Digital transformation evaluation method for small and medium-sized enterprises

Abstract

The invention provides a small and medium-sized enterprise digital transformation evaluation method, which comprises the following steps of: S11, analyzing and identifying regularly acquired small and medium-sized enterprise digital transformation evaluation files to obtain quantifiable multi-dimensional evaluation indexes; s12, constructing an evaluation index matching model based on the multi-dimensional evaluation indexes, and further constructing a smart contract architecture; s13, regularly collecting and analyzing the successful cases of the digital transformation of the small and medium-sized enterprises, performing scoring guidance for the quantifiable indexes corresponding to each successful case, and constructing a grade evaluation model; s14, determining quantifiable indexes corresponding to the small and medium-sized enterprises in transformation, deploying corresponding smart contracts in internal business systems of the small and medium-sized enterprises, and collecting key transformation data; and S15, analyzing the key transformation data through the grade evaluation model, and outputting a visual evaluation report. According to the invention, comprehensive and accurate quantitative evaluation of the digital transformation degree of small and medium-sized enterprises is realized, the objectivity, efficiency and credibility of evaluation are improved, and a user is helped to know the current situation of enterprise transformation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent evaluation, and in particular to a small and medium-sized enterprise digital transformation evaluation method. BACKGROUND

[0002] Enterprise digital transformation aims to comprehensively upgrade core links such as business model, business process, employee collaboration and customer service, in order to cope with market competition and changes in customer demand. It is essential to carry out transformation evaluation, which can clearly present the current situation of enterprise transformation (achievements, shortcomings, opportunities and challenges), position the industry competition status to guide strategic planning, and accurately identify the advantages and shortcomings of organization, process and technology, providing decision-making basis for subsequent optimization.

[0003] In the process of small and medium-sized enterprise digital transformation, there are problems such as non-uniform evaluation standards and inaccurate effect measurement. Traditional evaluation methods mainly rely on static files and experts' subjective experience, which lack objective and quantifiable index system and are difficult to dynamically adapt to changes in policy and market. Due to the use of general indicators, it is often difficult to accurately match the specific transformation needs and business scenarios of enterprises in different industries, scales and development stages. In terms of data collection, traditional manual reporting and third-party research methods are inefficient and costly, and there is a risk of data authenticity and commercial information leakage. At the same time, the evaluation results often only stay at the level of simple index scoring, lack of in-depth interpretation of actual business significance, and lack of horizontal comparison with successful cases, making it difficult for enterprises to develop specific improvement direction and target based on this. In addition, the cumbersome evaluation process and lengthy report form make it difficult for decision-makers to quickly extract core information, seriously affecting the actual application value of evaluation. SUMMARY

[0004] The purpose of the present application is to provide a small and medium-sized enterprise digital transformation evaluation method, which solves the problems of non-uniform evaluation standards, difficult data acquisition and low credibility, and inefficient process in small and medium-sized enterprise digital transformation evaluation, and realizes comprehensive and accurate quantitative evaluation of small and medium-sized enterprise digital transformation degree.

[0005] To achieve the above purpose, the present application provides a small and medium-sized enterprise digital transformation evaluation method, which comprises: S11, periodically obtaining an evaluation file of small and medium-sized enterprise digital transformation, analyzing and identifying the evaluation file, constructing an evaluation index analysis model, and outputting quantifiable multi-dimensional evaluation indexes; S12, constructing an evaluation index matching model for different types of small and medium-sized enterprise digital transformation based on the multi-dimensional evaluation indexes, and constructing an intelligent contract architecture based on the evaluation index matching model; S13, collect historical successful cases of digital transformation of different types of small and medium-sized enterprises regularly, determine the quantifiable indicators corresponding to the successful cases through the evaluation indicator matching model, analyze the successful cases, score and guide each corresponding quantifiable indicator, and build a level evaluation model; S14, determine the quantifiable indicators corresponding to the small and medium-sized enterprises in digital transformation through the evaluation indicator matching model, deploy the corresponding smart contract in the internal business system of the small and medium-sized enterprise, and collect key transformation data; S15, analyze the key transformation data through the level evaluation model, determine the transformation level of the digital transformation of the small and medium-sized enterprise, and output a visual evaluation report.

