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

Abstract

The invention relates to the technical field of multi-project collaboration, in particular to an enterprise multi-project collaboration management method and system based on data security analysis, and the method comprises the following steps: based on data security requirements, identifying key task nodes, evaluating resource composition and execution deviations, forming task tension indexes, and analyzing path propulsion fluctuation and interruption conditions according to the task tension indexes; identifying a calling aggregation structure of a key resource; judging a task time sequence, resource overlapping and dependency difference between paths; generating a conflict characteristic quantity; constructing a sorting rule by synthesizing a propelling state and a conflict relationship; according to the method, data security requirements are included in scheduling judgment, a cross-project state recognition mechanism is established, the task tension degree is evaluated in combination with task output field integrity and scheduling stage offset, so that structure sensitive tasks are captured, propulsion fluctuation and interruption frequency are superposed in a path, the stability of a task chain is measured, and an uneven scheduling area is positioned.

Description

Technical Field

[0001] The present invention relates to the field of multi-project collaboration technology, and in particular to an enterprise multi-project collaborative management method and system based on data security analysis. Background Art

[0002] Multi-project collaboration mainly involves the theories, methods and tools for simultaneously managing and executing multiple projects 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 progress control, avoid resource conflicts and progress delays, and improve the management efficiency and success rate of the entire project group. Multi-project collaborative management requires not only 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 method that combines data security technology and multi-project management strategies. It aims to achieve information sharing and collaborative advancement among multiple projects while ensuring the security of key enterprise data. This method can be used by enterprises to identify and analyze risks of sensitive data when operating multiple projects in parallel, ensuring the security and compliance of data when flowing between multiple projects.

[0004] Existing technologies rely on static resource settings and phased scheduling plans, lack real-time feedback on the multi-dimensional behavioral status of tasks, make it difficult to reveal dynamic conflict characteristics, task output structure is not included in the evaluation criteria, information transmission integrity is lost, path execution performance is only based on single advancement information, and continuous fluctuation risks cannot be tracked, 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, resulting in resource accumulation and scheduling blockages. Conflict judgment is only based on the static dependency relationship between tasks, and cannot handle actual startup intersections and resource reuse. The priority sorting indicators are single, and there is a lack of collaborative analysis of path competition and delay status, which affects the rationality of task coordination sequence. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose an enterprise multi-project collaborative management method and system based on data security analysis.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution: an enterprise multi-project collaborative management method 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 structure fields, and generate task intensity indicators;

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

[0009] S3: Based on the degree of fluctuation in path stability, count the call frequencies of key resources, compare the distribution of resources in tasks, select resources related to data protection and access control, and obtain resource concentration structure parameters;

[0010] S4: Based on the resource concentration structure parameters, determine whether the task starts between paths overlap, analyze the resource name overlap and dependency level changes, integrate the conflict dimension information, and obtain the path conflict combination feature quantity.

[0011] The improvements of the present invention are that the task intensity index includes the stage remaining load and the dependent node density, the path stability fluctuation degree includes the task scheduling continuity status and the interruption frequency statistical item, the resource concentration structure parameters include the key resource repetition rate, the authentication resource aggregation degree, and the task path exclusive ratio, and the path conflict combination characteristic quantity includes the startup time overlap, resource call overlap, and the dependency level similarity.

[0012] The present invention is improved in that the steps for obtaining the task 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 audit task nodes in the project, determine the resource category and usage method associated with each node, calculate the time difference between the actual progress of the task schedule 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 structure fields corresponding to each task node, analyze the changing trend between the number of field missing 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 field missing offset characteristics, analyze the adaptability of the log audit frequency and the transmission isolation frequency in the current task stage, optimize the scheduling classification standard of the tense node, and judge the difference in the degree of coordination load in the node operation to generate the task tension intensity index.

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

[0017] S211: Based on the task intensity index, analyze the continuous records of task progress status in multiple plans, determine the variation range of the 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 progress rhythm fluctuation range;

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

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

[0020] The present invention is improved in that the step of obtaining the structural parameters of the resource concentration is specifically as follows:

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

[0022] S312: Based on the resource call quantity distribution, analyze the aggregation trend and fluctuation degree of resources in the continuous path segment, determine whether the distribution range of resources in the task structure is concentrated, establish grouping and classification rules based on the repetitiveness of resources 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, screen 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 cross-repetition, determine whether the distribution concentration characteristics are met, and obtain the resource concentration structure parameters.

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

[0025] S411: Based on the structural parameters in the resource set, determine whether there is time overlap between the start periods of the tasks 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, and obtain a set of overlapping task resource names;

[0026] S412: Based on the overlapping task resource name set, analyzing the repeated call situation, identifying the task combination with repeated resource identifiers, screening the resource items with complete matches, and outputting the repeated resource comparison content by repeated combination group to obtain resource repeated combination data;

[0027] S413: Calculate the order difference between the start time and the dependency level in the task combination according to the resource duplication combination data, analyze the start and dependency spans 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 characteristic quantity, analyze the task progress and key node delay status, establish a sorting rule based on the path conflict intensity, complete the priority determination between tasks, and obtain the task collaboration sorting level;

[0030] The task collaborative sorting level includes task priority order, path conflict relief index, and scheduling priority level label.

