Industrial park project management control method and system based on big data

Abstract

The invention relates to the technical field of intelligent data management, in particular to an industrial park project management control method and system based on big data, and the method comprises the following steps: based on industrial park project data, controlling task progress, resource consumption and budget expenditure, screening progress delay and resource abnormity tasks, extracting a resource allocation path and task priority, and performing task scheduling; resource conflicts are identified, budget imbalance is identified, quality and cost benefit analysis is output, external fluctuation and policy changes are captured, and risk triggering nodes are identified. According to the invention, through comprehensive control and monitoring of industrial park project management data, tasks with progress delay and abnormal resource consumption can be efficiently screened, efficient management of project nodes is ensured, through analysis of budget configuration and task complexity proportion, a budget imbalance problem is accurately identified, risks are identified in advance, predictability and response speed of project management are improved, and project management efficiency is improved. Delay and resource waste are reduced, decision support capability and data collaboration efficiency are enhanced, and smooth execution of a project is ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent data management technology, and in particular to an industrial park project management and control method and system based on big data. Background Art

[0002] The field of intelligent data management technology includes the management of technical aspects such as automated collection, standardized processing, structured storage, and efficient scheduling of large-scale data resources. The core content of this technology field includes integrated data management, real-time update mechanisms, the construction of logical relationships between data, and data adaptation methods for different application requirements. Its overall technical system covers data lifecycle management, data quality monitoring, permission control mechanisms, data interface collaboration methods, and cross-platform data sharing strategies, aiming to improve data circulation efficiency and consistency of data usage, and form a data-driven management and decision-making support framework in various business scenarios.

[0003] Among them, the industrial park project management control method refers to the node decomposition of project establishment, budgeting, approval, execution, acceptance and other matters in the project life cycle in order to achieve dynamic collection of multiple types of project information, unified identification of key node progress and multi-department task collaboration in the process of industrial park management, and digital recording of the status of each node through a preset indicator system. This method classifies project types in a data grouping manner, combines progress reporting rules, time window mechanism settings and related task list verification rules to achieve process control and update synchronization of different project statuses, and completes the project data management of the entire process by recording the actual completion status of the node content and the information of the responsible party.

[0004] Although existing technologies can achieve node-based management and status recording of projects in industrial park project management, they lag significantly in dynamic monitoring, budget adjustments, and responses to external fluctuations. Traditional process control methods rely primarily on the collection and processing of static data, and are unable to reflect project progress delays and abnormal resource consumption in real time. This results in the inability of projects to adjust resource allocation or budget investment in a timely manner at certain key nodes or when external policies change, leading to unnecessary budget imbalances or resource conflicts. For example, due to unreasonable budget allocation in certain project phases, resource paths were not adjusted in a timely manner, resulting in project progress delays and even affecting the final quality score. Existing technologies have certain limitations in cross-platform data sharing and synchronization, lack an effective real-time update mechanism, and are unable to ensure the coordination and consistency of project data between different departments or teams, ultimately affecting decision-making efficiency and project progress synchronization. Summary of the Invention

[0005] In order to solve the problem that the existing technology in industrial park project management can realize node management and status recording of projects, but has a large lag in dynamic monitoring, budget adjustment and response to external fluctuations, the traditional process control method mainly relies on the collection and processing of static data, and cannot reflect the abnormal situation of project progress delays and resource consumption in real time. This leads to the inability of projects to adjust resource allocation or budget investment in time at some key nodes or external policy changes, thereby causing unnecessary budget imbalances or resource conflicts. For example, due to unreasonable budget allocation in certain project stages, resource paths cannot be adjusted in time, resulting in project progress delays and even affecting the final quality score. The existing technology has certain limitations in cross-platform data sharing and synchronization, lacks an effective real-time update mechanism, and cannot ensure the coordination and consistency of project data between different departments or teams, which ultimately affects the decision-making efficiency and project progress synchronization. The embodiment of the present invention provides an industrial park project management control method and system based on big data. The technical solution is as follows:

[0006] On the one hand, a big data-based industrial park project management and control method is provided, which includes:

[0007] S1: Based on the project management data of the industrial park, the execution time nodes of the park tasks are compared with the resource consumption. The task list with progress delays and abnormal resource consumption ratios is screened, the deviation intervals are classified, and the location distribution of the budget deviation behavior of the partition is determined to obtain the budget deviation risk task distribution dataset;

[0008] S2: Calling the budget deviation risk task distribution dataset, extracting the resource allocation path and task priority of the task node, identifying the resource matching conflict nodes, re-adjusting the resource path order, and obtaining a list of resource allocation conflict nodes;

[0009] S3: Based on the resource allocation conflict node list, extract the budget records and task complexity of the core development nodes of the industrial park project, analyze the proportional relationship between budget input and task complexity, identify node tasks with unreasonable budget allocation, and generate budget allocation imbalance data for key nodes;

[0010] S4: Call the budget configuration imbalance data of the key nodes, extract the stage quality score, budget execution status and time usage data, determine the matching relationship between time usage and quality score, identify the project stages with high cost input but low quality score, and output quality and cost-benefit analysis data.

[0011] As a further solution of the present invention, the budget offset risk task distribution data set includes progress delayed tasks, resource consumption abnormal tasks, budget offset positions, and offset intervals; the resource allocation conflict node list includes resource conflict nodes, task priorities, resource allocation paths, and adjusted resource path sequences; the key node budget configuration imbalance data specifically includes task budget records, task complexity, budget input and task complexity ratio, budget consumption trajectory, and budget imbalance task nodes; and the quality and cost-benefit analysis data includes stage quality scores, budget execution status, time usage, and cost-benefit ratio.

