture information and geological structure information; According to the mountain texture information and geological structure information, respectively query the preset regional geological survey database to obtain corresponding mountain texture survey characteristic data and geological structure survey characteristic data; The mountain texture survey characteristic data include mountain hardness data and mountain geological stability data, and the geological structure survey characteristic data include soil layer geological hardness data, soil base stability data and geological structure activity data.

[0007] Optionally, in the large model-based intelligent highway cost assessment method described in the present application, the model processing is performed according to the mountain texture survey feature data and the geological structure survey feature data to obtain the mountain geological intensity index and the corresponding mountain geological intensity grade of the preset highway construction area, including: Processing the mountain texture survey characteristic data and the geological structure survey characteristic data through a preset mountain geological strength assessment model to obtain a mountain geological strength index of the preset highway construction area; According to the mountain geological strength index, a reference query is performed through a preset mountain geological structure reference data table to obtain a corresponding mountain geological strength grade; The calculation formula of the mountain geological strength index is: ; in, is the mountain geological strength index, , , They are soil layer geological hardness data, soil base stability data, and geological structure activity data. , They are respectively the mountain hardness data and the mountain geological stability data. , , , , , is the preset characteristic coefficient.

[0008] Optionally, in the large model-based intelligent highway cost assessment method described in the present application, the adaptive pavement construction standard data set is obtained by querying the environmental geographic information in combination with the construction index information and the mountain geological strength grade, including the unit price data of highway facility layout, the material strength coefficient and the safety risk reinforcement coefficient, including: According to the regional location mark information combined with the special load-bearing level, functional administrative level and mountain geological strength level, a preset road and bridge construction standard queue table is queried to obtain a pavement construction standard data set adapted to the preset highway construction area, including highway facility layout unit price data, material strength coefficient and safety risk reinforcement coefficient; The unit price data of highway facility layout include the unit price data of bridge piers, the unit price data of culverts and tunnels, the unit price data of roadbeds, the unit price data of environmental control facilities and the unit price data of green crossing belt facilities.

[0009] Optionally, in the large model-based intelligent highway cost assessment method described in the present application, the mountain massif survey characteristic data and the geological structure survey characteristic data are processed separately to obtain the culvert and tunnel stability factor and the roadbed strength factor, including: According to the mountain massif survey characteristic data and the geological structure survey characteristic data, they are processed by a preset geological structure evaluation model to obtain a culvert and tunnel stability factor and a roadbed strength factor respectively; The calculation formulas for the culvert and tunnel stability factor and the roadbed strength factor are as follows: ; ; in, , They are the culvert and tunnel stability factor and the roadbed strength factor, , They are respectively the mountain hardness data and the mountain geological stability data. , , They are soil layer geological hardness data, soil base stability data, and geological structure activity data. , , , , , is the preset characteristic coefficient.

[0010] Optionally, in the large model-based intelligent highway cost assessment method described in the present application, the processing of the geographic morphology information, environmental geographic information, and construction index information to generate a regional highway landform distribution feature portrait, and extracting highway landform feature data and highway facility construction volume data, includes: The geographical features information, environmental geographical information and path distribution index information are processed by a preset regional highway landform feature description model to generate a regional highway landform distribution feature portrait of the preset highway construction area; Extracting highway geomorphic feature data according to the regional highway geomorphic distribution feature portrait, including mountain culvert and tunnel distribution feature data, gully bridge and tunnel distribution feature data, roadbed and pavement structure feature data, green crossing belt paving feature data, and lighting facility layout feature data; The highway facility construction quantity data are extracted respectively according to the highway geomorphic feature data, including the total volume data of bridge piers, the distribution volume data of culverts and tunnels, the roadbed laying volume data, the environmental control facility laying volume data and the green crossing belt distribution volume data.

[0011] Optionally, in the large model-based intelligent highway cost assessment method described in the present application, the highway facility construction quantity data and the corresponding highway facility layout unit price data are processed in combination with the material strength coefficient and the safety risk reinforcement coefficient to obtain the regional highway facility price assessment data of the preset highway construction area, including: According to the highway facility construction quantity data and the corresponding highway facility layout unit price data combined with the material strength coefficient, safety risk reinforcement coefficient, culvert and tunnel stability factor and roadbed strength factor, a correction aggregation process is performed to obtain the regional highway facility price estimation data of the preset highway construction area; The calculation formula for the regional highway facility valuation data is: ; in, It is the regional highway facility valuation data. , , , , They are the unit price data of culverts and tunnels, the unit price data of bridge piers, the unit price data of roadbeds, the unit price data of environmental control facilities, and the unit price data of green crossing belt facilities. , , , , They are the volume data of culvert and tunnel distribution, the total volume data of bridge piers, the volume data of roadbed laying, the volume data of environmental control facilities laying, and the volume data of green crossing belt distribution. , They are the culvert and tunnel stability factor and the roadbed strength factor, , are material strength coefficient and safety risk reinforcement coefficient respectively. , , , is the preset characteristic coefficient.

[0012] In a second aspect, the present application provides a highway cost intelligent evaluation system based on a large model, the system comprising: a memory and a processor, the memory comprising a program of a highway cost intelligent evaluation method based on a large model, the program of the highway cost intelligent evaluation method based on a large model being executed by the processor to implement the following steps: Obtain the geographical features and environmental geographical information of the preset highway construction area and the construction index information of the target highway, and obtain the mountain texture survey feature data and geological structure survey feature data based on the environmental geographical information query; Performing model processing according to the mountain texture survey characteristic data and the geological structure survey characteristic data to obtain the mountain geological intensity index and the corresponding mountain geological intensity grade of the preset highway construction area; According to the environmental geographic information combined with the construction index information and the mountain geological strength grade query, an adapted road construction standard data set is obtained, including highway facility layout unit price data, material strength coefficient and safety risk reinforcement coefficient; According to the mountain massif survey characteristic data and the geological structure survey characteristic data, respectively, a culvert and tunnel stability factor and a roadbed strength factor are obtained; Generate a regional highway landform distribution feature image based on the geographic morphology information, environmental geographic information and construction index information, and extract highway landform feature data and highway facility construction volume data; The highway facility construction quantity data and the corresponding highway facility layout unit price data are processed in combination with the material strength coefficient and the safety risk reinforcement coefficient to obtain the regional highway facility valuation data of the preset highway construction area.

