Method, device and equipment for generating budget calculation of engineering quantity of overhead line system and storage medium

Abstract

The invention relates to a catenary engineering quantity budget generation method, device and equipment and a storage medium, in particular to the technical field of catenary intelligent construction. Comprising the steps of directly obtaining infrastructure data input by a user, and skipping a traditional complex data collection and arrangement process; by means of a pre-established relational database system updated according to a preset period, a target engineering quantity budget list is automatically generated, manual one-by-one calculation is not needed, and the working process is simplified; meanwhile, the database system timely incorporates new data, the problem of data acquisition lagging is solved, and an arrangement and verification mechanism reduces manual estimation deviation, improves data validity and accuracy, and meets the requirement of railway construction for accurate data.

Description

Technical Field

[0001] This application relates to the technical field of intelligent catenary construction, and particularly to a method, device, equipment and storage medium for generating catenary project quantity estimates. Background Art

[0002] In the process of the rapid development of electrified railway construction in China, as a core facility, the design and construction technology of the catenary has been continuously iterated and innovated with remarkable achievements. The accurate acquisition of catenary project quantities is of crucial significance for the overall planning of railway projects, the efficient control of construction progress, and the accuracy of cost accounting.

[0003] Currently, the methods for obtaining catenary project quantities have obvious defects. Traditional methods mainly rely on large-scale catenary layout work to calculate quantities. This method has a complex process, not only requiring a huge amount of human and time costs, but also having a serious lag in data acquisition, unable to meet the urgent need for real-time data in current railway construction projects in a timely manner. This makes it difficult for project managers to flexibly adjust construction plans based on the latest data, easily leading to problems such as construction delays or resource misallocation. Another method is to use line data and estimate project quantities by referring to the empirical values of catenary kilometers per strip. However, due to the huge differences in terrain, design standards, and operation requirements among different railway lines, relying solely on empirical values is difficult to accurately reflect the actual project situation, and the effectiveness and accuracy of the data cannot be guaranteed. This may lead to budget deviations during the preparation of project budgets, resulting in waste of resources if it is too high or the risk of shortage of funds for the project if it is too low; in the procurement link of construction materials, inaccurate quantity estimation will also cause material backlogs or shortages, affecting the project progress and quality.

[0004] With the gradual rise of intelligent catenary construction technology, the requirements of railway projects for accurately controlling the actual progress and ensuring the effectiveness of catenary engineering design are constantly increasing. The existing methods for obtaining catenary project quantities can no longer keep up with the pace of industry development, and there is an urgent need for a method for generating catenary project quantity estimates that is efficient, convenient, and can ensure the real-time, effectiveness, and accuracy of data, injecting new impetus into the high-quality development of electrified railway construction. Summary of the Invention

[0005] To solve the above technical problems or at least partially solve the above technical problems, this application provides a method, device, equipment and storage medium for generating catenary project quantity estimates, which can improve work efficiency and enhance data reliability.

[0006] To achieve the above object, the technical solutions provided by the embodiments of this application are as follows:

[0007] In a first aspect, this application provides a method for generating catenary project quantity estimates, including:

[0008] Obtain the infrastructure data related to the catenary project input by the user;

[0009] Generate a target project quantity estimate list corresponding to the infrastructure data according to the infrastructure data and the pre-established relational database system;

[0010] Among them, the relational database system is updated according to a preset cycle, including the catenary project quantities of multiple catenary sub-units and their associated estimate lists.

[0011] As an alternative implementation provided by an embodiment of the present application, the steps for building the relational database system include: obtaining sample data, where the sample data includes basic data related to catenary project construction, catenary design drawings, and catenary project technical standards and specification documents; the basic data includes line data, main materials for catenary projects, unit prices for catenary projects, labor costs for catenary projects, and machine costs for catenary projects; inputting the basic data, catenary design drawings, and catenary project technical standards and specification documents into a pre-trained model to obtain the analysis results output by the pre-trained model; among them, the pre-trained model is used to reflect the calculation principles of catenary project quantities and estimates for sub-units under different basic data, and is a model that converges after comparative learning with actual catenary project quantities and actual estimate lists; according to the analysis results, combined with the influencing factors of catenary construction efficiency, generate the catenary project quantities of multiple catenary sub-units and their associated estimate lists; build a relational database system according to the line data and the catenary project quantities of multiple catenary sub-units and their associated estimate lists.

[0012] As an alternative implementation provided by an embodiment of the present application, after obtaining the sample data and before inputting the basic data, catenary design drawings, and catenary project technical standards and specification documents into the pre-trained model to obtain the analysis results output by the pre-trained model, the method further includes: preprocessing the basic data, including data cleaning and data normalization; extracting the drawing information of the catenary design drawings, including the geometric dimensions, position information, quantity, and connection relationships of each component.

[0013] As an alternative implementation provided by an embodiment of the present application, inputting the basic data, catenary design drawings, and catenary project technical standards and specification documents into the pre-trained model to obtain the analysis results output by the pre-trained model includes: extracting features from the preprocessed basic data and drawing information to obtain key features; using the pre-trained model to calculate and summarize the key features with reference to the catenary project technical standards and specification documents to generate the analysis results.

[0014] As an alternative implementation provided by the embodiments of the present application, feature extraction is performed on the preprocessed basic data and drawing information to obtain key features, including: extracting line features, main material features, price features, and quantity-related features from the preprocessed basic data; extracting component size features, component position features, component type features, equipment specification features, material material features, connection method features, assembly relationship features, special section features, and environmental condition features from the drawing information.

[0015] As an alternative implementation provided by the embodiments of the present application, the training steps of the pre-trained model include: dividing the sample data into a training set, a validation set, and a test set; using the training set to train the original machine learning model to obtain the predicted catenary project quantity and the predicted budget estimate list output by the original machine learning model; calculating the first loss function between the predicted catenary project quantity and the actual catenary project quantity, and the second loss function between the predicted budget estimate list and the actual budget estimate list; adjusting the model parameters of the original machine learning model based on the first loss function, the second loss function, and the validation set; calculating the performance metrics of the machine learning model on the test set after the model parameters are adjusted; and performing feedback training on the machine learning model according to the performance metrics until the performance metrics meet the preset conditions to obtain a converged pre-trained model.

