Business cost data management methods, devices, electronic equipment and readable storage media

Abstract

This disclosure relates to the field of data management technology, and provides a method, apparatus, electronic device, and readable storage medium for managing business cost data. The method includes: aggregating business data to be processed to obtain a aggregated dataset; performing baseline quantification on the aggregated dataset to obtain initial business cost data; performing corrective quantification on the initial business cost data and the aggregated dataset to obtain corrected business cost data; performing budget adaptation on the corrected business cost data to obtain target business cost data; and performing association and archiving on the target business cost data and the aggregated dataset to obtain full-lifecycle business cost management data. This improves the standardization and data consistency of cost calculation, enhances the objectivity of business cost data calculation, strengthens the dynamic linkage capability of cost data across different project stages and the reliability of cross-cycle comparative analysis, and improves the processing efficiency and version traceability of full lifecycle cost data.

Description

Technical Field

[0001] This disclosure relates to the field of data management technology, and in particular to a business cost data management method, apparatus, electronic device, and readable storage medium. Background Technology

[0002] Traditional property cost estimation relies on manual experience and decentralized recording, resulting in highly subjective and inefficient calculations. Furthermore, data from different projects cannot be compared horizontally, making it difficult to meet the needs of the large-scale, cross-regional property industry for refined cost control. This affects the accuracy of investment decisions and risk management capabilities. Current technologies typically employ manual calculations and decentralized recording methods based on spreadsheet software (Excel). While spreadsheets can improve the convenience of calculations, data still relies on personal experience. Alternatively, independent financial software can be used for recording and electronic data storage. However, the calculation processes at each stage (such as pre-investment, takeover, and exit) are fragmented, preventing data from forming a closed loop and enabling version traceability. It is also difficult to make objective and differentiated adjustments.

[0003] It is evident that existing technologies suffer from several problems: the calculation models rely on human experience, data is collected in a decentralized manner, the entire process is fragmented and dominated by manual operation, resulting in insufficient objectivity, poor data comparability, low calculation efficiency, and weak version traceability in business cost data calculation. Summary of the Invention

[0004] In view of this, the present disclosure provides a business cost data method, apparatus, electronic device, and readable storage medium to solve the problems in the prior art where the calculation model relies on human experience, the data is collected in a decentralized manner, the whole life cycle process is fragmented and dominated by manual operation, resulting in insufficient objectivity, poor data comparability, low calculation efficiency, and weak version traceability in business cost data calculation.

[0005] A first aspect of this disclosure provides a business cost data method, comprising: performing aggregation processing on business data to be processed to obtain an aggregated dataset; performing benchmark quantization processing on the aggregated dataset to obtain initial business cost data; performing correction quantization processing on the initial business cost data and the aggregated dataset to obtain corrected business cost data; performing budget adaptation processing on the corrected business cost data to obtain target business cost data; and performing association and archiving processing on the target business cost data and the aggregated dataset to obtain full-cycle business cost management data, wherein the full-cycle business cost management data is used to associate the full-cycle business cost data.

[0006] In some embodiments, benchmark quantification processing is performed on the aggregated dataset to obtain initial business cost data, including: performing engineering quantity accounting processing on the aggregated dataset to obtain engineering quantity accounting data; performing quantity-price matching processing on the engineering quantity accounting data and the aggregated dataset to obtain quantity-price matching data; and performing frequency aggregation processing on the quantity-price matching data and the aggregated dataset to obtain initial business cost data.

[0007] In some embodiments, the initial business cost data and the aggregated dataset are subjected to corrected quantitative processing to obtain corrected business cost data, including: performing indicator verification processing on the aggregated dataset to obtain indicator verification data; performing deviation comparison processing on the indicator verification data and the initial business cost data to obtain deviation comparison data; and performing parameter recalculation processing on the initial business cost data based on the deviation comparison data to obtain corrected business cost data.

[0008] In some embodiments, the initial business cost data is recalculated based on the deviation comparison data to obtain corrected business cost data, including: performing threshold determination processing on the deviation comparison data to obtain a threshold determination result; performing hierarchical correction processing on the initial business cost data based on the threshold determination result to obtain hierarchical corrected data; and performing convergence verification processing on the hierarchical corrected data to obtain corrected business cost data.

[0009] In some embodiments, budget adaptation processing is performed on the revised business cost data to obtain target business cost data, including: splitting the revised business cost data based on a preset period to obtain period-split data; performing item mapping processing on the period-split data based on preset dimension division data to obtain item mapping data; and performing numerical adaptation processing on the item mapping data to obtain target business cost data.

[0010] In some embodiments, the target business cost data and the aggregated dataset are associated and archived to obtain full-cycle business cost management data, including: performing version identification processing on the target business cost data to obtain version identification data; performing index binding processing on the version identification data and the aggregated dataset to obtain index-bound data; and performing time-series chained storage processing on the index-bound data to obtain full-cycle business cost management data.

[0011] In some embodiments, the business data to be processed is aggregated to obtain an aggregated dataset, including: performing field validation processing on the business data to be processed to obtain field validation data; performing missing data completion processing on the field validation data to obtain missing data; and performing structured encapsulation processing on the missing data to obtain the aggregated dataset.

[0012] A second aspect of this disclosure provides a business cost data apparatus, comprising: a first processing module for aggregating business data to be processed to obtain aggregated dataset; a second processing module for performing baseline quantization processing on the aggregated dataset to obtain initial business cost data; a third processing module for performing correction quantization processing on the initial business cost data and the aggregated dataset to obtain corrected business cost data; a fourth processing module for performing budget adaptation processing on the corrected business cost data to obtain target business cost data; and a fifth processing module for performing association and archiving processing on the target business cost data and the aggregated dataset to obtain full-cycle business cost management data, wherein the full-cycle business cost management data is used to associate the full-cycle business cost data.

[0013] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0014] A fourth aspect of this disclosure provides a readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0015] The beneficial effects of this disclosed embodiment compared with the prior art are as follows: by aggregating the business data to be processed, a aggregated dataset is obtained; then, the aggregated dataset is subjected to benchmark quantification processing to obtain initial business cost data; the initial business cost data and the aggregated dataset are subjected to correction quantification processing to obtain corrected business cost data; the corrected business cost data is subjected to budget adaptation processing to obtain target business cost data; and the target business cost data and the aggregated dataset are associated and archived to obtain full-cycle business cost management data. This improves the standardization and data consistency of cost calculation, enhances the objectivity of business cost data calculation, strengthens the dynamic linkage capability of cost data between different project stages and the reliability of cross-cycle comparative analysis, and improves the calculation and processing efficiency and version traceability of full life cycle cost data. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure; Figure 2This is a flowchart illustrating a business cost data management method provided in an embodiment of this disclosure; Figure 3 This is a flowchart illustrating another business cost data management method provided in this embodiment of the disclosure; Figure 4 This is a schematic diagram of the structure of a business cost data management device provided in an embodiment of this disclosure; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of this disclosure. However, those skilled in the art will understand that this disclosure may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this disclosure with unnecessary detail.

[0019] It should be noted that the user information (including but not limited to terminal device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0020] A business cost data management method and apparatus according to embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure. The application scenario may include terminal devices 1, 2, and 3, server 4, and network 5.

