Expert ability bid evaluation method based on digital simulation environment technology

Abstract

The invention discloses an expert ability bid evaluation method based on a digital simulation environment technology, and the method comprises the following steps: S1, constructing a digital simulation environment, simulating an actual scene in a bid evaluation process, building a virtual bid evaluation environment through the digital simulation technology, simulating a complex and changeable bid evaluation situation, and employing a nonlinear narrative structure in the simulation environment; according to the method, the complexity and reality sense of a digital simulation environment are remarkably improved by introducing a nonlinear narrative structure, the nonlinear narrative structure enables experts to make decisions under multiple situations by constructing a plurality of parallel decision paths and dynamic situation changes, so that the method is closer to the complex decision process of the real world, and the method is suitable for popularization and application. The method not only enhances the strain capability and decision-making flexibility of the experts, but also improves the interactivity and depth of the decision-making process, so that the coping capability and decision-making quality of the experts in a complex and dynamic environment can be more comprehensively evaluated in the bid evaluation process, and the comprehensive development of the experts in multi-dimensional decision making is further promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of expert capability evaluation, and in particular relates to an expert capability evaluation method based on digital simulation environment technology. Background Art

[0002] With the advancement of technology, the use of expert systems in bid evaluation is becoming more and more common. However, traditional expert bid evaluation methods usually rely on the subjective judgment of experts, making it difficult to fully quantify the experts' abilities and performance.

[0003] The expert capability evaluation methods in existing technologies lack the ability to adapt to complex and changing situations when evaluating expert capabilities. The linear structure usually assumes that the decision-making process is a one-way, sequential process, ignoring the variability and unexpected results that may exist in the real environment. This makes it impossible for expert evaluation to comprehensively examine the adaptability and decision-making depth of experts in complex environments, resulting in overly single or one-sided evaluation results that cannot fully reflect the expert's ability and adaptability to deal with multiple decision-making paths, and the actual evaluation effect is poor. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides an expert capability evaluation method based on digital simulation environment technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for evaluating expert competence based on digital simulation environment technology, comprising the following steps:

[0006] S1. Build a digital simulation environment to simulate the actual scenarios in the bid evaluation process

[0007] A virtual bid evaluation environment is established through digital simulation technology to simulate complex and ever-changing bid evaluation scenarios. The simulation environment adopts a non-linear narrative structure, supports multi-level decision paths and changes in complex scenarios, and provides multiple options at each decision node, which affect the subsequent evolution of scenarios and bid evaluation results.

[0008] S2. Collect and input experts’ background information, professional fields, and historical bid evaluation experience data

[0009] The system collects expert data, including personal information, professional background, and historical bid evaluation experience. It also incorporates sentiment analysis and social feedback mechanisms to assess the impact of expert emotional fluctuations on decision-making in real time and adjust the expert's decision-making path through feedback.

[0010] S3. Simulate experts to evaluate bids in a simulation environment and record the experts’ decision-making process and evaluation performance

[0011] When experts evaluate bids in a simulation environment, the system records their decision-making process and performance, focusing on the adverse impact of their decisions. Furthermore, a reverse scoring system is introduced to simulate the consequences of incorrect decisions and provide real-time feedback to the experts, helping them to correct themselves in subsequent reviews.

[0012] S4. Define competency assessment indicators, including expert knowledge depth, judgment accuracy, and scoring consistency.

[0013] New evaluation dimensions are introduced, including decision-making efficiency, emotional regulation ability, and the ability to correct cognitive biases. Combined with sentiment analysis and social feedback mechanisms, this approach quantitatively assesses experts' emotional fluctuations, cognitive biases, and social pressure factors during the decision-making process.

[0014] S5. Use expert systems to quantitatively evaluate expert capabilities based on their performance in the simulation environment

[0015] Based on the experts' scores and decision-making records in the simulation environment, the system uses adaptive cognitive bias correction and emotional feedback mechanisms to quantitatively evaluate the experts' abilities. The system dynamically monitors the experts' decision-making paths, corrects cognitive biases in real time, and provides AI-based recommendation algorithms to help experts break out of their single-minded thinking frameworks.

