Online new material enterprise evaluation service system

Abstract

The invention discloses an online new material enterprise evaluation service system, and relates to the technical field of information technology and new material science, and the system comprises the following components: integrated service platform construction, automatic process management, one-stop service experience and intelligent analysis and prediction, intellectual property protection and consultation, and dynamic updating and optimization. Through integrated service platform construction and automatic process management, intelligentization and automation of the evaluation process are realized, the evaluation period is greatly shortened, the evaluation efficiency is improved, meanwhile, the intelligent analysis and prediction function can deeply mine test data and market information based on big data and an algorithm model, and the evaluation efficiency is improved. Accurate cost analysis, market potential prediction and risk assessment reports are provided for enterprises, and the accuracy and objectivity of assessment results are effectively improved.

Description

Technical Field

[0001] The present invention relates to the fields of information technology and new material science and technology, and in particular to an online new material enterprise evaluation service system. Background Art

[0002] With the rapid development of the new materials industry, companies are facing more and more challenges in the process of material research and development, verification and commercialization. The performance evaluation, market potential prediction and risk assessment of new materials have become important bases for corporate decision-making. However, traditional new material evaluation methods have many shortcomings, such as difficulty in data collection, limited analysis methods, and inaccurate prediction results, which cannot meet the growing evaluation needs of companies.

[0003] Traditional new material evaluation technology mainly relies on laboratory testing and expert experience. First, the data collection process is cumbersome and costly, and it is difficult to fully cover all key indicators. Second, the data analysis methods are limited, and it is difficult to deeply explore the value behind the data. Third, the prediction results are greatly affected by human factors, and accuracy and objectivity are difficult to guarantee. Therefore, it is particularly important to develop an online new material enterprise evaluation service system. Summary of the invention

[0004] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide an online new material enterprise evaluation service system, which aims to solve the deficiencies in traditional new material evaluation methods, such as difficulty in data collection, limited analysis methods, and inaccurate prediction results, and to provide enterprises with more efficient, accurate and comprehensive evaluation services.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an online new material enterprise evaluation service system, which includes integrated service platform construction, automated process management, one-stop service experience and intelligent analysis and prediction, as well as intellectual property protection and consultation, dynamic update and optimization;

[0006] The integrated service platform construction: building a unified cloud platform, realizing data interconnection and intercommunication between modules, and forming a complete evaluation service chain;

[0007] The automated process management: using automated process management technology to intelligently allocate and track the assessment applications submitted by enterprises. The system automatically matches the most suitable service team and resources based on the material type and assessment requirements.

[0008] One-stop service experience: Enterprise users only need to submit an evaluation application once through the platform to enjoy the full process service from material concept verification to commercial application. The system provides real-time progress query, online communication and automatic report generation and download functions;

[0009] The intelligent analysis and prediction: through algorithms, the collected test data and market information are mined and analyzed to provide enterprises with accurate cost analysis, market potential forecast and risk assessment reports, helping enterprises make scientific decisions;

[0010] Dynamic update and optimization: The system supports continuous update and optimization, and continuously introduces new technologies and methods based on user feedback, technological development and market demand to improve service quality and efficiency.

[0011] Furthermore, the integrated service platform is constructed to determine the core service modules that the platform needs to cover, collect the needs and expectations of target users through questionnaires and interviews, ensure that the platform functions meet actual needs, design a clear evaluation service process based on the service modules, ensure smooth connection between each link, reduce redundant steps, select cloud service providers, build cloud platform infrastructure, ensure high availability, scalability and security of the system, adopt a microservice architecture, split the platform into multiple independent service modules, each module is independently deployed and expanded, and the flexibility and maintainability of the system are improved.

