A method for constructing an energy power industry equipment material price database

Abstract

The present application relates to the energy power industry equipment material supply chain data management technology, it discloses a kind of construction method of energy power industry equipment material price database, solve the problem of energy power industry equipment material price database construction, equipment material type is miscellaneous, there is no unified standard, price data source is single and credibility is low, and price adjustment lacks scientific quantization basis.The scheme in the present application, first, equipment material type parameter is standardized definition and modularization split, determine the price adjustment coefficient of basic type parameter, fixed description standard and additional function module, and scientifically set the density of basic type parameter;Second, collect historical contract / price inquiry data, regularly price inquiry data, project special price inquiry data three kinds of multi-source price data, according to time distance and credibility weight and after pre-processing, generate standard price;Finally, in combination with project region, project stage and other factors, through weight assignment and algorithm calculation, generate the price adjustment coefficient of actual price relative to standard price.

Description

Technical Field

[0001] This invention relates to data management technology for the supply chain of equipment and materials in the energy and power industry, specifically to a method for constructing a price database for equipment and materials in the energy and power industry. Background Technology

[0002] In the development of the energy and power industry, equipment and materials (such as transformers, cables, and switchgear) are core foundational elements in power production, transmission, and distribution. Their diverse categories and vast specifications directly impact the efficiency of supply chain management and the accuracy of project cost control. However, the industry currently faces several limiting factors in the construction of equipment and material price databases and the management of price information. These factors severely restrict the accuracy of budget preparation, the scientific nature of procurement decisions, and supply chain collaboration capabilities. Specifically:

[0003] (1) The model parameters lack unified standards, and the price calculation basis is chaotic:

[0004] The energy and power industry's equipment and materials involve multiple sub-sectors such as power generation, transmission, and distribution. The technical parameter systems for different equipment and materials vary significantly, and a unified standard for model parameter descriptions has not yet been established within the industry. On the one hand, different business departments use different descriptions of model parameters for the same type of equipment and materials (e.g., the difference between "110kV transformer" and "110 kV transformer"), leading to incompatibility in equipment and material list parameters during cross-departmental data transfer and a lack of unified reference for price comparisons. On the other hand, the additional functions of equipment and materials are not independently separated and calculated, and are usually bundled with the price of the basic model. This makes it impossible to accurately compare the prices of equipment and materials with the same basic model but different additional functions. Furthermore, the price database lacks a unified and standardized accounting benchmark, making it difficult to form a standardized model-price correlation system.

[0005] (2) The price data source is singular and lacks quality control, resulting in insufficient data credibility and comprehensiveness:

[0006] The current channels for obtaining equipment and material price information in the industry are relatively limited. Most companies rely on periodically published industry price lists or scattered historical contract data, failing to effectively integrate price data from multiple scenarios. This results in price information in the database failing to reflect the true market situation. First, a single data source cannot cover the dynamic changes in equipment and material prices. For example, historical contract data only reflects transaction prices at a specific point in time and cannot reflect price adjustments caused by fluctuations in raw materials and changes in market supply and demand. Second, the data collection process lacks an effective quality control mechanism. It fails to differentiate between the time validity of data and the relevance of supplier qualifications. For example, historical inquiry data from more than three years ago is used equally with recent market quotation data, ignoring the timeliness differences in prices. At the same time, the lack of outlier removal and duplicate data cleaning rules leads to some abnormal quotations that deviate from the reasonable market range and duplicate quotations from the same supplier being mixed into the database, further reducing the credibility of price data. This makes the price database built on such data unable to provide accurate support for project cost calculation.

[0007] (3) Price adjustments rely on human experience and lack a scientific quantitative mechanism:

[0008] Equipment and material prices are significantly affected by external environmental and project scenario factors, with project location, project stage, and raw material price fluctuations being the core influencing factors. However, current industry adjustments to equipment and material prices generally rely on manual estimations based on the experience of purchasing or cost estimators, lacking a systematic and quantifiable price adjustment mechanism. This experience-based approach to price adjustments is highly subjective, prone to errors, and difficult to adapt to the price requirements of different project scenarios. This not only leads to significant discrepancies between project budgets and actual procurement costs but may also cause disputes over supplier quotations due to unreasonable price adjustments, impacting procurement process efficiency and supply chain stability.

[0009] Therefore, there is an urgent need to design a systematic and standardized method for constructing an equipment and material price database to provide a high-quality data foundation for equipment and material price management in the energy and power industry, and to support the digital transformation of the industry's supply chain and refined cost control. Summary of the Invention

[0010] The technical problem to be solved by this invention is to provide a method for constructing a price database for equipment and materials in the energy and power industry, thereby addressing the problems of complex equipment and material models without unified standards, single and unreliable price data sources, and lack of scientific quantitative basis for price adjustments in the construction of such a database.

