Enterprise multi-project coordination management method and system based on data security analysis

Abstract

The present application relates to the technical field of multi-project coordination, in particular to an enterprise multi-project coordination management method and system based on data security analysis, comprising the following steps: identifying key task nodes and evaluating resource composition and execution deviation based on data security requirements, forming a task tension intensity index, analyzing path advancement fluctuations and interruption conditions based on the index, obtaining stability degree, identifying the calling aggregation structure of key resources, judging the task timing, resource overlap and dependency difference between paths, generating conflict characteristic quantity, constructing sequencing rules by comprehensively promoting state and conflict relationship, and determining task coordination level. The present application, by incorporating data security requirements into scheduling decision, establishes a cross-project state identification mechanism, evaluates task tension degree by combining task output field integrity and scheduling stage deviation, captures structure-sensitive tasks, superimposes path advancement fluctuations and interruption frequency, measures task chain stability, and locates scheduling uneven areas.

Description

Technical Field

[0001] This invention relates to the field of multi-project collaboration technology, and in particular to a method and system for enterprise multi-project collaboration management based on data security analysis. Background Technology

[0002] Multi-project collaboration mainly involves the theories, methods, and tools for managing and executing multiple projects simultaneously within the same organization or enterprise. This field focuses on how to achieve coordinated operation among multiple projects through reasonable resource allocation, task scheduling, information sharing, and schedule control, avoiding resource conflicts and schedule delays, and improving the overall project portfolio management efficiency and success rate. Multi-project collaborative management not only requires in-depth control over individual projects but also the ability to coordinate across projects. It has broad application prospects, especially in multi-task parallel environments such as large and medium-sized enterprises, engineering construction, software development, and manufacturing.

[0003] Among them, the enterprise multi-project collaborative management method of data security analysis is a comprehensive approach that combines data security technology with multi-project management strategies. It aims to achieve information sharing and collaborative advancement among multiple projects while ensuring the security of critical enterprise data. This method can be used by enterprises to ensure the security and compliance of data when it flows between multiple projects by identifying and analyzing the risks of sensitive data during the parallel operation of multiple projects.

[0004] Existing technologies rely on static resource settings and phased scheduling plans, lacking real-time feedback on the multi-dimensional behavioral states of tasks. This makes it difficult to reveal dynamic conflict characteristics. The task output structure is not included in the evaluation criteria, resulting in a lack of integrity in information transmission. Path execution performance is based solely on single-step information, failing to track continuous fluctuation risks and affecting the accuracy of path judgment. Resource usage frequency is not uniformly measured, and there is a lack of effective control over the occupancy trend of centralized components, leading to resource accumulation and scheduling blockages. Conflict determination is based solely on static dependencies between tasks, failing to handle actual startup intersections and resource reuse. Priority ranking indicators are singular, lacking collaborative analysis of path competition and delay states, which affects the rationality of task collaboration order. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a collaborative management method and system for enterprise multi-project management based on data security analysis.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a multi-project collaborative management method for enterprises based on data security analysis, comprising the following steps:

[0007] S1: Based on the data security requirements of enterprise projects, identify key task nodes for access control, transmission isolation and log auditing, determine their resource composition, stage deviation and missing structural fields, and generate task stress intensity indicators.

[0008] S2: Based on the task tension intensity index, analyze the task progress fluctuations and interruption records in the path, calculate the equilibrium state of task progress, and integrate the interruption frequency and path rhythm differences to obtain the path stability fluctuation degree.

[0009] S3: Based on the stability fluctuation of the path, count the frequency of key resource calls, compare the distribution of resources in the task, filter resources associated with data protection and access control, and obtain resource centralized structure parameters;

[0010] S4: Based on the resource set structure parameters, determine whether the task startup between paths overlaps, analyze the overlap of resource names and the change of dependency level, integrate the conflict dimension information, and obtain the path conflict combination feature quantity.

[0011] The present invention improves upon this invention by including the task stress intensity index as the remaining load of the stage and the density of dependent nodes, the path stability fluctuation degree as the task scheduling continuity status and interruption frequency statistics, the resource centralization structure parameters as the key resource duplication rate, the authentication resource aggregation degree and the task path exclusive ratio, and the path conflict combination characteristic quantity as the start time overlap, resource call overlap and dependency level similarity.

[0012] The present invention is improved in that the steps for obtaining the task stress intensity index are specifically as follows:

[0013] S111: Based on the data security requirements of enterprise projects, analyze the structural configuration of access control, transmission isolation and log auditing task nodes in the project, determine the resource categories and usage methods associated with each node, calculate the time difference between the actual progress in the task arrangement and the original node plan, and generate node resource offset characteristics.

[0014] S112: Based on the node resource offset characteristics, determine the integrity status of the structural fields corresponding to each task node, analyze the changing trend between the number of missing fields and the offset characteristics, compare the matching degree between the node field missing level and the resource configuration, filter out nodes with abnormal field distribution, and obtain the field missing offset characteristics.

[0015] S113: Based on the missing offset features of the field, analyze the adaptability of log audit frequency and transmission isolation frequency in the current task stage, optimize the scheduling classification criteria of tense nodes, and determine the differences in coordination load during node operation to generate a task tension intensity index.

[0016] The present invention is improved in that the step of obtaining the path stability fluctuation degree is specifically as follows:

[0017] S211: Based on the task tension intensity index, analyze the continuous records of task progress status in multiple plans, determine the variation range of progress rhythm in different time periods, calculate the fluctuation distance of each task progress data in the time series and the average progress difference, and obtain the fluctuation range of progress rhythm.

[0018] S212: Determine the distribution result of the propulsion rhythm fluctuation amplitude, match the cumulative record of interruption events of each node in the task path, calculate the correlation deviation level, compare the trend differences, and obtain the propulsion interruption correlation offset.

[0019] S213: Based on the aforementioned propulsion interruption associated offset, calculate the mission propulsion state fluctuation, the number of corresponding interruption events and their distribution differences, identify the degree of deviation between the propulsion balance and the interruption mode in the path, and obtain the path stability fluctuation degree.

[0020] The present invention is improved in that the steps for obtaining the resource centralization structure parameters are specifically as follows:

[0021] S311: Based on the stability fluctuation of the path, identify the resource items involved in the scheduling plan, calculate the number of times the resource is called in the path task, construct the association mapping data between the resource and the path according to the task order and path identifier, and determine the call frequency distribution of the resource in the task sequence to generate the resource call number distribution.

[0022] S312: Based on the distribution of the number of resource calls, analyze the aggregation trend and fluctuation of resources in the continuous segment of the path, determine whether the distribution range of resources in the task structure is concentrated, establish grouping and classification rules based on the resource repetition between tasks, filter resource tags that meet the classification conditions, and obtain the resource call aggregation structure.

