Intelligent scientific research project dynamic supervision and early warning method and system

Abstract

The invention relates to the field of information and data processing, and discloses an intelligent scientific research project dynamic supervision and early warning method and system, and the method comprises the steps: carrying out the encryption of scientific research project management data through employing an encryption algorithm, carrying out the storage and verification of the encrypted scientific research project management data through employing a distributed storage technology, and uploading the data to a distributed storage platform, performing automatic detection and management on the data by using an intelligent contract technology, and performing digital signature on scientific research project management data; a dynamic password management system is used, and a secret key is replaced regularly; the user accesses the corresponding scientific research project management data according to the authority; and monitoring the scientific research project management data in real time, and if the data exceed an abnormal threshold value, performing early warning. The system comprises an encryption module, an authority module and an early warning module. According to the invention, the risk of leakage and attack in the acquisition, transmission, use and storage process of a large amount of sensitive data is solved. According to the invention, the security and credibility of the data and the operation and maintenance efficiency of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of information and data processing technology, and in particular to a method and system for dynamic supervision and early warning of smart scientific research projects. Background Art

[0002] Dynamic supervision and early warning of scientific research projects refers to real-time monitoring and early warning of the progress, quality, and risks of scientific research projects through a series of scientific management methods and technical tools. This process aims to ensure that scientific research projects can proceed smoothly according to the predetermined plan, timely discover and solve potential problems, and improve scientific research efficiency and the quality of results. Generally, big data technology and cloud computing technology are used; big data technology collects data in scientific research projects in real time through data collection tools. Cloud computing technology: provides elastic computing and storage resources and supports large-scale data processing and analysis. A large amount of sensitive data in big data technology may face the risk of leakage and attack during transmission and storage; cloud computing technology systems involve multiple technologies and components, with high complexity and high maintenance costs.

[0003] Prior art 1, involving the technical field of information management of medical research projects, discloses an information management system and method for medical research projects, including: project management module, subject management module, form management module, drug and biological sample management module, message reminder module, file management module and data analysis and report generation module. It can flexibly and comprehensively record the test data of medical research projects, and automatically perform data analysis and generate professional reports. Although it improves the work efficiency of medical researchers, it does not take into account real-time monitoring of data and cannot ensure the safety factor of data.

[0004] Prior art 2 discloses a project progress monitoring system, including: a data acquisition module: used to collect the original data of the project from the designated nodes of the project process; a data preprocessing module: to clean, organize and standardize the original data to generate data to be analyzed; an indicator setting module: used to define monitoring indicators and the calculation rules of the indicators; a data analysis module: used to generate indicator data results according to the calculation rules of the indicators for the data to be analyzed; a result visualization module: used to lay out the indicator data results on the dashboard for visual display; a monitoring feedback module: used to monitor data changes in real time and timely feedback dynamic information on task completion. According to the above technical solution, although it is possible to understand, monitor and feedback the progress of the project without affecting the original multiple management systems, comprehensively and accurately reflect the completion of scientific research plan tasks, and provide a reliable basis for decision makers, a large amount of sensitive data in big data technology may face the risk of leakage and attack during transmission and storage.

[0005] Prior Art III discloses a remote online sharing system for experimental data based on a cloud platform, which relates to the field of data sharing and includes a data sharing platform, which includes an experiment creation module, a data upload module, a data integration module, a data storage module, a data sharing module, and a data collaboration module; the experiment creation module is used to obtain a research project application form and create a research project space; the data upload module is used to obtain corresponding scientific research experiment data in the research project space; the data integration module is used to uniformly process the format of the obtained scientific research experiment data; the data storage module is used to encrypt and store the corresponding scientific research experiment data; the data sharing module is used for researchers to obtain scientific research experiment data and generate a smart contract; the data collaboration module is used to collaboratively process the research project according to the scientific research experiment data and the smart contract; although the security and collaboration in the data sharing process are improved, the cloud computing technology system involves multiple technologies and components, with high complexity and high maintenance costs.

[0006] In the above prior art, there are problems that real-time monitoring data is not considered, the security factor of the data cannot be ensured, there may be risks of leakage and attack for a large amount of sensitive data in the process of transmission and storage in big data technology, and the cloud computing technology system involves multiple technologies and components, with high complexity and high maintenance costs. Summary of the Invention

[0007] In view of the above problems, the object of the present invention is to provide a method and system for dynamic supervision and early warning of intelligent research projects, which can effectively improve the security, credibility of the data and the operation and maintenance efficiency of the system.

[0008] To achieve the above object, in the first aspect, the technical solution adopted by the present invention is: a method for dynamic supervision and early warning of intelligent research projects, which includes: collecting key data in the project management process through a data collection tool; using an encryption algorithm to encrypt the scientific research project management data for the collected key data, storing and verifying the encrypted scientific research project management data by using a distributed storage technology, uploading it to a distributed storage platform, and using smart contract technology to automatically detect and manage the data, and performing digital signature on the scientific research project management data; using a dynamic password management system to regularly change the encryption key and adopting multi-factor authentication; setting different access permissions for users according to the roles and responsibilities of the users; the users access the corresponding scientific research project management data according to the permissions; using a big data analysis tool to perform real-time analysis on the collected data, and setting an anomaly threshold according to historical anomaly data and expert experience; if the analysis result is greater than the anomaly threshold, then trigger an early warning, and timely notify relevant personnel through text messages, emails, and mobile cloud push methods.

[0009] Further, the process of using an encryption algorithm to encrypt the scientific research project management data for the collected key data includes: Collect key data in the project management process in real time through a data collection tool, digitally process the key data to obtain scientific research management data, and perform importance analysis on the scientific research management data to obtain the importance analysis result; According to the importance analysis result, use the lattice addition dynamic encryption algorithm to encrypt the scientific research management data with high importance to obtain encrypted data; based on the distributed storage technology, store the encrypted technology and the unencrypted scientific research management data; Use smart contract technology to automatically execute processes based on real-time scientific research project management data, automatically execute data processing tasks on the scientific research project management data uploaded to the distributed storage platform according to preset conditions, automatically detect and verify the scientific research project management data, issue a digital signature request, and perform digital signature on the scientific research project management data.

[0010] Furthermore, the process of the importance analysis result includes: Preprocess the scientific research management data to obtain standardized data; perform cluster analysis on the standardized data; perform attribute fractal on the digitalized data of the files in each cluster analysis result, assign characteristic frequencies to the attributes of the digitalized data of scientific research management in each cluster, and construct a conversion function; Based on the conversion function, convert the attributes of the scientific research management data into one-dimensional variables, and calculate the first-order sensitivity index of each attribute of the scientific research management data; evaluate the importance of the scientific research management data in each cluster according to the first-order sensitivity index; Set permissions for the scientific research project management data according to the importance evaluation, and set a security factor.

