Multi-project parallel-oriented information system supervision collaborative management method and system

Abstract

The invention discloses a multi-project parallel-oriented information system supervision collaborative management method and system, and relates to the technical field of intelligent collaborative scheduling, and the method comprises the steps: obtaining supervision items of parallel implementation projects, dividing the supervision items in each project into supervision task units, and obtaining project state data corresponding to the supervision task units; based on the project state data, performing parallel scheduling on supervision task units of each project, and determining an execution sequence of supervision tasks; triggering multiple roles to cooperatively execute the supervision task according to the execution sequence, and synchronously managing the execution state of the supervision task; feedback information is collected in the supervision task execution process, and the supervision cooperation process is dynamically adjusted; performing association analysis based on the feedback information of each item, and triggering cross-item co-processing according to an analysis result; through task unitization modeling and self-organizing collaborative scheduling, the problems that multi-project parallel supervision collaborative efficiency is low and conflicts are difficult to manage are solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent collaborative scheduling technology, and more specifically, to a collaborative management method and system for information system supervision of multiple parallel projects. Background Technology

[0002] With the continuous expansion of information system construction, the parallel implementation of multiple information system projects has become the norm, placing higher demands on the organization and collaborative capabilities of supervision work. Existing information system supervision management methods are mostly project-centric, relying on manual experience to break down and schedule supervision tasks. This results in coarse-grained and inconsistently structured supervision tasks, making it difficult to achieve effective reuse and overall coordination in multi-project parallel scenarios. Furthermore, traditional supervision processes often employ centralized and static planning management methods, making it difficult to promptly perceive changes in project status and execution feedback. Faced with complex task dependencies and frequent resource competition, problems such as low scheduling efficiency, delayed collaborative responses, and difficulty in handling cross-project conflicts in a timely manner are prone to occur. Therefore, there is an urgent need for a collaborative management method for information system supervision in multi-project parallel environments to achieve refined modeling, parallel scheduling, and dynamic collaboration of supervision tasks, thereby improving the overall efficiency and controllability of information system supervision management in multi-project parallel environments.

[0003] For example, the invention patent with publication number CN120670116A discloses a method and system for enterprise multi-project collaborative management based on data security analysis. This includes the following steps: based on data security requirements, identifying key task nodes and assessing resource composition and execution deviations to form a task stress intensity index; analyzing path progression fluctuations and interruptions to obtain stability levels; identifying the aggregation structure of key resource calls; judging task timing, resource overlap, and dependency differences between paths to generate conflict characteristic quantities; constructing sorting rules by comprehensively considering progress status and conflict relationships; and determining the task collaboration level. By incorporating data security requirements into scheduling decisions, a cross-project status identification mechanism is established. Combining the completeness of task output fields and scheduling stage offsets, the task stress level is assessed to capture structurally sensitive tasks. Progress fluctuations and interruption frequencies are superimposed on the path to measure task chain stability and locate areas of uneven scheduling.

[0004] For example, the invention patent with announcement number CN120723479B discloses a task scheduling method and system based on multi-agent collaboration. First, it receives externally input target task requirement text, performs structured parsing processing to obtain a set of task elements, then calls the task planning module of the main agent to plan and decompose the task element set, generating a set of sub-task sequences. Each sub-task unit corresponds one-to-one with a preset professional agent type. Based on the professional attribute identifier of the sub-task unit, it is assigned to the matching professional agent for execution, generating sub-task execution instructions. Each professional agent executes the sub-task operation based on the instructions, generating a set of sub-task execution results and feeding them back to the main agent. Finally, the main agent performs multi-source data integration processing on the sub-task execution result set to generate a final task output file that meets the task output requirements. This improves task scheduling efficiency, quality, and flexibility, adapting to complex task needs.