[0013] Optionally, in the highway cost intelligent evaluation system based on a large model described in the present application, the acquisition of geographical features information and environmental geographical information of a preset highway construction area and construction index information of a target highway to obtain mountain texture survey feature data and geological structure survey feature data according to the environmental geographical information query includes: Obtain geographical features and environmental geographical information of the preset highway construction area, and obtain construction index information of the target highway, including path distribution index information, special load-bearing level and functional administrative level; The geographical information includes mountain shape information, gully distribution information and river distribution information, and the environmental geographical information includes regional location mark information, mountain texture information and geological structure information; According to the mountain texture information and geological structure information, respectively query the preset regional geological survey database to obtain corresponding mountain texture survey characteristic data and geological structure survey characteristic data; The mountain texture survey characteristic data include mountain hardness data and mountain geological stability data, and the geological structure survey characteristic data include soil layer geological hardness data, soil base stability data and geological structure activity data.

[0014] In the third aspect, the present application also provides a computer-readable storage medium, which stores a program for an intelligent highway cost assessment method based on a large model. When the program for an intelligent highway cost assessment method based on a large model is executed by a processor, the steps of the intelligent highway cost assessment method based on a large model as described in any one of the above items are implemented.

[0015] From the above, it can be seen that the large-scale model-based intelligent evaluation method, system and medium for highway cost provided in the present application obtain the geographical morphological information, environmental geographical information and construction index information of the preset highway construction area, query and obtain the survey characteristic data of the mountain texture and address structure, and process to obtain the mountain geological strength index and mountain geological strength grade as well as the culvert and tunnel stability factor and the roadbed strength factor, and then query and obtain the pavement construction standard data set according to the environmental geographical information, construction index information and mountain geological strength grade, generate the regional highway landform distribution characteristic portrait according to the geographical morphological information, environmental geographical information and construction index information, and extract the highway facility construction quantity data, and then combine the highway facility layout unit price data as well as the material strength coefficient and the safety risk reinforcement coefficient to obtain the regional highway facility price evaluation data of the preset highway construction area; thereby, the terrain and geomorphological information of the highway construction area is evaluated for the mountain foundation strength and the highway facility cost is calculated through the large model, so as to realize the intelligent evaluation technology for the cost of highways in complex terrain.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flowchart of a highway cost intelligent evaluation method based on a large model provided in an embodiment of the present application; Figure 2 A flowchart of obtaining mountain massif survey characteristic data and geological structure survey characteristic data of a highway cost intelligent evaluation method based on a large model provided in an embodiment of the present application; Figure 3 A flowchart of obtaining a mountain geological strength index and a mountain geological strength grade of a highway cost intelligent evaluation method based on a large model provided in an embodiment of the present application; Figure 4 A flowchart of obtaining a standard data set for pavement construction in a large-model-based intelligent highway cost assessment method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 The following is a flow chart of a highway cost intelligent evaluation method based on a large model in some embodiments of the present application. The highway cost intelligent evaluation method based on a large model is used in a terminal device, such as a computer, a mobile phone terminal, etc. The highway cost intelligent evaluation method based on a large model includes the following steps: S11, obtaining geographical features information and environmental geographical information of the preset highway construction area and construction index information of the target highway to be constructed, and obtaining mountain texture survey feature data and geological structure survey feature data according to the environmental geographical information query; S12, performing model processing according to the mountain texture survey characteristic data and the geological structure survey characteristic data to obtain the mountain geological intensity index and the corresponding mountain geological intensity grade of the preset highway construction area; S13, obtaining an adapted road construction standard data set according to the environmental geographic information combined with the construction index information and the mountain geological strength grade query, including highway facility layout unit price data, material strength coefficient and safety risk reinforcement coefficient; S14, respectively processing the mountain massif survey characteristic data and the geological structure survey characteristic data to obtain a culvert and tunnel stability factor and a roadbed strength factor; S15, generating a regional highway landform distribution feature image based on the geographic morphology information, environmental geographic information and construction index information, and extracting highway landform feature data and highway facility construction volume data; S16. Processing the highway facility construction quantity data and the corresponding highway facility layout unit price data in combination with the material strength coefficient and the safety risk reinforcement coefficient to obtain regional highway facility price estimation data for the preset highway construction area.

[0022] Among them, in order to achieve standardized construction and effective cost evaluation of highways in complex terrain with mountainous gullies, and to give weighted consideration to the stability and strength of mountain structure, geological structure, and foundation structure, the terrain, geomorphology, environmental geographical information, and construction indicators of the highway construction area are obtained, and the structural survey data of the mountain texture and foundation geology corresponding to the area are obtained. The mountain geological strength grade is evaluated through a large model. At the same time, the adapted road construction standard data is obtained, including the corresponding unit price of various facilities, material strength and safety reinforcement index, as well as the element factors of culvert and tunnel stability and roadbed strength. Then, by generating a characteristic portrait depicting the distribution of regional highway landforms, characteristic data including the construction volume of highway facilities are extracted. According to the construction volume and unit price combined with the material, safety strength stability coefficient and element factors, the large model is used for calculation and processing, and finally the total cost of highway facilities in the highway construction area is obtained, and the evaluation of the strength and stability of the mountain foundation in the highway construction area and the cost calculation of highway facilities are realized through the large model, so as to realize the intelligent evaluation technology of the cost of highways in complex mountainous gully terrain.