[0016] In a second aspect, the present application provides a device for generating a catenary project quantity budget estimate, and the device includes:

[0017] An acquisition module, configured to acquire infrastructure data related to the catenary project input by a user;

[0018] A generation module, configured to generate a target project quantity budget estimate list corresponding to the infrastructure data according to the infrastructure data and a pre-established relational database system;

[0019] Wherein, the relational database system is updated according to a preset period and includes the catenary project quantities of multiple catenary sub-units and their associated budget estimate lists.

[0020] As an alternative implementation provided by the embodiments of the present application, the device further includes a database construction module, which is configured to: obtain sample data, where the sample data includes basic data related to catenary project construction, catenary design drawings, and catenary project technical standards and specification documents; the basic data includes line data, main materials for catenary projects, unit prices of catenary projects, labor costs of catenary projects, and machine costs of catenary projects; input the basic data, catenary design drawings, and catenary project technical standards and specification documents into a pre-trained model to obtain the analysis results output by the pre-trained model; wherein, the pre-trained model is used to reflect the calculation principles of the catenary project quantity and budget estimate of sub-items under different basic data, and is a model that converges after comparative learning with the actual catenary project quantity and actual budget estimate list; according to the analysis results, combined with the influencing factors of catenary construction efficiency, generate the catenary project quantity and associated budget estimate list of multiple catenary sub-units; build a relational database system based on the line data and the catenary project quantity and associated budget estimate list of multiple catenary sub-units.

[0021] As an alternative implementation provided by the embodiments of the present application, the database construction module is further configured to: preprocess the basic data, including data cleaning and data normalization; extract the drawing information of the catenary design drawings, including the geometric dimensions, position information, quantity, and connection relationship of each component.

[0022] As an alternative implementation provided by the embodiments of the present application, the database construction module is specifically configured to: extract key features from the preprocessed basic data and drawing information; use the pre-trained model to calculate and summarize the key features with reference to the catenary project technical standards and specification documents to generate analysis results.

[0023] As an alternative implementation provided by the embodiments of the present application, the database construction module is specifically configured to: extract line features, main material features, price features, and usage-related features from the preprocessed basic data; extract component dimension features, component position features, component type features, equipment specification features, material material features, connection method features, assembly relationship features, special section features, and environmental condition features from the drawing information.

[0024] As an alternative implementation provided by the embodiments of the present application, the device further includes a model training module, which is configured to: divide the sample data into a training set, a validation set, and a test set; use the training set to train the original machine learning model to obtain the predicted catenary project quantity and the predicted budget estimate list output by the original machine learning model; calculate a first loss function between the predicted catenary project quantity and the actual catenary project quantity, and a second loss function between the predicted budget estimate list and the actual budget estimate list; adjust the model parameters of the original machine learning model based on the first loss function, the second loss function, and the validation set; calculate the performance metrics of the machine learning model on the test set after the model parameters are adjusted; perform feedback training on the machine learning model according to the performance metrics until the performance metrics meet the preset conditions to obtain a converged pre-trained model.

[0025] In a third aspect, the present application provides an electronic device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor, where when the computer program is executed by the processor, it implements the method for generating the catenary project quantity estimate as described in the first aspect or any of its alternative implementations.

[0026] In a fourth aspect, the present application provides a computer-readable storage medium, including: a computer program stored on the computer-readable storage medium, where when the computer program is executed by a processor, it implements the method for generating the catenary project quantity estimate as described in the first aspect or any of its alternative implementations.

[0027] In a fifth aspect, the present application provides a computer program product, including: the computer program product includes a computer program, and when the computer program runs on a computer, it causes the computer to implement the method for generating the catenary project quantity estimate as described in the first aspect or any of its alternative implementations.

[0028] The technical solutions provided by the embodiments of the present application have the following advantages compared with the prior art:

[0029] The embodiments of the present disclosure provide a method, apparatus, device, and storage medium for generating an estimate of catenary project quantities. The method directly obtains the infrastructure data input by the user, skipping the large amount of complex data collection and collation processes in the traditional method; uses a pre-built relational database system to automatically generate a target estimate list of project quantities, eliminating the need for manual calculation of each sub-project based on experience one by one, greatly simplifying the work process. The relational database system is updated according to a preset cycle, enabling timely incorporation of the latest catenary project data, thereby ensuring that the data obtained by the user at any time is relatively new, solving the problem of lagging data acquisition. The relational database system has been sorted and verified. When generating the target estimate list of project quantities, the system automatically matches and calculates based on the infrastructure data, reducing the deviation caused by manual experience estimation, improving the effectiveness and accuracy of the data, and meeting the needs of railway construction for accurate data. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application.

[0031] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 It is a schematic flowchart of a method for generating an estimate of catenary project quantities provided by an embodiment of the present application;

[0033] Figure 2 It is a schematic diagram of infrastructure data related to catenary projects provided by an embodiment of the present application;

[0034] Figure 3 It is a schematic diagram of a target estimate list of project quantities provided by an embodiment of the present application;

[0035] Figure 4 It is a schematic flowchart of building a relational database system provided by an embodiment of the present application;

[0036] Figure 5 It is a schematic diagram of the main materials of catenary projects provided by an embodiment of the present application;

[0037] Figure 6 It is a schematic diagram of the unit prices of catenary projects provided by an embodiment of the present application;

[0038] Figure 7 It is a schematic diagram of the labor costs of catenary projects provided by an embodiment of the present application;

[0039] Figure 8 Schematic diagram of the catenary engineering machine cost provided by the embodiment of the present application;

[0040] Figure 9 Schematic structural diagram of a device for generating an estimate of catenary project quantity provided by the embodiment of the present application;

[0041] Figure 10 Schematic structural diagram of an electronic device according to the embodiment of the present application. Detailed implementation manners

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the technical terms used in the description of the embodiments or the prior art.

[0043] The quantity of catenary projects refers to the quantitative statistics of various specific construction contents and workloads in catenary project items. For different railway lines or rail transit projects, the quantity of catenary projects will vary due to factors such as line length, design standards, and terrain conditions.