[0011] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0012] A method for constructing a database of equipment and material prices in the energy and power industry includes the following steps:

[0013] S1. Standardization and modular construction of equipment material model parameters:

[0014] S11. Determine the basic model parameters and fixed description standards for equipment and materials: For equipment and materials in the energy and power industry, select core technical parameters that include at least equipment type, core performance indicators, and specifications as basic model parameters, and formulate a unified parameter description format;

[0015] S12. Modular decomposition of additional functions and assignment of price adjustment coefficients: The non-basic additional functions of equipment and materials are decomposed into independent modules, each with a unique identifier. A fixed price adjustment coefficient is assigned to each additional function module through historical transaction data statistics and expert evaluation.

[0016] S13. Basic model parameter density setting: Based on the interpolation method, the interval between adjacent basic model parameters is determined to form a standardized and modular equipment and material model system.

[0017] S2. Weighted average of equipment and material price data from multiple sources is entered into the database to form a standard price:

[0018] S21. Multi-source price data collection: Collect equipment and material contract price data and historical inquiry data from multiple years, collect price data from cooperative suppliers on a regular basis, and obtain project-specific quotation data by initiating special inquiries with multiple qualified suppliers for projects with specific equipment and material procurement needs;

[0019] S22. Data preprocessing: Review the collected price data, remove outliers and duplicate data, and retain valid data;

[0020] S23. Weighted Calculation of Multi-Source Data: Assign time weights to valid data based on their proximity in time, and assign credibility weights to valid data based on their credibility. The standard price of materials for a single model of equipment is obtained through weighted calculation.

[0021] S24. Standardization and Storage: After associating the equipment and material model system constructed in step S1 with the standard price calculated in step S23, store it in the database;

[0022] S3. Generation of equipment and material price adjustment coefficients based on multiple factors:

[0023] S31. Determine the factors influencing price adjustments: Select factors that include at least the regional factors categorized by transportation cost differences and the phase factors categorized by demand urgency as factors influencing price adjustments;

[0024] S32. Weighting of Influencing Factors: Using the analytic hierarchy process combined with industry expert evaluation, weights are assigned to each factor affecting the price adjustment, and the sum of the weights of all factors is 1.

[0025] S33. Price Adjustment Coefficient Calculation: Input the regional level and stage level of the specific project, and calculate the price adjustment coefficient by combining the weight of influencing factors and the quantitative value corresponding to each level; the quantitative value is determined by statistical analysis of the ratio of historical actual price to standard price.

[0026] S34. Price Adjustment Coefficient Verification and Optimization: Select actual price data of completed projects in recent years to verify the deviation between the price adjustment coefficient and the ratio of actual price and standard price. If the deviation exceeds the preset range, adjust the weight or level quantification value of the influencing factors until the deviation meets the preset range.

[0027] S4. Database Update: Periodically repeat steps S2-S3 to update the standard prices and price adjustment coefficients in the database.

[0028] Furthermore, in step S11, the selection of basic model parameters refers to the GB / T series of national standards for the energy and power industry, and the core performance indicators include the equipment's rated power, rated voltage, and efficiency.

[0029] Furthermore, in step S13, the interval between adjacent basic model parameters must meet the following requirements: the price error of the interpolation calculation is ≤3%, and the number of basic models counted is ≤60% of the total number of models of the same type of equipment and materials; the interval between adjacent basic model parameters = (maximum parameter value - minimum parameter value) / preset number of basic models.

[0030] Furthermore, in step S21, the contract price data also includes the contract signing time and the purchase quantity, and the historical inquiry data also includes the inquiry time and the supplier name; the period for collecting price data from cooperative suppliers is monthly or quarterly, and the collection methods include sending standardized electronic inquiry forms or opening system visitor permissions for suppliers to fill in, and the collected data includes the validity period of the quotation and the payment method.

[0031] Furthermore, in step S23, the specific value of the time weight is:

[0032] Data from the past year is weighted at 30%-40%, data from the past 1-2 years at 20%-30%, and data from more than 2 years at 10%-20%.

[0033] The specific values ​​for the credibility weights are as follows: 80%-90% for signed contract data, 60%-70% for historical inquiry data, and 70%-80% for regularly collected price data and project-specific quotation data.

[0034] Standard price = ∑ (price of a single valid data entry × time weight × credibility weight) / ∑ (time weight × credibility weight).

[0035] Furthermore, in step S23, if the historical contract data contains different purchase quantities, quantity weights must first be assigned according to the purchase quantity: the quantity weight is 1.0 when the purchase quantity is ≥10 units, the quantity weight is 0.9 when the purchase quantity is 5-9 units, and the quantity weight is 0.8 when the purchase quantity is 1-4 units. Then, the weighted price is calculated by combining the time weight and the credibility weight.

[0036] Furthermore, in step S24, the standardized storage format for data entry is: basic model parameters + additional functional module identifier - standard price - data update time.

[0037] Furthermore, in step S31, the project's regional factors are divided into 3-5 levels based on differences in transportation costs, and the project's phase factors are divided into design phase, construction preparation phase, and construction phase based on the urgency of demand. The price adjustment influencing factors also include raw material price fluctuation factors, which are divided into three levels based on the year-on-year fluctuation range of raw material prices: ≤5%, 5%-10%, and >10%, with corresponding quantitative values ​​of 1.00, 1.05, and 1.10, respectively.

