A method and device for evaluating the value of data assets of postal service data potential

Abstract

This application provides a method, apparatus, equipment, and medium for assessing the value of data assets related to the potential energy of delivery and postal service data. The method includes: determining the historical actual cost of delivery and postal service data assets through weighted summation based on preset assessment indicators; constructing an evaluation system adapted to the characteristics of structured delivery and postal service data to evaluate the quality of the data, and calculating and determining the data quality factor coefficients; considering the breadth and depth of application of the delivery and postal service data assets, and combining dynamic adaptation mechanism parameters to determine the marginal increasing effect of scenarios, i.e., obtaining dynamic application dimension factors; determining macro-market dimension factors through the average return on investment in the delivery and postal service industry; finally, using the data quality factor coefficients to correct the historical actual cost to determine the effective cost value; and calculating and determining the data asset valuation value based on the dynamic application dimension factors, macro-market dimension factors, and effective cost value. This method fills the gap in a high-quality data assessment system for the delivery and postal service industry.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method, apparatus, equipment, and medium for assessing the value of data assets related to the potential energy of postal and express delivery business data. Background Technology

[0002] The rise of data asset valuation is the result of a concerted effort from four parties: policy (top-level design), market (circulation pricing), technology (capability support), and enterprises (management compliance), all aimed at achieving effective enterprise asset management. Current data asset valuation methods are mainly divided into three categories: cost approach, market approach, and income approach. However, they suffer from the following problems: Cost method: It only reflects the historical input costs, ignores the incremental business value of delivery data in multiple stages such as collection, transit and delivery, and the indirect costs of delivery data development and storage are difficult to allocate, and the relationship between cost and value is weak. Market approach: Relies on mature and active data trading markets, but the data trading markets in the current sub-sectors of the express delivery industry are not yet formed, and there are few similar data asset trading cases for reference, making it extremely impractical; Income Approach: The revenue boundaries of delivery data are vague, and its driving value for scenarios such as route optimization, promotional support, and customer service is difficult to quantify and predict. The setting of discount rate and revenue period depends on subjective judgment, and the reliability of the evaluation results is insufficient. Lack of industry compatibility: General data quality assessment tools are not well-suited to the delivery business scenario, failing to cover the core quality dimensions of delivery data and unable to meet the assessment needs of most typical business scenarios.

[0003] In view of this, there is an urgent need to provide a method for evaluating high-quality data in the delivery industry that covers the core quality of delivery data. Summary of the Invention

[0004] To overcome the problems existing in the related technologies, this disclosure provides a method, apparatus, equipment and medium for assessing the value of data assets based on the potential energy of postal service data, so as to solve the technical problems in the related technologies.

[0005] This specification provides one or more embodiments of a data asset valuation method based on the data potential of a delivery service, including the following steps: The historical actual cost of delivery business data assets is determined by weighted summation based on preset evaluation indicators.

[0006] Construct an evaluation system for assessing the quality of delivery business data that is adapted to the characteristics of structured delivery data, and then calculate and determine the data quality factor coefficient of delivery business data based on the evaluation data quality indicators. Taking into account the breadth and depth of application of postal data assets, and combining the parameters of the dynamic adaptation mechanism, the marginal incremental effect of the scenario is determined, that is, the dynamic application dimension factor is obtained. Determine the average return on investment in the delivery industry, i.e., obtain macro market dimension factors; The effective cost value is determined by correcting historical actual costs using data quality factor coefficients. The valuation of data assets in the delivery business is determined by dynamically applying dimensional factors and effective cost value calculations based on data quality factor coefficients.

[0007] This specification provides one or more embodiments of a data asset value assessment device for the data potential of a delivery service, comprising: The historical actual cost calculation module is used to determine the historical actual cost of delivery business data assets by weighted summation based on preset evaluation indicators.