[0022] Terminal devices 1, 2, and 3 can be hardware or software. When terminal devices 1, 2, and 3 are hardware, they can be various electronic devices with displays and supporting communication with server 4, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 1, 2, and 3 are software, they can be installed in the aforementioned electronic devices. Terminal devices 1, 2, and 3 can be implemented as multiple software programs or software modules, or as a single software program or software module; this disclosure does not limit this. Furthermore, various applications can be installed on terminal devices 1, 2, and 3, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0023] Server 4 can be a server that provides various services, such as a backend server that receives requests sent by terminal devices with which it has established communication connections. This backend server can receive and analyze the requests sent by the terminal devices and generate processing results. Server 4 can be a single server, a server cluster consisting of several servers, or a cloud computing service center. This disclosure embodiment does not limit this.

[0024] It should be noted that server 4 can be either hardware or software. When server 4 is hardware, it can be various electronic devices that provide various services to terminal devices 1, 2, and 3. When server 4 is software, it can be multiple software programs or software modules that provide various services to terminal devices 1, 2, and 3, or it can be a single software program or software module that provides various services to terminal devices 1, 2, and 3. This disclosure does not limit the scope of the embodiments.

[0025] Network 5 can be a wired network using coaxial cable, twisted pair, and fiber optic connection, or it can be a wireless network that enables interconnection of various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), and Infrared. This disclosure does not limit the scope of the network.

[0026] Users can establish a communication connection with server 4 via network 5 through terminal devices 1, 2, and 3 to receive or send information. Specifically, server 4 can obtain business data to be processed through terminal devices 1, 2, and 3, and obtain a collected dataset by aggregating the business data to be processed; then, it can perform baseline quantization processing on the collected dataset to obtain initial business cost data; it can perform correction quantization processing on the initial business cost data and the collected dataset to obtain corrected business cost data; it can perform budget adaptation processing on the corrected business cost data to obtain target business cost data; and it can perform association and archiving processing on the target business cost data and the collected dataset to obtain full-cycle business cost management data.

[0027] It should be noted that the specific types, quantities, and combinations of terminal devices 1, 2, and 3, server 4, and network 5 can be adjusted according to the actual needs of the application scenario, and this disclosure embodiment does not impose any restrictions on this.

[0028] Figure 2 This is a flowchart illustrating a business cost data management method provided in an embodiment of this disclosure. Figure 2 Business cost data management methods can be provided by Figure 1 The server executes this. For example... Figure 2 As shown, this business cost data management method includes: S201, perform aggregation processing on the business data to be processed to obtain the aggregated dataset.

[0029] Specifically, the business data to be processed can be a set of original business data that needs to be used for cost calculation. This business data can be used as basic data to characterize the project scale, service content and resource consumption. The business data to be processed can be used as the input source of the cost calculation model to provide raw materials for subsequent standardization processing and calculation.

[0030] Furthermore, aggregation processing can be a data processing procedure that summarizes, classifies, and organizes scattered and heterogeneous business data to be processed based on predefined rules and structures. Aggregation processing can be used to achieve preliminary data standardization, eliminate format differences and logical confusion between data sources, and serve as a preprocessing step for benchmark matching and automatic calculation, laying the foundation for subsequent steps to obtain aggregated datasets.

[0031] The aggregated dataset can be a collection of data with a unified structure and standardized format formed after aggregation processing. The aggregated dataset can include standardized project indicators and cost element information. The aggregated dataset can be used as input for benchmark quantification processing to ensure the uniformity and comparability of data sources in the measurement process.

[0032] Furthermore, the aggregation process can specifically include performing anomaly removal on the business data to be processed to obtain cleaned data; performing field mapping on the cleaned data to obtain mapped data; and performing dimensional aggregation on the mapped data to obtain the aggregated dataset.

[0033] This application embodiment removes duplicates and outliers from the business data to be processed, resulting in cleaned data. The cleaned data is then mapped to standardized fields to obtain mapped data. This mapped data can then be aggregated to obtain a aggregated dataset, thereby enhancing the initial standardization capability of multi-source heterogeneous data, improving the efficiency of data cleaning and field mapping, and providing a unified and standardized data source foundation for subsequent benchmark quantification processing.

[0034] For example, in the pre-investment calculation of a property project, the business data to be processed synchronized by the property management system may include multi-source heterogeneous data such as project type data, estimated area data, and service level data. This business data to be processed can be aggregated and processed. After removing duplicates and outliers through anomaly removal, the cleaned data can be processed by field mapping to unify it into standardized fields. Then, the mapped data can be aggregated according to the project dimension to obtain the aggregated dataset.

[0035] S202, perform benchmark quantization on the aggregated dataset to obtain initial business cost data.

[0036] Specifically, benchmark quantification can be a data processing process that calculates and transforms data in the aggregated dataset according to a preset standardized model. Benchmark quantification can be used to match project indicators with benchmark data. Benchmark quantification can also be used as a preprocessing step for cost value generation, transforming abstract project characteristics into specific cost values ​​to obtain initial business cost data.

[0037] The initial business cost data can be the preliminary cost calculation results obtained through benchmark quantification. This initial business cost data can be used to characterize the cost target calculated based on the standardized model. The initial business cost data can serve as the basis for the generation of subsequent cost versions and can be used to provide an initial quantitative benchmark for full-cycle cost management.

[0038] Furthermore, the benchmark quantification process may specifically include performing indicator parsing on the aggregated dataset to obtain indicator parsing data; matching the indicator parsing data with preset benchmark data to obtain matching data; and performing quantity-price calculations on the matching data to obtain initial business cost data.

[0039] This application embodiment extracts basic project indicators by parsing the aggregated dataset to obtain indicator parsing data; it then matches the indicator parsing data with preset benchmark data to obtain content standards, unit price standards, and frequency standards, resulting in matched data; finally, it performs quantity-price calculations on the matched data to aggregate the quantity, unit price, and frequency, thus obtaining initial business cost data. This enhances the standardization capability of the cost calculation starting point, improves the efficiency of matching project indicators with benchmark data, and enhances the objectivity and comparability of cost value generation.

[0040] For example, in the pre-investment cost calculation of a property project, the aggregated dataset synchronized by the property management system can include basic project indicators such as managed area data, service level data, and city level data, as well as corresponding preset benchmark data such as content standard data, unit price standard data, and frequency standard data. The aggregated dataset can be subjected to benchmark quantification processing, and the managed area data can be extracted through indicator analysis processing. The managed area data and content standard data can be matched to obtain the engineering quantity data. Then, the engineering quantity data can be compared with the unit price standard data and frequency standard data to perform quantity and price calculation processing to obtain the initial business cost data.

[0041] S203, the initial business cost data and the aggregated dataset are corrected and quantified to obtain corrected business cost data.

[0042] Specifically, the correction quantification process can be a data processing procedure that adjusts and calibrates the initial business cost data based on reference information in the aggregated dataset. This correction quantification process can be used to compare and correct the preliminary calculated cost with the actual standardized cost information. The correction quantification process can be used to improve the accuracy and applicability of cost data and obtain corrected business cost data.

[0043] Among them, the corrected business cost data can be cost data obtained through corrected quantitative processing. This corrected business cost data can be used to characterize the calibrated cost target. The corrected business cost data can serve as a reliable basis for investment decisions and cost control, and can be used to support full-cycle cost management.