[0012] As a further solution of the present invention, the step of distributing the budget deviation risk task data set is specifically as follows:

[0013] S101: Based on the industrial park project management data, extract task progress and resource consumption data, compare the planned time with the real-time completion time, filter according to resource proportion and usage limit, and obtain a set of abnormal task offsets;

[0014] S102: Extracting the task execution time period and the corresponding resource call frequency based on the abnormal task offset set, calculating the difference between the task time offset and the resource call frequency, setting boundary values to divide the tasks into differentiated categories, recording the partition number and category label of the task, and obtaining task offset interval classification data;

[0015] S103: Call the task offset interval classification data, count the frequency values of the partition numbers corresponding to each category, and screen the budget expenditure ratios, extract the expenditure ratios exceeding the budget threshold and the corresponding regional locations, and obtain the budget offset risk task distribution data set.

[0016] As a further solution of the present invention, the step of allocating a list of conflicting nodes is specifically as follows:

[0017] S201: Calling the budget deviation risk task distribution dataset, extracting the resource allocation path and task priority of the task node, comparing the resource supply order with the task priority arrangement order, determining the order consistency between the two, and obtaining the resource and priority deviation matching value;

[0018] S202: Based on the resource and priority offset matching values, select task nodes whose offset matching values exceed the resource matching deviation threshold, compare task priorities, determine whether to pre-provision high-priority tasks, mark task numbers that do not meet the conditions, and obtain a resource path conflict identification value;

[0019] S203: According to the resource path conflict identification value, adjust the resource supply order, give priority to supplying resources to high-priority task nodes, record the order change position, and obtain a resource allocation conflict node list.

[0020] As a further solution of the present invention, the step of allocating imbalanced data to key nodes is specifically as follows:

[0021] S301: Extracting budget and task complexity data of core development nodes of the industrial park project based on the resource allocation conflict node list, classifying them by task code, identifying the ratio of task quantity to budget amount, and obtaining the budget allocation difference rate;

[0022] S302: Based on the budget allocation difference rate, extract the phase budget consumption and time progress value in the node budget record, identify the ratio of budget consumption to time progress, and perform a difference judgment with the budget allocation difference rate to obtain the budget offset fluctuation;

[0023] S303: Filter node tasks with budget offset benchmark values according to the budget offset fluctuation amount, compare the complexity index with the complexity benchmark value, extract node tasks that meet two judgment conditions, and obtain budget configuration imbalance data of key nodes.

[0024] As a further solution of the present invention, the budget offset fluctuation amount adopts the formula:

[0025]

[0026] Among them, ΔD represents the budget deviation fluctuation, B i represents the stage budget consumption of the i-th node, T i Represents the time progress value of the i-th node, W i represents the budget weight coefficient of the i-th node, R represents the budget allocation difference rate, and n represents the total number of nodes.

[0027] As a further embodiment of the present invention, the steps of analyzing the quantity and cost-benefit data are specifically as follows:

[0028] S401: Calling the budget configuration imbalance data of the key node, extracting the quality score, budget execution status and time usage data of each stage, aggregating the quality score and time usage according to the stage code, identifying the score value corresponding to the unit time in each stage, and obtaining the time score correspondence rate;

[0029] S402: Based on the time score correspondence rate and the budget consumption in the stage budget execution status, the ratio of unit budget input to unit score is compared according to the stage code, the degree of deviation between the ratio and the score is determined, and the deviation direction and magnitude are extracted to obtain the cost score deviation;

[0030] S403: Based on the cost score offset, filter the stage records whose unit budget input is greater than the benchmark value and whose score offset is negative, calculate the budget consumption and score offset amplitude of each stage, and output quality and cost-benefit analysis data.

[0031] As a further embodiment of the present invention, the method further comprises step S5:

[0032] S5: Based on the quality and cost-benefit analysis data, capture external events and policy fluctuation data related to the industrial park zoning, determine whether the content change trend is consistent with the current quality deviation trend, and if so, mark the change point as a risk trigger node to obtain a task list affected by external fluctuations in the industrial park project;

[0033] The task list of external fluctuation impacts on the industrial park project includes external event data, policy fluctuations, quality deviation trends, and risk trigger nodes.

[0034] As a further solution of the present invention, the steps of the task list affected by external fluctuations of the industrial park project are specifically as follows:

[0035] S501: Extracting the partition number of the project node based on the quality and cost-benefit analysis data, identifying the external events and policy fluctuation data of the partition, and aggregating them into the partition content sequence in chronological order to obtain the external content fluctuation trend value;

[0036] S502: Based on the external content fluctuation trend value, the quality score deviation value and timestamp of the node task are called, the quality deviation trend is matched with the external change direction according to the partition number, the temporal consistency of the two is compared, and the position of the consistent interval is extracted to obtain the quality linkage consistent position value;

[0037] S503: According to the quality linkage consistent position quantity, match the external events and node task numbers within the trend consistent interval, collect related tasks and event type information, and obtain a task list affected by external fluctuations of the industrial park project.

[0038] On the other hand, an industrial park project management and control system based on big data is provided. The industrial park project management and control system based on big data is used to execute the above-mentioned industrial park project management and control method based on big data. The system includes:

[0039] The task progress analysis module extracts task progress and resource consumption anomaly data based on industrial park project management data. It compares and analyzes the execution time and resource consumption of each task node, screens tasks with delayed progress and abnormal resource consumption, categorizes them by offset interval, and generates a distribution dataset of tasks with budget offset risk.

