Innovation achievement evaluation system based on big data

Abstract

The invention relates to the technical field of data analysis, in particular to an innovation achievement evaluation system based on big data, and the system comprises a data capturing module which screens an original data set of an enterprise scientific and technological innovation project, extracts time series data, and distinguishes the data into cost data, an innovation project milestone date and a quality index; the cost change rate, the project progress deviation and the quality change index of the multiple innovation projects are calculated, the average value and the standard deviation of multiple indexes are counted, and preliminary project deviation analysis is established; according to the method, time sequence data of science and technology projects are accurately analyzed, cost, progress and quality indexes are monitored and analyzed, time sequence decomposition and independent analysis of seasonal and trend deviation are carried out, the recognition precision of periodic changes and long-term development trends of the projects is improved, key data changes can be accurately captured, and the recognition accuracy is improved. Accurate positioning and prediction of key change points in scientific and technological progress are promoted, and evaluation and optimization effects on research and development achievements are greatly enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an innovation achievement evaluation system based on big data. Background Art

[0002] The field of data analysis technology covers all aspects from basic data collection and storage to advanced data processing and analysis. The key task in this field is to transform large amounts of complex data into useful information and insights to support the decision-making process. Data analysis technologies include statistical analysis, machine learning, big data technology, and artificial intelligence, which are widely used in finance, healthcare, market research, operational optimization, and many other fields. Through data models and algorithms, analysts can identify trends, test hypotheses, predict market behavior, and improve business efficiency. With the advancement of technology, data analysis methods and tools are also constantly evolving, with an increasing focus on real-time data processing and the development of automated decision support systems.

[0003] Among them, the innovation results evaluation system refers to a tool that uses data analysis technology to evaluate and optimize the results of innovation activities. The main purpose of this system is to help enterprises or research institutions quantify and evaluate the effects of their innovation projects, thereby guiding future R&D investment and strategic adjustments. By analyzing data collected from various data sources, the system can provide detailed analysis of project performance, cost-effectiveness and market response, enabling decision makers to more wisely choose which innovations to continue investing in and which ones may need to be reconsidered.

[0004] Existing technologies are often limited to basic data collection and statistical analysis when dealing with large-scale and complex data sets, making it difficult to achieve instant identification and in-depth analysis of data changes. This results in traditional methods being unable to adjust strategies in a timely manner under rapidly changing market conditions, which in turn affects the cost-effectiveness and market responsiveness of projects. For example, failure to accurately analyze seasonal and long-term trend changes in data may lead to misjudgment of market demand. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an innovation achievement evaluation system based on big data.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution: an innovation achievement evaluation system based on big data includes:

[0007] The data capture module screens the original data set of enterprise technology projects, extracts time series data, and distinguishes them into cost data, project milestone dates, and quality indicators. It calculates the cost change rate, project schedule deviation, and quality change index of multiple projects, and calculates the average and standard deviation of multiple indicators to establish preliminary project deviation analysis.

[0008] The pattern recognition module extracts data items with standard deviations exceeding the norm from the preliminary project deviation analysis, performs time series decomposition on each data item, extracts seasonal and trend components, analyzes seasonal deviations and trend deviations separately, identifies abnormal change points corresponding to time nodes of scientific and technological progress, and extracts key morphological features;

[0009] The result optimization module analyzes the key morphological features, determines the cumulative effect of data deviation, reclassifies multiple data items by setting threshold classification, adjusts the deviation limit of each type of data, and establishes the deviation dynamic prediction analysis results;

[0010] The report generation module integrates the evaluation data based on the deviation dynamic prediction analysis results, conducts a multi-dimensional evaluation of the project including project health, risk assessment, and future development forecast, and forms a project analysis result.

[0011] As a further solution of the present invention, the steps for calculating the cost change rate are specifically as follows:

[0012] Filter cost data from the original data set of enterprise technology projects, classify them according to projects and time series, and generate classified cost data sets;

[0013] Utilizing the classified cost data set, calculating the total cost at each time point, comparing the cost at the current time point with the cost at the previous time point, and generating cost comparison data at each time point;

[0014] Based on the cost comparison data at each time point, the formula is used:

[0015]

[0016] Calculate and generate the cost change rate at each time point;

[0017] Where, ΔC i represents the cost change rate, C i Represents the total cost at the current time point, C i-1 Indicates the total cost at the previous point in time.

[0018] As a further solution of the present invention, the steps for calculating the project progress deviation are specifically as follows:

[0019] Filter project milestone date information from the original data set of enterprise technology projects, classify data according to projects and time series, and generate a project milestone classification data set;

[0020] Using the project milestone classification data set, calculate the difference between the actual completion date and the scheduled completion date of each project to generate time deviation data for each project;

[0021] From the time deviation data for each project, use the formula:

[0022]

[0023] Calculate and generate project schedule deviations;

[0024] Among them, P dev Represents the calculated project schedule deviation percentage, T actual Indicates the actual completion time, T planned Indicates the scheduled completion time.