[0031] The present invention is improved in that the steps for obtaining the task collaborative ranking level are specifically as follows:

[0032] S511: Based on the path conflict combination characteristic, analyzing the differences between the task start order and progress in the path overlap area, comparing the scheduling delay conditions of the task nodes in the intersecting paths, screening the paths with node delays, and obtaining the number of node delay paths;

[0033] S512: Based on the number of node delay paths, analyzing the interference degree between the node and the conflicting path, comparing the correspondence between the delayed position of the task node and the advancement degree, determining the task scheduling relevance of the intersection nodes in the critical path, and obtaining the task conflict sorting sequence;

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

[0035] An enterprise multi-project collaborative management system based on data security analysis, the system comprising:

[0036] The task intensity assessment module, based on the data security requirements of enterprise projects, locates key nodes involved in access control, transmission isolation, and log auditing tasks, determines the resource types used by the nodes, calculates the deviation between the remaining duration of the node 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 intensity index.

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

[0038] The resource aggregation analysis module calculates the call frequency of key resources involved in the scheduling plan in the path based on the degree of fluctuation in the path stability, compares the call range of each type of resource in the task list, screens 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 concentration structure parameters;

[0039] The path conflict identification module determines whether there is overlap between task start-up periods in the difference paths based on the resource concentration structural parameters, analyzes whether the resource names called in the tasks involved are repeated, compares the span changes between task dependency levels, integrates time intersection, resource overlap and dependency difference, constructs a path conflict expression mechanism, and obtains a path conflict combination feature quantity;

[0040] The collaborative sorting decision module analyzes the progress of the project tasks in the current stage based on the path conflict combination characteristic quantity, determines the scheduling lag status of key nodes, calls for a joint comparison of the task progress degree, key node delay status and path conflict relationship, 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:

[0042] In the present invention, by incorporating data security requirements into scheduling judgments, a cross-project status identification mechanism is established, and the integrity of task output fields and scheduling stage offsets are combined to evaluate task intensity in order to capture structurally sensitive tasks, superimpose advancement fluctuations and interruption frequencies in the path, measure task chain stability, and locate uneven scheduling areas. The resource layer establishes overlap judgment based on the concentration of key and authority components to characterize resource coupling trends. The paths are integrated with the overlap of startup periods, duplication of resource names, and density of dependency levels to obtain conflict identification feature groups. The sorting is based on advancement status, node lag, and path conflict intensity to complete the expression of task collaborative sorting levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of the main steps of the present invention;

[0044] Figure 2 This is a flow chart for obtaining the task intensity index in the present invention;

[0045] Figure 3 This is a flow chart for obtaining the degree of path stability fluctuation in the present invention;

[0046] Figure 4 This is a flow chart for obtaining the resource centralized structural parameters in the present invention;

[0047] Figure 5 This is a flow chart for obtaining the path conflict combination feature quantity in the present invention;

[0048] Figure 6 This is a flowchart for obtaining the task collaborative sorting level in the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present 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 only used to explain the present invention and are not intended to limit the present invention.

[0050] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0051] Example

[0052] See also Figure 1 The present invention provides a technical solution: a method for collaborative management of multiple projects in an enterprise based on data security analysis, comprising the following steps:

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

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

[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 concentration structure parameters;

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

[0057] S5: Based on the combined characteristic quantities of path conflicts, the progress of the current project tasks is analyzed, the scheduling lag status of key nodes is determined, the task progress degree, key node delay status and path conflict relationship are compared, and project sorting rules are constructed according to priority to obtain the task collaborative sorting level.

[0058] The task 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 concentration structure parameters include the key resource repetition rate, the authentication resource aggregation degree, and the task path exclusive ratio. The path conflict combination characteristics include the start time overlap, the resource call overlap, and the dependency level similarity. The task collaborative sorting level includes the task priority order, the path conflict relief indicator, and the scheduling priority level label.

[0059] The remaining duration refers to the length of time between the current time point and the planned completion point of the current task node under the actual scheduling status, reflecting the urgency of the task. The reference plan refers to the standard task progress schedule set after the project is established or updated in the current phase, which is used as a comparison benchmark for evaluating execution deviations at each node. The project task path refers to a series of related task nodes that need to be completed to form the project goal. It has clear dependencies and execution order and is the logical execution chain in project scheduling. The progress status refers to the degree of completion of the task within the specific scheduling cycle, such as progressing as planned, delayed, suspended, or completed ahead of schedule. The balance degree refers to whether the progress rhythm of each task in the path is relatively consistent, and whether some tasks are seriously lagging or advanced, disrupting the overall rhythm. Key resources are irreplaceable or highly dependent functional components when performing data security-related tasks, such as authentication interfaces and permission control modules. Call scope refers to the extent to which a resource is used by multiple tasks, specifically whether it is centrally used or distributed. Data protection tasks are project tasks responsible for data encryption, access isolation, and transmission hardening, and are core components for ensuring information security. Terminal access control tasks are tasks responsible for identifying user terminal permissions, determining connection legitimacy, and implementing access blocking or authentication verification. Identification objects, in this context, refer to the names of resource types identified by the system for resource classification, judgment, and screening, such as "key authentication" and "permission authentication." Task start period refers to the time range within which each task is scheduled to start, used to determine whether different tasks overlap in their execution timelines. Overlap refers to the overlap of the start time periods of two or more tasks, indicating a concurrency conflict on resources or paths. Task dependency level indicates the degree of coupling between tasks; a higher dependency level indicates a task's greater reliance on the completion of its predecessor. Path conflict expression mechanism refers to an assessment structure constructed by integrating comparative relationships among dimensions such as time, resources, and dependencies to identify the severity of conflicts between paths. Scheduling lag refers to the time delay caused by the actual progress of a task compared to the standard plan, and is an important dimension affecting priority sorting; project sorting rules refer to the project execution order judgment mechanism constructed based on factors such as path conflicts, progress status and node risks in multi-project scheduling.