[0005] The above-disclosed technical solutions have at least the following technical problems:

[0006] Traditional technical solutions for scheduling supervision tasks under multi-project parallel conditions rely primarily on manual planning or static rules, which are difficult to dynamically adapt to changes in project status. Furthermore, the feedback from the supervision process is insufficient, and anomaly handling is mostly reactive, failing to promptly mitigate the spread of risks. To address these issues, this invention proposes a solution. Summary of the Invention

[0007] To overcome the aforementioned shortcomings of the prior art, embodiments of the present invention provide a method and system for collaborative management of information system supervision for multiple parallel projects. By using task unit modeling and self-organizing collaborative scheduling, the method and system solve the problems of low efficiency and difficulty in managing conflicts in collaborative supervision of multiple parallel projects.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A collaborative management method for information system supervision of multiple parallel projects includes: acquiring supervision items for projects implemented in parallel; dividing the supervision items in each project into supervision task units and acquiring project status data corresponding to the supervision task units; scheduling the supervision task units of each project in parallel based on the project status data to determine the execution order of the supervision tasks; triggering multi-role collaborative execution of supervision tasks according to the execution order and synchronously managing the execution status of the supervision tasks; collecting feedback information during the execution of supervision tasks and dynamically adjusting the collaborative supervision process; performing correlation analysis based on the feedback information of each project and triggering cross-project collaborative handling based on the analysis results.

[0010] In a preferred technical solution, the step of acquiring the supervision items of the parallel implementation project involves dividing the supervision items in each project into supervision task units and acquiring the project status data corresponding to the supervision task units. Specifically, this includes: acquiring historical supervision execution data generated during the supervision execution of completed projects; processing the historical supervision execution data to extract the supervision execution behavior sequence corresponding to the supervision items; parsing the supervision execution behavior sequence to identify execution segments that repeatedly appear in different projects and using these repeated execution segments as candidate supervision task structures; mapping the candidate supervision task structures to supervision task units, where each supervision task unit corresponds to an independent set of executable supervision operations and is associated with a corresponding supervision item identifier; in the currently parallel implementation project, splitting the supervision items based on the supervision task units to generate a set of supervision task units; matching the set of supervision task units with their respective supervision item identifiers to obtain the project and task scope corresponding to each supervision task unit, and acquiring the real-time status data of the currently parallel implementation project; and based on the matching results, associating the extracted real-time status data with each supervision task unit to form the project status data of the supervision task unit.

[0011] In a preferred technical solution, mapping the candidate supervision task structure to a supervision task unit specifically involves: generating a corresponding structure identifier based on the supervision execution action identifiers and their order contained in the candidate supervision task structure; determining, based on historical supervision execution data, whether the candidate supervision task structure completes execution without relying on other supervision execution actions outside the structure; when the candidate supervision task structure meets the independent executability condition, obtaining the execution start action and execution end action corresponding to the structure to form an execution boundary; collecting the supervision operation records corresponding to the supervision execution action identifiers within the execution boundary to form a supervision operation set; registering the supervision operation set as a whole as a supervision task unit and assigning a task unit identifier to the supervision task unit; and associating and storing the generated supervision task unit with the corresponding supervision item identifier based on the source information of the candidate supervision task structure.

[0012] In a preferred technical solution, the parallel scheduling of supervision task units for each project based on project status data to determine the execution order of supervision tasks is specifically as follows: Analyze the dependencies and conflict states between supervision task units based on project status data; based on the dependency and conflict analysis results, combined with the priority of supervision task units and project status data, divide the supervision task units into task groups that can be executed in parallel; output the execution order of supervision task units within each task group based on task priority, project status, and resource availability, forming an executable task sequence list; integrate the sequences of each task group into a supervision task execution order; based on the supervision task execution order, perform correlation analysis on different project task groups, identify cross-project conflicts, and trigger cross-project optimization strategies.

[0013] In a preferred technical solution, the step of dividing the supervision task units into parallel executable task groups based on dependency and conflict analysis results, combined with the priority of supervision task units and project status data, is as follows: A directed acyclic graph (DAG) is constructed based on the supervision task units and their dependencies; the DAG is topologically sorted to identify the executable order of the supervision task units; critical task units and parallelizable task units are identified using the critical path method based on the topological sorting results and the weights of the DAG; the status information of each parallelizable task unit is integrated into a multi-dimensional feature vector to form the feature space of the supervision task units; and the supervision task units within the multi-dimensional feature space are clustered to form parallel executable task groups.