[0025] As a further solution of the present invention, the steps for calculating the mass change index are specifically as follows:

[0026] Extract indicators related to project quality from the original data set of enterprise technology projects, classify and sort the data by project and time series, and generate a classified quality indicator data set;

[0027] Using the quality indicator data set, statistically analyzing the data at each time point, calculating the average quality score at each time point, and generating average quality score data at each time point;

[0028] Based on the average quality score data at each time point, the formula is used:

[0029]

[0030] Calculate and generate quality change index;

[0031] Among them, Q index Represents the quality change index, which is used to evaluate the degree of change in the quality score in the time series. current , Q previous Represent the average quality scores of the current and previous time points, respectively.

[0032] As a further solution of the present invention, the steps for obtaining the preliminary project deviation analysis are specifically as follows:

[0033] Integrate the cost change rate, the project schedule deviation, and the quality change index, aggregate indicators from multiple data sources, and form a comprehensive performance indicator data set;

[0034] Based on the comprehensive performance indicator data set, a weight analysis is performed on the key performance indicators of each project, a standardization process is applied to ensure that the weight of each indicator matches, a weighted average score is calculated, and a comprehensive performance score of each project is obtained;

[0035] Using the composite performance score for each item, use the formula:

[0036] D score=α·ΔC i +β·P dev +γ·Q index

[0037] Calculate the deviation for each item and establish a preliminary item deviation analysis;

[0038] Among them, D score It represents the comprehensive deviation score of the project, which is used to comprehensively evaluate the overall performance of the project. α, β, and γ are the weights of the cost change rate, project schedule deviation, and quality change index, respectively. They are adjusted according to the focus of project management to reflect their criticality to project success. ΔC i , P dev , Q index They represent the cost change rate, project schedule deviation, and quality change index extracted from the dataset, respectively.

[0039] As a further solution of the present invention, the step of acquiring the key morphological features is specifically as follows:

[0040] Screening the data items in the preliminary project deviation analysis, calculating the standard deviation of each data item, comparing it with a preset threshold, identifying the data items whose standard deviation significantly exceeds the threshold, and generating a list of standard deviation abnormal data;

[0041] Performing time series decomposition on the abnormal data items identified in the standard deviation abnormal data list, extracting and separating the seasonality and trend components of each data item, ensuring the independence of each component, and generating decomposed seasonality and trend data;

[0042] The statistical model was applied to analyze the decomposed seasonality and trend data using the formula:

[0043] A i =β·S i +γ·T i

[0044] Identify key time nodes and abnormal change points, and generate abnormal change analysis results;

[0045] Among them, A i Represents the comprehensive anomaly analysis score, which is used to quantify the degree of anomaly. i 、T i Refers to the quantitative scores of seasonal and trend components, respectively, β and γ are coefficients used to adjust the impact of seasonal and trend components, providing a means of adjusting the sensitivity of the analysis;

[0046] From the abnormal change analysis results, key morphological features corresponding to the time nodes of scientific and technological progress are extracted.

[0047] As a further solution of the present invention, the step of obtaining the deviation dynamic prediction analysis result is specifically as follows:

[0048] Analyze the data extracted from the key morphological features, evaluate the cumulative effect of deviations for each data point, calculate the cumulative deviations, and determine which deviations are significantly higher than the normal level, and generate a list of cumulative deviation effects;

[0049] According to the list of cumulative effects of deviations, differentiated threshold levels are set, data items are reclassified through threshold grading logic, deviation limits are set for each category, and a list of reclassified data items is generated;

[0050] Using the results from the reclassified data item list, adjust the deviation limits for each category of data using the formula:

[0051] L new =θ·L old +ξ

[0052] Redefine the deviation limit for each category and generate adjusted deviation limit data;

[0053] Among them, L new Indicates the current deviation limit, which is used to control and predict data deviation, L old is the original deviation limit, representing the previous classification standard, and θ and ξ are adjustment coefficients used to fine-tune the deviation limit to match the current analysis requirements;

[0054] According to the adjusted deviation limit data, a dynamic prediction model for displacement is established to predict the deviation behavior that will occur in the future, and the dynamic prediction data change based on the adjusted deviation limit is dynamically predicted to generate the dynamic prediction analysis result of displacement.

[0055] As a further solution of the present invention, the steps of obtaining the project analysis results are specifically as follows:

[0056] Extract key data points from the dynamic prediction analysis results of the offset, combine each project dimension including cost, time and quality, calculate comprehensive health indicators, and generate preliminary comprehensive evaluation data;

[0057] Utilizing the comprehensive assessment data, applying a quantitative risk assessment model to analyze potential risks and impacts, and generating risk assessment results;

[0058] Combining the risk assessment results and the health index, the formula is adopted:

[0059] P future =β·R risk +γ·H current

[0060] Conduct forecast analysis of future development, predict the future performance of the project by adjusting the weights of risk and health, and generate forecast results of future development;

[0061] Among them, P future Represents the future forecast value of the project, R risk Represents the quantitative results of risk assessment,

[0062] H current It represents the current project health index, β and γ are the weight coefficients for adjusting the impact of risk and health;

[0063] Summarize the preliminary comprehensive assessment data, the risk assessment results and the future development forecast results, conduct a multi-dimensional assessment of the project, analyze the overall health status, risk level and future trends of the project, and generate project analysis results.

