Enterprise project database intelligent management method and system

Abstract

The application discloses an enterprise project database intelligent management method and system, and relates to the technical field of data management and resource optimization. The method comprises the following steps: verifying the integrity of business unit project data in real time through an intelligent cleaning engine and storing the data in a structured manner to a central project database; fusing progress, cost, risk fields and dynamically calculating a comprehensive value index of business value and strategic weight; based on the value index and real-time resource inventory, generating a resource allocation scheme through a hierarchical adaptation strategy and outputting a priority list; tracking progress deviation during execution, triggering data isolation and index optimization when the risk offset exceeds a threshold; dynamically adjusting the value weight by analyzing the risk root cause and feeding back the management rule library to realize system self-optimization. The application solves the problems of rigid resource allocation, lagging risk response and lack of dynamic value orientation in traditional project management, improves resource utilization and reduces project delay risk.

Description

Technical Field

[0001] This invention relates to the field of enterprise-level data governance technology, and in particular to an intelligent management method and system for enterprise project databases. Background Technology

[0002] Currently, enterprise project management commonly suffers from low data governance efficiency and insufficient resource optimization. Project data generated by various business units exhibits heterogeneous formats, leading to time-consuming and lengthy cleaning processes that are prone to losing critical information. Traditional resource allocation relies on static priority rules, which are difficult to adapt to dynamic changes in project value, often resulting in insufficient resource supply for high-strategy-value projects. Risk monitoring mechanisms mostly adopt a periodic inspection model, and the lag in response leads to a sharp increase in repair costs when deviations expand.

[0003] Existing technical solutions lack intelligent parsing capabilities for unstructured text in basic data cleaning methods, resource allocation algorithms do not fully consider the coupling relationship between real-time inventory constraints and business value, and risk handling processes are isolated and lack a closed-loop feedback mechanism. As the complexity of multi-project collaboration within enterprises increases, there is an urgent need for an intelligent management method and system for enterprise project databases. Summary of the Invention

[0004] This invention proposes an intelligent management method for enterprise project databases, comprising:

[0005] The system collects project data from each business unit in real time, performs integrity verification and structured processing through an intelligent cleaning engine, and stores the data in the central project database.

[0006] Based on the schedule, cost, and risk fields of the central project database, a comprehensive value index is calculated by integrating business value and strategic weights, and the value assessment indicators of the database are updated.

[0007] Based on value assessment indicators and real-time resource inventory, a resource allocation plan is generated through a hierarchical adaptation strategy, and a priority resource list is output to the execution system.

[0008] When executing the priority resource list, progress deviations are tracked synchronously. When the risk offset exceeds the threshold, data isolation and index optimization are triggered, and the database risk status record is updated.

[0009] Analyze the root causes of deviations in the risk status records, dynamically adjust the value calculation weights, and feed them back into the management rule base to achieve continuous self-optimization of the intelligent management system.

[0010] The enterprise project database intelligent management method described above, wherein the system collects project data from each business unit in real time, performs integrity verification and structured processing through an intelligent cleaning engine, and stores the data in a central project database, includes the following sub-steps:

[0011] Through standardized distributed data acquisition interfaces deployed in various business unit systems, the raw data stream of the project, including project identifiers, status information, and numerical indicators, is acquired in real time at preset time intervals or event triggering mechanisms.

[0012] The system uses a predefined set of field integrity verification rules stored in the rule base to scan and check the received data, automatically identify and fill in missing key data items, and use predefined data structuring transformation templates to uniformly transform heterogeneous data from different sources and with different formats into a standard structured format that meets the requirements of the central database.

[0013] The standardized data, after integrity verification and structure transformation, is associated and matched according to its unique project identifier and persistently stored in a pre-designed relational data table structure in the central project database.

[0014] The enterprise project database intelligent management method described above includes the following sub-steps: Based on the schedule, cost, and risk fields of the central project database, a comprehensive value index is calculated by integrating business value and strategic weights, and the database's value assessment indicators are updated.

[0015] Accurately extract the latest progress percentage, actual cost consumption, and risk level quantification values ​​obtained from the system assessment for all projects that need to be calculated within a specific assessment period from the designated data table in the central project database.

[0016] The project's pre-defined commercial value parameters, reflecting its expected revenue potential, and strategic weight parameters, reflecting its importance in the company's overall strategy, are input into a pre-defined weighted comprehensive calculation model for processing and calculation.

[0017] The comprehensive value index calculated by running the weighted comprehensive calculation model will be used as the core value assessment indicator for the project. This value index will be written into and updated to the corresponding value assessment indicator field of the project record in the central project database.

[0018] The enterprise project database intelligent management method described above involves inputting the project's pre-set commercial value parameters, reflecting its expected revenue potential, and strategic weight parameters, reflecting its importance in the company's overall strategy, into a pre-set weighted comprehensive calculation model for processing. This includes the following sub-steps:

[0019] The raw progress percentage values ​​extracted from the database are standardized to convert them into progress completion coefficients that can be compared across projects.