[0040] The data monitoring module identifies the task progress data, resource consumption data, and budget expenditure data of the task based on the budget deviation risk task distribution data set of the park, analyzes the relationship between task progress and resource consumption and budget expenditure, identifies task progress delays and resource consumption anomalies, and obtains task progress and resource consumption anomaly data;

[0041] The resource allocation optimization module extracts the resource allocation path and task priority of the task node based on the budget deviation risk task distribution data set, analyzes the matching relationship between the resource supply sequence and the task priority, identifies the resource matching conflict nodes, adjusts the conflict nodes, and obtains a list of resource allocation conflict nodes;

[0042] The budget risk identification module extracts the budget records and task complexity of the core development nodes of the industrial park project based on the resource allocation conflict node list, analyzes the proportional relationship between budget input and task complexity, and combines the budget consumption trajectory to identify node tasks with unreasonable budget allocation, thereby obtaining budget allocation imbalance data for key nodes;

[0043] The external impact monitoring module analyzes the relationship between time usage and quality scores based on the budget configuration imbalance data of the key nodes, identifies project stages with high cost input but low quality scores, and combines external events and policy fluctuation data to determine whether the change trend is consistent with the quality deviation trend. If consistent, it is marked as a risk trigger node and a task list of external fluctuation impacts on the industrial park project is generated.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] Through comprehensive control and monitoring of industrial park project management data, it is possible to efficiently screen tasks with delayed progress and abnormal resource consumption, automatically detect budget deviation risks, timely adjust resource paths and discover resource matching conflicts. This refined dynamic monitoring and data recording ensures efficient management of project nodes. In the proportional analysis of budget allocation and task complexity, it can accurately identify potential budget imbalances. By real-time tracking of external events and policy fluctuations, it can timely adjust change points consistent with quality deviation trends and mark them as risk trigger nodes to identify risks in advance. This optimization of processing logic improves the foresight and response speed of project management, enhances the management efficiency and decision-making support capabilities of industrial park projects, reduces delays and resource waste, ensures the smooth execution of projects, and further improves the data collaboration efficiency and overall circulation efficiency of each link. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0047] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0048] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0049] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0050] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0051] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0052] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0053] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0054] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0055] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0056] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0057] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0058] See also Figure 1 , an embodiment of the present invention provides an industrial park project management and control method based on big data, the processing flow of the method may include the following steps:

[0059] S1: Based on the industrial park project management data, including the task progress data, resource consumption data, and budget expenditure data for each sub-project task partition in the industrial park, the task execution time nodes and resource consumption quantities of the industrial park are compared. The task list with progress delays and abnormal resource consumption ratios is screened, the deviation intervals are classified, and the location distribution of the budget deviation behavior of the partition is determined to obtain the budget deviation risk task distribution dataset;

[0060] S2: Call the budget deviation risk task distribution dataset, extract the resource allocation path and task priority of the task node, determine the matching relationship between the resource supply order and the task priority, identify the resource matching conflict nodes, readjust the resource path order, and obtain the resource allocation conflict node list;

[0061] S3: Based on the resource allocation conflict node list, extract the budget records and task complexity of the core development nodes of the industrial park project, analyze the proportional relationship between budget input and task complexity, and combine it with real-time budget consumption trajectory to identify node tasks with unreasonable budget allocation and generate budget allocation imbalance data for key nodes;

[0062] S4: Calls up budget allocation imbalance data at key nodes, extracts stage quality scores, budget execution status, and time usage data, determines the relationship between time usage and quality scores, identifies project stages with high cost inputs but low quality scores, and outputs quality and cost-benefit analysis data.

[0063] S5: Based on the quality and cost-benefit analysis data, capture the external events and policy fluctuations related to the industrial park zoning, determine whether the content change trend is consistent with the current quality deviation trend, and if so, mark the change point as a risk trigger node to obtain a list of tasks affected by external fluctuations in the industrial park project;

[0064] The budget deviation risk task distribution data set includes progress delayed tasks, resource consumption abnormal tasks, budget deviation positions, and deviation intervals. The resource allocation conflict node list includes resource conflict nodes, task priorities, resource allocation paths, and adjusted resource path sequences. The key node budget configuration imbalance data specifically includes task budget records, task complexity, budget input and task complexity ratio, budget consumption trajectory, and budget imbalance task nodes. The quality and cost-benefit analysis data includes stage quality scores, budget execution status, time usage, and cost-benefit ratio. The list of tasks affected by external fluctuations in industrial park projects includes external event data, policy fluctuations, quality deviation trends, and risk trigger nodes.

[0065] Specifically, if Figure 2 As shown in the figure, the steps of the budget deviation risk task distribution dataset are as follows:

[0066] S101: Based on the industrial park project management data, extract task progress and resource consumption data, compare the planned time with the real-time completion time, filter according to resource proportion and usage limit, and obtain a set of abnormal task offsets;

[0067] First, the progress and resource consumption data of the relevant tasks are extracted from the database, which involves query operations on multiple database tables. For example, the task ID and planned completion time are obtained from the task table, and the actual amount of resources used is obtained from the resource usage table. Then, these two types of data are integrated through a join query. Assume that task A plans to use 100 units of electricity and actually consumes 150 units, with a progress of 75%. The comparison with the planned completion time uses a simple date difference calculation method, such as the difference in days between the planned date and the actual date, so that the time deviation can be intuitively seen. Next is the comparison and screening of resource consumption, which is screened according to the resource usage ratio of each task and the preset upper limit. For example, electricity consumption must not exceed 150% of the plan. , which is achieved by writing SQL query statements, which involve comparison operators and aggregate functions to calculate the total resource consumption and average consumption. Tasks that exceed the resource consumption limit or are seriously behind schedule are marked. The collection of tasks constitutes the abnormal task offset set, which can effectively identify tasks that need further attention and adjustment, thereby optimizing the resource allocation and schedule management of the entire project. The dataset of abnormal tasks is stored in a new table for subsequent further analysis and processing. The dataset includes task ID, resource consumption deviation and time deviation. For example, the power consumption deviation of task A is 50 units and the time deviation is -3 days. Such data provides clear quantitative information for subsequent project management decisions.