[0023] S313: Based on the resource call aggregation structure, filter the resources associated with the data protection task and the terminal access control task, calculate the number of calls and corresponding positions in the path task, analyze the distribution deviation and path intersection and repetition, determine whether the distribution is concentrated, and obtain the resource concentration structure parameters.

[0024] The present invention is improved in that the step of obtaining the path conflict combination feature quantity is specifically as follows:

[0025] S411: Based on the resource set structure parameters, determine whether there is time overlap in the start time of each task in the difference path, analyze the resource call records involved in the overlapping tasks, identify the resource names corresponding to the tasks, determine whether there are resources with the same name among them, and obtain the resource name set of overlapping tasks.

[0026] S412: Based on the overlapping task resource name set, analyze the repeated call situation, identify the task combination with repeated resource identifiers, filter out resource items with complete matching, and output the duplicate resource comparison content by grouping the duplicate combinations to obtain the resource duplicate combination data;

[0027] S413: Based on the resource repetition combination data, calculate the ranking difference between the start time and dependency level in the task combination, analyze the start and dependency span formed between them, and obtain the path conflict combination feature quantity.

[0028] The present invention is improved in that the steps further include:

[0029] S5: Based on the path conflict combination feature quantity, analyze the task progress and key node delay status, establish sorting rules in combination with the path conflict intensity, complete the priority determination between tasks, and obtain the task collaborative sorting level.

[0030] The task coordination and sorting levels include task priority order, path conflict mitigation indicators, and scheduling priority level labels.

[0031] The present invention is improved in that the step of obtaining the task collaboration ranking level is specifically as follows:

[0032] S511: Based on the path conflict combination feature quantity, analyze the difference between the task start order and progress in the path overlap area, compare the scheduling lag of task nodes in the cross path, filter the paths with node delay, and obtain the number of node delay paths.

[0033] S512: Based on the number of delayed paths of the node, analyze the degree of interference between the node and the conflicting path, compare the correspondence between the delayed position of the task node and the degree of progress, determine the task scheduling correlation of the intersection nodes in the critical path, and obtain the task conflict sorting sequence.

[0034] S513: Based on the task conflict sorting sequence, and according to the sequential relationship between tasks in the sorting structure, determine whether there is a conflict in the collaborative execution logic of the current task set, optimize the sorting structure, and re-output the corresponding level grouping to obtain the task collaborative sorting level.

[0035] A multi-project collaborative management system for enterprises based on data security analysis, the system comprising:

[0036] The task stress assessment module is based on the data security requirements of enterprise projects. It identifies key nodes involving access control, transmission isolation and log auditing tasks, determines the types of resources used by the nodes, calculates the deviation between the remaining duration of the node's stage and the reference plan, and combines the missing status of the structure fields in the task output to make a joint judgment and generate a task stress intensity index.

[0037] Based on the task stress intensity index, the path stability analysis module analyzes the fluctuation of task progress status in multiple plans within the project task path, determines the number of interruption event records for each task within the path, calculates the balance of progress status within the path, compares its correlation with interruption situations, and integrates judgment factors to obtain the degree of path stability fluctuation.

[0038] The resource aggregation degree analysis module calculates the call frequency of key resources involved in the scheduling plan in the path based on the stability fluctuation degree of the path, compares the call range of each type of resource in the task list, filters the resource list associated with data protection tasks and terminal access control tasks, limits the identification objects to key authentication and permission authentication, and obtains the resource aggregation structure parameters.

[0039] Based on the resource set structure parameters, the path conflict identification module determines whether there is an overlap between the task start times in the different paths, analyzes whether the resource names called in the involved tasks are repeated, compares the span changes between task dependency levels, integrates time intersection, resource overlap and dependency differences, constructs a path conflict expression mechanism, and obtains path conflict combination feature quantity.

[0040] The collaborative sorting decision module analyzes the progress of project tasks at the current stage based on the path conflict combination feature quantity, determines the scheduling lag status of key nodes, calls the task progress, key node delay status and path conflict relationship for joint comparison, constructs project sorting rules according to priority, and obtains the task collaborative sorting level.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0042] In this invention, by incorporating data security requirements into scheduling decisions, a cross-project status identification mechanism is established. By combining the integrity of task output fields and scheduling phase offsets, the task stress level is assessed to capture structurally sensitive tasks. Progress fluctuations and interruption frequencies are superimposed on the path to measure the stability of the task chain and locate areas of uneven scheduling. The resource layer establishes overlap judgment based on the concentration of key and permission components to characterize the resource coupling situation. The paths are integrated with the overlap of start-up time, duplicate resource names, and the density of dependency levels to obtain conflict identification feature groups. The sorting is based on the progress status, node lag, and path conflict intensity to complete the task collaborative sorting level expression. Attached Figure Description

[0043] Figure 1 This is a flowchart of the main steps of the present invention;

[0044] Figure 2 This is a flowchart illustrating the process of obtaining the task stress intensity index in this invention.

[0045] Figure 3 This is a flowchart illustrating the process of obtaining the path stability fluctuation level in this invention.

[0046] Figure 4 This is a flowchart illustrating the process of obtaining the resource centralized structure parameters in this invention.

[0047] Figure 5 This is a flowchart illustrating the acquisition of path conflict combination features in this invention.

[0048] Figure 6 This is a flowchart illustrating the process of obtaining the task collaboration ranking level in this invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0051] Example

[0052] Please see Figure 1 This invention provides a technical solution: a multi-project collaborative management method for enterprises based on data security analysis, comprising the following steps:

[0053] S1: Based on the data security requirements of enterprise projects, locate the key nodes involving access control, transmission isolation and log auditing tasks, determine the resource types used by the nodes, calculate the deviation between the remaining duration of the node's current stage and the reference plan, and combine the missing status of the structure fields in the task output to make a joint judgment and generate a task stress intensity index.

[0054] S2: Based on the task tension intensity index, analyze the fluctuation of task progress status in multiple plans in the project task path, determine the number of interruption event records for each task in the path, calculate the balance of progress status in the path, compare its correlation with interruption status, and integrate judgment factors to obtain the path stability fluctuation degree.

[0055] S3: Based on the degree of path stability fluctuation, calculate the call frequency of key resources involved in the scheduling plan in the path, compare the call range of each type of resource in the task list, filter the resource list associated with data protection tasks and terminal access control tasks, limit the identification objects to key authentication and permission authentication, and obtain the resource set structure parameters.

[0056] S4: Based on the resource set structure parameters, determine whether there is an overlap between the task start time periods in the differential paths, analyze whether the resource names called in the involved tasks are repeated, compare the span changes between task dependency levels, integrate time intersection, resource overlap and dependency differences, construct a path conflict expression mechanism, and obtain path conflict combination feature quantity;

[0057] S5: Based on the combined feature of path conflicts, analyze the progress of project tasks at the current stage, determine the scheduling lag status of key nodes, and jointly compare the task progress, key node delay status and path conflict relationship. Construct project sorting rules according to priority to obtain the task collaborative sorting level.