[0044] Catenary estimate refers to the comprehensive cost estimate carried out for the key part of the catenary project in the early stage of the project (such as the preliminary design period) in the electrified railway construction project. It is based on various elements such as detailed design drawings, current railway engineering quota standards, material and equipment market price information, and rich engineering construction experience, and calculates and summarizes various costs involved in the catenary project, such as the costs of pillar and foundation projects (including pillar procurement, transportation, and foundation construction costs), catenary suspension project costs (covering the purchase and installation costs of materials such as contact wires, carrier wires, and droppers), positioning device costs, anchor section joint and center anchor knot project costs, additional conductor project costs, electrical equipment installation project costs, and other related costs such as pillar protection, cable projects, equipment commissioning and testing, and temporary projects.

[0045] The catenary project quantity estimate list is a document that quantifies and prices various works and materials in the catenary project construction. When compiling the catenary project quantity estimate list, it is necessary to follow relevant design documents, construction drawings, railway engineering budget quotas, and compilation methods and other regulations to ensure that the list content is complete and accurate and can truly reflect the construction cost of the catenary project.

[0046] A relational database is a database management system based on the relational model, which organizes and stores data in the form of tables.

[0047] The main materials of the catenary project refer to the key materials that constitute the main structure and realize the main functions in the construction of the catenary system, and play an indispensable role in the project.

[0048] The unit price of catenary project refers to the cost standard required to complete a specific sub - project or unit project in the catenary project. It is comprehensively determined based on factors such as different parts of the project, different construction techniques, and different materials and equipment. The project unit price is an important basis for calculating project cost and is used to determine the total project cost.

[0049] The labor cost of catenary project refers to the labor remuneration paid to construction workers during the construction of catenary project. The calculation of labor cost is usually determined according to the number of man - days consumed in the project and the corresponding labor unit price.

[0050] The machinery cost of catenary project refers to the expenses incurred in using various construction machinery and equipment during the construction of catenary project.

[0051] The traditional work process of catenary plane layout is complex and the data acquisition is lagging. The method of relying on line data and empirical values for estimation cannot guarantee the validity and accuracy of data, and it is difficult to meet the requirements of railway construction for real - time and accurate data.

[0052] In order to solve some or all of the technical problems existing in the related technologies, the embodiments of the present application provide a method, device, equipment and storage medium for generating the catenary project quantity estimate. The method directly obtains the infrastructure data input by the user, skipping a large number of complex data collection and collation processes in the traditional method; uses a pre - built relational database system to automatically generate the target project quantity estimate list, eliminating the need for manual calculation of each sub - project based on experience one by one, greatly simplifying the work process. The relational database system is updated according to a preset cycle, and can timely incorporate the latest catenary project data, so as to ensure that the data obtained by the user at any time is relatively new, solving the problem of lagging data acquisition. The relational database system is sorted and verified. When generating the target project quantity estimate list, the system automatically matches and calculates based on the infrastructure data, reducing the deviation caused by manual empirical estimation, improving the validity and accuracy of data, and meeting the requirements of railway construction for accurate data.

[0053] In order to more clearly understand the above - mentioned objects, features and advantages of the present application, the solutions of the present application will be further described below. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0054] Many specific details are set forth in the following description in order to provide a thorough understanding of the present application, but the present application may be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present application, rather than all of the embodiments.

[0055] A method for generating an estimate of catenary project quantity provided in an embodiment of the present application can be implemented by a device or an electronic device for generating an estimate of catenary project quantity. The electronic device includes, but is not limited to, a personal computer, a laptop computer, a tablet computer, a smart phone, etc. The operating system of the electronic device may include Android, iOS developed by Apple Inc., Windows developed by Microsoft Corporation of the United States, etc., and the embodiments of the present application do not limit this. The electronic device can run alone to implement the present application, or can be connected to a network and implement the present application through interaction with other computer devices in the network. Among them, the network where the electronic device is located includes, but is not limited to, the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0056] It should be noted that the protection scope of a method for generating an estimate of catenary project quantity described in an embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. Any solution achieved by adding or reducing steps of the prior art and replacing steps according to the principle of the present application is included in the protection scope of the present application.

[0057] As Figure 1 shown, Figure 1 is a schematic flowchart of a method for generating an estimate of catenary project quantity according to an embodiment of the present application. This method can be executed by a device for generating an estimate of catenary project quantity, where the device can be implemented by software and / or hardware and is generally integrated in an electronic device. This method mainly includes the following steps S101 to S102:

[0058] S101. Obtain infrastructure data related to the catenary project input by the user.

[0059] Among them, the infrastructure related to the catenary project includes, but is not limited to: lines, bridges, tunnels.

[0060] Taking a line as an example, the infrastructure data related to the catenary project is line data, as Figure 2 shown, Figure 2In the ID, it represents the record number, which is used to distinguish different curve segment information in the table, facilitating data management and reference. The deflection angle refers to the deflection angle of the curve, which can be positive or negative, reflecting the degree of change in the line direction. The curve radius is a measure of the bending degree of the curve. The larger the value, the flatter the curve; the smaller the value, the sharper the curve. The transition curve is a curve that connects the straight line and the circular curve. The length of the front transition curve is the length of the transition curve before the start point of the curve, and the length of the rear transition curve is the length of the transition curve after the end point of the curve. The curve length represents the length of the circular curve part. Combining with the length of the transition curve, the length of the entire curve segment can be determined. The starting mileage prefix such as "DK" and "DIK" is an identifier for mileage marking; the starting mileage indicates the position of the starting point of the curve segment in the entire line, which is used for line positioning and length calculation.

[0061] S102. Generate a target project quantity budget list corresponding to the infrastructure data based on the infrastructure data and the pre-established relational database system.

[0062] Among them, the relational database system is updated according to a preset cycle, including the catenary project quantities of multiple catenary sub-units and their associated budget lists.

