Bidding quotation data analysis method for service-oriented project

Abstract

The invention discloses a bidding offer data analysis method for a service-oriented project, and belongs to the cross technical field of data processing and bidding management, and the specific implementation steps of the bidding offer data analysis method for the service-oriented project are as follows: S1, determining a service-oriented project offer data acquisition dimension, forming a standardized service purchase list; s2, a service project quotation tool is researched and developed, and a service type quotation data interface is determined; s3, constructing a quotation data analysis function of the service-oriented project; and S4, constructing a quotation data analysis model of the service-oriented project. According to the method, the fair bid evaluation target of price and quality matching can be effectively guaranteed, the quotation rationality and the service value matching degree can be considered, the service value matching degree of a service project can be visually calculated, and the dilemma that traditional quotation data is difficult to use and subsequent projects repeatedly face quotation chaos is thoroughly changed.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of data processing and bidding management, and more specifically, to a method for analyzing bid price data for service-oriented projects. Background Technology

[0002] In the current bidding and tendering system, there are significant irregularities in the bidding and tendering of service-oriented projects, mainly reflected in the following aspects: 1. Chaotic pricing formats; 2. Crude evaluation methods; 3. Difficulty in utilizing pricing data. Currently, the engineering field has widely adopted standardized pricing systems and data processing methods, achieving standardized collection and scientific analysis of pricing data for engineering projects.

[0003] Currently, the value realization path for engineering projects is clear (settlement is based on the completion of the work), and the core logic of the analysis model is "cost accounting verification". However, the value realization path for service projects depends on the service performance process (such as the fault repair efficiency of operation and maintenance services), and the analysis model needs to take into account both "price reasonableness" and "service value matching". The difference in the analysis objectives of the two makes the model logic unusable.

[0004] Secondly, the "service value matching degree" of service-oriented projects is difficult to calculate with a simple formula. It is necessary to establish a multi-dimensional correlation model by combining historical performance data. However, the engineering field model does not involve this kind of correlation logic, so the model architecture and training method need to be redesigned, and there is a lack of technical solutions that can be directly referenced.

[0005] Therefore, in view of this, we will study and improve the existing structure to provide a data analysis method for bidding prices of service projects, in order to achieve a more practical value. Summary of the Invention

[0006] 1. Technical problems to be solved In view of the problems existing in the prior art, the purpose of this invention is to provide a method for analyzing bidding price data for service projects. It can effectively ensure the achievement of the fair evaluation goal of "price-quality matching", while taking into account both "price reasonableness" and "service value matching degree". At the same time, it can make the "service value matching degree" of service projects intuitively calculated, completely changing the dilemma of traditional price data being difficult to use and subsequent projects facing "price chaos".

[0007] 2. Technical Solution To solve the above problems, the present invention adopts the following technical solution.

[0008] A method for analyzing bid price data in service projects, the specific implementation steps of which are as follows: Step S1: Determine the data collection dimensions for service project quotations and generate a standardized service procurement list: A service-oriented project classification system was established, and for each project category, core data collection dimensions covering basic information, service content, and pricing details were pre-defined. Step S2: Using the R&D service project quotation tool and the data interface for determining service-type quotations: Determine the service-type quotation data interface specifications, as well as the interface data format and transmission protocol. Then, in conjunction with step S1, develop a dedicated quotation tool that adapts to various service-type projects. Step S3: Construct a service-oriented project quotation data parsing function: Connect to the data interface and sequentially perform dedicated access to structured data, accurate verification of structured data, and standardized conversion of structured data; Step S4: Construct a service project pricing data analysis model: Based on the standardized pricing dataset, conduct a bid review analysis of service project bid pricing documents and a rationality analysis of service project bid pricing documents.

[0009] Furthermore, in step S1, when dividing the service project classification system: Based on the port group's historical service project procurement data, a service project classification system was established to clarify the core characteristics of different types of projects and ensure the relevance of data collection dimensions. The service project classification specifically includes consulting services, operation and maintenance services, and professional services.

[0010] Furthermore, in step S1, when presetting the core collection dimensions covering basic information, service content, and pricing details: For each type of service project, core data collection dimensions are preset based on its core characteristics to ensure the integrity and universality of the collected data.

