Business scenario confirmation method and device, equipment and storage medium

Abstract

This application relates to the field of enterprise digital transformation technology, and discloses a method, apparatus, device, and storage medium for business scenario identification. The method includes: determining candidate business scenarios; evaluating the candidate business scenarios using a predictive evaluation model to obtain a business scenario evaluation list; and identifying the top preset number of candidate business scenarios with the highest scenario value evaluation values ​​in the business scenario evaluation list as target business scenarios representing development goals. The technical solution provided by this application, by determining candidate business scenarios and evaluating and ranking them from multiple evaluation dimensions, can efficiently identify high-value target scenarios, achieve a comprehensive and objective evaluation of business scenario value, and effectively improve resource allocation efficiency and decision-making accuracy.

Description

Technical Field

[0001] This application relates to the field of enterprise digital transformation technology, specifically to a business scenario confirmation method, apparatus, device, and storage medium. Background Technology

[0002] Against the backdrop of the profound advancement of digital transformation in the new energy vehicle industry, new energy vehicle companies have accumulated massive amounts of high-value data throughout the entire lifecycle of vehicle production, operation, and service. Based on these data resources, companies can identify one or more key business scenarios for development, thereby determining their main future work direction. During the digital transformation process in the new energy vehicle industry, companies face the challenge of identifying high-value scenarios from numerous potential data assetization business scenarios to avoid blind and inefficient resource investment. Currently, related technologies primarily rely on subjective decision-making based on the experience of management or business departments, lacking data support, which can easily lead to judgment biases and an inability to objectively quantify and assess the value of scenarios. Summary of the Invention

[0003] In view of the above problems, this application provides a business scenario confirmation method, apparatus, device and storage medium to solve the problem that the determination of business scenarios in the prior art relies on the experience of management or business departments to make subjective decisions and lacks data support.

[0004] According to one aspect of the embodiments of this application, a business scenario confirmation method is provided, the method comprising: determining candidate business scenarios; evaluating the candidate business scenarios using a predictive evaluation model to obtain a business scenario evaluation list, wherein the predictive evaluation model is used to evaluate the value of each candidate business scenario from multiple evaluation dimensions; and determining the top preset number of candidate business scenarios with the highest scenario value evaluation values ​​in the business scenario evaluation list as target business scenarios representing development goals.

[0005] According to another aspect of the embodiments of this application, a business scenario confirmation device is provided, comprising: a first determining module, configured to determine candidate business scenarios; an evaluation module, configured to evaluate the candidate business scenarios using a predictive evaluation model to obtain a business scenario evaluation list, wherein the predictive evaluation model is configured to evaluate the value of each candidate business scenario from multiple evaluation dimensions; and a second determining module, configured to determine the top preset number of candidate business scenarios with the highest scenario value evaluation values ​​in the business scenario evaluation list as target business scenarios representing development goals.

[0006] According to another aspect of the embodiments of this application, a business scenario confirmation device is provided, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, wherein the executable instruction causes the processor to perform the operation of the business scenario confirmation method as described above.

[0007] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction, which, when run on a business scenario confirmation device / app, causes the business scenario confirmation device / app to perform the operation of the business scenario confirmation method as described in any of the above.

[0008] This application embodiment, by identifying candidate business scenarios and evaluating and ranking them from multiple assessment dimensions, can efficiently identify high-value target scenarios, achieve a comprehensive and objective assessment of the value of business scenarios, and effectively improve resource allocation efficiency and decision-making accuracy.

[0009] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0010] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a first embodiment of the business scenario confirmation method provided in this application is shown. Figure 2 A flowchart illustrating a second embodiment of the business scenario confirmation method provided in this application is shown. Figure 3 A flowchart illustrating a third embodiment of the business scenario confirmation method provided in this application is shown; Figure 4 A schematic diagram of the business scenario verification device provided in this application is shown; Figure 5 A schematic diagram of an embodiment of the business scenario verification device provided in this application is shown. Detailed Implementation

[0011] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein.

[0012] Against the backdrop of the profound advancement of digital transformation in the new energy vehicle industry, new energy vehicle companies have accumulated massive amounts of high-value data throughout the entire lifecycle of vehicle production, operation, and service. This data encompasses multi-dimensional resources such as vehicle operating status, battery lifecycle, user driving behavior, and charging network operation, making data one of the core assets of these companies. Based on these data resources, companies can focus on one or more key business scenarios for development. Examples include predictive maintenance, intelligent charging navigation, user profiling and precision marketing, personalized insurance pricing, and battery lifecycle traceability—all of which are potential application scenarios for the assetization of new energy vehicle data. Each scenario possesses potential application value in areas such as cost control, revenue growth, user experience improvement, and compliance requirements fulfillment, making them important directions for companies to unlock data value and enhance their core competitiveness.

[0013] However, new energy vehicle companies still face many common industry challenges when undertaking data assetization, selecting data application scenarios, and investing resources. The selection of data assetization scenarios is often determined by management or business departments based on industry experience and subjective judgment, without the establishment of quantitative evaluation standards and decision-making basis. This easily leads to a disconnect between the selected scenarios and the company's actual development needs. In some scenarios, after investing significant human, material, and financial resources, the actual return on investment is far lower than expected, resulting in ineffective waste of company resources.

[0014] In response, this application proposes a method for confirming business scenarios, such as... Figure 1 As shown, the method includes: Step S110: Determine candidate business scenarios.

[0015] Candidate business scenarios refer to potential application directions or specific business activities that enterprises may realize business value through data assetization during the digital transformation process. These scenarios are usually preliminary ideas or have undergone preliminary screening and are yet to be further evaluated and confirmed.

[0016] Specifically, candidate business scenarios can be identified manually. For example, internal business experts, managers, or project team members can propose a series of potential business scenarios through workshops, brainstorming sessions, or experience summaries. These scenarios can be based on observations of existing business processes, analysis of market trends, or predictions of technological potential. As an alternative approach, candidate business scenarios can also be initially identified by collecting and organizing historical project data, industry reports, or competitor analysis reports. For example, by reviewing past digital transformation cases, extracting business models that can be learned from or improved upon, and using them as candidate business scenarios. In addition, business innovation ideas can be solicited from internal employees or external partners through questionnaires or interviews, and these ideas can be compiled and summarized into candidate business scenarios.

[0017] Step S120: Use the prediction and evaluation model to evaluate the candidate business scenarios and obtain a business scenario evaluation list.

[0018] The prediction and evaluation model is used to evaluate the value of each candidate business scenario from multiple evaluation dimensions.

[0019] Evaluation dimensions refer to multiple different perspectives or standards used to measure the value of candidate business scenarios. These dimensions are designed to comprehensively reflect the intrinsic value and implementation conditions of the scenario, avoiding the bias of a single perspective. For example, the business value dimension focuses on the scenario's contribution to the company's strategic goals, economic benefits, and market competitiveness; the data support dimension focuses on the availability, quality, and integration difficulty of the data required to implement the scenario; and the implementation feasibility dimension focuses on technological maturity, required resource investment, and organizational readiness.

[0020] Specifically, for candidate business scenarios in the new energy vehicle sector, the predictive evaluation model can perform the following evaluation process: Business value dimension evaluation, for example, for the "battery lifecycle traceability" scenario, quantitative scoring is conducted based on expected revenue growth, strategic alignment, cost savings, and risk reduction; for the "precision marketing based on user driving behavior" scenario, indicators such as improved user conversion rate, increased repurchase rate, and increased revenue per vehicle are calculated and assigned values. Data support dimension evaluation, for example, assessing the availability, completeness, accuracy, and integration difficulty of the data required for each scenario, such as whether the data on vehicle operation, fault codes, and battery status required for the "predictive maintenance" scenario is complete, whether the quality meets standards, and the cost of cross-system integration. Implementation feasibility dimension evaluation, for example, scoring based on technology maturity, human and financial investment, project cycle, and organizational adaptability, such as whether the map and scheduling algorithms relied upon by the "intelligent charging navigation" scenario are mature, and whether the required R&D and maintenance resources are feasible.

