Power transmission line foundation geotechnical layering calculation and quota intelligent matching method and device

Abstract

The embodiment of the application provides a power transmission line foundation rock-soil layered calculation and quota intelligent matching method and device. The application relates to the field of data processing of power engineering design and engineering cost management, and the method comprises the following steps: acquiring engineering structured data and a soil quality division rule knowledge base, constructing a logical corresponding relationship among foundation types, rock-soil layer properties and excavation modes, and combining tower leg soil layer cumulative thickness and design pit depth to perform leg-by-leg layered calculation, forming rock-soil layer engineering quantities, and then realizing intelligent classification and set price output according to a list quota version index. The method is used to improve the processing efficiency of layered calculation and quota matching.

Description

Technical Field

[0001] This application relates to the field of data processing in power engineering design and engineering cost management, and in particular to a method and device for layered calculation and intelligent matching of quotas for the foundation soil of transmission lines. Background Technology

[0002] In the design and cost management of power transmission line projects, the foundation soil is usually classified and the engineering quantity is calculated based on the survey report, regional specifications and quota documents.

[0003] However, existing methods rely heavily on manual comparison of scattered rules and layer-by-layer calculation using tables. This makes it difficult to adapt rules to different regions, project attributes, and foundation types. Furthermore, inconsistencies in the data standards for surveying and technical and economic data can lead to low efficiency in quantity statistics and limited stability and accuracy of calculation results, making it difficult to adapt to timely updates to quota versions.

[0004] Therefore, how to improve the accuracy and efficiency of soil classification and engineering quantity calculation for transmission line foundations under the conditions of multiple source standards coexisting and dynamic rule changes has become a technical problem that needs to be solved. Summary of the Invention

[0005] This application provides a method and device for intelligent matching of soil and rock layer calculation and quota for transmission line foundations. Addressing the problem of poor coordination between soil classification, layered quantity calculation, and quota application in transmission line engineering, this application unifies the association between the foundation type, soil and rock layer characteristics, and construction compatibility of the project to be processed. It also establishes a collaborative processing mechanism by combining quantity results with quota rules, thereby achieving intelligent processing of soil and rock layer calculation and quota matching for transmission line foundations. This improves data processing consistency, calculation accuracy, and operational efficiency under conditions of multiple source standards coexisting and dynamic rule changes.

[0006] In a first aspect, embodiments of this application provide a method for layered calculation and intelligent matching of quotas for soil and rock layers in transmission line foundations, including:

[0007] Obtain the structured data of the project to be processed and the pre-set knowledge base of soil classification rules; the structured data includes foundation type information and soil and rock layer information, and the knowledge base of soil classification rules contains at least the mapping relationship between foundation type, soil and rock layer properties and excavation method;

[0008] Based on structured data and a knowledge base of soil classification rules, a correspondence matrix is ​​established. The correspondence matrix is ​​used to characterize the logical mapping relationship between the physical and mechanical properties of soil and rock layers and the appropriate excavation and construction methods under different foundation type constraints.

[0009] Based on the corresponding matrix, the cumulative soil layer thickness and pit depth matching algorithm is applied to determine the tower leg layer by layer and calculate the engineering quantity of each soil and rock layer.

[0010] A smart matching engine is built to match, classify, and price the project based on the quantity of work and the preset list quota version index, and output the processing results of the structured data of the project to be processed.

[0011] In one possible embodiment, prior to the step of obtaining the structured data of the project to be processed, the following steps are included:

[0012] The differences in soil classification standards caused by variations in region, voltage level, and foundation type were analyzed.

[0013] Collect engineering data submission forms, filter and abstract the granularity of basic information required for calculation, and establish a unified standard for basic information submission of transmission lines;

[0014] Receive the original design data, perform format validation and field mapping according to specifications, and generate structured data.

[0015] In one possible embodiment, the steps for constructing the soil classification rule knowledge base include:

[0016] Integrate industry and regional standards to build a dynamic and scalable initial knowledge base;

[0017] Blockchain technology is used to manage the version of the initial knowledge base, storing standard update records on blockchain nodes and generating a unique hash value for each version rule;

[0018] By monitoring policy document updates through smart contracts, when a new version of the standard is detected, the rule base is automatically updated to form a knowledge base of soil classification rules.

[0019] In one possible embodiment, a corresponding matrix is ​​established based on structured data and a knowledge base of soil classification rules, including:

[0020] The information on soil and rock layers in historical survey reports was statistically analyzed, and preliminary soil classification was carried out based on multi-source classification standards.

[0021] An adaptive classification model based on soil and rock layer features based on machine learning is introduced. The model uses the update rules in the soil classification rule knowledge base as training constraints.

[0022] Features of soil and rock layers are extracted using a convolutional neural network, and multidimensional feature vectors are constructed by combining density, water content, and compressive strength parameters from the exploration data.

[0023] The decision tree algorithm is used to match multidimensional feature vectors, output the optimal soil and rock layer classification result and its suitable excavation method, and generate the corresponding matrix.

[0024] In one possible embodiment, the step of performing leg-by-leg layer determination using the soil cumulative thickness and pit depth matching algorithm includes:

[0025] An edge computing framework is introduced to perform distributed processing for complex hierarchical tasks of generating corresponding matrices.

[0026] The task of determining the layer by layer is broken down into multiple sub-tasks, including layer thickness calculation, excavation method matching, and excavation volume accumulation, and then distributed to edge computing nodes for parallel execution.

[0027] Computational resources are dynamically allocated based on the geological complexity label of the tower legs, with priority given to computational tasks for tower legs with high geological complexity.

[0028] In one possible embodiment, a smart matching engine is constructed, including:

[0029] Establish a soil type-quota item mapping table and version matching index;

[0030] A multi-objective optimization algorithm is introduced, taking project attributes and different versions of quota rules as optimization objectives;

[0031] When a quota version conflict is detected, the difference in engineering quantity between different versions is calculated through a genetic algorithm to generate the optimal quota matching scheme. The optimal quota matching scheme includes suggestions for adjusting the soil and rock layer classification and suggestions for optimizing the excavation method.

[0032] In one possible embodiment, after outputting the processing result, the following is also included:

[0033] Integrate augmented reality (AR) devices and call upon the processing results and actual terrain model data;

[0034] The excavation volume distribution, soil and rock layer classification, and quota item matching information of each tower leg are overlaid and displayed in the AR field of view;

[0035] Identify and mark abnormal areas where the quota items do not match the properties of the soil and rock layers, display correction suggestions in the AR view, and receive user interaction instructions to correct the processing results.

[0036] Secondly, embodiments of this application provide a device for intelligent matching of geotechnical layer calculation and quota for transmission line foundations, comprising:

[0037] The acquisition module is used to acquire the structured data of the project to be processed and the pre-set soil classification rule knowledge base. The structured data includes foundation type information and soil and rock layer information. The soil classification rule knowledge base contains at least the mapping relationship between foundation type, soil and rock layer properties and excavation method.

[0038] The module is used to build a corresponding matrix based on structured data and a knowledge base of soil classification rules. The corresponding matrix is ​​used to characterize the logical mapping relationship between the physical and mechanical properties of soil and rock layers and the appropriate excavation and construction methods under different foundation type constraints.

[0039] The calculation module is used to determine the tower legs layer by layer based on the corresponding matrix and the matching algorithm of cumulative soil layer thickness and pit depth, and to calculate the engineering quantity of each soil and rock layer.

[0040] The processing module is used to build an intelligent matching engine, which matches, classifies, and prices the project based on the quantity of work and the preset list quota version index, and outputs the processing results of the structured data of the project to be processed.

[0041] In one possible embodiment, the processing module is further configured to:

[0042] The differences in soil classification standards caused by variations in region, voltage level, and foundation type were analyzed.

[0043] Collect engineering data submission forms, filter and abstract the granularity of basic information required for calculation, and establish a unified standard for basic information submission of transmission lines;

[0044] Receive the original design data, perform format validation and field mapping according to specifications, and generate structured data.

[0045] In one possible embodiment, the processing module is further configured to:

[0046] Integrate industry and regional standards to build a dynamic and scalable initial knowledge base;

[0047] Blockchain technology is used to manage the version of the initial knowledge base, storing standard update records on blockchain nodes and generating a unique hash value for each version rule;

[0048] By monitoring policy document updates through smart contracts, when a new version of the standard is detected, the rule base is automatically updated to form a knowledge base of soil classification rules.