[0020] By comparing the actual cost consumption of the project with the pre-approved budget benchmark, the cost deviation rate or cost-benefit ratio is calculated, and the cost-benefit factor value reflecting the cost control effect is derived based on this.

[0021] The obtained progress completion coefficient, cost-benefit factor value, and project risk level quantification value are used as basic input items. Combined with the project's specific business value parameters and strategic weight parameters, a multi-factor, multi-weight linear weighted combination calculation is performed in the weighted calculation model to obtain the project's comprehensive value index.

[0022] The enterprise project database intelligent management method described above includes the following sub-steps: Based on value assessment indicators and real-time resource inventory, a resource allocation plan is generated through a hierarchical adaptation strategy, and a priority resource list is output to the execution system.

[0023] Based on the value assessment index values ​​of each project in the central project database, preset score range thresholds are set to clearly divide all projects into three different levels: high value priority, medium value priority, and low value priority.

[0024] The system obtains real-time information on the total amount of available resources provided by the resource management system and calculates the total amount of resource quota to be allocated to each level according to the pre-configured resource allocation strategy rules and the proportion of resources occupied by different priority levels.

[0025] Within the same priority level, all projects are sorted in ascending order based on their risk field values, ultimately forming a priority resource list that details the project execution sequence and the amount of resources required. This list is then output to the project execution system.

[0026] The enterprise project database intelligent management method described above includes the following sub-steps: When executing the priority resource list, schedule deviations are tracked synchronously; when the risk deviation exceeds a threshold, data isolation and index optimization are triggered, and the database risk status record is updated.

[0027] When the risk offset of a project is detected to exceed the preset critical threshold, the system will automatically perform temporary isolation and storage operations on the project data partition or specific dataset associated with the risk event to prevent abnormal data from interfering with normal business analysis.

[0028] Based on the severity level of the risk event, the index structure of the relevant data tables in the central project database will be rebuilt or optimized to improve the efficiency of querying and processing high-risk status data.

[0029] The new data access paths, processing status, and risk level changes resulting from data isolation and index optimization are recorded in detail and updated in a dedicated risk status record table in the database.

[0030] The intelligent management method for enterprise project databases described above, which analyzes the root causes of deviations in risk status records, dynamically adjusts value calculation weights, and feeds back into the management rule base to achieve continuous self-optimization of the intelligent management system, includes the following sub-steps:

[0031] Based on the deviation between the quality assessment results and the target value, a parameter adjustment gradient vector is generated, and the sensitivity coefficients of the parameters in each processing stage are defined to determine the adjustment direction.

[0032] Using natural language processing or pattern recognition technology, the deviation description information recorded in the risk status record table is analyzed to extract the key root cause words or factor classifications that lead to the deviation.

[0033] Based on the identified root cause type, the strategic weight parameters of projects associated with that type of root cause are dynamically adjusted in the value assessment model to more accurately reflect their current actual value.

[0034] The dynamically adjusted strategic weight parameter values ​​are used as new configuration information and fed back into the weight configuration parameter table in the management rule knowledge base, thereby updating the basis for subsequent value calculations and realizing the self-iteration of the rules.

[0035] This invention also proposes an intelligent management system for enterprise project databases, comprising:

[0036] Data acquisition and preprocessing module: The system collects project data from each business unit in real time, performs integrity verification and structured processing through an intelligent cleaning engine, and stores the data in the central project database;

[0037] Dynamic Value Management Module: Based on the schedule, cost, and risk fields of the central project database, it calculates a comprehensive value index by integrating business value and strategic weights, and updates the value assessment indicators of the database; based on the value assessment indicators and real-time resource inventory, it generates a resource allocation plan through a hierarchical adaptation strategy and outputs a priority resource list to the execution system;

[0038] Resource execution monitoring module: Synchronously tracks progress deviations when executing priority resource lists. When the risk deviation exceeds the threshold, it triggers data isolation and index optimization, updates the database risk status record, analyzes the root causes of deviations in the risk status record, dynamically adjusts the value calculation weights and feeds back into the management rule base, and completes the continuous self-optimization of the intelligent management system.

[0039] The present invention also proposes a computer storage medium, comprising: at least one memory and at least one processor;

[0040] Memory, used to store one or more program instructions;

[0041] A processor for running one or more program instructions to execute an enterprise project database intelligent management method as described above.

[0042] The beneficial effects achieved by this invention are as follows:

[0043] By using an intelligent cleaning engine to collect and process data from various business units in real time, a unified, high-quality central project database is built, laying a reliable data foundation.

[0044] Based on this database, the system integrates core indicators such as progress, cost, and risk with business value and strategic weights, dynamically calculates and updates the comprehensive value index of the project, and provides a multi-dimensional value view that goes beyond traditional assessments.