[0006] Further, in step S11, the evaluation indicator analysis model is built, specifically including: Periodically obtain the evaluation indicator file of the digital transformation of small and medium-sized enterprises from official information sources; Use natural language processing technology to extract a plurality of feature information related to digital transformation in the evaluation indicator file, pre-process the plurality of feature information, remove stop words and repeated words, and screen quantifiable and non-quantifiable keywords according to the part of speech; Through context analysis, it is confirmed whether the non-quantifiable keywords have quantification function, if they have quantification function, the non-quantifiable keywords are converted to quantifiable keywords after quantification, and then divided into quantifiable keywords; The frequency of each quantifiable keyword in the text is counted, and the word rule is constructed based on the quantifiable keywords and their corresponding word frequencies; Based on the word rule, the word vector of the keyword is calculated, the cosine angle of the word vector is used to measure the semantic similarity, the synonyms are screened, and a synonym library is constructed; Based on the word rule and the synonym library, the language model is pre-trained to build the evaluation indicator analysis model.

[0007] Further, in step S12, the evaluation indicator matching model for digital transformation of different types of small and medium-sized enterprises is built based on multi-dimensional evaluation indicators, specifically including: Collect sample data of historical small and medium-sized enterprises, including the type of small and medium-sized enterprises and the evaluation indicators corresponding to each small and medium-sized enterprise, and pre-process the sample data; Identify the type of each historical small and medium-sized enterprise in the sample data, and output the type feature set corresponding to each small and medium-sized enterprise; Take the type features in several type feature sets and the corresponding evaluation indicators as nodes, and the relationship between the type features and the evaluation indicators as edges to construct an index matching graph database; Learn the matching rules in the index matching graph database through a machine learning algorithm to build an evaluation indicator matching model.

[0008] Further, in step S12, the smart contract architecture is constructed based on the evaluation index matching model, specifically including: According to the evaluation index matching model, the feature vector and quantifiable index of the digital transformation of small and medium-sized enterprises are determined, and the dynamic adjustment mechanism of the weight of the quantifiable index is determined to obtain the input demand of the evaluation index matching model; The data collection module in the smart contract is designed, the data source and data format that need to be collected are determined according to the input demand of the evaluation index matching model, and the data verification logic is used to determine whether the collected data meets the input demand of the dynamic level evaluation model; The weight adjustment module in the smart contract is designed, the dynamic adjustment data collection rule of the weight in the evaluation index matching model is determined, and the data collection module is fed back.

[0009] Further, in step S13, scoring guidance is performed for each corresponding quantifiable index, specifically including: Qualitative data and quantitative data related to the corresponding quantifiable index of each successful case are extracted, and the qualitative data and quantitative data are preprocessed; A first scoring standard for the corresponding quantifiable index is determined through the preprocessed quantitative data; It is determined whether the preprocessed qualitative data is quantifiable, if quantifiable, it is converted into quantitative data, and a second scoring standard for the corresponding quantifiable index is determined; The first scoring standard and the second scoring standard are integrated based on the corresponding quantifiable index, and scoring guidance is performed for the corresponding quantifiable index.

[0010] Further, in step S14, the corresponding smart contract is deployed in the internal business system of the small and medium-sized enterprise, specifically including: The blockchain client software is installed in the internal business system of the small and medium-sized enterprise, the compiled smart contract is sent to the blockchain client software through the blockchain network, and the key transformation data is collected.

[0011] Further, the key transformation data is encrypted before being chained.

[0012] Further, the key transformation data is encrypted, specifically including: Obtain the key transformation data in each preset time period; Perform feature analysis on the key transformation data in each preset time period to generate a target encryption key corresponding to each preset time period; According to the target encryption key corresponding to each preset time period, the key transformation data in each preset time period is encrypted to obtain an encrypted ciphertext corresponding to each preset time period; All encrypted ciphertexts corresponding to the preset time periods are sequentially stored in the blocks in the blockchain.