[0068] S102: Based on the abnormal task offset set, extract the task execution time period and the corresponding resource call frequency, calculate the difference between the task time offset and the resource call frequency, set a boundary value to divide the task into differentiated categories, record the task partition number and category label, and obtain task offset interval classification data;

[0069] Define the specific parameters of resource calls within the task execution time period, such as the call frequency of electricity, water, etc., and their correlation with the task time. For each task, analyze its resource usage records within a specific time period, for example, analyze the power consumption records of task A every hour during execution. If the consumption record shows an abnormal increase in frequency within a specific time period, it indicates that the equipment is unstable or overloaded. This analysis requires processing time series data, using techniques such as time window aggregation, and then calculating the difference between the time offset of the task and the resource call frequency. This is achieved through simple arithmetic operations, such as taking the difference between the resource usage frequency and the time offset after taking the average value, and setting boundary values to divide the task into Classification, which involves the calculation of standard deviation and mean in statistical analysis to determine what kind of difference constitutes a significant deviation. For example, a deviation exceeding twice the standard deviation of the mean is set as a significant deviation. Then, based on the calculation results, the tasks are divided into three categories: normal, warning, and dangerous. The partition number and category label of each task are recorded, and the data is stored in the database for further management decision-making. For example, task A is marked as dangerous because the deviation of its resource call frequency exceeds the set boundary value. Finally, the task offset interval classification data is obtained, including task ID, offset category and specific deviation value. The data provides a specific operational basis for project management to adjust resource allocation and optimize task execution strategies.

[0070] S103: Calling the task deviation interval classification data, counting the frequency value of the partition number corresponding to each category, and screening the budget expenditure ratio, extracting the expenditure ratio exceeding the budget threshold and the corresponding regional location, to obtain the budget deviation risk task distribution data set;

[0071] Calling classified data and counting the frequency of partition numbers corresponding to each category involves database query and data aggregation operations. For example, counting the number of tasks of each category in each partition can be achieved by writing SQL queries and using the GROUP BY and COUNT functions. Filtering the budget expenditure ratio, accessing the budget database, obtaining the budget and actual expenditure data for each task, and then calculating the expenditure ratio, such as actual expenditure divided by budget. This calculation is usually performed in data processing software, using tools such as Excel or professional data analysis. Tasks that exceed preset thresholds are marked. The threshold setting is based on the overall financial strategy of the project. For example, setting an overspending of 20% as a budget overspending. The expenditure ratio exceeding the budget threshold and its corresponding regional location are extracted. This information is summarized into a budget deviation risk task distribution dataset and stored in a dedicated analytical database table. The dataset includes task ID, overspending ratio, and geographic location information. This provides detailed data for project financial analysis and supports further optimization of resource allocation and budget management. For example, if it is determined that tasks in a certain area are generally overspending, it is necessary to adjust the budget allocation for that area or review the resource utilization efficiency of related tasks.

[0072] Specifically, if Figure 3 As shown in FIG, the steps for resource allocation conflict node list are as follows:

[0073] S201: Calling the budget deviation risk task distribution dataset, extracting the resource allocation path and task priority of the task node, comparing the resource supply order with the task priority arrangement order, determining the order consistency between the two, and obtaining the resource and priority deviation matching value;

[0074] Extract relevant data from the database, including task nodes, resource allocation paths, and task priorities for subsequent analysis and operation. Each task node is assigned a priority label, which is pre-defined based on the urgency and importance of the task. For example, task node A is marked as high priority because it is critical to the timely completion of the project. The resource allocation path defines the distribution route of resources from the source to each task node, such as the specific power supply line from the main power supply to each operating machine. Then, compare the resource supply order with the task priority. The steps mainly involve sorting and matching operations. For example, use the sorting function of Python or SQL to compare the resource supply order and task priority. The tasks are sorted in order of priority, and then a matching algorithm is written to determine the order consistency between the two. The order consistency is determined by comparing the actual resource arrival time of the task node and the predetermined priority. For example, task node A should be powered first, but if it is at the back in the actual power supply order, it is considered that there is an offset. This offset is calculated by a calculation method to obtain a specific offset matching value. This value is calculated by an algorithm, such as calculating the standard deviation or average offset of the offset, and finally obtaining the resource and priority offset matching value, providing a quantitative deviation indicator for each task node. For example, the offset matching value of task A is -2, which means that there is a two-level deviation between its resource supply and the predetermined priority.

[0075] S202: Based on the resource and priority offset matching values, select task nodes whose offset matching values exceed the resource matching deviation threshold, compare task priorities, determine whether to pre-provision high-priority tasks, mark task numbers that do not meet the conditions, and obtain a resource path conflict identification value;

[0076] Filtering out task nodes from the dataset whose offset match values exceed a preset threshold involves querying and filtering the dataset. For example, SQL queries can be used to filter out task nodes whose offset match values exceed a threshold of ±3. The screening process requires defining a resource match deviation threshold, which is set based on project management experience and historical data. For example, a threshold of 3 is set based on statistical data from past projects to identify nodes with large deviations that affect project progress. Next, task priorities are compared. This is primarily done through programming algorithms to sort task nodes by priority and then determine whether high-priority tasks are correctly allocated in advance. This involves logical judgment and conditional statements. For example, if a high-priority task does not receive resources before a low-priority task, it is marked as a conflict. The task numbers that do not meet the conditions are recorded for subsequent resource adjustments and management decisions. The marking process is implemented using a marking algorithm or identification function to obtain a resource path conflict identification value, which provides key conflict point information for project management. For example, if the resource path conflict identification value of task node A is 1, it indicates that there is an inconsistency between resource allocation and priority.