[0011] Furthermore, the process of obtaining the encrypted data includes the following steps: Set a security factor based on the storage requirements of the scientific research management data, and determine odd prime numbers and prime numbers according to the security factor; based on the security factor, odd prime numbers, and prime numbers, determine encryption parameters, use a generation algorithm to generate a random matrix and a trapdoor matrix, and use the random matrix and the trapdoor matrix as the public key and the private key respectively; Perform data encoding processing on the scientific research management data that meets the rating threshold to obtain a set of encoded vectors, randomly select uniformly distributed vectors from the set of encoded vectors, and encrypt the randomly selected vectors with the public key to obtain encrypted data; Based on the distributed storage technology, store the encrypted technology and the unencrypted scientific research management data; users can perform access and modification operations according to their own permissions.

[0012] Furthermore, the process of performing digital signature on the scientific research project management data includes: A digital signature request for obtaining scientific research project management data, and different digital signature requests are sent for different scientific research projects; updating the scientific research project management data with digital signatures to a pre-constructed in-memory database; reading the in-memory database according to a preset time period to detect updated data in the in-memory database; Writing the updated data to a distributed storage platform according to the data update timestamp. If the update data fails to be written to the distributed storage platform, read the original scientific research project management data sequence and convert the updated data in the in-memory database into original data; If the digital signature loading flag bit is changed from a first value to a second value, where the first value is different from the second value, assign a digital signature status flag for the data to be digitally signed. If the first value is output, the digital signature is not completed. If the second value is output, the digital signature is completed.

[0013] Furthermore, the process for a user to access corresponding scientific research project management data according to permissions includes: Determine a dynamic key based on the parameters obtained in real time from the scientific research project management data and send the dynamic key to the dynamic key management system. The terminal device obtains the dynamic key from the dynamic key management system, and the dynamic key management system forms a dynamic key based on the dynamic key, the master key, and the scientific research project management data timestamp; Set a unique identity identifier for each user, verify the user's identity through the identity verification mechanism when the user logs in. Each user is assigned a different role when registering or being allocated an account; define a permission set for different roles according to the scientific research project; According to the user's role and permission set, apply role-based access control rules. When a user requests to access a certain scientific research project management data, first monitor whether the user's role has the corresponding permissions. If the user's permissions are not higher than the sensitivity level of the data, reject the access request.

[0014] Furthermore, the process of forming a dynamic key includes: Determine the first target data in the corresponding master key for the first part of the data of the dynamic key, and calculate the first intermediate key by operating the first target data and the dynamic key according to a preset operation method; Determine the second target data in the corresponding master key for the second part of the data of the dynamic key, and calculate the second intermediate key by operating the second target data and the dynamic key according to a preset method; Merge the first intermediate key and the second intermediate key to obtain a third intermediate key, and calculate the dynamic key by operating the third intermediate key and the timestamp in the scientific research project management data according to a preset operation method.

[0015] Furthermore, the process of setting an exception threshold includes: Identify errors, inconsistencies, or missing values in the collected scientific research project management data, perform data cleaning operations such as removing duplicate records, filling in missing values, smoothing noisy data, identifying and deleting outliers, and merge data from different sources into a unified dataset, and standardize the data. Extract features from the standardized scientific research project management data, analyze the features of the scientific research project management data, start the outlier comparison database, retrieve historical outlier data and expert experience data in the outlier comparison database, and obtain the comparison result. Set the outlier threshold. If the comparison result is greater than the outlier threshold, trigger an alarm and notify relevant personnel in a timely manner via text message, email, and mobile cloud push; and regularly scan and repair system vulnerabilities.

[0016] Furthermore, the process of comparing the outlier threshold with the real-time analysis result includes: Start the outlier comparison database, where the comparison database contains the comparison relationship between historical outlier data and expert experience data and the scientific research project management data; divide the comparison table into a first structure table and a second structure table according to the scientific research project management data, and create a scientific research table in the comparison database. Extract and traverse the first data in the first table structure and the second table structure from the database, calculate the value of each piece of data inside, and the comparison database directly inserts the result of the first piece of scientific research project management data that is completed first into the scientific research table. The data result of each calculation is compared with the data in the scientific research table through a preset comparison method. The comparison database operates on the leaf nodes of the underlying tree of the scientific research table, inserts, deletes, or updates data in the scientific research table according to the comparison rules, and inputs the final scientific research table to obtain the comparison result of the scientific research project management data.

[0017] In a second aspect, the technical solution adopted by the present invention is as follows: A dynamic supervision and early warning system for intelligent scientific research projects, which includes: An encryption module, used to collect key data in the project management process through a data collection tool; encrypt the scientific research project management data using an encryption algorithm for the collected key data, store and verify the encrypted scientific research project management data using a distributed storage technology, upload it to a distributed storage platform, and use smart contract technology to automatically detect and manage the data, and perform digital signature on the scientific research project management data; A permission module, used to use a dynamic password management system to regularly change keys and adopt multi-factor authentication; set different access permissions for users according to the roles and responsibilities of the users; users access the corresponding scientific research project management data according to their permissions; An early warning module, used to perform real-time analysis on the collected data using a big data analysis tool, set an anomaly threshold according to historical abnormal data and expert experience; if the analysis result is greater than the anomaly threshold, trigger an early warning, and timely notify relevant personnel through text messages, emails, and mobile cloud push methods, so as to achieve the goal of dynamic supervision of scientific research projects.

[0018] Due to the above technical solution adopted by the present invention, it has the following advantages: The present invention collects key data in the project management process in real time through a data collection tool, such as project data, financial data, contract data, etc.; uses a big data analysis tool (such as Hadoop, Spark, etc.) to perform real-time analysis on the collected data to discover abnormal situations; sets an early warning threshold according to historical data and expert experience, and triggers an early warning when the data exceeds the threshold; timely notifies relevant personnel through text messages, emails, mobile cloud push, etc., to ensure that problems can be quickly responded to and processed. The present invention can effectively improve the security and credibility of data and the operation and maintenance efficiency of the system, realize the dynamic supervision of scientific research projects, and ensure the smooth progress of scientific research projects. Brief Description of the Drawings

[0019] Figure 1 It is a step flow chart of the dynamic supervision and early warning method for intelligent scientific research projects provided in Embodiment 1 of the present invention; Figure 2 It is a step flow chart of encrypting the scientific research project management data using an encryption algorithm for the collected key data provided in Embodiment 2 of the present invention; Figure 3 It is a step flow chart of obtaining the importance analysis result provided in Embodiment 3 of the present invention; Figure 4 It is a step flow chart of obtaining the encrypted data provided in Embodiment 4 of the present invention; Figure 5 It is a step flow chart of performing digital signature on the scientific research project management data provided in Embodiment 5 of the present invention; Figure 6This is the flowchart of the steps for a user to access corresponding scientific research project management data provided in Embodiment 6 of the present invention; Figure 7 This is the flowchart of the steps for forming a dynamic key provided in Embodiment 7 of the present invention; Figure 8 This is the flowchart of the steps for setting an exception threshold provided in Embodiment 8 of the present invention; Figure 9 This is the flowchart of the steps for comparing the exception threshold with the real-time analysis result provided in Embodiment 9 of the present invention; Figure 10 This is the block diagram of the intelligent scientific research project dynamic supervision and warning system provided in Embodiment 10 of the present invention; Figure 11 This is the block diagram of the electronic device provided in Embodiment 11 of the present invention; Figure 12 This is the block diagram of the computer-readable storage medium provided in Embodiment 12 of the present invention. Specific embodiments