[0014] In a preferred technical solution, the step of triggering multi-role collaborative execution of supervision tasks according to the execution order and synchronously managing the execution status of supervision tasks is as follows: A task dependency network is constructed; historical supervision execution behavior sequences are analyzed to obtain the logical sequence of each supervision task unit, and this logical sequence is written into the task dependency network as a prerequisite dependency; each task unit determines whether it has the conditions for execution based on its own node attributes and the completion status of prerequisite dependencies in the task dependency network, and broadcasts its execution intention in its local neighborhood, while simultaneously sensing the resource occupation and execution status of other task units in the neighborhood, forming a local information interaction network; the local information interaction network is iterated, and if a resource conflict or dependency conflict is detected locally, neighborhood collaboration is performed according to the request; continuous local iteration is performed, and the data accumulation and self-organizing behavior of the local information interaction network gradually emerge globally, naturally aggregating parallel task units into task groups, forming a globally executable task sequence; based on the globally emerging task groups and the globally executable task sequence, state changes are sensed through the local information interaction network during execution, and dynamic adjustments are made through local self-organizing rules.

[0015] In a preferred technical solution, each task unit determines whether it meets the execution conditions based on its own node attributes and the completion status of its prerequisites, and broadcasts its execution intention in its local neighborhood. Simultaneously, it senses the resource usage and execution status of other task units within the neighborhood, forming a local information interaction network. Specifically, the process is as follows: Based on the set of supervision operations corresponding to the supervision items and their execution order, the sequential constraints between each supervision task unit are analyzed, and task unit identifiers with sequential constraints or completion dependencies are associated to form a task unit dependency record; based on the task unit dependency record, a corresponding set of prerequisite task unit identifiers is generated for each supervision task unit; during the scheduling phase, the node attributes of each task unit are obtained, and the set of prerequisite task unit identifiers corresponding to that task unit is loaded as the basis for execution determination; the prerequisite tasks in the set of prerequisite task unit identifiers are detected. The execution status of a unit is determined as follows: when all preceding dependent task units within the set are in the completed state, the task unit is deemed to have the conditions for execution; otherwise, its own state is marked as waiting and it continuously monitors for changes in dependent states. When a task unit is determined to have the conditions for execution, it generates corresponding execution intention information. The task unit broadcasts the execution intention information to task units within its local neighborhood. The task unit receives the execution intention information and real-time status information broadcast by other task units within its local neighborhood and stores the received information as a neighborhood status record. Based on the neighborhood status record, it performs association resolution on other task units related to the current task unit, establishes dependency edges between task units with preceding dependencies, establishes resource conflict edges between task units with resource competition or sharing relationships, and generates corresponding local connection structures with the task unit as a node, forming a local information interaction network.

[0016] In a preferred technical solution, the step of collecting feedback information and dynamically adjusting the supervision collaboration process during the execution of the supervision task is as follows: Feedback information from each task unit is received based on a local information interaction network, and the feedback information is associated with the corresponding task unit identifier, its task group, and the global executable task sequence to form a real-time feedback dataset; based on the real-time feedback dataset, the planned execution status and actual execution status of the task unit are compared to identify abnormal states and determine the scope of their impact; when an abnormal state is detected, a dynamic adjustment mechanism for the supervision collaboration process is triggered; during the re-determination of execution conditions, if a prerequisite dependent task unit has not been completed, the corresponding task unit is marked as delayed, and the relevant task units are notified through the local information interaction network to synchronously adjust their execution order; if a resource conflict is detected, execution resources are reallocated within the local neighborhood based on the feedback information; the adjustment results are broadcast in real-time through the local information interaction network and synchronously updated to the global executable task sequence and task group status.