[0023] Please refer to Figure 2 , Figure 2The present invention is a flowchart of obtaining mountain texture survey characteristic data and geological structure survey characteristic data of the intelligent highway cost evaluation method based on a large model in some embodiments of the present application. According to the embodiment of the present invention, the geographical morphology information and environmental geographical information of the preset highway construction area and the construction index information of the target highway are obtained, and the mountain texture survey characteristic data and geological structure survey characteristic data are obtained according to the environmental geographical information query, including: S21. Obtaining geographical features and environmental geographical information of the preset highway construction area, and obtaining construction index information of the target highway, including path distribution index information, special load-bearing level and functional administrative level; S22, the geographical information includes mountain shape information, gully distribution information and river distribution information, and the environmental geographical information includes regional location mark information, mountain texture information and geological structure information; S23, obtaining corresponding mountain texture survey characteristic data and geological structure survey characteristic data through querying a preset regional geological survey database according to the mountain texture information and geological structure information; S24. The mountain texture survey characteristic data includes mountain hardness data and mountain geological stability data, and the geological structure survey characteristic data includes soil layer geological hardness data, soil base stability data and geological structure activity data.

[0024] Among them, in order to evaluate the stability and hardness of the mountain geology and ground geology in the mountain gully area, as well as the corresponding highway construction standards, it is first necessary to obtain the geographical morphological information and environmental geographical information of the preset highway construction area, as well as the construction index information of the target highway to be constructed, including the index information of the distribution of the construction road path, the special load-bearing level of the highway pavement and the functional administrative level of the highway. The geographical morphological information includes the contour morphological information of the mountain, the gully distribution information and the river distribution information. The environmental geographical information includes the marking information of the highway area location, the texture information of the mountain and the geological structure information. Then, according to the mountain texture information and the geological structure information, the corresponding mountain texture survey feature data and geological structure survey feature data are obtained by querying the preset regional geological survey database respectively. The preset regional geological survey database contains the record data of geological structure survey in each area, wherein the mountain texture survey feature data includes the hardness data of the mountain and the stability data of the mountain geological structure, and the geological structure survey feature data includes the hardness data of the ground soil layer geology, the stability data of the soil base layer and the crust activity data of the geological structure.

[0025] Please refer to Figure 3 , Figure 3The present invention is a flowchart of obtaining a mountain geological strength index and a mountain geological strength grade of a highway cost intelligent evaluation method based on a large model in some embodiments of the present application. According to an embodiment of the present invention, the mountain geological strength index and the corresponding mountain geological strength grade of the preset highway construction area are obtained by performing model processing based on the mountain geological survey feature data and the geological structure survey feature data, including: S31, processing the mountain texture survey characteristic data and the geological structure survey characteristic data through a preset mountain geological strength assessment model to obtain a mountain geological strength index of the preset highway construction area; S32, performing a comparison query through a preset mountain geological structure comparison data table according to the mountain geological strength index to obtain a corresponding mountain geological strength grade; The calculation formula of the mountain geological strength index is: ; in, is the mountain geological strength index, , , They are soil layer geological hardness data, soil base stability data, and geological structure activity data. , They are respectively the mountain hardness data and the mountain geological stability data. , , , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset mountain geological survey management database platform).

[0026] Among them, after obtaining the survey data of the mountain texture structure and the ground geological structure of the region, in order to evaluate the overall strength of the mountain geology so as to correspond to the highway construction standard information corresponding to the mountain geological strength, the mountain texture survey characteristic data and the geological structure survey characteristic data are processed through the calculation formula of the preset mountain geological strength assessment model to obtain the mountain geological strength index of the preset highway construction area, and then the preset mountain geological structure reference data table is used for reference query according to the index to obtain the corresponding mountain geological strength grade, that is, the strength grade is obtained by querying the reference data list according to the mountain geological strength index of the highway construction area.

[0027] Please refer to Figure 4 , Figure 4It is a flowchart of obtaining a pavement construction standard data set of a highway cost intelligent evaluation method based on a large model in some embodiments of the present application. According to an embodiment of the present invention, the adaptive pavement construction standard data set is obtained according to the environmental geographic information combined with the construction index information and the mountain geological strength grade query, including the unit price data of highway facility layout, the material strength coefficient and the safety risk reinforcement coefficient, including: S41, according to the regional location mark information combined with the special load-bearing level, functional administrative level and mountain geological strength level, query through the preset road and bridge construction standard queue table to obtain a road construction standard data set adapted to the preset highway construction area, including highway facility layout unit price data, material strength coefficient and safety risk reinforcement coefficient; S42. The highway facility layout unit price data includes bridge pier unit price data, culvert tunnel unit price data, roadbed unit price data, environmental control facility unit price data, and green crossing belt facility unit price data.

[0028] Among them, the preset road and bridge construction standard queue list is a queue list of road and bridge construction standards that contains road construction load-bearing levels, functional administrative levels, mountain geological strength levels and road construction standard data for various regional locations. Through this queue list, the corresponding construction standard data can be obtained based on the obtained regional location marks, special load-bearing levels, functional administrative levels and mountain geological strength levels, including unit price data of highway facilities such as piers, culverts, roadbeds, environmental control facilities, and green crossing belt facilities, as well as the strength coefficient of construction materials and the reinforcement coefficient of road and bridge safety risks.

[0029] According to an embodiment of the present invention, the step of processing the mountain massif survey characteristic data and the geological structure survey characteristic data to obtain the culvert and tunnel stability factor and the roadbed strength factor includes: According to the mountain massif survey characteristic data and the geological structure survey characteristic data, they are processed by a preset geological structure evaluation model to obtain a culvert and tunnel stability factor and a roadbed strength factor respectively; The calculation formulas for the culvert and tunnel stability factor and the roadbed strength factor are as follows: ; ; in, , They are the culvert and tunnel stability factor and the roadbed strength factor, , They are respectively the mountain hardness data and the mountain geological stability data. , , They are soil layer geological hardness data, soil base stability data, and geological structure activity data. , , , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset mountain geological survey management database platform).

[0030] Among them, in order to accurately correct the precise cost data of road facility construction, it is necessary to estimate the stability of culvert tunnels built in the mountain and the strength of the road bridge foundation. The stability of the culvert tunnel and the strength of the road bridge foundation are calculated respectively through the calculation formula of the preset geological structure evaluation model to obtain the culvert tunnel stability factor and the roadbed strength factor respectively.