[0020] To solve the foregoing problems existing in the prior art, the present invention provides an intelligent scientific research project dynamic supervision and warning method and system, including: encrypting scientific research project management data using an encryption algorithm, storing and verifying the encrypted scientific research project management data using a distributed storage technology, uploading it to a distributed storage platform, automatically detecting and managing the data using smart contract technology, and digitally signing the scientific research project management data; using a dynamic password management system to regularly change the key; allowing users to access corresponding scientific research project management data according to their permissions; and performing real-time monitoring on the scientific research project management data, and giving a warning if the exception threshold is exceeded. The system includes: an encryption module, a permission module, and a warning module. The present invention solves the risks of leakage and attack faced by a large amount of sensitive data during the processes of collection, transmission, use, and storage; effectively improves the security, credibility of the data, and the operation and maintenance efficiency of the system.

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention fall within the scope of protection of the present invention.

[0022] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] The embodiments of the present invention can be used in fields related to scientific research projects such as university laboratory safety management, smart park management, scientific research fund supervision, smart inspection and testing laboratories, industrial and civil construction safety supervision, chemical industrial park safety management, and power systems and energy fields.

[0024] The key technical features of the embodiments of the present invention are described as follows: Encrypt the scientific research project management data using a high-strength encryption algorithm (such as AES-256) to ensure the security of the data during transmission and storage; Implement end-to-end encryption from the data source to the data storage to prevent the data from being stolen or tampered with in the intermediate links; Use a dynamic key management system to regularly change the keys to enhance the data security; Adopt multi-factor authentication (such as passwords, fingerprints, dynamic verification codes, etc.) to ensure that only authorized personnel can access sensitive data; Set different access permissions according to the roles and responsibilities of users to ensure that the data can only be accessed by the required personnel; Record all access and operation logs for easy post-event auditing and tracing. Digitally sign the key result data (such as experimental results, research records, etc.) during the project management process to ensure the immutability of the data; Add timestamps to each data record to ensure the timeliness and sequentiality of the data; Utilize smart contracts to automatically execute data verification and auditing processes, reducing human intervention and improving the credibility of the data; Through a consensus mechanism involving multiple parties, ensure the authenticity and consistency of the data. Real-time collect the key data during the project management process through data collection tools, such as project data, financial data, contract data, etc.; Use big data analysis tools (such as Hadoop, Spark, etc.) to perform real-time analysis on the collected data to detect abnormal situations; Set warning thresholds based on historical data and expert experience, and trigger warnings when the data exceeds the thresholds; Through methods such as text messages, emails, mobile cloud pushes, etc., notify relevant personnel in a timely manner to ensure that problems can be quickly responded to and processed. Divide the system into multiple independent modules, each module responsible for a specific function, facilitating independent development and maintenance; Ensure loose coupling between modules, reduce dependencies between modules, and improve the flexibility and scalability of the system; Adopt automated deployment tools (such as Jenkins, Ansible, etc.) to achieve rapid deployment and update of the system; Use monitoring tools (such as Prometheus, Grafana, etc.) to monitor the system status in real time, automatically detect and warn of abnormal situations; Configure automated repair scripts to automatically repair common problems, reduce human intervention, and lower the maintenance cost.

[0025] Through the above, the intelligent scientific research project dynamic supervision and warning method can effectively improve the security and credibility of the data and the operation and maintenance efficiency of the system, ensuring the smooth progress of scientific research projects.

[0026] Embodiment 1: As Figure 1 shown, in the embodiment of the present invention, an intelligent project dynamic monitoring and warning method is provided, including the following steps: Step S100: Real-time collect key data in the project management process through a data collection tool; use an encryption algorithm to encrypt the scientific research project management data with the collected key data, and use a distributed storage technology to store and verify the encrypted scientific research project management data, upload it to a distributed storage platform, and use smart contract technology to automatically detect and manage the data, and digitally sign the scientific research project management data; Among them, the key data includes project management, financial management, contract management, etc.; Step S200: Use a dynamic password management system to regularly change the encryption key and adopt multi-factor authentication; set different access permissions for users according to their roles and responsibilities; users access the corresponding scientific research project management data according to their permissions; Step S300: Use big data analysis tools to perform real-time analysis on the collected data, and set an anomaly threshold based on historical anomaly data and expert experience; if the analysis result is greater than the anomaly threshold, trigger an alarm and notify relevant personnel in a timely manner through text messages, emails, mobile cloud pushes, etc.

[0027] In the above embodiments, step S100 collects key data in the project management process through a data collection tool, ensuring the timeliness and accuracy of the data; encrypts the scientific research project management data using an encryption algorithm, enhancing the security of the data and preventing data leakage; stores and verifies the encrypted scientific research project management data using distributed storage technology, ensuring the immutability and traceability of the data; uses smart contract technology to automatically detect and manage the data, improving the data processing efficiency and accuracy; guarantees the integrity, security, and credibility of the scientific research project management data, providing a reliable basis for management decisions; improves the management efficiency of the scientific research project management data and reduces the risk of human errors; step S200 uses a dynamic password management system to regularly change the encryption key, enhancing the security of the system; adopts multi-factor authentication to improve the accuracy and security of user authentication; sets different access permissions according to the roles and responsibilities of users, achieving fine-grained access control of the data; prevents unauthorized access and data leakage, protecting the confidentiality of the scientific research project management data; ensures that only appropriate users can access the corresponding data, maintaining the compliance of the scientific research project management data; step S300 uses big data analysis tools to perform real-time analysis on the collected data, enabling the timely discovery of abnormal data; sets abnormal thresholds based on historical abnormal data and expert experience, improving the accuracy of early warnings; notifies relevant personnel in a variety of ways in a timely manner, ensuring the timely transmission and processing of early warning information; improves the speed of discovery and handling of abnormal situations in the project management process, helping to take timely measures to avoid potential problems; enhances the controllability and security of the project management process, providing a guarantee for the smooth progress of scientific research projects. Encrypts the scientific research project management data using a high-strength encryption algorithm (such as AES-256) to ensure the security of the data during transmission and storage; achieves end-to-end encryption from the data source to the data storage to prevent the data from being stolen or tampered with in the intermediate links; uses a dynamic encryption key management system to regularly change the encryption key to increase the security of the data; adopts multi-factor authentication (such as passwords, fingerprints, dynamic verification codes, etc.) to ensure that only authorized personnel can access sensitive data; sets different access permissions according to the roles and responsibilities of users to ensure that the data can only be accessed by the personnel who need it; records all access and operation logs for easy post-event auditing and tracing. Digitally signs the key data (such as experimental results, research records, etc.) in the project management process to ensure the immutability of the data; adds a timestamp to each data record to ensure the timeliness and sequentiality of the data; uses smart contracts to automatically execute data verification and auditing processes, reducing human intervention and improving the credibility of the data; ensures the authenticity and consistency of the data through a consensus mechanism involving multiple parties.Real-time collect key data in the project management process through a data collection tool, such as project data, financial data, contract data, etc.; use big data analysis tools (such as Hadoop, Spark, etc.) to perform real-time analysis on the collected data to discover abnormal situations; set warning thresholds based on historical data and expert experience, and trigger a warning when the data exceeds the threshold; through methods such as text messages, emails, and mobile cloud pushes, notify relevant personnel in a timely manner to ensure that problems can be quickly responded to and processed. This embodiment can effectively improve the security and credibility of data and the operation and maintenance efficiency of the system, ensuring the smooth progress of scientific research projects.

