Enterprise digital transformation index generation method and platform based on natural language processing

Abstract

The present invention relates to a method and platform for generating an enterprise digital transformation index based on natural language processing, and relates to the field of enterprise development analysis. The platform includes: an intergenerational participation evaluation module, an intergenerational transformation demand identification module, an intergenerational demand quantitative correlation evaluation module, an effectiveness influencing factor index identification module, and a comprehensive transformation index calculation module. The method uses natural language processing technology to analyze employee feedback, identify the degree of participation and needs of employees of different generations in crisis response, and form quantitative intergenerational participation and demand indicators. By combining intergenerational participation and demand satisfaction, enterprises can evaluate the effectiveness of response measures, find room for improvement, and ensure that strategies are close to employee needs. At the same time, the method dynamically calculates the comprehensive transformation index, provides enterprises with real-time monitoring tools, supports managers to track the transformation process and adjust strategies in a timely manner, and ensures the success and sustainable development of digital transformation.

Description

Technical Field

[0001] The present invention relates to the field of enterprise development analysis, and specifically to a method and platform for generating an enterprise digital transformation index based on natural language processing. Background Art

[0002] The Enterprise Digital Transformation Index is a comprehensive indicator designed to assess the progress and effectiveness of enterprises in the process of digital transformation. The index usually covers multiple dimensions, including the degree of technology application, employee engagement, business process optimization, customer experience improvement, and data-driven decision-making capabilities. By quantifying these dimensions, enterprises can clearly understand their specific performance in digital transformation, identify potential areas for improvement, and formulate corresponding measures to promote a more efficient digital transformation process. This index not only provides data support for internal management of enterprises, but also provides transparent information on the progress of digital transformation for external stakeholders such as investors and customers.

[0003] Traditional methods lack a systematic and dynamic employee feedback analysis mechanism, which results in companies being unable to accurately grasp the real needs and feelings of employees when generating a digital transformation index, and failing to effectively identify the specific needs of employees of different generations, thus affecting the comprehensiveness and adaptability of transformation strategies. At the same time, the lack of quantitative indicators and real-time monitoring mechanisms means that companies lack a scientific basis for evaluating the effectiveness of crisis response measures and adjusting transformation strategies, which ultimately affects the overall effect and success rate of digital transformation. Summary of the invention

[0004] In response to the technical problems existing in the prior art, the present invention provides a method and platform for generating an enterprise digital transformation index based on natural language processing.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: a method for generating an enterprise digital transformation index based on natural language processing, the method comprising: Collect internal enterprise data and internal employee data, and based on the internal employee data and internal enterprise data, evaluate the participation of employees of different age groups in crisis response and generate intergenerational participation; Analyze employee feedback based on NLP technology to identify and quantify the transformation needs of employees of different generations, and determine the expectations of each generation for transformation based on the needs; Analyze the company's current response measures to the crisis and correlate the intergenerational engagement and needs quantification results with the measures to evaluate the effectiveness of the measures and identify and quantify the degree to which needs are met; Compare intergenerational participation, demand satisfaction and effectiveness of response measures, identify the factors and indicators that affect effectiveness in different generations, and calculate the maximum value of the factor indicator on the premise of maximizing effectiveness to generate improvement measures; Based on the improved measures, we re-analyze intergenerational participation and needs satisfaction, and perform a weighted analysis of intergenerational participation, needs satisfaction and effectiveness to calculate a comprehensive transformation index.

[0006] As a further solution of the present invention, the collection of internal enterprise data and internal enterprise employee data, and based on the internal enterprise employee data and internal enterprise data, the evaluation of the participation of employees of different age groups in crisis response, and the generation of intergenerational participation, specifically includes: Obtain all internal data on the company's response to the crisis, including response measures and performance indicators, and collect employee feedback data; Through NLP technology, we analyze employee work feedback data, identify relevant keywords and emotional tendencies of engagement, and classify employee participation by age group to generate intergenerational engagement.