[0044] Furthermore, the correction and quantification process may specifically include performing a difference comparison process on the initial business cost data and the aggregated dataset to obtain difference comparison data; performing weight configuration processing on the difference comparison data to obtain weight configuration data; and performing weighted adjustment processing on the weight configuration data and the initial business cost data to obtain corrected business cost data.

[0045] This application embodiment identifies cost deviations by comparing initial business cost data and aggregated datasets to obtain difference comparison data. This difference comparison data is then weighted and integrated with multi-dimensional reference information to obtain weighted configuration data. This weighted configuration data and initial business cost data can then be weighted and adjusted to obtain corrected business cost data. This enhances the adaptability of cost data to specific projects, improves the accuracy of multi-dimensional reference information integration and calibration, and enhances the reliability and applicability of cost calculation results.

[0046] For example, in the cost calculation of property project takeover, the initial business cost data may include labor cost data, material consumption cost data, and energy consumption cost data calculated based on the project's basic indicators and benchmark data. The aggregated dataset may include historical project cost data, group standard unit price data, and regional standard unit price data under the same city level. The initial business cost data and the aggregated dataset can be corrected and quantified. Cost deviations can be identified through difference comparison processing. The difference comparison data can be weighted and processed to integrate historical data and regional standards. Then, the weighted data and the initial business cost data can be weighted and adjusted to obtain the corrected business cost data.

[0047] S204 performs budget adaptation processing on the corrected business cost data to obtain the target business cost data.

[0048] Specifically, budget adaptation processing can be a data processing process that combines revised business cost data with budget preparation rules and historical budget execution data to perform data conversion and verification. This budget adaptation processing can be used to achieve format and logical adaptation of cost data with the budget management system; budget adaptation processing can be used to generate cost data that can serve the budget preparation and control process.

[0049] The target business cost data can be a set of cost data generated through budget adaptation processing. This target business cost data can be used to characterize the cost benchmark that meets the format requirements of the budget management system. The target business cost data can serve as a direct basis for budget preparation and cost control, and can be used to support full-cycle cost management.

[0050] Furthermore, the budget adaptation process may specifically include mapping the revised business cost data to obtain mapped data; splitting the mapped data into periodic data; and performing a reasonableness verification process on the split data and historical budget execution data to obtain the target business cost data.

[0051] This application embodiment obtains mapped data that conforms to the budget subject system by performing subject mapping processing on the corrected business cost data; obtains split data that adapts to the budget cycle by performing periodic split processing on the mapped data; and obtains target business cost data by performing reasonableness verification processing on the split data and historical budget execution data. This enhances the compatibility of cost data with budget preparation rules and historical execution data; improves the automation level of converting cost calculation results into budget management processes; and improves the consistency of budget preparation data and overall processing efficiency.

[0052] For example, in the preparation of property project budgets, the revised business cost data can include labor cost data, maintenance cost data and energy consumption cost data recalculated based on actual operating indicators. The revised business cost data can be processed for budget adaptation. Each expense item can be mapped to the budget item system through subject mapping. The mapped data can be split into quarterly budget data through periodic processing. Then, the split data and historical budget execution data can be processed for reasonableness verification to identify abnormal fluctuation items and obtain the target business cost data.

[0053] S205, link and archive the target business cost data and the aggregated dataset to obtain full-cycle business cost management data, in which the full-cycle business cost management data is used to link the full-cycle business cost data.

[0054] Specifically, the associated archiving process can be a data processing procedure that logically links and structures the target business cost data with the relevant data in the aggregated dataset. This associated archiving process can be used to establish a mapping relationship between the target cost data and its background information such as its source, version, and application scenario, without any limitations here. The associated archiving process can serve as the core processing for the full lifecycle management of cost data, and can be used to ensure that each target cost data can be traced back to its generation basis, application stage, and adjustment history, thereby obtaining full-cycle business cost management data.

[0055] Among them, the full-cycle business cost management data can be a structured data set obtained through associated archiving and processing, which can be used to characterize the cost status and evolution process of each stage of the project from pre-investment to exit. This full-cycle business cost management data can be used to associate full-cycle business cost data. The full-cycle business cost management data can serve as a data carrier for closed-loop cost control management and can be used to support unified query, comparative analysis and decision support of full-cycle costs.

[0056] Furthermore, the associated archiving process may specifically include version identification processing of the target business cost data to obtain version identification data; index binding processing of the version identification data and the aggregated dataset to obtain index-bound data; and time-series chained storage processing of the index-bound data to obtain full-cycle business cost management data.

[0057] This application embodiment obtains version identification data that distinguishes different lifecycle nodes by processing the target business cost data into version identifiers; it establishes a mapping relationship between cost data and background information by indexing and binding the version identifier data and the aggregated dataset, thus obtaining index-bound data; and it forms a chained data structure based on time dimension by performing time-series chained storage processing on the index-bound data, thereby obtaining full-cycle business cost management data. This achieves structured association and full-cycle archiving of cost data, enhances the continuity and traceability of cost data at each stage, improves the efficiency of full-cycle comparative analysis of cost data, and provides a solid data foundation for closed-loop management of cost control.

[0058] For example, in the full-cycle cost management of property projects, the target business cost data can include the target cost data of the pre-investment calculation version, the target cost data of the takeover calculation version, and the target cost data of the budget preparation version. The aggregated dataset can include the basic project indicator data, benchmark data version information, and historical cost records corresponding to each version. The target business cost data and the aggregated dataset can be linked and archived. By using version identification processing, a stage identifier can be assigned to each version of the target cost data. The version identifier data and the aggregated dataset can be indexed and bound to establish a mapping relationship. Then, the indexed and bound data can be stored in a time-series chain, forming a chain data structure according to the time sequence of pre-investment, takeover, budget, adjustment, and exit, to obtain the full-cycle business cost management data.

[0059] According to the technical solution provided in this disclosure, the business data to be processed is aggregated and processed by anomaly removal, field mapping, and dimension aggregation to obtain an aggregated dataset. The aggregated dataset is then subjected to benchmark quantification processing, and initial business cost data is obtained through indicator analysis, benchmark matching, and quantity-price calculation. The initial business cost data and the aggregated dataset are then subjected to corrected quantification processing, and corrected business cost data is obtained through difference comparison, weight configuration, and weighted adjustment. The corrected business cost data is then subjected to budget adaptation processing, and target business cost data is obtained through account mapping, period splitting, and reasonableness verification. Finally, the target business cost data and the aggregated data are combined... The system performs associated archiving processing, using version identifiers, index binding, and time-series chained storage to obtain full-cycle business cost management data for associating with full-cycle business cost data. This enhances the standardization of cost calculation starting points and the accuracy of multi-dimensional reference information fusion and calibration; improves the compatibility of cost data with budget preparation rules and historical execution data; enhances the standardization and data consistency of cost calculation; strengthens the objectivity of business cost data calculation; enhances the dynamic linkage capability of cost data across different project stages and the reliability of cross-cycle comparative analysis; and improves the efficiency of full-lifecycle cost data calculation and processing and version traceability.

[0060] In some embodiments, benchmark quantification processing is performed on the aggregated dataset to obtain initial business cost data, including: performing engineering quantity accounting processing on the aggregated dataset to obtain engineering quantity accounting data; performing quantity-price matching processing on the engineering quantity accounting data and the aggregated dataset to obtain quantity-price matching data; and performing frequency aggregation processing on the quantity-price matching data and the aggregated dataset to obtain initial business cost data.