[0049] In one possible embodiment, the building module is also used for:

[0050] The information on soil and rock layers in historical survey reports was statistically analyzed, and preliminary soil classification was carried out based on multi-source classification standards.

[0051] An adaptive classification model based on soil and rock layer features based on machine learning is introduced. The model uses the update rules in the soil classification rule knowledge base as training constraints.

[0052] Features of soil and rock layers are extracted using a convolutional neural network, and multidimensional feature vectors are constructed by combining density, water content, and compressive strength parameters from the exploration data.

[0053] The decision tree algorithm is used to match multidimensional feature vectors, output the optimal soil and rock layer classification result and its suitable excavation method, and generate the corresponding matrix.

[0054] In one possible embodiment, the processing module is further configured to:

[0055] An edge computing framework is introduced to perform distributed processing for complex hierarchical tasks of generating corresponding matrices.

[0056] The task of determining the layer by layer is broken down into multiple sub-tasks, including layer thickness calculation, excavation method matching, and excavation volume accumulation, and then distributed to edge computing nodes for parallel execution.

[0057] Computational resources are dynamically allocated based on the geological complexity label of the tower legs, with priority given to computational tasks for tower legs with high geological complexity.

[0058] In one possible embodiment, the processing module is further configured to:

[0059] Establish a soil type-quota item mapping table and version matching index;

[0060] A multi-objective optimization algorithm is introduced, taking project attributes and different versions of quota rules as optimization objectives;

[0061] When a quota version conflict is detected, the difference in engineering quantity between different versions is calculated through a genetic algorithm to generate the optimal quota matching scheme. The optimal quota matching scheme includes suggestions for adjusting the soil and rock layer classification and suggestions for optimizing the excavation method.

[0062] In one possible embodiment, the processing module is further configured to:

[0063] Integrate augmented reality (AR) devices and call upon the processing results and actual terrain model data;

[0064] The excavation volume distribution, soil and rock layer classification, and quota item matching information of each tower leg are overlaid and displayed in the AR field of view;

[0065] Identify and mark abnormal areas where the quota items do not match the properties of the soil and rock layers, display correction suggestions in the AR view, and receive user interaction instructions to correct the processing results.

[0066] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0067] The memory stores the instructions that the computer executes;

[0068] The processor executes computer execution instructions stored in memory, causing the processor to perform the methods described above.

[0069] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided above.

[0070] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0071] The method and apparatus for calculating and matching the soil and rock layers of transmission line foundations provided in this application obtains the structured data of the project to be processed and a pre-set knowledge base of soil classification rules. It establishes a logical mapping relationship between the physical and mechanical properties of soil and rock layers under the constraints of foundation type and the appropriate excavation and construction methods. Based on the corresponding matrix, it applies an algorithm that matches the cumulative thickness of soil layers with the pit depth to determine the layers of tower legs one by one, calculates the engineering quantity of each soil and rock layer, and then matches, classifies and prices the engineering quantity with the preset list quota version index. This method can integrate the foundation type, soil and rock characteristics, construction method, engineering quantity calculation and quota adaptation, reduce the caliber deviation and efficiency loss caused by manual comparison of scattered rules and layer-by-layer table calculation, and thus improve the accuracy, stability and adaptability of soil classification, engineering quantity statistics and quota matching of transmission line foundations. Attached Figure Description

[0072] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0073] Figure 1 A flowchart illustrating the method for layered calculation of geotechnical soil and rock layers and intelligent matching of quotas for transmission line foundations provided in this application. Figure 1 ;

[0074] Figure 2 A flowchart illustrating the method for layered calculation of geotechnical soil and rock layers and intelligent matching of quotas for transmission line foundations provided in this application. Figure 2 ;

[0075] Figure 3 A schematic diagram of the intelligent matching device for geotechnical layer calculation and quota of transmission line foundation provided in this application;

[0076] Figure 4 This is a hardware schematic diagram of the intelligent matching device for geotechnical layer calculation and quota matching of transmission line foundations provided in this application.

[0077] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0078] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and approaches consistent with some aspects of this application.

[0079] In the field of power transmission line engineering design and cost management, the determination of foundation soil and rock stratification, quantity statistics, and quota pricing are crucial aspects of foundation design. This process typically involves the collaborative processing of survey data, foundation type parameters, regional applicable rules, and bill of quantities documents. The results directly impact the selection of foundation construction methods, the accuracy of quantities, and the quality of cost estimation.

[0080] In existing technologies, the process typically begins by compiling information on the soil and rock layers for each tower location or leg based on the survey report. Then, considering regional regulations, engineering attributes, and foundation type, the soil type and excavation method are manually determined. Subsequently, spreadsheet tools are used to calculate the quantities layer by layer based on parameters such as cumulative thickness and design pit depth. Finally, different versions of the bill of quantities are compared to complete the classification and pricing. The working principle of this approach relies primarily on the designer's experienced understanding of the relationship between foundation type, soil and rock properties, and construction methods, as well as the manual searching and comparison of scattered rule documents.

[0081] However, in practical applications, there are often differences in scope between different regions, different foundation types, and different quota versions. The excavation methods and quota items corresponding to the same soil layer under different conditions are not completely consistent, making the rule adaptation process complex and prone to omissions. Especially when there are many tower legs and significant changes in the distribution of strata, manually matching the cumulative thickness of soil layers with the pit depth leg by leg can easily lead to problems such as deviations in layer determination, duplication of work quantities, or omissions in statistics.

[0082] Furthermore, survey data, structural design data, and cost statistics often lack a unified structured representation, making it difficult to establish a stable connection between front-end input and back-end pricing. When quota versions are updated or applicable rules are adjusted, existing calculation results need to be repeatedly checked and corrected, which not only reduces processing efficiency but also affects the stability and accuracy of the results.

[0083] In view of this, how to achieve efficient and accurate processing of soil and rock layer calculation and quota matching for transmission line foundations under the conditions of multiple source rules coexisting and dynamically changing has become an urgent technical problem to be solved. To solve the above problem, an intelligent matching method for soil and rock layer calculation and quota matching for transmission line foundations is proposed. By acquiring the structured data of the project to be processed and a pre-set knowledge base of soil classification rules, a corresponding matrix is ​​established based on the mapping relationship between foundation type information, soil and rock layer information, foundation type, soil and rock layer properties and excavation method.

[0084] Based on this, combined with a structured data processing architecture, the cumulative soil thickness and pit depth of the tower legs are matched for each leg layer, the engineering quantity of each soil layer is calculated, and the intelligent matching engine is used to match, classify and price the engineering quantity with the preset list quota version index, and output the structured data processing results of the project to be processed, thereby improving the consistency, accuracy and processing efficiency of layer calculation and quota matching.

[0085] Figure 1 A flowchart illustrating the method for layered calculation of geotechnical soil and rock layers and intelligent matching of quotas for transmission line foundations provided in this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0086] S101. Obtain the structured data of the project to be processed and the pre-set soil classification rule knowledge base; the structured data includes foundation type information and soil and rock layer information, and the soil classification rule knowledge base contains at least the mapping relationship between foundation type, soil and rock layer properties and excavation method.

[0087] In this embodiment, structured data serves as a standardized input carrier for the project to be processed, carrying the foundation type information and soil and rock layer information required for subsequent calculations. This data can be formed by uniformly extracting fields and converting the format of project cost estimates, foundation configuration tables, geological survey columnar sections, tower location details, and design pit depth data. Foundation type information characterizes the foundation structure form used for the tower location or tower leg, specifically including bored foundations, excavated foundations, cast-in-place pile foundations, slab foundations, or stepped foundations. Soil and rock layer information characterizes the stratum parameters directly related to foundation excavation calculations, including at least the distribution order of each layer, the top and bottom depths of each layer, the thickness of a single layer, the name of the soil / rock, the degree of weathering, the compaction state, the saturation state, and the corresponding physical and mechanical property labels. A soil classification rule knowledge base provides the mapping relationship between foundation type, soil and rock layer properties, and excavation method. Its data sources can include regional cost documents, transmission line foundation design regulations, construction quota instructions, list item characteristic explanation documents, and internal enterprise verification rules, and it is stored according to version number, applicable region, effective date, and rule priority.