[0013] Further, the key transformation data in each preset time period is subjected to feature analysis processing, specifically including: The target encryption key corresponding to the first preset time period is obtained through a key generation algorithm, and the preset time period other than the first preset time period is determined as a reference time period; According to the data amount, data update situation and data type of the key transformation data in the reference time period, the features of the key transformation data in the reference time period are analyzed and processed to obtain data feature factors corresponding to the reference time period; According to the data feature factors corresponding to the reference time period, a key derivation salt corresponding to the reference time period is generated, and the target encryption key corresponding to the reference time period is generated by key derivation on the target encryption key corresponding to the last preset time period of the reference time period according to the key derivation salt.

[0014] Compared with the prior art, the beneficial effects of the present application are: The small and medium-sized enterprise digital transformation evaluation method provided by the present application periodically acquires and analyzes evaluation files, automatically extracts quantifiable multi-dimensional evaluation indexes, ensures that the multi-dimensional evaluation indexes have the capabilities of objectivity, standardization and dynamic updating, constructs an evaluation index matching model, intelligently selects and matches the most relevant evaluation index set according to the specific characteristics of the enterprise, improves the pertinence and individualization of the evaluation, deploys an intelligent contract in the internal business system of the enterprise, automatically and safely collects key transformation data authorized by the enterprise, improves the data acquisition efficiency, real-time performance, security and credibility, constructs a level evaluation model based on successful case analysis, provides scoring guidance for each quantified index, outputs a level evaluation result with actual reference significance by collecting key transformation data through the level evaluation model, and directly presents and clearly shows the gap and improvement direction through a visual evaluation report, thereby improving the user decision-making efficiency. The present application realizes comprehensive and accurate quantitative evaluation of the digital transformation degree of small and medium-sized enterprises, improves the objectivity, efficiency and credibility of the evaluation, and helps users understand the enterprise transformation status. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings. Figure 1 A small and medium-sized enterprise digital transformation evaluation method flowchart provided by the present application. DETAILED DESCRIPTION

[0016] The application will be described in further detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the application and not to limit the application. In addition, it should be noted that, for the sake of description, only the parts related to the application are shown in the drawings and not all the structures.

[0017] With reference to Figure 1 The embodiment provides a small and medium-sized enterprise digital transformation evaluation method, and the method comprises the following steps: S11. Regularly obtaining an evaluation file of small and medium-sized enterprise digital transformation, performing analysis and identification on the evaluation file, constructing an evaluation index analysis model, and outputting quantifiable multi-dimensional evaluation indexes.

[0018] S12. Constructing an evaluation index matching model of different types of small and medium-sized enterprise digital transformation based on the multi-dimensional evaluation indexes, and constructing an intelligent contract architecture based on the evaluation index matching model.

[0019] S13. Regularly collecting successful cases of different types of small and medium-sized enterprise digital transformation, determining quantifiable indexes corresponding to the successful cases through the evaluation index matching model, analyzing the successful cases, scoring and guiding each corresponding quantifiable index, and constructing a level evaluation model.

[0020] S14. Determining quantifiable indexes corresponding to small and medium-sized enterprises in digital transformation through the evaluation index matching model, deploying corresponding intelligent contracts in the internal business system of the small and medium-sized enterprises, and collecting key transformation data.

[0021] S15. Analyzing the key transformation data through the level evaluation model, determining the transformation level of the small and medium-sized enterprise digital transformation, and outputting a visual evaluation report.

[0022] In the embodiment, policy files, industry white papers and enterprise self-evaluation reports published by relevant industry associations, government departments or professional institutions are collected, natural language processing and machine learning technologies are used to perform analysis and identification on the evaluation files, an evaluation index analysis model is constructed, and quantifiable multi-dimensional evaluation indexes are output. According to the multi-dimensional evaluation indexes, the digital transformation of different types of small and medium-sized enterprises is classified, an evaluation index matching model is constructed through a clustering algorithm or an expert rule base, and it is ensured that the evaluation index matching model can accurately match the specific needs of enterprises in different industries, scales and development stages. Based on the evaluation index matching model, an intelligent contract architecture is constructed, trigger conditions and execution rules of the intelligent contract are designed, and it is ensured that the intelligent contract can automatically perform related operations.