[0061] Specifically, the quantity calculation process can be a data processing procedure that calculates the basic indicators reflecting the project scale in the collected data based on a preset benchmark content standard. This quantity calculation process can be used to transform abstract project indicators into specific resource requirements. The quantity calculation process can be used to perform calculations based on the basic project indicators and benchmark content data in the collected data to obtain quantity calculation data.

[0062] Among them, the quantity accounting data can be a set of calculation results obtained through quantity accounting processing, which can be used to characterize the resource quantity required for each cost item. This quantity accounting data can be used to characterize the theoretical resource consumption of each operation in the project. The quantity accounting data can be used as a direct input for quantity-price matching processing, and can also be used to provide data on the scale of resource consumption to be matched.

[0063] Furthermore, the engineering quantity accounting process may specifically include extracting indicators from the aggregated dataset to obtain project indicator data; performing matching calculations on the project indicator data based on preset content standard data in the aggregated dataset to obtain theoretical resource requirements; and summarizing and standardizing the theoretical resource requirements to obtain engineering quantity accounting data.

[0064] In addition, quantity-price matching processing can be a data processing process that associates and combines engineering quantity accounting data with corresponding benchmark unit price data. This quantity-price matching processing can be used as a preprocessing step for calculating individual costs and expenses. It can be used to match the corresponding unit price standard from the aggregated dataset according to the cost item type identified in the engineering quantity accounting data to obtain quantity-price matching data.

[0065] Among them, the quantity-price matching data can be an intermediate data set generated by the quantity-price matching process, which associates the quantity of work with the unit price. This quantity-price matching data can be used to characterize the resource consumption of each operation and its corresponding unit price. The quantity-price matching data can be used as a direct input for frequency aggregation processing and can be used to provide structured data containing the quantity-price correspondence.

[0066] Furthermore, the quantity-price matching process can specifically include parsing the engineering quantity accounting data to obtain the subject type data; performing unit price retrieval processing on the aggregated dataset based on the subject type data to obtain candidate unit price data; and associating and binding the candidate unit price data and the engineering quantity accounting data to obtain quantity-price matching data.

[0067] In addition, frequency aggregation processing can be a data processing procedure that summarizes and converts quantity and price matching data based on the periodicity of cost occurrence. This frequency aggregation processing can be used to aggregate quantity and price data based on a single occurrence or unit time according to a preset frequency standard. Frequency aggregation processing can be used to calculate based on the operation frequency standard in the quantity and price matching data and the aggregated data to obtain the initial business cost data.

[0068] Furthermore, the frequency aggregation process can specifically include performing period identification processing on the quantity-price matching data to obtain period type data; performing frequency retrieval processing on the aggregated dataset based on the period type data to obtain occurrence frequency data; and performing product aggregation processing on the occurrence frequency data and the quantity-price matching data to obtain initial business cost data.

[0069] For example, in the benchmark cost calculation of a property project, the aggregated dataset can include basic project indicators such as building area data, elevator quantity data, and equipment list data, as well as benchmark content standard data, unit price standard data, and frequency standard data. The aggregated dataset can be subjected to benchmark quantification processing. The quantity calculation data can be obtained by calculating the resource requirements based on the benchmark content standards through quantity calculation processing. The quantity calculation data and the unit price standard data in the aggregated dataset can be matched to obtain quantity-price matching data. Then, the quantity-price matching data and the frequency standard data in the aggregated dataset can be aggregated to obtain initial business cost data based on the preset period dimension.

[0070] According to the technical solution provided in this disclosure, by performing quantity accounting on the collected dataset, calculating the project scale indicators based on preset benchmark content standards, and obtaining quantity accounting data, the quantity accounting data and the collected dataset are subjected to quantity-price matching processing. Based on the subject type, corresponding unit price standards are matched to obtain quantity-price matching data. The quantity-price matching data and the collected dataset are subjected to frequency aggregation processing, and time-dimensional summarization and conversion are performed based on the operation frequency standard to obtain initial business cost data. This enhances the standardization capability of the cost calculation starting point; improves the collaborative efficiency of project indicators with benchmark content, unit price, and frequency matching calculations; and enhances the objectivity and comparability of cost value generation.

[0071] In some embodiments, the initial business cost data and the aggregated dataset are subjected to corrected quantitative processing to obtain corrected business cost data, including: performing indicator verification processing on the aggregated dataset to obtain indicator verification data; performing deviation comparison processing on the indicator verification data and the initial business cost data to obtain deviation comparison data; and performing parameter recalculation processing on the initial business cost data based on the deviation comparison data to obtain corrected business cost data.

[0072] Specifically, indicator verification processing can be a data processing procedure that performs logical verification and range rationality confirmation on the benchmark parameters in the aggregated dataset. This indicator verification processing can be used to ensure the accuracy of the benchmark data and the consistency with business logic. Indicator verification processing can also be used as a preprocessing step for deviation comparison processing to perform rationality verification on the aggregated dataset and obtain indicator verification data.

[0073] Among them, the indicator verification data can be a set of standardized benchmark data confirmed through indicator verification processing. This indicator verification data can be used to characterize the standard engineering quantity coefficients, unit prices and frequency information applicable to the current project, etc., without limitation here; the indicator verification data can be used as a reference for deviation comparison processing, and can be used for quantitative comparison with the initial business cost data.

[0074] Furthermore, the indicator verification process may specifically include performing range verification on the aggregated dataset to obtain range verification data; performing logical consistency processing on the range verification data to obtain logically consistent data; and performing validity confirmation processing on the logically consistent data to obtain indicator verification data.

[0075] In addition, deviation comparison processing can be a data processing procedure that compares and analyzes the verified data of indicators and the initial business cost data. This deviation comparison processing can be used to quantify the degree of deviation between the initial estimate and the standardized benchmark. Deviation comparison processing can also serve as the trigger for parameter recalculation processing, and can be used to identify the adjustment direction and magnitude of key parameters to obtain deviation comparison data.

[0076] Among them, the deviation comparison data can be intermediate data generated through deviation comparison processing, which can be used to characterize the proportion or absolute difference between the initial calculation and the standard benchmark. This deviation comparison data can be used to characterize the direction and degree of cost deviation; the deviation comparison data can be used to guide the adjustment of key calculation parameters.

[0077] Furthermore, the deviation comparison processing can specifically include performing difference calculation processing on the indicator verification data and the initial business cost data to obtain difference calculation data; performing ratio analysis processing on the difference calculation data to obtain ratio analysis data; and performing deviation rating processing on the ratio analysis data to obtain deviation comparison data.

[0078] In addition, parameter recalculation can be a data processing procedure that adjusts and recalculates the key calculation parameters used when generating initial business cost data based on deviation comparison data. This parameter recalculation can be used to calibrate the initial cost through deviation information. Parameter recalculation can serve as the core processing for automated cost value calculation to obtain corrected business cost data.

[0079] Furthermore, the parameter recalculation process may specifically include performing parameter identification processing on the deviation comparison data to obtain parameter data to be corrected; performing parameter replacement processing on the initial business cost data based on the parameter data to be corrected to obtain parameter replacement data; and performing recalculation processing on the parameter replacement data to obtain corrected business cost data.