[0016] S6. Generate expert capability scoring report and provide targeted capability improvement suggestions

[0017] After the comprehensive assessment of expert capabilities is completed, the system generates an expert capability scoring report, including the expert's comprehensive score, and uses a deep retrospective analysis mechanism to display the expert's decision-making process, running a simulation hypothesis consequence program to display the corresponding results of different decision paths.

[0018] Preferably, in said S1, the nonlinear narrative structure design includes: plot complexity, situational change factors and interdisciplinary scenario simulation, and a scenario interaction system with multi-level and dynamic plot generation is designed to simulate the complex and sudden change situations faced by experts in the bid evaluation process, including: project plan modifications, time delays and external events, and each decision node generates different situational feedback.

[0019] Preferably, in S2, the system monitors the expert's speech, decision-making process, and reaction time behavior in real time based on a sentiment analysis algorithm to identify his or her emotional fluctuations, including anxiety and overconfidence.

[0020] Preferably, in said S2, based on the emotional state of the expert, the simulation system adopts a virtual social feedback mechanism, including simulating the opinions of other experts, exerting pressure, and regulating the emotional state of the expert.

[0021] Preferably, in S3, the system tracks the expert's decision path in real time and simulates the failure consequences caused by various wrong decisions made by the expert. The wrong consequences are presented to the expert through simulation results, so that the expert can understand the long-term impact of his decision-making errors and continuously optimize the decision-making ability through the feedback mechanism.

[0022] Preferably, in said S4, the judgment accuracy, decision-making speed and consistency of the experts under emotional fluctuations are evaluated, and the cognitive biases of the experts in the bid evaluation process are regularly evaluated and corrected in real time, including confirmation bias and overconfidence.

[0023] Preferably, in S5, the system monitors the decision-making path of the expert, identifies cognitive biases, and guides the expert to re-examine his judgment through virtual feedback, prompts, and reverse scenario design.

[0024] Preferably, in S6, the deep backtracking analysis mechanism is specifically to visualize the decision chain diagram to reflect the expert's review process, and provide multi-path reverse deduction to show the result changes of different decisions made by experts.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The present invention significantly enhances the complexity and realism of the digital simulation environment by introducing a nonlinear narrative structure. The nonlinear narrative structure enables experts to make decisions in multiple scenarios by constructing multiple parallel decision paths and dynamic situational changes, thus being closer to the complex decision-making process in the real world. This not only enhances the experts' adaptability and decision-making flexibility, but also improves the interactivity and depth of the decision-making process, enabling a more comprehensive assessment of the experts' coping capabilities and decision-making quality in complex and dynamic environments during the bid evaluation process, thereby promoting the comprehensive development of experts in multi-dimensional decision-making.

[0027] 2. This invention effectively improves the self-cognition and decision-making quality of experts in the bid evaluation process by introducing sentiment analysis and social feedback mechanisms. Sentiment analysis captures the emotional fluctuations and psychological state of experts during the decision-making process, helps identify their decision-making biases under pressure, conflict or uncertainty situations, and thus optimizes decision-making strategies. The social feedback mechanism simulates the impact of the external environment on expert decision-making, including peer evaluation and market response, thereby enhancing the experts' sensitivity and adaptability to external factors. The combination of the two can provide experts with richer and more accurate feedback, making them more forward-looking and comprehensive in actual decision-making, and promoting experts to improve their decision-making quality and adaptability in a highly interactive and complex environment.

[0028] 3. The present invention provides a new dynamic analysis perspective when evaluating expert capabilities through virtual "deep retrospective analysis". By deeply retrospecting and analyzing the expert's past decision-making path, it can comprehensively present the expert's thinking trajectory, decision-making preferences and response strategies in the decision-making process. Retrospective analysis not only helps experts understand the actual effects and potential risks of past decisions, but also provides targeted improvement suggestions through reflection and summary of historical decisions, prompting experts to improve their long-term strategic vision and decision-making depth in the process of constantly adjusting and optimizing their own decisions. In addition, deep retrospective analysis can also provide more accurate evaluation criteria for the bid evaluation method, making the measurement of expert capabilities more comprehensive and objective, and promoting the development of evaluation models in a more efficient and intelligent direction. DETAILED DESCRIPTION