[0088] In practice, the executing entity can be a data processing program deployed on a cost estimation terminal, local server, or cloud processing platform. This step begins by receiving the original project data from the engineering database or external file interface. This can be done by reading structured form files, parsing exported survey reports, calling design software interfaces, or receiving manually entered data tables. After receiving the original data, fields related to basic calculations are extracted and standardized, mapping synonymous fields to unified field names. Different units of measurement are converted, such as converting pit depth and thickness to meters, and converting area and volume units corresponding to engineering quantity measurement rules to internal system standard units. Finally, an indexable data organization relationship is established for the structured data, ensuring clear object boundaries for subsequent layered calculations.

[0089] In one possible implementation, if the exploration data is provided in the form of a columnar chart or text, the soil and rock stratification record corresponding to each tower leg is first obtained through template parsing or text extraction. Then, the layers are sorted from top to bottom according to the stratification sequence, and the depth of the top and bottom interfaces of each layer is calculated. In an optional data completion method, if the geological data is not directly detailed to the tower leg, it can be organized according to the existing geological information, and relevant descriptions can be retained in the structured record for subsequent verification. For foundation type information, in addition to the foundation type itself, necessary parameters for subsequent pit depth matching and engineering quantity calculation can also be recorded.

[0090] During the same process, a pre-built soil classification rule knowledge base is established during the system deployment phase, and the corresponding rule set is loaded at runtime according to the applicable engineering conditions. This knowledge base can use a relational table or key-value mapping table structure, and at least includes foundation type coding, soil and rock layer property coding, excavation method coding, applicable condition description, constraint conditions, and priority fields. For example, for different foundation types, the rule knowledge base can specify the compatibility relationships of soil and rock layer properties such as ordinary soil, firm soil, loose gravel, moderately firm rock, and moderately firm rock in manual excavation, mechanical excavation, rock drilling excavation, or blasting-assisted excavation; if the same soil and rock layer property exists under different foundation types with different excavation methods, it is distinguished by condition fields. After completing the structured data organization and loading of the rule knowledge base, the system outputs a standardized engineering input set and passes this input set to the subsequent corresponding matrix establishment step. Based on the above analysis, this step, by uniformly organizing the survey data, structural design data, and rule data, ensures that subsequent stratification determination no longer relies on manual searching of scattered files, but is based on calculable and indexable data. It should be understood that the above example is only for demonstration and not a limitation.

[0091] S102. Based on the structured data and the knowledge base of soil classification rules, establish a corresponding matrix.

[0092] In this embodiment, the correspondence matrix is ​​a calculation relationship table formed by associating the basic type information and soil and rock layer information in the structured data with the rule entries in the soil classification rule knowledge base. It is used to convert the original stratigraphic description into a basis for determining the excavation method that can be directly used for stratification and engineering quantity attribution. The row elements in the correspondence matrix can correspond to a specific soil and rock layer record under the tower leg, and the column elements include at least the basic type, soil and rock layer properties, physical and mechanical characteristic labels, applicable excavation method, rule source version, applicable area, excavation stratification classification identifier, and mapping key values ​​required for subsequent processing. The physical and mechanical properties of the soil and rock layers can be obtained by merging fields such as name, weathering degree, natural state, bearing capacity, compressibility, or integrity from the exploration data. Its function is to convert stratigraphic descriptions expressed in different ways into a unified rule category. For example, "strongly weathered mudstone" and "completely weathered mudstone with fragments" are merged into the corresponding rock weathering category or soil-rock category to ensure stable matching with the rule knowledge base.

[0093] In practice, the basic type fields in the structured data are first standardized with standard codes, and then the soil and rock layer property fields are standardized with standard classification and merging. Afterwards, using "basic type code + soil and rock layer property code + additional constraints" as the joint search key, records of excavation methods that meet the conditions are searched in the soil classification rule knowledge base, and the search results are written into a matrix. The additional constraints here can be summarized as engineering constraints, thereby ensuring that the mapping results are consistent with the current engineering environment.

[0094] In one possible embodiment, the corresponding matrix is ​​stored in a two-dimensional table format, but it can be implemented as a multi-dimensional index structure internally. Using the tower leg number as the first index, the stratigraphic sequence as the second index, and the foundation type, soil and rock properties, and regional rule version as joint conditions, the excavation method and quantity classification identifier corresponding to each layer are generated. For example, one matrix record can contain "Tower location A—Tower leg 1—Layer 2—Drilled foundation—Strongly weathered rock—Manual and mechanical excavation—List classification key K1—Quota mapping key D1"; another record can contain "Tower location A—Tower leg 2—Layer 3—Jarned pile foundation—Medium-dense gravel—Mechanical excavation—List classification key K2—Quota mapping key D2". In this way, different tower legs, different foundation types, and different strata are all assigned clear excavation method determination results within the same processing framework.

[0095] In another possible embodiment, when the knowledge base contains multiple applicable rules, the system makes decisions according to a preset priority order, such as region-specific rules over general rules, new version rules over old version rules, and rules with strong constraints over rules with weak constraints. If conflicts still exist, the relevant matrix records are marked as pending verification, and the candidate excavation method set is retained. However, the matrix still needs to output default judgment values ​​to ensure the continuous execution of subsequent calculation processes. After the correspondence matrix is ​​established, not only are the excavation method results for each layer of each tower leg generated, but also classification labels for subsequent processing are generated. Based on the above analysis, it can be seen that the correspondence matrix in this embodiment constitutes an intermediate mapping structure between the data layer and the rule layer, transforming the correlation between soil and rock properties and excavation methods from manual judgment to a traceable rule calculation process. It should be understood that the above example is only for demonstration and is not limiting.

[0096] S103. Based on the corresponding matrix, apply the cumulative soil layer thickness and pit depth matching algorithm to determine the tower leg layer by layer, and calculate the engineering quantity of each soil and rock layer.

[0097] In this embodiment, the cumulative soil thickness and pit depth matching algorithm is used to match the soil thickness sequence with the design pit depth layer by layer along the tower leg dimension, thereby determining the actual soil stratum interval involved in the excavation of each tower leg, the intercepted thickness of each layer, and the corresponding engineering quantity allocation. The input of this algorithm includes at least the tower leg number, design pit depth, foundation plane dimension parameters, the excavation method determination result of each layer in the corresponding matrix, and the soil and rock layer information sorted by depth; the output includes at least the effective excavation thickness of each soil and rock layer of each tower leg, the corresponding excavation method, the engineering quantity value, and the classification code. The layer-by-layer determination here means that the entire tower location is not treated as a single calculation object, but each tower leg is calculated independently, because different tower legs at the same tower location may have differences in ground elevation, pit depth, and soil stratum distribution. Only by processing each tower leg can the accuracy of the engineering quantity statistics boundary be guaranteed.

[0098] In practice, the system traverses the soil and rock layer sequence of a certain tower leg from top to bottom according to layer sequence n. Let the original thickness of the nth layer be hn, and the cumulative thickness be Hn, where Hn = Σhi, and i takes values ​​from 1 to n. The system compares the cumulative thickness Hn with the designed pit depth D of the tower leg: when Hn is less than D, it means that the entire nth layer falls within the excavation range, and the effective excavation thickness of the layer is hn; when Hn is greater than or equal to D and Hn-1 is less than D, it means that the designed pit depth falls within the nth layer, and the effective excavation thickness of the layer is D-Hn-1; when Hn-1 is already greater than or equal to D, subsequent layers are no longer included in the current excavation calculation. Through the above matching relationship, the thickness of each layer involved in each tower leg within the designed pit depth range can be accurately extracted. If the tower leg has backfill, cushion layer, wall protection expansion hole, or stepped excavation, the corresponding volume boundary is corrected according to the foundation size model after the effective thickness is determined.

