Scientific research fund management monitoring method and system

Abstract

The invention discloses a scientific research fund management monitoring method and system, and the method comprises the following steps: collecting user demands, and designing a micro-service architecture, a database, a technology stack, a budget control algorithm and a budget monitoring algorithm framework according to the user demands; utilizing the budget control algorithm to realize automatic matching of a budget template, automatic calculation of a budget use condition and prediction of a future budget consumption trend; and managing an early warning rule by using the early warning monitoring algorithm, setting a threshold value to trigger early warning, detecting whether the early warning rule is triggered in real time, and generating early warning information to be pushed. A micro-service architecture is adopted, so that the system has good expandability and flexibility and can easily cope with future business growth and change, meanwhile, budget control and an early warning monitoring algorithm framework are introduced, automatic matching of budget, real-time calculation and accurate pushing of early warning information are achieved, and the system is high in practicability and easy to popularize. And the scientific research fund management efficiency and accuracy are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fund management, and more specifically, to a scientific research fund management and monitoring method and system. Background Art

[0002] Scientific research funding is the monetary expression of the materialized and living labor expended in the development of scientific research capabilities and activities. Scientific research funding management is a crucial component of scientific research, encompassing multiple aspects, including fund raising, allocation, use, and oversight. Strengthening scientific research funding management aims to achieve optimal economic results with minimal investment, providing high-quality research results and high-caliber scientific talent for national economic development and social progress. This is also the fundamental purpose of implementing economic accounting, improving operational management, and rationally organizing scientific work.

[0003] Many existing scientific research funding management and monitoring systems use a traditional monolithic architecture, which is inadequate to cope with business growth and functional expansion. Monolithic systems often struggle to adapt to new business needs or technology upgrades. Modifying a single line of code may require redeploying the entire system, which not only increases development and maintenance costs but also limits the system's flexibility and scalability. Existing scientific research funding management and monitoring systems often rely on manually set thresholds and regular inspections for budget control and early warning monitoring. This approach is not only inefficient but also makes it difficult to achieve real-time monitoring and accurate early warnings. Manually set thresholds may not accurately reflect the actual needs of scientific research projects, while regular inspections can lead to lags in early warning information, making it impossible to take timely measures to prevent risks such as budget overruns. Summary of the Invention

[0004] One of the purposes of the present invention is to provide a scientific research funding management and monitoring method to solve the technical problems of the scientific research funding management and monitoring method in the prior art; the second purpose of the present invention is to provide a scientific research funding management and monitoring system.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A first aspect of the present invention provides a method for managing and monitoring scientific research funds, comprising the following steps:

[0007] Collect user requirements and design microservice architecture, database, technology stack, budget control algorithm and budget monitoring algorithm framework based on user requirements;

[0008] Utilize the budget control algorithm to automatically match budget templates, automatically calculate budget usage and predict future budget consumption trends;

[0009] The warning monitoring algorithm is used to manage warning rules, set thresholds to trigger warnings, and detect in real time whether the warning rules are triggered, and generate warning information for push.

[0010] Furthermore, we design microservice architecture, database, and technology stack based on user needs, including:

[0011] Design microservice architecture based on user needs, including project application services, budget control services, and early warning monitoring services;

[0012] Design data models for scientific research projects, budget items, and early warning rules based on user needs, and choose MySQL database for storage;

[0013] According to user needs, the Spring Cloud microservice framework is used for service governance, Spring Boot is used for service development, Redis is used as a cache to improve system performance, and Kafka is used as a message queue to implement asynchronous communication.

[0014] Furthermore, the budget control algorithm realizes automatic matching of budget templates, including:

[0015] Input scientific research project information, including type, scale and research period;

[0016] Calling the Drools rule engine to match the scientific research project information with the rules in the preset budget template library to obtain a matching result;

[0017] Selecting the optimal budget template from the preset template library based on the matching result;

[0018] Output the optimal budget template ID and information.

[0019] Furthermore, the scientific research project information is matched with the rules in the preset budget template library, including:

[0020] A decision tree classifier is selected to match the scientific research project information with the rules in the preset budget template library, and the decision tree classifier is trained using the scikit-learn library of Python.