[0021] The model calculates the scenario value assessment value of each candidate business scenario by weighting the scores of each dimension according to preset weights. Then, it sorts the scenarios from high to low according to the assessment values ​​to form a business scenario assessment list that includes scenario name, scores of each dimension, comprehensive score and priority level, providing a quantitative basis for subsequent target scenario selection.

[0022] Step S130: The top preset number of candidate business scenarios with the highest scenario value assessment values ​​in the business scenario assessment list are determined as target business scenarios representing development goals.

[0023] The scenario value assessment value refers to a numerical value derived by quantitatively evaluating the performance of candidate business scenarios across multiple assessment dimensions. This value objectively characterizes the overall value of each candidate business scenario, providing a basis for subsequent comparison and ranking.

[0024] Target business scenarios refer to those business scenarios that, after multi-dimensional value assessment and ranking, are identified as having the highest value and highly aligned with the company's development goals. These scenarios are typically considered key projects that the company should prioritize in terms of resource allocation for implementation and execution at specific stages.

[0025] Specifically, scenario value assessment values ​​can be directly sorted by numerical value. For example, all candidate business scenarios can be arranged from highest to lowest value to form a priority list. As an alternative implementation, scenario value assessment values ​​can be divided into different level ranges, such as "high value," "medium value," and "low value," and then sorted from highest to lowest level. Within the same level, a secondary sort can be performed based on specific numerical values. Furthermore, visualization tools can be used to display scenario value assessment values ​​in chart form, allowing for intuitive sorting based on the numerical values ​​within the chart.

[0026] The top 10 candidate business scenarios with the highest scenario value assessment values ​​are identified as the target business scenarios representing the development goals. Specifically, after ranking the scenario value assessment values, a preset number, such as the top 3 or top 5, can be used to directly select the top-ranked candidate business scenarios from the ranking results and identify them as target business scenarios. Alternatively, a minimum scenario value assessment value threshold can be set; all candidate business scenarios with values ​​above this threshold and ranking high are identified as target business scenarios. If the number of scenarios meeting the criteria exceeds a preset value, the top 100 are still selected. Furthermore, based on the ranking results, decision-makers can manually select the final target business scenario from the top-ranked scenarios according to the company's current strategic priorities and resource status, but the selection range is limited to a preset number.

[0027] It is understandable that this application, by introducing a multi-dimensional value assessment and ranking mechanism, effectively addresses the problems of subjective decision-making bias, delayed assessment results, and one-sidedness caused by single-dimensional considerations in traditional methods. By comprehensively and objectively quantifying the value of candidate business scenarios and prioritizing them based on the quantification results, it can help companies in the new energy vehicle industry accurately identify and focus on business scenarios with the highest return on investment and strategic value, thereby avoiding blind and inefficient resource investment and improving the success rate of digital transformation. In some of the embodiments described above in this application, a value assessment from multiple evaluation dimensions is proposed to objectively evaluate the value of a scenario. However, in the implementation process, the weight allocation lacks a basis, resulting in inaccurate or subjective evaluation results that cannot effectively reflect the strategic priorities of the enterprise.

[0028] In response, this application further proposes a method for confirming business scenarios, such as... Figure 2 As shown, the method includes: Step S210: Determine candidate business scenarios. See details in [link / reference]. Figure 1 Step S110 in the embodiment will not be described again here.

[0029] Step S220 involves evaluating candidate business scenarios using a predictive evaluation model to obtain a business scenario evaluation list. The predictive evaluation model is used to evaluate the value of each candidate business scenario from multiple evaluation dimensions. Specifically, this includes: Step S2201: Obtain corporate strategic information and convert it into the first weight value of the corresponding evaluation dimension. The evaluation dimension includes at least one of the following: business value dimension, data support dimension, and implementation feasibility dimension.

[0030] Specifically, this step aims to establish a direct link between corporate strategic goals and the importance of assessment dimensions, thereby ensuring the strategic orientation of the assessment process. Corporate strategic information can encompass various forms, including annual reports, strategic planning documents, and minutes of senior management meetings. Systems or human intervention can conduct in-depth analysis of this information to identify strategic priorities closely related to assessment dimensions such as business value, data support, and feasibility. For example, if a company's current strategic focus is on rapid market share expansion and breakthroughs in new business areas, the business value dimension may be assigned a higher first-weight value; conversely, if the strategy emphasizes the accumulation and utilization of data assets and the improvement of technological innovation capabilities, the data support and feasibility dimensions may receive higher first-weight values. The transformation process can be achieved through a pre-set rule base, expert systems, or machine learning-based models, effectively mapping qualitative strategic descriptions to quantitative first-weight values. Furthermore, corporate strategic information can also be specifically represented as a series of key performance indicators (KPIs) or explicit strategic goals, such as increasing specific market share or reducing operating costs. By defining the correlation strength between these strategic objectives and evaluation dimensions, and applying the corresponding transformation algorithm, the importance of strategic objectives can be directly converted into the first weight value of each evaluation dimension, ensuring the objectivity of weight allocation and strategic consistency.

[0031] Step S2202: For each candidate business scenario, calculate the score value for each evaluation dimension.

[0032] Specifically, this step aims to provide detailed and objective quantitative evidence for the performance of each candidate business scenario across various evaluation dimensions, laying a data foundation for subsequent weighted fusion. One approach is to organize experts in relevant fields, such as business experts, data architects, and technical experts, to professionally score each candidate business scenario in terms of business value, data support, and implementation feasibility, based on pre-defined scoring criteria. Simultaneously, quantitative analysis can be conducted using actual operational data. For example, for the business value dimension, the expected return on investment (ROI), market potential, or user growth rate can be analyzed; for the data support dimension, the completeness, accuracy, availability, and data governance level of existing data can be assessed; and for the implementation feasibility dimension, the maturity of the required technology stack, the capabilities of the project team, the required resource investment, and potential risks can be evaluated. Finally, the expert scores and data analysis results are combined to derive the score for each evaluation dimension. Another approach is to establish a detailed quantitative indicator system for each evaluation dimension. For example, the business value dimension can include sub-indicators such as "expected economic benefits," "strategic alignment," and "risk reduction benefits"; the data support dimension can include sub-indicators such as "data availability," "data quality level," and "data integration complexity"; and the implementation feasibility dimension can include sub-indicators such as "technology maturity," "required resource input," and "organizational readiness." By collecting objective data related to these sub-indicators and applying pre-set calculation models or algorithms, the score for each candidate business scenario on each evaluation dimension is automatically calculated.

[0033] Step S2203: Based on the score value of the corresponding evaluation dimension in each candidate business scenario and the first weight value corresponding to each evaluation dimension, the score values ​​are weighted and fused to obtain the scenario value evaluation value corresponding to each candidate business scenario.

[0034] Specifically, this step combines the specific performance of the scenario across various dimensions with the importance assigned to each dimension by the enterprise strategy, thereby generating a single quantitative indicator that comprehensively reflects the true value of the scenario. A common weighted fusion method is linear weighted summation. That is, for each candidate business scenario, its scenario value assessment value V can be calculated using the formula V=Σ(score i * first weight i), where i represents different assessment dimensions. For example, if the assessment dimensions include business value dimension, data support dimension, and implementation feasibility dimension, then the scenario value assessment value V can be expressed as: V=(business value score * business value first weight value) + (data support score * data support first weight value) + (implementation feasibility score * implementation feasibility first weight value). In addition, in some more complex scenarios, a multi-level weighted fusion model can also be used. For example, firstly, the sub-dimensions under each assessment dimension are weighted and fused to obtain the score value of that dimension, and then these dimension scores are weighted and fused with the first weight value. Alternatively, nonlinear functions or fuzzy logic can be introduced for fusion to better reflect the complex interactions or nonlinear effects between different dimensions. For example, through multiplication or exponential models, a low score in a certain dimension can have a greater negative impact on the overall evaluation value, thereby more rigorously screening out high-value scenarios.

[0035] Step S2204: Based on the scenario value assessment value, sort the candidate business scenarios to obtain the business scenario assessment list.

[0036] Step S230: The top 10 candidate business scenarios with the highest scenario value assessment values ​​in the business scenario assessment list are determined as the target business scenarios representing the development goals. (See details...) Figure 1 Step S130 in the embodiment will not be described again here.