[0017] In a preferred technical solution, the correlation analysis based on feedback information from each project, and the triggering of cross-project collaborative handling based on the analysis results, are as follows: Based on the real-time feedback dataset generated by each project during the execution of supervision tasks, the execution status of task units in each project is extracted and standardized to form a feedback feature set; based on the feedback feature set, a cross-project correlation model is constructed; based on the cross-project correlation model, correlation analysis is performed on abnormal task units and their abnormal impact range in different projects to determine cross-project collaborative conflicts and identify the project set and task unit set involved in the cross-project collaborative conflicts; for the determined cross-project collaborative conflicts, the overall impact intensity of the cross-project conflicts is evaluated by combining the current execution stage, task priority distribution, and remaining critical path length of each relevant project, generating cross-project collaborative handling triggering conditions; when the evaluation result exceeds a preset collaborative threshold, the cross-project collaborative handling mechanism is triggered; after the cross-project collaborative handling mechanism is triggered, the task groups and task units of relevant projects are jointly rearranged based on the cross-project correlation network; the cross-project collaborative handling results are synchronously fed back to the real-time feedback dataset of the affected projects and the global executable task sequence through a local information interaction network.

[0018] A system for collaborative management of information system supervision for multiple parallel projects includes a partitioning module, a parallel scheduling module, a synchronization management module, an adjustment module, and a collaborative handling module, with connections between the modules. The partitioning module acquires the supervision items for projects implemented in parallel, divides the supervision items in each project into supervision task units, and acquires the project status data corresponding to each supervision task unit. The parallel scheduling module performs parallel scheduling of the supervision task units of each project based on the project status data, determining the execution order of the supervision tasks. The synchronization management module triggers multi-role collaborative execution of supervision tasks according to the execution order and synchronously manages the execution status of the supervision tasks. The adjustment module collects feedback information during the execution of supervision tasks and dynamically adjusts the collaborative supervision process. The collaborative handling module performs correlation analysis based on the feedback information from each project and triggers cross-project collaborative handling based on the analysis results.

[0019] The technical effects and advantages of this invention, which describes a collaborative management method and system for information system supervision of multiple parallel projects, are as follows:

[0020] 1. This invention, through in-depth analysis of historical supervision execution data, identifies and abstracts repeatable and reusable supervision task structures, mapping these structures into independently executable supervision task units. This transforms supervision items from project-level descriptions to task-level structured units. This approach effectively avoids the subjectivity and inconsistencies inherent in traditional manual breakdown of supervision items by project, giving supervision task division data-driven characteristics and a unified structural standard, laying the foundation for subsequent cross-project scheduling and collaboration.

[0021] 2. This invention breaks through the traditional centralized scheduling and static process control model, constructing a multi-role collaborative execution mechanism based on a local information interaction network. Each supervision task unit can autonomously determine execution conditions based on its own node attributes and the completion status of its prerequisites, and broadcast its execution intentions and status awareness within its local neighborhood. Through self-organization and iterative collaboration, a globally executable task sequence gradually emerges. This distributed and adaptive collaborative approach reduces the system's dependence on the central scheduling node and improves the system's robustness and real-time response capabilities in complex and ever-changing project environments. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a collaborative management method for information system supervision oriented towards multiple parallel projects, as proposed in this invention.

[0023] Figure 2 This is a schematic diagram of the system structure of an information system supervision and collaborative management method for multiple parallel projects according to the present invention.

[0024] Specific technical solutions

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1, Figure 1 This invention presents a collaborative management method for information system supervision oriented towards multiple parallel projects, comprising:

[0027] S1, obtain the supervision items of the parallel implementation projects, divide the supervision items in each project into supervision task units, and obtain the project status data corresponding to the supervision task units;

[0028] In this embodiment, the supervision items of the parallel implementation projects are obtained, the supervision items in each project are divided into supervision task units, and the project status data corresponding to the supervision task units are obtained, as follows:

[0029] Through the information system, historical supervision execution data generated during the supervision process of multiple completed projects is obtained. The historical supervision execution data includes supervision item records, supervision operation records, and execution time records.

[0030] The historical supervision execution data is processed to extract the supervision execution behavior sequence corresponding to the supervision items. The supervision execution behavior sequence reflects the actual operation sequence of the supervisors when executing the supervision items.