[0007] As a further solution of the present invention, the generation of intergenerational participation is specifically: Calculate each employee's level of engagement in crisis response: ; in, Indicates employees The participation rate, is the total number of jobs involved, For work The weight of For employees at work The score in Based on work participation, the average participation of each age group is calculated as intergenerational participation: ; in, Indicates age group The average participation rate, that is, intergenerational participation, For age group Number of employees, For age group Staff participation.

[0008] As a further solution of the present invention, the identification and quantification of the needs of employees of different generations for transformation, and the determination of the expectations of each generation for transformation based on the needs, specifically include: Analyze employee work feedback data through NLP technology to identify demand information related to transformation, and combine sentiment analysis to determine employees' attitudes and emotional tendencies toward transformation; Define scores from high to low based on sentiment tendency, and assign a quantitative score to each identified need based on this; Comprehensively analyze the quantitative scores of each generation’s needs, combined with employee work feedback data, to identify each generation’s core expectations in the transformation.

[0009] As a further solution of the present invention, the analysis of the current measures taken by the enterprise to cope with the crisis, and associating the intergenerational participation and demand quantification results with the measures, evaluating the effectiveness of the measures, and identifying and quantifying the degree of satisfaction of the needs, specifically includes: Combine intergenerational participation and the company's response measures to identify the participation of employees of each generation in specific response measures, create a cross-tabulation, and mark the relationship between the intergenerational participation of each generation of employees and the corresponding response measures; Correlate the quantitative scores of needs with intergenerational participation to identify the degree to which employees of each generation meet the corresponding response measures; Specifically: ; in, Indicate measures The degree of satisfaction of needs, For measures The demand quantification score, For age group of employees responded to the measures intergenerational participation, the total number of generations scored for participation; Evaluate the effectiveness of each response measure based on the degree to which each measure meets the needs and corporate performance indicators.

[0010] As a further solution of the present invention, the effectiveness of the evaluation of each countermeasure is specifically as follows: ; ; in, For measures The effectiveness score, For and measures The relevant performance indicator normalized scores, and is the weight, and , For and measures The actual value of the relevant performance indicator, For and measures The minimum value of the relevant performance indicator, For and measures The maximum value of the relevant performance indicator.

[0011] As a further solution of the present invention, the factor indexes that affect effectiveness in different generations are identified, and the maximum value of the factor indexes is calculated under the premise of maximizing effectiveness to generate improvement measures, which specifically includes: Intergenerational engagement, needs satisfaction, and effectiveness scores were aggregated to form a comprehensive data set; Quantitatively analyze the factors that affect the effectiveness score, evaluate the relevance of intergenerational participation of each age group and the degree of satisfaction of each measure to effectiveness, sort them according to the size of the correlation, and identify the factors with the greatest correlation ; An optimization algorithm is used to calculate the best factor index that maximizes the effectiveness score under different conditions: ; in, is the best factor index, indicating the effectiveness score Get the maximum value ; Based on the optimal factor indicators as the implementation standard, the response measures are readjusted to generate improved measures.

[0012] As a further solution of the present invention, the evaluation of the correlation between the intergenerational participation of each age group and the degree of satisfaction of each measure on the effectiveness includes: Calculate separately , : ; ; in, Indicates age group Intergenerational participation and measures The corresponding effectiveness score The correlation coefficient between Indicate measures The degree of satisfaction of needs and measures The corresponding effectiveness score The correlation coefficient between .

[0013] As a further solution of the present invention, the improved measures are used to re-analyze intergenerational participation and demand satisfaction, and to perform weighted analysis on intergenerational participation, demand satisfaction and effectiveness to calculate the comprehensive transformation index, including: Reanalyze intergenerational participation and demand satisfaction based on improved measures to obtain improved intergenerational participation and improve demand satisfaction , and recalculate the improvement effectiveness score ; Calculate the final comprehensive transformation index: ; in, is the comprehensive transformation index, represents the weight coefficient, and .