[0060] See also Figure 2 ,The specific steps for obtaining the task intensity index are:

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

[0062] Based on the control requirements for data security in enterprise projects, resource structure disassembly operations are performed on the nodes that perform access control, transmission isolation and log auditing in multiple sub-projects, and the scheduling status of each type of node task in different project paths is extracted. The resource type called by the current configuration of each node is read in turn. For example, the access control task node calls the permission authentication resource, and the log audit task node calls the log collection service or the audit record storage module. After obtaining the resource type, the node start and end time in the task schedule is compared with the actual execution time record. The time interval for task completion is used as the actual progress, and then compared with the planned node completion time to obtain the time difference, so as to judge whether the task has been postponed or advanced. For example, an access control task is planned to be completed on May 10, but the actual completion time is May 13. Day, the offset is 3 days. Then search the resources called by the node and find that they have been called 12 times in the past 5 days, with an average of 2.4 times per day, while the planned average is 1.8 times per day, indicating that the call frequency has increased. Combined with the offset time and resource frequency changes, an offset association relationship between the node and resource usage is established. It is recorded whether the resource type corresponding to the node is a key resource, and whether it is a component reused by multiple tasks in the system, such as permission authentication or encryption interface. If the resource type appears repeatedly in multiple tasks and the call frequency increases by more than the historical average, the configuration and usage status of the resource is considered abnormal. The resource abnormal status and time offset are then jointly identified on the node to form a node resource offset feature marked with resource category, 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 structure fields corresponding to each task node, analyze the changing trend between the number of field missing 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;

[0064] In the nodes that have been marked as having node resource offset characteristics, further read the structural field configuration table of the task node, judge the non-empty value of each field item by item, record whether the field is missing, compare the number of missing fields with the total number of fields, and calculate the overall missing percentage of the field. For example, a log audit task node field has 20 items in total, but only 14 items are actually filled in with data, 6 items are missing, and the missing percentage is 30%. Record the field missing situation of the node, and then sort out the resource offset situation of the node. For example, the frequency of its resource call increases or decreases abnormally in scheduling, and there is a large difference with the average plan. At this time, it is necessary to compare the trend relationship between the degree of field missing and the change of resource offset, arrange the nodes according to the order of the nodes in the scheduling path, and compare whether the degree of missing of two or more nodes before and after increases or decreases synchronously. If there are multiple consecutive nodes, When the number of missing fields and the frequency of resource calls increase simultaneously, it is determined that the field missingness of this node is affected by resource configuration imbalance. For example, compared with its previous node B1, the number of missing fields of node B2 increases from 4 to 6, and the missing rate increases from 20% to 30%. At the same time, its resource call frequency increases from 1.5 times per day to 2.4 times, indicating that the structural integrity of this node is affected by resource scheduling fluctuations. Nodes with high field missing rates and abnormal resource frequencies are then screened as field abnormality distribution nodes. Based on task categories, such as access control, transmission isolation, or log audit, the number and proportion of abnormal nodes are counted separately. For example, the log audit task contains a total of 10 nodes, of which 4 are marked as field abnormality nodes. The proportion of field missing offset features of this type of task is 40%, and node B2 is identified as a field missing offset feature node.

[0065] S113: Based on the field missing offset characteristics, analyze the adaptability of the log audit frequency and the transmission isolation frequency in the current task phase, optimize the scheduling classification standard of the stress nodes, and determine the difference in the degree of coordination load in the node operation to generate the task stress intensity index;

[0066] Extract the execution frequency of its log audit and the number of records of transmission isolation operations, set a time window of 5 days, and count the number of log writes and data isolation actions executed by the node during this time period. For example, the number of log writes for node C3 during this time period is 22, and the number of transmission isolation records is 3 times. Compare them with the benchmark values ​​of 30 logs and 5 transmissions respectively. If both are lower than the set reference range, it is judged that the execution frequency of the node in the security task operation is too low. Combined with its field missing rate exceeding 25% and resource offset, the priority is adjusted. The node with the original priority label of "normal" will be re-evaluated as "low priority". At the same time, the node is retrieved. The execution time data of the node and the number of resources used are combined to calculate the resource load per unit time. If its load is higher than the average load value of the standard node statistically counted by the system and there is an increase of more than a certain degree, it is determined to be a high coordination load node. For example, node C3 processes three types of resources on average every day during the scheduling cycle, and the task takes 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 value by more than 0.5 and meets the load difference standard. Combined with factors such as its low execution frequency and high field missingness, this node is marked as a scheduling node with task intensity characteristics.