[0038] Furthermore, in step S32, the number of experts participating in the industry expert evaluation is 5, and the experts come from the fields of procurement, cost estimation, and energy and power equipment technology, respectively; in step S34, the preset range is a deviation ≤ 5%.

[0039] Furthermore, in step S4, the cycle of repeating steps S2-S3 is quarterly; step S4 also includes database access control: setting different data viewing and operation permissions for different business levels such as the budget department, the procurement department, and the management, and saving users' data access records to ensure data traceability.

[0040] The beneficial effects of this invention are:

[0041] (1) Resolve the problem of inconsistent model parameters and unify the price calculation basis:

[0042] This invention determines basic model parameters by screening core technical parameters and establishing unified description standards. Non-basic additional functions are broken down into independent modules and assigned price adjustment coefficients. Simultaneously, the density of basic model parameters is scientifically set, forming a standardized and modular equipment and material model system. This system effectively eliminates differences in equipment and material model descriptions within the energy and power industry, avoids accounting confusion caused by bundling additional functions with basic model prices, provides a unified data language for price database construction, and enables accurate comparison of equipment and material prices across departments.

[0043] (2) Improve the quality of price data and ensure its accuracy and comprehensiveness:

[0044] Compared to traditional single-source price information collection methods, this invention integrates three types of multi-source data: historical contract / inquiry data, periodic inquiry data, and project-specific inquiry data. It removes outliers and duplicate data through review and assigns dual weights based on time proximity and data reliability to calculate the standard price. This process balances data timeliness (high weight for data from the past year) and reliability (high weight for contract data), while also covering both routine and specific project procurement scenarios. This significantly reduces standard price errors and provides high-quality data support for budget preparation and procurement cost calculation.

[0045] (3) Achieve scientific price adjustments to adapt to the needs of projects in multiple scenarios:

[0046] This invention overcomes the subjective limitations of traditional manual experience-based price adjustments. It identifies and quantifies core influencing factors such as project location and stage, employs the analytic hierarchy process (AHP) combined with expert evaluation to assign weights to these factors, and then generates a price adjustment coefficient through formula calculation and deviation verification. This coefficient can be dynamically adjusted based on differences in regional transportation costs and the urgency of demand across different projects, ensuring a precise match between actual equipment and material prices and the project scenario. This reduces disputes over supplier quotations caused by unreasonable price adjustments, and improves the scientific nature of procurement decisions and the efficiency of the procurement process.

[0047] (4) Enhance the timeliness and security of databases to support supply chain collaboration:

[0048] This invention establishes a database update mechanism that involves quarterly recurring data collection, standard price calculation, and price adjustment coefficient optimization. This ensures that price data is synchronized with dynamic factors such as market conditions and raw material fluctuations, avoiding cost accounting errors caused by data lag. Simultaneously, by setting differentiated data permissions for different business levels and saving access records, data security and traceability are guaranteed. The standardized price database can directly connect to the intelligent supply chain platform for equipment and materials in the energy and power industry, reducing repetitive work in areas such as price inquiries and audits, and significantly improving the collaborative efficiency of all links in the supply chain. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating the method for constructing the energy and power industry equipment and material price database in this invention. Detailed Implementation

[0050] This invention aims to provide a method for constructing a price database for equipment and materials in the energy and power industry. It addresses the problems encountered in constructing such databases, including the complexity and lack of unified standards for equipment and material models, the single and unreliable source of price data, and the lack of scientific quantitative basis for price adjustments. The core idea is to address the issues of lack of standardized models, unreliable data, and unscientific price adjustments in the construction of equipment and material price databases for the energy and power industry. This is achieved through a systematic, end-to-end approach: standardizing and modularizing the model system; generating standard prices through multi-source data weighting; generating price adjustment coefficients through multi-factor quantification; and regularly updating the data to ensure its timeliness. This comprehensive system aims to create an accurate, adaptable, and dynamic price database for equipment and materials.

[0051] Specifically, the approach begins by identifying the core parameters of equipment and materials to establish basic model standards. Additional functions are then broken down into independent modules with assigned price adjustment coefficients. Simultaneously, the density of basic model parameters is scientifically set to construct a unified model system, resolving the issue of inconsistent price calculation benchmarks. Next, multi-source price data—historical, periodic, and project-specific—is integrated. After data cleaning, dual weights are assigned based on time validity and reliability to calculate standard prices that reflect the true market situation, addressing the problems of single data sources and low reliability. Then, considering factors such as project location (transportation cost differences) and project stage (urgency of demand), scientific price adjustment coefficients are generated through hierarchical quantification, weight assignment, formula calculation, and deviation verification, addressing the subjectivity of manual experience-based price adjustments. Finally, quarterly data collection, standard price calculation, and price adjustment coefficient optimization ensure the database's timeliness, ultimately providing high-quality data support for budget preparation, procurement decisions, and supply chain collaboration in the energy and power industry.