[0008] The quality factor calculation module is used to construct an evaluation system for evaluating the quality of delivery business data that is adapted to the characteristics of structured delivery data, and then calculate and determine the data quality factor coefficient of delivery business data based on the evaluation data quality indicators. The marginal increasing effect determination module is used to comprehensively consider the application breadth and depth of postal data assets, and combine the parameters of the dynamic adaptation mechanism to determine the marginal increasing effect of the scenario, that is, to obtain the dynamic application dimension factor. The average return on investment determination module is used to determine the average return on investment in the express delivery industry, that is, to obtain macro market dimension factors. The Historical Actual Cost Correction Module is used to correct historical actual costs using data quality factor coefficients to determine the effective cost value.

[0009] The asset valuation module is used to determine the valuation of delivery business data assets by dynamically applying dimensional factors and effective cost value calculations based on data quality factor coefficients.

[0010] This specification provides a computer device according to one or more embodiments, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the data asset value assessment method for the potential energy of postal service data as described above.

[0011] This specification provides one or more embodiments of a computer-readable storage medium storing a computer program that, when executed by a processor, implements the data asset valuation method for the potential data energy of postal services as described above.

[0012] This disclosure provides a data asset value assessment method, apparatus, equipment, and medium for the potential energy of delivery business data. Its advantages lie in its deep compliance with the national standard "Information Technology Big Data Data Asset Value Assessment" (GB / T46353—2025), providing a compliant and adaptable technical solution for industry data asset value assessment. Based on preset evaluation indicators and a system, it accurately determines the historical actual cost and data quality factor coefficients of delivery business data assets. Simultaneously, it introduces a hyperparameter dynamic adaptation mechanism to dynamically capture and quantify the marginal increasing effect of data in specific delivery scenarios, dynamically reflecting the application value of business scenarios. Combining dynamic application dimension factors, macro market dimension factors, and effective cost value, it ultimately determines the assessed value of delivery business data assets. This method fills the gap in a high-quality data assessment system for the delivery industry, overcomes the core bottleneck of difficulty in quantifying data value, breaks through the limitations of traditional reliance on expert experience and qualitative judgment, and establishes a quantitative analysis and calculation system entirely based on collectable, calculable, and verifiable indicators. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, 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 recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart illustrating a data asset valuation method for the data potential of a delivery service, provided for one or more embodiments of this specification; Figure 2 This specification provides a data quality factor coefficient dimension index system for delivery services, which is provided in one or more embodiments. Figure 3 A block diagram of a data asset value assessment device for the data potential of a delivery service, provided for one or more embodiments of this specification; Figure 4 This is a schematic diagram of the structure of a computer device provided for one or more embodiments of this specification. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this invention.

[0016] The present invention will now be described in detail with reference to specific embodiments and accompanying drawings.

[0017] Method Implementation Examples According to embodiments of the present invention, a method for assessing the value of data assets based on the potential energy of delivery business data is provided, such as... Figure 1 The diagram shown is a flowchart of the data asset value assessment method for the data potential energy of delivery services provided in this embodiment. The data asset value assessment method for the data potential energy of delivery services according to this embodiment includes the following steps: Step S1: Determine the historical actual cost C of the delivery business data assets by weighted summation based on preset evaluation indicators; Step S2: Construct an evaluation system adapted to the characteristics of structured data in the delivery business to assess the quality of delivery business data, and then calculate and determine the data quality factor coefficient of the delivery business data based on the evaluation data quality indicators. ; Step S3: Taking into account the breadth and depth of application of postal data assets, and combining the parameters of the dynamic adaptation mechanism, determine the marginal increasing effect h of the scenario, that is, obtain the dynamic application dimension factor. Step S4: Determine the average return on investment in the delivery industry, i.e., obtain the macro market dimension factor g; Step S5: Use the data quality factor coefficient to correct the historical actual cost and determine the effective cost value m; Step S6: Calculate and determine the valuation of the delivery business data assets based on dynamic application dimension factors, macro market dimension factors, and effective cost value.