[0064] Compared with the prior art, the advantages and positive effects of the present invention are:

[0065] In the present invention, the cost, progress and quality indicators are monitored and analyzed by accurately analyzing the time series data of scientific and technological projects. Time series decomposition and independent analysis of seasonality and trend deviations improve the accuracy of identifying cyclical changes and long-term development trends of projects. In this way, key data changes can be accurately captured, and the precise positioning and prediction of key change points in scientific and technological progress can be promoted, which greatly enhances the evaluation and optimization effect of R&D results. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a system flow chart of the present invention;

[0067] Figure 2 is a flow chart of the steps for calculating the cost change rate of the present invention;

[0068] Figure 3 A flow chart of the calculation steps of the project progress deviation of the present invention;

[0069] Figure 4 is a flow chart of the calculation steps of the quality change index of the present invention;

[0070] Figure 5 A flowchart of the steps for obtaining the preliminary project deviation analysis of the present invention;

[0071] Figure 6 is a flow chart of the steps for obtaining the key morphological features of the present invention;

[0072] Figure 7 A flowchart of the steps for obtaining the results of the dynamic prediction analysis of the displacement of the present invention;

[0073] Figure 8The present invention is a flowchart of the steps for obtaining the project analysis results. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0076] Embodiment 1

[0077] See also Figure 1 , an innovation achievement evaluation system based on big data includes:

[0078] The data capture module screens the original data set of enterprise technology projects, extracts time series data, and distinguishes them into cost data, project milestone dates, and quality indicators. It calculates the cost change rate, project schedule deviation, and quality change index of multiple projects, and calculates the average and standard deviation of multiple indicators to establish preliminary project deviation analysis.

[0079] The pattern recognition module extracts data items with standard deviations exceeding the norm from the preliminary project deviation analysis, performs time series decomposition on each data item, extracts seasonal and trend components, analyzes seasonal deviations and trend deviations separately, identifies abnormal change points corresponding to the time nodes of scientific and technological progress, and extracts key morphological features;

[0080] The result optimization module analyzes key morphological features, determines the cumulative effect of data deviation, reclassifies multiple data items by setting threshold classification, adjusts the deviation limit of each type of data, and establishes the deviation dynamic prediction analysis results;

[0081] The report generation module integrates the assessment data based on the deviation dynamic prediction analysis results, conducts a multi-dimensional assessment of the project including project health, risk assessment, and future development forecast, and forms the project analysis results.

[0082] The preliminary project deviation analysis specifically includes the cost change rate, project schedule deviation, and quality change index. The key morphological characteristics include seasonal deviation, trend deviation, and abnormal change points. The deviation dynamic prediction analysis results specifically refer to the cumulative effect of data deviation, threshold classification, and deviation limit adjustment. The multi-dimensional project evaluation includes project health, risk assessment, and future development forecast.

[0083] See also Figure 2 , the calculation steps of cost change rate are as follows:

[0084] Filter cost data from the original data set of enterprise technology projects, classify them according to projects and time series, and generate classified cost data sets;

[0085] To screen cost data, we first extract all cost-related data from the original data set of the enterprise's technology projects, including direct costs, indirect costs, fixed costs, and variable costs. Data classification involves distinguishing between projects and time series. This process involves batch data sorting and verification to ensure that the cost data of each project is accurately archived. In this process, the acquisition of key parameters depends on the accurate labeling and timestamp information of the cost items in the data set to ensure the timeliness and accuracy of the data. Through this series of operations, the classified cost data set is obtained.

[0086] Using the classified cost data set, calculate the total cost at each time point, compare the cost at the current time point with the cost at the previous time point, and generate cost comparison data at each time point;

[0087] Using the classified cost data, we continue to analyze the total cost of each project at multiple time points, and compare the cost at the current time point with the cost at the previous time point. This comparison relies on the cost records at each time point, including labor costs and material costs. Through comparison, we can monitor the cost change trend. Key parameters include the amount of multiple types of costs and accurate records of time points. These are all executed through the statistical function within the data system to ensure the accuracy and real-time nature of the data, and generate cost comparison data for each time point.

[0088] Based on the cost comparison data at each time point, the formula is used:

[0089]

[0090] Calculate and generate the cost change rate at each time point;

[0091] Where, ΔC i represents the cost change rate, C i Represents the total cost at the current time point, C i-1 Indicates the total cost at the previous point in time.

[0092] formula:

[0093]

[0094] The benefit of the formula is that it can quantify the rate of change of costs at each point in time, which not only helps management monitor the trend of cost changes, but also predicts future estimated cost fluctuations, thereby making more effective budget and cost control decisions.

[0095] Setting C i = 105,000 yuan is the cost of this month, C i-1 =100,000 yuan is the cost of last month. According to the formula:

[0096]

[0097] The results show that the cost has increased by 5% from last month to this month, indicating that cost management needs to pay attention to this growth trend and iteratively analyze the reasons for the cost increase, whether it is the increase in material costs or labor costs. It is necessary to investigate and verify the optimization of the cost structure.