[0077] S203: Adjust the resource supply order based on the resource path conflict identifier value, prioritize resources to high-priority task nodes, record the order change position, and obtain a list of resource allocation conflict nodes;

[0078] Calling a data set containing conflict identification values involves database access and data retrieval. For example, using SQL statements to extract all task nodes marked as conflicting, and then giving priority to high-priority task nodes. This requires programming adjustments to the resource allocation logic, such as modifying the resource allocation algorithm to ensure that high-priority task nodes get resources first. This can be achieved by writing optimization algorithms or modifying existing resource scheduling programs. During the adjustment process, the location of the order change also needs to be recorded. This can be done by recording the change log in the algorithm or using version control software. For example, a logging function is added to the resource allocation program to record the details of each resource adjustment, and finally a list of resource allocation conflict nodes is obtained. This list is generated by the program and lists all task nodes whose resource allocation has changed due to priority adjustments. The information is stored in the project database for further analysis and subsequent decision-making. For example, the resource supply order of task node A and task node B changes due to priority reordering.

[0079] Specifically, if Figure 4 As shown in the figure, the steps for configuring imbalanced data in the key node budget are as follows:

[0080] S301: Based on the resource allocation conflict node list, extract the budget and task complexity data of the core development nodes of the industrial park project, classify them by task code, identify the ratio of the number of tasks to the budget amount, and obtain the budget allocation difference rate;

[0081] Extracting budget and task complexity data related to the core development nodes of the industrial park project from project management involves querying the task and budget tables in the database. For example, to obtain the budget data and complexity of a task, it is necessary to execute an SQL query to extract information such as the task code, budget amount, and predetermined task complexity from the task table. Each task is classified and processed according to its code. Task complexity is determined by the required working hours, expertise, and diversity of resources. For example, if Task A involves multiple technical fields and has a long execution time span, its complexity is higher. After processing, the data is used to calculate the budget allocation variance rate for each task, that is, the ratio of the budget amount to the task complexity. Calculating this ratio involves a simple division operation. In this way, it is clear which tasks have unreasonable budget allocations. For example, the budget allocation variance rate of high-complexity tasks is lower than expected, indicating insufficient budget allocation. The variance rate results are used in subsequent project budget adjustments and resource reallocation decisions, thereby improving the utilization efficiency and execution effectiveness of project resources.

[0082] S302: Based on the budget allocation difference rate, extract the phase budget consumption and time progress value in the node budget record, identify the ratio of budget consumption to time progress, and perform a difference judgment with the budget allocation difference rate to obtain the budget offset fluctuation;

[0083] Budget deviation fluctuation, using the formula:

[0084]

[0085] Among them, ΔD represents the budget deviation fluctuation, B i represents the stage budget consumption of the i-th node, T i Represents the time progress value of the i-th node, W i represents the budget weight coefficient of the i-th node, R represents the budget allocation difference rate, and n represents the total number of nodes;

[0086] Phase budget consumption (B i ): Obtained through the actual budget consumption data of each stage recorded in the project management system;

[0087] Time progress value (T i ):Calculate the time progress percentage of each stage based on the comparison between the project plan and the actual progress;

[0088] Budget weight coefficient (W i): Based on the importance and complexity of tasks at each stage, set weight coefficients to reflect their relative impact on the overall project;

[0089] Budget allocation variance rate (R): calculated by comparing the difference between budget allocation and actual consumption in each stage;

[0090] Assume that the project is divided into three phases (n=3), and the parameters of each phase are as follows:

[0091] Phase 1:

[0092] Phase budget consumption (B1): 1 million yuan;

[0093] Time progress value (T1): 30% (i.e. 0.30);

[0094] Budget weight coefficient (W1): 1.2;

[0095] Phase 2:

[0096] Phase budget consumption (B2): 1.5 million yuan;

[0097] Time progress value (T2): 50% (i.e. 0.50);

[0098] Budget weight coefficient (W2): 1.5;

[0099] Phase 3:

[0100] Phase budget consumption (B3): 1.2 million yuan;

[0101] Time progress value (T3): 70% (i.e. 0.70);

[0102] Budget weight coefficient (W3): 1.3;

[0103] Budget allocation variance rate (R): 10% (i.e. 0.10);

[0104] Calculate the budget-to-schedule ratio adjustments for each phase:

[0105] Phase 1:

[0106] Phase 2:

[0107] Phase 3:

[0108] Sum calculation:

[0109] Calculate the budget deviation fluctuation (ΔD):

[0110]

[0111] The results show that the budget deviation fluctuation is 110860%, which means that the ratio of actual budget consumption to time progress deviates greatly from the budget allocation difference rate. Further analysis of the reasons and adjustment of the project budget and schedule are needed.

[0112] S303: Filter node tasks with budget offset benchmark values based on budget offset fluctuations, compare the complexity index with the complexity benchmark value, extract node tasks that meet the two judgment conditions, and obtain budget configuration imbalance data for key nodes;

[0113] Starting from the budget offset fluctuation, a screening and marking process is carried out to identify node tasks that deviate significantly from the budget offset baseline value. This involves the calculation and comparison of the complexity index. The complexity index is calculated based on factors such as the technical requirements of the task, the number of people involved, and the type of resources. It is compared with the pre-set complexity baseline value. For example, the calculated complexity index is compared with the baseline value to see if there is a significant difference. It is also necessary to consider whether the budget offset exceeds another preset baseline value. This is a percentage threshold. For example, a 10% excess of the budget is considered significant. When a node task meets both of these conditions at the same time, that is, the complexity is significantly higher or lower than the baseline value, and the budget offset also exceeds the threshold, the task will be marked as a critical node. The data of the node task is sorted out to form the budget allocation imbalance data of the critical node. The data provides in-depth insights into the risks and problems in the project, enabling the project team to take specific management measures for critical nodes to restore the balance of budget and resources and ensure the smooth progress of the project.