[0018] The data asset value assessment method for the potential energy of delivery business data provided in this embodiment accurately determines the historical actual cost and data quality factor coefficients of delivery business data assets based on preset evaluation indicators and evaluation system. At the same time, it introduces hyperparameter dynamic adaptation mechanism parameters, which can dynamically capture and quantify the marginal increasing effect of data in specific delivery scenarios, realize the dynamic reflection of the application value of business scenarios, and finally determine the value assessment value of delivery business data assets by combining dynamic application dimension factors, macro market dimension factors, and effective cost value. The method of this embodiment fills the gap in the high-quality data assessment system of the delivery industry, breaks through the core bottleneck of the difficulty in quantifying data value, breaks the limitations of traditional reliance on expert experience and qualitative judgment, and establishes a quantitative analysis and calculation system based entirely on collectable, calculable, and verifiable indicators.

[0019] In step S1 of this embodiment, the evaluation indicators include, but are not limited to, one or more cost indicators selected from data planning cost, acquisition cost, governance cost, modeling cost, service cost, operation and maintenance cost, storage cost, and security cost. This embodiment focuses on express logistics data and constructs a cost management design, including: Standardization of development workload: The customized development of the "Evaluation Standard for Data Development and Service Workload of Postal and Express Delivery Business" breaks down the complex data development work into more than 60 standardized execution units. The benchmark for man-day workload is set with reference to the industry's common efficiency level. Combined with accounting records and statistical survey data, various costs are accurately calculated and summed.

[0020] Refined Modeling of Storage Costs: Drawing on Gartner's data center cost model and cloud vendors' billing logic, a customized "Data Center Unit Data Storage Equipment Cost Estimation Model" was developed, enabling accurate estimation and allocation of data storage costs.

[0021] This embodiment describes an evaluation system for assessing the quality of delivery business data, adapted to the characteristics of structured delivery data. This system includes primary indicators such as accuracy, completeness, timeliness, standardization, consistency, and uniqueness, as well as twelve secondary indicators. A customized indicator library for delivery business data quality has been developed, forming a deeply customized evaluation system for the express delivery and logistics industry. For details, please refer to [link / reference]. Figure 2 As shown, it adapts to over 90% of typical delivery business scenarios. The primary indicators and twelve secondary indicators are as follows: Accuracy: This includes the accuracy of key information and the accuracy of business logic, with a focus on adapting to the verification requirements of core data such as waybill information and routing information; Completeness: This includes the completeness of data records and the completeness of data fields, covering key elements such as waybill number, mailing address, and contact information; Timeliness: This includes the timeliness of data generation and the timeliness of data processing; Standardization: This includes format standardization and value range standardization, which are measured by whether the dataset naming conforms to the postal and express delivery industry standards, the percentage of fields that meet the postal and express delivery field naming standards, and the percentage of data that conforms to the postal and express delivery data content standards, respectively. Consistency: Primarily measured by cross-system / table consistency and statistical data consistency.

[0022] Uniqueness: Primarily measured by the uniqueness of the primary key in the master data table and the uniqueness of the combination of core fields.

[0023] The AHP algorithm is then used to determine the weights of each dimension and its subordinate indicators. Based on the weights of the primary and secondary indicators of each indicator, the data quality factor coefficient is calculated. The calculation formula is as follows:

[0024] in, For the first The weight of each primary indicator; For the first The first primary indicator The weights of each secondary indicator; For the first The first primary indicator The scores of each secondary indicator.