[0080] For example, in the cost calculation of property project takeover, the aggregated dataset can include standardized quantity and price data applicable to specific city levels and service grades. The initial business cost data can be preliminary cost data estimated by the project team based on past experience. The aggregated dataset can be corrected and quantified. The standardized quantity and price data can be range-verified and logically consistent through indicator verification processing to obtain indicator verification data. The indicator verification data and the initial business cost data can be compared to calculate the difference ratio to obtain deviation comparison data. Then, based on the deviation comparison data, the initial business cost data can be recalculated to identify key deviation parameters and replace and recalculate them according to the standard benchmark to obtain corrected business cost data.

[0081] According to the technical solution provided in this disclosure, by performing indicator verification processing on the collected dataset, and obtaining indicator verification data through range verification and logical consistency confirmation, the indicator verification data and initial business cost data are compared for deviation. Through difference calculation, ratio analysis and deviation classification, deviation comparison data is obtained. Based on the deviation comparison data, the initial business cost data is recalculated for parameters. Through parameter identification, parameter replacement and recalculation, corrected business cost data is obtained. This enhances the adaptability of cost data to specific projects, improves the accuracy of multi-dimensional reference information fusion calibration, and enhances the reliability and applicability of cost calculation results.

[0082] In some embodiments, the initial business cost data is recalculated based on the deviation comparison data to obtain corrected business cost data, including: performing threshold determination processing on the deviation comparison data to obtain a threshold determination result; performing hierarchical correction processing on the initial business cost data based on the threshold determination result to obtain hierarchical corrected data; and performing convergence verification processing on the hierarchical corrected data to obtain corrected business cost data.

[0083] Specifically, the threshold determination process can be a data processing procedure that compares the deviation comparison data with a preset threshold range. This threshold determination process can be used to identify cost data whose deviation exceeds a reasonable range. The threshold determination process can also be used to provide decision conditions for subsequent differential correction and obtain the threshold determination result.

[0084] The threshold determination result can be a judgment conclusion obtained through threshold determination processing. The threshold determination result can be used to indicate whether the deviation comparison data exceeds the preset threshold range and the degree of exceedance. The threshold determination result can be used as the grade basis for graded correction processing and can also be used to guide the selection of differentiated adjustment strategies.

[0085] Furthermore, the threshold determination process may specifically include performing deviation calculation on the deviation comparison data to obtain deviation data; and performing interval comparison processing on the deviation data and the preset threshold data to obtain the threshold determination result.

[0086] Furthermore, the graded correction process can be a data processing procedure that applies differentiated adjustments to initial business cost data based on the deviation level indicated by the threshold determination result. This graded correction process can be used to adopt correction strategies of different strengths or different methods for different degrees of deviation.

[0087] Among them, the graded correction data can be cost data that has been initially adjusted according to the deviation level through graded correction processing. This graded correction data can be used to characterize the intermediate cost results in the correction process. The graded correction data can be used as direct input for convergence verification processing, and can also be used to provide data to be verified for quality verification.

[0088] Furthermore, the graded correction process may specifically include performing graded parsing on the threshold determination results to obtain graded parsing data; and performing differentiated adjustment on the initial business cost data based on the graded parsing data to obtain graded correction data.

[0089] In addition, convergence verification can be a data processing procedure to re-evaluate and verify the graded correction data to ensure that the corrected data is stable and reasonable. This convergence verification can be used to prevent over-correction or under-correction. Convergence verification can also be used as a quality checkpoint in the correction process to ensure the logical consistency and business rationality of the output cost data, thereby obtaining corrected business cost data.

[0090] Furthermore, the convergence verification process may specifically include performing reverse verification on the graded correction data to obtain reverse verification data; and performing consistency verification on the reverse verification data and the data within a preset reasonable range to obtain the corrected business cost data.

[0091] For example, in the cost calculation of property project takeover, the deviation comparison data can be the deviation data that characterizes the difference between the model calculation and human experience. The initial business cost data can be the draft target cost of the takeover version calculated based on the basic indicators and benchmark data of the project. The initial business cost data can be recalculated based on the deviation comparison data. The deviation degree of the deviation comparison data can be calculated through threshold judgment processing and compared with the preset threshold data to obtain the threshold judgment result. Based on the threshold judgment result, the initial business cost data can be graded and corrected to differentiate specific cost items to obtain graded and corrected data. Then, the graded and corrected data can be converged and verified by substituting the corrected data back into the calculation model for consistency verification to obtain the corrected business cost data.

[0092] According to the technical solution provided in this disclosure, by performing threshold determination processing on deviation comparison data, comparing the deviation degree calculation with a preset threshold data range, a threshold determination result is obtained. Based on the threshold determination result, the initial business cost data is subjected to graded correction processing. Through grade parsing and differential adjustment, graded correction data is obtained. The graded correction data is subjected to convergence verification processing. Through reverse verification and consistency verification, the corrected business cost data is obtained. This enhances the ability to manage different degrees of deviation during cost calibration; improves the pertinence and effectiveness of multi-level correction strategies; and enhances the logical consistency and business rationality of the corrected cost data.

[0093] In some embodiments, budget adaptation processing is performed on the revised business cost data to obtain target business cost data, including: splitting the revised business cost data based on a preset period to obtain period-split data; performing item mapping processing on the period-split data based on preset dimension division data to obtain item mapping data; and performing numerical adaptation processing on the item mapping data to obtain target business cost data.

[0094] Specifically, the preset period can be a pre-defined time division unit, which can be used to provide a standard for the time dimension division of cost data; the preset period can serve as a time benchmark for subsequent item mapping and numerical adaptation, and can also be used to adapt to budget management requirements of different granularities.

[0095] In addition, the splitting process can be a data processing procedure that divides the corrected business cost data into time dimensions based on a preset period. This splitting process can be used to decompose the annual or project full-cycle cost into cost data in smaller time units. The splitting process can also be used to provide cost data with time attributes for subsequent item mapping, resulting in periodic splitting data.

[0096] The periodic split data can be a set of data obtained by dividing the corrected business cost data according to a preset period. The periodic split data can include cost information within a specific time period. The periodic split data can be used as a direct input for itemized mapping processing, and can also be used to decompose the overall cost data into various time periods.

[0097] Furthermore, the splitting process may specifically include performing period type identification processing on the revised business cost data to obtain period type data; performing proportional allocation processing on the revised business cost data based on the period type data to obtain allocation data; and performing period aggregation processing on the allocation data to obtain period splitting data.

[0098] In addition, the preset dimension division data can be predefined cost classification or itemization standards. This preset dimension division data can be used to classify and organize cost data; the preset dimension division data can serve as the classification basis for itemization mapping processing, and can also be used to ensure that cost data meets the needs of multi-dimensional budget analysis.

[0099] Itemized mapping processing is a data processing procedure that categorizes cost items in periodic split data based on preset dimensions. This itemized mapping processing can be used to refine time-dimensional cost data into specific cost items. Itemized mapping processing can serve as the core processing for structuring budget data and can also be used to enhance the multi-dimensional analyzability of budget data to obtain itemized mapping data.

[0100] Among them, the itemized mapping data can be the data obtained by classifying and mapping the periodic split data according to the preset dimension classification standard. The itemized mapping data can include time period attributes and cost item attributes. The itemized mapping data can be used as direct input for numerical adaptation processing, and can also be used to provide a refined data foundation for subsequent budget preparation.