[0012] Define the data interaction interface between modules to ensure that data can be efficiently and accurately transmitted and shared between different modules according to business needs and data scale;

[0013] Select a database system to ensure efficient data storage and query, design a unified data model to ensure data consistency and shareability among modules, implement data encryption, access control and other security measures to ensure data security and privacy protection; at the same time, establish a data backup mechanism to prevent data loss, develop the functions of each service module according to the service process and technical architecture design, and conduct unit testing to ensure the correctness and stability of the module functions, realize interface docking between modules, ensure that data can be transmitted and shared according to predefined formats and protocols, conduct integration testing to verify the collaborative working ability of modules, and ensure the smoothness and accuracy of the entire service chain;

[0014] Build a test environment and a production environment on the cloud platform to ensure that the system can run stably in different environments. Write user manuals and training materials, conduct platform operation training for target users, and ensure that users can use the platform functions proficiently. After completing all testing and preparation work, officially launch the platform and start providing services to the outside world. Establish a user feedback mechanism to collect users' opinions and suggestions on the platform's functions, performance, and ease of use. Use big data analysis technology to conduct in-depth analysis of the platform's operating data to identify potential problems and areas for improvement.

[0015] Furthermore, the automated process management designs a user-friendly assessment application submission interface, allowing enterprise users to input key information including material type, assessment requirements, and project overview. The system performs preliminary verification on the submitted application information to ensure that all necessary fields are filled in and in the correct format, and parses the submitted assessment application data into a format recognizable by the system to prepare for subsequent intelligent allocation and tracking. According to business needs, a set of intelligent allocation rules are defined. These rules may include the correspondence between material type and expert team, the logic of matching assessment requirements with resources, and an algorithm is selected to implement the intelligent allocation logic. The algorithm should be able to automatically calculate and recommend the most suitable service team and resources based on the specific content of the assessment application;

[0016] Establish and maintain a database of service teams and resources. Based on the intelligent allocation strategy and algorithm calculation results, the system automatically retrieves and matches service teams and resources from the database, sends a notification to the matched service team, informs them that there is a new assessment project to be processed, and requests confirmation whether to accept the task. Once the service team confirms to accept the task, the system formally assigns the assessment project to the team and records the assignment information in the system. The system provides a progress tracking function, allowing enterprise users to view the current status of the assessment project, completed work and estimated completion time at any time. At key nodes in the assessment process, the system automatically sends reminder notifications to relevant parties to ensure the smooth progress of the process. The system monitors the execution of the assessment process and immediately marks and notifies relevant personnel if any abnormality is found.

[0017] Furthermore, the one-stop service experience enterprise users access the online new material evaluation service platform through a browser or mobile application. On the evaluation application page on the platform, the user fills in detailed evaluation requirements, including but not limited to material type, concept verification point and commercial application target information. After the user confirms that the information is correct, the evaluation application is submitted. The system automatically matches the service team and resources according to the evaluation application submitted by the user, and starts the evaluation process. The system notifies the user that the evaluation process has been started and provides a preliminary timetable through email, SMS and platform messages. The platform provides users with an intuitive progress tracking interface, showing each link of the evaluation process and its current status. Whenever there is new progress in a link in the evaluation process, the system automatically updates the progress information and informs the user through the notification function;

[0018] The platform has built-in instant messaging tools that allow corporate users to communicate with the service team in real time. During the evaluation process, users can ask questions to the service team at any time, and team members will answer and provide professional advice in a timely manner. Users and service teams can share relevant files through the platform for better collaboration. At each stage of the evaluation process, the system automatically collects and organizes relevant data and information. When the evaluation process is completed, the system automatically generates a detailed evaluation report based on the collected data and information. The service team reviews the automatically generated report to ensure the accuracy and completeness of the report. After the review is passed, the system allows corporate users to download the evaluation report through the platform. Users can also choose to view the electronic version of the report on the platform. Based on the evaluation results, the service team may provide corporate users with advice and guidance on the commercial application of materials. The system invites users to provide feedback on the evaluation service. For corporate users who need follow-up support, the platform provides continuous consulting and services to help their materials from concept verification to commercial application.