[0052] In specific implementation, the method for constructing the energy and power industry equipment and material price database provided by this invention is as follows: Figure 1 As shown, it includes the following steps:

[0053] S1. Standardization and modular construction of equipment material model parameters:

[0054] This step addresses the pain point of the complex range of equipment and material models in the energy and power industry by establishing a unified model parameter system to provide a consistent benchmark for price calculation. In one exemplary implementation plan, the specific methods are as follows:

[0055] S11. Determination of Basic Model Parameters and Fixed Description Standards:

[0056] Core parameter selection: Referring to industry standards such as the "General Technical Conditions for Energy and Power Equipment," basic parameters are determined for different types of equipment and materials. For example:

[0057] Transformer: Basic parameters are: Equipment type (oil-immersed / dry-type) - Rated capacity (kVA) - Rated voltage (kV) - Connection group.

[0058] Cable: Basic parameters are: cable type (cross-linked polyethylene insulation / polyvinyl chloride insulation) - conductor cross-section (mm²) - rated voltage (kV) - number of cores.

[0059] Standardized description: Establish standardized parameter description formats, such as: rated capacity: expressed as 'XXkVA', with values ​​rounded to the nearest integer; rated voltage: expressed as 'XXkV', ensuring that parameter descriptions in all internal lists and Requests for Quotation are completely consistent.

[0060] S12. Modular decomposition of additional functions and assignment of price adjustment coefficients:

[0061] Modular decomposition: Non-basic functions (such as the "temperature monitoring module" and "remote control module" of transformers, and the "flame retardant properties" and "corrosion resistance properties" of cables) are decomposed into independent modules, and each module is assigned a unique code (such as "B-YB-001" representing the transformer temperature monitoring module).

[0062] Price adjustment coefficient determination: By analyzing the price differences between contracts that include this module and those that do not over the past two years, and combining the evaluations of 3-5 industry experts, the coefficient value is determined. For example:

[0063] Transformer temperature monitoring module: Price adjustment coefficient = +5.00% (i.e., the price of equipment including this module = standard price of the basic model × 1.05).

[0064] Flame retardant properties of cables: Price adjustment factor = +3.50%.

[0065] S13. Basic Model Parameter Density Settings:

[0066] When setting it up, the core principle is to balance the accuracy of price calculation using interpolation with the difficulty of data statistics, avoiding excessively large intervals that lead to insufficient accuracy, or excessively small intervals that lead to a surge in statistical volume.

[0067] Specifically, the core parameters of similar equipment materials (such as the rated capacity of a transformer) can be used as a benchmark. The interval can be calculated using the formula "interval between adjacent parameters = (maximum parameter value - minimum parameter value) / number of preset basic models". Through trial calculations, if the interpolated price deviates from the actual contract price by ≤3%, and the number of basic models is ≤60% of the total number of models, then the interval is determined. For example:

[0068] The rated capacity range of the transformer is 500kVA-2000kVA. The number of basic models is preset to 6. Then the interval = (2000-500) / 6≈250kVA. The basic models are set as 500kVA, 750kVA, 1000kVA, 1250kVA, 1500kVA and 1750kVA. When interpolating the price of the 1100kVA transformer, the linear interpolation is based on the standard price of 1000kVA and 1250kVA. The error can be controlled within 2%.

[0069] S2. Weighted average of equipment and material price data from multiple sources is entered into the database to form a standard price:

[0070] This step addresses the issue of low reliability from single data sources by integrating multi-source price data and introducing a weighting mechanism, thereby generating accurate standard prices. In one exemplary implementation scheme, the specific methods are as follows:

[0071] S21. Multi-source price data collection method:

[0072] Historical data collection: Extract signed contract data from 2021 to 2023 from the enterprise's ERP system or contract management system (which needs to be associated with equipment and material models, prices, purchase quantities, and signing dates), and extract historical inquiry data for the same period from the inquiry record system (which also needs to be associated with inquiry models, quotations, suppliers, and inquiry dates) to ensure that the data covers different years and different suppliers.

[0073] Regularly collect price inquiry data: Before the 10th of each month, send standardized electronic price inquiry forms (including the model system, price quotation column, and validity period column constructed in step S1) to 10-15 cooperative suppliers. Suppliers are required to provide feedback within 5 working days. At the same time, open the "Supplier Visitor Channel" in the system. Suppliers can log in and fill in the latest price. The system will automatically record the time of filling in the form.

[0074] Project-specific price inquiry data collection: For the 110kV transformer requirement of a wind farm project, a specific price inquiry was initiated with 3 qualified suppliers to clarify the project schedule, delivery location and other requirements, and collect project-specific quotations to ensure that the data is adapted to the specific project scenario.

[0075] S22. Data Preprocessing Flow:

[0076] Manual review: The collected price data is cross-reviewed by two or more procurement specialists to verify the data's compatibility with the model system (such as whether there are any errors in the model parameter descriptions).