[0098] See also Figure 3 , the specific steps for calculating project schedule deviation are:

[0099] Filter project milestone date information from the original data set of enterprise technology projects, classify data according to projects and time series, and generate a project milestone classification data set;

[0100] Project milestone date information is extracted from the original data set of the enterprise's technology project and classified by project and time series. This process involves systematic classification and analysis of batch project data in order to effectively record and track the key time nodes of each project, ensure the integrity and availability of the data, and provide a reliable basic data source for subsequent progress deviation analysis. The classification processing of the data set includes not only data collection, but also preliminary data cleaning to remove invalid or erroneous time records, such as incorrect date formats or time nodes in future time periods. This step is a preliminary grasp of time control in project management, ensuring that each stage of the project can be executed as planned, thereby forming a milestone timeline for each project in the project management system. The data set obtained after classification directly affects the efficiency of project progress monitoring and deviation analysis.

[0101] Using the project milestone classification data set, the difference between the actual completion date and the scheduled completion date of each project is calculated to generate the time deviation data for each project;

[0102] Using the classified project milestone data set obtained, by comparing the actual completion date of each project with the scheduled completion date, this process requires calculating the date difference, referring to the difference between working days and non-working days, and the estimated holiday adjustment. This comparative analysis assists project managers in identifying potential delays or early completion stages in the project, and provides data support for project risk management and resource reallocation. Through this analysis, the project team can adjust resources and plans in a timely manner to cope with any uncertainty in the schedule. The generated time deviation data not only reflects the time management efficiency of each project, but is also a key data point for evaluating project management performance, thereby providing decision support for senior management.

[0103] From the time deviation data for each project, use the formula:

[0104]

[0105] Calculate and generate project schedule deviations;

[0106] Among them, P dev Represents the calculated project schedule deviation percentage, T actual Indicates the actual completion time, T planned Indicates the scheduled completion time.

[0107] formula:

[0108]

[0109] The benefit of the formula is that by calculating the percentage deviation between the actual project completion time and the scheduled time, this formula can not only quantify the extent of project delay or early completion, but also provide a standardized evaluation indicator to assist project teams in comparing the efficiency of project execution in differentiated projects or differentiated time periods.

[0110] Detailed explanation of the formula and the process of formula calculation and derivation:

[0111] Set the scheduled completion date of a project to December 31, 2021, and the actual completion date to January 10, 2022, and calculate the progress deviation using the formula. First calculate the date difference:

[0112] T actual =2022-01-10

[0113] T planned =2021-12-31

[0114] The date difference is 10 days. Substituting the value into the formula:

[0115]

[0116] The results show that the actual completion time of the project was about 2.74% later than planned, which means that the project encountered delays during execution. This delay percentage can assist the management team in assessing the severity of the delay and refer to whether measures need to be taken to adjust the project plan or optimize resource allocation.

[0117] See also Figure 4 , the calculation steps of the quality change index are as follows:

[0118] Extract indicators related to project quality from the original data set of enterprise technology projects, classify and sort the data by project and time series, and generate a classified quality indicator data set;

[0119] Quality-related indicators are extracted from the original data set of the project, and data classification is performed according to differentiated projects and time nodes. In this way, the quality performance of each project in the differentiated stage can be determined. This classification method not only helps in subsequent quality analysis, but also provides a data basis for comparing the quality performance between differentiated projects. Through data processing and classification, the accuracy and reliability of the data can be ensured, providing support for quality management.

[0120] Using the quality indicator data set, statistical analysis is performed on the data at each time point, the average quality score at each time point is calculated, and the average quality score data at each time point is generated;

[0121] Using the classified quality indicator data set, the data at each time point is analyzed and the average quality score at each time point is calculated. This process involves batch data processing and statistical calculations to ensure that the results of each calculation are accurate. This detailed data analysis helps to identify at which time points the project performs poorly and then take corresponding improvement measures.

[0122] According to the average quality score data at each time point, the formula is used:

[0123]

[0124] Calculate and generate quality change index;

[0125] Among them, Q index Represents the quality change index, which is used to evaluate the degree of change in the quality score in the time series. current , Q previous Represent the average quality scores of the current and previous time points, respectively.

[0126] formula:

[0127]

[0128] The benefit of the formula is that by combining the geometric mean of the quality scores at the current and previous time points, it improves the reflection of the sensitivity to changes in project quality and is suitable for scenarios with large changes, thereby more accurately measuring quality changes between projects.

[0129] Detailed explanation of the formula and the process of formula calculation and derivation:

[0130] Set Q current =85, Q previous =80, substitute into the formula:

[0131]

[0132] The results show that the quality score has improved from the previous time point to the current time point, increasing by about 6.06%, which shows a small improvement in the project's quality management and helps to quantitatively evaluate the effectiveness of improvement measures.