[0031] The supervision execution action identifiers and their sequence are parsed in the sequence of supervision execution behaviors. The execution segments that appear repeatedly in different projects are identified and the repeated execution segments are used as candidate supervision task structures. The supervision execution action identifier is used to uniquely identify a specific supervision operation performed in the supervision process. It contains the identification information of supervision operation type, execution object and execution sequence.

[0032] The candidate supervision task structure is mapped to supervision task units. Each supervision task unit corresponds to an independent set of executable supervision operations and is associated with a corresponding supervision item identifier. The supervision item identifier is used to uniquely identify the specific item that needs to be supervised, including the project to which the item belongs, the type of supervision item, and the corresponding inspection requirements.

[0033] In the current parallel implementation of the project, based on the generated supervision task units, the acquired supervision items are automatically split to generate a set of supervision task units for subsequent parallel scheduling;

[0034] After the task units are split, each generated supervision task unit is matched with its corresponding supervision item identifier to determine the project and task scope corresponding to the supervision task unit, and real-time status data of the currently parallel projects is obtained. The real-time status data includes project progress information, key node completion status, task priority, resource allocation information, completion status of prerequisite tasks, and historical execution data. The key nodes are project control nodes pre-identified in the project status data. The project control nodes include: project phase switching nodes, milestone completion nodes, and mandatory inspection nodes associated with supervision items.

[0035] Based on the matching results, the extracted real-time status data is associated with each supervision task unit to form the project status data of the supervision task unit, including: the current progress and milestone status of the project to which it belongs, the execution priority and dependency constraints of the task unit, and the completion status of the task unit's predecessor tasks.

[0036] In this embodiment, the candidate supervision task structure is mapped to supervision task units, as follows:

[0037] Based on the supervision execution action identifiers and their order contained in the candidate supervision task structure, a corresponding structure identifier is generated to distinguish different candidate supervision task structures;

[0038] Based on historical supervision execution data, determine whether the candidate supervision task structure has been completed without relying on other supervision execution actions outside the structure during the execution process;

[0039] When the candidate supervision task structure meets the independent executable condition, the execution start action and execution end action corresponding to the structure are determined to form the execution boundary;

[0040] Collect the supervision operation records corresponding to the supervision execution action identifiers located within the execution boundary to form a supervision operation set;

[0041] The set of supervision operations is registered as a whole as a supervision task unit, and a task unit identifier is assigned to the supervision task unit. The task unit identifier is used to uniquely identify the independent executable supervision task formed by splitting the supervision item, including its project, associated supervision item identifier and execution status information.

[0042] Based on the source information of the candidate supervision task structure, the generated supervision task units are associated and stored with the corresponding supervision item identifiers.

[0043] In this embodiment, based on the generated supervision task units, the acquired supervision items are automatically split to generate a set of supervision task units for subsequent parallel scheduling, as follows:

[0044] Obtain a list of supervision items that need to be performed for the currently parallel projects. Each supervision item includes a supervision item identifier, inspection object, and inspection requirements.

[0045] By matching the candidate task units with the supervision item identifiers, the candidate supervision task units for each supervision item are found.

[0046] Based on the set of supervision operations and execution order of the matched candidate supervision task units, the candidate supervision task units are divided into several supervision task sub-units. The principle for dividing the supervision task sub-units is that the set of actions contained in each task unit can be executed independently in logic and is independent of the action sets of other task units.

[0047] Each supervision task sub-unit is registered as a supervision task unit set, and a task unit identifier is assigned to each supervision task sub-unit, while retaining the association with the original supervision item identifier.

[0048] In this embodiment, based on the set of supervision operations and the execution order of the matched candidate supervision task units, the candidate supervision task units are divided into several supervision task sub-units, as follows:

[0049] Obtain the supervision execution action identifiers and their execution sequence information contained within each candidate supervision task unit, where each supervision execution action identifier corresponds to a specific supervision operation objective and inspection requirement;

[0050] Analyze the dependencies between actions within the set of supervisory operations within the candidate task unit, including execution order constraints, interaction or resource sharing of inspection objects, and the sequential relationship of actions in historical execution data;

[0051] Combining operation identifiers that do not depend on other actions together forms an independently executable set of actions. Multiple independent sets of actions form independent sub-units of supervision tasks.