[0014] Another object of the present invention is to provide an enterprise digital transformation index generation platform based on natural language processing, the platform comprising: The intergenerational participation assessment module is used to collect internal enterprise data and internal employee data, and based on the internal employee data and internal enterprise data, assess the degree of participation of employees of different age groups in crisis response and generate intergenerational participation; The generational transformation needs identification module is used to analyze employee feedback based on NLP technology, identify and quantify the transformation needs of employees of different generations, and determine the expectations of each generation for transformation based on the needs; The module for quantitative correlation and assessment of intergenerational needs is used to analyze the current measures taken by enterprises to deal with the crisis, and to correlate the intergenerational participation and quantitative results of needs with the measures, to evaluate the effectiveness of the measures, and to identify and quantify the degree of satisfaction of needs; The effectiveness influencing factor indicator identification module is used to compare the intergenerational participation, demand satisfaction and effectiveness of the response measures, identify the factor indicators that affect the effectiveness in different generations, and calculate the maximum value of the factor indicator on the premise of maximizing effectiveness to generate improvement measures; The comprehensive transformation index calculation module is used to re-analyze intergenerational participation and demand satisfaction based on improved measures, and to perform weighted analysis on intergenerational participation, demand satisfaction and effectiveness to calculate the comprehensive transformation index.

[0015] The beneficial effects of the present invention are: This method uses NLP technology to mine and analyze employee feedback, identify the level of participation and needs of intergenerational employees in crisis response, and form indicators for quantitative scores of intergenerational participation and needs. These indicators not only accurately reflect the true feelings of employees, but also reveal the specific expectations of employees of different age groups, thereby helping management to formulate more personalized and effective transformation strategies. When generating the enterprise digital transformation index, by combining intergenerational participation with demand satisfaction, companies can comprehensively evaluate the effectiveness of current response measures, discover potential room for improvement, and ensure that strategies are closer to the actual needs of employees. This data-driven approach reduces risks in the decision-making process, improves the efficiency of resource allocation, and enables companies to respond to market changes quickly and flexibly. In addition, the calculation of the comprehensive transformation index provides companies with a dynamic monitoring tool to help managers track the transformation process in real time and adjust strategies in a timely manner to ensure the success and sustainability of digital transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a method for generating an enterprise digital transformation index based on natural language processing provided in an embodiment of the present invention; Figure 2 A flowchart for evaluating the participation of employees of different age groups in crisis response and generating intergenerational participation provided by an embodiment of the present invention; Figure 3 A flowchart for identifying and quantifying the needs of employees of different generations for transformation, and determining the expectations of each generation for transformation based on the needs, provided in an embodiment of the present invention; Figure 4 A flowchart for associating intergenerational participation and demand quantification results with measures, evaluating the effectiveness of measures, and identifying and quantifying the degree of demand satisfaction provided by an embodiment of the present invention; Figure 5 The embodiment of the present invention provides a flowchart for identifying the factor index that affects the effectiveness in different generations, and calculating the maximum value of the factor index on the premise of maximizing the effectiveness, and generating improved measures; Figure 6 A flowchart of calculating a comprehensive transformation index provided by an embodiment of the present invention; Figure 7 A structural block diagram of the enterprise digital transformation index generation platform based on natural language processing provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0018] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0019] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0020] Figure 1 A flowchart of a method for generating an enterprise digital transformation index based on natural language processing provided in an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method includes: S100, collects internal enterprise data and internal employee data, and based on the internal employee data and internal enterprise data, evaluates the participation of employees of different age groups in crisis response and generates intergenerational participation; In this step, you need to systematically collect all relevant internal data implemented in the crisis response, including specific response measures, performance indicators, and information related to employee work feedback. This process provides important basic data for subsequent analysis and ensures that decisions are based on comprehensive information.

[0021] After data collection is completed, natural language processing (NLP) technology is used to conduct in-depth analysis of employees' work feedback. NLP technology can extract keywords related to participation and analyze employees' emotional tendencies towards crisis response measures. This analysis not only helps to understand employees' attitudes towards corporate response strategies, but also reveals differences in participation among employees of different ages. For example, young employees may be more inclined to actively participate, while older employees may be more concerned about the stability and sustainability of response measures.