[0045] Based on this value indicator and real-time resource inventory, a layered adaptation strategy is adopted to automatically generate a precise resource allocation plan and output a priority list to the execution system, so as to achieve efficient dynamic matching of resources with corporate strategy and project value.

[0046] During execution, progress deviations are tracked synchronously. When the risk exceeds the threshold, data isolation and index optimization are automatically triggered, the risk status is updated, and a proactive risk defense mechanism is built.

[0047] The system further analyzes the root causes of risk deviations, dynamically adjusts the value calculation weights, and optimizes the management rule base, forming a closed loop of "data governance - value assessment - resource adaptation - risk prevention and control - rule evolution," giving the system continuous self-learning and self-optimization capabilities.

[0048] Ultimately, this significantly improves the data quality, decision-making level, resource utilization efficiency, and risk control capabilities of enterprise project management, driving management towards intelligence and agility, and strongly supporting the achievement of strategic goals. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0050] Figure 1 This is a flowchart of an intelligent management method for enterprise project databases provided in an embodiment of this application.

[0051] Figure 2 This is a schematic diagram of an intelligent management system for enterprise project databases provided in an embodiment of this application. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Example 1

[0054] like Figure 1 As shown in the figure, an embodiment of this application provides an intelligent management method for enterprise project databases, comprising:

[0055] Step S1: The system collects project data from each business unit in real time, performs integrity verification and structured processing through the intelligent cleaning engine, and stores the data in the central project database.

[0056] Specifically, the system acquires raw data streams containing project unique identifiers, status, key numerical indicators, and descriptive text in real time through standardized distributed acquisition interfaces deployed in various business units, based on preset time intervals or specific event triggers. The intelligent data cleaning engine automatically scans the data using pre-defined integrity verification rules in the rule base, identifying and filling in missing items. Simultaneously, the engine applies predefined structured transformation templates to perform field mapping, type conversion, and format standardization on heterogeneous data from different sources, unifying it into a standard structure. The processed standardized data is then matched against existing records in the central database based on its unique identifier and finally persistently stored in a pre-designed relational data table structure, including the following sub-steps:

[0057] Step S11: Through the standardized distributed data acquisition interface deployed in each business unit system, the raw data stream of the project, including project identifier, status information, and numerical indicators, is acquired in real time at preset time intervals or event triggering mechanisms.

[0058] The system utilizes standardized distributed data acquisition interfaces pre-deployed in the information systems of each business unit to automatically and in real-time capture raw project data streams from different business units based on preset fixed time periods or specific business event triggering mechanisms. This data stream includes a unique project identifier, current status markers, key numerical metrics, and other relevant project attribute information.

[0059] Step S12: Using a predefined set of field integrity verification rules stored in the rule base, scan and check the received data, automatically identify and fill in missing key data items, and at the same time use a predefined data structuring transformation template to uniformly transform heterogeneous data from different sources and with different formats into a standard structured format that meets the requirements of the central database.

[0060] Step S13: After the standardized data has undergone integrity verification and structure transformation, it is associated and matched according to its unique project identifier, and persistently stored in the pre-designed relational data table structure of the central project database.

[0061] After integrity verification and structure transformation, the standardized project data, using its unique project identifier as the core association key, accurately matches and aggregates all relevant information. Subsequently, the system persistently stores these structurally complete and uniformly formatted data records according to the pre-designed relational data table structure of the central project database, writing them into the corresponding data tables in the central project database, thus completing the entire processing flow from raw data to standardized storage.

[0062] Step S2: Based on the schedule, cost, and risk fields of the central project database, calculate the comprehensive value index by integrating business value and strategic weight, and update the value assessment indicators of the database.

[0063] Specifically, based on the central project database, the latest progress percentage, actual cost, and risk level quantification of the target project are extracted; combined with the project's preset business value parameters and strategic weight parameters, preset rules are applied for processing: the progress value is standardized into a progress completion coefficient, and the cost-benefit factor between the actual cost and the budget benchmark is calculated; the progress coefficient, cost factor, risk value, business value, and strategic weight are weighted and calculated to generate a comprehensive value index; this index is then used as the core evaluation indicator to update the corresponding field in the database, specifically including the following sub-steps:

[0064] Step S21: Accurately extract the latest progress percentage, actual cost consumption, and risk level quantification values ​​obtained from the system assessment for all projects that need to be calculated within a specific assessment period from the designated data table in the central project database.

[0065] The system precisely retrieves and extracts the latest status data for all projects to be evaluated within the current assessment period from specific structured data tables in the central project database. This data includes actual measurements of the project's progress completion percentage, the actual cost incurred to date, and a quantified current risk level output by the system's built-in assessment mechanism. This ensures that all extracted data are the most recently updated and valid records within the assessment period, serving as the foundational input for subsequent value assessments.