[0063] Exemplarily, the target project quantity budget list is as Figure 3 shown, including columns such as serial number, material code, material name and specification, English description, unit, quantity, unit price (yuan), total price (yuan), remarks, etc., which are used for the system to record the data of each main material. Regarding wire types such as tin-magnesium alloy contact wire (specification: AC120mm 2 CuAg0.1), copper-magnesium alloy messenger wire (95mm 2 BzⅡ), etc., the specific material, specification and weight unit (kg) are given, and the quantity, unit price and total price are marked. Regarding pillar types, H-shaped steel columns are counted in "t" (tons), reflecting their usage and cost in the project. Regarding the upper assembly types, including single cantilever bases, aluminum alloy flat wrist arms, positioning pipes, etc. with various specifications, they are in units of "set" or "piece", and the specifications, quantities, unit prices and total prices of each material are listed in detail.

[0064] Referring to Figure 3 the target project quantity budget list shown, the project quantity budget list generated by this application can help engineering personnel accurately calculate costs and control budgets in the material procurement link of the catenary project. At the same time, during the construction process, it is also an important basis for material management and quality control to ensure that the materials used meet the design specification requirements.

[0065] In the above steps, the infrastructure data related to the catenary project directly input by the user is obtained, avoiding over-reliance on the data of a single line. Moreover, the data input by the user is usually collected based on the actual situation, which can better reflect the real situation of the current project, improving the timeliness and accuracy of the data. The relational database system is pre-built, which contains the quantity of the catenary project of multiple catenary sub-units and the associated budgetary estimate list. By using these existing data resources and combining with the infrastructure data input by the user, the target project quantity budgetary estimate list can be quickly generated. The calculation method based on the database is supported by a large amount of actual project data, which can effectively avoid errors caused by insufficient experience or subjective factors, improving the accuracy and reliability of the project quantity budgetary estimate. The relational database system is updated according to a preset cycle to ensure that the data in it can reflect the latest project situation and standards, meeting the needs of railway construction for real-time data.

[0066] In some embodiments, the steps for building the relational database system include the following steps S401 to S404, as Figure 4 shown.

[0067] S401. Obtain sample data.

[0068] The sample data includes the basic data related to the construction of the catenary project, the catenary design drawings, and the catenary project technical standards and specification documents.

[0069] Among them, the basic data includes line data, main materials of the catenary project, unit price of the catenary project, labor cost of the catenary project, and machine cost of the catenary project.

[0070] The line data includes length, curve radius, gradient, etc. The length includes the length of the anchor section and the span length. The length of the catenary anchor section is determined according to the tension difference caused by the temperature change of the contact wire and the carrier cable. The span length of the catenary varies according to different curve radii. The selection of the curve radius is related to the railway grade. For example, when calculating the length of the contact wire and the carrier cable in the curve section, the elongation of the wire needs to be considered; on the sloping line, parameters such as the length of the dropper will also be affected.

[0071] The main materials of the catenary project can also be called the labor materials of the catenary project, as Figure 5 shown, Figure 5 which contains columns such as serial number, material code, material name and specification, measurement unit, unit price, total price, etc., for the system to record and count various data of the materials. The material code is the unique number of each material, which is convenient for quickly and accurately identifying and retrieving materials in project management. For example, 4310112 represents the anchor plate pressure pipe JY-T70mm 2The material name and specification clarify the name and specification model of the specific material, which is very important for determining the applicability of the material during procurement and construction. The measurement unit records the measurement method of the material, such as piece, each, meter, ton, set, etc., which determines the statistical standard of the material quantity. The unit price refers to the price of a single measurement unit of the material, and the total price is the total amount obtained by multiplying the unit price of the material by the quantity. Through these two pieces of data, the cost of each material can be clearly understood, which helps to calculate and control the material cost of the entire catenary project.

[0072] The unit price of the catenary project, such as Figure 6 As shown, the unit price analysis table of "ANCHOR FOUNDATION (guy foundation)" in the catenary project includes the cost analysis of two parts: materials - equipment and labor - machinery. Regarding the materials - equipment part, "Code and Project" lists the work - material - machine codes and names of various materials and equipment, such as cement grade 32.5, cement with film grade 42.5, etc.; "Unit and Quantity" clarifies the measurement unit (such as kg, m 3 etc.) and quantity of each material to calculate the material cost; it also includes the cost of other material fees and water, and finally calculates the direct project cost, contract management fee and other fees of this section of the project, as well as the total cost. Regarding the labor - machinery part, "Labor and Machinery" lists different types of labor (Class III labor) and mechanical equipment (such as truck crane, single - drum slow winch, etc.); "Unit and Number of shifts" records the usage quantity of labor and machinery in units of work - days or shifts, such as 4.23 work - days of Class III labor; "Unit price and Amount" indicates the labor unit price and mechanical shift unit price, and then calculates the corresponding fees. The unit price of Class III labor is 70.00 yuan / work - day, and the fee is 297.17 yuan; "Fee summary" calculates the direct project cost, contract management fee and other fees, as well as the total cost, and finally obtains the total cost of 1052.25 yuan for the unit price analysis of the entire guy foundation project.

[0073] The labor cost of the catenary project, such as Figure 7 As shown, the column information of the labor cost plan includes serial number, number, cost name, unit, unit price (yuan), single weight (kg), remarks. It details different types of labor from Class I labor to Class VIII labor. Each type of labor has a corresponding work - day unit and different unit prices. For example, the unit price of Class I labor is 66 yuan / work - day, and the unit price of Class VIII labor is 82 yuan / work - day. These different types of workers may have differences in skill level, work intensity or work content, and the corresponding unit prices are also different.

[0074] The machinery cost of the catenary project, such as Figure 8As shown, the machine cost plan column information includes serial number, number, cost name, unit, unit price (yuan), unit weight (kg), and remarks. It covers a variety of construction machinery and equipment, such as frog rammers, wheel loaders, pneumatic leg rock drifters, etc., with the unit being workdays. The unit price corresponding to each type of machinery and equipment is marked, for example, the unit price of a frog rammer ≤ 250 Nm is 6.45 yuan / workday. The use of these equipment is indispensable in engineering construction. The machine cost plan provides a basis for calculating the mechanical workday cost and helps accurately estimate the mechanical usage cost in project cost management.

[0075] The catenary design drawings include catenary layout plans, installation drawings, mast assembly drawings, etc. For example, the mast type, quantity, installation location, etc. marked in the design drawings are the direct basis for calculating the mast engineering quantity.