[0022] By categorizing employee feedback data by age group, companies can generate more detailed intergenerational engagement indicators. These indicators not only reflect the participation of employees of each age group, but also reveal their specific contributions and needs in crisis response. This clear intergenerational engagement model provides companies with valuable insights into employee characteristics and needs, helping management to formulate strategies and policies more effectively.

[0023] Systematic data collection and NLP analysis can provide companies with data-driven insights, making the decision-making process more scientific and reasonable. Companies can identify the participation of employees of different generations in crisis response, thus providing a basis for the subsequent development of targeted training and communication strategies.

[0024] like Figure 2 As shown, the internal data of the enterprise and the internal employee data of the enterprise are collected, and based on the internal employee data and the internal data of the enterprise, the participation degree of employees of different age groups in crisis response is evaluated to generate intergenerational participation, which specifically includes: S110, obtain all internal data taken by the company in crisis response, including response measures and performance indicators, and collect employee work feedback data; S120 uses NLP technology to analyze employee work feedback data, identify relevant keywords and sentiment tendencies of engagement, and classify employee engagement by age group to generate intergenerational engagement.

[0025] In this step, the generation of intergenerational participation is specifically: Calculate each employee's level of engagement in crisis response: ; in, Indicates employees The participation rate, is the total number of jobs involved, For work The weight of For employees at work The score in Based on work participation, the average participation of each age group is calculated as intergenerational participation: ; in, Indicates age group The average participation rate, that is, intergenerational participation, For age group Number of employees, For age group Staff participation.

[0026] S200, using NLP technology to analyze employee feedback, identify and quantify the needs of employees of different generations for transformation, and determine the expectations of each generation for transformation based on the needs; This step uses NLP technology to comprehensively analyze employee feedback data and extract demand information related to transformation. This process includes semantic understanding of the text, identification of key demand keywords, and analysis of employee emotional tendencies to determine employees' attitudes and emotional colors toward transformation.

[0027] After identifying the needs, the company assigns scores to different needs based on emotional tendencies. These scores range from high to low, reflecting the importance employees attach to each need. Through quantitative methods, companies can convert employee needs into specific quantitative scores. These scores not only provide a basis for subsequent strategy formulation, but also provide a clear perspective on the core expectations of employees of different generations during the transformation process. By comprehensively analyzing the quantitative scores of the needs of each generation, companies will be able to identify the core expectations of each generation in the transformation, and then formulate more effective transformation strategies to adapt to the diverse needs of employees.

[0028] By using NLP technology, not only can the efficiency of analyzing employee feedback be greatly improved, but also the real feelings and needs of employees can be deeply understood. Such a data-driven approach enables management to formulate transformation strategies more scientifically, ensuring that these strategies can effectively meet employee expectations, thereby enhancing employee participation and satisfaction. In addition, clear identification of generational needs helps companies establish good communication channels between employees of different ages, promote the integration of corporate culture and team collaboration. This employee-centric transformation strategy not only enhances employees' sense of identity with the company, but also improves the overall flexibility and adaptability of the organization, laying a solid foundation for successful digital transformation.

[0029] like Figure 3 As shown, the identification and quantification of the needs of employees of different generations for transformation, and the determination of the expectations of each generation for transformation based on the needs, specifically include: S210, using NLP technology to analyze employee work feedback data, identify demand information related to transformation, and combine sentiment analysis to determine employees’ attitudes and emotional tendencies toward transformation; S220, defining scores from high to low according to sentiment tendency, and assigning a demand quantification score to each identified demand based on the scores; S230, comprehensively analyzes the quantitative scores of the needs of each generation, combines them with employee work feedback data, and identifies the core expectations of each generation in the transformation.

[0030] S300, analyzing the company’s current measures to deal with the crisis, and linking the intergenerational engagement and demand quantification results with the measures, evaluating the effectiveness of the measures, and identifying and quantifying the degree to which the needs are met; This step first requires combining intergenerational participation and implemented response measures to identify the specific participation of employees of each generation in these measures. This process can be achieved by constructing a cross-tabulation, which lists the relationship between the participation of each generation of employees and the corresponding response measures. This method can not only intuitively show the degree of participation of employees of different generations in crisis response, but also provide a clear data basis for subsequent analysis.