[0023] The success cases of digital transformation of different types of small and medium-sized enterprises are collected regularly by industry research, case library collection, etc. The quantifiable indicators corresponding to the success cases are determined by using the evaluation index matching model, and the success cases are analyzed in depth to score each indicator and guide the construction of a hierarchical evaluation model. The corresponding smart contract is deployed in the internal business system of the small and medium-sized enterprise to collect key transformation data in real time, and the hierarchical evaluation model is used to analyze the collected key transformation data to determine the transformation level of the digital transformation of the small and medium-sized enterprise, and generate an evaluation report to present the analysis results in a visual way to provide intuitive decision support for enterprise managers.

[0024] As a preferred embodiment, in step S11, the evaluation index analysis model is constructed, specifically including: Periodically obtain the evaluation index file of the digital transformation of small and medium-sized enterprises from official information sources.

[0025] Use natural language processing technology to extract a plurality of feature information related to digital transformation in the evaluation index file, pre-process the plurality of feature information, remove stop words and repeated words, and filter quantifiable and non-quantifiable keywords according to the part of speech.

[0026] Through context analysis, it is confirmed whether the non-quantifiable keywords have quantification function, if they have quantification function, the non-quantifiable keywords are converted to quantifiable keywords after quantifiable part of speech conversion, and then divided into quantifiable keywords.

[0027] The frequency of occurrence of each quantifiable keyword in the text is counted, and the word rule is constructed based on the quantifiable keywords and their corresponding word frequencies.

[0028] The word vector of the keyword is calculated based on the word rule, the cosine angle of the word vector is used to measure the semantic similarity, the synonyms are screened, and the synonym library is constructed.

[0029] Based on the word rule and the synonym library, the language model is pre-trained to construct the evaluation index analysis model.

[0030] In this embodiment, the key information in the evaluation index file is extracted through natural language processing technology, the feature information related to digital transformation is selected from a large amount of text, the stop words and repeated words are removed, and the keywords are selected according to the part of speech, so as to ensure that the extracted information is accurate and has practical significance, and to provide a reliable basis for subsequent quantitative analysis. Through context analysis, the originally unquantifiable keywords are converted into quantifiable parts of speech, the range of quantifiable indicators is expanded, and the evaluation system is more comprehensive and detailed. For example, "customer satisfaction" can be quantified through specific scoring or feedback data in the context, and "customer satisfaction" is converted into "customer satisfaction score". Based on the word rule and the synonym library, the pre-trained language model can be dynamically updated according to the new text data, so that the evaluation index analysis model has strong adaptability, and can timely reflect the influence of policy and market changes on the evaluation index of digital transformation. For example, using advanced natural language processing models such as BERT and GPT, it can automatically learn and adapt to new text data.

[0031] As a preferred embodiment, in step S12, an evaluation index matching model for digital transformation of different types of small and medium-sized enterprises is constructed based on multi-dimensional evaluation indexes, specifically including: Collecting sample data of historical small and medium-sized enterprises, including the types of small and medium-sized enterprises and the evaluation indexes corresponding to each small and medium-sized enterprise, and preprocessing the sample data.

[0032] Identifying the type of each historical small and medium-sized enterprise in the sample data, and outputting the type feature set corresponding to each small and medium-sized enterprise.

[0033] Taking the type features in several type feature sets and the corresponding evaluation indexes as nodes, and the relationship between the type features and the evaluation indexes as edges, an index matching graph database is constructed.

[0034] Learning the matching rules in the index matching graph database through a machine learning algorithm to construct an evaluation index matching model.

[0035] In this embodiment, sample data related to the digital transformation of small and medium-sized enterprises is collected from historical enterprise annual reports, industry reports, government statistics, and other data, including the types of enterprises and the evaluation indicators corresponding to each enterprise. The sample data is cleaned and preprocessed to remove noise data and missing values, ensuring data quality and consistency. Cluster algorithms or classification algorithms are used to identify the type characteristics of each historical small and medium-sized enterprise, extracting features related to enterprise type and business, such as enterprise size, industry sector, main business processes, etc. The output of each small and medium-sized enterprise corresponds to a set of type characteristics. For example, for manufacturing enterprises, features may include the number of production equipment and the degree of production process automation. The type characteristics in several type characteristic sets and the corresponding evaluation indicators are used as nodes, and the relationship between type characteristics and evaluation indicators is used as edges to construct an index matching graph database. Graph database technology is used to store and manage these nodes and edges, ensuring efficient querying and analysis of data. For example, nodes can be "manufacturing enterprises" and "production equipment automation rate," and edges can be "manufacturing enterprises - use - production equipment automation rate."