[0019] Furthermore, the intelligent analysis and prediction collects key test data on the performance, stability and durability of new materials through laboratory tests and field tests, and obtains market information related to new materials through market research and industry reports, including but not limited to market demand, competitive situation, and price trends. The collected test data and market information are cleaned and organized to ensure the accuracy and consistency of the data, and a unified data warehouse is built to remove duplicate, erroneous, and missing data, fill in reasonable missing values, ensure data quality, and appropriately convert and format the data according to analysis needs, extract features useful for analysis from the raw data, select an algorithm according to the analysis goal, and build an analysis model based on the preprocessed data through the selected algorithm. Depending on the analysis needs, the data set is divided into a training set, a validation set, and a test set for model training, validation, and testing.

[0020] Furthermore, the intelligent analysis and prediction uses training set data to train the model, adjusts model parameters to optimize performance, uses validation set data to evaluate model performance, and makes necessary adjustments and optimizations to the model based on the evaluation results. The model's hyperparameters are tuned through random search methods to further improve model performance. The constructed model is used to predict and analyze the production cost and R&D cost of new materials, helping companies understand cost structure and optimize resource allocation. Based on market information and model prediction results, the market demand and growth potential of new materials are analyzed, and market entry strategy recommendations are provided to companies. Considering material performance, market demand, and competitive situation factors, risk assessments are conducted on the commercial application of new materials. The results of intelligent analysis and prediction are organized into a report format, and charts and graphic visualization tools are used to present complex analysis results to corporate users in an intuitive manner. Based on the report content, scientific decision-making basis and recommendations are provided to companies, helping companies make wise choices in the process of new material R&D and commercial application.

[0021] Furthermore, the model training and validation:

[0022] Model training function L:

[0023]

[0024] Where θ is the model parameter, y is the true label, is the model prediction value, X is the input feature, and the validation set is used to evaluate the model performance and may trigger further model adjustments;

[0025] Model predictions:

[0026] Model predictions are the model's output for new data points:

[0027]

[0028] Where f is the model function, X new is a new data point, θ * are the optimized model parameters;

[0029] P(y t+1 |x t+1 ,D)=∫p(y t+1 |x t+1 ,θ)p(θ|D)dθ

[0030] Among them, y i +1 is the predicted value at a future time point, x i+1 is the corresponding feature, D is the historical data, θ is the model parameter, and p(θ|D) is the posterior distribution of the parameter. By sampling multiple θ values ​​from the posterior distribution and using these values ​​for prediction, we can get the distribution of the predicted value and estimate the prediction interval.

[0031] The steps of the random search method are: define the hyperparameter distribution: unlike grid search, random search defines a probability distribution for each hyperparameter instead of a fixed search range, random sampling: randomly extract a sample value from the distribution of each hyperparameter to form a set of hyperparameter combinations, evaluation and iteration: use this set of hyperparameters to train the model and evaluate its performance, repeat this process multiple times, and re-randomly sample each iteration, select the best model: from all evaluated models, select the hyperparameter combination corresponding to the model with the best performance.

[0032] Furthermore, the dynamic update and optimization collects user feedback through online surveys, user forums, social media, customer service hotlines and other channels, organizes, categorizes and prioritizes the collected feedback, identifies the issues and potential needs that users are most concerned about, closely follows industry trends, understands the latest technology trends and cutting-edge research results, evaluates their potential impact on the company's products or services, understands the changing trend of market demand, predicts future development directions, and comprehensively evaluates existing systems or services in combination with user feedback, technology development and market demand, and identifies key points that need to be improved and optimized;

[0033] Based on the evaluation results, formulate a specific update and optimization plan, clarify the update content, technology selection, resource investment and expected effects, break down the update and optimization plan into several small tasks, implement them in stages according to priority and dependency, and conduct comprehensive tests on the update content of each stage to ensure that the updated system or service runs stably and meets user needs and business goals. Actively invite users to participate in the testing phase, collect their feedback, and make necessary adjustments and optimizations based on the feedback. After completing all testing work, prepare the deployment environment to ensure a smooth deployment process. Adopt a grayscale release or phased launch strategy to gradually bring the updated system or service to the market. After the launch, closely monitor the operating status of the system or service, promptly discover and resolve potential problems, and make necessary adjustments and optimizations based on user feedback and market reactions. Regularly evaluate the effects of updates and optimizations, use the evaluation results and feedback as a reference for future updates and optimizations, and continuously optimize and improve the system or service.

