Science and technology project management method and system based on business fusion

Abstract

The invention discloses a science and technology project management method and system based on business fusion, and belongs to the technical field of project management, and the method comprises the following steps: S1, collecting business data and technical data of each department, and storing the collected data in a unified database according to a preset data standard; s2, performing format conversion, standardization processing and data version management on the business data and the technical data to generate a comprehensive management data set; and S3, based on the comprehensive management data set, risk factors and performance indicators in the project implementation process are automatically calculated by using a risk assessment algorithm and a performance assessment algorithm, the risk assessment algorithm adopts a machine learning model, the performance assessment algorithm adopts a weighted statistical analysis method, and risk early warning information and a performance feedback report are generated. Through standardized data management and a unified database, information islands are eliminated, efficient integration of business data and technical data is realized, and cross-department collaboration efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of project management, and more specifically, to a technology project management method and system based on business integration. Background Art

[0002] In the process of technology project management, the integration of business and technology is a key challenge. Traditional project management methods often have the following problems: business departments and technical teams usually use different data standards and management systems, which leads to poor communication and affects the efficiency of project promotion. Business needs are constantly changing, while the response speed of technical solutions is slow, making it difficult to achieve agile adjustments. The management system is complex and the data utilization rate is low. The existing management system is often composed of multiple subsystems (such as financial management, R&D management, market operations, etc.). The data is scattered and it is difficult to form a unified decision support system. The data storage and analysis methods are not unified, resulting in serious information island problems and affecting the accuracy of decision-making. Summary of the invention

[0003] Technical solution: A technology project management method based on business integration, including the following steps: S1. Collect business data and technical data from various departments, and store the collected data in a unified database according to preset data standards; S2. Perform format conversion, standardization processing and data version management on the business data and technical data to generate a comprehensive management data set; S3. Based on the comprehensive management data set, the risk factors and performance indicators in the project implementation process are automatically calculated using the risk assessment algorithm and the performance assessment algorithm, wherein the risk assessment algorithm adopts a machine learning model and the performance assessment algorithm adopts a weighted statistical analysis method to generate risk warning information and performance feedback reports; S4. Input the risk warning information and performance feedback report into the decision support module, which automatically generates project management suggestions and transmits the decision support information to the terminals of each department in a secure and encrypted manner through network communication. It also supports user interactive input to achieve real-time two-way communication between business managers and technical managers.

[0004] Preferably, the step S1 comprises: S1-1: Define preset data standards, including data format, data fields, and data storage specifications; S1-2: Extract data from the business and technical systems of each department to ensure the integrity and accuracy of the data; S1-3: The extracted data is converted according to the preset data standard and stored in a unified database.

[0005] Preferably, the step S2 comprises: S2-1: Convert the data stored in the unified database to ensure the consistency of data format; S2-2: Standardize the data, including data cleaning, missing value filling and outlier processing; S2-3: Implement data version management and record historical versions and change records of data; S2-4: Generate a comprehensive administrative dataset as the basis for subsequent analyses.

[0006] Preferably, step S3 comprises: S3-1: Apply machine learning models on comprehensive management data sets to identify and evaluate risk factors in project implementation and generate risk warning information; S3-2: Use weighted statistical analysis methods to calculate and evaluate performance indicators and generate performance feedback reports.

[0007] Preferably, step S4 comprises: S4-1: Input risk warning information and performance feedback reports into the decision support module; S4-2: The decision support module automatically generates project management recommendations based on the input information; S4-3: Deliver decision support information to each department’s terminal in a secure and encrypted manner through network communications; S4-4: Support user interactive input to achieve real-time two-way communication between business managers and technical managers.

[0008] A technology project management system based on business integration, including: Data collection module: used to collect business data and technical data from various departments and store them in a unified database according to preset data standards.

[0009] Data processing module: used to perform format conversion, standardization processing and data version management on the business data and technical data to generate a comprehensive management data set.