[0066] Step S22: Input the project's preset commercial value parameters, which reflect its expected revenue potential, and strategic weight parameters, which reflect its importance in the company's overall strategy, into the preset weighted comprehensive calculation model for processing and calculation.

[0067] Step S221: Standardize the raw progress percentage values ​​extracted from the database and convert them into progress completion coefficients that can be compared across projects.

[0068] The raw progress percentage values ​​extracted from the database are standardized and preprocessed. Considering the differences in project size, complexity, and measurement methods, a specific algorithm is used to convert the raw progress values ​​into standardized progress completion coefficients, expressed by the following formula: in, Indicates standardized progress values; This represents the sensitivity parameter used to control the shape of the Sigmoid function curve; This represents the actual progress data of the project; The reference point parameter represents the Sigmoid function; This represents the coefficient of the regularization term.

[0069] Step S222: Compare the actual cost consumption of the project with the pre-approved budget benchmark value, calculate the cost deviation rate or cost-benefit ratio, and derive the cost-benefit factor value that reflects the cost control effect based on this.

[0070] Based on actual cost and budget benchmarks Calculate the cost-benefit factor Introducing a cost performance index As a foundation, but with increased complexity through logarithmic-exponential transformation and ratio adjustment, the asymmetric effects of cost overruns / savings are addressed, and the cost-benefit factor is expressed by the following formula: in, Indicates the cost-benefit factor; This represents the parameters used to control the shape and sensitivity of the scalable portion of the Sigmoid curve; Indicates actual cost; Indicates the budget baseline; Represents an exponential function; Represents a logarithmic function; Represents a symbolic function;

[0071] Step S223: Take the obtained progress completion coefficient, cost-benefit factor value, and project risk level quantification value together as basic input items, and then combine them with the project's specific business value parameters and strategic weight parameters to perform a multi-factor, multi-weight linear weighted combination calculation in the weighted calculation model to obtain the project's comprehensive value index.

[0072] A comparative analysis is conducted between the actual cost of the project and the pre-approved budget baseline. By calculating the deviation ratio or benefit ratio between the two, a cost-benefit factor reflecting the project's cost execution efficiency is derived, and the comprehensive value index is expressed by the following formula: in, Indicates the comprehensive value index; The strategic weight parameter is used as a global scaling factor to amplify or reduce the overall value calculation result, reflecting the impact of the project's strategic importance on the overall value. The weighting coefficient representing the contribution to the schedule; Indicates the progress completion coefficient; The weighting coefficient representing the cost contribution; Indicates the cost-benefit factor; This represents the exponential decay coefficient within the risk adjustment factor; This represents the risk value, which is a numerical value indicating the actual level of risk faced by the project. Indicates the reference offset value of the risk adjustment factor; Weighting coefficients representing commercial value; Indicates commercial value parameters; Represents the coefficient of the interaction regularization term; This represents an exponential function.

[0073] The quantitative measure is the degree to which actual project expenditures match the planned budget or the output efficiency of cost inputs. A higher value indicates better cost control or higher cost-effectiveness.

[0074] Step S23: The comprehensive value index calculation result output after running the weighted comprehensive calculation model is used as the core value assessment indicator of the project and written into and updated to the value assessment indicator field corresponding to the project record in the central project database.

[0075] For each completed project, a unique record identifier is located in the central project database. Then, a database write operation is performed, precisely writing the calculated comprehensive value index value into a pre-defined field within the project record specifically used to store the value assessment results. Upon completion of the write, the record's modification timestamp is immediately updated, triggering relevant database index updates and cache refresh mechanisms to ensure that subsequent queries or report generation operations can obtain the latest project value assessment status in real time.

[0076] Step S3: Based on the value assessment indicators and real-time resource inventory, generate a resource allocation plan through a hierarchical adaptation strategy and output a priority resource list to the execution system;

[0077] Specifically, firstly, projects are divided into three priority levels—high, medium, and low—based on project value assessment indicators; then, the total resource quota for each level is calculated based on the total available resources in real time and the preset ratio; finally, projects within the same level are arranged in ascending order of risk value, generating a priority resource list that includes execution order and resource requirements, which is then output to the execution system. This process includes the following sub-steps:

[0078] Step S31: Based on the value assessment index values ​​of each project in the central project database, set a preset score range threshold and clearly divide all projects into three different levels: high value priority, medium value priority, and low value priority.

[0079] Based on the comprehensive score of multi-dimensional value assessment indicators pre-calculated and stored for each project in the central project database, and setting preset score range thresholds, the comprehensive project value score is expressed by the following formula: in, This indicates the overall score of the project's value; Indicates the first The overall value assessment score of each project; Indicates the total number of items; Project Index;

[0080] Based on these thresholds, all projects in the current database are strictly divided into three mutually exclusive levels: high-value priority projects, medium-value priority projects, and low-value priority projects. This clearly defines the strategic importance of projects and the urgency of resource allocation.