[0099] In one possible embodiment, the quantity calculation adopts the method of "effective excavation thickness × corresponding layer excavation cross-sectional area" to calculate layer by layer, where the excavation cross-sectional area is determined by the foundation type and geometric parameters. For straight-walled pits, a constant cross-section can be formed by adding the construction working face to the pit bottom dimensions; for sloping pits, the upper and lower opening areas can be calculated according to the sloping boundary at the top and bottom elevations of each layer, and then the volume of that layer can be calculated according to the frustum volume formula; for foundations with approximately circular cross-sections such as bored piles, the layered volume calculation can be performed according to a cylindrical or frustum model. If the nth layer is at the boundary of different excavation methods, the excavation method given by the corresponding matrix is ​​used as the basis for quantity classification, and the same geometric volume is classified into different quantity classification items. For example, if a tower leg has plain fill from 0 to 1.2 meters and strongly weathered rock from 1.2 to 2.8 meters, and the designed pit depth is 2.0 meters, then the effective thickness of the first layer is 1.2 meters and the effective thickness of the second layer is 0.8 meters. If the corresponding matrix determines that the first layer is mechanical excavation and the second layer is rock drilling excavation, then the volumes of the two layers are calculated separately and written into different engineering quantity classification records.

[0100] In another possible embodiment, to ensure the completeness of statistical results, the system iteratively executes the above algorithm on multiple tower legs under each tower location, and outputs a detailed engineering quantity table according to the dimensions of "tower location number - tower leg number - foundation type - soil and rock layer category - excavation method". This is then further summarized into tower location-level, foundation type-level, or full-line-level statistical results as needed. If the cumulative stratum thickness of a certain tower leg is less than the designed pit depth, the system can mark it according to the rule of insufficient final borehole depth and include the uncovered depth in the abnormal verification record. If there are missing strata, zero thickness, or reversed strata sequence, the system performs data cleaning before calculation, automatically corrects identifiable errors, and pauses automatic pricing for the tower leg and outputs the verification status for records that cannot be corrected. Based on the above analysis, by comparing the cumulative thickness with the pit depth layer by layer, the system can clearly identify the boundary position of each layer entering the excavation calculation, and complete the fine-grained breakdown of engineering quantities by combining the excavation method given by the corresponding matrix, ensuring that the engineering quantity attribution for different tower legs, different strata, and different excavation methods within the same tower location remains consistent. It should be understood that the above example is only for demonstration and not a limitation.

[0101] S104. Construct an intelligent matching engine to match, classify, and price the project based on the quantity of work and the preset list quota version index, and output the processing results of the structured data of the project to be processed.

[0102] In this embodiment, the intelligent matching engine automatically associates the detailed quantities output in the previous step with the bill of quantities and quota rules, and generates classification and pricing results that meet the requirements of the target quota version. The intelligent matching engine includes at least a quantity receiving unit, a version index retrieval unit, a quota mapping unit, a classification and summarization unit, and a pricing output unit. The bill of quantities and quota version index indicates the applicable scope, rule caliber, and mapping priority of different bill of quantities and quota versions. Internally, it records at least the version number, region code, professional category, applicable time interval, quota sub-item code, project characteristic constraints, and unit of measurement rules. The relationship between quantities and quotas is not a simple one-to-one correspondence; therefore, after receiving the detailed quantities, the intelligent matching engine needs to combine excavation method, soil and rock layer type, foundation type, unit of measurement, and version caliber to complete multi-condition matching.

[0103] In practice, the system first determines the applicable list of quota versions based on the project's basic information, such as the project's region, compilation year, construction unit requirements, or the effective date of the cost document. Then, it extracts a classification key from each record in the quantity details, including fields such as foundation type code, soil / rock layer category code, excavation method code, quantity unit, whether it's layered excavation, whether it requires manual labor, and whether it involves rock excavation. This classification key is then matched with the project feature key in the list of quota versions index. Upon successful matching, the corresponding list item code and quota sub-item code are obtained, and the measurement rules and unit price composition under that version are read. If the same quantity record needs to be split and matched with multiple quota sub-items, such as separate pricing for earthwork excavation, transportation, disposal, or blasting auxiliary items, the intelligent matching engine generates multiple pricing details according to preset splitting rules, retaining the association identifiers between them and the original quantity record.

[0104] In one possible embodiment, the intelligent matching engine internally establishes a three-level mapping table of "work quantity classification code - list code - quota sub-item code". The classification code output from the aforementioned correspondence matrix and the work quantity calculation steps directly serves as the mapping entry point, thereby reducing ambiguity in the text comparison process. For example, when the classification code of a work quantity record corresponds to "excavation foundation, strongly weathered rock, rock drilling, volume measurement", the system retrieves the rock excavation sub-item in the specified version through the version index and reads its unit of measurement as cubic meters, comprehensive unit price composition, and project characteristic description; when another record corresponds to "ordinary soil, mechanical excavation", the earthwork excavation sub-item is matched. If different versions differ in sub-item codes, project boundaries, or units of measurement, the versioning rules in the version index will limit this matching to only use the mapping relationship of the current version, preventing cross-version misuse.

[0105] In another possible embodiment, the pricing calculation can generate a direct cost result by multiplying the matched quantity by the corresponding unit price, and can further add labor, materials, machinery, management fees, or other components according to version rules. The final output of the system includes at least a detailed quantity table, a detailed quota matching table, a list of categories summary table, a pricing result table, and an anomaly check list. Each pricing result retains the source tower location, tower leg, soil and rock layer number, excavation method determination, and rule version information to meet the result traceability requirements. If the version index is updated or the rules are adjusted, the intelligent matching engine can re-execute the matching classification and pricing without re-entering the basic data, only replacing the version index and mapping rules, thereby outputting new processing results. Based on the above analysis, this step connects the front-end soil and rock layer calculation results with the back-end list of quota standards into a unified data flow, maintaining a consistent processing path for matching quantities and quotas under rule version changes. It should be understood that the above example is only for demonstration and not a limitation.

[0106] Based on the above analysis, this application provides a method for calculating the soil and rock layers of transmission line foundations and intelligently matching quotas. The method includes acquiring structured data of the project to be processed and a pre-set knowledge base of soil classification rules; establishing a corresponding matrix based on the structured data and the knowledge base of soil classification rules; applying a soil layer cumulative thickness and pit depth matching algorithm to determine the layering of each tower leg and calculate the engineering quantity of each soil and rock layer according to the corresponding matrix; and constructing an intelligent matching engine to match, classify, and price the engineering quantity with the pre-set list quota version index and output the processing results. In this embodiment, by incorporating survey data, foundation type parameters, soil classification rules, and quota version rules into the same structured processing chain, and using a corresponding matrix as an intermediate mapping structure between soil and rock layer properties and excavation methods, and then using a leg-by-leg cumulative thickness and pit depth matching algorithm to determine the actual excavation boundary of each layer, the intelligent matching engine automatically converts the engineering quantity into quota sub-items. This enables unified processing of the foundation soil and rock layer calculation and pricing process under conditions of multiple tower legs, multiple strata, and multiple rule versions, maintaining a continuous data correspondence between layer determination, engineering quantity attribution, and quota matching, and reducing the deviation caused by manual item-by-item verification. It should be understood that the above example is only for demonstration and not a limitation.

[0107] Figure 2 A flowchart illustrating the method for layered calculation of geotechnical soil and rock layers and intelligent matching of quotas for transmission line foundations provided in this application. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the examples, a detailed explanation of the method for layered calculation of soil and rock layers and intelligent matching of quotas for transmission line foundations is provided. This method includes:

[0108] S201. Analyze the differences in soil classification standards caused by different regions, voltage levels, and foundation types; collect engineering data submission forms, filter and abstract the granularity of basic information required for calculation, and establish a unified standard for basic information submission of transmission lines; receive original design data submissions, perform format verification and field mapping according to the standard, and generate structured data.

[0109] In practical processing, the soil classification standards for different regions, voltage levels, and foundation types are first compared to identify differences in classification caliber, excavation method correspondence, and applicable boundaries for the same soil and rock layer. These differences are then written into the rule base or the constraints of the data submission template. Subsequently, fields related to foundation design and geotechnical calculations are extracted from the engineering data submission form. These fields are then combined with tower location information, tower leg information, foundation type, stratum layering, pit depth, design elevation, and excavation conditions to merge and abstract the original fields, forming a unified requirement for the granularity of foundation information. Based on this, a standardized data submission template is generated. This template maintains consistency in field naming, field types, required fields, value ranges, and unit formats, ensuring that subsequent inputs can directly participate in stratification determination and quantity calculation.