[0029] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0030] The present invention provides a technical solution: a method for evaluating expert competence based on digital simulation environment technology, comprising the following steps:

[0031] S1. Build a digital simulation environment to simulate the actual scenarios in the bid evaluation process

[0032] A virtual bid evaluation environment is established through digital simulation technology to simulate complex and ever-changing bid evaluation scenarios. The simulation environment adopts a non-linear narrative structure, supports multi-level decision paths and changes in complex scenarios, and provides multiple options at each decision node, which affect the subsequent evolution of scenarios and bid evaluation results.

[0033] S2. Collect and input experts’ background information, professional fields, and historical bid evaluation experience data

[0034] The system collects expert data, including personal information, professional background, and historical bid evaluation experience. It also incorporates sentiment analysis and social feedback mechanisms to assess the impact of expert emotional fluctuations on decision-making in real time and adjust the expert's decision-making path through feedback.

[0035] S3. Simulate experts to evaluate bids in a simulation environment and record the experts’ decision-making process and evaluation performance

[0036] When experts evaluate bids in a simulation environment, the system records their decision-making process and performance, focusing on the adverse impact of their decisions. Furthermore, a reverse scoring system is introduced to simulate the consequences of incorrect decisions and provide real-time feedback to the experts, helping them to correct themselves in subsequent reviews.

[0037] S4. Define competency assessment indicators, including expert knowledge depth, judgment accuracy, and scoring consistency.

[0038] New evaluation dimensions are introduced, including decision-making efficiency, emotional regulation ability, and the ability to correct cognitive biases. Combined with sentiment analysis and social feedback mechanisms, this approach quantitatively assesses experts' emotional fluctuations, cognitive biases, and social pressure factors during the decision-making process.

[0039] S5. Use expert systems to quantitatively evaluate expert capabilities based on their performance in the simulation environment

[0040] Based on the experts' scores and decision-making records in the simulation environment, the system uses adaptive cognitive bias correction and emotional feedback mechanisms to quantitatively evaluate the experts' abilities. The system dynamically monitors the experts' decision-making paths, corrects cognitive biases in real time, and provides AI-based recommendation algorithms to help experts break out of their single-minded thinking frameworks.

[0041] S6. Generate expert capability scoring report and provide targeted capability improvement suggestions

[0042] After the comprehensive assessment of expert capabilities is completed, the system generates an expert capability scoring report, including the expert's comprehensive score, and uses a deep retrospective analysis mechanism to display the expert's decision-making process, running a simulation hypothesis consequence program to display the corresponding results of different decision paths.

[0043] Among them, the non-linear narrative structure design includes: plot complexity, situational change factors and interdisciplinary scenario simulation, and the design has a scenario interaction system with multi-level and dynamic plot generation to simulate the complex and sudden changes faced by experts in the bid evaluation process, including: project plan modifications, time delays and external events. Each decision node generates different situational feedback.

[0044] By building a multi-dimensional, dynamically changing simulation environment, we can enhance the ability of experts to cope with complex and dynamic decision-making situations and test the flexibility and adaptability of their decision-making.

[0045] Design a multi-level, widely branched decision tree. Each decision node should have multiple different paths to ensure that the scenario constantly changes during the simulation process to increase unpredictability; introduce uncertainties such as market fluctuations, emergencies, technological changes, and policy changes, and let experts respond to these changes in the simulation environment; when designing the simulation environment, simulate evaluation scenarios that cross multiple disciplines, involving multiple fields such as project management, technological innovation, financial analysis, and legal compliance, requiring experts to make multi-dimensional decisions in a complex context.

[0046] Set up instant feedback after each decision point so that experts can adjust their decisions based on real-time results. The simulation environment should simulate the impact of external factors on the review results, increase the display of multiple consequences of expert choices in the simulation environment, and display the results of different action paths in an intuitive way.