[0133] See also Figure 5 ,The specific steps for obtaining the preliminary project deviation analysis are:

[0134] Integrate cost change rate, project schedule deviation and quality change index, aggregate indicators from multiple data sources to form a comprehensive performance indicator data set;

[0135] To integrate the key performance indicators, first extract and clean the cost change rate, project schedule deviation and quality change index in the data set through the target script, and then use SQL query to merge the three indicators into one table. In this way, the consistency and integrity of the data are ensured. Next, apply data normalization to unify the scales of multiple indicators and create conditions for subsequent weighted calculations. This process involves calculating the mean and standard deviation of each indicator and adjusting each data point so that it is distributed on the same scale. Finally, generate a comprehensive performance score for each project, which will be used to evaluate the overall performance of the project.

[0136] Based on the comprehensive performance indicator data set, the key performance indicators of each project are weighted and analyzed, and standardized processing is applied to ensure that the weight of each indicator matches, and the weighted average score is calculated to obtain the comprehensive performance score of each project;

[0137] Based on the comprehensive performance indicator data set, correlation analysis and regression testing are carried out to determine the degree of correlation between multiple indicators and project success. This analysis helps to analyze which factors have a significant impact on project success. Then, based on the analysis results, the weights of multiple indicators are preliminarily set. The simulation model is used to test the changes in results under differentiated weight settings, optimize the weight configuration, and establish the weight coefficient of each indicator to ensure that these weights can accurately reflect the contribution of multiple indicators to project success.

[0138] Using the composite performance score for each project, the formula is:

[0139] D score =α·ΔC i +β·P dev +γ·Q index

[0140] Calculate the deviation for each item and establish a preliminary item deviation analysis;

[0141] Among them, D score It represents the comprehensive deviation score of the project, which is used to comprehensively evaluate the overall performance of the project. α, β, and γ are the weights of the cost change rate, project schedule deviation, and quality change index, respectively. They are adjusted according to the focus of project management to reflect their criticality to project success. ΔC i , P dev , Q index They represent the cost change rate, project schedule deviation, and quality change index extracted from the dataset, respectively.

[0142] formula:

[0143] D score =α·ΔC i +β·P dev +γ·Q index

[0144] The benefit of the formula is that by dynamically adjusting the weight coefficients, it can flexibly reflect the needs and characteristics of differentiated projects, thereby improving the accuracy and practicality of project deviation analysis.

[0145] Detailed explanation of the formula and the process of formula calculation and derivation:

[0146] Set the cost change rate ΔC for a project i is 0.05, indicating that the cost has increased by 5%, and the project schedule deviation P dev -0.1 means the project is 10% ahead of schedule. index If the weight coefficients α = 0.3, β = 0.5, and γ = 0.2 are set, the calculation process is as follows:

[0147] D score =0.3 0.05 + 0.5 (-0.1) + 0.2 0.02 = 0.015 - 0.05 + 0.004 = -0.031

[0148] The results show that although the project has improved in quality and slightly increased in cost, the overall deviation score is negative due to the early progress of the schedule, indicating that the overall performance of the project is good, but attention should be paid to other potential problems caused by schedule management.

[0149] See also Figure 6 , the steps to obtain key morphological features are as follows:

[0150] Screen the data items in the preliminary project deviation analysis, calculate the standard deviation of each data item, compare it with the preset threshold, identify the data items whose standard deviation significantly exceeds the threshold, and generate a list of standard deviation abnormal data;

[0151] Screen the data items in the preliminary project deviation analysis, calculate the standard deviation of each data item, compare it with the preset threshold, and determine which data items have standard deviations that significantly exceed the threshold. This step calculates the standard deviation of each data item and then compares it with the pre-set threshold to identify those abnormal data items whose standard deviations are significantly higher than the average level. These data items are identified as the focus of subsequent analysis, and then a list of all standard deviation abnormal data items is generated. This list will be used for time series decomposition analysis to understand the changing trends and cyclical changes of the data.

[0152] Perform time series decomposition on the abnormal data items identified in the standard deviation abnormal data list, extract and separate the seasonality and trend components of each data item, ensure the independence of each component, and generate decomposed seasonality and trend data;

[0153] Perform time series decomposition on the data items identified as abnormal to separate the seasonal and trend components of each data item. The key to this step is to apply time series analysis technology to analyze the data and separate the main components of the data through mathematical modeling, including seasonal factors and long-term trends. This analysis helps to better analyze the cyclical fluctuations and trend changes of the data, and provides a basis for outlier analysis. The decomposition results will be recorded and used for the next step of seasonal and trend deviation analysis.

[0154] Apply statistical models to analyze the decomposed seasonality and trend data using the formula:

[0155] A i =β·S i +γ·T i

[0156] Identify key time nodes and abnormal change points, and generate abnormal change analysis results;

[0157] Among them, A i Represents the comprehensive anomaly analysis score, which is used to quantify the degree of anomaly. i 、T i Refers to the quantitative scores of seasonal and trend components, respectively, β and γ are coefficients used to adjust the impact of seasonal and trend components, providing a means of adjusting the sensitivity of the analysis;

[0158] formula:

[0159] A i =β·S i +γ·T i

[0160] The benefit of the formula is that by combining the weighted sum of seasonal and trend components, the weights can be flexibly adjusted to highlight the target behavior of the data, thereby more accurately identifying key change points. The β and γ in the formula provide this flexibility, and analysts can adjust the parameters according to the situation to reflect the actual influence of seasonality and trend.