[0077] Outlier removal: Using the “3σ principle”, the mean and standard deviation of material price data for similar equipment models are calculated, and data that deviates from the mean by more than 3 times the standard deviation are removed (e.g., if the average price of a certain model of transformer is 120,000 yuan and the standard deviation is 5,000 yuan, prices above 140,000 yuan are considered outliers).

[0078] Deduplication: Delete duplicate quotes from the same supplier for the same model and within the same time period, and retain the latest quote.

[0079] S23. Multi-source data weighted calculation logic:

[0080] Taking a certain type of transformer as an example, the weighting rules are shown in Table 1:

[0081] Table 1 Weighting Rules

[0082]

[0083] Standard Price Calculation: Assuming the valid data for a certain type of transformer is: 2023 contract price of RMB 120,000 (comprehensive weight 36%), 2022 contract price of RMB 118,000 (27%), 2023 inquiry price of RMB 121,000 (28%), and January 2024 regular inquiry price of RMB 122,000 (32%), then:

[0084] Standard price = (12.0×36% + 11.8×27% + 12.1×28% + 12.2×32%) / (36%+27%+28%+32%) ≈ 120,500 yuan.

[0085] Standardized data entry format: The data is stored in the structure of "model code - basic model standard price - additional module code and coefficient - data update time", for example: "B-110KV-001 (110kV oil-immersed transformer, 1000kVA) - 120,500 yuan - B-YB-001 (+5.00%) - 2024.03", to ensure clear data association and easy retrieval.

[0086] S3. Generation of equipment and material price adjustment coefficients based on multiple factors:

[0087] This step comprehensively considers project scenario factors, quantifies the price adjustment logic, and addresses the issue of subjectivity in manual adjustments. In one exemplary implementation plan, the specific methods are as follows:

[0088] S31. Determination and Quantification of Factors Affecting Price Adjustments:

[0089] Based on the characteristics of the energy and power industry, the core influencing factors and their levels are classified, as shown in Table 2:

[0090] Table 2 Core Influencing Factors and Their Classification

[0091]

[0092] S32. Assigning weights to influencing factors:

[0093] The Analytic Hierarchy Process (AHP) was used to construct a hierarchical structure of target layer (price adjustment coefficient) - criterion layer (region, stage) - scheme layer (each level). Five procurement and cost experts were invited to compare the importance of the factors in the criterion layer pairwise, generate a judgment matrix, and calculate the weights. Example weights: project region weight 40%, project stage weight 30%, raw material fluctuation weight 30% (total 100%).

[0094] S33. Calculation of Price Adjustment Coefficient:

[0095] Calculation Example: For a transformer procurement project in the construction phase in a certain northwestern region, if the copper price fluctuates by 7% year-on-year, then:

[0096] Price adjustment coefficient = (regional weight × regional quantitative value) + (stage weight × stage quantitative value) + (raw material weight × raw material quantitative value) = (40% × 1.07) + (30% × 1.05) + (30% × 1.05) = 0.428 + 0.315 + 0.315 = 1.058 ≈ 1.06;

[0097] The actual reference price of the transformer for this project = standard price of the basic model × (1 + additional module coefficient) × price adjustment coefficient = 12.05 × 1.05 × 1.06 ≈ 134,500 yuan.

[0098] S34. Verification and Optimization of Price Adjustment Coefficient: Select 5 transformer procurement projects in the Northwest region during the construction phase in 2023, compare the actual transaction price with the reference price calculated according to the coefficient, and if the average deviation is >5% (e.g., the actual average price is 132,000 yuan, the calculated average price is 139,000 yuan, the deviation is 5.3%), then adjust the weight (e.g., increase the regional weight to 45% and reduce the phase weight to 25%), and recalculate until the deviation is ≤5%.

[0099] S4. Database Update:

[0100] To ensure that the data is synchronized with market conditions and raw material fluctuations and to maintain the timeliness of the database, steps S2-S3 can be repeated periodically to update the standard prices and price adjustment coefficients in the database.

[0101] The following example, using the "110kV oil-immersed transformer," a typical piece of equipment in the energy and power industry, illustrates the implementation process of the present invention.

[0102] Preparations before implementation:

[0103] Establish an implementation team: Establish an implementation team consisting of 2 procurement specialists, 2 cost experts, and 1 data specialist. The procurement specialists are responsible for data collection and review, the cost experts are responsible for parameter screening, weight assignment and coefficient evaluation, and the data specialist is responsible for data cleaning, calculation and data entry.

[0104] Basic data preparation: Extract signed contract data (120 records, including contract signing time, purchase quantity, and equipment model parameters) and historical inquiry data (80 records, including inquiry time, supplier name, and quotation amount) for 110kV oil-immersed transformers from the enterprise's ERP system, contract management system, and inquiry record system from 2021 to 2023. At the same time, sort out the resources of 15 cooperative suppliers to ensure that the data covers different years, different suppliers, and different procurement scenarios.