[0034] Compared with the existing technology, an online new material enterprise evaluation service system has the following beneficial effects:

[0035] 1. The present invention realizes the intelligence and automation of the evaluation process through the construction of an integrated service platform and automated process management, greatly shortens the evaluation cycle, and improves the evaluation efficiency. At the same time, the intelligent analysis and prediction functions are based on big data and algorithm models, which can deeply mine test data and market information, and provide enterprises with accurate cost analysis, market potential forecasts and risk assessment reports, effectively improving the accuracy and objectivity of the evaluation results.

[0036] 2. This system provides a one-stop service experience. Enterprise users only need to submit an evaluation application once through the platform to enjoy the full process service from material concept verification to commercial application. This service model not only simplifies the evaluation process and reduces the operating costs of the enterprise, but also improves the user experience and enhances user satisfaction through real-time progress inquiry, online communication and automatic report generation and download functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 An operation flow chart of an online new material enterprise evaluation service system. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1

[0041] Enterprise users access the online new material evaluation service platform through a browser or mobile application, enter their username and password, complete identity authentication, and successfully log in to the system.

[0042] After logging in, the user enters the platform homepage and browses various service options. In the service list, the user explicitly selects the "Project Concept Proof of Concept Report Upload" service and clicks to enter the relevant page.

[0043] On the project information filling page, the user enters the basic information of the project in detail, including but not limited to: clearly marking the full name or abbreviation of the project so that the system and service team can quickly identify it, selecting the corresponding material classification based on the specific properties of the R&D materials, outlining the key achievements and breakthroughs achieved by the project in the concept verification stage, and briefly describing the project's future commercial application goals and market positioning. After the user confirms that the information is correct, click "Next" to continue.

[0044] Enter the report upload page, click the "Upload Report" button, and a file selection dialog box pops up. In the dialog box, the user browses the local file and selects the PDF or Word file containing the project concept verification report. After selecting the file, click the "OK" or "Upload" button to upload the report to the system. The system automatically performs preliminary verification of the file type and size to ensure that the file meets the upload requirements.

[0045] After the report is successfully uploaded, the user confirms again that the project information and report content are correct, clicks the "Submit Application" button, and formally submits the evaluation application to the system. The system automatically matches the most suitable service team and resources based on the application information submitted by the user, and starts the evaluation process.

[0046] After submitting the application, the user can view the current status of the assessment project, completed work and estimated completion time in real time through the platform's progress tracking interface. At key points in the assessment process, the system will automatically send reminder notifications to the user via email, SMS or platform messages. Users can use the platform's built-in instant messaging tools to communicate with the service team in real time to inquire about the progress of the assessment, raise questions or share additional information.

[0047] After the evaluation process is completed, the system automatically generates a detailed evaluation report based on the collected data and information. The service team reviews the automatically generated report to ensure its accuracy and completeness. After the review is passed, the system notifies the user through email or platform message that the evaluation report has been completed. The user logs in to the system and clicks the "Download Report" button to save the evaluation report to the local device, or directly view the electronic version of the report on the platform.

[0048] Based on the content of the evaluation report, the service team may provide corporate users with advice and guidance on the commercial application of materials. Users can further optimize project plans and accelerate the commercialization process of materials based on these suggestions.

[0049] The system invites users to provide feedback on the evaluation service and collects users' opinions and suggestions on the platform's functions, performance, and usability. User feedback will serve as an important basis for continuous optimization and improvement of the system.