[0114] Specifically, if Figure 5 As shown, the steps for analyzing the quantity and cost-effectiveness of data are as follows:

[0115] S401: Calling the budget configuration imbalance data of key nodes, extracting the quality score, budget execution status and time usage data of each stage, aggregating the quality score and time usage by stage code, identifying the score value corresponding to the unit time in each stage, and obtaining the time score correspondence rate;

[0116] This is achieved through phased data call instructions, which compare the budget configuration data of key nodes with the actual consumption data. Special software is used to capture and preliminarily analyze the data. Afterwards, the data is classified and integrated through phase coding. For example, if in the management of an industrial park project, the budget configuration of the first phase is 2 million, and the actual consumption is 2.5 million, the excess 500,000 needs to be recorded and this part of the data needs to be specially marked. Then, the relationship between the data and the overall operational efficiency of the industrial park project is analyzed, especially the aggregation process of quality score and time usage. The quality score and time usage data of different stages need to be compared, which can be completed through the aggregation tools in data management. For example, by calculating the average quality score and average time usage of each stage, the quality score per unit time of each stage can be obtained, which helps project managers understand the changing trend of project quality per unit time, and thus adjust the subsequent budget allocation and time management to ensure that the project can be completed within the scheduled time and maintain high quality standards, and obtain the time score correspondence rate.

[0117] S402: Based on the time score correspondence rate and the budget consumption in the stage budget execution status, the ratio of unit budget input to unit score is compared by stage code, the degree of deviation between the ratio and the score is determined, and the deviation direction and magnitude are extracted to obtain the cost score deviation;

[0118] The specific execution process of the budget consumption in the stage budget execution status includes matching the time score correspondence rate with the budget consumption data. First, the budget consumption standard for each stage needs to be set. For example, in a certain industrial park project, the budget consumption standard for stage one is set to 1,000 yuan per unit score. Then, based on the actual budget consumption data, if the actual consumption of a certain stage is 1,200 yuan / unit score, this shows a deviation from the standard. This deviation can be displayed in more detail by calculating the ratio between the budget consumption and the unit score of each stage, and then judging the direction and magnitude of the deviation. This is done using professional analysis software. The software will calculate the offset through an algorithm model and quantify the direction and magnitude of the offset. For example, a positive offset direction indicates that the budget is exceeded, and a negative offset direction indicates that the budget is not reached. The offset magnitude indicates the degree of offset, which directly affects subsequent budget adjustments and resource allocation. By comparing data from different stages, the cost-effectiveness of the entire project can also be analyzed to obtain the cost score offset.

[0119] S403: Based on the cost score offset, filter the phase records where the unit budget input is greater than the benchmark value and the score offset is negative, calculate the budget consumption and score offset of each phase, and output quality and cost-benefit analysis data;

[0120] The specific implementation process of screening stage records with unit budget investment greater than the benchmark value and negative score offset includes first setting a reasonable benchmark value. For example, in the project management of an industrial park, the benchmark value can be set to a unit score cost of no more than 1,000 yuan. Based on the actual data, all stages that exceed this benchmark value are screened out, and further analysis is conducted to see whether the stage score is too low, that is, the score offset is negative. This involves complex data analysis and calculation processes, such as using database query and data processing technology to identify qualified records. For data that meets the screening conditions, it is necessary to further count the budget consumption and score offset of each stage. The data not only helps managers identify stages with excessively high costs and low benefits, but also provides a basis for adjusting strategies. For example, for stages with excessive consumption, it is necessary to adjust the budget or improve the work process to improve resource utilization efficiency and the overall quality and cost-effectiveness of the project, and output quality and cost-effectiveness analysis data.

[0121] Specifically, if Figure 6 As shown in the figure, the steps of the task list for the impact of external fluctuations on the industrial park project are as follows:

[0122] S501: Based on the quality and cost-benefit analysis data, extract the partition number of the project node, identify the external events and policy fluctuation data of the partition, and aggregate them into the partition content sequence in chronological order to obtain the external content fluctuation trend value;

[0123] Use database query technology to retrieve detailed information on each project node from comprehensive management, where each node is assigned a unique partition number. The number is generated through automated numbering to ensure the unique identification of each project node, and analyze the external events and policy fluctuation data of each partition. The data comes from government announcements or market research reports. The data analysis software will integrate external event and policy data into the corresponding partition content sequence in chronological order. For example, in the project management of a certain industrial park, if partition A is affected by a new environmental protection policy in the first quarter, this will be recorded and aggregated in the content sequence of the partition. The analysis tool will process the aggregated data and calculate the external content fluctuation trend value affecting each partition. This value is obtained by comparing the data changes at each time point. For example, the change ratio of the investment cost and operating efficiency of partition A before and after the implementation of the policy can be calculated to obtain the external content fluctuation trend value.

[0124] S502: Based on the external content fluctuation trend value, the quality score deviation value and timestamp of the node task are called, the quality deviation trend is matched with the external change direction according to the partition number, the temporal consistency of the two is compared, and the position of the consistent interval is extracted to obtain the quality linkage consistent position value;

[0125] Determining the external content fluctuation trend of each partition involves big data analysis and trend prediction technology. For example, in a certain industrial park project, data analysis software is used to analyze the quality score deviation value and related timestamps of each partition. For example, if partition A1 shows a downward trend in quality at the end of the first quarter, then analysts need to find external change data within the time period, such as adjustments to tax policies or changes in market demand. The data is then sorted and paired according to the partition number. For example, the quality deviation of partition A1 is corresponded to the policy adjustment of the same period. By comparing the quality deviation trend with the external change direction, statistical methods such as correlation analysis are used to determine the temporal consistency of the two. This includes finding corresponding points in time and judging whether there is a consistent interval, that is, whether the trends of the two are consistent within a certain period of time. For example, if the quality score of partition A1 is significantly improved during the policy preferential period, then this period is regarded as a consistent interval. The analysis is verified by the data model to ensure its scientificity and accuracy, and outputs the quality linkage consistent position quantity.