[0037] It is understood that, through the technical solution described in this embodiment, the weights of the evaluation dimensions are no longer subjectively determined, but directly derived from the corporate strategy. This ensures a high degree of consistency between the evaluation process and the company's development goals, effectively solving the problems of lack of basis for weight allocation, inaccurate evaluation results, or subjectivity. This allows the evaluation results to accurately reflect the company's strategic priorities, avoiding blind and inefficient resource allocation. Simultaneously, by calculating the score for each evaluation dimension for each candidate business scenario, objective and detailed basic data is provided for subsequent weighted fusion. Finally, based on the strategic priority (i.e., the first weight value) and the actual performance of the scenario (i.e., the score value), a comprehensive and objective scenario value assessment is generated, ensuring that the identified target business scenario truly aligns with the company's development goals and possesses higher potential.

[0038] In some of the embodiments described above in this application, a weighted fusion method based on weight values ​​is proposed to evaluate the value of a scenario. However, in its implementation, the potential mutual support relationships between candidate business scenarios are not considered, resulting in the evaluation value not fully reflecting the true value of the scenario.

[0039] To address this, this application further proposes a weighted fusion of the score values ​​based on the corresponding evaluation dimensions in each candidate business scenario and the first weight value corresponding to each evaluation dimension, to obtain the scenario value evaluation value for each candidate business scenario. Figure 2 As shown, the method also includes: Step S240: Traverse each candidate business scenario and determine the data correlation between the current candidate business scenario and other candidate business scenarios.

[0040] Specifically, this assessment aims to identify whether there are synergies or dependencies between different candidate business scenarios, i.e., whether the implementation of one scenario can provide foundational data, technical support, or business prerequisites for the value realization of other scenarios. Data correlation is a correlation factor in evaluating the overall value of a scenario. For example, this can be determined through expert review meetings, where business experts, data experts, and technical experts jointly analyze the functions, data flows, and technical dependencies of each candidate business scenario to identify scenario pairs with data sharing, function reuse, or preconditional relationships. Alternatively, it can be determined by constructing scenario dependency graphs or matrices, for example, by analyzing the data sources required by each scenario, the data assets produced, and their business processes. If the output of one scenario is the input of another scenario, then a data correlation is considered to exist. In addition, natural language processing technology can be used to analyze scenario description documents and identify keyword correlations to assist in the assessment.

[0041] Step S250: If the data correlation degree meets the score compensation condition, then the scenario value assessment value of the current candidate business scenario is compensated.

[0042] Specifically, this score compensation step quantifies and incorporates the additional value generated by mutual support between scenarios, thus more comprehensively reflecting the true contribution of the scenario. Score compensation avoids underestimating business scenarios that serve as foundations or empower other high-value scenarios. For example, a fixed-ratio compensation method can be used; if a scenario is determined to provide numerical support for other scenarios, its scenario value assessment value can be increased by a preset percentage, such as 5% or 10%. Alternatively, a dynamic compensation method based on the scope of influence or importance can be used. This involves calculating a dynamic compensation coefficient or score based on the number of other scenarios supported by the current scenario, the potential value of the supported scenarios, or the closeness of the support relationship, and adding it to the current scenario's scenario value assessment value.

[0043] It is understood that, through the technical solution described in this embodiment, this application addresses the problem of biased evaluation results caused by neglecting the synergistic effects between scenarios in traditional evaluation methods by introducing a judgment and score compensation mechanism for the mutual support relationships between candidate business scenarios. Based on a weighted fusion of enterprise strategic information and scores from various evaluation dimensions to obtain a preliminary scenario value assessment, it further identifies "empowering" scenarios that can provide data, technology, or business support to other scenarios. By compensating the scores of these supporting scenarios, their strategic value and potential contribution within the entire business ecosystem can be more accurately reflected, avoiding underestimation due to their insignificant direct economic benefits. This makes the final scenario value assessment more comprehensive and objective, helping enterprises identify "foundational" business scenarios that not only have value themselves but also drive the realization of value in other scenarios, thereby optimizing resource allocation and improving the overall return on investment in digital transformation.

[0044] In some of the solutions mentioned above in this application, value assessment is proposed from multiple evaluation dimensions to objectively quantify the value of candidate business scenarios. However, in this process, the evaluation dimensions may be too macroscopic and lack detailed consideration, resulting in one-sided and inaccurate evaluation results that cannot fully reflect the true value of the business scenarios.

[0045] In some optional embodiments, this application further proposes to calculate the score value for each evaluation dimension for each candidate business scenario, specifically including: Step c1 involves quantifying each evaluation dimension in the candidate business scenario into multiple evaluation sub-dimensions.

[0046] Specifically, this step aims to refine the macro-level assessment dimensions into more specific and actionable measurement units. For example, by organizing expert workshops and using methods such as the Delphi method or brainstorming, the "business value dimension" can be broken down into multiple sub-dimensions such as "market potential," "user growth rate," and "cost saving potential." Alternatively, based on internal data analysis and industry best practices, a data-driven indicator system can be built to quantify the "data support dimension" into sub-dimensions such as "data availability," "data quality level," and "data integration complexity." This helps avoid the generality of the assessment and improves its precision.

[0047] Step c2: Based on the evaluation sub-dimensions, perform value evaluation on the corresponding evaluation dimensions and obtain the dimension evaluation value for each evaluation dimension.

[0048] Specifically, this step ensures that the value of each evaluation dimension is based on the specific performance of its constituent sub-dimensions rather than subjective experience. For example, a scoring standard, such as 1-5 points, can be set for each evaluation sub-dimension. Evaluators score each sub-dimension based on the candidate business scenario's performance on each sub-dimension, and then the scores of these sub-dimensions are aggregated into the dimensional evaluation value of that evaluation dimension using a weighted average method or a simple summation method. Alternatively, fuzzy comprehensive evaluation can be used to handle the fuzziness and uncertainty that may exist in the sub-dimension evaluation, thereby obtaining a more robust dimensional evaluation value.

[0049] Step c3: Sum the dimensional evaluation values ​​of each evaluation dimension to obtain the scenario value evaluation value corresponding to each candidate business scenario.

[0050] Specifically, this step integrates the evaluation values ​​of various assessment dimensions, such as business value, data support, and implementation feasibility, to form a comprehensive quantitative score that fully reflects the overall value of the candidate business scenario. For example, all dimension evaluation values ​​can be simply summed, which is suitable for situations where all dimensions are considered equally important; alternatively, different weights can be assigned to each evaluation dimension based on the company's strategic priorities, and then a weighted sum can be performed to more accurately reflect the contribution of each dimension to the overall value.

[0051] It is understood that, through the technical solution described in this embodiment, this application decomposes the originally macroscopic evaluation dimensions into more specific and operable sub-dimensions, effectively solving the problem of overly macroscopic dimensions and lack of detailed consideration in the evaluation process. Value assessment is conducted based on these sub-dimensions, and the evaluation values ​​of each dimension are finally summed, ensuring the objectivity, accuracy, and comprehensiveness of the evaluation results. This makes the value assessment of candidate business scenarios more comprehensive, more accurately reflecting their true value, thereby providing data support for enterprises to identify high-value scenarios with the greatest return on investment, avoiding blind and inefficient resource investment, and ultimately improving the scientific nature of business scenario confirmation and the effectiveness of decision-making. In some of the solutions mentioned above in this application, value assessment is proposed from multiple evaluation dimensions to objectively quantify the value of the scenario and determine the target business scenario. However, in this process, the assessment is static and cannot respond to changes in external events such as market information, policy information, competitive information or corporate strategic adjustment information. This results in the assessment results lagging behind the actual situation, making it impossible to dynamically optimize resource allocation decisions and causing blind and inefficient investment.

[0052] In response, this application further proposes a method for confirming business scenarios, such as... Figure 3 As shown, the method includes: Step S310: Determine candidate business scenarios. See details below. Figure 1Step S110 in the embodiment will not be described again here.

[0053] Step S320: The candidate business scenarios are evaluated using a predictive evaluation model to obtain a list of evaluated business scenarios. The predictive evaluation model is used to evaluate the value of each candidate business scenario from multiple evaluation dimensions. Specifically, this includes: Step S3201: Obtain corporate strategic information and convert it into the first weight value of the corresponding evaluation dimension. The evaluation dimension must include at least one of the following: business value dimension, data support dimension, and implementation feasibility dimension. (See details...) Figure 2 Step S2201 in the embodiment will not be described again here.