[0021] Furthermore, the automatic calculation of budget usage includes:

[0022] Initialize the budget status of the scientific research project, which includes the total budget and the used budget;

[0023] Monitoring budget item increase and decrease events, including expenditures and budget adjustments;

[0024] Update the budget status of the scientific research project according to the increase or decrease change event;

[0025] According to the updated scientific research project budget status, the budget usage is calculated and output in real time, and the budget usage includes the remaining budget and the budget utilization rate.

[0026] Furthermore, the real-time calculation and output of the budget usage includes using a sliding window algorithm to calculate the budget usage within the current time window:

[0027] Use the Pandas library to read financial data files;

[0028] Set the size and step size of the sliding window as required;

[0029] Calculate budget usage within a sliding window and save it in a result set.

[0030] Furthermore, the ARIMA model is used to predict future budget consumption trends.

[0031] Furthermore, the setting of a threshold to trigger an early warning includes:

[0032] Build an early warning rule engine based on the Drools rule engine;

[0033] Defining warning rules, including warning conditions, warning levels, and triggering actions;

[0034] Recording the warning rules into the warning rule engine;

[0035] Obtaining the budget usage in real time;

[0036] When the budget usage meets the warning condition, a corresponding warning event is triggered.

[0037] Furthermore, early warning information is generated and pushed, including:

[0038] Receiving the warning event, parsing it to obtain the warning type and warning level;

[0039] Generate an early warning information template according to the early warning type and early warning level;

[0040] Fill in the scientific research project name and budget usage into the warning information template;

[0041] Call the email or SMS service API to push the warning information template to relevant personnel.

[0042] A second aspect of the present invention provides a scientific research funding management and monitoring system, comprising:

[0043] A user demand collection module, which is used to collect user needs and design a microservice architecture, database, technology stack, and budget control algorithm and budget monitoring algorithm framework based on user needs;

[0044] A budget control module, which uses the budget control algorithm to automatically match budget templates, automatically calculate budget usage, and predict future budget consumption trends;

[0045] The budget monitoring module uses the early warning monitoring algorithm to manage early warning rules, sets thresholds to trigger early warnings, and detects in real time whether early warning rules are triggered, and generates early warning information for push.

[0046] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0047] The present invention adopts a microservice architecture, which makes the system have good scalability and flexibility, and can easily cope with future business growth and changes. At the same time, the introduction of the budget control and early warning monitoring algorithm framework realizes automatic matching of budgets, real-time calculation and accurate push of early warning information, effectively improving the efficiency and accuracy of scientific research funding management. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flow chart of a method for managing and monitoring scientific research funds provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of a process for automatically matching budget templates provided by an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of a process for automatically calculating budget usage according to an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of a process for setting a threshold to trigger an early warning according to an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of a process for generating and pushing warning information provided by an embodiment of the present invention;

[0053] Figure 6 A schematic diagram of the structure of a scientific research funding management and monitoring system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0055] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0056] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0057] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0058] Example 1

[0059] This embodiment provides a scientific research funding management and monitoring method, such as Figure 1 As shown, the following steps are included:

[0060] Collect user requirements and design microservice architecture, database, technology stack, budget control algorithm and budget monitoring algorithm framework based on user requirements;

[0061] Utilize the budget control algorithm to automatically match budget templates, automatically calculate budget usage and predict future budget consumption trends;

[0062] The warning monitoring algorithm is used to manage warning rules, set thresholds to trigger warnings, and detect in real time whether the warning rules are triggered, and generate warning information for push.

[0063] In this embodiment, through in-depth interviews with key users including scientific research management departments, financial departments, and project leaders, the specific needs of users for the scientific research funding management and monitoring system are collected and recorded, and the needs are analyzed to clarify the core functions, user interface requirements, data interaction requirements, security requirements, and performance requirements of the system. A detailed requirements specification can be written and a requirements review meeting can be held to ensure that all key users have a consistent understanding of the requirements; then, based on the results of the requirements analysis, a microservice architecture blueprint for the system is designed to clarify the responsibilities and boundaries of each microservice, including project application services, budget control services, and early warning monitoring services; scientific research projects, budget items, and early warning rule data models are designed, and a MySQL database storage solution is selected to ensure data consistency and integrity; a technology stack is selected, including the use of the Spring Cloud microservice framework for service governance, Spring Boot for service development, Redis as a cache to improve system performance, and Kafka as a message queue to implement asynchronous communication.