[0010] Risk and performance assessment module: used to automatically calculate risk factors and performance indicators during project implementation based on a comprehensive management data set using risk assessment algorithms and performance assessment algorithms. The risk assessment algorithm uses a machine learning model, and the performance assessment algorithm uses a weighted statistical analysis method to generate risk warning information and performance feedback reports.

[0011] Decision support module: used to receive the risk warning information and performance feedback report, automatically generate project management suggestions, and transmit decision support information to the terminals of each department in a secure and encrypted manner through network communication. It also supports user interactive input to achieve real-time two-way communication between business managers and technical managers.

[0012] Preferably, the data acquisition module further comprises: Data standard definition unit: used to define preset data standards, including data format, data field and data storage specifications.

[0013] Data extraction unit: used to extract data from the business systems and technical systems of various departments to ensure the integrity and accuracy of the data.

[0014] Data conversion unit: used to convert the extracted data according to preset data standards and store them in a unified database.

[0015] Preferably, the data processing module further comprises: Format conversion unit: used to convert the format of data stored in the unified database to ensure consistency of data format.

[0016] Standardization processing unit: used to perform standardization processing on data, including data cleaning, missing value filling and outlier processing.

[0017] Version management unit: used to implement data version management and record historical versions and change records of data.

[0018] Comprehensive data generation unit: used to generate comprehensive management data sets as the basis for subsequent analysis.

[0019] Preferably, the risk and performance assessment module further includes: Risk Assessment Unit: Used to apply machine learning models on comprehensive management data sets to identify and assess risk factors during project implementation and generate risk warning information.

[0020] Performance evaluation unit: used to calculate and evaluate performance indicators using weighted statistical analysis methods and generate performance feedback reports.

[0021] Preferably, the decision support module further comprises: Information receiving unit: used to receive risk warning information and performance feedback reports.

[0022] Proposal generation unit: used to automatically generate project management proposals based on the received information.

[0023] Information transmission unit: used to transmit decision support information to terminals of various departments in a secure and encrypted manner through network communication.

[0024] User interaction unit: used to support user interaction input and realize real-time two-way communication between business managers and technical managers.

[0025] Compared with the prior art, the advantages of the present invention are: (1) Through standardized data management and unified database, information silos are eliminated, efficient integration of business data and technical data is achieved, and cross-departmental collaboration efficiency is improved.

[0026] (2) Use machine learning models to conduct risk assessment, automatically identify potential risks, and improve the accuracy of early warnings; use weighted statistical analysis methods to conduct performance evaluation, provide quantitative decision-making basis, and reduce the impact of human subjective judgment.

[0027] (3) Through automated data analysis and intelligent decision support systems, we can reduce manual data processing and coordination costs, achieve real-time feedback and efficient management, and improve the speed and success rate of project advancement. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the overall system of the present invention. DETAILED DESCRIPTION

[0029] Embodiment 1, a technology project management method based on business integration, comprises the following steps: S1. Collect business data and technical data from various departments, and store the collected data in a unified database according to preset data standards; S2. Perform format conversion, standardization processing and data version management on the business data and technical data to generate a comprehensive management data set; S3. Based on the comprehensive management data set, the risk factors and performance indicators in the project implementation process are automatically calculated using the risk assessment algorithm and the performance assessment algorithm, wherein the risk assessment algorithm adopts a machine learning model and the performance assessment algorithm adopts a weighted statistical analysis method to generate risk warning information and performance feedback reports; S4. Input the risk warning information and performance feedback report into the decision support module, which automatically generates project management suggestions and transmits the decision support information to the terminals of each department in a secure and encrypted manner through network communication. It also supports user interactive input to achieve real-time two-way communication between business managers and technical managers.

[0030] The step S1 comprises: S1-1: Define preset data standards, including data format, data fields, and data storage specifications; S1-2: Extract data from the business and technical systems of each department to ensure the integrity and accuracy of the data; S1-3: The extracted data is converted according to the preset data standard and stored in a unified database.