[0050] Embodiment 2

[0051] Service team members use their exclusive accounts and passwords to log in to the backend management system of the online new material evaluation service platform. The system verifies the validity of the account information to ensure that only authorized users can access sensitive data.

[0052] After successful login, the service team member enters the "Workspace" or "Project List" page, and finds the specific project that currently needs to upload the assessment report by searching, filtering or browsing. Click the project name or enter the project details page to view the basic information and current progress of the project.

[0053] The service team writes the assessment report directly in the system based on the assessment results. The system provides a rich text editor or an upload function compatible with multiple document formats so that team members can easily write, edit and format the report. If the assessment report has been completed in external software, team members can export it to a format supported by the system and prepare for upload.

[0054] On the project details page or the assessment report upload area, the service team member clicks the "Upload Assessment Report" button to open the file upload dialog box, selects the prepared assessment report file, and clicks the "Upload" button. The system starts uploading the file and displays the upload progress. During the upload process, the system may perform checks on the file size, type, etc. to ensure that the uploaded file meets the system requirements.

[0055] After the evaluation report is uploaded, the system will automatically or according to the set rules trigger the report review process. The system's built-in AI-assisted review function can perform preliminary screening to identify possible format errors, content omissions and other problems. For parts that require manual review, the system will assign the report to the corresponding reviewer for detailed review and confirmation. The reviewer has the right to modify, supplement or reject the report to ensure its accuracy and completeness.

[0056] After the evaluation report passes the review, the system updates the status information of the evaluation project and marks it as "completed" or "reviewed". The system automatically sends a notification message to the enterprise user, informing him that the evaluation report has been uploaded and can be downloaded for viewing. After the enterprise user logs into the system, he can find and download the evaluation report in the "My Project" or "Evaluation Report" area for subsequent analysis and decision-making.

[0057] For corporate users who need further consultation or support, the service team can provide continuous services through the platform's built-in instant messaging tools or telephone, email, etc. The system also supports corporate users to provide feedback and evaluation of the evaluation services so that the service team can continuously optimize and improve service quality.

[0058] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An online new material enterprise evaluation service system, characterized in that: The system includes integrated service platform construction, automated process management, one-stop service experience and intelligent analysis and prediction, as well as intellectual property protection and consulting, dynamic update and optimization; The integrated service platform construction: building a unified cloud platform, realizing data interconnection and intercommunication between modules, and forming a complete evaluation service chain; The automated process management: using automated process management technology to intelligently allocate and track the assessment applications submitted by enterprises. The system automatically matches the most suitable service team and resources based on the material type and assessment demand factors; The one-stop service experience: Enterprise users only need to submit an evaluation application once through the platform to enjoy the full process service from material concept verification to commercial application. The system provides real-time progress query, online communication, and automatic report generation and download functions; Intelligent analysis and prediction: Through algorithms, the collected test data and market information are mined and analyzed to provide enterprises with accurate cost analysis, market potential forecasts and risk assessment reports, helping enterprises make scientific decisions; Dynamic update and optimization: The system supports continuous update and optimization, and continuously introduces new technologies and methods based on user feedback, technological development and market demand to improve service quality and efficiency.

2. According to claim 1, an online new material enterprise evaluation service system is characterized in that: The integrated service platform construction determines the core service modules that the platform needs to cover, collects the needs and expectations of target users through questionnaires and interviews, designs a clear evaluation service process based on the service modules, selects cloud service providers, builds cloud platform infrastructure, and adopts microservice architecture to split the platform into multiple independent service modules. Each module is independently deployed and expanded to improve the flexibility and maintainability of the system, and defines the data interaction interface between modules according to business needs and data scale; Select a database system, design a unified data model, implement data encryption and access control security measures, and establish a data backup mechanism to prevent data loss. According to the service process and technical architecture design, develop the functions of each service module separately, conduct unit testing, realize interface docking between modules, and conduct integration testing to verify the collaborative working ability of modules; Build a test environment and production environment on the cloud platform, write user manuals and training materials, and conduct platform operation training for target users. After completing all testing and preparation work, officially launch the platform and start providing services to the outside world. Establish a user feedback mechanism to collect users' opinions and suggestions on the platform's functions, performance, and ease of use. Use big data analysis technology to conduct in-depth analysis of the platform's operating data to identify potential problems and areas for improvement.