[0036] The graph structure data is processed by machine learning algorithms to learn the matching rules in the index matching graph database and extract features of nodes and edges. The evaluation index matching model is trained using historical sample data, and the matching relationship between different types of enterprises and evaluation indicators is learned through supervised learning. For example, input the type characteristics of an enterprise, and output the corresponding evaluation indicators. The performance of the evaluation index matching model is verified by cross-validation and other methods, and the model parameters are adjusted based on the verification results to optimize the model structure and improve the accuracy and generalization ability of the model. The trained evaluation index matching model is deployed in practical applications to match the evaluation indicators of new small and medium-sized enterprises, and new sample data is collected regularly to update and optimize the evaluation index matching model to maintain its accuracy and effectiveness.

[0037] As a preferred embodiment, in step S12, an intelligent contract architecture is constructed based on the evaluation index matching model, specifically including: According to the evaluation index matching model, the feature vector and quantifiable indicators of the digital transformation of small and medium-sized enterprises are determined, and a dynamic adjustment mechanism for the weight of the quantifiable indicators is determined to obtain the input requirements of the evaluation index matching model.

[0038] The data collection module in the smart contract is designed to clearly define the data sources and data formats required for collection based on the input requirements of the evaluation index matching model, and to determine whether the collected data meets the input requirements of the dynamic level evaluation model through data verification logic.

[0039] The weight adjustment module in the smart contract is designed to collect data according to the dynamic adjustment data collection rules of the weight in the evaluation index matching model and feedback to the data collection module.

[0040] In this embodiment, according to the evaluation index matching model, the feature vector of the digital transformation of small and medium-sized enterprises is extracted, including the type characteristics, business process characteristics, and technology application characteristics of the enterprise. Based on the feature vector, quantifiable indexes related to digital transformation are determined, such as the production equipment automation rate, data collection coverage rate, and customer satisfaction score. A dynamic weight adjustment mechanism is designed to dynamically adjust the weights of each quantifiable index according to market changes, policy adjustments, and enterprise development, so that the smart contract architecture can dynamically adjust according to market policy changes, improve the applicability and flexibility of the smart contract, and improve the accuracy and effectiveness of data collection. The data source and data format required for collection are clearly defined, and data verification logic is designed to ensure that the collected data meets the input requirements of the dynamic level evaluation model. The verification logic can include data integrity checks, data consistency checks, and data timeliness checks.

[0041] According to the weight dynamic adjustment rules in the evaluation index matching model, a weight adjustment module is designed in the smart contract. According to the data collection rules of the weight dynamic adjustment in the evaluation index matching model, the weight adjustment rules can be based on market changes, policy adjustments, and enterprise performance to establish a feedback mechanism between the weight adjustment module and the data collection module, ensuring that the data collection rules can be dynamically optimized according to the weight adjustment. For example, if the weight of a certain index increases, the data collection module can increase the collection frequency and accuracy of the data for that index. The data collection module and the weight adjustment module are integrated into the smart contract architecture to work together, and the smart contract is deployed in the enterprise's internal business system to collect key transformation data. The smart contract architecture is tested to verify its functions and performance, and optimized according to the test results to ensure that the smart contract architecture can efficiently and accurately support the evaluation of the enterprise's digital transformation.

[0042] As a preferred embodiment, in step S13, scoring guidance is performed for each corresponding quantifiable index, specifically including: Extracting qualitative and quantitative data related to each successful case and corresponding quantifiable index, and preprocessing the qualitative and quantitative data.

[0043] Determining a first scoring standard for the corresponding quantifiable index through the preprocessed quantitative data.

[0044] Evaluating whether the preprocessed qualitative data is quantifiable, and if so, converting it into quantitative data to determine a second scoring standard for the corresponding quantifiable index.

[0045] Integrating the first and second scoring standards based on the corresponding quantifiable index to perform scoring guidance for the corresponding quantifiable index.