[0110] Upon receiving the original design data, the system performs format validation according to specifications, verifying data integrity, field order, field type, and unit consistency. If a field is missing, has an incompatible type, or inconsistent units, it is marked as an item requiring correction. Through field mapping, similar fields from different project data submissions are uniformly converted into preset standard fields. For example, different descriptions of foundation type, soil layer name, excavation method, and soil / rock layer thickness are mapped to the same set of fields, ultimately generating structured data, which is then output to the subsequent soil / rock layer calculation and quota matching modules. This structured data has a unified data structure and computability, directly supporting matrix creation, leg-by-leg layer determination, and quantity classification and pricing.

[0111] This implementation method first analyzes the differences in soil classification, then standardizes the granularity of the data and verifies and maps the original design data, transforming heterogeneous data information into a consistent structured representation. As a result, subsequent processing modules can directly access foundation type information and geotechnical layer information based on unified fields, reducing manual comparison and redundant corrections, and improving the consistency and accuracy of layered calculations and quota matching results.

[0112] S202. Integrate industry and regional standards to build a dynamic and scalable initial knowledge base; use blockchain technology to manage the version of the initial knowledge base, store standard update records on blockchain nodes, and generate a unique hash value for each version rule; monitor policy document updates through smart contracts, and automatically trigger the synchronization update operation of the rule base when a new version standard is detected, forming a soil classification rule knowledge base.

[0113] In this embodiment, in practical implementation, industry standards and regional standards can first undergo text extraction, field normalization, and terminology mapping processing before being written into a data table using a hierarchical index to form an initial knowledge base that is easy to retrieve and expand. Blockchain nodes can be deployed using a consortium blockchain approach, with nodes synchronizing and updating records through a consensus mechanism. Hash values ​​can be generated jointly from rule text, version number, and timestamp, allowing for rapid verification of different rule versions. Smart contracts can monitor change signals from policy release platform interfaces or document repositories, and upon identifying a new version standard, automatically compare the differences between old and new entries, complete rule replacement, supplementation, and invalidation marking, ultimately outputting a soil classification rule knowledge base consistent with the latest standard.

[0114] This knowledge base achieves rule traceability through versioned storage, verifies version consistency through hash verification, and is driven by smart contracts for automatic updates, ensuring the rule base evolves synchronously with changes in standards. Therefore, subsequent soil classification determinations can directly call the latest rules, reducing discrepancies caused by manual searching and comparison, and maintaining consistency in rule application across different regions and standard versions.

[0115] S203. Obtain structured data of the project to be processed, statistically analyze the soil and rock layer information in the historical survey report, and perform preliminary soil classification based on multi-source classification standards; introduce a machine learning-based adaptive classification model of soil and rock layer features, using update rules in the soil classification rule knowledge base as training constraints; extract soil and rock layer image features through convolutional neural networks, and construct multi-dimensional feature vectors by combining density, water content, and compressive strength parameters in the survey data; use decision tree algorithm to match the multi-dimensional feature vectors, output the optimal soil and rock layer classification result and its suitable excavation method, and generate the corresponding matrix.

[0116] In practical implementation, historical survey reports can first be structured and parsed, mapping core logging, layer descriptions, test indicators, and image data into sample records. Initial labels are then generated based on soil classification standards from multiple sources. These initial labels, along with update rules from a rule base, are then input into an adaptive classification model. The update rules serve as constraints in the loss function, allowing the model to correct category boundaries during training and inference. The soil and rock layer images are processed through convolutional, pooling, and fully connected layers to obtain visual features. These features are then combined with density, water content, and compressive strength to form a vector, which is fed into a decision tree model. The decision tree outputs the optimal classification and corresponding excavation method based on node thresholds. Finally, the classification results and the foundation type constraints are written into the corresponding matrix. Other models or implementations of the convolutional neural network and decision tree model can be selected in practical applications; this application does not limit their specific implementation.

[0117] In this way, the correspondence matrix can unify multi-source rules, image features, and exploration parameters into a single mapping framework, creating a computable correlation between soil and rock layer classification results and excavation methods. This matrix can be directly used in subsequent basic stratification determination, reducing manual comparison and repeated verification, and improving the consistency and adaptability of classification results under different rule versions.

[0118] S204. An edge computing framework is introduced to perform distributed processing for complex layered tasks that generate corresponding matrices. The task of determining the layered structure leg by leg is decomposed into multiple sub-tasks, such as layer thickness calculation, excavation method matching, and excavation volume accumulation, and then distributed to edge computing nodes for parallel execution. Computational resources are dynamically allocated according to the geological complexity label of the tower leg, and the calculation tasks of tower legs with high geological complexity are given priority.

[0119] Layer thickness calculation is used to read the thickness of the soil and rock layers of each tower leg and compare it with the cumulative thickness threshold. Excavation method matching is used to establish a correspondence between the corresponding soil and rock layers and the appropriate excavation method based on the corresponding matrix. Excavation volume accumulation is used to summarize the excavation volume calculated segment by segment for each layer. Edge computing nodes can be composed of general-purpose processors, memory, and network communication interfaces. Nodes are connected to the scheduling unit through wired or wireless networks. A single node can carry multiple parallel threads to complete the calculation of different tower legs or different sub-tasks.

[0120] Geological complexity tags are used to characterize the degree of variation in soil and rock layers at the location of the tower leg. Their generation can be determined based on the number of soil and rock layers, the frequency of inter-layer variation, and the difficulty of matching the pit depth, and is written into the task queue index. Upon receiving the tag, the scheduling module allocates higher computing quotas, cache quotas, or higher priority queues to high-complexity tower legs according to preset resource weights, allowing them to enter the stratification and result aggregation stage before low-complexity tasks. To adapt to different project scales, other models of edge computing nodes can be selected in practical applications; this application does not limit this selection.

[0121] By breaking down the leg-by-leg hierarchical decision-making into multiple parallelizable subtasks and combining them with edge computing scheduling, the leg-by-leg hierarchical calculation can be completed in a distributed environment. Legs in complex geological conditions can also obtain higher processing priority, thereby improving the response speed of hierarchical decision-making and ensuring the consistency and stability of engineering quantity calculation results.

[0122] S205. Acquire real-world images of the construction site using AR devices, and based on the coordinates of the tower legs and the processing results, overlay and display the excavation volume distribution, soil and rock layer classification, and quota item matching information of each tower leg in the AR view; compare the currently displayed soil and rock layer attributes with the applicable conditions in the quota rule base to determine whether the quota item matches; if the quota item is found to be inconsistent with the soil and rock properties, highlight the abnormal area in the AR screen and pop up a prompt box containing correction suggestions; receive confirmation commands from users via gestures or voice, correct the soil and rock parameters or adjust the quota version according to the commands, and trigger the system to recalculate the project quantity and update the displayed data in the AR screen in real time.

[0123] The core of this step lies in using Augmented Reality (AR) technology to immerse and interactively visualize the engineering quantity calculations and quota matching results generated in the previous steps, thereby bridging the final gap between "data processing" and "on-site verification." The design principle is to leverage the spatial positioning and image overlay capabilities of AR devices to display the excavation volume distribution of each tower leg, the soil and rock layer classification results, and the matching information with quota sub-items in real time on the actual terrain, using 3D labels, heat maps, or highlighted color blocks. This allows designers or construction personnel to intuitively see the thickness, classification, and corresponding quota of each soil layer calculated by the system during on-site surveys, eliminating the need to consult paper drawings or spreadsheets and greatly improving the efficiency and intuitiveness of information acquisition.

[0124] Based on this, the system can automatically identify and mark abnormal areas where the quota item does not match the properties of the soil and rock layers. For example, when the system determines a soil layer as "loose sand and gravel" based on the survey data, but according to the quota rules of the current project location, the soil layer should be subject to the "hard soil" quota item, the AR view will highlight the area with a conspicuous flashing mark or warning icon, and simultaneously display a correction suggestion based on knowledge base reasoning, such as "It is recommended to adjust the quota item here from 'loose sand and gravel' to 'hard soil,' and the excavation unit price will increase by XX yuan / cubic meter after the adjustment." This real-time presentation of abnormality markings and suggestions can help on-site personnel quickly locate potential data conflicts or rule application deviations, avoiding cost errors caused by information delays or poor communication.