[0161] Detailed explanation of the formula and the process of formula calculation and derivation:

[0162] Set at a certain point in time, the seasonal score S i is 0.3, and the trend score T i is 0.7, and weights β = 0.5 and γ = 0.5 are set, then the calculation is:

[0163] A i =0.5·0.3+0.5·0.7=0.15+0.35=0.5

[0164] This means that the overall score of the current data point is 0.5, and a higher score indicates that there is a significant anomaly or change at this point.

[0165] The results show that the analysis successfully identifies key time points associated with scientific and technological progress, and the scores directly affect the overall evaluation and decision-making of the project.

[0166] From the results of abnormal change analysis, key morphological features corresponding to the time nodes of scientific and technological progress are extracted.

[0167] Extract key morphological features from the abnormal change analysis results. This process includes quantitative analysis of the identified abnormal change points, revealing the key behavioral patterns of the data through graphical and numerical analysis methods. These patterns represent potential technological advances or risk points that need attention. By quantifying these patterns, the key morphological features of the data can be extracted. The features help guide subsequent project decisions and strategy adjustments, ensuring that scientific and technological development and project management proceed in sync, thereby generating a result that includes all key morphological features and providing empirical support for project management.

[0168] See also Figure 7 , the specific steps for obtaining the results of the offset dynamic prediction analysis are:

[0169] Analyze the data extracted from key morphological features, evaluate the cumulative effect of deviations at each data point, calculate the cumulative deviations, and determine which deviations are significantly higher than the normal level, and generate a list of cumulative deviation effects;

[0170] After analyzing the data of key morphological features, the data items that significantly exceeded the normal level were determined by calculating the cumulative effect list. This process involved evaluating the deviation of each data point and identifying anomalies through statistical methods and threshold judgment. This strategy not only simplified the subsequent data processing steps, but also ensured the accuracy and efficiency of the analysis, and obtained a classified deviation cumulative effect list.

[0171] According to the list of cumulative effects of deviations, differentiated threshold levels are set, data items are reclassified through threshold grading logic, deviation limits are set for each category, and a list of reclassified data items is generated;

[0172] Based on the list of cumulative effects of deviations that have been obtained, differentiated threshold levels are reset and data items are reclassified. This process adjusts the deviation limits of each category by comprehensively referring to the deviation size and the actual impact of the data. Statistical methods are used to dynamically adjust the deviation thresholds. With reference to the seasonal and cyclical characteristics of the data, data classification is made more precise and personalized, and a list of data items classified according to the new standards is obtained, providing accurate data support for subsequent predictive analysis.

[0173] Using the results from the reclassified data item list, adjust the deviation limits for each category of data using the formula:

[0174] L new =θ·L old +ξ

[0175] Redefine the deviation limit for each category and generate adjusted deviation limit data;

[0176] Among them, L new Indicates the current deviation limit, which is used to control and predict data deviation, L old is the original deviation limit, representing the previous classification standard, and θ and ξ are adjustment coefficients used to fine-tune the deviation limit to match the current analysis requirements;

[0177] formula:

[0178] L new =θ·L old +ξ

[0179] The benefit of the formula is that by introducing adjustment parameters θ and ξ, the deviation limit can be flexibly adjusted to match differentiated data analysis needs, thereby more accurately predicting data deviation in future time periods.

[0180] Detailed explanation of the formula and the process of formula calculation and derivation:

[0181] Set the raw deviation limit L old=10, the adjustment coefficient θ is 1.2 for enlarging the limit, and the fixed value ξ is 2 for handling system errors. Insert the numerical value for calculation, L new =1.2×10+2=14, current deviation limit L new It is 14, indicating that the adjusted limit is more relaxed, matching the current data trend;

[0182] The results show that by adjusting the deviation bound, the sensitivity and responsiveness of data classification can be more effectively controlled, allowing the model to match a wider range of data fluctuations and improving the flexibility and accuracy of the forecast.

[0183] According to the adjusted deviation limit data, a dynamic prediction model for deviation is established to predict future deviation behaviors. Based on the adjusted deviation limit dynamic prediction data changes, the dynamic prediction analysis results of deviation are generated.

[0184] Through the adjusted deviation limits, a dynamic prediction model is established to predict data deviations in future time periods. This process uses time series analysis technology and machine learning models to evaluate the development trend of the deviation, and refers to the currently adjusted deviation limits. The established prediction model is not only highly adaptable, but also able to dynamically adjust the prediction strategy according to actual data changes. The generated deviation dynamic prediction analysis results provide a scientific basis for future data management and risk control, and have high practical value and strategic guidance significance.

[0185] See also Figure 8 , the specific steps for obtaining project analysis results are:

[0186] Extract key data points from the results of the dynamic forecast analysis of the deviation, combine each project dimension including cost, time and quality, calculate the comprehensive health index, and generate preliminary comprehensive assessment data;

[0187] Extract key data points from the results of the dynamic forecast analysis of the deviation, and calculate the comprehensive health index based on each project dimension including cost, time and quality. Through data standardization and multi-dimensional weighted analysis, calculate the comprehensive influence of multiple dimensions, and generate preliminary comprehensive evaluation data based on this. This data helps project managers understand the overall health of the project, and effectively monitor and adjust the project in a timely manner.