[0031] The step S2 comprises: S2-1: Convert the data stored in the unified database to ensure the consistency of data format; S2-2: Standardize the data, including data cleaning, missing value filling and outlier processing; S2-3: Implement data version management and record historical versions and change records of data; S2-4: Generate a comprehensive administrative dataset as the basis for subsequent analyses.

[0032] The step S3 comprises: The machine learning model support vector machine (SVM) is used to assess project risks. The specific steps are as follows: 1. Feature selection: Select risk-related features from the comprehensive management data set and construct a feature vector .

[0033] 2. Model training: Kernel function selection: Radial basis function (RBF) is selected as the kernel function: ; in, and is the feature vector, is the kernel function parameter. Solve the optimization problem: Determine the model parameters by solving the following optimization problem: ; The constraints are: ; Among them, w is the weight vector, b is the bias, and c is the penalty parameter. is the slack variable, For labels, is the mapping function. 3. Model evaluation: Use methods such as cross-validation to evaluate model performance and adjust parameters to improve the generalization ability of the model. 4. Risk prediction: Input the feature vector of the new project into the trained SVM model and output the risk probability P (risk). Use the weighted statistical analysis method to evaluate project performance. The specific steps are as follows: 1. Indicator selection: Determine key performance indicators (KPIs), such as cost control, progress achievement, quality indicators, etc.

[0034] 2. Weight determination: According to the importance of the indicator, the weight of each indicator is determined by using methods such as the analytic hierarchy process (AHP) or the entropy weight method. Taking the entropy weight method as an example: Calculate the indicator weight: For the jth indicator, the indicator weight of the i-th project is: ; in, is the value of the i-th item on the j-th indicator, and m is the total number of items.

[0035] Calculate information entropy: The information entropy of the jth indicator is: ; Calculate entropy weight: The weight of the jth indicator is: ; Where n is the total number of indicators.

[0036] 3. Performance score: Calculate the overall performance score for each project : ; in, is the standardized score of the i-th item on the j-th indicator.

[0037] The step S4 comprises: Data input and integration, the risk warning information generated by the risk assessment module (recorded as risk vector ) and the performance feedback report generated by the performance evaluation module (recorded as performance vector ) is input into the decision support module. Data integration formula: Define the project management suggestion comprehensive index M as the weighted sum of risk factors and performance indicators: ; in: represents the evaluation value of the ith risk factor; represents the evaluation value of the jth performance indicator; and is a pre-set weight parameter determined according to the importance of risk and performance indicators; n and m are the total number of risk factors and performance indicators, respectively.

[0038] Automatically generate project management suggestions based on the comprehensive index M and preset decision rules.

[0039] The following rule logic can be used: If M exceeds the set high risk threshold , then generate the suggestion "strengthen risk control and immediately initiate emergency response"; If M is lower than the set safety threshold , then generate the suggestion "continue to maintain the current execution plan"; Otherwise, a suggestion is generated to "make appropriate adjustments based on performance feedback and optimize resource allocation."

[0040] A technology project management system based on business integration, including: Data collection module: used to collect business data and technical data from various departments and store them in a unified database according to preset data standards.

[0041] Data processing module: used to perform format conversion, standardization processing and data version management on the business data and technical data to generate a comprehensive management data set.

[0042] Risk and performance assessment module: used to automatically calculate risk factors and performance indicators during project implementation based on a comprehensive management data set using risk assessment algorithms and performance assessment algorithms. The risk assessment algorithm uses a machine learning model, and the performance assessment algorithm uses a weighted statistical analysis method to generate risk warning information and performance feedback reports.

[0043] Decision support module: used to receive the risk warning information and performance feedback report, automatically generate project management suggestions, and transmit decision support information to the terminals of each department in a secure and encrypted manner through network communication. It also supports user interactive input to achieve real-time two-way communication between business managers and technical managers.

[0044] The data acquisition module further comprises: Data standard definition unit: used to define preset data standards, including data format, data field and data storage specifications.