[0058] Task stress intensity indicators include stage remaining load and dependency node density; path stability fluctuation degree includes task scheduling continuity status and interruption frequency statistics; resource concentration structure parameters include key resource duplication rate, authentication resource aggregation degree, and task path exclusive ratio; path conflict combination characteristics include startup time overlap, resource call overlap, and dependency level similarity; task coordination ranking level includes task priority order, path conflict mitigation indicators, and scheduling priority level label.

[0059] Remaining time refers to the length of time from the current point in time to the planned completion point of the current task node under actual scheduling conditions, reflecting the urgency of the task; Reference plan: refers to the standard task progress schedule set after project initiation or current stage update, used as a benchmark for evaluating the execution deviation of each node; Project task path refers to a set of related task nodes that constitute the project objectives, with clear dependencies and execution order, and is the logical execution chain in project scheduling; Progress status refers to the degree of completion of tasks within a specific scheduling cycle, such as progressing as planned, delayed, suspended, or completed ahead of schedule; Balance level refers to whether the progress pace of each task in the path is relatively consistent, and whether there are some tasks that are seriously delayed or advanced, disrupting the overall rhythm. Key resources are irreplaceable or highly dependent functional components when performing data security-related tasks, such as identity authentication interfaces and access control modules; the scope of invocation refers to the breadth of a resource used by multiple tasks, i.e., whether the resource is used centrally or in a distributed manner; data protection tasks refer to project tasks that undertake functions such as data encryption, access isolation, and transmission hardening, and are the core units for ensuring information security; terminal access control tasks refer to the task types responsible for identifying user terminal permissions, judging connection legitimacy, and implementing access blocking or authentication verification; the identified object in this context refers to the resource type name identified by the system for resource classification and screening, such as "key authentication" and "authorization authentication". Task start time refers to the time range within which each task is scheduled to start, used to determine whether different tasks overlap in execution on the timeline; overlap refers to the overlap of the start time periods of two or more tasks, indicating concurrent conflicts on resources or paths; task dependency level: indicates the degree of coupling between tasks, the higher the dependency level, the more the execution of a task depends on the completion of the preceding task; path conflict expression mechanism refers to an evaluation structure constructed by integrating the comparison relationship of time, resources, and dependencies to identify the severity of conflicts between paths. Scheduling lag refers to the time delay that occurs in the actual progress of a task compared to the standard plan, and it is an important dimension affecting priority ranking. Project ranking rules refer to the mechanism for determining the order of project execution in multi-project scheduling based on factors such as path conflicts, progress status, and node risks.

[0060] Please see Figure 2 The specific steps for obtaining the task intensity index are as follows:

[0061] S111: Based on the data security requirements of enterprise projects, analyze the structural configuration of access control, transmission isolation and log auditing task nodes in the project, determine the resource categories and usage methods associated with each node, calculate the time difference between the actual progress in the task arrangement and the original node plan, and generate node resource offset characteristics.

[0062] Based on the data security control requirements of enterprise projects, resource structure decomposition operations are performed on nodes executing access control, transmission isolation, and log auditing in multiple sub-projects. The scheduling status of each type of node task in different project paths is extracted, and the resource types called by the current configuration of each node are read sequentially. For example, access control task nodes call authorization authentication resources, while log auditing task nodes call log collection services or audit record storage modules. After obtaining the resource types, the start and end times of the nodes in the task plan table are compared with the actual execution times. The time interval between task completion is used as the actual progress, and then compared with the planned node completion time to obtain the time difference. This is used to determine whether there is a delay or advance deviation in the task. For example, an access control task was planned to be completed on May 10, but the actual completion time was May 13. If the offset is 3 days, then the resources called by this node were retrieved. In the past 5 days, they were called 12 times, averaging 2.4 times per day, while the planned average is 1.8 times per day, indicating an increase in the frequency of calls. Combining the offset time with the changes in resource frequency, an offset correlation relationship between the node and resource usage is established. It is recorded whether the resource type corresponding to the node is a critical resource, and whether it is a component reused by multiple tasks in the system, such as an authentication or encryption interface. If the resource type appears repeatedly in multiple tasks and the call frequency increases beyond the historical average, the configuration and usage status of the resource is considered abnormal. The abnormal resource status and time offset are then jointly marked on the node to form a node resource offset feature labeled with resource type, call frequency change and time offset value, which is used as the input basis for the subsequent field matching process.

[0063] S112: Based on the node resource offset characteristics, determine the integrity status of the corresponding structural fields of each task node, analyze the changing trend between the number of missing fields and the offset characteristics, compare the matching degree between the node field missing level and resource configuration, filter out nodes with abnormal field distribution, and obtain the field missing offset characteristics.

[0064] For nodes already marked as having node resource offset characteristics, further read the structure field configuration table of that task node, check each field for non-null values, record whether any fields are missing, compare the number of missing fields with the total number of fields, and calculate the overall missing field percentage. For example, if a log audit task node has 20 fields set, but only 14 are actually filled in, 6 are missing, resulting in a missing field percentage of 30%, record the field missing information for that node. Then, organize the node's resource offset information. For example, if the frequency of resource calls during scheduling increases or decreases abnormally, and there is a significant difference from the average plan, it is necessary to compare the trend relationship between the degree of field missing and the changes in resource offset. Arrange the nodes according to their order in the scheduling path, and compare whether the degree of missing between two or more consecutive nodes increases or decreases synchronously. If in multiple consecutive nodes... If a node simultaneously shows an increase in both the number of missing fields and the frequency of resource calls, it is determined that the missing fields of that node are affected by resource allocation imbalance. For example, compared with its predecessor B1, node B2 has increased the number of missing fields from 4 to 6, and the missing rate has increased from 20% to 30%. At the same time, its resource call frequency has increased from 1.5 times per day to 2.4 times per day. This indicates that the structural integrity of this node is affected by fluctuations in resource scheduling. Nodes with such high field missing rates and abnormal resource frequencies are then screened as nodes with abnormal field distribution. Based on task category, such as access control, transmission isolation, or log auditing, the proportion of abnormal nodes is counted. For example, if the log auditing task contains 10 nodes, and 4 of them are marked as nodes with abnormal fields, then the proportion of field missing offset features for this type of task is 40%, and node B2 is identified as a node with field missing offset features.

[0065] S113: Based on the field missing offset feature, analyze the adaptability of log audit frequency and transmission isolation frequency in the current task stage, optimize the scheduling classification criteria of tense nodes, and determine the differences in coordination load during node operation to generate a task tension intensity index.