[0028] Embodiment 2: As Figure 2 shown, based on Embodiment 1, in step S100 of this embodiment, the collected key data is used to encrypt the scientific research project management data using an encryption algorithm, including the following steps: Step S101: Real-time collect key data in the project management process through a data collection tool, perform digital processing on the key data to obtain scientific research management data, and perform importance analysis on the scientific research management data to obtain an importance analysis result; Step S102: According to the importance analysis result, use the lattice-based additive dynamic encryption algorithm to encrypt the scientific research management data with high importance to obtain encrypted data; based on the distributed storage technology, store the encrypted technology and the unencrypted scientific research management data. Among them, the process of the lattice-based additive dynamic encryption algorithm includes: Construct a multi-layer lattice basis system , and each lattice basis corresponds to a secret key ; Scientific research management data m is encrypted through the lattice basis , and introduce non-linear perturbations and quantum random numbers to improve security;

[0029] In the formula, is the encrypted scientific research management data, and enhance the unpredictability of the algorithm; Dynamically adjust the lattice basis according to the historical encrypted scientific research management data and decryption situation A ;

[0030] The decryption process adopts a multi-stage decryption and threshold secret sharing mechanism, and multiple key fragments are required to decrypt:

[0031] Step S103: Automatically execute processes based on real-time scientific research project management data using smart contract technology, automatically execute data processing tasks on the scientific research project management data uploaded to the distributed storage platform according to preset conditions, automatically detect and verify the scientific research project management data, send a digital signature request, and digitally sign the scientific research project management data.

[0032] In the above embodiments, in step S101 of this embodiment, key data in the project management process is collected in real time and accurately through a data collection tool and converted into a digital form for easy processing and analysis; the scientific research management data is deeply analyzed to evaluate its importance for the scientific research project, improving the efficiency and accuracy of data collection. Through importance analysis, key data can be protected targeted, ensuring the smooth progress of the scientific research project; in step S102, the lattice addition dynamic encryption algorithm is used to encrypt important scientific research management data, enhancing data security and preventing data leakage and illegal access; based on distributed storage technology, the encrypted data and unencrypted scientific research management data are stored, ensuring data immutability and traceability; the security and privacy of scientific research project management data are guaranteed, providing a secure storage environment for scientific research activities; through the distributed storage and consensus mechanism of the distributed storage platform, the reliability and credibility of data are improved; in step S103, smart contract technology is used to automatically execute data processing tasks according to real-time scientific research project management data and preset conditions, improving the efficiency and accuracy of data processing; automatically detect and verify the scientific research project management data uploaded to the distributed storage platform to ensure data integrity and consistency; according to the detection results, automatically send a digital signature request and digitally sign the eligible scientific research project management data, realizing data transparency and traceability; through the automatic execution of smart contracts, the risk of human intervention is reduced, and the efficiency and accuracy of scientific research project management data processing are improved; automatic detection and verification ensure data integrity and consistency, providing strong guarantee for the smooth progress of scientific research projects; after data digital signature, data transparency and traceability are realized, facilitating data analysis and scientific research activities.

[0033] Embodiment 3: As Figure 3 shown, based on Embodiment 2, the importance analysis results obtained in step S101 of this embodiment include the following steps: Step S1011: Preprocess the scientific research management data to obtain standardized data; perform cluster analysis on the standardized data; perform attribute fractal on the digitalized data of the files in each cluster analysis result, assign characteristic frequencies to the attributes of the scientific research management data in each cluster, and construct a conversion function. Step S1012: Convert the scientific research management data attributes into one-dimensional variables based on the conversion function, and calculate the first-order sensitivity index of each scientific research management data attribute; evaluate the importance of the scientific research management data in each cluster according to the first-order sensitivity index; Among them, the expression of the conversion function:

[0034] In the formula, represents the conversion function, which converts the multi-dimensional attribute into a one-dimensional variable, represents the vector of scientific research management data attributes, which contains n attributes, that is , represents the Sigmoid function, which is used to convert the linear output into a value within the interval (0,1) and is commonly used in binary classification problems, represents the intercept term, indicating the initial value of the converted one-dimensional variable when all attribute values are represents the p th weight coefficient of the attribute, indicating the influence degree of a single attribute on the conversion result, represents the p th and q th interaction coefficient between attributes, indicating the influence of the possible interaction between attributes on the conversion result, represents the p th non-linear function of the attribute, which can be a polynomial, exponential or other form of function, used to capture the non-linear relationship of the attribute value, represents the linear combination of attributes, indicating the weighted sum of all attributes, represents the interaction term between attributes, considering the interaction between different attributes, represents the non-linear term of the attribute, capturing the non-linear characteristics of each attribute; by adding the interaction term and the non-linear term, the conversion function can more flexibly simulate the complex relationship between scientific research management data attributes and the specific influence of each attribute on the final result, and can provide more accurate conversion and prediction results; Step S1013: Set the permissions for the scientific research project management data according to the importance evaluation and set the security coefficient.

[0035] In the above embodiments, step S1011 of this embodiment enables scientific research management data from different sources and in different formats to be uniformly processed; the data after preprocessing and standardization eliminates noise, outliers, and inconsistencies in the original data, facilitating subsequent analysis; through cluster analysis, similar data can be grouped together to form different clusters, which helps to identify natural groupings in the data and provides a basis for subsequent attribute analysis; through attribute fractal processing, the characteristics of the data in each cluster can be quantified, and characteristic frequencies can be assigned to these characteristics, further refining the structure and attributes of the data; constructing a transformation function converts complex data attributes into one-dimensional variables that are easier to analyze, and the transformed function enables the attributes and characteristics of the data to be quantified and compared. Step S1012 simplifies the data, making it easier to process and analyze, and reducing the analysis complexity; calculating the first-order sensitivity index of each attribute can help identify which attributes have the greatest impact on scientific research management data, that is, which attributes are key factors; through the sensitivity index, the scientific research management data can be ranked according to importance, thereby identifying the most important data attributes. Step S1013 sets permissions for scientific research project management data based on the obtained importance evaluation results to ensure that only authorized personnel can access important data, enhancing the security of the data; the setting of the security coefficient is to further improve the security and stability of the data and avoid the risk of data leakage caused by improper setting of data access permissions.