[0045] Data extraction unit: used to extract data from the business systems and technical systems of various departments to ensure the integrity and accuracy of the data.

[0046] Data conversion unit: used to convert the extracted data according to preset data standards and store them in a unified database.

[0047] The data processing module further comprises: Format conversion unit: used to convert the format of data stored in the unified database to ensure consistency of data format.

[0048] Standardization processing unit: used to perform standardization processing on data, including data cleaning, missing value filling and outlier processing.

[0049] Version management unit: used to implement data version management and record historical versions and change records of data.

[0050] Comprehensive data generation unit: used to generate comprehensive management data sets as the basis for subsequent analysis.

[0051] The risk and performance assessment module further includes: Risk Assessment Unit: Used to apply machine learning models on comprehensive management data sets to identify and assess risk factors during project implementation and generate risk warning information.

[0052] Performance evaluation unit: used to calculate and evaluate performance indicators using weighted statistical analysis methods and generate performance feedback reports.

[0053] The decision support module further comprises: Information receiving unit: used to receive risk warning information and performance feedback reports.

[0054] Proposal generation unit: used to automatically generate project management proposals based on the received information.

[0055] Information transmission unit: used to transmit decision support information to terminals of various departments in a secure and encrypted manner through network communication.

[0056] User interaction unit: used to support user interaction input and realize real-time two-way communication between business managers and technical managers.

[0057] Embodiment 2: Step S1: Collect business data and technical data of each department, and store the collected data in a unified database according to preset data standards.

[0058] By designing standardized data templates, we ensure that the data formats of different departments are consistent. We use data interfaces or ETL (Extract, Transform, Load) tools to extract, transform and load the data of each department into a unified database.

[0059] Step S2: Perform format conversion, standardization and data version management on the business data and technical data to generate a comprehensive management data set. Use data cleaning and conversion tools to unify and standardize the data format. Introduce a data version control mechanism to ensure data traceability and consistency.

[0060] Step S3: Based on the comprehensive management data set, the risk assessment algorithm and performance assessment algorithm are used to automatically calculate the risk factors and performance indicators in the project implementation process, and generate risk warning information and performance feedback reports. Machine learning models (such as decision trees, random forests, etc.) are used to predict potential risks based on historical data and current project data. Weighted statistical analysis methods are used to quantitatively evaluate the key performance indicators (KPIs) of the project and generate performance feedback reports.

[0061] Step S4: Input the risk warning information and performance feedback report into the decision support module, which automatically generates project management suggestions and transmits decision support information to the terminals of each department in a secure and encrypted manner through network communication. It also supports user interactive input to achieve real-time two-way communication between the business manager and the technical manager. Based on the risk and performance assessment results, project management suggestions are automatically generated using a decision tree or rule engine. The SSL / TLS protocol is used to encrypt the transmitted data to ensure information security. A user interface that supports real-time interaction is developed to facilitate communication and decision-making between the business manager and the technical manager.

[0062] Through the above method, the effective integration of business data and technical data is achieved, the efficiency and quality of project management are improved, the coordination cost is reduced, and the success rate of the project is increased.

[0063] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A technology project management method based on business integration, characterized in that: The following steps are involved: S1. Collect business data and technical data from various departments, and store the collected data in a unified database according to preset data standards; S2. Perform format conversion, standardization processing and data version management on the business data and technical data to generate a comprehensive management data set; S3. Based on the comprehensive management data set, the risk factors and performance indicators in the project implementation process are automatically calculated using the risk assessment algorithm and the performance assessment algorithm, wherein the risk assessment algorithm adopts a machine learning model and the performance assessment algorithm adopts a weighted statistical analysis method to generate risk warning information and performance feedback reports; S4. Input the risk warning information and performance feedback report into the decision support module, which automatically generates project management suggestions and transmits the decision support information to the terminals of each department in a secure and encrypted manner through network communication. It also supports user interactive input to achieve real-time two-way communication between business managers and technical managers.