[0126] S503: Match external events and node task numbers within the trend consistency interval based on the quality linkage consistent position quantity, aggregate related tasks and event type information, and obtain a list of tasks affected by external fluctuations in the industrial park project;

[0127] Through advanced data matching technology, all node task numbers and external events within a consistent interval are screened out. For example, in the industrial park project, if partition A1 has consistent quality improvements and policy benefits between March and May, then all relevant task numbers during this period are matched with event types, such as Task 001 (technology upgrade) and event (tax exemption). This step requires the use of the database's advanced query function to collect and organize qualified task and event information into a list format. Each task is associated with a specific external event. This not only makes it easier for managers to quickly identify tasks affected by external changes, but also facilitates subsequent resource allocation and strategy adjustments. For example, by analyzing data, project managers can decide whether to continue the current preferential policies or adjust the key tasks of the project to adapt to changes in the external environment, and ultimately output a list of tasks affected by external fluctuations in the industrial park project.

[0128] like Figure 7 As shown in the figure, the industrial park project management and control system based on big data includes:

[0129] The task progress analysis module extracts task progress and resource consumption anomaly data based on industrial park project management data. It compares and analyzes the execution time and resource consumption of each task node, screens tasks with delayed progress and abnormal resource consumption, categorizes them by offset interval, and generates a distribution dataset of tasks with budget offset risk.

[0130] The data monitoring module identifies the task progress data, resource consumption data, and budget expenditure data based on the park's budget deviation risk task distribution data set, analyzes the relationship between task progress and resource consumption and budget expenditure, identifies task progress delays and resource consumption anomalies, and obtains task progress and resource consumption anomaly data;

[0131] The resource allocation optimization module extracts the resource allocation path and task priority of task nodes based on the budget deviation risk task distribution dataset, analyzes the matching relationship between resource supply sequence and task priority, identifies resource matching conflict nodes, adjusts the conflict nodes, and obtains a list of resource allocation conflict nodes;

[0132] The budget risk identification module extracts the budget records and task complexity of the core development nodes of the industrial park project based on the resource allocation conflict node list, analyzes the proportional relationship between budget input and task complexity, and combines the budget consumption trajectory to identify node tasks with unreasonable budget allocation, thereby obtaining budget allocation imbalance data for key nodes.

[0133] The external impact monitoring module analyzes the relationship between time usage and quality scores based on the budget allocation imbalance data of key nodes, identifies project stages with high cost input but low quality scores, and combines external events and policy fluctuation data to determine whether the change trend is consistent with the quality deviation trend. If consistent, it is marked as a risk trigger node and a task list of external fluctuation impacts on the industrial park project is generated.

[0134] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. The industrial park project management and control method based on big data is characterized by: The following steps are involved: S1: Based on the project management data of the industrial park, the execution time nodes of the park tasks are compared with the resource consumption. The task list with progress delays and abnormal resource consumption ratios is screened, the deviation intervals are classified, and the location distribution of the budget deviation behavior of the partition is determined to obtain the budget deviation risk task distribution dataset; S2: Calling the budget deviation risk task distribution dataset, extracting the resource allocation path and task priority of the task node, identifying the resource matching conflict nodes, re-adjusting the resource path order, and obtaining a list of resource allocation conflict nodes; S3: Based on the resource allocation conflict node list, extract the budget records and task complexity of the core development nodes of the industrial park project, analyze the proportional relationship between budget input and task complexity, identify node tasks with unreasonable budget allocation, and generate budget allocation imbalance data for key nodes; S4: Call the budget configuration imbalance data of the key nodes, extract the stage quality score, budget execution status and time usage data, determine the matching relationship between time usage and quality score, identify the project stages with high cost input but low quality score, and output quality and cost-benefit analysis data.

2. The industrial park project management and control method based on big data according to claim 1 is characterized in that: The budget deviation risk task distribution data set includes progress delayed tasks, resource consumption abnormal tasks, budget deviation positions, and deviation intervals. The resource allocation conflict node list includes resource conflict nodes, task priorities, resource allocation paths, and adjusted resource path sequences. The key node budget configuration imbalance data specifically includes task budget records, task complexity, budget input and task complexity ratio, budget consumption trajectory, and budget imbalance task nodes. The quality and cost-benefit analysis data includes stage quality scores, budget execution status, time usage, and cost-benefit ratio.

3. The industrial park project management and control method based on big data according to claim 1 is characterized in that: The steps of distributing the budget deviation risk task data set are as follows: S101: Based on the industrial park project management data, extract task progress and resource consumption data, compare the planned time with the real-time completion time, filter according to resource proportion and usage limit, and obtain a set of abnormal task offsets; S102: Extracting the task execution time period and the corresponding resource call frequency based on the abnormal task offset set, calculating the difference between the task time offset and the resource call frequency, setting boundary values to divide the tasks into differentiated categories, recording the partition number and category label of the task, and obtaining task offset interval classification data; S103: Call the task offset interval classification data, count the frequency values of the partition numbers corresponding to each category, and screen the budget expenditure ratios, extract the expenditure ratios exceeding the budget threshold and the corresponding regional locations, and obtain the budget offset risk task distribution data set.