[0031] According to an embodiment of the present invention, the processing of generating a regional highway landform distribution feature portrait based on the geographic morphology information, environmental geographic information, and construction index information, and extracting highway landform feature data and highway facility construction volume data, includes: The geographical features information, environmental geographical information and path distribution index information are processed by a preset regional highway landform feature description model to generate a regional highway landform distribution feature portrait of the preset highway construction area; Extracting highway geomorphic feature data according to the regional highway geomorphic distribution feature portrait, including mountain culvert and tunnel distribution feature data, gully bridge and tunnel distribution feature data, roadbed and pavement structure feature data, green crossing belt paving feature data, and lighting facility layout feature data; The highway facility construction quantity data are extracted respectively according to the highway geomorphic feature data, including the total volume data of bridge piers, the distribution volume data of culverts and tunnels, the roadbed laying volume data, the environmental control facility laying volume data and the green crossing belt distribution volume data.

[0032] Among them, in order to obtain the total construction amount of bridge piers, culvert tunnels, roadbeds, environmental control lighting equipment and facilities, and green intersection belt facilities included in the highway facility construction projects in the highway construction area, the regional geographical morphology, environmental geography, and path distribution information are processed through a preset regional highway landform feature description model to generate a regional highway landform distribution feature portrait of the preset highway construction area, that is, the distribution of highway facility construction in the region is portrayed through the feature portrait description model. The feature portrait depicts the distribution characteristics of regional highway facility construction, and then the highway landform feature data are extracted according to the generated feature portrait, including the feature data of mountain culverts and tunnels, gully bridges and tunnels, roadbeds and pavements, green intersection belts, and lighting facilities. According to each feature data, the data corresponding to the construction amount of each facility is extracted to obtain the construction and layout usage of bridge piers, culvert tunnels, roadbeds, environmental control lighting equipment and facilities, and green intersection belt facilities for highway construction.

[0033] According to an embodiment of the present invention, the highway facility construction quantity data and the corresponding highway facility layout unit price data are processed in combination with the material strength coefficient and the safety risk reinforcement coefficient to obtain the regional highway facility price estimation data of the preset highway construction area, including: According to the highway facility construction quantity data and the corresponding highway facility layout unit price data combined with the material strength coefficient, safety risk reinforcement coefficient, culvert and tunnel stability factor and roadbed strength factor, a correction aggregation process is performed to obtain the regional highway facility price estimation data of the preset highway construction area; The calculation formula for the regional highway facility valuation data is: ; in, It is the regional highway facility valuation data. , , , , They are the unit price data of culverts and tunnels, the unit price data of bridge piers, the unit price data of roadbeds, the unit price data of environmental control facilities, and the unit price data of green crossing belt facilities. , , , , They are the volume data of culvert and tunnel distribution, the total volume data of bridge piers, the volume data of roadbed laying, the volume data of environmental control facilities laying, and the volume data of green crossing belt distribution. , They are the culvert and tunnel stability factor and the roadbed strength factor, , are material strength coefficient and safety risk reinforcement coefficient respectively. , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset mountain geological survey management database platform).

[0034] Finally, according to the construction volume and corresponding unit price data of various facilities in the highway facility construction area, combined with the material strength coefficient, safety risk reinforcement coefficient, culvert and tunnel stability factor and roadbed strength factor, formula aggregation calculation is performed to obtain the regional highway facility valuation data of the preset highway construction area, that is, to obtain the cost correction estimate of the highway construction area.

[0035] It is worth mentioning that it also includes: The road, bridge and tunnel cost ratio index is obtained by processing the regional highway facility construction valuation data of the preset highway construction area with the total volume data of bridge piers, the volume data of culvert and tunnel distribution, and the roadbed laying volume data; Comparing the road, bridge and tunnel cost ratio index with the average road, bridge and tunnel cost ratio index of a plurality of similar highway opening areas, obtaining road, bridge and tunnel cost ratio comparison data; Performing a threshold comparison based on the road-bridge-tunnel price ratio comparison data and a preset road-bridge-tunnel price ratio threshold, and judging the highway paving construction effect of the preset highway construction area based on the threshold comparison result; The calculation formula of the road, bridge and tunnel cost weight index is: ; The calculation formula for the comparison data of road-bridge-tunnel price ratio is: ; in, is the cost ratio index of roads, bridges and tunnels, For comparison data of road, bridge and tunnel price ratio, It is the regional highway facility valuation data. , , They are the culvert and tunnel distribution volume data, bridge pier total volume data, and roadbed paving volume data. , They are the culvert and tunnel stability factor and the roadbed strength factor, is the sum of the road, bridge and tunnel cost ratio indexes of n similar highway opening areas, , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset mountain geological survey management database platform).

[0036] Among them, in order to measure and evaluate the effectiveness of regional highway construction, the proportion of the piers, culverts, and roadbed paving quantities and cost valuation data of the main facilities of regional highway construction, that is, the road, bridge, and tunnel cost proportion index, is compared with the average of the proportion index of similar highway construction and opening areas to obtain control data, that is, the facility quantity cost proportion data of the highway construction area is compared with the average cost proportion data of similar highway construction to generate control data, and then the control data of the road, bridge, and tunnel price ratio is compared with the preset road, bridge, and tunnel price ratio threshold for threshold comparison, and the regional highway paving construction effectiveness is judged according to the threshold comparison results. If the threshold comparison result does not meet the preset threshold comparison requirements, it indicates that the construction proportion of the core highway facilities in the region is insufficient, the planning rationality of the highway construction is not good, and the highway facility construction plan needs to be re-optimized to achieve the effectiveness evaluation of highway facility construction.