[0054] Step S3202: For each candidate business scenario, calculate the score for each evaluation dimension. See details in the attached document. Figure 2 Step S2202 in the embodiment will not be described again here.

[0055] Step S3203: Based on the score value of the corresponding evaluation dimension in each candidate business scenario and the first weight value corresponding to each evaluation dimension, a weighted fusion of the score values ​​is performed to obtain the scenario value evaluation value corresponding to each candidate business scenario. See details. Figure 2 Step S2203 in the embodiment will not be repeated here.

[0056] Step S3204: Based on the scenario value assessment value, the candidate business scenarios are sorted to obtain the business scenario assessment list. See details. Figure 2 Step S2204 in the embodiment will not be described again here.

[0057] Step S330: The top 10 candidate business scenarios with the highest scenario value assessment values ​​in the business scenario assessment list are determined as the target business scenarios representing the development goals. (See details below.) Figure 1 Step S130 in the embodiment will not be described again here.

[0058] Step S340: Traverse each candidate business scenario and determine the data correlation between the current candidate business scenario and other candidate business scenarios. See details... Figure 2 Step S240 in the embodiment will not be described again here.

[0059] Step S350: If the data correlation degree meets the score compensation condition, then score compensation is applied to the scenario value assessment value of the current candidate business scenario. (See details below.) Figure 2 Step S250 in the embodiment will not be described again here.

[0060] Step S360, in response to the triggering of event information, returns to the step of evaluating candidate business scenarios using a predictive evaluation model to obtain a business scenario evaluation list, wherein the event information includes at least one of the following: market information, policy information, competitive information, and corporate strategic adjustment information related to the candidate business scenarios.

[0061] In this context, triggering in response to event information refers to the system's ability to perceive and react to external or internal changes that impact the value of the business scenario. This can be achieved in ways including, but not limited to: one approach is to configure a scheduled task to periodically, for example, daily, weekly, or monthly, check preset data sources or interfaces to obtain the latest event information; another approach is to integrate a real-time monitoring module into the system, which subscribes to external data services, such as market analysis report APIs, policy release platforms, or internal business system message queues, and immediately triggers the subsequent reassessment process upon detecting the release or status update of relevant event information.

[0062] The step of returning to evaluate candidate business scenarios using a predictive evaluation model to obtain a list of evaluated business scenarios refers to the process by which the system re-executes the previously defined process of quantifying the value of candidate business scenarios based on multiple evaluation dimensions after the event information is triggered. This ensures that a comprehensive and consistent evaluation framework can still be used during dynamic adjustments. Its implementation methods may include, but are not limited to: one method is for the system to call the predictive evaluation model to perform a comprehensive, end-to-end value evaluation of all identified candidate business scenarios; another method is for the system to intelligently identify and re-evaluate only the specific candidate business scenarios or specific evaluation dimensions affected by the event information based on its specific content and scope of impact, thereby improving evaluation efficiency.

[0063] Event information includes at least one of the following related to the candidate business scenario: market information, policy information, competitive information, and corporate strategic adjustment information, clearly identifying the key factors triggering dynamic evaluation. Market information can refer to industry development trends, changes in consumer demand, and breakthroughs in emerging technologies; policy information can refer to issued industry regulations, subsidy policies, and environmental standards; competitive information can refer to competitors' new product launches, market share changes, and strategic partnerships; and corporate strategic adjustment information can refer to the company's internal annual strategic planning, adjustments to business priorities, and changes in resource allocation.

[0064] It is understood that, through the above-described technical solution in this embodiment, this application establishes a dynamic response mechanism, making the value assessment of business scenarios no longer a static, one-off process, but capable of adapting in real time to changes in the external environment and internal strategies. When market, policy, competition, or corporate strategy changes, the system can promptly detect and automatically trigger a reassessment process, using a multi-dimensional assessment framework to requantify the value of candidate business scenarios. This ensures the real-time nature and accuracy of scenario value assessment results, solves the problem of assessment lag, and avoids blind and inefficient resource investment caused by information asymmetry or outdated information. Based on the latest assessment results, it can more flexibly and accurately adjust its digital transformation direction and resource allocation, ensuring that the selected target business scenarios always have the highest value and best return on investment, improving the scientific nature and effectiveness of decision-making.

[0065] In some embodiments described above, a method is proposed to recalculate the scenario value assessment value in response to event information to dynamically adjust the assessment results. However, in its implementation, the weight values ​​are not updated according to the event information, resulting in the assessment results not accurately reflecting the latest situation. To address this, this application further proposes a step of returning to the evaluation of candidate business scenarios using a predictive evaluation model to obtain a list of business scenario assessments, including: Step a1: Based on the event information, redetermine the second weight value of the corresponding evaluation dimension. The event information includes one or more of the technical information, regulatory information, and market competition dynamics information associated with the candidate business scenario. The second weight value is used as the first weight value, and the step of calculating the score value of each evaluation dimension for each candidate business scenario is returned.

[0066] The re-determination of the second weight value for the corresponding evaluation dimensions refers to the process of dynamically adjusting the importance of the evaluation dimensions based on changes in the external environment or specific events. For example, a rule-based expert system can be used, with a set of pre-set rules to determine the impact of different types of event information, such as technological breakthroughs or policy changes, on the weights of various evaluation dimensions, such as business value, data support, and implementation feasibility, and adjust the weights accordingly. Alternatively, machine learning models can be used, trained on historical data, to automatically predict and output the optimal weight values ​​for each evaluation dimension based on the input event information. Furthermore, through human-computer interaction, domain experts can manually adjust and confirm the weights of each evaluation dimension based on event information and system-provided suggestions.

[0067] Event information is the external factor that triggers weight adjustments. For example, technical information can refer to major breakthroughs in new energy vehicle battery technology, updates to autonomous driving algorithms, etc.; regulatory information can refer to new environmental protection standards and data security regulations issued by the state, etc.; market competition dynamics information can refer to the release of new products by major competitors, changes in market share, and shifts in consumer preferences, etc. The system can acquire and identify this event information in various ways. For example, it can use web crawling technology to scrape information from public websites, such as news media, industry reports, and government announcements, and use natural language processing technology to perform semantic analysis to extract key information related to specific candidate business scenarios; or it can integrate with the enterprise's internal information system to obtain internal event information such as enterprise strategic adjustments and R&D progress; or it can have business analysts or decision-makers manually input and mark important external events.

[0068] After determining the new second weight value, this second weight value will be used as the new first weight value for subsequent evaluation calculations. The system will update its internally stored evaluation dimension weight parameters and then return to the step of calculating the score value for each evaluation dimension for each candidate business scenario. For example, the system can maintain a weight configuration table, and when the new second weight value is generated, the corresponding weight item in the configuration table will be directly updated. Subsequently, the evaluation module will re-call the calculation function, which will automatically read the updated weight configuration and, based on the score value of each candidate business scenario on each evaluation dimension, perform a weighted fusion calculation in combination with the new weight to obtain the updated scenario value evaluation value.

[0069] It is understood that, through the above-described technical solution in this embodiment, this application can effectively solve the problem of delayed evaluation results caused by the failure to update evaluation weights in a timely manner when response event information is triggered. Specifically, when event information associated with a candidate business scenario is received, the system no longer uses the old evaluation dimension weights, but intelligently or through preset logic re-determines the second weight values ​​of each evaluation dimension based on these event information. This second weight value is then adopted as the new first weight value and used to restart the evaluation dimension scoring calculation process for each candidate business scenario. This mechanism ensures that the evaluation model can reflect changes in the external environment in real time. For example, when a new technological breakthrough occurs, the weight of the technology maturity dimension may increase; when new regulations are introduced, the weight of the compliance or implementation feasibility dimension may be adjusted accordingly. Through dynamic weight adjustment and re-evaluation, this application can generate more accurate and timely scenario value assessments, enabling enterprises to more accurately identify the most promising target business scenarios when facing a rapidly changing market environment, avoiding decision-making biases and resource waste caused by outdated evaluation parameters, and significantly improving the adaptability and accuracy of business scenario selection.