[0011] Furthermore, in step S2, when determining the service-type pricing data interface specification: To ensure efficient integration between the data exported from the dedicated quotation tool and the bidding party's data collection system, avoiding duplicate data entry or format conversion errors, a standardized service-oriented quotation data interface was designed, clearly defining the interface type, data format, transmission protocol, and verification rules. Specifically: The core interface types include data upload interface, data download interface, and data verification interface; In step S2, when determining the interface data format and transmission protocol: Data transmission is performed using JSON format, with field names consistent with those in the standardized service procurement list, resulting in a clear hierarchical structure.

[0012] Furthermore, in step S2, when developing a dedicated pricing tool adapted to various service projects, the specific pricing tool includes: A standardized list of service-oriented project classification systems that incorporates tools; Structured data entry modules are set up for different types of service items; The data export and encryption module allows the tool to export data as an encrypted standard data interface file after the bidder completes the form, meeting the needs of bidding scenarios.

[0013] Furthermore, in step S3, when performing dedicated access to structured data: It directly connects to a standardized data interface to receive JSON-formatted structured data uploaded by a dedicated quotation tool, and performs data reception, unpacking, and source data recording.

[0014] Furthermore, in step S3, during the precise verification of structured data: To address the field characteristics of JSON-formatted structured data, a three-layer validation logic is designed to comprehensively investigate data anomalies. The validation during data anomaly investigation includes field integrity verification, format compliance verification, and calculation verification.

[0015] Furthermore, in step S3, when performing structured data standardization transformation: The validated JSON structured data is standardized and optimized according to unified rules to ensure that the data is fully adapted to subsequent analysis needs. The scope of standardization includes standardization of basic information dimensions, standardization of quotation details dimensions, and standardization of service content dimensions.

[0016] Furthermore, in step S4, during the bid evaluation analysis of service project tender documents: Focusing on two core dimensions—"whether the tender documents are responsive" and "whether there are calculation errors"—and based on current mature practices, the system determines the compliance level of tender documents through standardized verification processes and quantitative validation rules. It accurately identifies various calculation deviations and logical contradictions, outputs clear bid evaluation conclusions, provides objective basis for bid evaluation, and avoids compliance risks and performance disputes.

[0017] Furthermore, in step S4, when conducting a reasonableness analysis of the service project bid price document: Once the bid price documents pass the bid clearing analysis, the substantive price analysis can begin. Based on the characteristics of the service project, and focusing on the three core dimensions of "price reasonableness, service cost-effectiveness, and price competitiveness," an end-to-end analysis model is constructed, which includes "data preprocessing → feature engineering → model training → quantitative scoring → result output." The specific analysis includes: the three core dimensions and their sub-indicators, bid weight allocation, core algorithm design and implementation, and comprehensive scoring model algorithm. Beneficial effects

[0018] Compared with the prior art, the advantages of this invention are: ① This solution breaks the chaotic situation of traditional service project pricing by clarifying unified dimensions for price data collection and forming a standardized service procurement list. It makes the prices of different bidders directly comparable, completely solves the problem of difficulty in bid evaluation and comparison caused by differences in pricing formats in similar projects, and greatly reduces the complexity of the bid evaluation process.

[0019] By constructing a multi-dimensional bidding data analysis model, covering core dimensions such as bid clearing analysis, bid rationality, service cost-effectiveness, and bid competitiveness, and calculating a comprehensive score through weight configuration, it replaces the traditional extensive bid evaluation model that relies on the subjective experience of bid evaluators. It provides objective and accurate data basis for bid evaluation, effectively ensuring the achievement of the fair bid evaluation goal of "price-quality matching", while taking into account both "bid rationality" and "service value matching degree". ② This solution, through the development of a dedicated quotation tool, the establishment of standardized data interfaces, and the construction of quotation data parsing functions, achieves automated and accurate extraction of quotation data from different media such as PDF files and paper materials. It avoids the tedious operation of manual word-by-word extraction, significantly reduces data extraction time and error rate, and greatly improves the efficiency of quotation data processing.