[0031] On this basis, the correlation analysis between the demand quantification score and intergenerational participation is particularly important. By matching the demand score with the intergenerational participation, companies can identify the degree to which employees of different generations meet their needs for specific responses. This information not only helps to understand which measures have received positive feedback from employees, but also reveals which measures are insufficient in meeting employee needs. Combining the degree to which each measure meets the needs and the company's performance indicators, companies can clearly evaluate the effectiveness of each response.

[0032] This step can provide companies with a comprehensive perspective, helping management to fully identify the real needs and participation attitudes of employees of different generations in the process of crisis response. This in-depth analysis prompts companies to consider employee feedback and needs more when formulating and adjusting response measures, thereby improving the overall response effect. In addition, through quantitative analysis of intergenerational participation and demand satisfaction, companies can not only discover the advantages and disadvantages of current measures, but also provide data support and optimization suggestions for future response strategies, creating a more favorable environment for digital transformation.

[0033] like Figure 4 As shown, the analysis of the company's current measures to deal with the crisis, and the association of intergenerational participation and demand quantification results with the measures, evaluate the effectiveness of the measures, and identify and quantify the degree of demand satisfaction, including: S310, combining the intergenerational participation and the enterprise's response measures, identifying the participation of employees of each generation in specific response measures, creating a cross-tabulation, and marking the relationship between the intergenerational participation of each generation of employees and the corresponding response measures; S320, associating the demand quantification score with the intergenerational participation to identify the satisfaction degree of the needs of employees of each generation for the corresponding response measures; Specifically: ; in, Indicate measures The degree of satisfaction of needs, For measures The demand quantification score, For age group of employees responded to the measures intergenerational participation, the total number of generations scored for participation; By combining the needs quantification score with the intergenerational engagement, companies can assess whether the needs of employees of different generations for specific responses are fully met. This analysis not only reveals employees' true feelings about the measures, but also helps companies understand to what extent the needs of employees of different generations have been responded to. Through this quantification, companies can adjust and optimize response strategies more specifically to improve employee satisfaction and engagement.

[0034] S330, evaluate the effectiveness of each response measure based on the degree of demand satisfaction of each measure and the company's performance indicators.

[0035] In this step, the effectiveness of each response measure is evaluated, specifically: ; ; in, For measures The effectiveness score, For and measures The relevant performance indicator normalized scores, and is the weight, and , For and measures The actual value of the relevant performance indicator, For and measures The minimum value of the relevant performance indicator, For and measures The maximum value of the relevant performance indicator.

[0036] The algorithm quantifies the relationship between intergenerational engagement and various response measures to form an effectiveness score. This process not only helps managers intuitively understand the effects of various measures, but also provides a basis for decision-making. By analyzing the scores of different measures, companies can identify which measures have been successful in implementation and which need further improvement, thus forming an effective feedback loop to ensure continuous optimization and improvement of measures.

[0037] S400, compare intergenerational participation, demand satisfaction and effectiveness of response measures, identify the factor indicators that affect effectiveness in different generations, and calculate the maximum value of the factor indicator on the premise of maximizing effectiveness to generate improvement measures; This step forms a comprehensive data set by aggregating the generational engagement, needs fulfillment, and effectiveness scores, which provides the basis for subsequent analysis. With this data, companies can deeply analyze the impact of different generations of employees on effectiveness scores, evaluate the correlation between the engagement and needs fulfillment of employees of different ages and effectiveness, and sort them according to these correlations to identify the factors that have the greatest impact on effectiveness.

[0038] In the specific steps, the company established a multi-factor model and used optimization algorithms to handle these complex relationships, and calculated the factor indicators that maximize the effectiveness score under different conditions. The core of this process is that by analyzing the correlation that affects effectiveness, the company can clearly identify which combination of intergenerational participation and demand satisfaction can best improve the effectiveness of the overall measures. For each factor, the company can not only identify its relative importance, but also provide data support for subsequent decision-making and adjustments.