[0067] See also Figure 3 , the specific steps for obtaining the degree of path stability fluctuation are:

[0068] S211: Based on the task intensity index, analyze the continuous records of task progress status in multiple plans, determine the variation range of the 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 progress rhythm fluctuation range;

[0069] Read the progress status records of the corresponding task nodes in multiple plan versions in different time periods, and correspond the task start time, completion time, status labels such as "on schedule", "delayed", "ahead of schedule" and "paused" recorded in each plan to the degree of progress of the task in that stage, and convert them into progress identification values. Arrange them in chronological order to generate a progress status sequence, and then perform numerical difference processing between adjacent time periods on the progress status sequence to reflect the fluctuation range of the task progress rhythm. For example, the progress status of task D1 in plan versions 1, 2, and 3 are marked as 2, 3, and 1 respectively. The difference operation is performed in chronological order to obtain +1 and -2, that is, the progress status fluctuation has an increase and a decrease. Then the absolute value of the fluctuation amplitude is accumulated to obtain the total amplitude of the task progress fluctuation of 3. If the total amplitude is greater than the set stable baseline value of 5, the task is pushed The advancement rhythm is relatively stable, otherwise it is considered an unstable advancement rhythm. Then, the fluctuation distance of all tasks in each time period is counted, and the total value and frequency of fluctuations are recorded. The advancement change value of each task is further compared with the average value of the overall advancement rhythm. For example, if the average advancement change value is 1.2 and the change of task D1 is 1.5, then its deviation is 0.3, indicating that its advancement rhythm offset is slightly higher than the average. Then, it is judged whether the fluctuation of each task is concentrated in a specific stage or evenly distributed. By counting the changing trend of the advancement status of the same node in two or more consecutive versions, it is judged whether there is a sudden change in the task advancement change. For example, the advancement values ​​of a task in three versions are 3, 3, and 1 respectively, then it is judged 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 its fluctuation behavior occurs is recorded to obtain the fluctuation amplitude of the advancement rhythm.

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

[0071] Extract all nodes in each task path, match the nodes in the path with their advancement fluctuation amplitude one by one, and at the same time retrieve whether each node has any interruption events during the execution phase, such as scheduling suspension, task stagnation, approval process freeze, etc., archive all interruption behaviors in the form of an event list, and count the cumulative number of interruptions corresponding to each node. For example, if node E1 has two interruptions in three plan versions, the cumulative number is 2. Compare the number of interruptions and the advancement fluctuation amplitude side by side to analyze whether there is a significant synchronous growth trend. By arranging the comparison matrix formed by the number of interruptions and advancement rhythm fluctuation amplitude of multiple nodes, the proportion of nodes with the same direction of change is counted. If more than 40% of the nodes have both high fluctuation amplitude and high interruption frequency, the path is marked as a path with strong interruption correlation. Then map the density of the interruption behavior of each node to the deviation level, and define the deviation level There are three categories: low deviation (interruption number ≤ 1, fluctuation amplitude ≤ 1.0), medium deviation (interruption number 2, fluctuation amplitude 1.0 to 2.0) and high deviation (interruption number ≥ 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 deviation level distribution on the entire path is cumulatively counted. If the proportion of high deviation nodes in the total number of path nodes exceeds 30%, the path is classified as an unstable advancement path, and the trend change value of the interruption and fluctuation matching curve is recorded. The distribution sequence of the interruption event on the time axis is time-aligned with the fluctuation sequence to form a rhythm interruption curve group. The trend peaks of the two are compared to see whether they occur at the same position. If the consistency of the change trend of the two is greater than the set correlation ratio of 0.6, the path is marked as a high trend consistency path, and the advancement interruption correlation offset is obtained.

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

[0073]

[0074] Identify the degree of deviation between the advancement balance and the interruption pattern in the path and obtain the path stability fluctuation degree F s , where B i It represents the fluctuation of the progress status of the i-th task in multiple plans, reflecting the discrete degree of the progress of the task, P i Indicates the number of interruption events recorded by the i-th task, which is the frequency of interruption during the task advancement. i Indicates the degree of difference between the progress state and the interruption event of the i-th task, representing the corresponding offset between the two data, M iIt represents the sequential number of the i-th task in the task path, which is numbered sequentially from the starting task of the path and is used to construct the structural parameters related to the position. n represents the total number of tasks included in the current task path;

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

[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 is the degree of path stability fluctuation, indicating that under the current path, there is a moderate degree of deviation between the advancement state and the interruption event. It is suitable for the basic judgment parameter of path adjustment and task control. The formula is introduced by the path position number Mi and the propulsion rhythm offset term Δ i By performing joint normalization calculations, a multi-dimensional evaluation mechanism correlating propulsion status with structural position was constructed, avoiding local misjudgments caused by a single indicator and improving the responsiveness and coverage of the overall stability measurement.

[0088] See also Figure 4 ,The specific steps for obtaining the structure parameters of the resource concentration are:

[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, build the association mapping data between the resource and the path based on the task sequence and path identifier, and determine the call frequency distribution of the resource in the task sequence to generate the resource call quantity distribution;