[0046] In this embodiment, qualitative data and quantitative data related to each quantifiable indicator are extracted from the success case. Qualitative data may include customer feedback, employee evaluation, etc., and quantitative data may include specific numerical indicators, such as production efficiency, customer satisfaction score, etc. The extracted data is preprocessed, including data cleaning and data standardization, to ensure the quality and consistency of the data. By completing the preprocessed quantitative data, the first scoring standard for the corresponding quantifiable indicator is determined. For example, for the indicator "customer satisfaction score", the scoring range and standard can be determined based on historical data, such as 80 points or above for excellent, 70-79 points for good, etc. Evaluate whether the preprocessed qualitative data is quantifiable. For example, "very satisfied" in customer feedback can be converted into a high score, and "unsatisfied" can be converted into a low score.

[0047] If qualitative data is quantifiable, convert it into quantitative data and determine the secondary scoring criteria for the corresponding quantifiable indicators. For example, convert "very satisfied" in customer feedback to 90 points, "satisfied" to 80 points, and so on. Based on the corresponding quantifiable indicators, integrate the primary and secondary scoring criteria, and use this integrated scoring criteria to provide scoring guidance for the corresponding quantifiable indicators. Apply this scoring guidance to actual digital transformation assessments to help companies understand their performance across various indicators. Based on feedback from actual applications, continuously optimize the scoring guidance to ensure it consistently meets market and business development needs.

[0048] As a preferred embodiment, in step S14, deploying the corresponding smart contract in the internal business system of the SME specifically includes: Install blockchain client software in the internal business systems of small and medium-sized enterprises, send the compiled smart contracts to the blockchain client software through the blockchain network, and collect key transformation data.

[0049] In this embodiment, blockchain client software is installed within the internal business systems of small and medium-sized enterprises (SMEs), ensuring compatibility and proper operation of the software with the enterprise's business systems. Parameters of the blockchain client software, such as node addresses, network configuration, and permission settings, are configured based on the enterprise's specific needs. Based on the enterprise's business needs and the evaluation indicator matching model, smart contract code is written, including data collection logic and weight adjustment logic. Blockchain development tools are used to compile the smart contract code, generating a deployable smart contract file. The compiled smart contract is then sent to the blockchain client software via the blockchain network, completing the deployment of the smart contract and automatically executing data collection and processing tasks.

[0050] As a preferred embodiment, the key transformation data is encrypted before being uploaded to the chain.

[0051] In this embodiment, the sensitive information of the enterprise is protected by encryption before the key transformation data is chained, effectively preventing the data from being leaked or tampered during transmission and storage. Before the key transformation data is chained, the data is preprocessed to ensure that the data format meets the requirements of the encryption algorithm, and the selected encryption algorithm is used to encrypt the key transformation data. For example, the data is encrypted using the AES algorithm, and then the AES key is encrypted using the RSA algorithm. After encryption is completed, the integrity and correctness of the encrypted data are verified to ensure that the data has not been damaged or tampered with during encryption, and the encrypted data is stored on the blockchain, and an index is created for the encrypted data on the blockchain to facilitate subsequent query and retrieval.

[0052] As a preferred embodiment, the key transformation data is encrypted, specifically including: Obtain the key transformation data in each preset time period.

[0053] Perform feature analysis on the key transformation data in each preset time period to generate a target encryption key corresponding to each preset time period.

[0054] According to the target encryption key corresponding to each preset time period, the key transformation data in each preset time period is encrypted to obtain the encrypted ciphertext corresponding to each preset time period.

[0055] All encrypted ciphertexts corresponding to the preset time periods are sequentially stored in the blocks in the blockchain.

[0056] In this embodiment, the key transformation data in each preset time period is obtained by a smart contract, and the preset time period can be set according to specific needs, such as daily, weekly, or monthly. The key transformation data in each preset time period is analyzed and processed according to its type, size, content, etc. to determine the appropriate encryption strategy. According to the results of the feature analysis, a target encryption key corresponding to each preset time period is generated, and the AES key generation algorithm is used to generate the key. The key transformation data in each preset time period is encrypted using the generated target encryption key, and the integrity and correctness of the encrypted data are verified after encryption to ensure that the data has not been damaged or tampered with during encryption, such as comparing the hash values of the data before and after encryption using a hash algorithm.