[0047] Among them, the system monitors the experts' words, decision-making process, and reaction time behavior in real time based on the sentiment analysis algorithm, identifies their emotional fluctuations, including anxiety and overconfidence, and based on the experts' emotional state, the simulation system adopts a virtual social feedback mechanism, including simulating the opinions of other experts, exerting pressure, and regulating the experts' emotional state.

[0048] Ensure that the experts' background information provides necessary contextual support for decision-making during the bid evaluation process, and optimize the bid evaluation results by quantitatively evaluating the experts' cognitive and emotional states.

[0049] Add a function to quantify expert background and historical experience. By systematically evaluating the depth of experts' knowledge in specific fields, analyzing their professional expertise and potential limitations, we ensure effective adaptation in multi-field bid evaluations. We also integrate experts' past bid evaluation historical data and analyze the successes and mistakes in their historical decision-making, which serves as an important reference for expert evaluation.

[0050] The system tracks the decision-making path of experts in real time and simulates the failure consequences caused by various wrong decisions made by experts. The consequences of errors are presented to experts through simulation results, allowing experts to understand the long-term impact of their decision-making errors and continuously optimize their decision-making capabilities through feedback mechanisms.

[0051] By simulating the bidding evaluation process, the decision-making behavior and performance of experts are recorded in real time, ensuring that the decision-making patterns, decision-making deviations and their possible consequences can be effectively analyzed.

[0052] Reverse scoring system and decision consequence feedback: For the expert's choice at each decision point, the system should immediately display the possible consequences of their choice (such as the consequences of a wrong decision or failure caused by underestimating the potential of a certain technology); each wrong decision should not only show the immediate consequences, but also evaluate the long-term potential risks, simulate the consequences of project failure, resource waste, etc. caused by the expert's wrong decision, and provide a detailed reverse analysis report.

[0053] Among them, the judgment accuracy, decision-making speed and consistency of evaluation experts under emotional fluctuations are evaluated regularly, and the cognitive biases of experts in the bid evaluation process are corrected in real time, including confirmation bias and overconfidence.

[0054] Through multi-dimensional capability assessment indicators, the experts' bid evaluation capabilities are comprehensively quantified to provide data support for subsequent evaluation and improvement.

[0055] In knowledge assessment, it is necessary to examine not only the theoretical knowledge of experts, but also their ability to apply knowledge to complex decision-making situations. In addition to evaluating the professional ability of experts, it is also necessary to evaluate their decision-making performance under emotional fluctuations or cognitive biases, such as overconfidence, group thinking, confirmation bias, etc., and make appropriate corrections to their decisions. When experts conduct reviews at different times and in different situations, the consistency of their scores is quantitatively analyzed. The system evaluates the stability of experts in the long-term bid evaluation process through multiple rounds of simulated reviews.

[0056] Among them, the system monitors the decision-making path of experts, identifies cognitive biases, and guides experts to re-examine their judgments through virtual feedback, prompts, and reverse scenario design.

[0057] The expert system conducts a comprehensive quantitative assessment of expert capabilities, relying not only on traditional scores but also on comprehensive evaluation based on multiple data such as the expert's behavior, emotional regulation, cognitive ability, etc. during the decision-making process.

[0058] The system predicts the behavior of experts through machine learning and deep learning algorithms, and provides real-time decision guidance during the bid evaluation process, helping experts make more reasonable and effective decisions.

[0059] Among them, the deep retrospective analysis mechanism is specifically to visualize the decision chain diagram to reflect the expert's review process, and provide multi-path reverse reasoning to show the changes in the results of different decisions made by experts.

[0060] Provide experts with detailed capability scoring reports and personalized improvement suggestions to ensure that experts can be further improved in future bid evaluation processes.