[0064] In this embodiment, through in-depth demand research and refinement, it is ensured that the system can accurately meet the actual needs of scientific research management departments, financial departments and project leaders, and improve the practicality of the system and user satisfaction. Secondly, the system architecture design adopts a microservice architecture, which improves the scalability and maintainability of the system. At the same time, an efficient technology stack is selected to ensure the performance and stability of the system. In terms of algorithm framework design, the innovative budget control algorithm and early warning monitoring algorithm, combined with the Drools rule engine and event monitoring mechanism, realize the automatic matching of budgets, real-time calculation and accurate push of early warning information, and effectively improve the efficiency and accuracy of scientific research funding management. In summary, these optimization measures have jointly promoted the development of scientific research funding management and monitoring systems in a more intelligent and efficient direction.

[0065] Example 2

[0066] This embodiment provides a detailed description of the budget control algorithm in Embodiment 1.

[0067] The budget control algorithm includes budget template matching and real-time budget calculation. It uses the Drools rule engine to implement automatic matching of budget templates and definition of calculation rules for budget items.

[0068] In a further embodiment, budget template matching is used to automatically match the most suitable budget template according to the characteristics of the scientific research project type, scale, and research cycle; the algorithm first analyzes the basic information of the scientific research project, and then uses the matching capability of the Drools rule engine to find the most suitable template in the predefined budget template library; the budget control algorithm realizes automatic matching of budget templates, such as Figure 2 Shown, including:

[0069] Input scientific research project information, including type, scale and research period;

[0070] Calling the Drools rule engine to match the scientific research project information with the rules in the preset budget template library to obtain a matching result;

[0071] Selecting the optimal budget template from the preset template library based on the matching result;

[0072] Output the optimal budget template ID and information.

[0073] In a further embodiment, matching the scientific research project information with rules in a preset budget template library includes:

[0074] Analyze the relationship between scientific research project types and budget templates, and select a suitable classification algorithm for automatic matching of budget templates. In this embodiment, a decision tree classifier is selected to match the scientific research project information with the rules in the preset budget template library. The decision tree classifier is trained using Python's scikit-learn library.

[0075] In a specific embodiment, a decision tree classification algorithm is used to automatically select an appropriate budget template based on multiple attributes of a scientific research project (such as research field, research objectives, research methods, etc.). The decision tree classifier is trained using the Python scikit-learn library. The specific steps are as follows:

[0076] (1) Prepare the dataset: collect historical data on scientific research project types and corresponding budget templates;

[0077] (2) Data preprocessing: Encode the data of the research project type (such as using one-hot encoding to process categorical variables) and construct a feature matrix;

[0078] (3) Train the decision tree classifier: Use the DecisionTreeClassifier class in the scikit-learn library to train the classifier;

[0079] (4) Prediction and validation: Use the test dataset to verify the accuracy of the classifier and adjust and optimize it as needed;

[0080] (5) Algorithm verification and optimization: Optimize algorithm performance through cross-validation, adjustment of decision tree depth, pruning strategy and other parameters to ensure the accuracy and efficiency of budget template matching.

[0081] In a further embodiment, the real-time budget calculation is used to calculate the budget usage of scientific research projects in real time, including key indicators such as used budget, remaining budget, and budget utilization rate; the algorithm dynamically updates the budget status by monitoring the increase and decrease of budget items, and the automatic calculation of budget usage, such as Figure 3 Shown, including:

[0082] Initialize the budget status of the scientific research project, which includes the total budget and the used budget;

[0083] Monitoring budget item increase and decrease events, including expenditures and budget adjustments;

[0084] Update the budget status of the scientific research project according to the increase or decrease change event;

[0085] According to the updated scientific research project budget status, the budget usage is calculated and output in real time, and the budget usage includes the remaining budget and the budget utilization rate.