[0025] In step S3 of this embodiment, the application dimension factor reflects the application value of data assets in business scenarios. It comprehensively considers the breadth and depth of application of data assets. For example, the application breadth covers scenarios such as pickup / transfer / delivery / signing. The application depth considers the intensity of business driving force. The dynamic quantitative model of business value, "Business Value Growth Momentum of Structured Data in Postal and Express Delivery Business", is developed. Referring to the nonlinear combination model proposed by PwC, the core meaning is that the number of multidimensional application scenarios and the economic value of data assets usually grow nonlinearly, and the marginal utility increases. That is, the value of data assets shows a rapid nonlinear trend of growth as the number of application scenarios increases. Combined with the actual application of data assets, the concept of "hyperparameter" is innovatively introduced, which can dynamically capture and quantify the marginal increasing effect of data in specific postal and express delivery scenarios, and then calculate h, so as to more scientifically measure the actual driving force of data on business operations and decision-making. The marginal increasing effect is calculated using the following formula:

[0026] in, It is the marginal utility index. Premium cap coefficient, control coefficient range.

[0027] In this embodiment, step S4, the average return on investment (g) of the postal and express delivery industry is calculated by dividing the total revenue from intangible assets of the postal group by the total amount of intangible assets of the postal group. If relevant data is lacking, the average return on investment of intangible assets in the industry can be used as a substitute.

[0028] In one embodiment, a data quality factor coefficient is used to correlate historical actual costs. C To correct for deviations and determine the effective cost value, follow the formula below: m = C × ; In this embodiment, the final valuation of the delivery business data asset is determined based on the obtained application dimension factor h, the dynamic application dimension factor, and the effective cost value, as shown in the following formula: V = mgh .

[0029] In this embodiment, the data quality factor coefficient The model corrects for historical cost biases in delivery data, dynamically reflects the application value of business scenarios using the application dimension factor h, and accurately anchors industry average return on investment g to the industry benchmark. Referring to the evaluation framework of the latest national standard "Information Technology Big Data Data Asset Valuation GB / T46353—2025," the model incorporates data quality and application elements from its data evaluation section as factors for determining the valuation of delivery business data assets. These two elements are then quantified and incorporated into the model as adjustment coefficients.

[0030] The data asset value assessment method for the potential energy of delivery business data provided by this invention has the following beneficial effects: (1) Foresight: Leading the industry's data asset management technology upgrade and filling technological gaps in niche areas. The project's independently developed "Data Potential Energy" model is deeply aligned with the national standard "Information Technology Big Data Data Asset Valuation" (GB / T46353—2025), providing a compliant and adaptable technical solution for industry data asset valuation. It not only possesses advanced technology but also ensures compliance, providing a practical path for the industry to implement the marketization of data elements.

[0031] (2) Industry universality: It fills the gap in the high-quality data evaluation system of the express delivery industry. Through the independent development of the "Customized Index Library of Data Quality Dimensions of Express Delivery Business", it has for the first time built a structured data quality evaluation framework covering more than 90% of business scenarios, which solves the problem of general data quality tools being "unsuitable" in logistics scenarios and provides the industry with a reusable and scalable data governance infrastructure.

[0032] (3) Quantifiability: It breaks through the core bottleneck of the difficulty in quantifying the value of data. It breaks away from the limitations of traditional reliance on expert experience and qualitative judgment, and establishes a quantitative analysis system based entirely on collectable, calculable and verifiable indicators.

[0033] (4) Multidimensional integration: For the first time in the industry, the data evaluation dimension and the monetary value dimension are organically integrated. By constructing the "data potential energy" model, the system integrates three core elements: the cost of the entire data lifecycle, the data quality level, and the business application value.