[0076] Among them, the catenary layout plan takes an interval, a station yard, or a tunnel as a unit, and draws the situation within the unit in the form of a legend into a plan view, reflecting the catenary layout direction, anchor section length, span length, pull-out value size and direction, line conditions, geological conditions along the line, types, categories, quantities of equipment, and their installation locations, etc.

[0077] The installation drawings include types such as mast arm column installation drawings, flexible catenary installation drawings, tunnel suspension installation drawings, rigid catenary installation drawings, additional conductor installation drawings, equipment installation drawings, etc. The main contents include installation structure and installation dimension diagrams, table columns, material statistics tables, instructions, icons, etc. It is an important basis for construction and operation and maintenance. Construction personnel can install catenary equipment according to the installation drawings, and operation and maintenance personnel also need to refer to the installation drawings during equipment inspection and replacement to ensure that the equipment installation meets the design requirements and guarantee the normal operation of the catenary.

[0078] The mast assembly drawing mainly refers to the assembly of the upper part of the mast, the support device, and the catenary suspension, that is, the form of the combination of the catenary suspension, positioning device, mast arm, and mast. Generally, it is divided into the assembly of intermediate masts, conversion masts, turnout positioning masts, anchor masts, and center masts according to the different uses of the masts, as well as the assembly of straight and curved masts, and is divided into different assembly types according to different catenary heights, side clearances, catenary suspensions, meteorological conditions, etc. It provides detailed guidance for the mast assembly work, clarifies the assembly methods and specific requirements of various types of masts, and ensures that the masts can correctly support the catenary suspension and other equipment to meet the working requirements of the catenary.

[0079] The catenary engineering technical standards and specification documents include "Measures for Compiling Design Estimates (Preliminary Estimates) of Railway Capital Construction Projects", "Budget Quota for Railway Electric Traction Power Supply Projects", etc. The standards and specifications uniformly stipulate the specific methods for calculating engineering quantities, measurement units, etc., to ensure the accuracy and consistency of calculations.

[0080] In some embodiments, after obtaining the above sample data, the basic data is preprocessed first, including data cleaning and data normalization; meanwhile, the drawing information of the catenary design drawing is extracted, including the geometric dimensions, position information, quantity and connection relationship of each component.

[0081] Specifically, check and process the missing values and outliers in the basic data. For missing values, methods such as mean filling and regression prediction can be used for complementation; for outliers, it is judged whether to eliminate or correct according to the actual situation. Data cleaning can effectively eliminate these bad data and improve the accuracy and reliability of the data. Normalize the basic data of different magnitudes so that the basic data is within the same numerical range, improving the efficiency and accuracy of model training. For example, use the minimum-maximum normalization or Z-score normalization method. Data normalization unifies data of different magnitudes and distributions into a specific interval or standard scale, making the data comparable in subsequent analysis and processing. After normalization processing, the dimension influence can be eliminated and the precision of data analysis can be improved.

[0082] Optionally, first conduct integrity, compliance and accuracy audits on the catenary design drawing. For example, check whether the drawing is complete, including whether various types of drawings such as plan views, elevation views, sectional views, and assembly drawings are complete, and whether there are missing or omitted parts; check whether the layout, spacing, installation methods, etc. of each component of the catenary meet safety and technical standards; check whether the dimension markings, technical parameters and other information on the drawing are accurate and clear, and whether there are contradictions or errors. Then, extract the geometric dimensions and position information of each component of the catenary from the drawing, such as the height of the pillar, the size of the cross sleeper, the length and direction of the contact wire, etc.; count the quantity of various equipment and components in the drawing, such as the number of insulators, the number of positioners, the number of disconnectors, etc.; sort out the connection relationship between each component of the catenary, and clarify which components need to be assembled and which need to be installed in specific positions, etc. Further, the information extracted from the drawing can be combined with line data (such as line length, slope, terrain and landform, etc.), or the content of the design drawing can be compared with the requirements of catenary engineering technical standards and specifications to supplement or correct relevant information.

[0083] The above embodiments can improve data quality, ensure design accuracy, improve design efficiency, and optimize resource allocation by processing basic data and drawing information, play an important role in the design, construction and management stages of the catenary project, and provide strong support for the smooth progress of the entire project.

[0084] S402. Input the basic data, the catenary design drawing, and the catenary engineering technical standards and specification documents into the pre-trained model to obtain the analysis result output by the pre-trained model.

[0085] Among them, the pre-trained model is used to reflect the calculation principles of the catenary project quantity and budget estimate of sub-items under different basic data, and it is a model that converges after comparative learning with the actual catenary project quantity and the actual budget estimate list. Exemplarily, the calculation principles include but are not limited to: calculating the number of poles according to the pole spacing and line length, and calculating the consumption of catenary wire according to the length and tension requirements of the catenary wire; preparing the budget estimate list of the catenary project according to the project quantity and the unit prices of various materials, equipment, labor, and machinery.

[0086] In some embodiments, the training steps of the pre-trained model include the following steps S501 to S506:

[0087] S501. Divide the sample data into a training set, a validation set, and a test set.

[0088] Before model training, a large amount of actual data of catenary projects is collected, covering the actual project quantities and corresponding budget estimate lists of catenary projects in railway projects of different regions and scales.

[0089] Specifically, the sample data is divided into a training set, a validation set, and a test set according to a preset ratio. For example, the ratio is 70%, 15%, and 15%.

[0090] S502. Use the training set to train the original machine learning model to obtain the predicted catenary project quantity and the predicted budget estimate list output by the original machine learning model.

[0091] Among them, the original machine learning model can be a decision tree, a random forest, a support vector machine, a neural network, etc.

[0092] In the training process, the model will process different basic data input based on its own initially set algorithm, and deduce the catenary project quantity and budget estimate results of sub-items.

[0093] S503. Calculate the first loss function between the predicted catenary project quantity and the actual catenary project quantity, and the second loss function between the predicted budget estimate list and the actual budget estimate list.