[0066] Extract the execution frequency of log audits and the number of records for transmission isolation operations. Set a 5-day time window, and count the number of log writes and data isolation actions performed by the node within this period. For example, if node C3 has 22 log writes and 3 transmission isolation records within this period, compare them with the baseline values ​​of 30 logs and 5 transmissions. If both are lower than the set reference range, it is determined that the node's execution frequency in security tasks is too low. Then, combined with its field missing rate exceeding 25% and resource offset, priority adjustment is performed. Nodes originally with the priority label "normal" will be re-evaluated as "low priority". At the same time, retrieve the node's... The execution time of a node is compared with the amount of resources used to calculate the resource load per unit time. If its load is higher than the average load of standard nodes in the system statistics and there is an increase beyond a certain level, it is determined to be a high-coordination-load node. For example, node C3 processes 3 types of resources on average every day during the scheduling cycle, with a task time of about 1 hour. Its unit time load is 3 times / hour, while the system average is 2.5 times / hour. This node has exceeded the standard average by more than 0.5, which meets the load difference standard. Combined with its low execution frequency and high field missingness, this node is marked as a scheduling node with task intensity characteristics.

[0067] Please see Figure 3 The specific steps for obtaining the path stability fluctuation level are as follows:

[0068] S211: Based on the task tension intensity index, analyze the continuous records of task progress status in multiple plans, determine the change range of progress rhythm in different time periods, calculate the fluctuation distance of each task progress data in the time series and the average progress difference, and obtain the fluctuation range of progress rhythm.

[0069] The system reads the progress status records of corresponding task nodes in different time periods from multiple plan versions. The start time, completion time, and status labels (e.g., "on schedule," "delayed," "early," and "paused") recorded in each plan are then assigned to correspond to the task's progress level within that stage. These are uniformly converted into progress identifier values, arranged chronologically to generate a progress status sequence. This sequence is then subjected to numerical difference processing between adjacent time periods to reflect the fluctuation range of the task's progress rhythm. For example, if task D1 has progress status identifiers of 2, 3, and 1 in plan versions 1, 2, and 3 respectively, the difference operation in chronological order yields +1 and -2, indicating a fluctuation of one increase and one decrease in progress status. The absolute values ​​of these fluctuations are then summed to obtain a total task progress fluctuation range of 3. If the total range is greater than or equal to a set stable baseline value of 5, the task progress is considered to be... If the progress rhythm is relatively stable, it is considered an unstable progress rhythm. Then, the fluctuation distance of all tasks in each time period is statistically analyzed, and the total fluctuation value and frequency of occurrence are recorded. Furthermore, the progress change value of each task is compared with the average progress rhythm of the whole. For example, if the average progress change value is 1.2 and the change of task D1 is 1.5, then the deviation is 0.3, indicating that its progress rhythm deviation is slightly higher than the average. Then, it is determined whether the fluctuation of each task is concentrated in a specific stage or evenly distributed. By statistically analyzing the change trend of the progress status of the same node in two or more consecutive versions, it is determined whether the progress change of the task has abruptly changed. For example, if the progress value of a certain task is 3, 3, and 1 in three versions, it is determined that there is a sudden drop in the third planned version. Then, the same judgment is made for all task nodes, and the number of times their fluctuation behavior occurs is recorded to obtain the fluctuation amplitude of the progress rhythm.

[0070] S212: Determine the distribution of the amplitude of the propulsion rhythm fluctuation, match the cumulative records of interruption events of each node in the task path, calculate the correlation deviation level, compare the trend differences, and obtain the propulsion interruption correlation offset.

[0071] Extract all nodes from each task path and map each node to its corresponding progress fluctuation amplitude. Simultaneously, retrieve whether each node experienced any interruption events during execution, such as scheduling pauses, task stalls, or approval process freezes. Archive all interruption behaviors as an event list and count the cumulative interruption count for each node. For example, if node E1 experienced two interruptions in three planning versions, its cumulative count would be 2. Compare the interruption count with the progress fluctuation amplitude side-by-side to analyze whether there is a significant synchronous growth trend. By arranging a comparison matrix of interruption counts and progress fluctuation amplitudes for multiple nodes, count the proportion of nodes that change in the same direction. If more than 40% of the nodes simultaneously exhibit high fluctuation amplitude and high interruption frequency, the path is marked as a path with strong interruption correlation. Then, map the density of interruption behavior for each node to a deviation level, defining the deviation level. The deviations are categorized into three types: low deviation (interruptions ≤ 1, fluctuation amplitude ≤ 1.0), medium deviation (interruptions ≤ 2, fluctuation amplitude ≤ 1.0 to 2.0), and high deviation (interruptions ≥ 3, fluctuation amplitude ≥ 2.0). For example, if node E2 has 3 interruption records and a fluctuation amplitude of 2.8, it is judged as a high deviation node. Then, the distribution of deviation levels along the entire path is cumulatively statistically analyzed. If the proportion of high deviation nodes to the total number of nodes on the path exceeds 30%, the path is classified as an unstable path. The trend change values ​​of the interruption and fluctuation matching curves are recorded. The distribution sequence of interruption events on the time axis is time-aligned with the fluctuation sequence to form a rhythm interruption curve group. The trend peaks are compared to see if they occur at the same position. If the consistency of the two trends is greater than the set correlation ratio of 0.6, the path is marked as a high trend consistency path, and the propulsion interruption correlation offset is obtained.

[0072] S213: Based on the offset associated with the propulsion interruption, calculate the mission propulsion state fluctuation, the number of corresponding interruption events, and their distribution differences, using the following formula:

[0073]

[0074] The degree of deviation between propulsion equilibrium and interruption mode in the path is identified to obtain the path stability fluctuation degree F. s Among them, B i P represents the volatility of the progress status of the i-th task across multiple plans, reflecting the dispersion of the task's progress. i Δ represents the number of interrupt events recorded by the i-th task, which is the frequency of interruptions during the task's execution. i M represents the degree of difference between the progress state and the interruption event of the i-th task, and represents the corresponding offset between the two data points. irepresents the sequential number of the i-th task in the task path. Tasks are numbered sequentially from the beginning of the path. It is used to construct location-related structural parameters. n represents the total number of tasks contained in the current task path.

[0075] Call the propulsion fluctuation parameter B recorded in different planning cycles of tasks T1 to T5. i For example, if the progress record of task T1 in the four-round advancement plan is 60%, 80%, 50%, and 90%, then the volatility B1 = 3.2 can be obtained by calculating its standard deviation. Similarly, the advancement status volatility of T2 to T5 is obtained as B2 = 4.1, B3 = 2.8, B4 = 5.0, and B5 = 3.7 respectively. Next, the number of interruption events that occur in each task within the advancement cycle is counted and set as P1 = 2, P2 = 3, P3 = 1, P4 = 4, and P5 = 2 respectively. Then, the degree of difference between the advancement status volatility and the number of interruption events is quantified as the offset difference parameter Δ. i For example, by using the normalized deviation difference between the two parameters, we can obtain Δ1 = 0.5, Δ2 = 0.7, Δ3 = 0.4, Δ4 = 0.9, and Δ5 = 0.6. Finally, to introduce the structural position influence factor of the task in the path, we set them sequentially in the path as M1 = 0, M2 = 1, M3 = 2, M4 = 3, and M5 = 4. We then substitute the above parameters into the formula for calculation and input the task T1 data for calculation.