[0101] Furthermore, the item mapping process may specifically include parsing the subject code of the periodic split data to obtain subject code data; performing matching and retrieval processing on the preset dimension-divided data based on the subject code data to obtain matching result data; and classifying and reorganizing the matching result data and the periodic split data to obtain item mapping data.

[0102] In addition, numerical adaptation processing can be a data processing procedure that adjusts or formats the cost values ​​in the itemized mapping data. This numerical adaptation processing can be used to make the cost values ​​meet specific budget formats or control requirements. Numerical adaptation processing can be used as the core processing for budget data standardization, and can also be used to ensure that the generated target business cost data meets the budget system standards in terms of numerical accuracy and format, thus obtaining the target business cost data.

[0103] Furthermore, the numerical adaptation process may specifically include precision adjustment processing of the sub-item mapping data to obtain precision-adjusted data; unit conversion processing of the precision-adjusted data to obtain unit-converted data; and format encapsulation processing of the unit-converted data to obtain target business cost data.

[0104] For example, in the preparation of the annual budget for a property project, the revised business cost data can include the estimated total cost of the project for the whole year. The preset period can be monthly. The preset dimension division data can include cost item standards such as labor costs, energy costs, material consumption, facility maintenance and administrative office costs. The revised business cost data can be processed for budget adaptation. By splitting the data, the period type can be identified and proportionally allocated according to business rules to obtain period split data. Based on the preset dimension division data, the period split data can be processed for item mapping, and each cost can be matched and classified according to the subject code to obtain item mapping data. Then, the item mapping data can be processed for numerical adaptation, uniform precision rounding and conversion to standard unit format to obtain the target business cost data.

[0105] According to the technical solution provided in this disclosure, the revised business cost data is split based on a preset period. Period type identification, proportional allocation, and period aggregation are used to obtain period-splittered data. The period-splittered data is then divided into items based on preset dimensions and mapped to specific items. Item mapping data is obtained through account code parsing, matching retrieval, and classification reorganization. The item mapping data undergoes numerical adaptation processing, including precision adjustment, unit conversion, and format encapsulation, to obtain the target business cost data. This enhances the compatibility of cost data with budget preparation rules and historical execution data; improves the automation of converting cost calculation results into budget management processes; and enhances the consistency of budget preparation data and overall processing efficiency.

[0106] In some embodiments, the target business cost data and the aggregated dataset are associated and archived to obtain full-cycle business cost management data, including: performing version identification processing on the target business cost data to obtain version identification data; performing index binding processing on the version identification data and the aggregated dataset to obtain index-bound data; and performing time-series chained storage processing on the index-bound data to obtain full-cycle business cost management data.

[0107] Specifically, version identification processing can be a data processing process that adds version information to target business cost data to form a unique identifier. This version identification processing can be used to distinguish target business cost data generated at different time points or different business stages. Version identification processing can be used as a preprocessing step for associated archiving processing, and can also be used to provide identifiable cost data for subsequent index binding, thus obtaining version identification data.

[0108] The version identifier data can be data generated through version identifier processing to uniquely identify and distinguish different versions of target business cost data. This version identifier data can be used to represent cost version identifiers with temporal or logical significance. The version identifier data can be used as direct input for index binding processing, and can also be used to support the tracking, comparison and management of subsequent cost data.

[0109] Furthermore, the version identification processing may specifically include parsing the stage tags of the target business cost data to obtain stage tag data; and generating an identifier for the target business cost data based on the stage tag data to obtain version identification data.

[0110] In addition, index binding processing can be a data processing procedure that establishes a relationship between version identifier data and related data entries in the aggregated dataset. This index binding processing can be used to establish a logical link between target business cost data and its corresponding aggregated dataset. Index binding processing can also be used to ensure the traceability of the relationship between cost data and its calculation basis, thus obtaining index-bound data.

[0111] Among them, the index-bound data can be structured data that has been linked through the index binding process. This index-bound data can be used to characterize the mapping relationship between cost data and the original calculation basis. The index-bound data can be used as the direct input for time-series chained storage processing, and can also be used to provide a locationable data foundation for full-cycle cost queries.

[0112] Furthermore, the index binding process may specifically include extracting associated fields from the version identifier data to obtain associated field data; performing matching retrieval on the aggregated dataset based on the associated field data to obtain matching record data; and mapping and binding the matching record data and version identifier data to obtain index binding data.

[0113] Furthermore, time-series chained storage processing can be a data organization process that stores index-bound data in series based on time order or business logic order. This time-series chained storage processing can be used to organize cost data and its associated aggregated data of different stages and versions in chronological order. Time-series chained storage processing can be used to form a complete, coherent and traceable data chain to obtain full-cycle business cost management data.

[0114] Furthermore, the time-series chained storage processing can specifically include performing timestamp parsing on the index-bound data to obtain timestamp data; performing sequential arrangement processing on the index-bound data based on the timestamp data to obtain sequentially arranged data; and performing chained pointer encapsulation processing on the sequentially arranged data to obtain full-cycle business cost management data.

[0115] For example, in the full-cycle cost archiving of a property project, the target business cost data can include target cost data from the pre-investment calculation version, the takeover calculation version, and the budget preparation version. The aggregated dataset can include the project's basic indicator data, benchmark data version information, and historical cost records corresponding to each version. The target business cost data and the aggregated dataset can be linked and archived. By adding stage tags and generation identifiers to the target cost data of each version through version identification processing, version identification data can be obtained. The version identification data and the aggregated dataset can be indexed and bound to establish a mapping relationship between cost data and the original calculation basis, resulting in index-bound data. Then, the index-bound data can be processed by time-series chained storage, arranged in series according to the business logic order of pre-investment, takeover, budget, adjustment, and exit, and encapsulated with chain pointers to obtain full-cycle business cost management data.

[0116] According to the technical solution provided in this disclosure, version identification processing is performed on the target business cost data. Version identification data is obtained through stage tag parsing and identifier generation. The version identification data and the aggregated dataset are indexed and bound. Indexed and bound data is obtained through related field extraction, matching retrieval, and mapping binding. The indexed and bound data is processed by time-series chained storage. Through timestamp parsing, sequential arrangement, and chained pointer encapsulation, full-cycle business cost management data is obtained. This achieves structured association and full-cycle archiving of cost data, enhances the continuity and traceability of cost data at each stage, and improves the efficiency of full-cycle comparative analysis of cost data.

[0117] In some embodiments, the business data to be processed is aggregated to obtain an aggregated dataset, including: performing field validation processing on the business data to be processed to obtain field validation data; performing missing data completion processing on the field validation data to obtain missing data; and performing structured encapsulation processing on the missing data to obtain the aggregated dataset.

[0118] Specifically, field validation processing can be a data processing procedure that performs compliance checks on the format type and value range of each field in the business data to be processed. This field validation processing can be used to identify and filter data entries with incorrect format or type mismatch; field validation processing can also be used to ensure the basic quality of data in subsequent processing and obtain field validation data.

[0119] Among them, the field validation data can be a set of data that conforms to the preset data specifications obtained through field validation processing. This field validation data can be used to characterize the usable data that has passed the compliance check. The field validation data can be used as direct input for missing data completion processing, and can also be used to provide a validated basis for data integrity processing.