2. A technology project management method based on business integration according to claim 1, characterized in that: The step S1 comprises: S1-1: Define preset data standards, including data format, data fields, and data storage specifications; S1-2: Extract data from the business and technical systems of each department to ensure the integrity and accuracy of the data; S1-3: The extracted data is converted according to the preset data standard and stored in a unified database.

3. According to the technology project management method based on business integration according to claim 1, it is characterized in that: The step S2 comprises: S2-1: Convert the data stored in the unified database to ensure the consistency of data format; S2-2: Standardize the data, including data cleaning, missing value filling and outlier processing; S2-3: Implement data version management and record historical versions and change records of data; S2-4: Generate a comprehensive administrative dataset as the basis for subsequent analyses.

4. The technology project management method based on business integration according to claim 1 is characterized in that: The step S3 comprises: S3-1: Apply machine learning models on comprehensive management data sets to identify and evaluate risk factors in project implementation and generate risk warning information; S3-2: Use weighted statistical analysis methods to calculate and evaluate performance indicators and generate performance feedback reports.

5. The technology project management method based on business integration according to claim 1 is characterized in that: The step S4 comprises: S4-1: Input risk warning information and performance feedback reports into the decision support module; S4-2: The decision support module automatically generates project management recommendations based on the input information; S4-3: Deliver decision support information to each department’s terminal in a secure and encrypted manner through network communications; S4-4: Support user interactive input to achieve real-time two-way communication between business managers and technical managers.

6. A technology project management system based on business integration, according to a technology project management method design based on business integration according to any one of claims 1-5, characterized in that: include: Data collection module: used to collect business data and technical data from various departments and store them in a unified database according to preset data standards; Data processing module: used to perform format conversion, standardization processing and data version management on the business data and technical data to generate a comprehensive management data set; Risk and performance assessment module: used to automatically calculate risk factors and performance indicators in the project implementation process based on the comprehensive management data set using risk assessment algorithms and performance assessment algorithms. The risk assessment algorithm uses a machine learning model, and the performance assessment algorithm uses a weighted statistical analysis method to generate risk warning information and performance feedback reports; Decision support module: used to receive the risk warning information and performance feedback report, automatically generate project management suggestions, and transmit decision support information to the terminals of each department in a secure and encrypted manner through network communication. It also supports user interactive input to achieve real-time two-way communication between business managers and technical managers.

7. A technology project management system based on business integration according to claim 6, characterized in that: The data acquisition module further comprises: Data standard definition unit: used to define preset data standards, including data format, data field and data storage specifications; Data extraction unit: used to extract data from the business systems and technical systems of various departments to ensure the integrity and accuracy of the data; Data conversion unit: used to convert the extracted data according to preset data standards and store them in a unified database.

8. A technology project management system based on business integration according to claim 6, characterized in that: The data processing module further comprises: Format conversion unit: used to convert the format of data stored in the unified database to ensure the consistency of data format; Standardization processing unit: used to perform standardization processing on data, including data cleaning, missing value filling and outlier processing; Version management unit: used to implement data version management and record historical versions and change records of data; Comprehensive data generation unit: used to generate comprehensive management data sets as the basis for subsequent analysis.

9. A technology project management system based on business integration according to claim 6, characterized in that: The risk and performance assessment module further includes: Risk Assessment Unit: used to apply machine learning models on comprehensive management data sets to identify and assess risk factors during project implementation and generate risk warning information; Performance evaluation unit: used to calculate and evaluate performance indicators using weighted statistical analysis methods and generate performance feedback reports.

10. A technology project management system based on business integration according to claim 6, characterized in that: The decision support module further comprises: Information receiving unit: used to receive risk warning information and performance feedback reports; Proposal generation unit: used for automatically generating project management proposals based on the received information; Information transmission unit: used to transmit decision support information to terminals of various departments in a secure and encrypted manner through network communication; User interaction unit: used to support user interaction input and realize real-time two-way communication between business managers and technical managers.

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