[0037] In a second aspect, the present invention further discloses a highway cost intelligent evaluation system based on a large model, comprising a memory and a processor, wherein the memory comprises a highway cost intelligent evaluation method program based on a large model, and when the highway cost intelligent evaluation method program based on a large model is executed by the processor, the following steps are implemented: Obtain the geographical features and environmental geographical information of the preset highway construction area and the construction index information of the target highway, and obtain the mountain texture survey feature data and geological structure survey feature data based on the environmental geographical information query; Performing model processing according to the mountain texture survey characteristic data and the geological structure survey characteristic data to obtain the mountain geological intensity index and the corresponding mountain geological intensity grade of the preset highway construction area; According to the environmental geographic information combined with the construction index information and the mountain geological strength grade query, an adapted road construction standard data set is obtained, including highway facility layout unit price data, material strength coefficient and safety risk reinforcement coefficient; According to the mountain massif survey characteristic data and the geological structure survey characteristic data, respectively, a culvert and tunnel stability factor and a roadbed strength factor are obtained; Generate a regional highway landform distribution feature image based on the geographic morphology information, environmental geographic information and construction index information, and extract highway landform feature data and highway facility construction volume data; The highway facility construction quantity data and the corresponding highway facility layout unit price data are processed in combination with the material strength coefficient and the safety risk reinforcement coefficient to obtain the regional highway facility valuation data of the preset highway construction area.

[0038] Among them, in order to achieve standardized construction and effective cost evaluation of highways in complex terrain with mountainous gullies, and to give weighted consideration to the stability and strength of mountain structure, geological structure, and foundation structure, the terrain, geomorphology, environmental geographical information, and construction indicators of the highway construction area are obtained, and the structural survey data of the mountain texture and foundation geology corresponding to the area are obtained. The mountain geological strength grade is evaluated through a large model. At the same time, the adapted road construction standard data is obtained, including the corresponding unit price of various facilities, material strength and safety reinforcement index, as well as the element factors of culvert and tunnel stability and roadbed strength. Then, by generating a characteristic portrait depicting the distribution of regional highway landforms, characteristic data including the construction volume of highway facilities are extracted. According to the construction volume and unit price combined with the material, safety strength stability coefficient and element factors, the large model is used for calculation and processing, and finally the total cost of highway facilities in the highway construction area is obtained, and the evaluation of the strength and stability of the mountain foundation in the highway construction area and the cost calculation of highway facilities are realized through the large model, so as to realize the intelligent evaluation technology of the cost of highways in complex mountainous gully terrain.

[0039] According to an embodiment of the present invention, the step of obtaining geographical features information and environmental geographical information of a preset highway construction area and construction index information of a target highway to obtain mountain texture survey feature data and geological structure survey feature data according to the environmental geographical information query includes: Obtain geographical features and environmental geographical information of the preset highway construction area, and obtain construction index information of the target highway, including path distribution index information, special load-bearing level and functional administrative level; The geographical information includes mountain shape information, gully distribution information and river distribution information, and the environmental geographical information includes regional location mark information, mountain texture information and geological structure information; According to the mountain texture information and geological structure information, respectively query the preset regional geological survey database to obtain corresponding mountain texture survey characteristic data and geological structure survey characteristic data; The mountain texture survey characteristic data include mountain hardness data and mountain geological stability data, and the geological structure survey characteristic data include soil layer geological hardness data, soil base stability data and geological structure activity data.

[0040] Among them, in order to evaluate the stability and hardness of the mountain geology and ground geology in the mountain gully area, as well as the corresponding highway construction standards, it is first necessary to obtain the geographical morphological information and environmental geographical information of the preset highway construction area, as well as the construction index information of the target highway to be constructed, including the index information of the distribution of the construction road path, the special load-bearing level of the highway pavement and the functional administrative level of the highway. The geographical morphological information includes the contour morphological information of the mountain, the gully distribution information and the river distribution information. The environmental geographical information includes the marking information of the highway area location, the texture information of the mountain and the geological structure information. Then, according to the mountain texture information and the geological structure information, the corresponding mountain texture survey feature data and geological structure survey feature data are obtained by querying the preset regional geological survey database respectively. The preset regional geological survey database contains the record data of geological structure survey in each area, wherein the mountain texture survey feature data includes the hardness data of the mountain and the stability data of the mountain geological structure, and the geological structure survey feature data includes the hardness data of the ground soil layer geology, the stability data of the soil base layer and the crust activity data of the geological structure.

[0041] According to an embodiment of the present invention, the model processing is performed according to the mountain texture survey feature data and the geological structure survey feature data to obtain the mountain geological intensity index and the corresponding mountain geological intensity grade of the preset highway construction area, including: Processing the mountain texture survey characteristic data and the geological structure survey characteristic data through a preset mountain geological strength assessment model to obtain a mountain geological strength index of the preset highway construction area; According to the mountain geological strength index, a reference query is performed through a preset mountain geological structure reference data table to obtain a corresponding mountain geological strength grade; The calculation formula of the mountain geological strength index is: ; in, is the mountain geological strength index, , , They are soil layer geological hardness data, soil base stability data, and geological structure activity data. , They are respectively the mountain hardness data and the mountain geological stability data. , , , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset mountain geological survey management database platform).

[0042] Among them, after obtaining the survey data of the mountain texture structure and the ground geological structure of the region, in order to evaluate the overall strength of the mountain geology so as to correspond to the highway construction standard information corresponding to the mountain geological strength, the mountain texture survey characteristic data and the geological structure survey characteristic data are processed through the calculation formula of the preset mountain geological strength assessment model to obtain the mountain geological strength index of the preset highway construction area, and then the preset mountain geological structure reference data table is used for reference query according to the index to obtain the corresponding mountain geological strength grade, that is, the strength grade is obtained by querying the reference data list according to the mountain geological strength index of the highway construction area.

[0043] According to an embodiment of the present invention, the adaptive pavement construction standard data set is obtained by querying the environmental geographic information in combination with the construction index information and the mountain geological strength grade, including the unit price data of highway facility layout, the material strength coefficient and the safety risk reinforcement coefficient, including: According to the regional location mark information combined with the special load-bearing level, functional administrative level and mountain geological strength level, a preset road and bridge construction standard queue table is queried to obtain a pavement construction standard data set adapted to the preset highway construction area, including highway facility layout unit price data, material strength coefficient and safety risk reinforcement coefficient; The highway facility layout unit price data includes bridge pier unit price data, culvert tunnel unit price data, roadbed unit price data, environmental control facility unit price data and green crossing belt facility unit price data.