[0188] Utilize comprehensive assessment data and apply quantitative risk assessment models to analyze potential risks and impacts and generate risk assessment results;

[0189] A quantitative risk assessment model is applied to analyze potential risks and their impacts. The model combines field monitoring data with statistical analysis methods and machine learning techniques to assess the various risks faced by the project, including budget overruns and schedule delays. The model also generates risk assessment results by calculating the severity and likelihood of potential impacts. This result is crucial to project risk management and helps the project team develop corresponding risk response strategies.

[0190] Combining the risk assessment results and health indicators, the formula is used:

[0191] P future =β·R risk +γ·H current

[0192] Conduct forecast analysis of future development, predict the future performance of the project by adjusting the weights of risk and health, and generate forecast results of future development;

[0193] Among them, P future Represents the future forecast value of the project, R risk Represents the quantitative results of risk assessment,

[0194] H current It represents the current project health index, β and γ are the weight coefficients for adjusting the impact of risk and health;

[0195] formula:

[0196] P future =β·R risk +γ·H current

[0197] The benefit of the formula is that by adjusting the weight parameters of risk and health, it can flexibly reflect the predictions of future project performance in different project management environments and project characteristics. This approach increases the accuracy of the prediction and the breadth of its application.

[0198] Detailed explanation of the formula and the process of formula calculation and derivation:

[0199] Set in the project, R risk is the current risk assessment value, which is determined to be 0.65 through analysis of similar situations in past projects. current is the current health index, which is 0.85 through real-time data monitoring; the weight parameters β and γ are set to 0.6 and 0.4 respectively according to the data and expert scheme. The calculation process is as follows:

[0200] P future =0.6 0.65 + 0.4 0.85 = 0.39 + 0.34 = 0.73

[0201] The results show that, referring to the current risk and health status, the future performance of the project is assessed at 0.73, which indicates that the project is in a relatively stable state, but potential risks still need to be paid attention to.

[0202] Summarize preliminary comprehensive assessment data, risk assessment results and future development forecast results, conduct multi-dimensional project assessment, analyze the overall health status, risk level and future trends of the project, and generate project analysis results.

[0203] A multi-dimensional project assessment is conducted based on preliminary comprehensive assessment data, risk assessment results and future development forecast results. This process includes analysis of the overall health status, risk level and future trends of the project. By using comprehensive data models and multivariate statistical methods, project analysis results are formed to provide a scientific basis for project decision-making and ensure that the project can proceed smoothly on the expected track.

[0204] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An innovation achievement evaluation system based on big data, characterized in that: The system comprises: The data capture module screens the original data set of the enterprise's scientific and technological innovation projects, extracts time series data, and distinguishes them into cost data, innovation project milestone dates, and quality indicators. It calculates the cost change rate, project schedule deviation, and quality change index of multiple innovation projects, and calculates the average and standard deviation of multiple indicators to establish a preliminary project deviation analysis. The pattern recognition module extracts data items with standard deviations exceeding the norm from the preliminary project deviation analysis, performs time series decomposition on each data item, extracts seasonal and trend components, analyzes seasonal deviations and trend deviations separately, identifies abnormal change points corresponding to time nodes of scientific and technological progress, and extracts key morphological features; The result optimization module analyzes the key morphological features, determines the cumulative effect of data deviation, reclassifies multiple data items by setting threshold classification, adjusts the deviation limit of each type of data, and establishes the deviation dynamic prediction analysis results; The report generation module integrates the evaluation data based on the deviation dynamic prediction analysis results, conducts a multi-dimensional evaluation of the innovation project including project health, risk assessment, and future development forecast, and forms a project analysis result.

2. The innovation achievement evaluation system based on big data according to claim 1 is characterized in that: The calculation steps of the cost change rate are specifically as follows: Filter cost data from the original data set of enterprise technology projects, classify them according to projects and time series, and generate classified cost data sets; Utilizing the classified cost data set, calculating the total cost at each time point, comparing the cost at the current time point with the cost at the previous time point, and generating cost comparison data at each time point; Based on the cost comparison data at each time point, the formula is used: Calculate and generate the cost change rate at each time point; Where, ΔC i represents the cost change rate, C i Represents the total cost at the current time point, C i-1 Indicates the total cost at the previous point in time.

3. The innovation achievement evaluation system based on big data according to claim 2 is characterized in that: The specific steps for calculating the project progress deviation are as follows: Filter project milestone date information from the original data set of enterprise technology projects, classify data according to projects and time series, and generate a project milestone classification data set; Using the project milestone classification data set, calculating the difference between the actual completion date and the scheduled completion date of each project, and generating time deviation data for each project; From the time deviation data for each project, use the formula: Calculate and generate project schedule deviations; Among them, P dev Represents the calculated project schedule deviation percentage, T actual Indicates the actual completion time, T planned Indicates the scheduled completion time.