[0125] Furthermore, AR devices also support receiving user interaction commands, enabling real-time correction of processing results. Field engineers can directly manipulate abnormal areas within the AR view using gestures, voice commands, or styluses. For example, they can click the "Accept Correction" button to adopt system suggestions, or manually drag and adjust soil layer boundaries to correct stratification results. Each interaction is recorded by the system and fed back to the backend database, triggering a recalculation of the corresponding tower leg engineering quantity and quota, and updating the content displayed in the AR view in real time. For instance, during the on-site review of a UHV transmission line project, an engineer discovered through the AR view that the "rock" classification of a tower leg did not match the actual "strongly weathered rock" revealed on site. Using a voice command, the engineer accessed the correction panel, changed the classification to "strongly weathered rock," and the system automatically rematched the quota items and updated the excavation quantity. The entire process was completed within minutes, whereas the traditional method required returning to the office to review documents, modify forms, and re-approval, taking hours or even days.

[0126] Through the above design, this step achieves deep integration and real-time interaction between data processing results and the physical world on site, resulting in multifaceted technical effects. Firstly, it significantly improves the accuracy and efficiency of on-site verification. The automatic identification of abnormal areas and the immediate presentation of correction suggestions greatly reduce the workload of manual comparison and judgment, lowering the risk of errors in quota application due to human negligence. Secondly, it achieves closed-loop feedback in data processing. On-site correction results can be updated to the backend database in real time, ensuring that the final output of engineering quantities and costs is completely consistent with the actual site conditions, avoiding the problem of design and construction disconnect. Thirdly, the AR visualization interaction lowers the technical threshold, enabling even on-site personnel unfamiliar with complex tables and quota systems to intuitively understand and participate in the data correction process, improving the efficiency and quality of team collaboration. In summary, this step deeply integrates AR technology with engineering data processing, serving not only as a visualization tool but also as an intelligent on-site operation assistance and quality control method, providing strong technical support for the accurate implementation of soil classification and engineering quantity calculation in power transmission projects.

[0127] In one possible implementation, an intelligent matching engine is constructed, including: establishing a soil type-quota item mapping table and version matching index; introducing a multi-objective optimization algorithm, taking engineering attributes and quota rules of different versions as optimization objectives; when quota version conflicts are detected, calculating the engineering quantity differences between different versions through a genetic algorithm to generate the optimal quota matching scheme, which includes suggestions for adjusting the soil and rock layer classification and suggestions for optimizing the excavation method.

[0128] In practical implementation, the version matching index can be constructed using a database index table or a hash index table, and the mapping table can be stored as an associative data structure. Fields should at least include soil type code, quota item code, version number, and applicable conditions. The multi-objective optimization algorithm can employ a constrained non-dominated sorting solution method, using engineering attributes as input vectors and the fit, difference, and conflict degrees under each version rule as evaluation metrics, outputting candidate matching solutions. When calculating engineering quantity differences, the genetic algorithm recalculates the engineering quantity for the same engineering object based on the soil and rock layer classification, excavation cross-section, and measurement boundaries under different versions, then compares the results to form a difference value, thereby generating the optimal quota matching scheme. After the scheme is output, the system simultaneously provides suggestions for adjusting the soil and rock layer classification and optimizing the excavation method, so as to map the existing stratification results to the target version quota rule. While a specific database engine or optimization solver may have advantages in index retrieval speed or iterative convergence, other models can also be selected for this component in practical applications; this application does not impose any limitations on this.

[0129] This intelligent matching engine establishes a stable association between soil type and quota rules through a mapping table and version index. Then, a multi-objective optimization algorithm jointly filters the project attributes and rule versions. When there is a version conflict, a genetic algorithm recalculates the difference in project quantity and outputs correction suggestions, so that the matching results can be consistent across different quota versions.

[0130] With this implementation method, the process of classifying and pricing quantities can maintain a unified judgment basis when multiple versions of rules coexist. Version conflicts can be quantified and transformed into executable classification adjustments and excavation method correction results, thereby improving the accuracy and stability of quota matching.

[0131] In this embodiment, in the case of a UHV transmission line project under complex geological conditions, for a certain UHV project located in a mountainous and hilly area with complex geological conditions, large differences in the thickness of the soil and rock layers and diverse soil types, the designers first analyzed the differences in soil classification standards caused by different regions, voltage levels and foundation types, based on the differences in the project location, UHV voltage level and foundation type, and also considered foundation types such as bored piles and cast-in-place piles.

[0132] The designers collected data from the ultra-high voltage (UHV) project, including soil and rock layer thickness data from the survey report, such as 1.5 meters for Class II rock and 2.0 meters for Class III rock, foundation type as bored piles, and project attributes as UHV + a specific province. They then filtered and abstracted the granularity of information required for calculations, including foundation type, tower leg layout, pit depth, soil and rock layer thickness, soil and rock physical and mechanical parameters, and excavation conditions. The granularity of foundation information included soil and rock layer thickness, foundation type, and excavation method. A unified "Specification for Data Collection on Transmission Line Foundations" was established, requiring soil and rock layer thickness to be recorded with meter-level accuracy and mandating the labeling of foundation type and excavation method, ultimately forming a structured data template.

[0133] After receiving the original design data, the system performs format verification, missing field checking, field mapping, and unit unification processing in accordance with the "Specification for Basic Information Submission of Transmission Lines" to generate structured data for the project to be processed.

[0134] Simultaneously, industry and local standards, including the "Budget Quota for Power Grid Engineering Construction," a provincial standard, and the "Budget Quota for Power Grid Engineering Construction (2025 Edition)," were integrated to construct a dynamically expanding initial knowledge base. Rules relating to foundation types, soil and rock properties, and excavation methods were incorporated into the base. Blockchain technology was used for version management, writing standard update records to blockchain nodes and generating a unique hash value for each version rule. Smart contracts monitored policy document updates, automatically triggering synchronous updates when a new version of the standard was released, thus forming a soil classification rule knowledge base. The knowledge base includes the rule "Ultra-high voltage project + a certain province + bored pile foundation," with the corresponding soil classification rule being "When the thickness of Class II rock is ≥1.5 meters, layered excavation is required." After the 2025 edition of the quota is released, the "Class II rock excavation method" field is automatically updated to ensure consistency with the 2025 edition of the quota.

[0135] In one possible implementation, the soil and rock layer information in historical survey reports is statistically analyzed, and the soil and rock layer data in the historical survey reports, such as Class II rock, Class III rock, and clay layers, are systematically sorted out. A preliminary soil classification is completed based on multi-source classification standards, including the "Budget Quota for Power Grid Engineering Construction" and a local standard of a certain province. During the classification process, a correspondence is established between Class II rock and bored pile foundations, and their excavation method is marked as "mechanical excavation." Simultaneously, an initial correspondence matrix of "foundation type - soil and rock layer properties" is constructed; for example, bored pile foundations correspond to Class II and Class III rock, while cast-in-place pile foundations correspond to clay layers.

[0136] Then, an adaptive classification model for soil and rock layer features based on machine learning was introduced. The update rules in the knowledge base of soil classification rules were used as training constraints. The model extracts soil and rock layer image features through a convolutional neural network and constructs a multi-dimensional feature vector by combining parameters such as density, water content, and compressive strength in the exploration data. Then, the decision tree algorithm is used to match the multi-dimensional feature vector and output the soil and rock layer classification results and the corresponding excavation methods.

[0137] Based on the basic type information, soil and rock layer information and knowledge base rules in the structured data, the logical relationship between the physical and mechanical properties of soil and rock layers and the appropriate excavation and construction methods under different basic type constraints is organized into a corresponding matrix. The "basic type-soil and rock layer properties-excavation method" corresponding matrix can be represented as bored pile foundations corresponding to Class II and Class III rocks, and the excavation method is mechanical excavation.

[0138] When performing layered calculations for tower legs, an edge computing framework is introduced to handle the complex layered tasks that generate the corresponding matrices. First, geological complexity labels are generated based on the geological data of the tower legs, and then computing resources are dynamically allocated according to the complexity, prioritizing the scheduling of tower leg tasks with high complexity.