[0039] By clarifying the participation and satisfaction of employees of different generations, companies can optimize their response measures in a targeted manner, thereby improving overall crisis response capabilities and work efficiency. In addition, through the application of multi-factor models and optimization algorithms, companies can identify the optimal response strategy in a complex environment, making resource allocation more efficient. This systematic analysis method not only helps companies succeed in the current crisis, but also lays a scientific decision-making foundation for future digital transformation.

[0040] like Figure 5 As shown, the factor indicators that affect effectiveness in different generations are identified, and the maximum value of the factor indicator is calculated under the premise of maximizing effectiveness, and improvement measures are generated, including: S410, aggregated intergenerational participation, needs satisfaction, and effectiveness scores to form a comprehensive data set; S420, quantitatively analyze the factors that affect the effectiveness score, evaluate the relevance of intergenerational participation of each age group and the degree of satisfaction of each measure to effectiveness, sort them according to the size of the correlation, and identify the factors with the greatest correlation ; S430, uses an optimization algorithm to calculate the best factor index to maximize the effectiveness score under different conditions: ; in, is the best factor index, indicating the effectiveness score Get the maximum value ; The algorithm aims to find the best combination of factors that can maximize the effectiveness score, helping companies to develop more scientific response strategies. By integrating multiple influencing factors, the algorithm provides a comprehensive perspective that enables companies to identify the best solution in a complex environment. In addition, companies can also use this algorithm to simulate effectiveness under different conditions, thereby predicting the effects of different strategies and providing empirical support for decision-making. By optimizing the algorithm, companies can not only improve the effectiveness of response measures, but also quickly adjust strategies according to the changing environment and employee feedback to achieve dynamic response. This integrated approach ultimately helps companies improve their ability to cope with challenges during the digital transformation process and ensure a solid foundation for long-term development.

[0041] S440, based on the optimal factor index as the implementation standard, readjust the response measures and generate improved measures.

[0042] In this step, the evaluation of intergenerational participation in each age group and the relevance of each measure's need satisfaction to effectiveness includes: Calculate separately , : ; ; in, Indicates age group Intergenerational participation and measures The corresponding effectiveness score The correlation coefficient between Indicate measures The degree of satisfaction of needs and measures The corresponding effectiveness score The correlation coefficient between .

[0043] The main function of this algorithm is to analyze the relationship between intergenerational participation, demand satisfaction and effectiveness. It not only identifies factors that have a significant impact on effectiveness, but also quantifies the correlation coefficient to clarify the strength of the relationship between different variables. This process enables managers to intuitively understand which factors have a greater impact on decision-making, and rank them on this basis, giving priority to those factors with high correlation. This data-driven approach can effectively reduce decision-making risks, improve resource allocation efficiency, and enhance the company's ability to respond to changes.

[0044] S500, re-analyzes intergenerational participation and needs satisfaction based on improved measures, and performs weighted analysis on intergenerational participation, needs satisfaction and effectiveness to calculate a comprehensive transformation index.

[0045] like Figure 6As shown, the intergenerational participation and demand satisfaction are re-analyzed based on the improved measures, and the intergenerational participation, demand satisfaction and effectiveness are weighted and analyzed to calculate the comprehensive transformation index, including: S510, reanalyze intergenerational participation and demand satisfaction based on improved measures to obtain improved intergenerational participation and improve demand satisfaction , and recalculate the improvement effectiveness score ; S520, calculate the final comprehensive transformation index: ; in, is the comprehensive transformation index, represents the weight coefficient, and .