[0020] By building a service project price database, the bidding price data of various service projects can be systematically accumulated and reused to form a recyclable data asset. This provides historical data support for the formulation of prices and the optimization of evaluation criteria for subsequent similar projects, and enables the "service value matching degree" of service projects to be calculated intuitively. This completely changes the dilemma of traditional price data being difficult to use and subsequent projects facing "pricing chaos". ③ This solution is specifically designed for the characteristics of service-oriented projects, which are characterized by "abstract service content and diverse pricing dimensions". Compared with the traditional pricing analysis system that is only applicable to engineering projects, it is suitable for various service-oriented project scenarios such as consulting services, operation and maintenance services, and technical support services. It has a wider range of applications and is more practical. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the method for analyzing bidding price data for service projects in this invention. Figure 2 This is a schematic diagram of the hierarchical structure of the interface data format and transmission protocol in this invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Example

[0023] Please see Figure 1 - Figure 2 A method for analyzing bid price data in service projects, the specific implementation steps of which are as follows: Step S1: Determine the data collection dimensions for service project quotations and generate a standardized service procurement list: A service-oriented project classification system was established, and for each project category, core data collection dimensions covering basic information, service content, and pricing details were pre-defined. Specifically, when classifying service-type projects: Based on the port group's historical service project procurement data, a service project classification system was established to clarify the core characteristics of different types of projects and ensure the relevance of data collection dimensions. The service project classification specifically includes consulting services, operation and maintenance services, and professional services.

[0024] Consulting services, such as cost consulting, management consulting, technical consulting, and industry research consulting, are characterized by "knowledge output and solution provision"; Operation and maintenance services, such as IT system operation and maintenance, equipment operation and maintenance, and facility operation and maintenance, are characterized by "continuous support, fault repair, and regular inspection"; Professional services, such as legal agency services, financial audit services, and design services, are characterized by "qualification dependence and process-oriented services".

[0025] Specifically, when pre-setting the core data collection dimensions to cover basic information, service content, and pricing details: For each type of service project, based on its core characteristics, core data collection dimensions covering "basic information, service content, and pricing details" are preset to ensure the completeness and universality of the collected data.

[0026] The core data collection dimensions for each project category are detailed in the table below:

[0027] In summary, step S1 involves constructing a standardized service procurement list by defining clear, unified, and adaptable dimensions for quotation data collection that can be tailored to individual needs. This provides a unified benchmark for subsequent data collection, standardized processing, and analysis, and addresses the data incomparability issues caused by the lack of unified standards and missing core elements in existing solutions.

[0028] Step S2: Using the R&D service project quotation tool and the data interface for determining service-type quotations: Determine the service-type quotation data interface specifications, as well as the interface data format and transmission protocol. Then, in conjunction with step S1, develop a dedicated quotation tool that adapts to various service-type projects. Specifically, when determining the service-based pricing data interface specifications: To ensure efficient integration between the data exported from the dedicated quotation tool and the bidding party's data collection system, avoiding duplicate data entry or format conversion errors, a standardized service-oriented quotation data interface was designed, clearly defining the interface type, data format, transmission protocol, and verification rules. Specifically: The core interface types include data upload interface, data download interface, and data verification interface; Data upload interface: Allows bidders to directly upload encrypted structured file data to the bidding party's core data acquisition system using a dedicated quotation tool; Data download interface: Allows the bidding party's data collection system to extract data from encrypted files exported by the tool, or directly read data synchronized by the tool over the network; Data verification interface: Connects to the real-time verification logic of the dedicated quotation tool, and performs secondary verification of data integrity, format correctness and logical consistency during the data generation process to ensure error-free data transmission.

[0029] See Figure 2 Specifically, when determining the interface data format and transmission protocol: Data transmission is performed using JSON format, with field names consistent with those in the standardized service procurement list, resulting in a clear hierarchical structure.

[0030] Specifically, when developing dedicated quotation tools adapted to various service projects, the specific quotation tools include: A standardized list of service-oriented project classification systems that incorporates tools; The tool includes a structured data entry module for different types of service projects, as well as a data export and encryption module. After the bidder completes the data entry, the tool supports exporting the data as an encrypted standard data interface file, meeting the needs of bidding scenarios.