[0120] Furthermore, the field validation process can specifically include parsing the format type of the business data to be processed to obtain format type data; performing rule matching processing on the preset compliance rule data based on the format type data to obtain rule matching data; and performing anomaly filtering processing on the rule matching data to obtain field validation data.

[0121] In addition, missing value completion processing can be a data processing procedure that identifies and fills in missing or null values ​​in field validation data. This missing value completion processing can be used to repair data integrity defects; missing value completion processing can also be used to ensure that data entries entering the encapsulation stage are complete, resulting in missing value completion data.

[0122] Among them, missing completion data can be a set of data with integrity repair obtained through missing completion processing. This missing completion data can be used to represent intermediate data with complete field content. Missing completion data can be used as direct input for structured encapsulation processing, and can also be used to provide a complete data foundation for standardized encapsulation.

[0123] Furthermore, the missing completion processing can specifically include performing null value scanning processing on the field validation data to obtain null value scan data; performing missing filling processing on the data with the preset completion strategy based on the null value scan data to obtain missing filled data; and performing integrity verification processing on the missing filled data to obtain missing completed data.

[0124] Furthermore, structured encapsulation processing can be a data processing procedure that organizes, transforms, and encapsulates missing and completed data according to a predefined data model. This structured encapsulation processing can be used to transform raw business data from diverse sources with inconsistent formats into standardized input; structured encapsulation processing can also be used to make data directly available for benchmark quantification processing to obtain aggregated datasets.

[0125] Furthermore, the structured encapsulation process can specifically include performing field mapping processing on the missing completion data to obtain field mapping data; performing model adaptation processing on the preset standard model data based on the field mapping data to obtain model adaptation data; and performing object encapsulation processing on the model adaptation data to obtain the aggregated dataset.

[0126] For example, in the collection of cost data for property projects, the business data to be processed can be the raw data synchronized from the property management system, which includes multiple fields such as project type, managed area, and service level. This business data to be processed can be aggregated and processed. The format type of each field can be parsed through field validation processing, and abnormal values ​​can be filtered by matching preset compliance rules to obtain field validation data. Missing data can be processed by scanning for null values ​​and filling missing items according to preset completion strategies to obtain missing data. Then, the missing data can be structured and encapsulated to map the fields to a standardized model and encapsulate them into a unified structure to obtain the aggregated dataset.

[0127] According to the technical solution provided in this disclosure, field validation processing is performed on the business data to be processed. Field validation data is obtained through format type parsing, rule matching, and anomaly filtering. Missing data is then filled in by scanning for null values, filling in missing values, and verifying completeness. The filled data is then encapsulated in a structured manner. Through field mapping, model adaptation, and object encapsulation, a aggregated dataset is obtained. This enhances the initial standardization capability of multi-source heterogeneous data, improves the efficiency of data cleaning and field mapping, and provides a unified and standardized data source foundation for subsequent benchmark quantification processing.

[0128] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this disclosure, and will not be described in detail here.

[0129] Figure 3 This is a schematic diagram of another business cost data management method provided in an embodiment of this disclosure. For example... Figure 3 As shown, this business cost data management method includes: Project basic indicator layer: Core indicators (business data to be processed) such as number of households, managed area, number of escalators, and number of entrances and exits are automatically synchronized from the property management system, without the need for manual input; Level 3 baseline data layer: Enterprise Standards: Establish unified standards for expense items, content, unit price, and frequency.

[0130] Regional Standards: Each business unit develops its own regional standards based on the company's standards, taking into account local prices and salary levels.

[0131] Project Standards: Individual projects are automatically matched with corresponding benchmark data based on their service level (e.g., S / A / B level).

[0132] Automatic calculation engine: Based on project indicators and matching benchmark data, it automatically calculates the workload, substitutes it into the core formula to generate the target cost, and can support annual or monthly dimension splitting.

[0133] The model review and acceptance process includes: Template setup: Output a unified basic model data collection template, clearly defining core fields such as indicators, fees, quantity and price.

[0134] Typical project pilot: For example, typical projects with excellent cost management can be selected from each city level, and data can be filled in and reported according to the template.

[0135] Standard setting: Based on pilot data, standard fees and quantity and price data can be sorted out, and after verification, enterprise standards can be formed, which can then be revised to form regional standards.

[0136] Acceptance criteria: The deviation between the "model-calculated target cost" and the "offline manual cost calculation" of a typical project is lower than a preset threshold to ensure the accuracy of the model.

[0137] Model lifecycle application process 1. Pre-investment stage: Create business opportunities and import basic information, call the benchmark model to automatically calculate investment costs, and support rapid investment decisions.

[0138] 2. Takeover Phase: After the project is taken over, the basic indicators are synchronized to the system, the benchmark model is used to generate the takeover version of the target cost, and the initial cost control benchmark is clarified.

[0139] 3. Budgeting Stage: Automatically inherits cost data from the takeover version, generates target costs for the budget version, and supports manual fine-tuning and version comparison.

[0140] 4. Adjustment Phase: During project operation, if changes occur in area, service standards, etc., the project quantity will be automatically updated and the adjusted cost will be recalculated.

[0141] 5. Exit Phase: Generate exit version costs and complete the closed-loop recording of full lifecycle costs.

[0142] According to the technical solution provided in this disclosure, core indicators such as the number of households and managed area are automatically synchronized from the property management system as business data to be processed; a three-level benchmark data layer (enterprise standard, regional standard, project standard) is used to automatically match corresponding benchmark data for the project; the calculation engine calculates the engineering quantity based on the project indicators and the matched benchmark data, and substitutes it into the core formula to generate the target cost, supporting the splitting of the year and month dimensions. The accuracy of the model is ensured through template construction, typical project pilot, standard formulation and deviation acceptance (the deviation between model calculation and manual calculation is less than a preset threshold); the full-cycle application process covers the pre-investment stage (automatically calculating the investment model cost to support decision-making), the takeover stage (generating the takeover version target cost), the budget stage (generating the budget version target cost and supporting version comparison), the adjustment stage (automatically updating the engineering quantity and recalculating the adjusted version cost), and the exit stage (generating the exit version cost), thereby realizing the full-cycle automated management of cost calculation from pre-investment to exit; improving the consistency and traceability of cost data in multiple stages; and reducing the workload and error risk of manual entry and calculation.

[0143] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0144] Figure 4 This is a schematic diagram of a business cost data management device provided in an embodiment of this disclosure. Figure 4 As shown, the business cost data management device includes: The first processing module 401 is used to collect and process the business data to be processed to obtain a collected dataset. The second processing module 402 is used to perform benchmark quantization processing on the aggregated dataset to obtain initial business cost data. The third processing module 403 is used to perform correction and quantification processing on the initial business cost data and the aggregated dataset to obtain corrected business cost data. The fourth processing module 404 is used to perform budget adaptation processing on the corrected business cost data to obtain the target business cost data. The fifth processing module 405 is used to associate and archive the target business cost data and the aggregated dataset to obtain full-cycle business cost management data, wherein the full-cycle business cost management data is used to associate the full-cycle business cost data.