[0070] In some of the embodiments described above in this application, the weight values ​​of the evaluation dimensions are redefined based on event information to dynamically adjust the scene value assessment. However, in this process, there is a lack of objective standards for how to specifically quantify the promoting or hindering effect of event information on the evaluation dimensions. This may lead to the weight adjustment relying on subjective judgment, failing to accurately reflect the actual impact of event information, and thus affecting the real-time performance and reliability of the evaluation results.

[0071] In response, this application further proposes to redetermine the second weight value of the corresponding evaluation dimension based on event information, including: Step b1: Determine the correlation between event information and evaluation dimensions.

[0072] The purpose of determining the correlation between event information and evaluation dimensions is to objectively identify the mutual influence between external dynamic event information and business scenario evaluation dimensions. This step provides a scientific basis for subsequent weight adjustments, avoiding subjective assumptions. Specifically, this can be achieved in several ways. For example, an expert knowledge base and rule engine can be established, pre-setting correlation rules between various types of event information, such as market information, policy information, and technical information, and different evaluation dimensions, such as business value, data support, and implementation feasibility. When the system receives new event information, the rule engine matches and judges it according to these pre-set rules to determine whether there is a correlation between the event information and a specific evaluation dimension, and the direction of that correlation. Another approach is to utilize historical data analysis and machine learning models. By collecting a large amount of historical event data, evaluation dimension weight adjustment records, and business scenario evaluation results, a machine learning model can be trained—for example, a model based on decision trees, support vector machines, or neural networks—to learn and identify correlation patterns between event information and evaluation dimensions from the data, thereby predicting the correlation—whether it is positive, negative, or uncorrelated.

[0073] Step b2: If the event information is positively correlated with the evaluation dimension, then increase the weight value of the evaluation dimension. Positive correlation is used to characterize that the event information has a positive effect on the evaluation dimension.

[0074] Specifically, after identifying a positive correlation, this step increases the weight value of the corresponding evaluation dimension to reflect the increased importance of that dimension in the current environment. Its purpose is to ensure that the evaluation of business scenarios can respond promptly to favorable external factors, prioritizing scenarios that become more valuable due to external promotion. In practice, a fixed-increment adjustment strategy can be used, where after confirming a positive correlation, the current weight value of the evaluation dimension is increased by a preset fixed value, such as 0.05 or 0.1. Alternatively, a proportional incremental adjustment strategy can be used, increasing the weight value proportionally based on the strength of the event information's promotional effect on the evaluation dimension. For example, if the event information is determined to have a "strong promotional effect" on a certain dimension, the weight increase is larger; if it has a "weak promotional effect," the increase is smaller. Furthermore, a weight adjustment function can be designed, which takes the correlation strength or event information type as input and outputs the corresponding weight increment, thereby achieving more refined adjustments.

[0075] Step b3: If the event information is negatively correlated with the evaluation dimension, then reduce the weight value of the evaluation dimension. Positive correlation is used to characterize that the event information has an hindering effect on the evaluation dimension.

[0076] Specifically, after identifying a negative correlation, this step reduces the weight of the corresponding evaluation dimension to reflect the decreased importance or increased risk of that dimension in the current environment. Its purpose is to avoid over-evaluating business scenarios whose value is diminished or whose implementation becomes more difficult due to external obstacles. In practice, a fixed reduction adjustment strategy can be used, where the current weight of the evaluation dimension is reduced by a preset fixed value after confirming a negative correlation. Alternatively, a proportional reduction adjustment strategy can be used, reducing the weight proportionally based on the strength of the event information's hindering effect on the evaluation dimension. For example, if the event information is determined to have a "strong hindering effect" on a certain dimension, the weight reduction ratio will be larger. Simultaneously, a weight lower limit can be set to ensure that even in the case of a strong negative correlation, the weight will not drop below zero, thus preserving the basic consideration value of that dimension.

[0077] I understand that, through the technical solution described in this embodiment, this application effectively solves the problem of how to objectively quantify the promoting or hindering effect of event information on evaluation dimensions during the dynamic adjustment of scenario value assessment. By clearly determining the correlation between event information and evaluation dimensions, and increasing or decreasing the weight of evaluation dimensions according to the direction of the correlation, the drawbacks of relying on subjective judgment in traditional methods are avoided, significantly improving the accuracy and reliability of weight adjustment. Furthermore, this solution, combined with the aforementioned step of re-determining the second weight value of the corresponding evaluation dimension based on event information, enables the value assessment of business scenarios to more accurately reflect changes in the external environment. When event information such as market, policy, and technology changes, the system can quickly identify the impact of these changes on different evaluation dimensions and dynamically adjust the weight of each dimension accordingly. For example, if a technological breakthrough makes the "technology maturity assessment sub-dimension" more critical, the system will increase its weight; if a policy has a negative impact on the "expected economic benefit assessment sub-dimension," its weight will be reduced accordingly. This dynamic and objective weighting adjustment mechanism ensures that the scenario value assessment of each candidate business scenario is always in sync with the latest external environment and corporate strategy, thereby providing enterprises with more timely and valuable decision support, effectively avoiding the blindness and inefficiency of resource investment, and improving the scientificity and adaptability of business scenario selection. In some optional embodiments, this application further proposes that the scenario value assessment value corresponding to the candidate business scenario is calculated by the following expression: V = Wb*Vb + Wd * Vd + Wf*Vf, where V is the scenario value assessment value, Wb is the business value weight, Wd is the data support weight, Wf is the implementation feasibility weight, Vb is the business value assessment value, Vd is the data support assessment value, and Vf is the implementation feasibility assessment value.

[0078] Here, V = Wb*Vb + Wd * Vd + Wf*Vf is a weighted linear combination model used to synthesize the evaluation values ​​of multiple evaluation dimensions into a single scenario value assessment value. By assigning different weights to different evaluation dimensions, the relative importance of each dimension in the overall value composition can be more accurately reflected, thus avoiding evaluation biases that may be caused by simple summation. This expression can be implemented by integrating a calculation module into the business scenario evaluation system, which receives the evaluation values ​​of each dimension and preset weights as input and outputs the final scenario value assessment value; alternatively, it can be implemented through a configurable data analysis tool that allows users to customize weights and perform weighted summation operations. The scenario value assessment value V mentioned above refers to a comprehensive indicator obtained after weighted calculation, used to quantify the overall value of candidate business scenarios. This value is a key quantitative basis for measuring the potential return on investment and strategic significance of a business scenario, and its role is to provide a unified and comparable numerical value for ranking and selecting different candidate business scenarios. For example, the value could be a standardized score between 0 and 100, with a higher score indicating greater scenario value; or, the value could be a dimensionless composite index used directly for prioritizing scenarios.

[0079] The aforementioned business value weight Wb represents a numerical factor indicating the relative importance of the business value dimension in the overall scenario value assessment. This weight ensures the proportion of the business value assessment value Vb in the final scenario value assessment value V. For example, this weight can be set by senior management based on current strategic priorities and the market environment; or it can be determined through multi-expert decision-making to reflect the perspectives of different stakeholders. The aforementioned data support weight Wd represents a numerical factor indicating the relative importance of the data support dimension in the overall scenario value assessment. This weight adjusts the contribution of the data support assessment value Vd to the final scenario value assessment value V, reflecting the company's emphasis on data infrastructure and the crucial role of data in specific business scenarios. For example, this weight can be dynamically adjusted based on the company's data maturity, data governance level, and the richness of its data assets; or it can be set based on the degree of dependence of a specific business scenario on data quality and availability. The aforementioned implementation feasibility weight Wf represents a numerical factor indicating the relative importance of the implementation feasibility dimension in the overall scenario value assessment. The purpose of this weight is to adjust the contribution of the feasibility assessment value Vf to the final scenario value assessment value V, ensuring that the technical difficulty of implementation, resource investment requirements, and organizational acceptance are fully considered when evaluating the value of a business scenario. For example, this weight can be determined based on the company's existing technical capabilities, project management experience, and resource reserves; or it can be set with reference to the success rate and complexity of similar projects in the industry.