[0031] In summary, step S2 standardizes the data entry behavior of bidders through a dedicated quotation tool, and achieves efficient and accurate transmission and collection of quotation data through a standardized data interface. This solves the problems of poor data carrier adaptability, low extraction efficiency, and insufficient accuracy in existing solutions, while also providing a structured input basis for subsequent data standardization processing.

[0032] Step S3: Construct a service-oriented project quotation data parsing function: Connect to the data interface and sequentially perform dedicated access to structured data, accurate verification of structured data, and standardized conversion of structured data; Specifically, when performing dedicated access to structured data: It directly connects to a standardized data interface to receive JSON-formatted structured data uploaded by a dedicated quotation tool, and performs data reception, unpacking, and source data recording.

[0033] Data reception and unpacking: Receive encrypted JSON data packets transmitted by the interface, decrypt them according to preset decryption rules, and restore the complete JSON structured data; Metadata Recording: Automatically extracts and records data-related metadata, including bidder identifier, data upload time, interface transmission status, and unique data packet number, forming the basis for data traceability.

[0034] Specifically, when performing precise validation of structured data: To address the field characteristics of JSON-formatted structured data, a three-layer validation logic is designed to comprehensively investigate data anomalies. The validation during data anomaly investigation includes field integrity verification, format compliance verification, and calculation verification.

[0035] (1) Field integrity verification: The "JSONSchema verification + mandatory field library comparison technology" is adopted. The validator compares the JSON to be verified with the schema template to realize the verification of mandatory fields and field level verification. (2) Format conformity verification: The "regular expression matching + field type strong verification technology" is adopted. By using a predefined regular expression template, the string type field is matched to verify whether the format conforms to the preset rules, and the verification of field type and encoding format is realized. (3) Calculation verification: Through numerical analysis, precision control, error calculation and other algorithms, the logical verification of quotation data is realized, including the verification of single price calculation, tax-inclusive and tax-exclusive calculation, and total price calculation.

[0036] Specifically, when performing structured data standardization transformation: The validated JSON structured data is standardized and optimized according to unified rules to ensure that the data is fully adapted to subsequent analysis needs. The scope of standardization includes standardization of basic information dimensions, standardization of quotation details dimensions, and standardization of service content dimensions.

[0037] In summary, step S3 involves building a dedicated parsing function for the JSON-formatted structured data output from the standardized data interface in step 2. Through precise verification and unified standardized conversion, it ensures that the data fields are complete, the format is consistent, and the logic is consistent, outputting a high-quality standardized quotation dataset to provide reliable data input for subsequent analysis model applications.

[0038] Step S4: Construct a service project pricing data analysis model: Based on the standardized pricing dataset, conduct a bid review analysis of service project bid pricing documents and a rationality analysis of service project bid pricing documents.

[0039] Specifically, when conducting bid evaluation analysis of service project tender documents: Focusing on two core dimensions—"whether the tender documents are responsive" and "whether there are calculation errors"—and based on current mature practices, the system determines the compliance level of tender documents through standardized verification processes and quantitative validation rules. It accurately identifies various calculation deviations and logical contradictions, outputs clear bid evaluation conclusions, provides objective basis for bid evaluation, and avoids compliance risks and performance disputes.

[0040] Specifically, when conducting a reasonableness analysis of the tender price documents for service projects: Once the bid price documents pass the bid clearing analysis, the substantive price analysis can begin. Based on the characteristics of the service project, and focusing on the three core dimensions of "price reasonableness, service cost-effectiveness, and price competitiveness," an end-to-end analysis model is constructed, which includes "data preprocessing → feature engineering → model training → quantitative scoring → result output." The specific analysis includes: the three core dimensions and their sub-indicators, bid weight allocation, core algorithm design and implementation, and comprehensive scoring model algorithm.

[0041] Example 2: Based on the above embodiment 1, further description is provided.

[0042] When performing structured data standardization: (1) Standardization of basic information dimensions Time units are standardized: the service period is converted to the standard unit of "month" (e.g., "1 year" → "12 months", "3 years" → "36 months"). The bidding date and service start date remain in the "YYYY-MM-DD" format. If the original data is already in this format, it will be retained directly. Standardization of regional information: Service coverage and delivery locations are standardized in the format of "province / municipality + city / district" to ensure that regional information is structured; (2) Standardization of quotation details Consistent units of measurement: Ensure that all quotation-related fields use the same units of measurement to avoid calculation errors or comparison failures caused by unit differences.