[0090] According to the statistical results of the degree of fluctuation of path stability, the scheduling plan document corresponding to each path task node is read, the resource items recorded in the document are extracted one by one, mapped and bound according to the task path number, and the specific position and calling node of each resource in the task path are recorded. Then the total number of times each resource is called is counted, and a mapping data table of task sequence and resources is established. For example, resource R1 is called by tasks T1, T3, and T5 in path P1, then the number of calls of R1 is 3. The tasks T1 to T5 are numbered from 1 to 5, and then R1 is called at positions 1, 3, and 5 of tasks. After completing the same record for all resources, a distribution table of resource call frequencies in the task path is constructed, and the call distribution range and concentration of each resource in the entire path are calculated. For example, R2 is only called once in task T2. R3 is called four times consecutively in tasks T1 to T6, while R4 is called twice alternately between T1 and T6. The corresponding call frequency differences clearly reflect the centralized and discrete distribution. Based on the position label of the resource in the task sequence, the call position gap is counted. For example, the position number difference of adjacent tasks of continuously called resources is 1. If 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 centralized call resource, otherwise it is a discrete resource. The ratio of the number of tasks called by each type of resource to the path length is used as the reference value of the frequency coefficient. The frequency boundary intervals are set as low frequency (the number of tasks accounts for less than 20%), medium frequency (the number of tasks accounts for between 20% and 60%), and high frequency (the number of tasks accounts for more than 60%). Based on the joint characteristics of resource distribution position and task sequence, the resource call quantity distribution is generated.

[0091] S312: Based on the distribution of resource call quantities, analyze the aggregation trend and fluctuation of resources within the continuous path segments to determine whether the distribution range of resources in the task structure is concentrated. Then, establish grouping and classification rules based on the repetitiveness of resources between tasks, select resource tags that meet the classification criteria, and obtain the resource call aggregation structure.

[0092] Select resource records with continuous or adjacent call positions in each task path and mark them as continuous segment resources. Then, draw a resource call heat curve according to the specific order in which the resources appear in the task path, monitor whether the resource concentrated call area forms a peak segment, and judge whether the resources are highly aggregated in a certain task stage by calculating the span of the peak segment. For example, in tasks T3 to T7, resources R5, R6, and R7 are repeatedly called more than three times, and the call interval between resources does not exceed two task nodes. It is judged that there is a resource aggregation trend in the path. Then compare the number of tasks that appear in each type of resource label with the difference in adjacent task numbers. If the proportion of the number of tasks where the resource appears in the total number of tasks in the path is greater than 50%, and the interval between task numbers is no more than 2, it is classified as a concentrated distributed resource. If the resource is distributed at the first and last task nodes of the path, it is classified as a concentrated distributed resource. If a resource does not appear in the same order and does not appear continuously in the middle, it is judged as a discretely distributed resource. All resources are then grouped according to the calling rules, and resources with the same calling distribution pattern are placed in the same classification group. A resource grouping and classification table is established, and resource tags that meet the following classification conditions are filtered from the classification results: the resource is called continuously by three or more tasks, the difference in 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 and encryption interface resources. Such resources are recorded as key aggregation structure resources in the path. For example, resource R9 in path P2 is called in T2, T3, T4, and T5, accounting for 66.7% of the tasks, and is a permission control resource. R9 is marked as a label item in the resource call aggregation structure to obtain the resource call aggregation structure.

[0093] 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 their distribution deviation and path intersection and duplication, and use the formula:

[0094]

[0095] Determine whether the distribution concentration characteristics are met and obtain the resource concentration structure parameters, where RS kj It represents the cross-concentration structural parameter of resource j in path k, which is used to reflect the call concentration degree and path cross-distribution of the resource in the multi-path task distribution. kj Indicates the number of calls of resource j in path k, that is, the total number of times the resource is called in the corresponding path task, CS kj Indicates the classification identification 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, and a value of 0 indicates that it is not related. LS kjIndicates the calling position number of resource j in path k, reflecting the relative sequence position of the resource in the path task. It represents the average value of the calling position number of resource j in all paths, which is used to evaluate the overall distribution deviation of the resource in different path tasks. j represents the number of segments in which resource j appears in the task path, that is, the number of segments covered by the resource in different paths, μS j It represents the mean number of segment numbers in all paths of resource j, and is used to compare whether the path distribution of the resource is uniform;

[0096] Filter the resource categories marked as associated with data protection tasks and terminal access control tasks, extract the resource numbered R7, and identify the four task paths in the path set, namely P1, P2, P3, and P4. Calculate the number of times the resource is called in each path. For example, the number of calls in path P1 is 9, in path P2 is 12, in path P3 is 15, and in path P4 is 11. Record the 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 square of the offset between the call position number in each path and the average value is calculated to obtain the square of the position offset within the path, which are:

[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] At the same time, 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 segments, and the average number of segment numbers is also 3 segments. The calculated path segment coverage difference items are:

[0104] (DS R7 -μS R7 ) 2 =(3-3) 2 =0;

[0105] This resource is associated with data protection or access control tasks, so the classification identifier is CS.kj Take 1 and substitute it into the formula to calculate the path-by-path cross-lumped structural parameters:

[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 maximum aggregation characteristic in path P3, with a calculated value of 12, while P2 and P4 are lower, at 3.692 and 3.385, respectively. This result shows that resource R7 is called and concentrated in path P3, with a position distribution close to the average position, and the path segment coverage is consistent with the overall distribution, with a strong cross-concentration characteristic. Based on this, it can be determined that R7 forms a resource concentration structure parameter in path P3. By introducing two influencing factors, the square of the position offset and the path segment distribution offset, the formula adjusts the original call quantity, so that the result can better reflect the aggregation and cross-coverage of the actual resource distribution, and effectively distinguish the differences in call structure in different paths.