[0046] Figure 7 The structural block diagram of the enterprise digital transformation index generation platform based on natural language processing provided by an embodiment of the present invention is as follows: Figure 7 As shown, the platform includes: An intergenerational participation evaluation module 100 is used to collect internal enterprise data and internal enterprise employee data, and based on the internal enterprise employee data and internal enterprise data, evaluate the participation of employees of different age groups in crisis response and generate intergenerational participation; The generational transformation needs identification module 200 is used to analyze employee feedback based on NLP technology, identify and quantify the needs of employees of different generations for transformation, and determine the expectations of each generation for transformation based on the needs; Intergenerational demand quantification correlation evaluation module 300 is used to analyze the current measures of the enterprise to deal with the crisis, and to correlate the intergenerational participation and demand quantification results with the measures, evaluate the effectiveness of the measures, and identify and quantify the degree of demand satisfaction; The effectiveness influencing factor indicator identification module 400 is used to compare the intergenerational participation, the degree of satisfaction of needs and the effectiveness of the response measures, identify the factor indicators that affect the effectiveness in different generations, and calculate the maximum value of the factor indicator on the premise of maximizing the effectiveness, and generate improvement measures; The comprehensive transformation index calculation module 500 is used to re-analyze the intergenerational participation and demand satisfaction based on the improved measures, and to perform a weighted analysis on the intergenerational participation, demand satisfaction and effectiveness to calculate the comprehensive transformation index.

[0047] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0048] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0052] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for generating an enterprise digital transformation index based on natural language processing, characterized in that: The method includes: collecting internal data of the enterprise and internal employee data of the enterprise, and based on the internal employee data and internal data of the enterprise, evaluating the participation of employees of different age groups in crisis response and generating intergenerational participation; analyzing employee feedback based on NLP technology, identifying and quantifying the needs of employees of different generations for transformation, and determining the expectations of each generation for transformation based on the needs; analyzing the current measures of the enterprise to cope with the crisis, and associating the intergenerational participation and demand quantification results with the measures, evaluating the effectiveness of the measures, and identifying and quantifying the degree of satisfaction of the needs; comparing the intergenerational participation, the degree of satisfaction of the needs and the effectiveness of the response measures, identifying the factor indicators that affect the effectiveness in different generations, and calculating the maximum value of the factor indicator on the premise of maximizing the effectiveness, and generating improved measures; re-analyzing the intergenerational participation and the degree of satisfaction of the needs based on the improved measures, and performing a weighted analysis on the intergenerational participation, the degree of satisfaction of the needs and the effectiveness, and calculating the comprehensive transformation index.

2. The method according to claim 1, characterized in that The method of collecting internal enterprise data and internal employee data, and based on the internal employee data and internal enterprise data, evaluating the degree of participation of employees of different age groups in crisis response, and generating intergenerational participation, specifically includes: obtaining all internal data taken by the enterprise in crisis response, including response measures and performance indicators, and collecting employee work feedback data; analyzing employee work feedback data through NLP technology, identifying relevant keywords and emotional tendencies of participation, and classifying employee participation by age group to generate intergenerational participation.

3. The method according to claim 2, characterized in that The generation of intergenerational participation is specifically as follows: Calculate each employee’s work participation in crisis response: ;in, Indicates employees The participation rate, is the total number of jobs involved, For work The weight of For employees at work Based on work participation, the average participation of each age group is calculated as intergenerational participation: ;in, Indicates age group The average participation rate, that is, intergenerational participation, For age group Number of employees, For age group Staff participation.

4. The method according to claim 2, characterized in that: The identifying and quantifying the needs of employees of different generations for transformation, and determining the expectations of each generation for transformation based on the needs, specifically includes: analyzing employee work feedback data through NLP technology to identify demand information related to transformation, and combining sentiment analysis to judge employees' attitudes and emotional tendencies towards transformation; defining scores from high to low based on emotional tendencies, and assigning a demand quantification score to each identified need based on this; comprehensively analyzing the demand quantification score of each generation, combining employee work feedback data, and identifying the core expectations of each generation in transformation.