[0043] Unified Tax Inclusion: Ensure that all bidders' price quotes clearly indicate whether they include tax, and uniformly use either "tax-inclusive price" or "tax-exclusive price" for further analysis.

[0044] (3) Standardization of service content dimensions Based on the "Standardized Service Procurement List", a "Service Content Standard Field - Mapping Rule" library is constructed. Through field matching, content splitting, and structured reorganization, the service content data filled in by the bidders is transformed into the field structure and expression form specified in the standard list, thereby achieving unified structuring of service content.

[0045] Load the service content standard list template for the corresponding project category. Based on the fields in the JSON data (such as "Operation and Maintenance Service" and "Consulting Service"), automatically load the corresponding category "Service Content Dimension Standard List Template" preset by S1, and clarify the required fields, optional fields, field structure and filling specifications of the service content of this project category. The service content field matching and structured splitting adopts a technology that combines "precise field name matching + semantic similarity matching" to associate the service content data filled in by the bidder with the standard list template fields; Reorganize the split content according to the field structure and format requirements of the standard list template to ensure that the data format is consistent; Output the service content after the result is converted.

[0046] In summary, step S3 involves building a dedicated parsing function for the JSON-formatted structured data output from the standardized data interface in step 2. Through precise verification and unified standardized conversion, it ensures that the data fields are complete, the format is consistent, and the logic is consistent, outputting a high-quality standardized quotation dataset to provide reliable data input for subsequent analysis model applications.

[0047] Example 3: Further description is provided based on the above embodiments 1 and 2.

[0048] a. The three core dimensions and their detailed indicators are shown in the table below:

[0049] b. Weighting Weighting determination method: The analytic hierarchy process (AHP) is used, combined with industry expert scoring, to construct a judgment matrix and calculate the weights of each indicator, ensuring the scientific nature of the weight allocation, as detailed in the table below:

[0050] c. Core Algorithm Design and Implementation c1. Overall Algorithm Architecture Based on the three dimensions of "price reasonableness, service cost-effectiveness, and price competitiveness", a four-layer core algorithm architecture is constructed, which consists of "data preprocessing (comparability / outlier removal) → indicator calculation → standardized scoring → weighted comprehensive scoring". Through high-precision numerical calculation, statistical analysis, normalization processing and other technologies, the quantitative calculation and comprehensive score output of each indicator are realized.

[0051] c2. Data preprocessing algorithm (quotation comparability conversion algorithm) Core objective: To eliminate interference from non-price factors such as service content, duration, tax calculation method, and unit of measurement, ensuring the comparability of all quotations. Algorithm formula:

[0052] Key parameter processing: Service content coverage: The matching ratio between the bidder's service items and the standard service list is calculated using the BERT semantic similarity algorithm (e.g., if there are 10 standard service items and 8 bidder service items, the coverage is 80%). Unified tax calculation coefficient: When the control price / historical quotation is a tax-exclusive price, the coefficient = 1 / (1 + tax rate); when it is a tax-inclusive price, the coefficient = 1. Unit conversion factor: For example, the conversion factor for "ten thousand yuan" to "yuan" is 10,000, and the conversion factor for "US dollar" to "RMB" is the midpoint of the exchange rate.