[0081] Step S32: Obtain the current available total resource information provided by the resource management system in real time, and calculate the total resource quota to be allocated to each level according to the pre-configured resource allocation strategy rules and the proportion of different priority levels.

[0082] The system accesses the resource management system in real time, providing precise data on the total available resources. Based on preset hierarchical resource allocation strategies, and using a proportional calculation method, it dynamically calculates the total resource quota to be allocated to each priority level based on the number of projects included in each level, average resource requirements, and preset weighting factors. The total resource quota is expressed by the following formula: in, Indicates hierarchy Average resource requirements; Indicates the first Resource requirements for each project; Indicates hierarchy, used to distinguish different project levels; Indicates hierarchy The average resource requirements of internal projects; Indicates hierarchy Standard deviation of internal project resource requirements; This represents an exponential function.

[0083] Ensure that resource allocation matches the project's value level and make full use of real-time inventory information.

[0084] Step S33: Within the same priority level, sort all projects in ascending order of their risk field values ​​to form a priority resource list that details the order of project execution and the amount of resources required. Output this list to the project execution system.

[0085] Within each priority tier where resource quotas have been allocated, all projects within that tier are sorted in ascending order based on their key risk indicator field values. For projects with the same risk value, further sub-sorting can be performed based on auxiliary fields such as project urgency or value assessment score. After sorting, the final order of projects across all tiers and their corresponding estimated resource requirements are integrated to generate a structured priority resource list.

[0086] Step S4: When executing the priority resource list, track the progress deviation synchronously. When the risk offset exceeds the threshold, trigger data isolation and index optimization, and update the database risk status record.

[0087] Specifically, during the execution of the priority resource list generated according to preset rules, the system monitors in real time the deviation between the actual progress and the planned progress of each project's key milestone nodes. Once the system detects that the risk deviation of a specific project exceeds a preset critical threshold, it immediately and automatically activates the built-in data isolation and index optimization mechanism, and synchronously updates the risk status record in the database, including the following sub-steps:

[0088] Step S41: When the risk offset of a certain project is detected to exceed the preset critical threshold, the system automatically performs temporary isolation storage operations on the project data partition or specific dataset associated with the risk event to prevent abnormal data from interfering with normal business analysis.

[0089] Once the system detects that the risk offset exceeds the limit, it immediately initiates a data isolation process. Based on preset data association mapping rules, it automatically identifies the project data partitions or specific key datasets directly associated with the high-risk event. Subsequently, these identified data entities are physically or logically separated from the regular operating environment of the central project database and transferred to a dedicated, temporarily isolated storage area with strict access control.

[0090] Step S42: Based on the severity level of the risk event, rebuild or optimize the index structure of the relevant data tables in the central project database to improve the efficiency of querying and processing high-risk status data.

[0091] After data isolation is completed, targeted index optimization operations are performed on the data tables in the central project database that are closely related to the high-risk project, based on the preset severity level of the risk event. For high-severity risk events, the system will trigger a complete reconstruction of the index structure of the relevant core data tables; for medium- and low-severity events, the focus is on performing index optimization adjustments.

[0092] Step S43: Record and update the new data access path, processing status, and risk level change information formed after data isolation and index optimization in detail in the dedicated risk status record table of the database.

[0093] The system will automatically integrate the established isolated data access path information, the latest processing status of relevant data tables after index optimization, and the project risk level information updated based on the development or handling feedback of risk events. Subsequently, these key information elements, along with precise timestamps and operation identifiers, will be recorded in detail and updated in real time to a dedicated risk status record table in the database.

[0094] Step S5: Analyze the root causes of deviations in the risk status records, dynamically adjust the value calculation weights and feed them back into the management rule base to complete the continuous self-optimization of the intelligent management system.

[0095] Specifically, firstly, natural language processing or pattern recognition technology is used to analyze the deviation descriptions in the risk status record table to extract key root causes; secondly, the strategic weights of relevant projects are dynamically adjusted in the value assessment model based on the root cause type; finally, the adjusted weights are fed back into the management rule knowledge base as a new configuration to update the basis for subsequent value calculations, achieving rule self-iteration. This process includes the following sub-steps:

[0096] Step S51: Using natural language processing or pattern recognition technology, analyze the deviation description information recorded in the risk status record table, and extract the key root cause words or factor classifications that lead to the deviation.

[0097] The system automatically parses the textual deviation description information stored in the risk status record table, uses text feature extraction technology to identify and extract core words and phrases that appear repeatedly or have specific semantic meanings in the description, and at the same time, according to the preset risk root cause classification system, the system maps the identified key root cause words or phrases to the corresponding root cause type classification, so as to realize the structured identification and classification of deviation root causes.

[0098] Step S52: Based on the identified root cause type, dynamically adjust the strategic weight parameter values ​​of projects associated with that type of root cause in the value assessment model to more accurately reflect their current actual value.