[0086] In a further embodiment, the real-time calculation and output of the budget usage includes using a sliding window algorithm to calculate the budget usage within the current time window:

[0087] Use the Pandas library to read financial data files, such as Excel, CSV, etc.;

[0088] Set the size and step size of the sliding window as required;

[0089] Calculate budget usage within a sliding window and save it in a result set.

[0090] In a further embodiment, the method further comprises the steps of:

[0091] Perform multi-dimensional statistics and queries on the result set as needed.

[0092] In this example, the Pandas library is used to process financial data, enabling real-time budget calculations and ensuring data accuracy and real-time performance. The real-time budget calculation logic is designed, employing a sliding window algorithm to calculate budget usage in real time, supporting multi-dimensional statistics and queries. The efficiency of real-time budget calculations can also be improved by optimizing Pandas operations, reducing unnecessary calculations, and using vectorized operations.

[0093] In a further embodiment, the real-time warning calculation algorithm is implemented based on an event monitoring mechanism (such as Spring Event) to detect in real time whether the budget usage of scientific research projects meets the warning conditions; when the conditions are met, a warning event is triggered; Figure 4 As shown, specifically including:

[0094] (1) Define the types of warning events, including budget overspending warning and excessive budget utilization warning;

[0095] (2) Monitor changes in the use of scientific research project budgets;

[0096] (3) When an event occurs, calculate whether the current budget usage meets the warning conditions;

[0097] (4) If the conditions are met, the warning event is triggered and passed to the warning information push algorithm for processing.

[0098] In a further embodiment, an ARIMA model is used to predict future budget consumption trends.

[0099] The warning information push algorithm is used to generate warning information based on warning events and push it to relevant personnel through email or SMS services; the warning information includes warning type, warning level, scientific research project name, and budget usage; Figure 5As shown, specifically including:

[0100] (1) Receive warning events and analyze warning types and levels;

[0101] (2) Generate corresponding warning information templates according to warning type and level;

[0102] (3) Fill in the key information of the scientific research project name and budget usage into the template;

[0103] (4) Call the email or SMS service API to push the warning information to relevant personnel.

[0104] In a further embodiment, a budget usage threshold is set, and an alert is triggered when the budget usage reaches or exceeds the threshold;

[0105] Introducing the ARIMA time series forecasting model to predict future budget consumption trends and provide decision support for early warning;

[0106] Implement the ARIMA model and use the statsmodels library for time series data preprocessing, model training, and forecasting;

[0107] Verify and optimize the forecast results to ensure the accuracy and reliability of the forecast.

[0108] The ARIMA time series forecasting model predicts future budget consumption trends, including:

[0109] (1) Data preprocessing: Perform stationarity test and difference processing on budget usage data to meet the input requirements of the ARIMA model;

[0110] (2) Model training: Use the ARIMA class in the statsmodels library to train the model and determine the order of the model (p, d, q);

[0111] (3) Predict future trends: Use the trained ARIMA model to predict future budget consumption trends;

[0112] (4) Setting warning thresholds: Set reasonable warning thresholds based on prediction results and actual needs;

[0113] (5) Verification and optimization of prediction results: Optimize the prediction results by calculating the prediction error (such as mean square error (MSE)), adjusting model parameters (such as order p, d, q), and introducing other prediction models to ensure the accuracy and reliability of the prediction.

[0114] In this embodiment, by introducing the decision tree classification algorithm, the intelligent matching of scientific research projects and budget templates is realized, and the accuracy and efficiency of matching are improved. Secondly, the combination of the sliding window algorithm and the Pandas library realizes the real-time calculation and multi-dimensional statistics of budget usage, ensuring the accuracy and real-time nature of the data. Finally, the introduction of the ARIMA time series prediction model provides scientific decision-making support for budget overspending warning, and realizes the advance and accuracy of warning by predicting future budget consumption trends. These optimization measures jointly enhance the intelligent level of scientific research funding management, and provide strong guarantees for the efficient execution of scientific research projects and the rational use of funds. At the same time, the performance optimization of the algorithm also ensures the stable operation of the system in high concurrency and large data volume scenarios.