[0076]

[0077] Task T2:

[0078]

[0079] Task T3:

[0080]

[0081] Task T4:

[0082]

[0083] Task T5:

[0084]

[0085] Calculate the average across all tasks:

[0086]

[0087] The value F s ≈0.809 represents the degree of path stability fluctuation, indicating a moderate deviation between the current progress state and the interruption event. It is a fundamental judgment parameter suitable for path adjustment and task control. The formula incorporates the path position number M.i With the propulsion rhythm offset term Δ i By performing joint normalization calculations, a multi-dimensional evaluation mechanism relating propulsion status and structural position was constructed, avoiding local misjudgments caused by a single indicator, thereby improving the responsiveness and coverage of the overall stability measurement.

[0088] Please see Figure 4 The specific steps for obtaining the resource centralization structure parameters are as follows:

[0089] S311: Based on the degree of path stability fluctuation, identify the resource items involved in the scheduling plan, calculate the number of times the resource is called in the path task, construct the association mapping data between resources and paths according to the task order and path identifier, and determine the frequency distribution of resource calls in the task sequence to generate the resource call quantity distribution.

[0090] Based on the statistical results of path stability fluctuations, the scheduling plan documents corresponding to each path task node are read, and the resource items recorded in the documents are extracted one by one. These resources are then mapped and bound according to the task path number, recording the specific location and calling node of each resource in the task path. Subsequently, the total number of times each resource is called is counted, and a mapping data table between task sequences and resources is established. For example, if resource R1 is called by tasks T1, T3, and T5 in path P1, then R1 is called 3 times. Tasks T1 to T5 are numbered sequentially from 1 to 5, and the number of times R1 is called in tasks 1, 3, and 5 is recorded. After completing the same recording for all resources, a distribution table of resource call frequency in the task path is constructed. The call distribution range and concentration of each resource in the entire path are calculated. For example, if R2 is only called once in task T2... R3 is called 4 times consecutively in tasks T1 to T6, while R4 is called twice alternately between T1 and T6. The difference in call frequency clearly reflects the concentrated and discrete distribution. Based on the position number of the resource in the task sequence, the difference in its call position is calculated. For example, if the difference in the position number of adjacent tasks that call the resource consecutively is 1, and the average difference in the call position of the resource in the path is less than or equal to 1.5, it is considered a concentrated resource call; otherwise, it is a discrete resource call. The ratio of the number of tasks called for each type of resource to the path length is used as a frequency coefficient reference value. The frequency limit range is set as low frequency (the proportion of tasks is less than 20%), medium frequency (the proportion of tasks is between 20% and 60%), and high frequency (the proportion of tasks is higher than 60%). Based on the joint characteristics of the resource distribution position and the task order, the distribution of the number of resource calls is generated.

[0091] S312: Based on the distribution of resource call quantity, analyze the aggregation trend and fluctuation of resources in continuous segments of the path, determine whether the distribution range of resources in the task structure is concentrated, establish grouping and classification rules based on the resource repetition between tasks, filter resource tags that meet the classification conditions, and obtain the resource call aggregation structure.

[0092] Select resource records with consecutive or adjacent call locations in each task path and mark them as continuous segment resources. Then, according to the specific order in which the resources appear in the task path, draw a resource call heat curve to monitor whether the concentrated resource call area forms a peak segment. By calculating the span of the peak segment, determine whether the resources are highly aggregated in a certain task stage. For example, if resources R5, R6, and R7 are repeatedly called more than three times in tasks T3 to T7, and the call interval between resources does not exceed two task nodes, it is judged that there is a resource aggregation trend in this path. Then, compare the number of tasks appearing for each type of resource tag with the difference between the adjacent task numbers. If the proportion of the number of tasks in which the resource appears is greater than 50% of the total number of tasks in the path, and the interval between task numbers does not exceed 2, it is classified as a concentrated distribution resource. If the resource is distributed at the beginning and end of the task nodes of the path, it is classified as a concentrated distribution resource. If a resource does not appear consecutively, it is considered a discretely distributed resource. All resources are then grouped according to their calling patterns, and resources with the same calling distribution pattern are placed in the same category group. A resource grouping classification table is established. From the classification results, resource tags that meet the following classification conditions are selected: the resource is called by 3 or more tasks consecutively, the difference between task numbers does not exceed 2, the resource frequency coefficient is greater than 60%, and the resource type is related to data protection tasks, such as key authentication resources, encryption interface resources, etc. Such resources are recorded as key aggregate structure resources within the path. For example, if resource R9 in path P2 is called in T2, T3, T4, and T5, accounting for 66.7% of the tasks, and belongs to the access control resource, then R9 is marked as a tag item in the resource calling aggregate structure, thus obtaining the resource calling aggregate structure.

[0093] S313: Based on the resource call aggregation structure, filter the resources associated with data protection tasks and terminal access control tasks, calculate the number of calls and corresponding positions in the path tasks, analyze their distribution deviation and path overlap, and use the following formula:

[0094]

[0095] Determine whether the distribution is concentrated to obtain the resource concentration structure parameters, where RS kj This represents the cross-concentration structure parameter of resource j in path k, used to reflect the degree of call concentration and path cross-distribution of this resource in multi-path task distribution. FS kj This represents the number of times resource j is invoked in path k, i.e., the total number of times the resource is invoked in the corresponding path task. CS kj This represents the classification identifier factor of resource j in path k. A value of 1 indicates that the resource is related to data protection or terminal access control tasks, while a value of 0 indicates that it is not related. LS kjThis indicates the location number of resource j in path k, reflecting the relative order of the resource within the path's tasks. DS represents the average of the call location numbers of resource j across all paths, used to evaluate the overall distribution offset of this resource across tasks on different paths. j This represents the number of segment numbers in which resource j appears in the task path, i.e., the number of segment numbers covered by this resource in different paths, μS j This represents the average number of segment numbers for resource j across all paths, used to compare whether the path distribution of this resource is uniform.