3. According to claim 1, an online new material enterprise evaluation service system is characterized in that: The automated process management designs a user-friendly assessment application submission interface, allowing enterprise users to input key information including material type, assessment requirements, and project overview. The system conducts preliminary verification of the submitted application information and parses the submitted assessment application data into a format recognizable by the system to prepare for subsequent intelligent allocation and tracking. According to business needs, a set of intelligent allocation rules are defined. These rules may include the correspondence between material types and expert teams, the logic of matching assessment requirements with resources, and the selection of algorithms to implement intelligent allocation logic. The algorithm should be able to automatically calculate and recommend the most suitable service team and resources based on the specific content of the assessment application; Establish and maintain a database of service teams and resources. Based on the intelligent allocation strategy and algorithm calculation results, the system automatically retrieves and matches service teams and resources from the database, sends a notification to the matched service team, informs them that there is a new assessment project to be processed, and requests confirmation whether to accept the task. Once the service team confirms to accept the task, the system formally assigns the assessment project to the team and records the assignment information in the system. The system provides a progress tracking function, allowing enterprise users to view the current status of the assessment project, completed work and estimated completion time at any time. At key nodes in the assessment process, the system automatically sends reminder notifications to relevant parties. The system monitors the execution of the assessment process. Once an abnormal situation is found, it will be marked immediately and the relevant personnel will be notified.

4. According to claim 1, an online new material enterprise evaluation service system is characterized in that: The one-stop service experience enterprise users access the online new material evaluation service platform through a browser or mobile application. On the evaluation application page on the platform, users fill in detailed evaluation requirements, including but not limited to material type, concept verification points and commercial application target information. After the user confirms that the information is correct, the evaluation application is submitted. The system automatically matches the service team and resources based on the evaluation application submitted by the user, and starts the evaluation process. The system notifies the user that the evaluation process has been started and provides a preliminary timetable through emails, text messages and platform messages. The platform provides users with an intuitive progress tracking interface that displays each link of the evaluation process and its current status. Whenever there is new progress in a link in the evaluation process, the system automatically updates the progress information and informs the user through the notification function; The platform has built-in instant messaging tools that allow corporate users to communicate with the service team in real time. During the evaluation process, users can ask questions to the service team at any time, and team members will answer and provide professional advice in a timely manner. Users and service teams can share relevant files through the platform. At each stage of the evaluation process, the system automatically collects and organizes relevant data and information. When the evaluation process is completed, the system automatically generates a detailed evaluation report based on the collected data and information. The service team reviews the automatically generated report. After the review is passed, the system allows corporate users to download the evaluation report through the platform. Users can also choose to view the electronic version of the report on the platform. Based on the evaluation results, the service team may provide corporate users with advice and guidance on the commercial application of materials. The system invites users to provide feedback on the evaluation service. For corporate users who need follow-up support, the platform provides continuous consulting and services to help their materials from concept verification to commercial application.

5. According to claim 1, an online new material enterprise evaluation service system is characterized in that: The intelligent analysis and prediction collects key test data on the performance, stability and durability of new materials through laboratory tests and field tests, and obtains market information related to new materials through market research and industry reports, including but not limited to market demand, competitive situation, and price trends. The collected test data and market information are cleaned and organized, and a unified data warehouse is built to remove duplicate, erroneous, and missing data, fill in reasonable missing values, and appropriately convert and format the data according to analysis needs, extract features useful for analysis from the original data, select an algorithm according to the analysis goal, and build an analysis model based on the preprocessed data through the selected algorithm. Depending on the analysis needs, the data set is divided into a training set, a validation set, and a test set for model training, validation, and testing.