5. The method according to claim 4, characterized in that The analysis of the current measures taken by the enterprise to cope with the crisis, and associating the intergenerational participation and demand quantification results with the measures, evaluating the effectiveness of the measures, and identifying and quantifying the degree of satisfaction of the needs, specifically includes: combining the intergenerational participation and the enterprise's response measures, identifying the participation of employees of each generation in specific response measures, creating a cross-table, marking the relationship between the intergenerational participation of each generation of employees and the corresponding response measures; associating the demand quantification score with the intergenerational participation, and identifying the degree of satisfaction of the needs of employees of each generation for the corresponding response measures; specifically: ;in, Indicate measures The degree of satisfaction of needs, For measures The demand quantification score, For age group of employees responded to the measures intergenerational participation, The total number of generations that participated was scored; the effectiveness of each response was evaluated based on the degree to which each measure met needs and corporate performance indicators.

6. The method according to claim 4, characterized in that The effectiveness of the various response measures mentioned above is specifically assessed as follows: ; ;in, For measures The effectiveness score, For and measures The relevant performance indicator normalized scores, and is the weight, and , For and measures The actual value of the relevant performance indicator, For and measures The minimum value of the relevant performance indicator, For and measures The maximum value of the relevant performance indicator.

7. The method according to claim 5, characterized in that The identification of the factor indicators that affect effectiveness in different generations, and the calculation of the maximum value of the factor indicator under the premise of maximizing effectiveness, to generate improved measures, specifically includes: summarizing the intergenerational participation, demand satisfaction and effectiveness scores to form a comprehensive data set; quantitatively analyzing the factors that affect the effectiveness score, evaluating the correlation between the intergenerational participation of each age group and the demand satisfaction of each measure on effectiveness, sorting according to the size of the correlation, and identifying the factor with the greatest correlation ; An optimization algorithm is used to calculate the best factor index to maximize the effectiveness score under different conditions: ;in, is the best factor index, indicating the effectiveness score Get the maximum value ; Based on the optimal factor indicators as the implementation standard, readjust the response measures and generate improved measures.

8. The method according to claim 7, characterized in that The evaluation of the relevance of intergenerational participation in each age group and the degree of satisfaction of each measure to effectiveness includes: calculating , : ; ;in, Indicates age group Intergenerational participation and measures The corresponding effectiveness score The correlation coefficient between Indicate measures The degree of satisfaction of needs and measures The corresponding effectiveness score The correlation coefficient between .

9. The method according to claim 8, characterized in that The re-analysis of intergenerational participation and demand satisfaction based on improved measures, and weighted analysis of intergenerational participation, demand satisfaction and effectiveness to calculate the comprehensive transformation index include: re-analyzing intergenerational participation and demand satisfaction based on improved measures to obtain improved intergenerational participation and improve demand satisfaction , and recalculate the improvement effectiveness score ; Calculate the final comprehensive transformation index: ;in, is the comprehensive transformation index, represents the weight coefficient, and .

10. The enterprise digital transformation index generation platform based on natural language processing is characterized by: The platform includes: an intergenerational participation evaluation module, which is used to collect internal enterprise data and internal employee data, and based on the internal employee data and internal enterprise data, evaluate the participation of employees of different age groups in crisis response and generate intergenerational participation; an intergenerational transformation demand identification module, which is used to analyze employee feedback based on NLP technology, identify and quantify the transformation needs of employees of different generations, and determine the expectations of each generation for transformation based on the needs; an intergenerational demand quantitative correlation evaluation module, which is used to analyze the current measures of the enterprise to deal with the crisis, and associate the intergenerational participation and demand quantification results with the measures, evaluate the effectiveness of the measures, and identify and quantify the degree of satisfaction of the needs; an effectiveness influencing factor index identification module, which is used to compare the intergenerational participation, the degree of satisfaction of needs and the effectiveness of the response measures, identify the factor indicators that affect the effectiveness in different generations, and calculate the maximum value of the factor indicator on the premise of maximizing the effectiveness, and generate improved measures; a comprehensive transformation index calculation module, which is used to re-analyze the intergenerational participation and the degree of satisfaction of needs based on the improved measures, and perform weighted analysis on the intergenerational participation, the degree of satisfaction of needs and the effectiveness to calculate the comprehensive transformation index.

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