[0105] Specific implementation process:

[0106] I. Standardization and modular construction of equipment material model parameters:

[0107] 1. Determine the basic model parameters and fixed description standards:

[0108] Referring to the "General Technical Conditions for Energy and Power Equipment" and the GB / T series of industry standards, the core technical parameters of the 110kV oil-immersed transformer are selected as the basic model parameters, specifically "Equipment Type (Oil-immersed) - Rated Capacity (kVA) - Rated Voltage (kV) - Connection Group". The rated voltage is fixed at 110kV, the connection group is fixed at Dyn11 (a commonly used type in the industry), and the core variable is the rated capacity.

[0109] Establish a unified parameter description format: rated capacity is expressed as "XXkVA" with the value rounded to the nearest integer; rated voltage is expressed as "110kV"; and connection group is expressed as "Dyn11" to ensure that the parameter descriptions are completely consistent when the equipment material list is transferred across departments.

[0110] 2. Modular breakdown of additional functions and assignment of price adjustment coefficients:

[0111] The non-basic additional functions of the 110kV oil-immersed transformer were sorted out and divided into three independent modules: "temperature monitoring", "remote control" and "noise reduction", and each module was assigned a unique identifier: B-YB-001 (temperature monitoring), B-YB-002 (remote control) and B-YB-003 (noise reduction).

[0112] First, we analyzed the price differences between contracts with and without the aforementioned additional functions from 2021 to 2023 (e.g., the average price of contracts with temperature monitoring modules was 5.2% higher than those without). Then, we invited three experts with more than five years of experience in the field of energy and power equipment materials to adjust the difference values ​​based on market supply and demand and functional costs. Finally, we determined the price adjustment coefficients for each module: +5.00% for B-YB-001, +8.00% for B-YB-002, and +4.50% for B-YB-003.

[0113] 3. Basic model parameter density settings:

[0114] First, determine the parameter range of the rated capacity of the 110kV oil-immersed transformer as 500kVA-2000kVA (covering commonly used enterprise specifications), and preset the number of basic models as 6 (to balance the accuracy of calculation and the difficulty of data statistics).

[0115] The interval is calculated using the formula "interval between adjacent basic models = (maximum parameter value - minimum parameter value) / number of preset basic models": (2000-500) / 6≈250kVA. Therefore, the basic models are set to 500kVA, 750kVA, 1000kVA, 1250kVA, 1500kVA, and 1750kVA.

[0116] Verification of the rationality of the density: The actual contract price of 1100kVA (between 1000kVA and 1250kVA) was selected and compared with the price calculated by linear interpolation of the standard prices of 1000kVA and 1250kVA. The results showed that the interpolation error was 1.8%≤3%, and the number of basic models (6) was ≤60% (7.2) of the total number of models of the same type of equipment and materials (12), which met the technical requirements and formed a standardized and modular 110kV oil-immersed transformer model system.

[0117] II. Weighted average of equipment and material price data from multiple sources is entered into the database to form a standard price:

[0118] 1. Multi-source price data collection:

[0119] Historical data collection: From the basic data for preparation, the following data were extracted: 2023 contract data for 110kV oil-immersed transformers (1000kVA): price 120,000 yuan, quantity 10 units; 2022 contract data: price 118,000 yuan, quantity 8 units; 2023 historical inquiry data: average price of 3 suppliers 121,000 yuan.

[0120] Regular price inquiry data collection: In January 2024, standardized electronic price inquiry forms were sent to 15 cooperative suppliers; 8 valid quotations were finally received, with an average value of RMB 122,000 and a validity period of 3 months.

[0121] Project-specific price inquiry data collection: For the specific procurement needs of a wind farm (requiring 110kV oil-immersed transformer) in February 2024, a special price inquiry was initiated with 3 qualified suppliers, specifying the delivery location (Northwest region), construction period (within 3 months), and other requirements. The final quotations obtained from the 3 suppliers were RMB 121,000, RMB 123,000, and RMB 122,000, respectively. The average of RMB 122,000 was taken as the project-specific price quotation data.

[0122] 2. Data preprocessing:

[0123] The price data collected was cross-checked by two procurement specialists, and outliers were removed using the "3σ principle": the mean of all collected data (120,000 yuan, 118,000 yuan, 121,000 yuan, 122,000 yuan, and 122,000 yuan) was calculated to be 120,600 yuan, the standard deviation was 1,600 yuan, and three times the standard deviation was 4,800 yuan. It was determined that there were no outliers deviating from the mean by more than three times the standard deviation. In addition, there were no duplicate data, and all five valid data points were retained.

[0124] 3. Weighted calculation of multi-source data:

[0125] Time weighting: 2023 data weight 40%, 2022 data weight 30%, and January-February 2024 data weight 40%.

[0126] Credibility weighting: Contract data accounts for 90%, historical inquiry data accounts for 70%, and periodic inquiry data and project-specific inquiry data account for 80%.