[0053] c3, Core Algorithm for Single Indicators (1) Price Reasonableness Dimension - Big Data Deviation - Calculation of Median of Reasonable Range The median of the reasonable range is the core benchmark value for calculating the deviation of service item pricing data. Its calculation relies on sample pricing data of similar services in the service price database and is achieved through a standardized process of "sample screening → data preprocessing → quartile calculation → reasonable range definition → median extraction". The specific steps are as follows: Step 1: Screening sample quotes for similar services: Project type matches perfectly: The service type of the project being bid on is exactly the same as the service type of the current project (such as IT operation and maintenance services, consulting and planning services, equipment maintenance services); Service content similarity ≥ 90%: The BM25 algorithm is used to extract semantic features from the service content description. Through similarity calculation, it is ensured that the matching degree between the sample service items and service standards and the current project is not less than 90%. Similar service periods: The difference between the service period of the sample project and the current project Step 2: Interquartile Range (IQR) Preprocessing Sort the price quotes in the sample pool in ascending order, and calculate the lower quartile (Q1, the value at the 25th percentile of the sample), the upper quartile (Q3, the value at the 75th percentile of the sample), and the interquartile range (IQR = Q3 - Q1). Remove extreme outliers from the sample pool that meet the following conditions: price < Q1 - 1.5 × IQR or price > Q3 + 1.5 × IQR; Step 3: Defining a Reasonable Interval Based on the preprocessed effective sample pool, recalculate the quartile parameters and define a reasonable interval: The lower limit of the reasonable range = Q1' - 1.5 × IQR' The upper limit of the reasonable range = Q3' + 1.5 × IQR' Step 4: Extracting the median of a reasonable price range. If the number of samples in the reasonable price range set is odd, the median is the value in the middle position after sorting. If the sample size of the reasonable price range set is even, the median is the arithmetic mean of the two middle values ​​after sorting.

[0054] (2) Reasonableness of Quotation - Historical Quotation Deviation - Historical Quotation Benchmark Value The calculation of historical price deviation focuses on the historical price of the current bidding unit for the same service, and is achieved through "historical data extraction → benchmark value calculation → deviation calculation". The specific implementation steps are the same as (1).

[0055] (3) Service cost-effectiveness dimension - unit service cost The unit service cost is the price quoted for each "standardized service resource unit". Its core purpose is to eliminate the differences in service cycle and manpower allocation among different bidders and make cost comparisons fairer.

[0056]

[0057] (4) Service cost-effectiveness dimension_service value matching degree Service value matching is a core indicator for quantifying the "equivalence between price and service value". It achieves an accurate assessment of the cost-effectiveness of services by matching the service scope with the unit service cost.

[0058] Service Scope Coverage: The BM25 algorithm is used to calculate the matching ratio (0-1, rounded to two decimal places) between the bidder's service list and the "Standard Service List" specified in the tender documents. The specific calculation logic is as follows: The standard service list is broken down into a set of keywords for core service items; The service list of the bidders is segmented into words, and the matching score is calculated using the BM25 algorithm and mapped to the coverage. Coverage = Number of matching service items / Total number of standard service items; Algorithm formula:

[0059] (5) Core calculation algorithm for price competitiveness dimension The calculation of relative competitiveness focuses on comparing the "current bidder's price" with the "extreme value of all bidders' prices". The specific steps are as follows: Define the core input parameters: the comparable bid price is the current bidder's bid price after comparison processing; the highest bid price is the maximum value among all comparable bid prices; the lowest bid price is the minimum value among all comparable bid prices. Substituting into the formula: Relative competitiveness = (Highest bid - Comparable bids by one party) / (Highest bid - Lowest bid) × 100%; Special case handling: If all bidders' comparable prices are the same (highest price = lowest price), then the relative competitiveness of all bidders is marked as 100.0; if the calculation result exceeds 100 points, it is counted as 100 points; if it is less than 0 points, it is counted as 0 points, and the final result is rounded to two decimal places.

[0060] c4. Comprehensive scoring model algorithm The comprehensive scoring model is the core closed-loop algorithm for bid evaluation and clearing analysis of service projects. Through the logic of "single indicator standardization → dimensional weighted summation → comprehensive score output", it integrates the quantitative results of three dimensions: price rationality, service cost-effectiveness, and price competitiveness, and generates a comprehensive score of 0-100 points, providing a comprehensive and objective quantitative basis for bid evaluation decisions.

[0061] Specific algorithm formula:

[0062] Where i=3 (three core dimensions), and n is the number of sub-indicators under each dimension.

[0063] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concepts, should be covered within the scope of protection of the present invention.

Claims

1. A method for analyzing bid price data in service projects, characterized in that: The specific implementation steps of the bid price data analysis method are as follows: Step S1: Determine the data collection dimensions for service project quotations and generate a standardized service procurement list: A service-oriented project classification system was established, and for each project category, core data collection dimensions covering basic information, service content, and pricing details were pre-defined. Step S2: Using the R&D service project quotation tool and the data interface for determining service-type quotations: Determine the service-type quotation data interface specifications, as well as the interface data format and transmission protocol. Then, in conjunction with step S1, develop a dedicated quotation tool that adapts to various service-type projects. Step S3: Construct a service-oriented project quotation data parsing function: Connect to the data interface and sequentially perform dedicated access to structured data, accurate verification of structured data, and standardized conversion of structured data; Step S4: Construct a service project pricing data analysis model: Based on the standardized pricing dataset, conduct a bid review analysis of service project bid pricing documents and a rationality analysis of service project bid pricing documents.