4. The industrial park project management and control method based on big data according to claim 3 is characterized in that: The steps of listing the resource allocation conflict nodes are specifically as follows: S201: Calling the budget deviation risk task distribution dataset, extracting the resource allocation path and task priority of the task node, comparing the resource supply order with the task priority arrangement order, determining the order consistency between the two, and obtaining the resource and priority deviation matching value; S202: Based on the resource and priority offset matching values, select task nodes whose offset matching values exceed the resource matching deviation threshold, compare task priorities, determine whether to pre-provision high-priority tasks, mark task numbers that do not meet the conditions, and obtain a resource path conflict identification value; S203: According to the resource path conflict identification value, adjust the resource supply order, give priority to supplying resources to high-priority task nodes, record the order change position, and obtain a resource allocation conflict node list.

5. The industrial park project management and control method based on big data according to claim 4 is characterized in that: The steps of allocating imbalance data for key node budget are as follows: S301: Extracting budget and task complexity data of core development nodes of the industrial park project based on the resource allocation conflict node list, classifying them by task code, identifying the ratio of task quantity to budget amount, and obtaining the budget allocation difference rate; S302: Based on the budget allocation difference rate, extract the phase budget consumption and time progress value in the node budget record, identify the ratio of budget consumption to time progress, and perform a difference judgment with the budget allocation difference rate to obtain the budget offset fluctuation; S303: Filter node tasks with budget offset benchmark values according to the budget offset fluctuation amount, compare the complexity index with the complexity benchmark value, extract node tasks that meet two judgment conditions, and obtain budget configuration imbalance data of key nodes.

6. The industrial park project management and control method based on big data according to claim 5 is characterized in that: The budget offset fluctuation is calculated using the formula: Among them, ΔD represents the budget deviation fluctuation, B i represents the stage budget consumption of the i-th node, T i Represents the time progress value of the i-th node, W i represents the budget weight coefficient of the i-th node, R represents the budget allocation difference rate, and n represents the total number of nodes.

7. The industrial park project management and control method based on big data according to claim 5 is characterized in that: The steps of analyzing the quantity and cost-effectiveness of data are as follows: S401: Calling the budget configuration imbalance data of the key node, extracting the quality score, budget execution status and time usage data of each stage, aggregating the quality score and time usage according to the stage code, identifying the score value corresponding to the unit time in each stage, and obtaining the time score correspondence rate; S402: Based on the time score correspondence rate and the budget consumption in the stage budget execution status, the ratio of unit budget input to unit score is compared according to the stage code, the degree of deviation between the ratio and the score is determined, and the deviation direction and magnitude are extracted to obtain the cost score deviation; S403: Based on the cost score offset, filter the stage records whose unit budget input is greater than the benchmark value and whose score offset is negative, calculate the budget consumption and score offset amplitude of each stage, and output quality and cost-benefit analysis data.

8. The industrial park project management and control method based on big data according to claim 1 is characterized in that: The method further comprises step S5: S5: Based on the quality and cost-benefit analysis data, capture external events and policy fluctuation data related to the industrial park zoning, determine whether the content change trend is consistent with the current quality deviation trend, and if so, mark the change point as a risk trigger node to obtain a task list affected by external fluctuations in the industrial park project; The task list of external fluctuation impacts on the industrial park project includes external event data, policy fluctuations, quality deviation trends, and risk trigger nodes.

9. The industrial park project management and control method based on big data according to claim 8 is characterized in that: The specific steps of the task list for the impact of external fluctuations on the industrial park project are: S501: Extracting the partition number of the project node based on the quality and cost-benefit analysis data, identifying the external events and policy fluctuation data of the partition, and aggregating them into the partition content sequence in chronological order to obtain the external content fluctuation trend value; S502: Based on the external content fluctuation trend value, the quality score deviation value and timestamp of the node task are called, the quality deviation trend is matched with the external change direction according to the partition number, the temporal consistency of the two is compared, and the position of the consistent interval is extracted to obtain the quality linkage consistent position value; S503: According to the quality linkage consistent position quantity, match the external events and node task numbers within the trend consistent interval, collect related tasks and event type information, and obtain a task list affected by external fluctuations of the industrial park project.

10. The industrial park project management and control system based on big data is characterized by: According to any one of claims 1 to 9, the industrial park project management and control method based on big data comprises: The task progress analysis module extracts task progress and resource consumption anomaly data based on industrial park project management data. It compares and analyzes the execution time and resource consumption of each task node, screens tasks with delayed progress and abnormal resource consumption, categorizes them by offset interval, and generates a distribution dataset of tasks with budget offset risk. The data monitoring module identifies the task progress data, resource consumption data, and budget expenditure data of the task based on the budget deviation risk task distribution data set of the park, analyzes the relationship between task progress and resource consumption and budget expenditure, identifies task progress delays and resource consumption anomalies, and obtains task progress and resource consumption anomaly data; The resource allocation optimization module extracts the resource allocation path and task priority of the task node based on the budget deviation risk task distribution data set, analyzes the matching relationship between the resource supply sequence and the task priority, identifies the resource matching conflict nodes, adjusts the conflict nodes, and obtains a list of resource allocation conflict nodes; The budget risk identification module extracts the budget records and task complexity of the core development nodes of the industrial park project based on the resource allocation conflict node list, analyzes the proportional relationship between budget input and task complexity, and combines the budget consumption trajectory to identify node tasks with unreasonable budget allocation, thereby obtaining budget allocation imbalance data for key nodes; The external impact monitoring module analyzes the relationship between time usage and quality scores based on the budget configuration imbalance data of the key nodes, identifies project stages with high cost input but low quality scores, and combines external events and policy fluctuation data to determine whether the change trend is consistent with the quality deviation trend. If consistent, it is marked as a risk trigger node and a task list of external fluctuation impacts on the industrial park project is generated.

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