[0094] Exemplarily, the predicted catenary project quantity and the actual catenary project quantity have similar characteristics at the sample level, such as the same line type, similar catenary structure forms, etc. The original data on which the actual catenary project quantity is calculated, including line parameters, the specifications and quantities of main materials used, actual labor costs and machine costs, etc., are matched with the input data used for the predicted catenary project quantity. The first loss function can be the absolute error or the relative error to quantify the difference degree between the predicted catenary project quantity and the actual catenary project quantity.

[0095] S504. Adjust the model parameters of the original machine learning model based on the first loss function, the second loss function, and the validation set.

[0096] Adjusting the model parameters based on these loss functions enables the model output to continuously approach the true value, improving the accuracy of predicting the quantity and budget estimate list of catenary projects. The selected model is trained using the training set. Through the backpropagation algorithm, the parameters of the model are optimized to gradually reduce the error and make the model output continuously approach the actual data. Adjusting the model parameters (such as the depth of the decision tree, the learning rate of the neural network, etc.) through the validation set can prevent the model from overfitting on the training set and enable the model to achieve better performance on the validation set. The cross-validation method can be adopted to improve the stability and generalization ability of the model.

[0097] S505. Calculate the performance metrics of the machine learning model on the test set after adjusting the model parameters.

[0098] S506. Perform feedback training on the machine learning model according to the performance metrics until the performance metrics meet the preset conditions to obtain a converged pre-trained model.

[0099] Use performance metrics such as accuracy, recall rate, and mean squared error (MSE) to evaluate the performance of the model on the test set and determine whether the prediction effect of the model meets the requirements. Conduct multiple rounds of iteration until the performance metrics meet the preset conditions to obtain a converged pre-trained model. Each round of iteration is based on the results of the previous round to further improve the calculation principle and the accuracy and reliability of the model.

[0100] With repeated training of a large amount of data, the model continuously adjusts its internal parameters, gradually learns the internal correlations and calculation logics between different basic data, the quantity of catenary projects, and the budget estimates, and finally achieves convergence. The converged model can accurately reflect the calculation principles of the quantity and budget estimates of the catenary project sub-units under different basic data.

[0101] The above training steps effectively improve the accuracy, generalization ability, and stability of the model by scientifically dividing sample data, calculating loss functions in multiple dimensions, continuously adjusting model parameters, and performing feedback training based on performance metrics, providing reliable support for predicting the quantity and budget estimate list of catenary projects. The fully trained model can stably output accurate prediction results when facing actual engineering data, reducing fluctuations and errors, and providing stable and reliable support for the planning, design, and budget of catenary projects.

[0102] In some embodiments, after obtaining a converged pre-trained model, it is applied for inference, including first extracting features from the pre-processed basic data and drawing information to obtain key features; then, using the pre-trained model to calculate and summarize the key features with reference to the engineering technical standards and specification documents of the catenary, and generating an analysis result.

[0103] Among them, the key features include line features, main material features, price features, usage-related features, as well as component size features, component position features, component type features, equipment specification features, material material features, connection method features, assembly relationship features, special section features, and environmental condition features, etc.

[0104] Specifically, extract the curve radius, slope, line length, etc. as the key features of the line, and these features will affect the structural design and material usage of the catenary. For example, the smaller the curve radius, the greater the tension adjustment and installation difficulty of the contact wire and the carrier cable, and more compensation devices may be required. Extract the features of the main material such as material, specification, model, etc. Different materials and specifications will affect the unit price and usage of the material. For example, contact wires with different cross-sections have different current-carrying capacities, and the appropriate specifications will be selected according to the power supply requirements of the line. Extract the key features in the project unit price, labor unit price, and machine unit price, such as the unit price difference in the installation of different types of poles, and the unit price difference of different types of labor. Analyze the correlation between the line data and the main material data, and extract the features that affect the usage of the main material, such as calculating the theoretical number of poles according to the span and line length; calculating the usage of the contact wire and the carrier cable according to the structural form of the catenary and the line length.

[0105] Extract the component size features from the drawing information, such as the height of the pole, the length and width of the cross sleeper, the cross-sectional size of the contact wire and the carrier cable, etc.; the component position features such as the position coordinates of the components and the spacing between them; the component type features such as material, model, structural form, specification, etc.; the material material features such as the copper alloy material composition of the contact wire, the steel material grade of the pole, etc.; the connection method features such as bolt connection, welding, hinge connection, etc.; the assembly relationship features indicating the assembly sequence and cooperation relationship between the components; the special section features such as areas like tunnels, bridges, stations, etc.; the environmental condition features such as wind speed, ice coating thickness, seismic intensity, etc.

[0106] According to the results of the model analysis, extract the calculation principles for the engineering quantity of the catenary. For example, if the decision tree model shows that the line curve radius and slope are important factors affecting the calculation of the number of poles, the calculation principles for the number of poles under different curve radius and slope conditions can be summarized.

[0107] The above embodiments extract features from the preprocessed basic data and drawing information, which can quickly screen out key features without having to analyze a large amount of complex data from scratch. By using a pre-trained model to calculate and summarize against technical standards and specification documents, the cumbersome process of manual comparison and calculation one by one is avoided, greatly shortening the time to generate analysis results and improving work efficiency.

[0108] S403. According to the analysis results and in combination with the influencing factors of catenary construction efficiency, generate the catenary project quantities of multiple catenary sub-units and the associated budgetary estimates list.

[0109] Among them, the influencing factors of catenary construction efficiency include construction organization costs, reserved length, and material losses. Construction organization costs include labor and machinery costs, construction measures costs, special construction increase costs, and transition project costs. Regarding the reserved length, for example, when calculating the lengths of wires such as catenary wires and messenger wires, a certain reserved length should be considered according to design requirements and relevant regulations. For example, the reserved length is calculated at 3% of the anchor section length to meet the requirements of wire tension adjustment, maintenance and replacement, etc. Regarding material losses, for example, when calculating the quantities of various materials, reasonable losses during the construction process need to be considered. The loss rate is generally determined based on empirical data or relevant regulations. For small materials such as bolts and nuts, there may be certain losses during transportation and installation, and this part needs to be taken into account during calculation.

[0110] Considering factors such as material losses, reserved quantities, and construction organization adjustments, calculate the total project cost, including direct project costs, indirect costs, profits, and taxes, etc.