[0096] Filter resource categories associated with data protection tasks and endpoint access control tasks, extract resource number R7, and identify its four task paths in the path set: P1, P2, P3, and P4. Calculate the number of times this resource is called in each path. For example, it is called 9 times in path P1, 12 times in path P2, 15 times in path P3, and 11 times in path P4. Record its call position numbers in each path as 4, 6, 5, and 3 respectively. Calculate the average position number of R7 in these four paths:

[0097]

[0098] Then, the squared offset is calculated by comparing the call location number in each path with the average value to obtain the squared term of the location offset within the path, which are as follows:

[0099] P1: (4-4.5) 2 =0.25;

[0100] P2: (6-4.5) 2 =2.25;

[0101] P3: (5-4.5) 2 =0.25;

[0102] P4: (3-4.5) 2 =2.25;

[0103] Simultaneously, determine the number of segment numbers of the resource in the path task structure. That is, the number of logical segments covered by the resource in each path is 3, and the average number of segment numbers is also 3. Calculate the path segment coverage difference item as follows:

[0104] (DS R7 -μS R7 ) 2 =(3-3) 2 =0;

[0105] This resource is associated with data protection or access control tasks, therefore it is categorized under CS.kj Take 1, substitute it into the formula to calculate the structural parameters of the path-by-path intersection set:

[0106] Path P1:

[0107]

[0108] Path P2:

[0109]

[0110] Path P3:

[0111]

[0112] Path P4:

[0113]

[0114] The calculation process reflects that the resource has the greatest aggregation characteristic in path P3, with a calculated value of 12, while P2 and P4 have lower values ​​of 3.692 and 3.385, respectively. This result indicates that resource R7 is concentrated in path P3, with its location distribution close to the average position. The path segment coverage is consistent with the overall distribution, exhibiting strong cross-concentration characteristics. Based on this, it can be determined that R7 forms a concentrated resource structure parameter in path P3. The formula adjusts the original number of calls by introducing two types of influencing factors: the square of the position offset and the path segment distribution offset. This makes the results more reflective of the aggregation and cross-coverage of the actual resource distribution, effectively distinguishing the differences in call structure in different paths.

[0115] Please see Figure 5 The specific steps for obtaining the path conflict combination feature quantity are as follows:

[0116] S411: Based on the resource set structure parameters, determine whether there is time overlap in the start time of each task in the difference path, analyze the resource call records involved in the overlapping tasks, identify the resource names corresponding to the tasks, determine whether there are resources with the same name among them, and obtain the resource name set of overlapping tasks.

[0117] Based on the key resource duplication rate and the certification resource aggregation degree, the difference path numbers and their task lists are extracted from all project paths. The planned start time period of each task is read, and a horizontal comparison is performed according to the project and number to which the task path belongs. A calendar-style alignment method is used to map the task start time periods to a unified time axis with date segment markers. The intersection length between any two task time periods is calculated. If the intersection length is greater than 1 day, it is considered that there is a time overlap. For example, if the start time of task A1 is from May 3 to May 6, and task B4 is from May 5 to May 7, then the intersection is from May 5 to May 6, a total of 2 days, which is marked as an overlap relationship. Then, the resource allocation is read from the overlapping task set. The system extracts the name and type information of the called resources from the records and generates a resource call list. Then, it performs a name comparison operation on the resource list of each pair of overlapping tasks. If two tasks have the same resource name, it is recorded as a name matching item. For example, if task A1 calls "Permission Authentication Interface 001" and task B4 also calls "Permission Authentication Interface 001", the resource name is considered to be the same, and the repetition count is incremented by one. The same comparison operation is performed on all overlapping tasks. Finally, the system outputs a list of resource name intersections for each pair of tasks. If two tasks have two or more identical resource names, they are considered to be task groups with high resource overlap. Such task groups are marked as overlapping task resource name sets.

[0118] S412: Based on the overlapping task resource name set, analyze the repeated call situation, identify the task combination with repeated resource identifiers, filter out resource items with complete matching, and output the duplicate resource comparison content by grouping the duplicate combinations to obtain the resource duplicate combination data;

[0119] The frequency of resource names appearing in task combinations is statistically analyzed. For each task combination, the identifiers of duplicate resources are compared and numbered. Resource entries with identical names and completely identical resource identifier codes are identified. For example, if both task C2 and task D3 call resource "RKEY_20240501", then this resource identifier is determined to be completely identical. Next, task pairs with two or more identical resource identifiers are selected from all task combinations as analysis targets. The task pair number, the list of duplicate resources, and the resource location index in the corresponding task are recorded. A resource duplicate combination mapping table is established for task pairs with completely identical resources, and the results are processed by task pair. Using the ID as the primary key, the system outputs the details and quantity of the corresponding duplicate resources in each task pair. For example, if task E1 and task F2 have three duplicate resources, namely "KEYMOD_1", "LOGTRACK_3", and "ACCESSAUTH_8", then the resource duplicate combination data items for task pair "E1-F2" are the above three resources. Then, all duplicate task combinations are sorted in descending order of resource overlap. The resource overlap is divided into high overlap (≥3 items), medium overlap (2 items), and low overlap (1 item). The high overlap combination group is selected as the key comparison output item to obtain the resource duplicate combination data.

[0120] S413: Based on resource repetition data, calculate the ranking differences between start time and dependency level in task combinations, analyze the start and dependency spans formed by them, and use the formula:

[0121]

[0122] Obtain the path conflict combination feature quantity CR zx This is used to measure the joint conflict between task z and task x in terms of startup time and dependency level, where, This represents the start time parameter for task z, which is the initial time point at which task z is scheduled in the differential path. This represents the start time parameter for task x, which is the initial time point at which task x is scheduled in the differential path. RL z The dependency level parameter for task z indicates the relative dependency level of the task within the task chain. (RL) x The dependency level parameter for task x indicates the relative dependency level of the task in the task chain;

[0123] First, extract the start times of the startup periods for task z and task x from the task scheduling plan, and denot them as follows: and By combining the hierarchical numbers in the task dependency network structure, the dependency levels between task z and task x are extracted and represented as RL. z With RL x The startup time is derived from the start time field in the scheduling table, and the dependency level is the task's position level in the logical task chain. It is typically assigned an increasing positive integer value from top to bottom. For example, if task z is scheduled at level 2 and task x is scheduled at level 4, then the corresponding dependency level is... z =2、RL x =4, if the scheduling start time for task z is 3.5 hours and task x is 6.0 hours, then the corresponding We use Euclidean distance to calculate the joint ranking offset of the task combination in both time and dependency dimensions. Substituting the parameters above, we get:

[0124]

[0125] The result of 3.20 indicates that the task combination has a compound offset in both time and dependency dimensions. The calculated path conflict combination feature is the quantitative measure of the task pair. This result is used to describe the joint conflict intensity of scheduling and dependency between resource-overlapping tasks. It can be used for comparative analysis between task paths. The formula introduces both the scheduling time start point and the dependency level at the same time to avoid misjudgment of conflict measurement caused by single time or logical structure analysis, thus improving the ability to analyze conflict features in complex task networks.

[0126] Please see Figure 6 The specific steps for obtaining the task collaboration ranking level are as follows:

[0127] S511: Based on the path conflict combination feature quantity, analyze the difference between the task start order and progress in the path overlap area, compare the scheduling lag of task nodes in the cross path, filter the paths with node delay, and obtain the number of node delay paths.