6. According to claim 5, an online new material enterprise evaluation service system is characterized in that: The intelligent analysis and prediction uses training set data to train the model, adjusts model parameters to optimize performance, uses validation set data to evaluate model performance, and makes necessary adjustments and optimizations to the model based on the evaluation results. The model's hyperparameters are tuned through random search methods to further improve model performance. The constructed model is used to predict and analyze the production cost and R&D cost of new materials, helping companies understand cost structures and optimize resource allocation. Based on market information and model prediction results, the market demand and growth potential of new materials are analyzed, and market entry strategy recommendations are provided to companies. Considering material properties, market demand, and competitive situation factors, risk assessments are conducted on the commercial application of new materials. The results of intelligent analysis and prediction are organized into a report format, and charts and graphic visualization tools are used to present complex analysis results to corporate users in an intuitive manner. Based on the report content, scientific decision-making basis and recommendations are provided to companies, helping companies make wise choices in the process of new material R&D and commercial application.

7. According to claim 5, an online new material enterprise evaluation service system is characterized in that: The model training and prediction: Model training function L: Where θ is the model parameter, y is the true label, is the model prediction value, X is the input feature, and the validation set is used to evaluate the model performance and may trigger further model adjustments; Model predictions: Model predictions are the model's output for new data points: Where f is the model function, X new is a new data point, θ * are the optimized model parameters; Py t+1 |x t+1 ,D=∫py t+1 |x t+1 ,θpθ|Ddθ Among them, y i +1 is the predicted value at a future time point, x i +1 is the corresponding feature, D is the historical data, θ is the model parameter, and pθ|D is the posterior distribution of the parameter. By sampling multiple θ values ​​from the posterior distribution and using these values ​​for prediction, we can get the distribution of the predicted value and estimate the prediction interval. The steps of the random search method are: define the hyperparameter distribution: unlike grid search, random search defines a probability distribution for each hyperparameter instead of a fixed search range, random sampling: randomly extract a sample value from the distribution of each hyperparameter to form a set of hyperparameter combinations, evaluation and iteration: use this set of hyperparameters to train the model and evaluate its performance, repeat this process multiple times, and re-randomly sample each iteration, select the best model: from all evaluated models, select the hyperparameter combination corresponding to the model with the best performance.

8. The online new material enterprise evaluation service system according to claim 1, characterized in that: The dynamic update and optimization collects user feedback through online surveys, user forums, social media, customer service hotlines and other channels, organizes, categorizes and prioritizes the collected feedback, identifies the issues and potential needs that users are most concerned about, closely follows industry trends, understands the latest technology trends and cutting-edge research results, evaluates their potential impact on the company's products and services, understands the changing trend of market demand, predicts future development directions, and comprehensively evaluates existing systems and services in combination with user feedback, technological development and market demand, identifying key points that need to be improved and optimized; Based on the evaluation results, formulate a specific update and optimization plan, clarify the update content, technology selection, resource investment and expected effects, break down the update and optimization plan into several small tasks, implement them in stages according to priority and dependency, conduct comprehensive tests on the update content of each stage, and at the same time meet user needs and business goals. Actively invite users to participate in the testing phase, collect their feedback, and make necessary adjustments and optimizations based on the feedback. After completing all testing work, prepare the deployment environment, adopt a grayscale release and phased launch strategy, and gradually push the updated system or service to the market. After the launch, closely monitor the operating status of the system and services, promptly discover and resolve potential problems, and make necessary adjustments and optimizations based on user feedback and market reactions. Regularly evaluate the effects of updates and optimizations, use the evaluation results and feedback as a reference for future updates and optimizations, and continuously optimize and improve the system or service.

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