[0115] See also Figure 5 ,The specific steps for obtaining the path conflict combination feature are:

[0116] S411: Based on the structural parameters in the resource set, determine whether there is time overlap between the start periods of the tasks 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, and obtain the overlapping task resource name set;

[0117] Based on the key resource repetition rate and the certified resource aggregation degree, the difference path number and its task list are extracted from all project paths. The planned start time period of each task is read. A horizontal comparison is performed according to the project and number to which the task path belongs. The task start time period is mapped to a unified time axis with a date segment mark using a calendar alignment method. 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, the start time of task A1 is from May 3 to May 6, and the start time of task B4 is from May 5 to May 7. The intersection is from May 5 to May 6, a total of 2 days, which is marked as an overlapping relationship. Then, the resource scheduling of each task is read from the overlapping task set. Use the records to extract the name and type information of the called resources and generate a resource call list table. Then, perform a name comparison operation on the resource lists of each pair of overlapping tasks. If the same resource name exists in the two tasks, it will be recorded as a consistent name item. For example, if task A1 calls "Authorization Interface 001" and task B4 also calls "Authorization Interface 001", the resource name is considered consistent. The number of repetitions is increased by one, and the same comparison operation is performed on all overlapping tasks. Finally, a resource name intersection list of each pair of tasks is output. If there are two or more identical resource names between two tasks, it is considered a task group with a high degree of resource overlap, and such task combinations are identified as overlapping task resource name sets.

[0118] S412: Based on the overlapping task resource name set, analyze the repeated call situation, identify the task combinations with repeated resource identifiers, select the resource items with complete matches, group the repeated combinations and output the repeated resource comparison content to obtain the resource repeated combination data;

[0119] Count the frequency of occurrence of resource names in task combinations, compare the numbers of duplicate resource identifiers in each pair of task combinations, and identify resource entries with the same name and completely consistent resource identifier codes. For example, if both task C2 and task D3 call resource "RKEY_20240501", then the resource identifier is judged to be a completely consistent resource. Then, select task pairs with two or more consistent resource identifiers from all task combinations as analysis targets, record the task pair number, duplicate resource list, and resource location index in the corresponding task, and establish a resource duplicate combination mapping table for task pairs with completely consistent resources. The number is used as the primary key to output the detailed content and quantity of the corresponding duplicate resources in each pair of tasks. For example, there are three duplicate resources in tasks E1 and F2, namely "KEYMOD_1", "LOGTRACK_3", and "ACCESSAUTH_8". Then the resource duplicate combination data items of task pair "E1-F2" are the above three resources. Then all duplicate task combinations are sorted in descending order according to the 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 control output item to obtain the resource duplicate combination data.

[0120] S413: Based on the resource duplication combination data, calculate the order difference between the start time and the dependency level in the task combination, analyze the start and dependency span formed between them, and use the formula:

[0121]

[0122] Get the path conflict combination characteristic CR zx , used to measure the joint conflict between task z and task x in terms of startup time and dependency level, where Indicates the starting parameter of the start period of task z, which is the initial time point when task z is scheduled in the difference path. Indicates the starting parameter of the start period of task x, which is the initial time point when task x is scheduled in the difference path. RL z Represents the dependency level parameter of task z, which is used to indicate the relative dependency level of the task in the task chain. x The dependency level parameter of task x is used to indicate the relative dependency level of the task in the task chain;

[0123] First, extract the start time of the task z and task x in the task scheduling plan, and record them as and Combined with the hierarchical number in the task dependency network structure, the dependency level of task z and task x is extracted and expressed as RL z With RL x , where the start time comes from the start time field in the scheduling table, and the dependency level is the position level of the task in the logical task chain, which is usually assigned as an increasing positive integer from top to bottom. For example, if task z is scheduled at the 2nd level and task x is scheduled at the 4th level, then the corresponding RL z =2, RL x =4, if the scheduling starting point of task z is 3.5 hours and task x is 6.0 hours, then the corresponding The Euclidean distance is used to calculate the joint sorting offset of the task combination in the time and dependency dimensions. Substituting the above parameters into the result is:

[0124]

[0125] The result is 3.20, indicating that there is a compound offset in the task combination in terms of both time and dependency. The calculated path conflict combination characteristic quantity is the quantitative measurement basis for task pairs. This result is used to describe the combined conflict intensity of scheduling and dependency between tasks with overlapping resources, and can subsequently be used for comparative analysis between task paths. By simultaneously introducing the scheduling time starting point and dependency hierarchy, the formula avoids misjudgment of conflict measurement caused by single time or logical structure analysis, thereby improving the ability to analyze conflict characteristics in complex task networks.

[0126] See also Figure 6 ,The specific steps for obtaining the task collaborative ranking level are:

[0127] S511: Based on the combined characteristic quantity of path conflicts, the differences between the task start order and progress in the path overlap area are analyzed, the scheduling delay conditions of the task nodes in the intersecting paths are compared, and the paths with node delays are screened to 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, group the tasks by the paths they belong to, and map the start times of all tasks along the time axis to form a time overlap matrix. Compare whether the start times of tasks in the two paths overlap in the same time period. For example, tasks T1 and T6 are located in paths P1 and P2 respectively. The start time of T1 is from June 1 to June 3, and the start time of T6 is from June 2 to June 4. The overlap is 2 days, which is marked as a path overlap section. Then arrange the path task list in order of task number, extract the actual start time of each task in the scheduling log and compare it with the planned time to determine whether there is a delay. For example, task T6 is scheduled to start on June 2, but The task was actually started on June 5th, a delay of three days. This was recorded as a delayed task, and the path to which it belonged was marked as a node delayed path. The number of delayed paths in all paths was then counted. The judgment criterion was set as follows: a task start time delay of more than two days was considered a delayed node. If two or more task nodes within a path met this condition, the entire path was recorded as a path with node delays. The number of paths that met this condition within each path group was then counted. For example, in the path group P1-P2-P3, P2 and P3 each had two delayed nodes, while P1 had no delayed nodes. Therefore, the number of delayed paths was two. The statistical values ​​of the paths that met this condition in all path groups were summed to obtain the number of node delayed paths.