[0128] Read the planned start time and path number information of the corresponding tasks in each path group. After grouping the tasks according to their paths, map the start times of all tasks onto a time axis to form a time overlay matrix. Compare whether the start times of tasks in two paths overlap within the same time period. For example, if tasks T1 and T6 are located in paths P1 and P2 respectively, and T1's start time is from June 1st to June 3rd, while T6's is from June 2nd to June 4th, then their overlap is 2 days, marked as the path overlap segment. Then, arrange the path task list in order of task number, extract the actual start time of each task from the scheduling log, and compare it with the planned time to determine if there is a delay. For example, task T6 is scheduled to start on June 2nd, but... The task was actually started on June 5th, a delay of 3 days, and was recorded as a delayed task. The path to which the delayed task belonged was marked as a delayed path. Then, the number of paths marked as delayed was counted in all paths. The criterion was set as a delayed node if the task start time was delayed by more than 2 days. If two or more task nodes in a path met this condition, the entire path was recorded as a path with a delayed node. Then, the number of paths in each path group that met this condition was counted in turn. For example, in path group P1-P2-P3, P2 and P3 each had 2 delayed nodes, while P1 had no delayed nodes, so the number of delayed paths was 2. The total number of delayed paths was obtained by summing up the statistical values ​​of all paths in all path groups that met this condition.

[0129] S512: Based on the number of delayed paths of nodes, analyze the degree of interference between them and conflicting paths, compare the correspondence between the delayed position of task nodes and the degree of progress, determine the task scheduling correlation of the intersection nodes in the critical path, and obtain the task conflict sorting sequence.

[0130] Read the node numbers and start delay days of all intersecting task nodes in the path and its overlapping paths. Compare the position of the intersecting node in the scheduling sequence with the percentage of completion progress in its task progress record to calculate the task progress. Determine whether the delayed node is related to the progress lag. If a node is delayed by 3 days and its actual progress is only 40%, which is lower than the average progress standard of 70%, it is considered a strongly related node. Then, count the number of times such nodes appear in the critical path and their distribution range. For example, if the intersecting node T9 in path P4 is delayed and has low progress, a similar node T10 appears in path P5. Record the task interference between paths P4 and P5, and combine the corresponding tasks into task pairs with conflict interference relationship. Then sort the task pairs according to the sum of the scheduling delay days and the progress deviation. The larger the value, the higher the degree of task conflict. For example, if task T11 is delayed by 4 days and progresses by only 30%, the conflict value is 4+40=44. If task T12 is delayed by 2 days and progresses by 50%, the conflict value is 2+20=22. The former is ranked first. Sort all tasks in descending order according to this conflict value.

[0131] S513: Based on the task conflict sorting sequence, and according to the sequential relationship between tasks in the sorting structure, determine whether there is a conflict in the collaborative execution logic of the current task set, optimize the sorting structure, and re-output the corresponding level grouping to obtain the task collaborative sorting level.

[0132] Read the task dependency fields and collaborative execution tags between adjacent tasks in the sorting structure to determine whether the two tasks overlap in resource calls, path branches, or structure fields. For example, if tasks T15 and T16 both depend on the resource "ENCODER_005" and have a duplicate entry "Transmission Authentication Segment" in their structure fields, and their path numbers are marked as P6 and P7, then an execution conflict exists. Then, combine the priority and dependency levels of the tasks in the sorting to analyze whether the task scheduling arrangement conforms to the logical order. If a downstream task is ranked higher than an upstream task and has a resource dependency, the sorting structure is deemed unreasonable and needs to be rearranged. Related tasks are rearranged to satisfy the logic of dependency before execution. Based on the task conflict value, priority levels are defined in the conflict sorting: a conflict value greater than or equal to 40 is a level 1 task, between 20 and 39 is a level 2 task, and less than 20 is a level 3 task. The task numbers and their corresponding levels are then re-output to obtain the task collaborative sorting level.

[0133] A multi-project collaborative management system for enterprises based on data security analysis, the system includes:

[0134] The task stress assessment module is based on the data security requirements of enterprise projects. It identifies key nodes involving access control, transmission isolation and log auditing tasks, determines the types of resources used by the nodes, calculates the deviation between the remaining duration of the node's stage and the reference plan, and combines the missing status of the structure fields in the task output to make a joint judgment and generate a task stress intensity index.

[0135] The path stability analysis module analyzes the fluctuation of task progress status in multiple plans within the project task path based on the task stress intensity index, determines the number of interruption event records for each task within the path, calculates the balance of progress status within the path, compares its correlation with interruption situations, and integrates judgment factors to obtain the degree of path stability fluctuation.

[0136] The resource aggregation degree analysis module calculates the call frequency of key resources involved in the scheduling plan in the path based on the degree of path stability fluctuation, compares the call range of each type of resource in the task list, filters the resource list associated with data protection tasks and terminal access control tasks, limits the identification objects to key authentication and permission authentication, and obtains the resource aggregation structure parameters.

[0137] The path conflict identification module, based on the resource set structure parameters, determines whether there is overlap between the task start times in the different paths, analyzes whether the resource names called in the involved tasks are repeated, compares the span changes between task dependency levels, integrates time intersection, resource overlap and dependency differences, constructs a path conflict expression mechanism, and obtains path conflict combination feature quantity.

[0138] The collaborative sorting decision module analyzes the progress of project tasks at the current stage based on path conflict combination features, determines the scheduling lag status of key nodes, and performs a joint comparison of task progress, key node delay status, and path conflict relationships. It then constructs project sorting rules according to priority to obtain the task collaborative sorting level.

[0139] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A multi-project collaborative management method for enterprises based on data security analysis, characterized in that: Includes the following steps: S1: Based on the data security requirements of enterprise projects, locate the key nodes involving access control, transmission isolation and log auditing tasks, determine the resource types used by the nodes, calculate the deviation between the remaining duration of the node's current stage and the reference plan, and combine the missing status of the structure fields in the task output to make a joint judgment and generate a task stress intensity index. S2: Based on the task tension intensity index, analyze the fluctuation of task progress status in multiple plans in the project task path, determine the number of interruption event records for each task in the path, calculate the balance of progress status in the path, compare its correlation with interruption status, and integrate judgment factors to obtain the path stability fluctuation degree. S3: Based on the degree of path stability fluctuation, calculate the call frequency of key resources involved in the scheduling plan in the path, compare the call range of each type of resource in the task list, filter the resource list associated with data protection tasks and terminal access control tasks, limit the identification objects to key authentication and permission authentication, and obtain the resource set structure parameters. S4: Based on the resource set structure parameters, determine whether there is an overlap between the task start time periods in the differential paths, analyze whether the resource names called in the involved tasks are repeated, compare the span changes between task dependency levels, integrate time intersection, resource overlap and dependency differences, construct a path conflict expression mechanism, and obtain path conflict combination feature quantity; The task stress intensity indicators include the remaining load of the stage and the density of dependent nodes. The path stability fluctuation degree includes the task scheduling continuity status and interruption frequency statistics. The resource centralization structure parameters include the key resource duplication rate, the degree of aggregation of certified resources, and the proportion of exclusive use of task paths. The path conflict combination characteristics include the start time overlap, resource call overlap, and dependency level similarity.