[0099] Based on the identified and categorized root cause types, the system automatically triggers a weight adjustment mechanism for specific root causes during project value assessment calculations. Specifically, the system identifies strategic weight parameters in the value assessment model that have a pre-defined correlation with that type of root cause. Using a pre-defined weight decay algorithm, the system dynamically quantifies the risk impact based on the frequency, severity, or correlation strength of the identified root causes, using a multi-dimensional impact coefficient formula, expressed as follows: in, This represents the multi-dimensional influence coefficient. Representing time-related variables, Indicates the severity of the root cause. Indicates the frequency of root cause identification; Represents the natural constant; Indicates the sensitivity coefficient for severity; Indicates the severity baseline value; Indicates the reference frequency; Indicates the current reference time point; This represents the time decay constant.

[0100] By immediately reducing the specific values ​​of relevant strategic weight parameters, the project's valuation results can more sensitively and objectively reflect the value loss or increased uncertainty caused by specific risk factors.

[0101] Step S53: The dynamically adjusted strategic weight parameter values ​​are used as new configuration information, fed back and written into the weight configuration parameter table in the management rule knowledge base, thereby updating the basis for subsequent value calculation and realizing the self-iteration of the rules.

[0102] The dynamically adjusted strategic weight parameter values ​​are automatically captured by the system and considered the latest valid configuration. These updated weight parameter values ​​are encapsulated into standardized configuration data packages and written back to the corresponding weight configuration parameter table in the centrally stored management rule knowledge base via a preset data interface. After the update is completed, all subsequent project value assessment calculation processes will automatically call this latest weight parameter configuration stored in the knowledge base as the calculation basis. Through a closed-loop feedback mechanism, the system achieves automated and continuous calibration and optimization of core value assessment rules based on the root causes of risks identified in actual operation, ensuring that the management rule knowledge base can dynamically adapt to changes in the project environment and continuously iterate and evolve.

[0103] Example 2

[0104] like Figure 2 As shown, Embodiment 2 of this application provides an intelligent management system for enterprise project databases, including:

[0105] Data Acquisition and Preprocessing Module 21: The system collects project data from each business unit in real time, performs integrity verification and structured processing through an intelligent cleaning engine, and stores the data in the central project database; it includes the following sub-modules:

[0106] Data Acquisition Submodule 211: Through standardized distributed data acquisition interfaces deployed in various business unit systems, the module acquires raw project data streams, including project identifiers, status information, and numerical indicators, in real time at preset time intervals or event triggering mechanisms.

[0107] Data Cleaning and Structuring Submodule 212: It uses a predefined set of field integrity verification rules stored in the rule base to scan and check the received data, automatically identify and fill in missing key data items, and at the same time use a predefined data structuring transformation template to uniformly transform heterogeneous data from different sources and with different formats into a standard structured format that meets the requirements of the central database.

[0108] Association and Storage Submodule 213: After the standardized data has undergone integrity verification and structure transformation, it performs association matching according to its unique project identifier and persists the data to the pre-designed relational data table structure in the central project database.

[0109] Dynamic Value Management Module 22: Based on the schedule, cost, and risk fields of the central project database, it calculates a comprehensive value index by integrating business value and strategic weights, and updates the database's value assessment indicators; based on the value assessment indicators and real-time resource inventory, it generates a resource allocation plan through a hierarchical adaptation strategy and outputs a priority resource list to the execution system; it includes the following sub-modules:

[0110] Data extraction submodule 221: Accurately extracts from the specified data table of the central project database the latest progress percentage, actual cost consumption, and risk level quantification values ​​obtained from the system assessment for all projects that need to be calculated within a specific assessment period.

[0111] Value Calculation Submodule 222: The project's preset commercial value parameters, which reflect its expected revenue potential, and strategic weight parameters, which reflect its importance in the company's overall strategy, are input into the preset weighted comprehensive calculation model for processing and calculation.

[0112] Result Update Submodule 223: The result of the comprehensive value index calculation after running the weighted comprehensive calculation model is used as the core value assessment indicator of the project and written to and updated to the value assessment indicator field of the project record in the central project database.

[0113] Project Hierarchy Submodule 224: Based on the value assessment index values ​​of each project in the central project database, a preset score range threshold is set to clearly divide all projects into three different levels: high value priority, medium value priority, and low value priority.

[0114] Resource quota calculation submodule 225: Real-time acquisition of the total amount of currently available resources provided by the resource management system, and calculation of the total amount of resource quota to be allocated to each level according to the pre-configured resource allocation strategy rules and the proportion of different priority levels.

[0115] List generation and output submodule 226: Within the same priority level, all projects are sorted in ascending order based on their risk field values, ultimately forming a priority resource list that details the project execution order and the amount of resources required, and this list is output to the project execution system.