[0080] The aforementioned business value assessment value Vb refers to a numerical value that quantifies the value that a candidate business scenario can generate at the business level. This value reflects the scenario's potential to increase revenue, reduce costs, enhance customer experience, or align with corporate strategy. For example, this value can be derived by comprehensively scoring sub-dimensions such as expected economic benefits, strategic alignment, and risk reduction benefits; or it can be calculated using methods such as financial model forecasting and market potential analysis. The aforementioned data support assessment value Vd refers to a numerical value that quantifies the availability, quality, and integration difficulty of the data required for the candidate business scenario. It reflects the support capability of existing data assets for the business scenario and the data investment required to realize the scenario. For example, this value can be derived by comprehensively scoring sub-dimensions such as data availability, data quality level, and data integration complexity; or it can be obtained through data asset inventory and data quality inspection reports. The aforementioned implementation feasibility assessment value Vf refers to a numerical value that quantifies the difficulty of implementing the candidate business scenario at the technical, resource, and organizational levels. It reflects the technological maturity, resource investment, and organizational readiness required to transform the business scenario from concept to reality. For example, this value can be obtained by comprehensively scoring sub-dimensions such as technology maturity, required resource input, and organizational readiness; or it can be obtained through methods such as technology stack assessment, resource budget analysis, and organizational capability assessment.

[0081] It is understood that, through the above technical solution in this embodiment, this application solves the problem of simply summing and ignoring weights by introducing a weighted expression to calculate the scenario value assessment value, thereby improving the accuracy and objectivity of the assessment. Specifically, when assessing the value of candidate business scenarios, each assessment dimension in the candidate business scenario is first quantified into multiple assessment sub-dimensions, and the dimension assessment value of each assessment dimension is obtained based on these assessment sub-dimensions. On this basis, this application no longer simply sums the dimension assessment values ​​of each assessment dimension, but adopts a weighted fusion method. It can adjust the contribution ratio of different assessment dimensions in the overall assessment according to their importance. For example, when the enterprise strategy focuses more on quickly realizing business value, the business value weight Wb can be appropriately increased, so that scenarios with high business value can obtain a higher comprehensive score. Conversely, if the enterprise's current data foundation is weak, the data support weight Wd can be increased to prioritize scenarios with high data support. In this way, the evaluation values ​​can more accurately reflect the relative importance of each dimension, avoid evaluation bias caused by uneven dimension weights, and thus support more scientific business scenario decisions that are more in line with the actual strategic needs of enterprises, effectively avoiding the blindness and inefficiency of resource investment.

[0082] In some optional embodiments, this application further proposes that the evaluation sub-dimensions in the business value dimension include the expected economic benefit evaluation sub-dimension, the strategic alignment evaluation sub-dimension, and the risk reduction benefit evaluation sub-dimension; the evaluation sub-dimensions in the data evaluation dimension include the data availability evaluation sub-dimension, the data quality level evaluation sub-dimension, and the data integration complexity evaluation sub-dimension; and the evaluation sub-dimensions in the implementation feasibility dimension include the technology maturity evaluation sub-dimension, the required resource input evaluation sub-dimension, and the organizational readiness evaluation sub-dimension.

[0083] The expected economic benefits assessment sub-dimension aims to quantify the potential economic benefits that a business scenario may bring. Specifically, this can be assessed by predicting future revenue growth, cost savings, and profit increases through financial indicators. For example, market research, historical data analysis, and expert interviews can be used to estimate the net present value or return on investment that the business scenario may generate over a specific future period. Alternatively, specific economic goals can be set, such as increasing market share, reducing operating costs, and increasing customer lifetime value, and the likelihood and extent to which the business scenario can achieve these goals can be evaluated. The strategic alignment assessment sub-dimension measures the consistency between the business scenario and the company's overall strategic goals. This can be achieved by comparing the business scenario with the company's annual strategic plan and long-term development goals to assess the extent to which the business scenario supports or promotes the achievement of these strategic goals. For example, if the company's strategy is to expand into emerging markets, the contribution of the business scenario to this expansion can be assessed. Another approach is to have the strategy department or senior management assess the alignment between the business scenario and the company's strategy through expert scoring, questionnaires, etc., quantifying it using dimensions such as "high alignment," "partial alignment," and "no alignment." The risk reduction benefit assessment sub-dimension is used to evaluate the ability of a business scenario to mitigate potential risks. Specifically, this can be achieved by identifying the types of risks that can be avoided after the implementation of the business scenario, such as market risk, operational risk, compliance risk, and technological risk, and quantifying the probability of these risks occurring and their potential losses, thereby assessing the effectiveness of the business scenario in reducing these risks. For example, introducing a new data analysis model can reduce the incidence of fraud. Furthermore, the contribution of the business scenario to enterprise resilience, crisis response capabilities, and compliance improvements can also be evaluated; for instance, establishing a more robust data governance system can reduce the compliance risk of data breaches.

[0084] The data availability assessment sub-dimensional aims to determine whether the data required for a business scenario is available and accessible. Specifically, this can be achieved by taking stock of existing internal data assets to assess the existence, storage location, and access permissions of the required data. For example, checking whether relevant business systems have recorded the required data and whether data interfaces are open. Another approach is to assess the availability, cost, and compliance of external data sources, such as public datasets and third-party data services. For instance, assessing whether the required market data can be obtained through purchase or partnership. The data quality level assessment sub-dimensional is used to evaluate the accuracy, completeness, consistency, and timeliness of the data required for a business scenario. This can be achieved by cleaning and validating sample data, calculating metrics such as error rate, missing rate, and duplication rate to quantify data quality. For example, sampling customer information data to assess its accuracy and completeness. Furthermore, automated quality checks can be performed using data governance tools, combined with business experts' understanding of the data's business implications, to assess whether the data meets the needs of business analysis and decision-making. The data integration complexity assessment sub-dimensional measures the difficulty of integrating different data sources. Specifically, the complexity of integration can be assessed by evaluating the number and heterogeneity of the required data sources, such as differences in data format, storage systems, data models, data volume, and data transfer frequency. For example, integrating customer data from different departments and databases requires considering the complexity of the data standardization process. Another approach is to quantify the difficulty of data integration by evaluating the capabilities of existing data integration tools and platforms, as well as the required development workload and technical personnel skills.

[0085] The Technology Maturity Assessment sub-dimensional aims to evaluate the maturity of the technologies required for the business scenario. Specifically, it can assess, by referring to the Technology Maturity Level (TML) model, whether the key technologies relied upon by the business scenario, such as AI algorithms, big data platforms, and cloud computing services, are widely used in the industry and whether there are mature solutions or products supporting them. Furthermore, it can assess the company's internal technical team's mastery of the relevant technologies, their experience in successfully implementing similar technologies, and whether there are any technical risks or bottlenecks. The Required Resource Input Assessment sub-dimensional quantifies the resource costs required to implement the business scenario. This can be achieved by estimating the human resources required for the project, such as developers, data scientists, project managers, hardware resources such as servers and storage devices, software resources such as licenses and tools, and financial investment, to quantify resource costs. Another approach is to compare with historical data from similar projects or consult external experts to conduct a detailed budget and assessment of each resource input. The Organizational Readiness Assessment sub-dimensional aims to determine the readiness of the company's organizational structure and processes for implementing the business scenario. Specifically, this can be achieved by assessing the collaboration mechanisms and communication efficiency between relevant business departments and IT departments, as well as whether there is a clear division of responsibilities and support processes. For example, assessing whether business departments are willing to adopt new solutions and whether the IT department can provide timely technical support. Furthermore, it can also be assessed by evaluating the company culture's acceptance of innovation, whether employees' skill levels meet the needs of new business scenarios, and whether organizational restructuring or personnel training is necessary.