[0115] Example 3

[0116] This embodiment provides a detailed description of the early warning monitoring algorithm in Example 1, specifically including:

[0117] Alert rules engine

[0118] Define warning conditions, warning levels, and trigger actions based on the Drools rule engine to achieve flexible configuration and management of warning rules;

[0119] Write Drools rule files to define the logic and trigger conditions of warning rules;

[0120] Verify and optimize the rules to ensure the accuracy and effectiveness of the warning rules;

[0121] Real-time warning calculation algorithm

[0122] Use Spring Event to implement event monitoring mechanism to detect whether the warning conditions are met in real time;

[0123] Design the warning calculation logic, trigger the warning event when the warning conditions are met, and call the corresponding warning processing function;

[0124] Optimize the performance of real-time warning calculation algorithms to ensure the timeliness and accuracy of warnings;

[0125] Early warning information push algorithm

[0126] Use template engines (such as Thymeleaf) to generate warning information templates to achieve dynamic generation of warning information;

[0127] Push warning information to relevant personnel through email services (such as JavaMail) or SMS services (such as Alibaba Cloud SMS Service);

[0128] Implement the warning information push function, integrate JavaMail and Alibaba Cloud SMS service API to ensure the timely delivery of warning information.

[0129] The Drools rule engine enables flexible configuration and management of warning rules, enhancing the system's adaptability and scalability. The introduction of the Spring Event event monitoring mechanism ensures real-time detection and rapid response to warning conditions, improving the timeliness of warnings. Furthermore, the integration of the template engine with email and SMS services enables the automated generation and multi-channel push of warning information, ensuring accurate delivery and widespread coverage. These optimization measures collectively enhance the warning effectiveness of the scientific research funding management and monitoring system, providing strong assurance for the safe use of research project funds.

[0130] Example 4

[0131] This embodiment provides the following steps:

[0132] Developed project application, project approval review, financial budget control, early warning monitoring, and data query modules; used SpringCloud and other frameworks to implement microservice registration, discovery, and load balancing, integrating Redis cache and Kafka message queues to improve performance; ensured system stability and reliability through unit testing, integration testing, and performance testing; and ultimately used Docker and Kubernetes for containerized deployment and automatic expansion to ensure smooth system operation. Specifically, this included:

[0133] Functional module development:

[0134] Develop project application and project approval modules, including front-end application interface and back-end logic;

[0135] Integrate financial budget control module to realize budget template matching, real-time budget calculation and budget overspending warning functions;

[0136] Integrate the early warning monitoring module to realize the early warning rule engine, real-time early warning calculation and early warning information push functions;

[0137] Develop data query and statistical analysis modules, provide data visualization tools and multi-dimensional data screening and statistical analysis functions;

[0138] System integration and testing

[0139] Use Spring Cloud Netflix to implement microservice registration and discovery, load balancing, and circuit breakers;

[0140] Integrate MyBatis ORM framework to implement data access layer;

[0141] Integrate Redis cache and Kafka message queue to improve system performance;

[0142] Conduct unit testing, integration testing, and performance testing to ensure system functional correctness and stability;

[0143] System deployment and configuration

[0144] Use Docker containerization to deploy the system and configure Kubernetes clusters for service management and automatic expansion; configure the server, database, and middleware infrastructure to ensure smooth system operation.

[0145] The microservices architecture and Spring Cloud technology stack improve the system's scalability, availability, and fault tolerance. The integration of the MyBatis ORM framework with Redis and Kafka enhances data access efficiency and system performance. A comprehensive testing strategy ensures system stability and correctness. Docker containerization and Kubernetes cluster deployment enable rapid deployment, automatic scalability, and efficient management. These optimization measures collectively improve the overall effectiveness and operational efficiency of the scientific research funding management and monitoring system.

[0146] Example 5

[0147] This embodiment provides a scientific research funding management and monitoring system. Figure 6 Shown, including:

[0148] A user demand collection module, which is used to collect user needs and design a microservice architecture, database, technology stack, and budget control algorithm and budget monitoring algorithm framework based on user needs;

[0149] A budget control module, which uses the budget control algorithm to automatically match budget templates, automatically calculate budget usage, and predict future budget consumption trends;

[0150] The budget monitoring module uses the early warning monitoring algorithm to manage early warning rules, sets thresholds to trigger early warnings, and detects in real time whether early warning rules are triggered, and generates early warning information for push.