[0127] The overall weight and weighted price of each data point were calculated, and the results are shown in Table 3 below:

[0128] Table 3. Data Overall Weighting and Weighted Price Table

[0129]

[0130] Standard price = (4.32 + 3.186 + 3.388 + 3.904 + 3.904) / (36% + 27% + 28% + 32% + 32%) ≈ 120,800 yuan.

[0131] 4. Standardized warehousing:

[0132] The stored content is: B-110KV-001 (110kV oil-immersed transformer, 1000kVA, Dyn11) - 120,800 yuan - B-YB-001 (+5.00%), B-YB-002 (+8.00%), B-YB-003 (+4.50%) - 2024.03", ensuring a clear association between the model and price for easy subsequent retrieval.

[0133] III. Generation of equipment and material price adjustment coefficients based on multiple factors:

[0134] 1. Determine and quantify the factors influencing price adjustments:

[0135] Each factor was classified and quantified, and the quantified value was determined by the ratio of historical actual price to standard price: the project region was "Northwest (far)", with a quantified value of 1.07; the project stage was "construction stage (high urgency)", with a quantified value of 1.05; and the copper price fluctuated by 7% year-on-year, with a quantified value of 1.05.

[0136] 2. Assigning weights to influencing factors:

[0137] A hierarchical structure was constructed: "Target Layer (Price Adjustment Coefficient) - Criterion Layer (Region, Stage, Raw Material Fluctuations) - Solution Layer (Various Levels)". Five experts (two from the procurement field, two from the cost engineering field, and one from the energy and power equipment technology field) were invited to compare the importance of the criteria layer factors pairwise, generate a judgment matrix, and calculate the weight of each factor.

[0138] The project's geographical location has a weight of 40%, the project stage has a weight of 30%, and raw material price fluctuations have a weight of 30%, with a total weight of 1, which meets the technical requirements.

[0139] 3. Calculation of price adjustment coefficient:

[0140] For a 110kV oil-immersed transformer procurement project for a wind farm in Northwest China (during the construction phase, copper prices fluctuated by 7% year-on-year), input the quantitative values ​​corresponding to each factor level and calculate the price adjustment coefficient: Price adjustment coefficient = (40% × 1.07) + (30% × 1.05) + (30% × 1.05) = 0.428 + 0.315 + 0.315 = 1.058 ≈ 1.06.

[0141] 4. Verification and optimization of price adjustment coefficient:

[0142] According to the technical requirements, five 110kV oil-immersed transformer procurement projects (of the same model) in the Northwest region during the construction phase in 2023 were selected, and the average actual transaction price was 133,000 yuan. Based on the price adjustment coefficient of 1.06 and the standard price of the basic model of 120,800 yuan, the reference price is calculated as 120,800 × 1.06 ≈ 128,000 yuan (excluding additional modules).

[0143] Calculation deviation: (13.3-12.80) / 12.80≈3.8%≤5% (preset deviation range of technical solution), the price adjustment coefficient is deemed valid, and there is no need to adjust the weight or grade quantification value of the influencing factors.

[0144] V. Database Update:

[0145] In June 2024, the database was updated: multi-source price data for 110kV oil-immersed transformers from March to May 2024 were re-collected. After preprocessing and weighted calculation, the updated standard price was RMB 121,500. At the same time, due to the year-on-year fluctuation of copper prices dropping to 5%, the price adjustment coefficient was recalculated to 1.04. The updated standard price, price adjustment coefficient, and data update time (June 2024) were synchronized to the database to ensure that the data is synchronized with market conditions and raw material fluctuations, and to maintain the timeliness of the database.

[0146] Although embodiments of the present invention have been described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present invention, and all such changes and alterations shall not depart from the protection scope of the present invention.