[0111] When determining the project quantities of catenary sub-units in the above embodiments, considering construction organization costs, reserved length, material losses, etc., can make the project plan more in line with actual construction needs and avoid waste of resources caused by unreasonable planning. By comprehensively considering influencing factors such as construction organization costs and material losses, the generated budgetary estimates list can more accurately reflect the project cost.

[0112] S404. According to the line data, the catenary project quantities of multiple catenary sub-units, and the associated budgetary estimates list, build a relational database system.

[0113] Among them, the relational database system is updated according to a preset period, and the preset period can be a quarter. Update the relational database regularly according to quarters, so as to ensure the timeliness and accuracy of catenary project quantity budget estimates. The built relational database can accurately manage data and efficiently generate catenary project quantity budget estimates after obtaining the infrastructure data of the catenary project input by the user.

[0114] The steps for setting up the above relational database system achieve efficient data management, improve the accuracy and reliability of engineering data by integrating multi-source data, analyzing with pre-trained models, generating lists by combining impact factors, and constructing the database. When setting up the system, various data related to catenary engineering construction are obtained, including basic data, design drawings, and technical standards and specification documents. Integrating these scattered data into the relational database system realizes centralized data management, facilitates users to quickly search for and use the data, and improves the data utilization efficiency. When personnel from different departments are engaged in work such as the design, construction, and budget of catenary engineering, they can obtain the required data in the same database, avoiding the inconvenience of searching and data inconsistency problems caused by scattered data.

[0115] The input data is analyzed using a pre-trained model. After convergence through comparative learning with the actual catenary engineering quantity and the actual budget estimate list, the model can reflect the calculation principles of the catenary engineering quantity and budget estimate for sub-items under different basic data. Compared with the traditional method of estimating based on empirical values, the accuracy of the catenary engineering quantity and the associated budget estimate list generated through model analysis is significantly improved. When calculating the engineering quantity of a specific catenary sub-item, the model can comprehensively consider various factors, reduce human errors, and provide more reliable data support for engineering decisions.

[0116] A relational database system is set up based on the line data and the generated catenary engineering quantity and budget estimate list, establishing the association between the line data and the engineering information of each sub-item. When querying the catenary engineering information related to a certain line, detailed information such as the engineering quantity and budget estimate list of each catenary sub-item on this line can be quickly located and obtained through the line data, facilitating the analysis and evaluation of the overall engineering situation of the line and providing convenience for line planning, maintenance, and other work.

[0117] In summary, the method for generating the catenary engineering quantity budget estimate provided by the embodiment of this application has the following beneficial effects compared with the related technologies:

[0118] (1) Improve work efficiency: The way of simplifying the process and automatically generating the list greatly shortens the time for the engineering quantity budget estimate in the catenary layout work. Engineering personnel do not need to spend a lot of time on data collection, collation, and complex calculations, and can invest more energy in other important work such as design optimization, thus overall improving the work efficiency in the design and planning stages of catenary engineering.

[0119] (2) Enhance data reliability: The real-time updated database and calculations based on accurate data ensure that the generated engineering quantity budget estimate list has high reliability. This provides a reliable basis for cost budgeting, material procurement plan formulation, etc. in railway construction, reducing problems such as cost overruns, material waste, or project delays caused by inaccurate data.

[0120] (3) Promote the informatization of railway construction: Using a relational database system for data processing and list generation is an important manifestation of the transformation of catenary projects towards informatization and digitization. This method facilitates data interaction and sharing with other information systems in railway construction, which is conducive to realizing the overall informatization management of railway construction projects and improving the coordination and management level of construction projects.

[0121] (4) Meet the development needs of railway construction: With the rapid development of railway construction, the demand for real-time and accurate data is increasing day by day. The method provided in the above steps can provide catenary project-related data in a timely and accurate manner, enabling railway construction to make decisions based on reliable data at all stages such as planning, design, and construction, and better meeting the ever-developing needs of railway construction.

[0122] As Figure 9 shown, Figure 9 is a schematic structural diagram of a device for generating an approximate estimate of catenary project quantities provided by an embodiment of the present application. The device includes:

[0123] An acquisition module 901, configured to acquire infrastructure data related to catenary projects input by a user;

[0124] A generation module 902, configured to generate a target project quantity approximate estimate list corresponding to the infrastructure data according to the infrastructure data and a pre-established relational database system;

[0125] Among them, the relational database system is updated according to a preset period, and includes the catenary project quantities of multiple catenary sub-units and their associated approximate estimate lists.

[0126] As an optional implementation manner provided by an embodiment of the present application, the device further includes a database construction module, configured to: acquire sample data, where the sample data includes basic data related to catenary project construction, catenary design drawings, and catenary project technical standards and specification documents; the basic data includes line data, main materials of catenary projects, unit prices of catenary projects, labor costs of catenary projects, and machine costs of catenary projects; input the basic data, catenary design drawings, and catenary project technical standards and specification documents into a pre-trained model to obtain an analysis result output by the pre-trained model; where the pre-trained model is used to reflect the calculation principles of catenary project quantities and approximate estimates of sub-units under different basic data, and is a model that converges after comparative learning with actual catenary project quantities and actual approximate estimate lists; according to the analysis result, combined with the influencing factors of catenary construction efficiency, generate the catenary project quantities of multiple catenary sub-units and their associated approximate estimate lists; build a relational database system according to the line data and the catenary project quantities of multiple catenary sub-units and their associated approximate estimate lists.

[0127] As an alternative implementation provided by the embodiments of the present application, the database construction module is further configured to: preprocess the basic data, including data cleaning and data normalization; extract the drawing information of the catenary design drawings, including the geometric dimensions, position information, quantity, and connection relationship of each component.

[0128] As an alternative implementation provided by the embodiments of the present application, the database construction module is specifically configured to: extract key features from the preprocessed basic data and drawing information; use a pre-trained model to calculate and summarize the key features with reference to the catenary engineering technical standards and specification documents to generate an analysis result.

[0129] As an alternative implementation provided by the embodiments of the present application, the database construction module is specifically configured to: extract line features, main material features, price features, and usage-related features from the preprocessed basic data; extract component size features, component position features, component type features, equipment specification features, material material features, connection method features, assembly relationship features, special section features, and environmental condition features from the drawing information.