[0129] S512: Based on the number of node delay paths, analyze the degree of interference with the conflicting paths, compare the correspondence between the delayed position of the task node and the advancement degree, determine the task scheduling relevance of the cross nodes in the critical path, and obtain the task conflict sorting sequence;

[0130] The numbers and start delay days of all intersecting task nodes in the path and its overlapping paths are read. The position of the intersecting nodes in the scheduling sequence is compared with the completion progress percentage in the task progress record. The task progress degree is calculated and the correlation between the delayed node and the progress delay is determined. If a node is delayed by 3 days and the actual progress is only 40%, which is lower than the average progress standard of 70%, it is considered a strongly correlated node. The number of occurrences of such nodes in the critical path and their distribution range are then counted. For example, if the intersecting node T9 in path P4 is delayed and has low progress, and a similar node T10 appears in path P5, the task interference between paths P4 and P5 is recorded, and the corresponding tasks are combined into task pairs with conflict interference relationships. The task pairs are then sorted based on the sum of the scheduling delay days and the progress deviation amplitude. The larger the value, the higher the task conflict degree. For example, if task T11 is delayed by 4 days and has only 30% progress, the conflict value is 4+40=44, and task T12 is delayed by 2 days and has 50% progress, the conflict value is 2+20=22. The former is ranked higher, and all tasks are sorted in descending order according to this conflict value.

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

[0132] The task dependency fields and collaborative execution tags between adjacent tasks in the sorting structure are read to determine whether the two tasks overlap in resource calls, path branches, or structure fields. For example, tasks T15 and T16 both depend on the resource "ENCODER_005" and there is a duplicate entry "Transmission Authentication Segment" in the structure field. Path numbers P6 and P7 are also present. If such overlap exists, an execution conflict is determined. The task scheduling structure is then analyzed based on the priority and dependency levels of the tasks in the sorting structure to determine whether it conforms to the sequential order logic. If a downstream task is ranked higher than an upstream task and there is a resource dependency, the sorting structure is deemed unreasonable and requires reordering. The order of the associated tasks is reordered to meet the dependency-first, execution-later logic. Priority levels are assigned in the conflict sorting process based on the task conflict value: tasks with a conflict value greater than or equal to 40 are considered level 1, tasks with a conflict value between 20 and 39 are considered level 2, and tasks with a conflict value less than 20 are considered level 3. The task numbers and their corresponding levels are then re-output to determine the task collaborative sorting level.

[0133] An enterprise multi-project collaborative management system based on data security analysis, including:

[0134] The task intensity assessment module, based on the data security requirements of enterprise projects, locates key nodes involved in access control, transmission isolation, and log auditing tasks, determines the resource types used by the nodes, calculates the deviation between the remaining duration of the node 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 intensity index.

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

[0136] The resource aggregation 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, screens 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 concentration structure parameters;

[0137] The path conflict identification module determines whether there is overlap between task start-up periods in different paths based on resource concentration structural parameters, analyzes whether resource names called in the tasks involved 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 the path conflict combination feature quantity.

[0138] The collaborative sorting decision module analyzes the progress of the current project tasks based on the combined characteristic quantities of path conflicts, determines the scheduling lag status of key nodes, and performs a joint comparison of the task progress degree, key node delay status, and path conflict relationship. It constructs project sorting rules according to priority and obtains the task collaborative sorting level.

[0139] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An enterprise multi-project collaborative management method based on data security analysis, characterized by: The following steps are involved: 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 structure fields, and generate task intensity indicators; S2: Based on the task intensity index, analyze the task progress fluctuations and interruption records in the path, calculate the equilibrium state of task progress, integrate the interruption frequency and path rhythm differences, and obtain the path stability fluctuation degree; S3: Based on the degree of fluctuation in path stability, count the call frequencies of key resources, compare the distribution of resources in tasks, select resources related to data protection and access control, and obtain resource concentration structure parameters; S4: Based on the resource concentration structure parameters, determine whether the task starts between paths overlap, analyze the resource name overlap and dependency level changes, integrate the conflict dimension information, and obtain the path conflict combination feature quantity.

2. The enterprise multi-project collaborative management method based on data security analysis according to claim 1 is characterized in that: The task intensity index includes 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 concentration structure parameters include the key resource repetition rate, the authentication resource aggregation degree, and the task path exclusive ratio. The path conflict combination feature includes the startup time overlap, resource call overlap, and dependency level similarity.

3. The enterprise multi-project collaborative management method based on data security analysis according to claim 1 is characterized in that: The steps for obtaining the task 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 audit task nodes in the project, determine the resource category and usage method associated with each node, calculate the time difference between the actual progress of the task schedule 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 structure fields corresponding to each task node, analyze the changing trend between the number of field missing 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 field missing offset characteristics, analyze the adaptability of the log audit frequency and the transmission isolation frequency in the current task stage, optimize the scheduling classification standard of the tense node, and judge the difference in the degree of coordination load in the node operation to generate the task tension intensity index.