4. The innovation achievement evaluation system based on big data according to claim 3 is characterized in that: The calculation steps of the quality change index are specifically as follows: Extract indicators related to project quality from the original data set of enterprise technology projects, classify and sort the data by project and time series, and generate a classified quality indicator data set; Using the quality indicator data set, statistically analyzing the data at each time point, calculating the average quality score at each time point, and generating average quality score data at each time point; Based on the average quality score data at each time point, the formula is used: Calculate and generate quality change index; Among them, Q index Represents the quality change index, which is used to evaluate the degree of change in the quality score in the time series. current , Q previous Represent the average quality scores of the current and previous time points, respectively.

5. The innovation achievement evaluation system based on big data according to claim 4 is characterized in that: The steps for obtaining the preliminary project deviation analysis are specifically as follows: Integrate the cost change rate, the project schedule deviation, and the quality change index, aggregate indicators from multiple data sources, and form a comprehensive performance indicator data set; Based on the comprehensive performance indicator data set, a weight analysis is performed on the key performance indicators of each project, standardization is applied to ensure that the weight of each indicator matches, and a weighted average score is calculated to obtain a comprehensive performance score for each project; Using the composite performance score for each item, use the formula: D score =α·ΔC i +β·P dev +γ·Q index Calculate the deviation for each item and establish a preliminary item deviation analysis; Among them, D score It represents the comprehensive deviation score of the project, which is used to comprehensively evaluate the overall performance of the project. α, β, and γ are the weights of the cost change rate, project schedule deviation, and quality change index, respectively. They are adjusted according to the focus of project management to reflect their criticality to project success. ΔC i , P dev , Q index They represent the cost change rate, project schedule deviation, and quality change index extracted from the dataset, respectively.

6. The innovation achievement evaluation system based on big data according to claim 5 is characterized in that: The steps for obtaining the key morphological features are specifically as follows: Screening the data items in the preliminary project deviation analysis, calculating the standard deviation of each data item, comparing it with a preset threshold, identifying the data items whose standard deviation significantly exceeds the threshold, and generating a list of standard deviation abnormal data; Performing time series decomposition on the abnormal data items identified in the standard deviation abnormal data list, extracting and separating the seasonality and trend components of each data item, ensuring the independence of each component, and generating decomposed seasonality and trend data; The statistical model was applied to analyze the decomposed seasonality and trend data using the formula: A i =β·S i +γ·T i Identify key time nodes and abnormal change points, and generate abnormal change analysis results; Among them, A i Represents the comprehensive anomaly analysis score, which is used to quantify the degree of anomaly. i , T i Refers to the quantitative scores of seasonal and trend components, respectively, β and γ are coefficients used to adjust the impact of seasonal and trend components, providing a means of adjusting the sensitivity of the analysis; From the abnormal change analysis results, key morphological features corresponding to the time nodes of scientific and technological progress are extracted.

7. The innovation achievement evaluation system based on big data according to claim 6 is characterized in that: The steps for obtaining the deviation dynamic prediction analysis results are specifically as follows: Analyze the data extracted from the key morphological features, evaluate the cumulative effect of deviations for each data point, calculate the cumulative deviations, and determine which deviations are significantly higher than the normal level, and generate a list of cumulative deviation effects; According to the list of cumulative effects of deviations, differentiated threshold levels are set, data items are reclassified through threshold grading logic, deviation limits are set for each category, and a list of reclassified data items is generated; Using the results from the reclassified data item list, adjust the deviation limits for each category of data using the formula: L new =θ·L old +ξ Redefine the deviation limit for each category and generate adjusted deviation limit data; Among them, L new Indicates the current deviation limit, which is used to control and predict data deviation, L old is the original deviation limit, representing the previous classification standard, and θ and ξ are adjustment coefficients used to fine-tune the deviation limit to match the current analysis requirements; According to the adjusted deviation limit data, a dynamic prediction model for displacement is established to predict the deviation behavior that will occur in the future, and the dynamic prediction data change based on the adjusted deviation limit is dynamically predicted to generate the dynamic prediction analysis result of displacement.

8. The innovation achievement evaluation system based on big data according to claim 7 is characterized in that: The steps for obtaining the project analysis results are specifically as follows: Extract key data points from the dynamic prediction analysis results of the offset, combine each project dimension including cost, time and quality, calculate comprehensive health indicators, and generate preliminary comprehensive evaluation data; Utilizing the comprehensive assessment data, applying a quantitative risk assessment model to analyze potential risks and impacts, and generating risk assessment results; Combining the risk assessment results and the health index, the formula is adopted: P future =β·R risk +γ·H current Conduct forecast analysis of future development, predict the future performance of the project by adjusting the weights of risk and health, and generate forecast results of future development; Among them, P future Represents the future forecast value of the project, R risk Represents the quantitative result of risk assessment, H current It represents the current project health index, β and γ are the weight coefficients for adjusting the impact of risk and health; Summarize the preliminary comprehensive assessment data, the risk assessment results and the future development forecast results, conduct a multi-dimensional assessment of the project, analyze the overall health status, risk level and future trends of the project, and generate project analysis results.

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