[0034] Device Examples According to embodiments of the present invention, a data asset value assessment device for the data potential of postal and express delivery services is provided, such as... Figure 3 The diagram shown is a block diagram of a data asset value assessment device for delivery business data potential provided in this embodiment. The data asset value assessment device for delivery business data potential according to this embodiment includes: The historical actual cost calculation module 10 is used to determine the historical actual cost C of the delivery business data assets by weighted summation based on preset evaluation indicators; The quality factor calculation module 20 is used to construct an evaluation system for assessing the quality of delivery business data that is adapted to the characteristics of structured delivery data, and then calculates and determines the quality factor coefficients of the delivery business data based on the evaluation data quality indicators. ; The marginal increasing effect determination module 30 is used to comprehensively consider the application breadth and depth of postal data assets, and combine the parameters of the dynamic adaptation mechanism to determine the scenario marginal increasing effect h, that is, to obtain the dynamic application dimension factor. The Average Return on Investment (ROI) Determination Module 40 is used to determine the average ROI of the express delivery industry, i.e., to obtain macroeconomic market dimension factors. The historical actual cost correction module 50 is used to correct historical actual costs using data quality factor coefficients to determine the effective cost value m.

[0035] The asset valuation module 60 is used to determine the valuation of postal service data assets based on dynamic application dimension factors, macro market dimension factors, and effective cost value.

[0036] The data asset value assessment method for the potential energy of delivery business data provided in this embodiment accurately determines the historical actual cost and quality factor coefficients of delivery business data assets based on preset evaluation indicators and evaluation system. At the same time, it introduces hyperparameter dynamic adaptation mechanism parameters, which can dynamically capture and quantify the marginal increasing effect of data in specific delivery scenarios, realize the dynamic reflection of the application value of business scenarios, and finally determine the value assessment value of delivery business data assets by combining dynamic application dimension factors, macro market dimension factors, and effective cost value. This method fills the gap in the high-quality data assessment system of the delivery industry, breaks through the core bottleneck of the difficulty in quantifying data value, breaks the limitations of traditional reliance on expert experience and qualitative judgment, and establishes a quantitative analysis and calculation system based entirely on collectable, calculable, and verifiable indicators.

[0037] The embodiments of the present invention are device embodiments corresponding to the above method embodiments. The specific operations of each module processing step can be understood with reference to the description of the method embodiments, and will not be repeated here.

[0038] like Figure 4 As shown, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the data asset value assessment method for the potential energy of postal service data in the above embodiments.

[0039] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the data asset value assessment method for the potential energy of postal service data in the above embodiments. When the computer program is executed by the processor, it implements the following method steps: Step S1: Determine the historical actual cost of delivery business data assets by weighted summation based on preset evaluation indicators.

[0040] Step S2: Construct an evaluation system for evaluating the quality of delivery business data that is adapted to the characteristics of structured delivery data, and then calculate and determine the data quality factor coefficient of delivery business data based on the evaluation data quality indicators. Step S3: Taking into account the breadth and depth of application of postal data assets, and combining the parameters of the dynamic adaptation mechanism, determine the marginal increasing effect of the scenario, that is, obtain the dynamic application dimension factor. Step S4: Determine the average return on investment in the delivery industry, i.e., obtain the macro market dimension factor; Step S5: Use the data quality factor coefficient to correct the historical actual cost and determine the effective cost value.

[0041] Step S6: Based on the data quality factor coefficient, dynamically apply the dimensional factors and effective cost value to calculate and determine the valuation of the data assets for the delivery business.

[0042] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0043] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0044] Furthermore, the functional modules in the various embodiments of this disclosure 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 in the form of hardware plus software functional modules.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention 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 or all of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and the contents not described in detail in the specification of the present invention are well known to those skilled in the art.

Claims

1. A method for assessing the value of data assets based on the potential energy of delivery business data, characterized in that, Includes the following steps: The historical actual cost of delivery business data assets is determined by weighted summation based on preset evaluation indicators; Construct an evaluation system for assessing the quality of delivery business data that is adapted to the characteristics of structured delivery data, and then calculate and determine the data quality factor coefficient of delivery business data based on the evaluation data quality indicators. Taking into account the breadth and depth of application of postal data assets, and combining the parameters of the dynamic adaptation mechanism, the marginal incremental effect of the scenario is determined, that is, the dynamic application dimension factor is obtained. Determine the average return on investment in the delivery industry, i.e., obtain macro market dimension factors; The effective cost value is determined by correcting historical actual costs using data quality factor coefficients. The valuation of data assets in the delivery business is determined based on dynamic application dimension factors, macro market dimension factors, and effective cost value.