[0130] As an alternative implementation provided by the embodiments of the present application, the device further includes a model training module, which is configured to: divide the sample data into a training set, a validation set, and a test set; use the training set to train the original machine learning model to obtain the predicted catenary project quantity and predicted budget estimate list output by the original machine learning model; calculate the first loss function between the predicted catenary project quantity and the actual catenary project quantity, and the second loss function between the predicted budget estimate list and the actual budget estimate list; adjust the model parameters of the original machine learning model based on the first loss function, the second loss function, and the validation set; calculate the performance index of the machine learning model after the model parameters are adjusted on the test set; perform feedback training on the machine learning model according to the performance index until the performance index meets the preset conditions to obtain a converged pre-trained model.

[0131] For the specific limitations on the catenary project quantity budget generation device, reference may be made to the limitations on the catenary project quantity budget generation method in the foregoing text, which will not be elaborated here. Each module in the above catenary project quantity budget generation device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0132] In one embodiment, the present application provides an electronic device, which may be a terminal, and its internal structure diagram may be as Figure 10As shown in the figure. The electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, near-field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for detecting lags. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, a touchpad, or a mouse, etc.

[0133] Those skilled in the art can understand that Figure 10 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0134] In one embodiment, the generating device for the catenary project quantity budget provided by this application can be implemented in the form of a computer program, and the computer program can run on an electronic device such as Figure 10 shown in the figure. Each program module that composes the generating device for the catenary project quantity budget can be stored in the memory of the electronic device. The computer program composed of each program module enables the processor to execute the steps in the method for generating the catenary project quantity budget in each embodiment of this application described in this specification.

[0135] In one embodiment, this application provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps in the method for generating the catenary project quantity budget in each embodiment of this application.

[0136] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the computer, it implements the steps in the method for generating the catenary project quantity budget in each embodiment of this application.

[0137] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0138] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0139] In the present application, the processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc.

[0140] In the present application, the memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0141] In this application, computer-readable media include both permanent and non-permanent, removable and non-removable storage media. The storage media can implement information storage by any method or technology, and the information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media do not include transitory media such as modulated data signals and carrier waves.

[0142] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0143] The above are only specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to these embodiments herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating an approximate estimate of the catenary project quantity, characterized in that Including: Obtain infrastructure data related to the catenary project input by the user; Generate a target project quantity estimate list corresponding to the infrastructure data according to the infrastructure data and a pre-established relational database system; Wherein, the relational database system is updated according to a preset period, and includes the catenary project quantities of multiple catenary sub-units and their associated estimate lists.

2. The method according to claim 1, characterized in that, The steps for building the relational database system include: Obtain sample data, where the sample data includes basic data related to the construction of the catenary project, catenary design drawings, and catenary project technical standards and specification documents; the basic data includes line data, main materials for the catenary project, unit prices of the catenary project, labor costs of the catenary project, and machine costs of the catenary project; Input the basic data, the catenary design drawings, and the catenary project technical standards and specification documents into a pre-trained model to obtain the analysis result output by the pre-trained model; wherein, the pre-trained model is used to reflect the calculation principles of the catenary project quantities and estimates of sub-units under different basic data, and is a model that converges after comparative learning with the actual catenary project quantities and actual estimate lists; Generate the catenary project quantities of multiple catenary sub-units and their associated estimate lists according to the analysis result and in combination with the influencing factors of catenary construction efficiency; Build the relational database system according to the line data and the catenary project quantities of multiple catenary sub-units and their associated estimate lists.

3. The method according to claim 2, wherein After obtaining the sample data and before inputting the basic data, the catenary design drawings, and the catenary project technical standards and specification documents into the pre-trained model to obtain the analysis result output by the pre-trained model, the method further includes: Preprocess the basic data, including data cleaning and data normalization; Extract the drawing information of the catenary design drawings, including the geometric dimensions, position information, quantity, and connection relationship of each component.

4. The method according to claim 3, wherein The inputting the basic data, the catenary design drawings, and the catenary project technical standards and specification documents into the pre-trained model to obtain the analysis result output by the pre-trained model includes: Extract features from the preprocessed basic data and the drawing information to obtain key features; Use the pre-trained model to calculate and summarize the key features with reference to the catenary project technical standards and specification documents to generate the analysis result.

5. The method according to claim 4, wherein The extracting features from the preprocessed basic data and the drawing information to obtain key features includes: Extract line features, main material features, price features, and usage-related features from the preprocessed basic data; Extract component size features, component position features, component type features, equipment specification features, material material features, connection method features, assembly relationship features, special section features, and environmental condition features from the drawing information.

6. The method according to claim 2, characterized in that The training steps of the pre-trained model include: Divide the sample data into a training set, a validation set, and a test set; Train the original machine learning model using the training set to obtain the predicted catenary project quantity and the predicted budget estimate list output by the original machine learning model; Calculate the first loss function between the predicted catenary project quantity and the actual catenary project quantity, and the second loss function between the predicted budget estimate list and the actual budget estimate list; Adjust the model parameters of the original machine learning model based on the first loss function, the second loss function, and the validation set; Calculate the performance metrics of the machine learning model on the test set after the model parameters are adjusted; Perform feedback training on the machine learning model according to the performance metrics until the performance metrics meet the preset conditions to obtain the converged pre-trained model.

7. An apparatus for generating an approximate estimate of catenary project quantity, characterized in that, It includes: An acquisition module for acquiring infrastructure data related to the catenary project input by the user; A generation module for generating a target project quantity budget estimate list corresponding to the infrastructure data according to the infrastructure data and a pre-established relational database system; Wherein, the relational database system is updated according to a preset period and includes the catenary project quantities of multiple catenary sub-units and their associated budget estimate lists.

8. An electronic device, characterized in that, It includes: A processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the method for generating the catenary project quantity budget estimate according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It includes: A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, it implements the method for generating the catenary project quantity budget estimate according to any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes: The computer program product includes a computer program. When the computer program runs on a computer, it causes the computer to implement the method for generating the catenary project quantity budget estimate according to any one of claims 1 to 6.

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