[0086] It is understood that, through the technical solutions described in this embodiment, this application defines specific sub-dimensions for each evaluation dimension, constructing a more detailed and comprehensive evaluation framework. Specifically, by introducing sub-dimensions for expected economic benefits, strategic alignment, and risk reduction benefits, the evaluation of business value is no longer limited to a single economic indicator, but can comprehensively consider its supporting role in corporate strategy and risk avoidance capabilities, thereby avoiding the one-sidedness of the evaluation. Simultaneously, through sub-dimensions for data availability, data quality level, and data integration complexity, the evaluation of data support capabilities becomes more in-depth and objective, fully considering the difficulty of data acquisition, intrinsic quality, and integration complexity, ensuring the accuracy of the data-based evaluation. Furthermore, through sub-dimensions for technology maturity, required resource input, and organizational readiness, the feasibility assessment covers multiple key aspects such as technology, resources, and organization, making the evaluation results more operational and reliable. These refined sub-dimensions serve as a foundation, providing more accurate and convincing quantitative evidence for the business value assessment, data support assessment, and implementation feasibility assessment required when calculating the scenario value assessment value V in the above methods. This enhances the comprehensiveness, objectivity, and accuracy of business scenario value assessment, effectively solving the problem of insufficient comprehensiveness and objectivity in assessment, and enabling enterprises to more accurately identify the high-value scenarios with the greatest return on investment.

[0087] In one example, the following provides a more detailed explanation of the above technical solution through a more specific example: Company A, a new energy vehicle manufacturer, is currently undergoing digital transformation. The challenge for Company A is how to objectively, comprehensively, and dynamically identify the most valuable scenarios from numerous potential digital business opportunities, in order to avoid blind and inefficient resource allocation. Traditional decision-making methods often rely on the experience and judgment of management, resulting in a single evaluation dimension and assessment results that are difficult to adapt to market changes once formed, leading to a disconnect between the assessment results and reality.

[0088] The method in this application first identifies candidate business scenarios. Company A, through internal discussions, market research, and analysis of existing business processes, identified several potential digital business scenarios, such as "smart charging pile operation optimization," "supply chain finance risk warning," "personalized marketing driven by user behavior data," and "predictive maintenance of production lines." These scenarios constitute the initial set of candidate business scenarios.

[0089] Next, this method uses a predictive evaluation model to assess the candidate business scenarios, resulting in a list of evaluated business scenarios. Unlike traditional evaluation methods that only focus on economic benefits, this method introduces a multi-dimensional, quantitative evaluation system. Specifically, the evaluation dimensions include at least business value, data support, and implementation feasibility.

[0090] To make the assessment more refined and objective, this method quantifies each assessment dimension into multiple sub-dimensions. For example, the sub-dimensions of the business value dimension include the expected economic benefit assessment sub-dimension, the strategic alignment assessment sub-dimension, and the risk reduction benefit assessment sub-dimension; the sub-dimensions of the data support dimension include the data availability assessment sub-dimension, the data quality level assessment sub-dimension, and the data integration complexity assessment sub-dimension; and the sub-dimensions of the implementation feasibility dimension include the technology maturity assessment sub-dimension, the required resource input assessment sub-dimension, and the organizational readiness assessment sub-dimension.

[0091] For each candidate business scenario, such as the "smart charging pile operation optimization" scenario, the system will evaluate the value of the corresponding evaluation dimensions based on these evaluation sub-dimensions, and obtain the dimension evaluation value for each evaluation dimension. For example, by evaluating the performance of the "smart charging pile operation optimization" scenario in improving charging pile utilization, aligning with Company A's strategy to expand its charging service ecosystem, and reducing operational failures, its business value evaluation value is obtained; by evaluating the availability, quality, and integration difficulty of the required data, its data support evaluation value is obtained; and by evaluating the maturity of the required technology, resource investment, and organizational acceptance, its implementation feasibility evaluation value is obtained.

[0092] To reflect Company A's current strategic priorities, the system acquires the company's strategic information and converts it into the first weight value for the corresponding evaluation dimensions. For example, if Company A's current strategic priority is "improving user experience," the weight of the "strategic alignment" sub-dimension within the business value dimension may increase; if the priority is "reducing operating costs," the weight of the "expected economic benefits" sub-dimension may increase. These weight values ​​are dynamically determined based on the company's strategic information.

[0093] Subsequently, the system performs a weighted fusion of the scores for each candidate business scenario based on the scores of the corresponding evaluation dimensions and the first weight value of each evaluation dimension, to obtain the scenario value assessment value for each candidate business scenario. This assessment value is calculated using the expression V=Wb*Vb + Wd * Vd + Wf*Vf. This avoids the bias caused by subjective judgment in traditional assessments, making the scenario value assessment more objective and comprehensive.

[0094] After obtaining the scenario value assessment value, the system will also determine whether the current candidate business scenario can provide numerical support for other candidate business scenarios. For example, the "smart charging pile operation optimization" scenario will generate a large amount of charging behavior data and equipment operation data during implementation. This data can provide key data support for the "user behavior data-driven personalized marketing" scenario. If the current scenario can provide numerical support for other scenarios, the system will compensate for the current scenario's scenario value assessment value to reflect its additional value as a data foundation or enabling scenario, making up for the shortcomings of traditional assessments that only focus on the direct benefits of a single scenario and ignore its synergistic value.

[0095] Finally, this method ranks all candidate business scenarios by their scenario value assessment values ​​and identifies the top three candidate business scenarios with the highest scenario value assessment values ​​as the target business scenarios representing the development goals. For example, Company A initially selects the top three scenarios as its key development goals for the current stage. After the above multi-dimensional, weighted fusion, and compensation-based evaluation and ranking, "intelligent charging pile operation optimization," "predictive maintenance of production lines," and "personalized marketing driven by user behavior data" are ultimately identified as Company A's target business scenarios for the current stage. The decision-making process based on quantitative evaluation is significantly superior to traditional decision-making methods that rely on subjective experience, ensuring the accuracy of resource allocation.

[0096] Furthermore, this method also possesses dynamic adjustment capabilities to address the assessment lag issues caused by market and strategic changes. In response to event-triggered information, such as the emergence of new charging technology standards in the market, the government's introduction of new energy vehicle subsidy policies, competitors launching innovative service models, or Company A adjusting its annual strategy, the system will return to the step of assessing the value of each candidate business scenario from multiple evaluation dimensions, recalculating the scenario value assessment value corresponding to each candidate business scenario.

[0097] Specifically, when event information, such as "the release of a new charging technology standard," is triggered, the system redetermines the second weight value of the corresponding evaluation dimension based on this event information. The system assesses the correlation between the event information and the evaluation dimension. If the technology standard has a positive effect on the "technology maturity assessment sub-dimension" within the "implementation feasibility dimension," the weight value of that evaluation dimension is increased; if the technology standard introduces a higher technological threshold, the weight value of that evaluation dimension is decreased. Subsequently, the system uses this new second weight value as the first weight value and returns to the step of calculating the scores of each evaluation dimension, re-weighting and integrating them to obtain the updated scenario value assessment value. Through this dynamic adjustment mechanism, Company A can ensure that its target business scenario remains consistent with the latest market environment and corporate strategy, avoiding the lag problem caused by traditional static assessments.

[0098] In summary, this method provides a comprehensive, objective, and real-time business scenario confirmation solution through multi-dimensional quantitative evaluation, corporate strategy weighted integration, inter-scenario value compensation, and dynamic adjustment mechanisms. It effectively solves the problems of subjective decision-making, static evaluation, and single-dimensional consideration faced in the digital transformation of the new energy vehicle industry, and significantly improves the efficiency and success rate of enterprise digital transformation.

[0099] Figure 4 A schematic diagram of an embodiment of the business scenario verification device of this application is shown. Figure 4 As shown, the device 400 includes: a first determining module 410, an evaluation module 420, and a second determining module 430.

[0100] The first determining module 410 is used to determine candidate business scenarios.

[0101] The evaluation module 420 is used to evaluate candidate business scenarios using a predictive evaluation model to obtain a list of business scenario evaluations. The predictive evaluation model is used to evaluate the value of each candidate business scenario from multiple evaluation dimensions.

[0102] The second determining module 430 is used to determine the top preset number of candidate business scenarios with the highest scenario value assessment value in the business scenario assessment list as the target business scenarios that represent the development goals.

[0103] In an alternative embodiment, the evaluation module 420 includes: The first unit is used to acquire corporate strategic information and transform it into the first weight value of the corresponding evaluation dimension. The evaluation dimension includes at least one of the following: business value dimension, data support dimension, and implementation feasibility dimension.

[0104] The second unit is used to calculate the score for each evaluation dimension for each candidate business scenario.