[0061] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating expert competence based on digital simulation environment technology, characterized by: The following steps are involved: S1. Build a digital simulation environment to simulate the actual scenarios in the bid evaluation process A virtual bid evaluation environment is established through digital simulation technology to simulate complex and ever-changing bid evaluation scenarios. The simulation environment adopts a non-linear narrative structure, supports multi-level decision paths and changes in complex scenarios, and provides multiple options at each decision node, which affect the subsequent evolution of scenarios and bid evaluation results. S2. Collect and input experts’ background information, professional fields, and historical bid evaluation experience data The system collects expert data information, including personal information, professional background, and historical bid evaluation experience; Incorporating sentiment analysis and social feedback mechanisms to assess in real time the impact of expert emotional fluctuations on decision-making and adjust the expert's decision path through feedback; S3. Simulate experts in a simulation environment to evaluate bids and record their decision-making process and evaluation performance. When experts evaluate bids in a simulation environment, the system records their decision-making process and performance, focusing on the adverse impact of their decisions. A reverse scoring system is introduced to simulate the consequences of incorrect expert decisions and provide real-time feedback to the experts, helping them to correct themselves in subsequent evaluations. S4. Define ability assessment indicators, including the expert's knowledge depth, judgment accuracy, and scoring consistency. Introduce new assessment dimensions, including decision-making efficiency, emotional regulation ability, and the ability to correct cognitive biases. Combine sentiment analysis with social feedback mechanisms to quantitatively assess experts' emotional fluctuations, cognitive biases, and social pressure factors during the decision-making process; S5. Use expert systems to quantitatively evaluate expert capabilities based on their performance in the simulation environment Based on the experts' scores and decision-making records in the simulation environment, the system uses adaptive cognitive bias correction and emotional feedback mechanisms to quantitatively evaluate the experts' abilities. The system dynamically monitors the experts' decision-making paths, corrects cognitive biases in real time, and provides AI-based recommendation algorithms to help experts break out of their single-minded thinking frameworks. S6. Generate expert capability scoring report and provide targeted capability improvement suggestions After the comprehensive assessment of expert capabilities is completed, the system generates an expert capability scoring report, including the expert's comprehensive score, and uses a deep retrospective analysis mechanism to display the expert's decision-making process, running a simulation hypothesis consequence program to display the corresponding results of different decision paths.

2. The expert capability evaluation method based on digital simulation environment technology according to claim 1 is characterized by: In S1, the nonlinear narrative structure design includes: plot complexity, situational change factors and interdisciplinary scenario simulation, and a situational interaction system with multi-level and dynamic plot generation is designed to simulate the complex and sudden change situations faced by experts in the bid evaluation process, including: project plan modifications, time delays and external events, and each decision node generates different situational feedback.

3. The expert capability evaluation method based on digital simulation environment technology according to claim 1 is characterized by: In S2, the system monitors the expert's speech, decision-making process, and reaction time behavior in real time based on the sentiment analysis algorithm to identify his or her emotional fluctuations, including anxiety and overconfidence.

4. The method for evaluating expert competence based on digital simulation environment technology according to claim 3 is characterized by: In S2, based on the emotional state of the experts, the simulation system adopts a virtual social feedback mechanism, including simulating the opinions of other experts, exerting pressure, and regulating the emotional state of the experts.

5. The expert capability evaluation method based on digital simulation environment technology according to claim 1 is characterized by: In S3, the system tracks the expert's decision path in real time and simulates the failure consequences caused by various wrong decisions made by the expert. The error consequences are presented to the expert through simulation results, so that the expert can understand the long-term impact of his decision-making errors and continuously optimize the decision-making ability through the feedback mechanism.

6. The expert capability evaluation method based on digital simulation environment technology according to claim 1 is characterized by: In the aforementioned S4, the judgment accuracy, decision-making speed and consistency of experts under emotional fluctuations are evaluated, and the cognitive biases of experts in the bid evaluation process are regularly evaluated and corrected in real time, including confirmation bias and overconfidence.

7. The method for evaluating expert competence based on digital simulation environment technology according to claim 1, characterized in that: In S5, the system monitors the decision-making path of the experts, identifies cognitive biases, and guides the experts to re-examine their judgments through virtual feedback, prompts, and reverse scenario design.

8. The method for evaluating expert competence based on digital simulation environment technology according to claim 1, characterized in that: In S6, the deep backtracking analysis mechanism is specifically to visualize the decision chain diagram to reflect the expert's review process, and provide multi-path reverse deduction to show the changes in the results of different decisions made by experts.