[0057] Specifically, the key transformation data of the day is collected from the enterprise internal business system through the smart contract every month, including production efficiency, equipment status, customer satisfaction, etc. The collected data is analyzed for features, the data type and size are determined, the target encryption key of the day is generated using the AES key generation algorithm, the key transformation data of the day is encrypted using the generated key, and the integrity and correctness of the encrypted data are verified to ensure that the data is not damaged or tampered with during the encryption process. The encrypted data is formatted into JSON format, including the timestamp and hash value of the data, the encrypted data is stored in a new block in the blockchain, and an index is created for the block. When the data is needed, the corresponding key is obtained, the encrypted data is decrypted, and the decrypted data is used for digital transformation evaluation of the enterprise.

[0058] As a preferred embodiment, the key transformation data in each preset time period is processed for feature analysis, specifically including: The target encryption key corresponding to the first preset time period is obtained through the key generation algorithm, and the preset time period other than the first preset time period is determined as the reference time period.

[0059] According to the data amount, data update situation and data type of the key transformation data in the reference time period, the features of the key transformation data in the reference time period are analyzed and processed to obtain the data feature factor corresponding to the reference time period.

[0060] According to the data feature factor corresponding to the reference time period, the key derivation salt corresponding to the reference time period is generated, and the target encryption key corresponding to the reference time period is generated by key derivation according to the key derivation salt corresponding to the reference time period.

[0061] In this embodiment, the key generation algorithm is used to generate an initial target encryption key for the first preset time period, and other preset time periods other than the first preset time period are determined as reference time periods. For example, if the first preset time period is the first day, then the second day and subsequent time periods are reference time periods. The key transformation data in each reference time period is processed for feature analysis, and the extracted data features include data amount, data update situation and data type. According to the extracted data features, the data feature factor corresponding to the reference time period is calculated, which is a comprehensive numerical value reflecting the complexity and importance of the data in the time period. The key derivation salt is generated according to the data feature factor corresponding to the reference time period, which is a randomly generated value combined with the data feature factor to ensure that the salt value of each time period is unique.

[0062] The target encryption key corresponding to the last preset time period of the reference time period is subjected to key derivation using a key derivation salt, and is operated through a key derivation function to generate the target encryption key corresponding to the reference time period. The key transition data in the reference time period is encrypted using the generated target encryption key, and the integrity and correctness of the encrypted data are verified to ensure that the data is not damaged or tampered with during the encryption process. The encrypted data is stored in the blockchain to ensure the non-tamperability and traceability of the data.

[0063] The above merely describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating the digital transformation of small and medium-sized enterprises, characterized by: The method comprises: S11. Regularly obtain assessment documents for the digital transformation of small and medium-sized enterprises, parse and identify the assessment documents, build an assessment indicator parsing model, and output quantifiable multi-dimensional assessment indicators; S12. Build an evaluation indicator matching model for the digital transformation of different types of SMEs based on multi-dimensional evaluation indicators, and build a smart contract architecture based on the evaluation indicator matching model; S13. Regularly collect historical success stories of digital transformation of different types of SMEs, determine the corresponding quantifiable indicators through the evaluation indicator matching model, analyze the success stories, provide scoring guidance for each corresponding quantifiable indicator, and build a graded evaluation model; S14. Determine quantifiable indicators corresponding to SMEs undergoing digital transformation through the evaluation indicator matching model, deploy corresponding smart contracts in the SMEs’ ​​internal business systems, and collect key transformation data; S15. Analyze key transformation data through the grade assessment model, determine the transformation level of digital transformation of small and medium-sized enterprises, and output a visual assessment report.

2. The method for evaluating the digital transformation of small and medium-sized enterprises according to claim 1 is characterized in that: In step S11, an evaluation index analysis model is constructed, which specifically includes: Regularly obtain evaluation indicator documents for the digital transformation of SMEs from official information sources; Use natural language processing technology to extract multiple feature information related to digital transformation from the evaluation indicator file. Preprocess the multiple feature information to remove stop words and repeated words, and filter quantifiable and non-quantifiable keywords based on part of speech. Through context analysis, confirm whether non-quantifiable keywords have quantifiable functions. If they do, perform quantifiable part-of-speech conversion and classify them as quantifiable keywords. By counting the frequency of occurrence of each quantifiable keyword in the text, word patterns are constructed based on the quantifiable keywords and their corresponding word frequencies; Calculate keyword word vectors based on word patterns, measure semantic similarity using the cosine angle of word vectors, filter synonyms, and build a synonym database; Based on word patterns and synonyms, the pre-trained language model constructs an evaluation index parsing model.