[0105] The third unit is used to perform weighted fusion of the score values ​​based on the corresponding evaluation dimensions in each candidate business scenario and the first weight value corresponding to each evaluation dimension, so as to obtain the scenario value evaluation value corresponding to each candidate business scenario.

[0106] The fourth unit is used to sort candidate business scenarios based on their scenario value assessment values ​​to obtain a business scenario assessment list.

[0107] The fifth unit is used to traverse each candidate business scenario and determine the data correlation between the current candidate business scenario and other candidate business scenarios.

[0108] The sixth unit is used to compensate the scenario value assessment value of the current candidate business scenario if the data correlation meets the score compensation conditions.

[0109] Figure 5 The diagram shows a structural schematic of an embodiment of the business scenario confirmation device of this application. The specific embodiments of this application do not limit the specific implementation of the business scenario confirmation device.

[0110] like Figure 5 As shown, the device for confirming this business scenario may include: processor 502, communication interface 504, memory 506, and communication bus 508.

[0111] The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508. Communication interface 504 is used to communicate with other network elements, such as clients or other servers. The processor 502 executes program 510, specifically performing the relevant steps described in the above-described embodiment of the business scenario confirmation method.

[0112] Specifically, program 510 may include program code, which includes computer-executable instructions.

[0113] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The business scenario confirms that the device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0114] Memory 506 is used to store program 510. Memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0115] Specifically, program 510 can be called by processor 502 to enable the business scenario to confirm the device to perform the following operations: Identify candidate business scenarios; evaluate the candidate business scenarios using a predictive evaluation model to obtain a business scenario evaluation list, wherein the predictive evaluation model is used to evaluate the value of each candidate business scenario from multiple evaluation dimensions; and determine the top 10 candidate business scenarios with the highest scenario value evaluation values ​​in the business scenario evaluation list as target business scenarios representing development goals.

[0116] In an alternative approach, program 510 is invoked by processor 502 to cause the business scenario to confirm that the device performs the following operations: Acquire corporate strategic information and transform it into the first weight value of the corresponding evaluation dimensions. The evaluation dimensions include at least one of the following: business value dimension, data support dimension, and implementation feasibility dimension. For each candidate business scenario, calculate the score value of each evaluation dimension. Based on the score value of the corresponding evaluation dimension in each candidate business scenario and the first weight value of each evaluation dimension, perform a weighted fusion of the score values ​​to obtain the scenario value evaluation value for each candidate business scenario. Based on the scenario value evaluation values, sort the candidate business scenarios to obtain the business scenario evaluation list.

[0117] This application provides a computer-readable storage medium storing at least one executable instruction. When the executable instruction is run on a business scenario confirmation device / app, it causes the business scenario confirmation device / app to perform the business scenario confirmation method in any of the above method embodiments.

[0118] Specifically, executable instructions can be used to enable a business scenario to confirm that the device / receiver performs the following operations: Identify candidate business scenarios; evaluate the candidate business scenarios using a predictive evaluation model to obtain a business scenario evaluation list, wherein the predictive evaluation model is used to evaluate the value of each candidate business scenario from multiple evaluation dimensions; and determine the top 10 candidate business scenarios with the highest scenario value evaluation values ​​in the business scenario evaluation list as target business scenarios representing development goals.

[0119] In one alternative approach, the executable instructions cause the business scenario confirmation device / device to perform the following operations: acquire enterprise strategic information and convert the enterprise strategic information into a first weight value for the corresponding evaluation dimension, wherein the evaluation dimension includes at least one of the following: business value dimension, data support dimension, and implementation feasibility dimension; calculate the score value for each evaluation dimension for each candidate business scenario; perform a weighted fusion of the score values ​​for the corresponding evaluation dimensions in each candidate business scenario and the first weight value for each evaluation dimension to obtain the scenario value evaluation value for each candidate business scenario; and sort the candidate business scenarios based on the scenario value evaluation values ​​to obtain a business scenario evaluation list.

[0120] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments in this application are not directed to any particular programming language.

[0121] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. Similarly, for the purpose of simplification and aiding understanding of one or more aspects of the invention, in the above description of exemplary embodiments of this application, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0122] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

[0123] It should be noted that the above embodiments are illustrative of this application and not restrictive, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for confirming a business scenario, characterized in that, The method includes: Identify candidate business scenarios; The candidate business scenarios are evaluated using a predictive evaluation model to obtain a business scenario evaluation list. The predictive evaluation model is used to evaluate the value of each candidate business scenario from multiple evaluation dimensions. The top 10 candidate business scenarios with the highest scenario value assessment values ​​in the business scenario assessment list are identified as target business scenarios representing development goals.

2. The method according to claim 1, characterized in that, The process of evaluating the candidate business scenarios using a predictive evaluation model yields a business scenario evaluation list, including: Obtain corporate strategic information and convert the corporate strategic information into the first weight value of the corresponding evaluation dimension, wherein the evaluation dimension includes at least one of the following: business value dimension, data support dimension, and implementation feasibility dimension. For each of the candidate business scenarios, a score value for each evaluation dimension is calculated. The score values ​​of each candidate business scenario are weighted and fused with the first weight value corresponding to each evaluation dimension, and the scenario value evaluation value corresponding to each candidate business scenario is obtained. Based on the scenario value assessment, the candidate business scenarios are sorted to obtain the business scenario assessment list.

3. The method according to claim 2, characterized in that, For each of the candidate business scenarios, a score value for each evaluation dimension is calculated, including: Each evaluation dimension in the candidate business scenario is quantified into multiple evaluation sub-dimensions; Based on the evaluation sub-dimensions, value evaluation is performed on the corresponding evaluation dimensions to obtain the dimension evaluation value for each evaluation dimension; The dimensional evaluation values ​​of each of the evaluation dimensions are summed to obtain the scenario value evaluation value corresponding to each candidate business scenario.

4. The method according to claim 2, characterized in that, After obtaining the scenario value assessment value for each candidate business scenario by weighted fusion of the score values ​​of the corresponding evaluation dimensions in each candidate business scenario and the first weight value corresponding to each evaluation dimension, the method further includes: Iterate through each candidate business scenario and determine the data correlation between the current candidate business scenario and other candidate business scenarios; If the data correlation meets the score compensation condition, then the scenario value assessment value of the current candidate business scenario will be compensated.

5. The method according to claim 2, characterized in that, The method further includes: In response to the triggering of event information, the step of evaluating the candidate business scenarios using a predictive evaluation model to obtain a business scenario evaluation list is returned, wherein the event information includes at least one of the following: market information, policy information, competitive information, and corporate strategic adjustment information associated with the candidate business scenarios.

6. The method according to claim 5, characterized in that, The step of returning the evaluation list of candidate business scenarios by using the predictive evaluation model includes: Based on the event information, the second weight value of the corresponding evaluation dimension is re-determined, wherein the event information includes one or more of the technical information, regulatory information, and market competition dynamics information associated with the candidate business scenario; The steps are as follows: using the second weight value as the first weight value, the score value for each evaluation dimension is calculated for each candidate business scenario.

7. The method according to claim 6, characterized in that, The step of re-determining the second weight value of the corresponding evaluation dimension based on the event information includes: Determine the correlation between the event information and the evaluation dimensions; If the event information is positively correlated with the evaluation dimension, then the weight value of the evaluation dimension is increased. The positive correlation is used to characterize that the event information has a positive effect on the evaluation dimension. If the event information is negatively correlated with the evaluation dimension, the weight value of the evaluation dimension is reduced. The positive correlation is used to characterize that the event information has an hindering effect on the evaluation dimension.

8. A business scenario confirmation device, characterized in that, The device includes: The first determination module is used to determine candidate business scenarios; An evaluation module is used to evaluate the candidate business scenarios using a predictive evaluation model to obtain a business scenario evaluation list, wherein the predictive evaluation model is used to evaluate the value of each candidate business scenario from multiple evaluation dimensions. The second determining module is used to determine the top preset number of candidate business scenarios with the highest scenario value assessment value in the business scenario assessment list as the target business scenarios representing the development goals.

9. A business scenario confirmation device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation of the business scenario confirmation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when run on the business scenario confirmation device / app, causes the business scenario confirmation device / app to perform the operation of the business scenario confirmation method as described in any one of claims 1-7.

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