[0145] According to the technical solution provided in this disclosure, the business data to be processed is aggregated and processed by anomaly removal, field mapping, and dimension aggregation to obtain an aggregated dataset. The aggregated dataset is then subjected to benchmark quantification processing, and initial business cost data is obtained through indicator analysis, benchmark matching, and quantity-price calculation. The initial business cost data and the aggregated dataset are then subjected to corrected quantification processing, and corrected business cost data is obtained through difference comparison, weight configuration, and weighted adjustment. The corrected business cost data is then subjected to budget adaptation processing, and target business cost data is obtained through account mapping, period splitting, and reasonableness verification. Finally, the target business cost data and the aggregated data are combined... The system performs associated archiving processing, using version identifiers, index binding, and time-series chained storage to obtain full-cycle business cost management data for associating with full-cycle business cost data. This enhances the standardization of cost calculation starting points and the accuracy of multi-dimensional reference information fusion and calibration; improves the compatibility of cost data with budget preparation rules and historical execution data; enhances the standardization and data consistency of cost calculation; strengthens the objectivity of business cost data calculation; enhances the dynamic linkage capability of cost data across different project stages and the reliability of cross-cycle comparative analysis; and improves the efficiency of full-lifecycle cost data calculation and processing and version traceability.

[0146] In some embodiments, the second processing module 402 is specifically used to: perform engineering quantity accounting processing on the aggregated dataset to obtain engineering quantity accounting data; perform quantity-price matching processing on the engineering quantity accounting data and the aggregated dataset to obtain quantity-price matching data; and perform frequency aggregation processing on the quantity-price matching data and the aggregated dataset to obtain initial business cost data.

[0147] In some embodiments, the third processing module 403 is specifically used to: perform indicator verification processing on the aggregated dataset to obtain indicator verification data; perform deviation comparison processing on the indicator verification data and the initial business cost data to obtain deviation comparison data; and perform parameter recalculation processing on the initial business cost data based on the deviation comparison data to obtain corrected business cost data.

[0148] In some embodiments, the process of recalculating parameters of the initial business cost data based on the deviation comparison data to obtain corrected business cost data is specifically used as follows: threshold determination processing is performed on the deviation comparison data to obtain a threshold determination result; the initial business cost data is then subjected to hierarchical correction processing based on the threshold determination result to obtain hierarchical corrected data; and convergence verification processing is performed on the hierarchical corrected data to obtain corrected business cost data.

[0149] In some embodiments, the fourth processing module 404 is specifically used to: split the modified business cost data based on a preset period to obtain period split data; perform item mapping processing on the period split data based on preset dimension division data to obtain item mapping data; and perform numerical adaptation processing on the item mapping data to obtain target business cost data.

[0150] In some embodiments, the fifth processing module 405 is specifically used to: perform version identification processing on the target business cost data to obtain version identification data; perform index binding processing on the version identification data and the aggregated dataset to obtain index binding data; and perform time-series chained storage processing on the index binding data to obtain full-cycle business cost management data.

[0151] In some embodiments, the first processing module 401 is specifically used to perform field validation processing on the business data to be processed to obtain field validation data; perform missing data completion processing on the field validation data to obtain missing data; and perform structured encapsulation processing on the missing data to obtain aggregated dataset.

[0152] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this disclosure.

[0153] Figure 5 This is a schematic diagram of the electronic device 5 provided in an embodiment of this disclosure. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program 503, it implements the steps in the various method embodiments described above. Alternatively, when the processor 501 executes the computer program 503, it implements the functions of each module / unit in the various device embodiments described above.

[0154] Electronic device 5 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 5 may include, but is not limited to, processor 501 and memory 502. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown, or different components.

[0155] The processor 501 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0156] The memory 502 can be an internal storage unit of the electronic device 5, such as a hard disk or RAM of the electronic device 5. The memory 502 can also be an external storage device of the electronic device 5, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 5. The memory 502 can also include both internal and external storage units of the electronic device 5. The memory 502 is used to store computer programs and other programs and data required by the electronic device.

[0157] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0158] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a readable storage medium (e.g., a computer-readable storage medium). Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0159] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A method for managing business cost data, characterized in that, include: The business data to be processed is aggregated and processed to obtain an aggregated dataset. The collected dataset is subjected to benchmark quantization to obtain initial business cost data; The initial business cost data and the aggregated dataset are subjected to correction and quantification processing to obtain corrected business cost data. The revised business cost data is subjected to budget adaptation processing to obtain the target business cost data; The target business cost data and the aggregated dataset are associated and archived to obtain full-cycle business cost management data, wherein the full-cycle business cost management data is used to associate the full-cycle business cost data.

2. The business cost data management method according to claim 1, characterized in that, The step of performing benchmark quantization on the aggregated dataset to obtain initial business cost data includes: The collected dataset is processed to calculate the engineering quantities, resulting in engineering quantity calculation data. The quantity calculation data and the aggregated dataset are subjected to quantity-price matching processing to obtain quantity-price matching data; The initial business cost data is obtained by frequency aggregation of the quantity-price matching data and the aggregated dataset.

3. The business cost data management method according to claim 1, characterized in that, The step of performing corrected quantization processing on the initial business cost data and the aggregated dataset to obtain corrected business cost data includes: The aggregated dataset is subjected to indicator verification processing to obtain indicator verification data; The deviation comparison data is obtained by comparing the verified data of the indicators with the initial business cost data. Based on the deviation comparison data, the initial business cost data is recalculated to obtain the corrected business cost data.

4. The business cost data management method according to claim 3, characterized in that, The step of recalculating the parameters of the initial business cost data based on the deviation comparison data to obtain the corrected business cost data includes: The deviation comparison data is subjected to threshold determination processing to obtain the threshold determination result; Based on the threshold determination result, the initial business cost data is subjected to hierarchical correction processing to obtain hierarchical corrected data; The graded correction data is subjected to convergence verification processing to obtain the corrected business cost data.

5. The business cost data management method according to claim 1, characterized in that, The process of performing budget adaptation on the revised business cost data to obtain target business cost data includes: The revised business cost data is split based on a preset period to obtain period-split data; The periodic split data is divided into items based on a preset dimension to obtain itemized mapping data. The itemized mapping data is subjected to numerical adaptation processing to obtain the target business cost data.

6. The business cost data management method according to claim 1, characterized in that, The step of associating and archiving the target business cost data and the aggregated dataset to obtain full-cycle business cost management data includes: The target business cost data is processed to obtain version identification data; The version identifier data and the aggregated dataset are indexed and bound together to obtain index-bound data; The index-bound data is processed using time-series chained storage to obtain the full-cycle business cost management data.

7. The business cost data management method according to claim 1, characterized in that, The process of aggregating the business data to be processed to obtain a aggregated dataset includes: The data to be processed is subjected to field validation to obtain field validation data; The field validation data is processed to complete the missing data, resulting in the completed data. The missing data is structured and encapsulated to obtain the aggregated dataset.

8. A business cost data management device, characterized in that, include: The first processing module is used to aggregate the business data to be processed and obtain the aggregated dataset. The second processing module is used to perform benchmark quantization processing on the aggregated dataset to obtain initial business cost data. The third processing module is used to perform correction and quantification processing on the initial business cost data and the aggregated dataset to obtain corrected business cost data. The fourth processing module is used to perform budget adaptation processing on the revised business cost data to obtain the target business cost data. The fifth processing module is used to associate and archive the target business cost data and the aggregated dataset to obtain full-cycle business cost management data, wherein the full-cycle business cost management data is used to associate the full-cycle business cost data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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