[0044] Among them, the preset road and bridge construction standard queue list is a queue list of road and bridge construction standards that contains road construction load-bearing levels, functional administrative levels, mountain geological strength levels and road construction standard data for various regional locations. Through this queue list, the corresponding construction standard data can be obtained based on the obtained regional location marks, special load-bearing levels, functional administrative levels and mountain geological strength levels, including unit price data of highway facilities such as piers, culverts, roadbeds, environmental control facilities, and green crossing belt facilities, as well as the strength coefficient of construction materials and the reinforcement coefficient of road and bridge safety risks.

[0045] According to an embodiment of the present invention, the step of processing the mountain massif survey characteristic data and the geological structure survey characteristic data to obtain the culvert and tunnel stability factor and the roadbed strength factor includes: According to the mountain massif survey characteristic data and the geological structure survey characteristic data, they are processed by a preset geological structure evaluation model to obtain a culvert and tunnel stability factor and a roadbed strength factor respectively; The calculation formulas for the culvert and tunnel stability factor and the roadbed strength factor are as follows: ; ; in, , They are the culvert and tunnel stability factor and the roadbed strength factor, , They are respectively the mountain hardness data and the mountain geological stability data. , , They are soil layer geological hardness data, soil base stability data, and geological structure activity data. , , , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset mountain geological survey management database platform).

[0046] Among them, in order to accurately correct the precise cost data of road facility construction, it is necessary to estimate the stability of culvert tunnels built in the mountain and the strength of the road bridge foundation. The stability of the culvert tunnel and the strength of the road bridge foundation are calculated respectively through the calculation formula of the preset geological structure evaluation model to obtain the culvert tunnel stability factor and the roadbed strength factor respectively.

[0047] According to an embodiment of the present invention, the processing of generating a regional highway landform distribution feature portrait based on the geographic morphology information, environmental geographic information, and construction index information, and extracting highway landform feature data and highway facility construction volume data, includes: The geographical features information, environmental geographical information and path distribution index information are processed by a preset regional highway landform feature description model to generate a regional highway landform distribution feature portrait of the preset highway construction area; Extracting highway geomorphic feature data according to the regional highway geomorphic distribution feature portrait, including mountain culvert and tunnel distribution feature data, gully bridge and tunnel distribution feature data, roadbed and pavement structure feature data, green crossing belt paving feature data, and lighting facility layout feature data; The highway facility construction quantity data are extracted respectively according to the highway geomorphic feature data, including the total volume data of bridge piers, the distribution volume data of culverts and tunnels, the roadbed laying volume data, the environmental control facility laying volume data and the green crossing belt distribution volume data.

[0048] Among them, in order to obtain the total construction amount of bridge piers, culvert tunnels, roadbeds, environmental control lighting equipment and facilities, and green intersection belt facilities included in the highway facility construction projects in the highway construction area, the regional geographical morphology, environmental geography, and path distribution information are processed through a preset regional highway landform feature description model to generate a regional highway landform distribution feature portrait of the preset highway construction area, that is, the distribution of highway facility construction in the region is portrayed through the feature portrait description model. The feature portrait depicts the distribution characteristics of regional highway facility construction, and then the highway landform feature data are extracted according to the generated feature portrait, including the feature data of mountain culverts and tunnels, gully bridges and tunnels, roadbeds and pavements, green intersection belts, and lighting facilities. According to each feature data, the data corresponding to the construction amount of each facility is extracted to obtain the construction and layout usage of bridge piers, culvert tunnels, roadbeds, environmental control lighting equipment and facilities, and green intersection belt facilities for highway construction.

[0049] According to an embodiment of the present invention, the highway facility construction quantity data and the corresponding highway facility layout unit price data are processed in combination with the material strength coefficient and the safety risk reinforcement coefficient to obtain the regional highway facility price estimation data of the preset highway construction area, including: According to the highway facility construction quantity data and the corresponding highway facility layout unit price data combined with the material strength coefficient, safety risk reinforcement coefficient, culvert and tunnel stability factor and roadbed strength factor, a correction aggregation process is performed to obtain the regional highway facility price estimation data of the preset highway construction area; The calculation formula for the regional highway facility valuation data is: ; in, It is the regional highway facility valuation data. , , , , They are the unit price data of culverts and tunnels, the unit price data of bridge piers, the unit price data of roadbeds, the unit price data of environmental control facilities, and the unit price data of green crossing belt facilities. , , , , They are the volume data of culvert and tunnel distribution, the total volume data of bridge piers, the volume data of roadbed laying, the volume data of environmental control facilities laying, and the volume data of green crossing belt distribution. , They are the culvert and tunnel stability factor and the roadbed strength factor, , are material strength coefficient and safety risk reinforcement coefficient respectively. , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset mountain geological survey management database platform).

[0050] Finally, according to the construction volume and corresponding unit price data of various facilities in the highway facility construction area, combined with the material strength coefficient, safety risk reinforcement coefficient, culvert and tunnel stability factor and roadbed strength factor, formula aggregation calculation is performed to obtain the regional highway facility valuation data of the preset highway construction area, that is, to obtain the cost correction estimate of the highway construction area.

[0051] It is worth mentioning that it also includes: The road, bridge and tunnel cost ratio index is obtained by processing the regional highway facility construction valuation data of the preset highway construction area with the total volume data of bridge piers, the volume data of culvert and tunnel distribution, and the roadbed laying volume data; Comparing the road, bridge and tunnel cost ratio index with the average road, bridge and tunnel cost ratio index of a plurality of similar highway opening areas, obtaining road, bridge and tunnel cost ratio comparison data; Performing a threshold comparison based on the road-bridge-tunnel price ratio comparison data and a preset road-bridge-tunnel price ratio threshold, and judging the highway paving construction effect of the preset highway construction area based on the threshold comparison result; The calculation formula of the road, bridge and tunnel cost weight index is: ; The calculation formula for the comparison data of road-bridge-tunnel price ratio is: ; in, is the cost ratio index of roads, bridges and tunnels, For comparison data of road, bridge and tunnel price ratio, It is the regional highway facility valuation data. , , They are the culvert and tunnel distribution volume data, bridge pier total volume data, and roadbed paving volume data. , They are the culvert and tunnel stability factor and the roadbed strength factor, is the sum of the road, bridge and tunnel cost ratio indexes of n similar highway opening areas, , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset mountain geological survey management database platform).