2. The data asset value assessment method for the data potential of postal and express delivery services as described in claim 1, characterized in that, The preset evaluation indicators include any one or more of the following: data planning cost, acquisition cost, governance cost, modeling cost, service cost, operation and maintenance cost, storage cost, and security cost.

3. The data asset value assessment method for the data potential of postal and express delivery services as described in claim 1, characterized in that, The evaluation system for assessing the quality of delivery service data includes six primary indicators and twelve secondary indicators. The primary indicators include accuracy, completeness, timeliness, standardization, consistency, and uniqueness. Accuracy includes the accuracy of key information and the accuracy of business logic; Integrity includes the integrity of data records and the integrity of data fields; Timeliness includes both the timeliness of data generation and the timeliness of data processing; Standardization includes format standardization and value range standardization; Consistency includes cross-system / table consistency and statistical data consistency; Uniqueness includes the uniqueness of the primary key in the master data table and the uniqueness of the combination of core fields.

4. The data asset value assessment method based on the data potential of postal and express delivery services as described in claim 1, characterized in that, The marginal increasing effect of the scenario is specifically calculated as follows: in, It is the marginal utility index. Premium cap coefficient, control coefficient range.

5. The data asset value assessment method for the data potential of postal and express delivery services as described in claim 1, characterized in that, The method of using data quality factor coefficients to correct for deviations in historical actual costs and determine the effective cost value is as follows: m = C × ； Where C represents the historical actual cost. This represents the data quality factor coefficient.

6. A data asset value assessment device for the data potential energy of postal and express delivery services, characterized in that, include: The historical actual cost calculation module is used to determine the historical actual cost of delivery business data assets by weighted summation based on preset evaluation indicators; The quality factor calculation module is used to construct an evaluation system for evaluating the quality of delivery business data that is adapted to the characteristics of structured delivery data, and then calculate and determine the data quality factor coefficient of delivery business data based on the evaluation data quality indicators. The marginal increasing effect determination module is used to comprehensively consider the application breadth and depth of postal data assets, and combine the parameters of the dynamic adaptation mechanism to determine the marginal increasing effect of the scenario, that is, to obtain the dynamic application dimension factor. The average return on investment (ROI) determination module is used to determine the average ROI of the delivery industry; that is, to obtain macroeconomic market dimension factors. The historical actual cost correction module is used to correct historical actual costs using data quality factor coefficients to determine the effective cost value. The asset valuation module is used to determine the valuation of delivery business data assets by dynamically applying dimensional factors and effective cost value calculations based on data quality factor coefficients.

7. The data asset value assessment device for the data potential energy of postal services as described in claim 6, characterized in that, The evaluation system for assessing the quality of delivery service data includes six primary indicators and twelve secondary indicators. The primary indicators include accuracy, completeness, timeliness, standardization, consistency, and uniqueness. Accuracy includes the accuracy of key information and the accuracy of business logic; Integrity includes the integrity of data records and the integrity of data fields; Timeliness includes both the timeliness of data generation and the timeliness of data processing; Standardization includes format standardization and value range standardization; Consistency includes cross-system / table consistency and statistical data consistency; Uniqueness includes the uniqueness of the primary key in the master data table and the uniqueness of the combination of core fields.

8. The data asset value assessment device for the data potential energy of postal services as described in claim 6, characterized in that, The marginal increasing effect of the scenario is specifically calculated as follows: in, It is the marginal utility index. Premium cap coefficient, control coefficient range.

9. A computer 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 data asset value assessment method for the data potential of postal services as described in any one of claims 1 to 5.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the data asset value assessment method for the data potential of postal services as described in any one of claims 1 to 5.

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