[0151] The same or similar reference numerals correspond to the same or similar components;

[0152] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0153] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A scientific research fund management and monitoring method, characterized in that: The following steps are involved: Collect user requirements and design microservice architecture, database, technology stack, budget control algorithm and budget monitoring algorithm framework based on user requirements; Utilize the budget control algorithm to automatically match budget templates, automatically calculate budget usage and predict future budget consumption trends; The warning monitoring algorithm is used to manage warning rules, set thresholds to trigger warnings, and detect in real time whether the warning rules are triggered, and generate warning information for push.

2. The scientific research funding management and monitoring method according to claim 1, characterized in that: Design microservice architecture, database, and technology stack based on user needs, including: Design microservice architecture based on user needs, including project application services, budget control services, and early warning monitoring services; Design data models for scientific research projects, budget items, and early warning rules based on user needs, and choose MySQL database for storage; According to user needs, the Spring Cloud microservice framework is used for service governance, Spring Boot is used for service development, Redis is used as a cache to improve system performance, and Kafka is used as a message queue to implement asynchronous communication.

3. The scientific research funding management and monitoring method according to claim 1, characterized in that: The budget control algorithm realizes automatic matching of budget templates, including: Input scientific research project information, including type, scale and research period; Calling the Drools rule engine to match the scientific research project information with the rules in the preset budget template library to obtain a matching result; Selecting the optimal budget template from the preset template library based on the matching result; Output the optimal budget template ID and information.

4. The scientific research funding management and monitoring method according to claim 3, characterized in that: Match the scientific research project information with the rules in the preset budget template library, including: A decision tree classifier is selected to match the scientific research project information with the rules in the preset budget template library, and the decision tree classifier is trained using the scikit-learn library of Python.

5. The scientific research funding management and monitoring method according to claim 1, characterized in that: The automatic calculation of budget usage includes: Initialize the budget status of the scientific research project, which includes the total budget and the used budget; Monitoring budget item increase and decrease events, including expenditures and budget adjustments; Update the budget status of the scientific research project according to the increase or decrease change event; According to the updated scientific research project budget status, the budget usage is calculated and output in real time, and the budget usage includes the remaining budget and the budget utilization rate.

6. The scientific research funding management and monitoring method according to claim 5, characterized in that: The real-time calculation and output of the budget usage includes using a sliding window algorithm to calculate the budget usage within the current time window: Use the Pandas library to read financial data files; Set the size and step size of the sliding window as required; Calculate budget usage within a sliding window and save it in a result set.

7. The scientific research funding management and monitoring method according to claim 1, characterized in that: Use the ARIMA model to predict future budget consumption trends.

8. The scientific research funding management and monitoring method according to claim 4, characterized in that: Setting a threshold to trigger an early warning includes: Build an early warning rule engine based on the Drools rule engine; Defining warning rules, including warning conditions, warning levels, and triggering actions; Recording the warning rules into the warning rule engine; Obtaining the budget usage in real time; When the budget usage meets the warning condition, a corresponding warning event is triggered.

9. The scientific research funding management and monitoring method according to claim 8, characterized in that: Generate early warning information for push, including: Receiving the warning event, parsing it to obtain the warning type and warning level; Generate an early warning information template according to the early warning type and early warning level; Fill in the scientific research project name and budget usage into the warning information template; Call the email or SMS service API to push the warning information template to relevant personnel.

10. A scientific research funding management and monitoring system, characterized in that: include: A user demand collection module, which is used to collect user needs and design a microservice architecture, database, technology stack, and budget control algorithm and budget monitoring algorithm framework based on user needs; A budget control module, which uses the budget control algorithm to automatically match budget templates, automatically calculate budget usage, and predict future budget consumption trends; The budget monitoring module uses the early warning monitoring algorithm to manage early warning rules, sets thresholds to trigger early warnings, and detects in real time whether early warning rules are triggered, and generates early warning information for push.

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