[0052] Among them, in order to measure and evaluate the effectiveness of regional highway construction, the proportion of the piers, culverts, and roadbed paving quantities and cost valuation data of the main facilities of regional highway construction, that is, the road, bridge, and tunnel cost proportion index, is compared with the average of the proportion index of similar highway construction and opening areas to obtain control data, that is, the facility quantity cost proportion data of the highway construction area is compared with the average cost proportion data of similar highway construction to generate control data, and then the control data of the road, bridge, and tunnel price ratio is compared with the preset road, bridge, and tunnel price ratio threshold for threshold comparison, and the regional highway paving construction effectiveness is judged according to the threshold comparison results. If the threshold comparison result does not meet the preset threshold comparison requirements, it indicates that the construction proportion of the core highway facilities in the region is insufficient, the planning rationality of the highway construction is not good, and the highway facility construction plan needs to be re-optimized to achieve the effectiveness evaluation of highway facility construction.

[0053] The third aspect of the present invention provides a readable storage medium, which stores a program for an intelligent highway cost assessment method based on a large model. When the program for an intelligent highway cost assessment method based on a large model is executed by a processor, the steps of the intelligent highway cost assessment method based on a large model as described in any one of the above items are implemented.

[0054] The present invention discloses a large-model-based intelligent highway cost assessment method, system and medium. By acquiring geographic morphological information, environmental geographic information and construction index information of a preset highway construction area, the survey characteristic data of mountain texture and address structure are queried and obtained, and the mountain geological strength index and mountain geological strength grade as well as culvert and tunnel stability factor and roadbed strength factor are processed. Then, a pavement construction standard data set is queried according to the environmental geographic information, construction index information and mountain geological strength grade. A regional highway landform distribution characteristic portrait is generated according to the geographic morphological information, environmental geographic information and construction index information, and highway facility construction quantity data is extracted. Then, the regional highway facility price assessment data of the preset highway construction area is obtained by combining the highway facility layout unit price data as well as the material strength coefficient and the safety risk reinforcement coefficient. Thus, the terrain and geomorphological information of the highway construction area is evaluated for the mountain foundation strength and the highway facility cost is calculated through the large model, so as to realize the intelligent assessment technology for the cost of highways in complex terrains.

[0055] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0056] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0057] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0058] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0059] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. The intelligent evaluation method of highway cost based on the large model includes the following steps: Obtain the geographical features and environmental geographical information of the preset highway construction area and the construction index information of the target highway, and obtain the mountain texture survey feature data and geological structure survey feature data based on the environmental geographical information query; Performing model processing according to the mountain texture survey characteristic data and the geological structure survey characteristic data to obtain the mountain geological intensity index and the corresponding mountain geological intensity grade of the preset highway construction area; According to the environmental geographic information combined with the construction index information and the mountain geological strength grade query, an adapted road construction standard data set is obtained, including highway facility layout unit price data, material strength coefficient and safety risk reinforcement coefficient; According to the mountain massif survey characteristic data and the geological structure survey characteristic data, respectively, a culvert and tunnel stability factor and a roadbed strength factor are obtained; Generate a regional highway landform distribution feature image based on the geographic morphology information, environmental geographic information and construction index information, and extract highway landform feature data and highway facility construction volume data; The highway facility construction quantity data and the corresponding highway facility layout unit price data are processed in combination with the material strength coefficient and the safety risk reinforcement coefficient to obtain the regional highway facility valuation data of the preset highway construction area.

2. The highway cost intelligent evaluation method based on a large model according to claim 1 is characterized in that: The method of obtaining geographical features information and environmental geographical information of the preset highway construction area and construction index information of the target highway to obtain mountain texture survey feature data and geological structure survey feature data according to the environmental geographical information query includes: Obtain geographical features and environmental geographical information of the preset highway construction area, and obtain construction index information of the target highway, including path distribution index information, special load-bearing level and functional administrative level; The geographical information includes mountain shape information, gully distribution information and river distribution information, and the environmental geographical information includes regional location mark information, mountain texture information and geological structure information; According to the mountain texture information and geological structure information, respectively query the preset regional geological survey database to obtain corresponding mountain texture survey characteristic data and geological structure survey characteristic data; The mountain texture survey characteristic data include mountain hardness data and mountain geological stability data, and the geological structure survey characteristic data include soil layer geological hardness data, soil base stability data and geological structure activity data.

3. The method for intelligent highway cost assessment based on a large model according to claim 2 is characterized in that: The model processing is performed according to the mountain texture survey characteristic data and the geological structure survey characteristic data to obtain the mountain geological intensity index and the corresponding mountain geological intensity grade of the preset highway construction area, including: Processing the mountain texture survey characteristic data and the geological structure survey characteristic data through a preset mountain geological strength assessment model to obtain a mountain geological strength index of the preset highway construction area; According to the mountain geological strength index, a reference query is performed through a preset mountain geological structure reference data table to obtain a corresponding mountain geological strength grade; The calculation formula of the mountain geological strength index is: ； in, is the mountain geological strength index, , , They are soil layer geological hardness data, soil base stability data, and geological structure activity data. , They are respectively the mountain hardness data and the mountain geological stability data. , , , , , is the preset characteristic coefficient.