2. The enterprise multi-project collaborative management method based on data security analysis according to claim 1, characterized in that, The specific steps for obtaining the task stress intensity index are as follows: S111: Based on the data security requirements of enterprise projects, analyze the structural configuration of access control, transmission isolation and log auditing task nodes in the project, determine the resource categories and usage methods associated with each node, calculate the time difference between the actual progress in the task arrangement and the original node plan, and generate node resource offset characteristics. S112: Based on the node resource offset characteristics, determine the integrity status of the structural fields corresponding to each task node, analyze the changing trend between the number of missing fields and the offset characteristics, compare the matching degree between the node field missing level and the resource configuration, filter out nodes with abnormal field distribution, and obtain the field missing offset characteristics. S113: Based on the missing offset features of the field, analyze the adaptability of log audit frequency and transmission isolation frequency in the current task stage, optimize the scheduling classification criteria of tense nodes, and determine the differences in coordination load during node operation to generate a task tension intensity index.

3. The enterprise multi-project collaborative management method based on data security analysis according to claim 1, characterized in that, The specific steps for obtaining the path stability fluctuation level are as follows: S211: Based on the task tension intensity index, analyze the continuous records of task progress status in multiple plans, determine the variation range of progress rhythm in different time periods, calculate the fluctuation distance of each task progress data in the time series and the average progress difference, and obtain the fluctuation range of progress rhythm. S212: Determine the distribution result of the propulsion rhythm fluctuation amplitude, match the cumulative record of interruption events of each node in the task path, calculate the correlation deviation level, compare the trend differences, and obtain the propulsion interruption correlation offset. S213: Based on the aforementioned propulsion interruption associated offset, calculate the mission propulsion state fluctuation, the number of corresponding interruption events and their distribution differences, identify the degree of deviation between the propulsion balance and the interruption mode in the path, and obtain the path stability fluctuation degree.

4. The enterprise multi-project collaborative management method based on data security analysis according to claim 1, characterized in that, The specific steps for obtaining the resource central structure parameters are as follows: S311: Based on the stability fluctuation of the path, identify the resource items involved in the scheduling plan, calculate the number of times the resource is called in the path task, construct the association mapping data between the resource and the path according to the task order and path identifier, and determine the call frequency distribution of the resource in the task sequence to generate the resource call number distribution. S312: Based on the distribution of the number of resource calls, analyze the aggregation trend and fluctuation of resources in the continuous segment of the path, determine whether the distribution range of resources in the task structure is concentrated, establish grouping and classification rules based on the resource repetition between tasks, filter resource tags that meet the classification conditions, and obtain the resource call aggregation structure. S313: Based on the resource call aggregation structure, filter the resources associated with the data protection task and the terminal access control task, calculate the number of calls and corresponding positions in the path task, analyze the distribution deviation and path intersection and repetition, determine whether the distribution is concentrated, and obtain the resource concentration structure parameters.

5. The enterprise multi-project collaborative management method based on data security analysis according to claim 1, characterized in that, The specific steps for obtaining the path conflict combination feature quantity are as follows: S411: Based on the resource set structure parameters, determine whether there is time overlap in the start time of each task in the difference path, analyze the resource call records involved in the overlapping tasks, identify the resource names corresponding to the tasks, determine whether there are resources with the same name among them, and obtain the resource name set of overlapping tasks. S412: Based on the overlapping task resource name set, analyze the repeated call situation, identify the task combination with repeated resource identifiers, filter out resource items with complete matching, and output the duplicate resource comparison content by grouping the duplicate combinations to obtain the resource duplicate combination data; S413: Based on the resource repetition combination data, calculate the ranking difference between the start time and dependency level in the task combination, analyze the start and dependency span formed between them, and obtain the path conflict combination feature quantity.

6. The enterprise multi-project collaborative management method based on data security analysis according to claim 1, characterized in that, The steps also include: S5: Based on the path conflict combination feature quantity, analyze the progress of the project tasks in the current stage, determine the scheduling lag status of key nodes, call the task progress, key node delay status and path conflict relationship for joint comparison, construct project sorting rules according to priority, and obtain the task collaboration sorting level. The task coordination and sorting levels include task priority order, path conflict mitigation indicators, and scheduling priority level labels.

7. The enterprise multi-project collaborative management method based on data security analysis according to claim 6, characterized in that, The specific steps for obtaining the task collaboration ranking level are as follows: S511: Based on the path conflict combination feature quantity, analyze the difference between the task start order and progress in the path overlap area, compare the scheduling lag of task nodes in the cross path, filter the paths with node delay, and obtain the number of node delay paths. S512: Based on the number of delayed paths of the node, analyze the degree of interference between the node and the conflicting path, compare the correspondence between the delayed position of the task node and the degree of progress, determine the task scheduling correlation of the intersection nodes in the critical path, and obtain the task conflict sorting sequence. S513: Based on the task conflict sorting sequence, and according to the sequential relationship between tasks in the sorting structure, determine whether there is a conflict in the collaborative execution logic of the current task set, optimize the sorting structure, and re-output the corresponding level grouping to obtain the task collaborative sorting level.

8. An enterprise multi-project collaborative management system based on data security analysis, characterized in that: The system is used to implement the enterprise multi-project collaborative management method based on data security analysis as described in any one of claims 1-7, and the system includes: The task stress assessment module is based on the data security requirements of enterprise projects. It identifies key nodes involving access control, transmission isolation and log auditing tasks, determines the types of resources used by the nodes, calculates the deviation between the remaining duration of the node's stage and the reference plan, and combines the missing status of the structure fields in the task output to make a joint judgment and generate a task stress intensity index. Based on the task stress intensity index, the path stability analysis module analyzes the fluctuation of task progress status in multiple plans within the project task path, determines the number of interruption event records for each task within the path, calculates the balance of progress status within the path, compares its correlation with interruption situations, and integrates judgment factors to obtain the degree of path stability fluctuation. The resource aggregation degree analysis module calculates the call frequency of key resources involved in the scheduling plan in the path based on the stability fluctuation degree of the path, compares the call range of each type of resource in the task list, filters the resource list associated with data protection tasks and terminal access control tasks, limits the identification objects to key authentication and permission authentication, and obtains the resource aggregation structure parameters. Based on the resource set structure parameters, the path conflict identification module determines whether there is an overlap between the task start times in the different paths, analyzes whether the resource names called in the involved tasks are repeated, compares the span changes between task dependency levels, integrates time intersection, resource overlap and dependency differences, constructs a path conflict expression mechanism, and obtains path conflict combination feature quantity. The collaborative sorting decision module analyzes the progress of project tasks at the current stage based on the path conflict combination feature quantity, determines the scheduling lag status of key nodes, calls the task progress, key node delay status and path conflict relationship for joint comparison, constructs project sorting rules according to priority, and obtains the task collaborative sorting level.

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