[0036] In summary, this embodiment identifies important attributes in scientific research management data and sets permissions and security accordingly to protect important scientific research project management data.

[0037] Embodiment 4: As Figure 4 shown, based on Embodiment 2, the steps for obtaining encrypted data in step S102 provided by the embodiment of the present invention include the following steps: Step S1021: Set a security coefficient based on the storage requirements of scientific research management data, and determine an odd prime number and a prime number according to the security coefficient; based on the security coefficient, the odd prime number, and the prime number, determine encryption parameters, use a generation algorithm to generate a random matrix and a trapdoor matrix, and use the random matrix and the trapdoor matrix as the public key and the private key respectively; Among them, the process of using the generation algorithm to generate a random matrix and a trapdoor matrix includes: it is necessary to determine two large odd prime numbers a and b , which will be used to generate matrices, create two matrices, one is a random matrix A and the other is a trapdoor matrix B, and the two matrices will be used as the public key and the private key respectively; Use the public key matrix A to encrypt the scientific research management data, and then decrypt the data through the private key matrix B; Determine a and b :

[0038] In the formula, n is the product of two prime numbers, defining the range of elements in the matrix; Generate a random matrix A:

[0039] The elements in the random matrix A are randomly selected integers that satisfy < n the condition; Generate a trapdoor matrix B:

[0040] The elements in the trapdoor matrix B are calculated so that B becomes the inverse matrix of A for decryption; Encryption process: Suppose there is a scientific research management data vector , the encrypted vector C is calculated as follows:

[0041] represents matrix multiplication, while represents the modulo n operation; Decryption process: Use the trapdoor matrix B to decrypt the encrypted vector C to obtain the original data vector V:

[0042] Step S1022: Perform data encoding processing on the scientific research management data that meets the rating threshold to obtain a set of encoded vectors, randomly select vectors with a uniform distribution from the set of encoded vectors, and encrypt the randomly selected vectors using the public key to obtain encrypted data; Step S1023: Store the encrypted technology and unencrypted scientific research management data based on the distributed storage technology; users can perform operations such as access and modification according to their own permissions.

[0043] In the above embodiments, in step S1021 of this embodiment, the security factor is set and the key is generated. To meet the storage requirements of scientific research management data, the security factor determines the data protection level; the determination of odd prime numbers and prime numbers is directly related to the encryption strength and efficiency. The larger the number, the more secure the encryption is theoretically; through a specific algorithm, a random matrix (public key) and a trapdoor matrix (private key) are generated; the public key can be made public and others can use it to encrypt information; the private key must be kept secret and only the owner can use it to decrypt information. In step S1022, data encoding and encryption processing are performed. Data meeting specific rating thresholds is encoded. The encoded data is easier to manage, transmit, and is more secure; randomly selected from the set of encoding vectors and encrypted using the public key to ensure data confidentiality, and only those with the corresponding private key can decrypt and read the data. In step S1023, the encrypted data is stored using distributed storage technology. The encrypted and unencrypted data is stored through distributed storage technology, and the immutability and transparency of distributed storage are used to enhance data security and reliability; users can access and modify the data according to their permissions; the operation management based on permissions not only protects data security but also ensures data availability.

[0044] In summary, this embodiment constructs a scientific research management data storage and processing system that is both secure and efficient. It not only protects data privacy and security but also enhances data transparency and immutability through distributed storage technology, making the management and use of scientific research project management data more convenient and reliable.

[0045] Embodiment 5: As Figure 5 shown, based on Embodiment 2, in step S103 provided by the embodiment of the present invention for digital signature of scientific research project management data, the following steps are included: Step S1031: Obtain the digital signature request for scientific research project management data, and send different digital signature requests according to different scientific research projects; update the scientific research project management data with digital signature to the pre-constructed in-memory database; read the in-memory database according to a preset time period to detect updated data in the in-memory database. Step S1032: Write the updated data into the distributed storage platform according to the data update timestamp. If the write of the updated data into the distributed storage platform fails, read the original scientific research project management data sequence, and convert the updated data that appears in the in-memory database into the original data. Step S1033: If the digital signature loading flag bit is changed from a first value to a second value, where the first value is different from the second value, assign a digital signature status flag to the data to be digitally signed; if the first value is output, the digital signature is not completed; if the second value is output, the digital signature is completed.

[0046] In the above embodiments, step S1031 can accurately obtain the digital signature requests for scientific research project management data and send different digital signature requests according to different scientific research projects; the scientific research project management data with digital signatures is updated in real time to a pre-constructed in-memory database; the in-memory database has high-speed read and write capabilities and can quickly respond to data update requirements; read the in-memory database according to a preset time period to detect whether there is updated data; through accurate acquisition and distribution of digital signature requests, the efficiency and accuracy of data processing are improved; the introduction of the in-memory database enhances the speed and response ability of data update, providing strong support for real-time digital signature of data; the data update detection mechanism ensures the timeliness and consistency of data, providing a reliable guarantee for data processing; step S1032 writes the updated data into the distributed storage platform according to the data update timestamp; if the write of the updated data to the distributed storage platform fails, read the original scientific research project management data sequence and convert the updated data that appears in the in-memory database into the original data; by writing the data into the distributed storage platform, the persistence and security protection of the data are realized, providing strong support for the long-term storage and sharing of scientific research project management data; the write failure handling mechanism ensures the integrity and consistency of the data and prevents the risk of data loss or damage; step S1033 assigns a digital signature status flag to the data to be digitally signed according to the change of the digital signature filling flag bit; by outputting the first value or the second value, it indicates that the digital signature is not completed and the digital signature is completed respectively; the digital signature status flag and output mechanism realize the real-time monitoring and status feedback of the digital signature process, improving the transparency and controllability of data processing; through clear digital signature status indication, users or system administrators can timely understand the digital signature progress and status of the data, providing convenience for data processing and management.

[0047] Embodiment 6: As Figure 6 shown, on the basis of Embodiment 1, in step S200 provided by the embodiment of the present invention, the user accesses the corresponding scientific research project management data according to the permission, including the following steps: Step S201: Determine the dynamic key according to the parameters obtained in real time from the scientific research project management data and send the dynamic key to the dynamic key management system. The terminal device obtains the dynamic key from the dynamic key management system, and the dynamic key management system forms the dynamic key according to the dynamic key, the master key, and the scientific research project management data timestamp; Step S202: Set a unique identity identifier for each user, verify the user identity through the identity authentication mechanism when the user logs in. Each user is assigned a different role when registering or allocating an account; define the permission sets for different roles according to the scientific research project; Step S203: According to the user's role and permission set, apply role-based access control rules. When a user requests access to certain scientific research project management data, first monitor whether the user's role has the corresponding permissions. If the user's permissions are not higher than the sensitivity level of the data, reject the access request.