4. The highway cost intelligent evaluation method based on a large model according to claim 3 is characterized in that: The method of obtaining an adapted road construction standard data set based on the environmental geographic information combined with the construction index information and the mountain geological strength grade query includes the unit price data of highway facility layout, material strength coefficient and safety risk reinforcement coefficient, including: According to the regional location mark information combined with the special load-bearing level, functional administrative level and mountain geological strength level, a preset road and bridge construction standard queue table is queried to obtain a pavement construction standard data set adapted to the preset highway construction area, including highway facility layout unit price data, material strength coefficient and safety risk reinforcement coefficient; The unit price data of highway facility layout include the unit price data of bridge piers, the unit price data of culverts and tunnels, the unit price data of roadbeds, the unit price data of environmental control facilities and the unit price data of green crossing belt facilities.

5. The highway cost intelligent evaluation method based on a large model according to claim 4 is characterized in that: The method of processing the mountain massif survey characteristic data and the geological structure survey characteristic data to obtain the culvert and tunnel stability factor and the roadbed strength factor includes: According to the mountain massif survey characteristic data and the geological structure survey characteristic data, they are processed by a preset geological structure evaluation model to obtain a culvert and tunnel stability factor and a roadbed strength factor respectively; The calculation formulas for the culvert and tunnel stability factor and the roadbed strength factor are as follows: ； ； in, , They are the culvert and tunnel stability factor and the roadbed strength factor, , They are respectively the mountain hardness data and the mountain geological stability data. , , They are soil layer geological hardness data, soil base stability data, and geological structure activity data. , , , , , is the preset characteristic coefficient.

6. The highway cost intelligent evaluation method based on a large model according to claim 5 is characterized in that: The processing of generating a regional highway landform distribution feature image based on the geographic morphology information, environmental geographic information and construction index information, and extracting highway landform feature data and highway facility construction volume data, includes: The geographical features information, environmental geographical information and path distribution index information are processed by a preset regional highway landform feature description model to generate a regional highway landform distribution feature portrait of the preset highway construction area; Extracting highway geomorphic feature data according to the regional highway geomorphic distribution feature portrait, including mountain culvert and tunnel distribution feature data, gully bridge and tunnel distribution feature data, roadbed and pavement structure feature data, green crossing belt paving feature data, and lighting facility layout feature data; The highway facility construction quantity data are extracted respectively according to the highway geomorphic feature data, including the total volume data of bridge piers, the distribution volume data of culverts and tunnels, the roadbed laying volume data, the environmental control facility laying volume data and the green crossing belt distribution volume data.

7. The method for intelligent highway cost assessment based on a large model according to claim 6 is characterized in that: The processing of the highway facility construction quantity data and the corresponding highway facility layout unit price data in combination with the material strength coefficient and the safety risk reinforcement coefficient to obtain the regional highway facility price assessment data of the preset highway construction area includes: According to the highway facility construction quantity data and the corresponding highway facility layout unit price data combined with the material strength coefficient, safety risk reinforcement coefficient, culvert and tunnel stability factor and roadbed strength factor, a correction aggregation process is performed to obtain the regional highway facility price estimation data of the preset highway construction area; The calculation formula for the regional highway facility valuation data is: ； in, It is the regional highway facility valuation data. , , , , They are the unit price data of culverts and tunnels, the unit price data of bridge piers, the unit price data of roadbeds, the unit price data of environmental control facilities, and the unit price data of green crossing belt facilities. , , , , They are the volume data of culvert and tunnel distribution, the total volume data of bridge piers, the volume data of roadbed laying, the volume data of environmental control facilities laying, and the volume data of green crossing belt distribution. , They are the culvert and tunnel stability factor and the roadbed strength factor, , are material strength coefficient and safety risk reinforcement coefficient respectively. , , , is the preset characteristic coefficient.

8. The intelligent evaluation system for highway construction cost based on large models is characterized by: The system includes: a memory and a processor, wherein the memory includes a program of a highway cost intelligent evaluation method based on a large model, and when the program of the highway cost intelligent evaluation method based on a large model is executed by the processor, the following steps are implemented: Obtain the geographical features and environmental geographical information of the preset highway construction area and the construction index information of the target highway, and obtain the mountain texture survey feature data and geological structure survey feature data based on the environmental geographical information query; Performing model processing according to the mountain texture survey characteristic data and the geological structure survey characteristic data to obtain the mountain geological intensity index and the corresponding mountain geological intensity grade of the preset highway construction area; According to the environmental geographic information combined with the construction index information and the mountain geological strength grade query, an adapted road construction standard data set is obtained, including highway facility layout unit price data, material strength coefficient and safety risk reinforcement coefficient; According to the mountain massif survey characteristic data and the geological structure survey characteristic data, respectively, a culvert and tunnel stability factor and a roadbed strength factor are obtained; Generate a regional highway landform distribution feature image based on the geographic morphology information, environmental geographic information and construction index information, and extract highway landform feature data and highway facility construction volume data; The highway facility construction quantity data and the corresponding highway facility layout unit price data are processed in combination with the material strength coefficient and the safety risk reinforcement coefficient to obtain the regional highway facility valuation data of the preset highway construction area.

9. The highway cost intelligent evaluation system based on large model according to claim 8 is characterized in that: The method of obtaining geographical features information and environmental geographical information of the preset highway construction area and construction index information of the target highway to obtain mountain texture survey feature data and geological structure survey feature data according to the environmental geographical information query includes: Obtain geographical features and environmental geographical information of the preset highway construction area, and obtain construction index information of the target highway, including path distribution index information, special load-bearing level and functional administrative level; The geographical information includes mountain shape information, gully distribution information and river distribution information, and the environmental geographical information includes regional location mark information, mountain texture information and geological structure information; According to the mountain texture information and geological structure information, respectively query the preset regional geological survey database to obtain corresponding mountain texture survey characteristic data and geological structure survey characteristic data; The mountain texture survey characteristic data include mountain hardness data and mountain geological stability data, and the geological structure survey characteristic data include soil layer geological hardness data, soil base stability data and geological structure activity data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a program for an intelligent highway cost assessment method based on a large model. When the program for an intelligent highway cost assessment method based on a large model is executed by a processor, the steps of the intelligent highway cost assessment method based on a large model as described in any one of claims 1 to 7 are implemented.

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