[0116] Resource execution monitoring module 23: Synchronously tracks progress deviations when executing priority resource lists; triggers data isolation and index optimization when risk offset exceeds a threshold, and updates database risk status records; analyzes the root causes of deviations in risk status records, dynamically adjusts value calculation weights, and feeds back into the management rule base to complete the continuous self-optimization of the intelligent management system; includes the following sub-modules:

[0117] Data Isolation Submodule 231: When the risk offset of a project is detected to exceed the preset critical threshold, the system automatically partitions or temporarily isolates and stores the project data associated with the risk event or a specific dataset to prevent abnormal data from interfering with normal business analysis.

[0118] Index Optimization Submodule 232: Based on the severity level of the risk event, rebuild or optimize the index structure of the relevant data tables in the central project database to improve the efficiency of querying and processing high-risk status data.

[0119] Status Update Submodule 233: Records and updates the new data access path, processing status, and risk level change information formed after data isolation and index optimization in detail to a dedicated risk status record table in the database.

[0120] Root Cause Analysis Submodule 234: Using natural language processing or pattern recognition technology, the deviation description information recorded in the risk status record table is parsed to extract the key root cause words or factor classifications that lead to the deviation.

[0121] Weight Adjustment Submodule 235: Based on the identified root cause type, dynamically adjust the strategic weight parameter values ​​of projects associated with that type of root cause in the value assessment model to more accurately reflect their current actual value;

[0122] Rule Iteration Submodule 236: The dynamically adjusted strategic weight parameter values ​​are fed back as new configuration information and written into the weight configuration parameter table in the management rule knowledge base, thereby updating the basis for subsequent value calculation and realizing the self-iteration of the rules.

[0123] Corresponding to the above embodiments, the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0124] The memory is used to store one or more program instructions;

[0125] A processor is used to run one or more program instructions to execute an intelligent management method for enterprise project databases.

[0126] Corresponding to the above embodiments, this embodiment of the invention provides a computer-readable storage medium containing one or more program instructions, which are executed by a processor to provide an intelligent management method for an enterprise project database.

[0127] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions, which, when executed on a computer, cause the computer to perform the aforementioned intelligent management method for enterprise project databases.

[0128] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0129] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0130] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0131] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0132] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0133] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0134] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0135] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, or improvements made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent management method for enterprise project databases, characterized by, Comprise: The system collects project data of each business unit in real time, performs integrity check and structured processing through an intelligent cleaning engine, and stores the data in a central project database; Based on the progress, cost, and risk fields of the central project database, the comprehensive value index is calculated by integrating business value and strategic weight, and the value evaluation index of the database is updated; According to the value evaluation index and real-time resource inventory, a resource allocation scheme is generated through hierarchical adaptation strategy, and a priority resource list is output to the execution system; When executing the priority resource list, the progress deviation is tracked synchronously, and when the risk deviation exceeds the threshold, data isolation and index optimization are triggered, and the risk state record of the database is updated; The deviation root cause in the risk state record is analyzed, the value calculation weight is dynamically adjusted, and the management rule library is fed back, and the continuous self-optimization of the intelligent management system is completed; According to the value evaluation index and real-time resource inventory, a resource allocation scheme is generated through hierarchical adaptation strategy, and a priority resource list is output to the execution system, including the following sub-steps: According to the value evaluation index of each project in the central project database, set a preset score interval threshold, and clearly divide all projects into three different levels of high value priority, medium value priority and low value priority; Real-time acquisition of the total amount of current available resources provided by the resource management system, and according to the pre-configured resource allocation strategy rules, the total amount of resource allocation for each level is calculated according to the proportion of different priority levels; In all projects within the same priority level, according to the risk field value, a priority resource list is formed, which lists the execution order of the projects and the required resource amount in detail, and the list is output to the project execution system; When executing the priority resource list, the progress deviation is tracked synchronously, and when the risk deviation exceeds the threshold, data isolation and index optimization are triggered, and the risk state record of the database is updated, including the following sub-steps: When the risk deviation of a project exceeds the preset threshold, the system automatically partitions or stores the project data associated with the risk event in a specific data set for temporary isolation to prevent abnormal data from interfering with normal business analysis; According to the severity level of the risk event, the index structure of the related data table in the central project database is reconstructed or optimized to improve the query and processing efficiency of high-risk state data; The new data access path, processing state, and risk level change information formed after data isolation and index optimization are recorded and updated in detail in the database's special risk state record table.

2. The method for intelligent management of enterprise project databases according to claim 1, characterized in that, The system collects project data of each business unit in real time, performs integrity check and structured processing through an intelligent cleaning engine, and stores the data in a central project database, including the following sub-steps: Through the standardized distributed data collection interface deployed in each business unit system, the project raw data stream containing project identifiers, state information, and numerical indicators is acquired in real time at a preset time interval or event trigger mechanism; The received data is scanned and checked by using a set of field integrity check rules predefined and stored in a rule base, missing key data items are automatically identified and filled in, and heterogeneous data from different sources and in different formats are uniformly converted into a standard structured format conforming to the requirements of the central database by using predefined data structure conversion templates; The standardized data after integrity check and structure conversion processing are associated and matched according to their unique project identifiers, and are persistently stored into the pre-designed relational data table structure of the central project database.