[0139] The task of determining the layering of each tower leg is broken down into multiple subtasks, including layer thickness calculation, excavation method matching, and excavation volume accumulation, and is distributed to multiple edge computing nodes for parallel execution. For cases where the distribution of soil and rock layers differs significantly among different tower legs, a "cumulative soil layer thickness-pit depth matching algorithm" is used. This algorithm stacks the cumulative thickness of each soil and rock layer according to the pit depth direction, matching the cumulative thickness with the actual pit depth of the tower leg. If the cumulative thickness does not reach the pit depth, the next soil and rock layer is added downwards. When the cumulative thickness exceeds or equals the pit depth, the soil and rock layer thickness within the corresponding depth range is extracted to determine the layering boundary for that tower leg in the calculation.

[0140] For example, a tower leg contains 1.5 meters of Class II rock and 2.0 meters of Class III rock, with a designed pit depth of 3.5 meters. The system automatically determines that layered excavation is required, and after accumulating 1.5 meters, it continues to add the next 2.0 meters, until the total depth reaches 3.5 meters, at which point the layer boundary is determined. Subsequently, it reads the soil and rock layer classification and appropriate excavation method for each layer using the corresponding matrix, calculates the engineering quantity for each soil and rock layer, and accumulates the excavation volume of the tower leg. For example, it calculates the excavation volume for each layer as 1.5 cubic meters of Class II rock and 2.0 cubic meters of Class III rock, and finally outputs the engineering quantity result for the tower leg, which is then summarized in the bill of quantities.

[0141] After completing the quantity calculation for each tower leg, a mapping table between soil type and quota item is established, along with a matching index for quota version. An intelligent matching engine is then built, inputting the quantity data, project attributes, and quota rules from different versions into the matching and classification module. For example, in the mapping table, the quota item corresponding to Class II rock excavation is "Mechanical excavation of Class II rock (2025 version)," and the quota item corresponding to clay layer excavation is "Manual excavation of clay (2020 version)."

[0142] The engine employs a multi-objective optimization algorithm to comprehensively solve for soil type, foundation type, excavation method, regional attributes, and quota version compatibility. Based on project attributes such as UHVDC and a specific province, it automatically matches the applicable quota version, categorizes and summarizes the quantities by sub-item, and then applies prices. When a quota version conflict is detected, a genetic algorithm is invoked to calculate the differences in quantities between different versions, outputting the quota matching result and providing suggestions for adjusting the soil and rock layer classification and optimizing the excavation method. Simultaneously, it completes the pricing calculation, forming the processed structured data of the project. For example, if the excavation volume for a tower leg in Class II rock is 1.5 cubic meters, the system automatically matches the unit price of the 2025 version of the quota and outputs a standard cost list.

[0143] After outputting the processing results, the system integrates augmented reality (AR) devices, calls upon the processing results and actual terrain model data, and overlays the excavation volume distribution, soil and rock layer classification information, and quota item matching information of each tower leg in the AR view. Within the AR view, the system identifies abnormal areas where the quota items do not match the soil and rock layer properties, marks these areas, displays corresponding correction suggestions, and then receives user interaction commands to correct the soil and rock layer classification results, excavation method matching results, or quota item matching results, while simultaneously updating the displayed content. In this ultra-high voltage project in a hilly area, designers, based on provincial standards, the latest 2025 version of the quota rules, and the relationships between bored pile foundations, cast-in-place pile foundations, Class II rock, Class III rock, and clay layers, linked and viewed and corrected the layered calculation results, bill of quantities, and cost list for each tower leg.

[0144] Figure 3 This is a schematic diagram of the structure of the intelligent matching device for geotechnical layer calculation and quota of transmission line foundation provided in this application, as shown below. Figure 3 As shown, the intelligent matching device 30 for geotechnical layer calculation and quota matching of transmission line foundations provided in this embodiment includes:

[0145] The acquisition module 301 is used to acquire the structured data of the project to be processed and the pre-set soil classification rule knowledge base; the structured data includes foundation type information and soil and rock layer information, and the soil classification rule knowledge base contains at least the mapping relationship between foundation type, soil and rock layer properties and excavation method;

[0146] Module 302 is used to build a corresponding matrix based on structured data and a knowledge base of soil classification rules. The corresponding matrix is ​​used to characterize the logical mapping relationship between the physical and mechanical properties of soil and rock layers and suitable excavation and construction methods under different foundation type constraints.

[0147] Calculation module 303 is used to determine the layer by layer of the tower leg according to the corresponding matrix by applying the cumulative thickness of the soil layer and the pit depth matching algorithm, and to calculate the engineering quantity of each soil and rock layer.

[0148] Processing module 304 is used to build an intelligent matching engine, which performs matching, classification and pricing based on the quantity of work and the preset list quota version index, and outputs the processing results of the structured data of the project to be processed.

[0149] In one possible embodiment, the processing module 304 is further configured to:

[0150] The differences in soil classification standards caused by variations in region, voltage level, and foundation type were analyzed.

[0151] Collect engineering data submission forms, filter and abstract the granularity of basic information required for calculation, and establish a unified standard for basic information submission of transmission lines;

[0152] Receive the original design data, perform format validation and field mapping according to specifications, and generate structured data.

[0153] In one possible embodiment, the processing module 304 is further configured to:

[0154] Integrate industry and regional standards to build a dynamic and scalable initial knowledge base;

[0155] Blockchain technology is used to manage the version of the initial knowledge base, storing standard update records on blockchain nodes and generating a unique hash value for each version rule;

[0156] By monitoring policy document updates through smart contracts, when a new version of the standard is detected, the rule base is automatically updated to form a knowledge base of soil classification rules.

[0157] In one possible embodiment, the building module 302 is further configured to:

[0158] The information on soil and rock layers in historical survey reports was statistically analyzed, and preliminary soil classification was carried out based on multi-source classification standards.

[0159] An adaptive classification model based on soil and rock layer features based on machine learning is introduced. The model uses the update rules in the soil classification rule knowledge base as training constraints.

[0160] Features of soil and rock layers are extracted using a convolutional neural network, and multidimensional feature vectors are constructed by combining density, water content, and compressive strength parameters from the exploration data.

[0161] The decision tree algorithm is used to match multidimensional feature vectors, output the optimal soil and rock layer classification result and its suitable excavation method, and generate the corresponding matrix.

[0162] In one possible embodiment, the processing module 304 is further configured to:

[0163] An edge computing framework is introduced to perform distributed processing for complex hierarchical tasks of generating corresponding matrices.

[0164] The task of determining the layer by layer is broken down into multiple sub-tasks, including layer thickness calculation, excavation method matching, and excavation volume accumulation, and then distributed to edge computing nodes for parallel execution.

[0165] Computational resources are dynamically allocated based on the geological complexity label of the tower legs, with priority given to computational tasks for tower legs with high geological complexity.

[0166] In one possible embodiment, the processing module 304 is further configured to:

[0167] Establish a soil type-quota item mapping table and version matching index;

[0168] A multi-objective optimization algorithm is introduced, taking project attributes and different versions of quota rules as optimization objectives;

[0169] When a quota version conflict is detected, the difference in engineering quantity between different versions is calculated through a genetic algorithm to generate the optimal quota matching scheme. The optimal quota matching scheme includes suggestions for adjusting the soil and rock layer classification and suggestions for optimizing the excavation method.

[0170] In one possible embodiment, the processing module 304 is further configured to:

[0171] Integrate augmented reality (AR) devices and call upon the processing results and actual terrain model data;

[0172] The excavation volume distribution, soil and rock layer classification, and quota item matching information of each tower leg are overlaid and displayed in the AR field of view;

[0173] Identify and mark abnormal areas where the quota items do not match the properties of the soil and rock layers, display correction suggestions in the AR view, and receive user interaction instructions to correct the processing results.

[0174] By acquiring basic type information, soil and rock layer information, and rule knowledge base through module 301, a consistent input standard can be formed for surveying, design, and cost data. After constructing the corresponding matrix, module 302 can transform the scattered rules between basic type constraints, soil and rock layer properties, and excavation methods into a computable mapping, thereby reducing the discrepancies in manual judgment. Calculation module 303 combines the cumulative soil layer thickness and pit depth matching algorithm to determine the layered structure of each tower leg, accurately identifying the boundaries of each layer and completing the quantity statistics, thus reducing the risk of repeated calculations and omissions under conditions of multiple tower legs and complex strata. Processing module 304 further completes matching, classification, and pricing based on the list quota version index, thereby achieving rapid adaptation and result output under different quota standards. Therefore, it can improve the consistency, accuracy, and processing efficiency of soil and rock layer calculation, quantity statistics, and quota pricing for transmission line foundations.