2. The method for analyzing bid price data for service projects according to claim 1, characterized in that: In step S1, when classifying service-type projects: Based on the port group's historical service project procurement data, a service project classification system was established to clarify the core characteristics of different types of projects and ensure the relevance of data collection dimensions. The service project classification specifically includes consulting services, operation and maintenance services, and professional services.

3. The method for analyzing bid price data for service projects according to claim 1, characterized in that: In step S1, when presetting the core collection dimensions covering basic information, service content, and pricing details: For each type of service project, core data collection dimensions are preset based on its core characteristics to ensure the integrity and universality of the collected data.

4. The method for analyzing bid price data for service projects according to claim 1, characterized in that: In step S2, when determining the service-type pricing data interface specification: To ensure efficient integration between the data exported from the dedicated quotation tool and the bidding party's data collection system, avoiding duplicate data entry or format conversion errors, a standardized service-oriented quotation data interface was designed, clearly defining the interface type, data format, transmission protocol, and verification rules. Specifically: The core interface types include data upload interface, data download interface, and data verification interface; In step S2, when determining the interface data format and transmission protocol: Data transmission is performed using JSON format, with field names consistent with those in the standardized service procurement list, resulting in a clear hierarchical structure.

5. The method for analyzing bid price data for service projects according to claim 1, characterized in that: In step S2, when developing a dedicated pricing tool adapted to various service projects, the specific pricing tool includes: A standardized list of service-oriented project classification systems that incorporates tools; Structured data entry modules are set up for different types of service items; The data export and encryption module allows the tool to export data as an encrypted standard data interface file after the bidder completes the form, meeting the needs of bidding scenarios.

6. The method for analyzing bid price data for service projects according to claim 1, characterized in that: In step S3, when performing dedicated access to structured data: It directly connects to a standardized data interface to receive JSON-formatted structured data uploaded by a dedicated quotation tool, and performs data reception, unpacking, and source data recording.

7. The method for analyzing bid price data for service projects according to claim 1, characterized in that: In step S3, when performing precise verification of structured data: To address the field characteristics of JSON-formatted structured data, a three-layer validation logic is designed to comprehensively investigate data anomalies. The validation during data anomaly investigation includes field integrity verification, format compliance verification, and calculation verification.

8. The method for analyzing bid price data for service projects according to claim 1, characterized in that: In step S3, when performing structured data standardization transformation: The validated JSON structured data is standardized and optimized according to unified rules to ensure that the data is fully adapted to subsequent analysis needs. The scope of standardization includes standardization of basic information dimensions, standardization of quotation details dimensions, and standardization of service content dimensions.

9. The method for analyzing bid price data for service projects according to claim 1, characterized in that: In step S4, during the bid evaluation analysis of service project tender documents: Focusing on two core dimensions—"whether the tender documents are responsive" and "whether there are calculation errors"—and based on current mature practices, the system determines the compliance level of tender documents through standardized verification processes and quantitative validation rules. It accurately identifies various calculation deviations and logical contradictions, outputs clear bid evaluation conclusions, provides objective basis for bid evaluation, and avoids compliance risks and performance disputes.

10. The method for analyzing bid price data for service projects according to claim 1, characterized in that: In step S4, when performing the rationality analysis of the service project bid price document: Once the bid price documents pass the bid clearing analysis, the substantive price analysis can begin. Based on the characteristics of the service project, and focusing on the three core dimensions of "price reasonableness, service cost-effectiveness, and price competitiveness," an end-to-end analysis model is constructed, which includes "data preprocessing → feature engineering → model training → quantitative scoring → result output." The specific analysis includes: the three core dimensions and their sub-indicators, bid weight allocation, core algorithm design and implementation, and comprehensive scoring model algorithm.

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