4. The enterprise multi-project collaborative management method based on data security analysis according to claim 1 is characterized in that: The steps for obtaining the degree of path stability fluctuation are specifically as follows: S211: Based on the task intensity index, analyze the continuous records of task progress status in multiple plans, determine the variation range of the 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 progress rhythm fluctuation range; S212: Determine the distribution result of the advancement rhythm fluctuation amplitude, match the cumulative records of interruption events of each node in the task path, calculate the correlation deviation level, and compare the trend differences to obtain the advancement interruption correlation offset; S213: Based on the advancement interruption associated offset, calculate the task advancement state fluctuation, the number of corresponding interruption events and their distribution differences, identify the deviation degree between the advancement balance and the interruption pattern in the path, and obtain the path stability fluctuation degree.

5. The enterprise multi-project collaborative management method based on data security analysis according to claim 1 is characterized in that: The steps for obtaining the structure parameters of the resource set are specifically as follows: S311: Based on the degree of fluctuation in path stability, identify resource items involved in the scheduling plan, calculate the number of times the resource is called in the path task, construct association mapping data between the resource and the path based on the task sequence and path identifier, and determine the call frequency distribution of the resource in the task sequence to generate a resource call quantity distribution; S312: Based on the resource call quantity distribution, analyze the aggregation trend and fluctuation degree of resources in the continuous path segment, determine whether the distribution range of resources in the task structure is concentrated, establish grouping and classification rules based on the repetitiveness of resources 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, screen 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 cross-repetition, determine whether the distribution concentration characteristics are met, and obtain the resource concentration structure parameters.

6. The enterprise multi-project collaborative management method based on data security analysis according to claim 1 is characterized in that: The steps for obtaining the path conflict combination feature are specifically as follows: S411: Based on the structural parameters in the resource set, determine whether there is time overlap between the start periods of the tasks 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, and obtain a set of overlapping task resource names; S412: Based on the overlapping task resource name set, analyzing the repeated call situation, identifying the task combination with repeated resource identifiers, screening the resource items with complete matches, and outputting the repeated resource comparison content by repeated combination group to obtain resource repeated combination data; S413: Calculate the order difference between the start time and the dependency level in the task combination according to the resource duplication combination data, analyze the start and dependency spans formed between them, and obtain the path conflict combination feature quantity.

7. The enterprise multi-project collaborative management method based on data security analysis according to claim 1 is characterized in that: The steps also include: S5: Based on the path conflict combination characteristic quantity, analyze the task progress and key node delay status, establish a sorting rule based on the path conflict intensity, complete the priority determination between tasks, and obtain the task collaboration sorting level; The task collaborative sorting level includes task priority order, path conflict relief index, and scheduling priority level label.

8. The enterprise multi-project collaborative management method based on data security analysis according to claim 7 is characterized in that: The steps for obtaining the task collaborative ranking level are specifically as follows: S511: Based on the path conflict combination characteristic, analyzing the differences between the task start order and progress in the path overlap area, comparing the scheduling delay conditions of the task nodes in the intersecting paths, screening the paths with node delays, and obtaining the number of node delay paths; S512: Based on the number of node delay paths, analyzing the interference degree between the node and the conflicting path, comparing the correspondence between the delayed position of the task node and the advancement degree, determining the task scheduling relevance of the intersection nodes in the critical path, and obtaining the task conflict sorting sequence; S513: Based on the task conflict sorting sequence and the order correlation between tasks in the sorting structure, determine whether there is a conflict in the collaborative execution logic of the current task set, optimize the arrangement structure, and re-output the corresponding level grouping to obtain the task collaborative sorting level.

9. Enterprise multi-project collaborative management system based on data security analysis, characterized by: The system is used to implement the enterprise multi-project collaborative management method based on data security analysis according to any one of claims 1 to 8, and the system includes: The task intensity assessment module, based on the data security requirements of enterprise projects, locates key nodes involved in access control, transmission isolation, and log auditing tasks, determines the resource types used by the nodes, calculates the deviation between the remaining duration of the node 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 intensity index. The path stability analysis module analyzes the fluctuation of task progress status in the project task path over multiple plans based on the task intensity index, determines the number of interruption event records for each task in the path, calculates the balance of progress status in the path, compares its correlation with the interruption situation, and integrates the judgment factors to obtain the degree of path stability fluctuation; The resource aggregation analysis module calculates the call frequency of key resources involved in the scheduling plan in the path based on the degree of fluctuation in the path stability, compares the call range of each type of resource in the task list, screens 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 concentration structure parameters; The path conflict identification module determines whether there is overlap between task start-up periods in the difference paths based on the resource concentration structural parameters, analyzes whether the resource names called in the tasks involved are repeated, compares the span changes between task dependency levels, integrates time intersection, resource overlap and dependency difference, constructs a path conflict expression mechanism, and obtains a path conflict combination feature quantity; The collaborative sorting decision module analyzes the progress of the project tasks in the current stage based on the path conflict combination characteristic quantity, determines the scheduling lag status of key nodes, calls for a joint comparison of the task progress degree, key node delay status and path conflict relationship, constructs project sorting rules according to priority, and obtains the task collaborative sorting level.

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