3. The method for evaluating the digital transformation of small and medium-sized enterprises according to claim 1 is characterized in that: In step S12, an evaluation indicator matching model for the digital transformation of different types of SMEs is constructed based on multi-dimensional evaluation indicators, specifically including: Collect historical sample data of SMEs, including the types of SMEs and the evaluation indicators corresponding to each SME, and pre-process the sample data; Identify the type of each historical SME in the sample data and output the type feature set corresponding to each SME; The type features and corresponding evaluation indicators in several type feature sets are used as nodes, and the relationship between type features and evaluation indicators is used as edges to build an indicator matching graph database; The matching rules in the indicator matching graph database are learned through machine learning algorithms to build an evaluation indicator matching model.

4. The method for evaluating the digital transformation of small and medium-sized enterprises according to claim 1 is characterized in that: In step S12, a smart contract architecture is constructed based on the evaluation indicator matching model, specifically including: Determine the characteristic vectors and quantifiable indicators of digital transformation for SMEs based on the evaluation indicator matching model, and determine the dynamic adjustment mechanism for the weights of quantifiable indicators to obtain the input requirements of the evaluation indicator matching model; Design the data collection module in the smart contract, match the input requirements of the evaluation index model, clarify the data source and data format to be collected, and use data verification logic to determine whether the collected data meets the input requirements of the dynamic grade evaluation model; Design a weight adjustment module in the smart contract, dynamically adjust the data collection rules based on the weights in the evaluation indicator matching model, and feed back to the data collection module.

5. The method for evaluating the digital transformation of small and medium-sized enterprises according to claim 1 is characterized in that: In step S13, scoring guidance is provided for each corresponding quantifiable indicator, specifically including: Extract qualitative and quantitative data related to each success case and the corresponding quantifiable indicators, and pre-process the qualitative and quantitative data; Determine the first scoring standard corresponding to the quantifiable indicator by completing the pre-processed quantitative data; Evaluate whether the pre-processed qualitative data is quantifiable. If so, convert it into quantitative data and determine the second scoring criteria for the corresponding quantifiable indicators; The first scoring criteria and the second scoring criteria are integrated based on the corresponding quantifiable indicators to provide scoring guidance for the corresponding quantifiable indicators.

6. The method for evaluating the digital transformation of small and medium-sized enterprises according to claim 1 is characterized in that: In step S14, the corresponding smart contract is deployed in the internal business system of the SME, specifically including: Install blockchain client software in the internal business systems of small and medium-sized enterprises, send the compiled smart contracts to the blockchain client software through the blockchain network, and collect key transformation data.

7. The method for evaluating the digital transformation of small and medium-sized enterprises according to claim 6 is characterized in that: Key transformation data is encrypted before being uploaded to the chain.

8. The method for evaluating the digital transformation of small and medium-sized enterprises according to claim 7 is characterized in that: Encrypt key transformation data, including: Obtain key transformation data within each preset time period; Perform feature analysis on key transformation data within each preset time period to generate a target encryption key corresponding to each preset time period; Encrypt the key transformation data within each preset time period according to the target encryption key corresponding to each preset time period to obtain the encrypted ciphertext corresponding to each preset time period; The encrypted ciphertexts corresponding to all preset time periods are stored in blocks in the blockchain in sequence.

9. The method for evaluating the digital transformation of small and medium-sized enterprises according to claim 8 is characterized in that: Perform feature analysis on key transformation data within each preset time period, including: The target encryption key corresponding to the first preset time period is obtained by a key generation algorithm, and a preset time period other than the first preset time period is determined as a reference time period; Analyze and process the characteristics of the key transformation data within the reference time period based on the data volume, data update status, and data type of the key transformation data within the reference time period to obtain the data characteristic factors corresponding to the reference time period; According to the data characteristic factors corresponding to the reference time period, a key derivation salt corresponding to the reference time period is generated, and a target encryption key corresponding to the previous preset time period of the reference time period is derived based on the key derivation salt to generate the target encryption key corresponding to the reference time period.

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