3. The method of claim 1, wherein, Based on the progress, cost, and risk fields of the central project database, the business value and strategic weight are fused to calculate a comprehensive value index, and the value evaluation indicators of the database are updated, including the following sub-steps: From the specified data table of the central project database, the latest progress percentage values, actual cost consumption values, and risk level quantitative values of all projects currently requiring calculation in a specific evaluation period are accurately extracted; The business value parameters reflecting the expected revenue potential of the project and the strategic weight parameters reflecting the importance of the project in the overall strategy of the company are jointly input into the preset weighted comprehensive calculation model for processing and operation; The comprehensive value index calculation result output by the weighted comprehensive calculation model is taken as the core value evaluation indicator of the project, and is written into and updated to the value evaluation indicator field corresponding to the project record in the central project database.

4. The method of claim 3, wherein, The business value parameters reflecting the expected revenue potential of the project and the strategic weight parameters reflecting the importance of the project in the overall strategy of the company are jointly input into the preset weighted comprehensive calculation model for processing and operation, including the following sub-steps: The original progress percentage values extracted from the database are standardized for data processing, and are converted into progress completion coefficients for cross-project comparison; The cost deviation rate or cost benefit ratio is calculated based on the comparison between the actual cost consumption value and the pre-approved budget benchmark value, and the cost benefit factor value reflecting the cost control effect is derived based on this; The progress completion coefficient and cost benefit factor values are taken as the basic input items, and are combined with the specific business value parameters and strategic weight parameters of the project in the weighted calculation model for multi-factor, multi-weight linear weighted combination operation to obtain the comprehensive value index of the project.

5. The method for intelligent management of enterprise project databases according to claim 1, wherein, The deviation root causes in the risk status record are analyzed, the value calculation weight is dynamically adjusted, and the management rule base is fed back to complete the continuous self-optimization of the intelligent management system, including the following sub-steps: The deviation description information recorded in the risk status record table is analyzed by using natural language processing or pattern recognition technology to extract key root cause vocabulary or factor classification causing the deviation; According to the identified root cause types, the strategic weight parameter values of the projects associated with the root causes are dynamically adjusted in the value evaluation model to more accurately reflect their current actual value. The strategy weight parameter value after dynamic adjustment is fed back and written into the weight configuration parameter table in the management rule knowledge base as new configuration information, thereby updating the basis for subsequent value calculation and realizing self-iteration of the rule.

6. An intelligent management system for enterprise project databases, characterized by, The method comprises the following steps: A data acquisition and preprocessing module: the system acquires project data of each business unit in real time, performs integrity check and structured processing through an intelligent cleaning engine, and stores the data into a central project database; A dynamic value management module: based on the progress, cost and risk fields of the central project database, a comprehensive value index is calculated by combining business value and strategy weight, and the value evaluation index of the database is updated; according to the value evaluation index and real-time resource inventory, a resource allocation scheme is generated through hierarchical adaptive strategy, and a priority resource list is output to an execution system; A resource execution monitoring module: when the priority resource list is executed, the progress deviation is tracked synchronously, data isolation and index optimization are triggered when the risk deviation exceeds a threshold, the risk state record of the database is updated; the deviation root cause in the risk state record is analyzed, the value calculation weight is dynamically adjusted, and the management rule library is fed back, thereby completing the continuous self-optimization of the intelligent management system; The dynamic value management module specifically comprises: according to the value evaluation index values of each project in the central project database, a preset score interval threshold is set, and all projects are clearly divided into three different levels of high-value priority, medium-value priority and low-value priority; the total amount of current available resources provided by the resource management system is acquired in real time, and according to the pre-configured resource allocation strategy rule, the total amount of resource quota allocated to each level is calculated according to the proportion relationship of different priority levels; in all projects within the same priority level, the risk field values are arranged in ascending order according to the risk field values, and finally a priority resource list detailing the execution order of the projects and the required resource amount is formed, and the list is output to the project execution system; The resource execution monitoring module specifically comprises: when it is monitored that the risk deviation of a project exceeds a preset critical threshold, the system automatically performs temporary isolation storage operation on the project data or specific data set associated with the risk event to prevent abnormal data from interfering with normal business analysis; according to the severity level of the risk event, the index structure of the related data table in the central project database is reconstructed or optimized to improve the query and processing efficiency of high-risk state data; the new data access path, processing state and risk level change information formed after data isolation and index optimization are recorded and updated in detail in the database risk state record table.

7. A computer storage medium, comprising, The method comprises the following steps: At least one memory and at least one processor; The memory is used to store one or more program instructions; The processor is used to run one or more program instructions to execute the enterprise project database intelligent management method according to any one of claims 1-5.

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