[0175] Figure 4 This is a hardware schematic diagram of the intelligent matching device for geotechnical layer calculation and quota matching of transmission line foundations provided in this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0176] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0177] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0178] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0179] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0180] The electronic device 40 can be deployed in the design and cost management of power transmission line projects. The memory 402 stores survey data, foundation type parameters, regional rules, list quota version indexes, and corresponding processing programs. The processor 401 calls instructions to execute structured data acquisition, foundation type and soil layer mapping, tower leg-by-leg layer determination, quantity calculation, and quota matching, classification, and pricing. Through the collaboration of the memory 402 and the processor 401, scattered survey data, structural design data, and cost rules can be processed in a unified computing architecture. This reduces deviations caused by manual searching, comparison, and repeated calculations, ensuring consistency in layered calculation and pricing processes across different regions, foundation types, and quota versions. This improves the accuracy of quantity statistics, rule adaptation efficiency, and result output stability, thus providing more efficient support for power transmission line foundation design and cost estimation.

[0181] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0182] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0183] This computer program product encapsulates the processing logic for geotechnical stratification calculation and intelligent quota matching for transmission line foundations in the form of program instructions. When executed, the processor can call upon structured data, a soil classification rule knowledge base, and a list quota version index to uniformly correlate foundation type information, geotechnical layer information, and excavation method mapping relationships. This enables the completion of stratified determination of tower locations or tower legs, quantity calculation, and quota matching and pricing. Because the method's capabilities are encapsulated as an executable program, a stable connection is established between survey data, design data, and cost data, thereby reducing deviations caused by manual searching, comparison, and repeated verification. Therefore, it improves the consistency, accuracy, and execution efficiency of processing results under different regions, foundation types, and quota versions.

[0184] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0185] Once the computer execution instructions are embedded in a computer-readable storage medium, the processor can uniformly process the structured survey data, foundation type parameters, regional applicable rules, and bill of quantities (BOQ) documents in transmission line engineering. It can also reproduce the processes of soil and rock stratification determination, quantity calculation, and quota matching, thus avoiding manual searching and switching between multiple rule sources. By saving the mapping relationship between foundation type, soil and rock properties, and excavation method, as well as the BOQ version index, as program instructions, the rule invocation, tower leg-by-tower stratification matching, and pricing classification processes can be executed stably. This improves the consistency and repeatability of batch processing for different projects. Therefore, even in scenarios involving rule updates, version replacements, or project recalculations, processing efficiency, result accuracy, and standardized output can still be guaranteed.

[0186] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0187] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0188] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, methods, or units, and may be electrical, mechanical, or other forms.

[0189] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0190] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0191] If a function is implemented as 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 this invention, or the part that contributes to the prior art, or a part of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0192] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0193] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for layered calculation and intelligent matching of quotas for soil and rock layers in transmission line foundations, characterized in that, include: Obtain the structured data of the project to be processed and the pre-set knowledge base of soil classification rules; The structured data includes foundation type information and soil and rock layer information, and the soil classification rule knowledge base contains at least the mapping relationship between foundation type, soil and rock layer properties and excavation method; Based on the structured data and the knowledge base of soil classification rules, a corresponding matrix is ​​established; wherein, the corresponding matrix is ​​used to characterize the logical mapping relationship between the physical and mechanical properties of soil and rock layers and the appropriate excavation and construction methods under different foundation type constraints. Based on the corresponding matrix, the cumulative soil layer thickness and pit depth matching algorithm is applied to determine the tower leg layer by layer and calculate the engineering quantity of each soil and rock layer. A smart matching engine is constructed to match, classify, and price the project based on the project quantity and the preset list quota version index, and output the processing results of the structured data of the project to be processed.

2. The method according to claim 1, characterized in that, Before the step of obtaining the structured data of the project to be processed, the following steps are included: The differences in soil classification standards caused by variations in region, voltage level, and foundation type were analyzed. Collect engineering data submission forms, filter and abstract the granularity of basic information required for calculation, and establish a unified standard for basic information submission of transmission lines; Receive the original design data, perform format validation and field mapping according to the specifications, and generate the structured data.

3. The method according to claim 1, characterized in that, The steps for constructing the soil classification rule knowledge base include: Integrate industry and regional standards to build a dynamic and scalable initial knowledge base; Blockchain technology is used to manage the version of the initial knowledge base, storing standard update records on blockchain nodes and generating a unique hash value for each version rule; By monitoring policy document updates through smart contracts, when a new version of the standard is detected to be released, the rule base is automatically updated synchronously, forming the soil classification rule knowledge base.

4. The method according to claim 3, characterized in that, The step of establishing a corresponding matrix based on the structured data and soil classification rule knowledge base includes: The information on soil and rock layers in historical survey reports was statistically analyzed, and preliminary soil classification was carried out based on multi-source classification standards. An adaptive classification model based on soil and rock layer features based on machine learning is introduced, wherein the model uses the update rules in the soil classification rule knowledge base as training constraints. Features of soil and rock layers are extracted using a convolutional neural network, and multidimensional feature vectors are constructed by combining density, water content, and compressive strength parameters from the exploration data. The multidimensional feature vectors are matched using a decision tree algorithm to output the optimal soil and rock layer classification result and its suitable excavation method, generating the corresponding matrix.

5. The method according to claim 4, characterized in that, The step of applying the cumulative soil layer thickness and pit depth matching algorithm to determine the tower leg layer by layer includes: An edge computing framework is introduced to perform distributed processing on the complex hierarchical task of generating the corresponding matrix. The task of determining the layer by layer is broken down into multiple sub-tasks, including layer thickness calculation, excavation method matching, and excavation volume accumulation, and then distributed to edge computing nodes for parallel execution. Computational resources are dynamically allocated based on the geological complexity label of the tower legs, with priority given to computational tasks for tower legs with high geological complexity.

6. The method according to claim 5, characterized in that, The construction of the intelligent matching engine includes: Establish a soil type-quota item mapping table and version matching index; A multi-objective optimization algorithm is introduced, taking project attributes and different versions of quota rules as optimization objectives; When a quota version conflict is detected, the difference in engineering quantity between different versions is calculated by a genetic algorithm to generate an optimal quota matching scheme. The optimal quota matching scheme includes suggestions for adjusting the soil and rock layer classification and suggestions for optimizing the excavation method.

7. The method according to any one of claims 1 to 6, characterized in that, Following the output processing result, the following is also included: The AR device acquires real-world images of the construction site, and based on the coordinates of the tower legs and the processing results, overlays and displays the excavation volume distribution, soil and rock layer classification, and quota item matching information of each tower leg in the AR field of view. Compare the currently displayed soil and rock layer properties with the applicable conditions in the quota rule base to determine whether the quota item matches; If the quota item is found to be inconsistent with the soil and rock properties, the abnormal area will be highlighted in the AR screen and a prompt box containing correction suggestions will pop up. The system receives confirmation commands from users via gestures or voice, corrects geotechnical parameters or adjusts quota versions according to the commands, and triggers the system to recalculate the engineering quantity and update the data displayed in the AR screen in real time.

8. A device for layered calculation and quota matching of geotechnical soil for transmission line foundations, characterized in that, include: The acquisition module is used to acquire the structured data of the project to be processed and the pre-set knowledge base of soil classification rules; The structured data includes foundation type information and soil and rock layer information, and the soil classification rule knowledge base contains at least the mapping relationship between foundation type, soil and rock layer properties and excavation method; A construction module is used to establish a corresponding matrix based on the structured data and the soil classification rule knowledge base; wherein, the corresponding matrix is ​​used to characterize the logical mapping relationship between the physical and mechanical properties of soil and rock layers and the appropriate excavation construction methods under different foundation type constraints; The calculation module is used to determine the tower leg layer by layer according to the corresponding matrix by applying the cumulative soil layer thickness and pit depth matching algorithm, and to calculate the engineering quantity of each soil and rock layer. The processing module is used to build an intelligent matching engine, which matches, classifies, and prices the project quantity with the preset list quota version index, and outputs the processing results of the structured data of the project to be processed.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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