[0048] In the above embodiments, in step S201, the dynamic key is determined according to the parameters obtained in real time from the scientific research project management data, and the dynamic key is sent to the dynamic key management system, from which the terminal device obtains it; the dynamic key management system forms the final dynamic key based on the dynamic key, the master key, and the time stamp of the scientific research project management data; by generating the dynamic key in real time, the flexibility and security of data access are increased, and the security risks that may be brought by static keys are prevented; the introduction of the key management system realizes the unified management and distribution of keys, improves the efficiency and convenience of key management; the key generated by combining multiple factors enhances the complexity and unpredictability of the key, and improves the security of the data; in step S202, a unique identity identifier is set for each user, ensuring the uniqueness and traceability of the user identity; the user identity is verified through the identity authentication mechanism during user login, preventing unauthorized users from accessing the system and ensuring the security of the system; according to the scientific research project, different roles are assigned to each user, and the permission sets of different roles are defined; through the unique identity identifier and the identity authentication mechanism, the authenticity and security of the user identity are ensured, preventing impersonation and illegal access; role-based access control realizes the fine-grained management of permissions, improving the security and maintainability of the system; the flexible allocation of roles and permissions adapts to the different requirements of different scientific research projects for data access permissions; in step S203, according to the user's role and permission set, apply role-based access control rules. When a user requests access to certain scientific research project management data, first monitor whether the user's role has the corresponding permissions. If the user's permissions are not higher than the sensitivity level of the data, reject the access request; by applying role-based access control rules, fine-grained access control of data is realized, improving the security and confidentiality of the data; the permission monitoring and access control mechanism ensures that only users with corresponding permissions can access specific scientific research project management data, preventing illegal access and abuse of data; it helps to maintain the integrity and credibility of the scientific research project management data and provides a strong guarantee for the smooth progress of scientific research activities.

[0049] Embodiment 7: As Figure 7 shown, on the basis of Embodiment 6, in step S201 of the embodiment of the present invention for forming the dynamic key, the following steps are included: Step S2011: Determine the first target data in the corresponding master key for the first part of the data of the dynamic key, and perform an operation on the first target data and the dynamic key according to a preset operation method to obtain a first intermediate key; Step S2012: Determine the second target data in the corresponding master key for the second part of the dynamic key data, and perform an operation on the second target data and the dynamic key according to a preset YunSai method to obtain a second intermediate key; Step S2013: Combine the first intermediate key and the second intermediate key to obtain a third intermediate key, and perform an operation on the third intermediate key and the timestamp in the scientific research project management data according to a preset operation method to obtain the dynamic key.

[0050] In the above embodiments, in step S2011, the first intermediate key is generated by combining the first part of the dynamic key data with the first target data in the master key using a preset operation method. This combination method ensures the complexity and security of the key generation process. Through the combination method, the randomness and unpredictability of key generation can be improved, thereby enhancing the security of communication. Step S2012 is similar to step S2011, but for the second part of the dynamic key data. It also uses a preset operation method to combine the second part of the data with the second target data in the master key to generate a second intermediate key. By means of segmented processing, the complexity of the key generation process can be further enhanced, ensuring that each generated intermediate key has high security and uniqueness. Step S2013 combines the first intermediate key and the second intermediate key generated in the previous two steps to form a third intermediate key. Then, the third intermediate key is combined with the timestamp in the scientific research project management data, and the final dynamic key is generated through a preset operation method. By introducing the timestamp as part of the operation, it can be ensured that each generated dynamic key is unique and has a temporal correlation. This not only improves the security of the system but also enhances the flexibility of dynamic key update and management. This embodiment ensures the security and efficiency of the dynamic key generation process through technical means such as segmented processing, combining master key data, and introducing timestamps, thereby enhancing the security and reliability of the overall system.

[0051] Embodiment 8: As Figure 8 shown, on the basis of Embodiment 1, the steps for setting an exception threshold in step S300 provided by the embodiment of the present invention include the following steps: Step S301: Identify errors, inconsistencies, or missing values in the collected scientific research project management data, perform data cleaning operations such as removing duplicate records, filling in missing values, smoothing noisy data, identifying and deleting outliers, etc., and merge data from different sources into a unified data set, and standardize the data; Step S302: Extract features from the standardized scientific research project management data, analyze the features of the scientific research project management data, start the outlier comparison database, retrieve historical outlier data and expert experience data in the outlier comparison database, and obtain a comparison result; Step S303: Set an anomaly threshold. If the comparison result is greater than the anomaly threshold, a warning will be triggered and relevant personnel will be notified in a timely manner via SMS, email, mobile cloud push, etc.; and the system will be regularly scanned for vulnerabilities and repaired.

[0052] In the above embodiment, step S301 identifies and processes errors, inconsistencies, or missing values in the scientific research project management data, performs operations such as removing duplicate records, filling in missing values, smoothing noisy data, identifying and deleting outliers, etc., and merges data from different sources into a unified dataset and standardizes the data. Through data cleaning, the accuracy, integrity, and consistency of the data are ensured, providing a solid foundation for subsequent analysis and decision-making; it helps to reduce analysis bias and improve the reliability of research results. Step S302 can identify abnormal patterns or trends in the data through feature extraction and outlier detection, thus helping researchers discover potential problems or opportunities. In addition, combining historical anomaly data and expert experience can improve the accuracy and reliability of anomaly detection. Step S303 sets an anomaly threshold. If the comparison result is greater than the anomaly threshold, a warning will be triggered and relevant personnel will be notified in a timely manner via SMS, email, mobile cloud push, etc.; the system will be regularly scanned for vulnerabilities and repaired. By setting the anomaly threshold and the warning system, abnormal situations can be discovered and responded to in a timely manner, reducing potential risks and losses. Regular system maintenance ensures the stability and security of the system, guaranteeing the continuous and effective operation of the data processing process. This embodiment significantly improves the quality of scientific research project management data and the accuracy of analysis by combining data cleaning, feature extraction, anomaly detection, and the warning system, providing strong support for scientific management.

[0053] Embodiment 9: As Figure 9 shown, on the basis of Embodiment 8, the comparison of the anomaly threshold with the real-time analysis result in step S303 provided by the embodiment of the present invention includes the following steps: Step S3031: Start the outlier comparison database, where the comparison database contains the comparison relationships between historical anomaly data and expert experience data and the scientific research project management data; according to the scientific research project management data, the comparison table is divided into a first structure table and a second structure table, and a scientific research table is created in the comparison database; Step S3032: Extract and traverse the first data in the first table structure and the second table structure from the database, calculate the value of each piece of data inside, and the comparison database directly inserts the result of the first piece of scientific research project management data that is completed first into the scientific research table; Step S3033: The calculated data results of each item are compared with the data in the scientific research table through a preset comparison method. The comparison database operates on the leaf nodes of the underlying tree of the scientific research table, and inserts, deletes, or updates data in the scientific research table according to the comparison rules. The final scientific research table is input to obtain the comparison result of scientific research project management data.