Claims

1. A method for constructing a database of equipment and material prices in the energy and power industry, characterized in that, Includes the following steps: S1. Standardization and modular construction of equipment material model parameters: S11. Determine the basic model parameters and fixed description standards for equipment and materials: For equipment and materials in the energy and power industry, select core technical parameters that include at least equipment type, core performance indicators, and specifications as basic model parameters, and formulate a unified parameter description format; S12. Modular decomposition of additional functions and assignment of price adjustment coefficients: The non-basic additional functions of equipment and materials are decomposed into independent modules, each with a unique identifier. A fixed price adjustment coefficient is assigned to each additional function module through historical transaction data statistics and expert evaluation. S13. Basic model parameter density setting: Based on the interpolation method, the interval between adjacent basic model parameters is determined to form a standardized and modular equipment and material model system. S2. Weighted average of equipment and material price data from multiple sources is entered into the database to form a standard price: S21. Multi-source price data collection: Collect equipment and material contract price data and historical inquiry data from multiple years, collect price data from cooperative suppliers on a regular basis, and obtain project-specific quotation data by initiating special inquiries with multiple qualified suppliers for projects with specific equipment and material procurement needs; S22. Data preprocessing: Review the collected price data, remove outliers and duplicate data, and retain valid data; S23. Weighted Calculation of Multi-Source Data: Assign time weights to valid data based on their proximity in time, and assign credibility weights to valid data based on their credibility. The standard price of materials for a single model of equipment is obtained through weighted calculation. S24. Standardization and Storage: After associating the equipment and material model system constructed in step S1 with the standard price calculated in step S23, store it in the database; S3. Generation of equipment and material price adjustment coefficients based on multiple factors: S31. Determine the factors influencing price adjustments: Select factors that include at least the regional factors categorized by transportation cost differences and the phase factors categorized by demand urgency as factors influencing price adjustments; S32. Weighting of Influencing Factors: Using the analytic hierarchy process combined with industry expert evaluation, weights are assigned to each factor affecting the price adjustment, and the sum of the weights of all factors is 1. S33. Price Adjustment Coefficient Calculation: Input the regional level and stage level of the specific project, and calculate the price adjustment coefficient by combining the weight of influencing factors and the quantitative value corresponding to each level; the quantitative value is determined by statistical analysis of the ratio of historical actual price to standard price. S34. Price Adjustment Coefficient Verification and Optimization: Select actual price data of completed projects in recent years to verify the deviation between the price adjustment coefficient and the ratio of actual price and standard price. If the deviation exceeds the preset range, adjust the weight or level quantification value of the influencing factors until the deviation meets the preset range. S4. Database Update: Periodically repeat steps S2-S3 to update the standard prices and price adjustment coefficients in the database.

2. The method for constructing a database of equipment and material prices in the energy and power industry as described in claim 1, characterized in that, In step S11, the selection of basic model parameters refers to the GB / T series of national standards for the energy and power industry. The core performance indicators include the equipment's rated power, rated voltage, and efficiency.

3. The method for constructing a database of equipment and material prices in the energy and power industry as described in claim 1, characterized in that, In step S13, the interval between adjacent basic model parameters must meet the following requirements: the price error of the interpolation calculation is ≤3%, and the number of basic models counted is ≤60% of the total number of models of the same type of equipment and materials; the interval between adjacent basic model parameters = (maximum parameter value - minimum parameter value) / preset number of basic models.

4. The method for constructing a database of equipment and material prices in the energy and power industry as described in claim 1, characterized in that, In step S21, the contract price data also includes the contract signing time and the purchase quantity, and the historical inquiry data also includes the inquiry time and the supplier name; the period for collecting price data from cooperative suppliers is monthly or quarterly, and the collection methods include sending standardized electronic inquiry forms or opening system visitor permissions for suppliers to fill in, and the collected data includes the validity period of the quotation and the payment method.

5. The method for constructing a database of equipment and material prices in the energy and power industry as described in claim 1, characterized in that, In step S23, the specific value of the time weight is: Data from the past year is weighted at 30%-40%, data from the past 1-2 years at 20%-30%, and data from more than 2 years at 10%-20%. The specific values ​​for the credibility weights are as follows: 80%-90% for signed contract data, 60%-70% for historical inquiry data, and 70%-80% for regularly collected price data and project-specific quotation data. Standard price = ∑ (price of a single valid data entry × time weight × credibility weight) / ∑ (time weight × credibility weight).

6. The method for constructing a database of equipment and material prices in the energy and power industry as described in claim 5, characterized in that, In step S23, if the historical contract data contains different purchase quantities, quantity weights must first be assigned according to the purchase quantity: the quantity weight is 1.0 when the purchase quantity is ≥10 units, the quantity weight is 0.9 when the purchase quantity is 5-9 units, and the quantity weight is 0.8 when the purchase quantity is 1-4 units. Then, the weighted price is calculated by combining the time weight and the credibility weight.

7. The method for constructing a database of equipment and material prices in the energy and power industry as described in claim 1, characterized in that, In step S24, the standardized storage format for data entry is: basic model parameters + additional functional module identifier - standard price - data update time.

8. The method for constructing a database of equipment and material prices in the energy and power industry as described in claim 1, characterized in that, In step S31, the project's regional factors are divided into 3-5 levels based on differences in transportation costs, and the project's phase factors are divided into design phase, construction preparation phase, and construction phase based on the urgency of demand. The price adjustment influencing factors also include raw material price fluctuation factors, which are divided into three levels based on the year-on-year fluctuation range of raw material prices: ≤5%, 5%-10%, and >10%, with corresponding quantitative values ​​of 1.00, 1.05, and 1.10, respectively.

9. The method for constructing a database of equipment and material prices in the energy and power industry as described in claim 8, characterized in that, In step S32, the number of experts participating in the industry expert evaluation is 5, and the experts come from the fields of procurement, cost estimation, and energy and power equipment technology, respectively; in step S34, the preset range is a deviation of ≤5%.

10. A method for constructing a database of equipment and material prices in the energy and power industry as described in any one of claims 1-9, characterized in that, In step S4, the cycle of repeating steps S2-S3 is quarterly; step S4 also includes database access management: setting different data viewing and operation permissions for different business levels such as the budget department, the purchasing department, and the management, and saving users' data access records to ensure data traceability.

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