[0054] In the above embodiment, step S3031 starts a comparison database containing historical abnormal data and expert experience data, divides the comparison table into a first structure table and a second structure table according to the scientific research project management data, and creates a scientific research table in the comparison database. By integrating historical abnormal data and expert experience data, a comprehensive comparison benchmark is provided for the scientific research project management data, ensuring the accuracy and reliability of the scientific research project management data. Creating a scientific research table helps to systematically manage the comparison results and facilitates subsequent data analysis and decision support. Step S3032 extracts and traverses the first piece of data from the first table structure and the second table structure, calculates the value of each piece of data, and directly inserts the first completed scientific research project management data result into the scientific research table. By traversing and calculating the data item by item, it is ensured that each piece of data can be accurately processed and recorded. The method of processing item by item improves the accuracy of data processing and reduces the errors that may be brought by batch processing. Step S3033 uses a preset comparison method to compare the calculated data results of each item, operates on the leaf nodes of the underlying tree of the scientific research table, and inserts, deletes, or updates data in the scientific research table according to the comparison rules. Through the preset comparison method, the scientific research project management data is systematically analyzed and processed to ensure the consistency and integrity of the data. The dynamic data update mechanism of this embodiment can timely reflect the changes in the scientific research project management data, improving the timeliness and accuracy of data analysis. In terms of the systematization, automation, and accuracy of data processing in this embodiment, the efficiency and reliability of scientific research project management data management are improved, providing strong support for management decision-making.

[0055] Embodiment 10: As Figure 10 shown, on the basis of Embodiments 1-9, the intelligent scientific research project dynamic supervision and warning system provided by the embodiment of the present invention includes: An encryption module 1, which is used to collect key data in the project management process through a data collection tool; encrypt the scientific research project management data using an encryption algorithm for the collected key data, store and verify the encrypted scientific research project management data using a distributed storage technology, upload it to a distributed storage platform, and use smart contract technology to automatically detect and manage the data, and perform digital signature on the scientific research project management data; Among them, the key data includes project management, financial management, contract management, etc.; The permission module 2 is used to manage the system with dynamic passwords, regularly change keys, and adopt multi-factor authentication; set different access permissions for users according to their roles and responsibilities; and users access the corresponding scientific research project management data according to their permissions. The warning module 3 is used to use big data analysis tools to analyze the collected data in real time, set abnormal thresholds based on historical abnormal data and expert experience; if the analysis result is greater than the abnormal threshold, trigger a warning, and notify relevant personnel in a timely manner through text messages, emails, mobile cloud pushes, etc.

[0056] In the above embodiments, the system is divided into multiple independent modules, and each module is responsible for a specific function, which is convenient for independent development and maintenance; ensure loose coupling between modules, reduce dependencies between modules, and improve the flexibility and scalability of the system; use automated deployment tools (such as Jenkins, Ansible, etc.) to achieve rapid deployment and update of the system; use monitoring tools (such as Prometheus, Grafana, etc.) to monitor the system status in real time, automatically discover and warn of abnormal situations; configure automated repair scripts to automatically repair common problems, reduce manual intervention, and lower maintenance costs.

[0057] Figure 11 A block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present invention is shown.

[0058] The electronic device may include a central processing unit / microprocessor / master control chip, etc. 4; a storage medium 5, coupled to the central processing unit / microprocessor / master control chip, etc. 4, and storing computer-executable instructions therein for performing the steps of the various methods of the embodiments of the present invention when executed by the processor.

[0059] The central processing unit / microprocessor / master control chip, etc. 4 may include, but are not limited to, for example, one or more processors or microprocessors, etc.

[0060] The storage medium 5 may include, but are not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (such as hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0061] In addition, the electronic device may further include (but is not limited to) a data bus 6, an input / output bus / external bus / device bus, etc. 7, a display 8, and input / output devices 9 (such as a keyboard, a mouse, a speaker, etc.).

[0062] The central processing unit / microprocessor / master control chip, etc. 4 may communicate with external devices (8, 9, etc.) via the I / O bus 7 through a wired or wireless network (not shown).

[0063] The storage medium 5 may also store at least one computer-executable instruction for performing each function and / or method step in the embodiments described in the present technology when run by the central processing unit / microprocessor / master control chip, etc. 4.

[0064] In one embodiment, the at least one computer-executable instruction may also be compiled into or form a software product, and when one or more computer-executable instructions are run by a processor, each function and / or method step in the embodiments described in the present technology is performed.

[0065] Figure 12 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0066] As Figure 12 shown, instructions are stored on the non-transitory computer-readable storage medium 11, and the instructions are, for example, computer-readable instructions 10. When the computer-readable instructions 10 are run by a processor, each method described above can be executed. The non-transitory computer-readable storage medium includes but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium 11 may be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions 10 stored on the non-transitory computer-readable storage medium 11, each method described above can be performed.

[0067] In several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other may be through some interfaces, and the indirect couplings or communication connections of devices or units may be in electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0068] In addition, in each embodiment of the present invention, each functional unit may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods in each embodiment of the present invention through a computer device (which may be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (English full name: Read-Only Memory, English abbreviation: ROM), random access memories (English full name: Random Access Memory, English abbreviation: RAM), magnetic disks, or optical discs that can store program codes.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A dynamic supervision and early warning method for intelligent scientific research projects, characterized in that, Including: Collecting key data in the project management process through a data collection tool; Using an encryption algorithm to encrypt the scientific research project management data with the collected key data, storing and verifying the encrypted scientific research project management data using distributed storage technology, uploading it to a distributed storage platform, and using smart contract technology to automatically detect and manage the data, and performing digital signature on the scientific research project management data; Using a dynamic password management system to regularly change the encryption key and adopting multi-factor authentication; setting different access permissions for users according to their roles and responsibilities; and allowing users to access the corresponding scientific research project management data according to their permissions; Using big data analysis tools to perform real-time analysis on the collected data and setting anomaly thresholds based on historical anomaly data and expert experience; If the analysis result is greater than the anomaly threshold, trigger an alarm and notify relevant personnel in a timely manner via text message, email, or mobile cloud push.

2. The dynamic supervision and early warning method for intelligent scientific research projects according to claim 1, characterized in that The process of using an encryption algorithm to encrypt the scientific research project management data with the collected key data includes: Real-time collecting key data in the project management process through a data collection tool, digitally processing the key data to obtain scientific research management data, and performing importance analysis on the scientific research management data to obtain an importance analysis result; According to the importance analysis result, using the lattice addition dynamic encryption algorithm to encrypt the scientific research management data with high importance, obtaining encrypted data; based on distributed storage technology, storing the encrypted and unencrypted scientific research management data separately; Using smart contract technology to automatically execute processes based on real-time scientific research project management data, automatically executing data processing tasks on the scientific research project management data uploaded to the distributed storage platform according to preset conditions, automatically detecting and verifying the scientific research project management data, sending a digital signature request, and performing digital signature on the scientific research project management data.

3. The dynamic supervision and early warning method for intelligent scientific research projects according to claim 2, characterized in that, The process of obtaining the importance analysis result includes: Preprocessing the scientific research management data to obtain standardized data; performing cluster analysis on the standardized data; performing attribute fractal on the digital archive data of each cluster analysis result, assigning characteristic frequencies to the scientific research management data attributes in each cluster, and constructing a transformation function; Based on the transformation function, converting the scientific research management data attributes into one-dimensional variables and calculating the first-order sensitivity index of each scientific research management data attribute; evaluating the importance of the scientific research management data in each cluster according to the first-order sensitivity index; Setting permissions for the scientific research project management data according to the importance evaluation and setting a security factor.

4. The dynamic supervision and early warning method for intelligent scientific research projects according to claim 2, characterized in that, The process of obtaining the encrypted data includes the following steps: Setting a security factor based on the storage requirements of the scientific research management data and determining odd prime numbers and prime numbers according to the security factor; determining encryption parameters based on the security factor, odd prime numbers, and prime numbers, generating a random matrix and a trapdoor matrix using a generation algorithm, and using the random matrix and the trapdoor matrix as the public key and the private key respectively; Performing data encoding processing on the scientific research management data that meets the rating threshold to obtain a set of encoded vectors, randomly selecting uniformly distributed vectors from the set of encoded vectors, and encrypting the randomly selected vectors using the public key to obtain encrypted data; Based on distributed storage technology, encrypted technology and unencrypted scientific research management data are stored separately; users can perform access and modification operations according to their own permissions.

5. The dynamic supervision and early warning method for intelligent scientific research projects according to claim 2, wherein The process of digital signature for scientific research project management data includes: Obtain digital signature requests for scientific research project management data, and send different digital signature requests according to different scientific research projects; update the scientific research project management data with digital signatures into a pre-constructed in-memory database; read the in-memory database according to a preset time period to detect updated data in the in-memory database; Write the updated data that appears into the distributed storage platform according to the data update timestamp. If the update data fails to be written into the distributed storage platform, read the original scientific research project management data sequence and convert the updated data that appears in the in-memory database into the original data; If the digital signature loading flag bit is changed from the first value to the second value, and the first value is different from the second value, assign a digital signature status flag for the data to be digitally signed. If the first value is output, the digital signature is not completed; if the second value is output, the digital signature is completed.

6. The dynamic supervision and early warning method for intelligent scientific research projects according to claim 1, characterized in that The process for users to access corresponding scientific research project management data according to their permissions includes: Determine a dynamic key based on the parameters obtained in real time from the scientific research project management data, and send the dynamic key to the dynamic key management system. The terminal device obtains the dynamic key from the dynamic key management system, and the dynamic key management system forms a dynamic key based on the dynamic key, the master key, and the timestamp of the scientific research project management data; Set a unique identity identifier for each user, verify the user's identity through the identity authentication mechanism during user login. Each user is assigned a different role when registering or being allocated an account; define the permission sets for different roles according to scientific research projects; According to the user's role and permission set, apply the role-based access control rule. When a user requests to access a certain scientific research project management data, first monitor whether the user's role has the corresponding permission. If the user's permission is not higher than the sensitivity level of the data, reject the access request.

7. The dynamic supervision and early warning method for intelligent scientific research projects according to claim 6, characterized in that, The process of forming a dynamic key includes: Determine the first target data in the corresponding master key for the first part of the dynamic key data, and perform an operation on the first target data and the dynamic key according to a preset operation method to obtain the first intermediate key; Determine the second target data in the corresponding master key for the second part of the dynamic key data, and perform an operation on the second target data and the dynamic key according to a preset cloud computing method to obtain the second intermediate key; Merge the first intermediate key and the second intermediate key to obtain the third intermediate key, and perform an operation on the third intermediate key and the timestamp in the scientific research project management data according to a preset operation method to obtain the dynamic key.

8. The dynamic supervision and early warning method for intelligent scientific research projects according to claim 1, wherein, The process of setting the anomaly threshold includes: Identify and collect errors, inconsistencies, or missing values in the scientific research project management data, perform data cleaning operations such as removing duplicate records, filling in missing values, smoothing noisy data, identifying and deleting outlier data, and merge data from different sources into a unified dataset, and standardize the data; Extract the features of the standardized scientific research project management data, analyze the features of the scientific research project management data, start the outlier comparison database, retrieve the historical outlier data and expert experience data in the outlier comparison database, and obtain the comparison result; Set the outlier threshold. If the comparison result is greater than the outlier threshold, trigger an alarm and notify the relevant personnel in a timely manner via text messages, emails, and mobile cloud push; and regularly scan and repair the system for vulnerabilities.

9. The dynamic supervision and early warning method for intelligent scientific research projects as claimed in claim 8, wherein The process of comparing the outlier threshold with the real-time analysis result includes: Start the outlier comparison database, where the comparison database contains the comparison relationships between the historical outlier data and expert experience data and the scientific research project management data; according to the scientific research project management data, divide the comparison table into a first structure table and a second structure table, and create a scientific research table in the comparison database; Extract and traverse the first data in the first table structure and the second table structure of the database, calculate the value of each piece of data inside, and the comparison database directly inserts the result of the first piece of scientific research project management data that is completed first into the scientific research table; The result of each calculated piece of data is compared with the data in the scientific research table through a preset comparison method. The comparison database operates on the leaf nodes of the underlying tree of the scientific research table, and performs data insertion, data deletion, or data update operations on the scientific research table according to the comparison rules, and inputs the final scientific research table to obtain the comparison result of the scientific research project management data.

10. A dynamic supervision and early warning system for intelligent scientific research projects, which is used to implement the dynamic supervision and early warning method for intelligent scientific research projects described in any one of claims 1 to 9, and is characterized in that, Including: An encryption module for collecting key data in the project management process through a data collection tool; Use an encryption algorithm to encrypt the scientific research project management data for the collected key data, store and verify the encrypted scientific research project management data using a distributed storage technology, upload it to a distributed storage platform, and use smart contract technology to automatically detect and manage the data, and perform digital signature on the scientific research project management data; A permission module for using a dynamic password management system to regularly change the encryption key and adopt multi-factor authentication; set different access permissions for users according to their roles and responsibilities; users access the corresponding scientific research project management data according to their permissions; An early warning module for using big data analysis tools to perform real-time analysis on the collected data, and setting the outlier threshold according to historical outlier data and expert experience; If the analysis result is greater than the outlier threshold, trigger an alarm and notify the relevant personnel in a timely manner via text messages, emails, and mobile cloud push.

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