[0052] Assign a unique task unit identifier to each supervision task sub-unit and retain the association information with the candidate task units and supervision item identifiers.

[0053] S2, based on project status data, performs parallel scheduling of the supervision task units of each project to determine the execution order of the supervision tasks;

[0054] In this embodiment, based on project status data, the supervision task units of each project are scheduled in parallel to determine the execution order of the supervision tasks, as follows:

[0055] Based on the project status data, analyze the dependencies and conflict states between each supervision task unit. The dependencies include task order constraints, time window constraints, and the feasibility of parallel execution within the group. The conflict states include the repeated occupation of supervision personnel, equipment, tools, or sites, and record the conflict type and scope of impact.

[0056] Based on the dependency and conflict analysis results, combined with the priority of the supervision task units and the project status data, the supervision task units are divided into task groups that can be executed in parallel, ensuring that the tasks within the group are independent of each other and will not cause resource conflicts, while preserving the execution order constraints between groups.

[0057] Based on task priority, project status, and resource availability, the execution order of supervision task units within each task group is dynamically output, forming an executable task sequence list.

[0058] The sequences of each task group are integrated into the overall supervision task execution order, while the scheduling basis and constraint information of each supervision task unit are recorded to provide data support for subsequent dynamic adjustments and cross-project collaboration.

[0059] Based on the generated execution order of supervision tasks, correlation analysis is performed on different project task groups to identify potential cross-project conflicts and trigger cross-project optimization strategies, thereby achieving efficient collaboration in multi-project supervision.

[0060] In this embodiment, based on dependency and conflict analysis results, and combined with the priority of the supervision task units and project status data, the task units are divided into task groups that can be executed in parallel, as follows:

[0061] The supervision task units and their dependencies are modeled as a directed acyclic graph (DAG), where nodes represent task units, edges represent prerequisite dependencies, and edges are assigned a comprehensive weight, which is adjusted by the urgency of the task.

[0062] Perform topological sorting on the directed acyclic graph to identify the executable order of the supervision task units, ensure that prerequisite dependencies are satisfied, and provide input for critical path analysis;

[0063] Based on the topological sorting results and the weights of the directed acyclic graph, the earliest start time, latest completion time, and total float time of each supervision task unit are output using the critical path method. Critical task units and parallelizable task units are identified, and the critical path status, time constraints, and dependencies of each task unit are recorded. Tasks with a total float time of 0 are identified as critical tasks, and tasks with a total float time greater than 0 are marked as parallelizable tasks.

[0064] The state information of each task unit is integrated into a multi-dimensional feature vector to form the feature space of the task unit. The state information includes topology level, critical path status, resource consumption, task priority, and project progress.

[0065] Clustering is performed on task units within the multidimensional feature space, and task units with weak dependencies, low resource conflicts, and parallel critical paths are grouped into the same task group.

[0066] After the task groups are divided, a preliminary execution sequence is generated within each task group. The sequence is dynamically sorted according to task priority, resource availability, and real-time project progress to form a list of tasks that can be executed directly within the group.

[0067] In this embodiment, based on the generated task execution order, correlation analysis is performed on different project task groups to identify potential cross-project conflicts and trigger cross-project optimization strategies, thereby achieving efficient collaboration in multi-project supervision, as detailed below:

[0068] Based on the task groups generated by each project and their execution sequences within each group, we analyze the cross-project dependencies, resource consumption, and key node time overlaps between different project supervision task units to identify potential conflicts, including resource conflicts, time window conflicts, and dependency conflicts.

[0069] For different types of conflicts, optimization strategies are adopted, such as adjusting the execution order of parallel task units, reallocating resources, or splitting and merging task units to ensure that critical tasks are prioritized and prerequisite dependencies are met.

[0070] After optimization, a new cross-project task group execution sequence is generated. At the same time, the scheduling basis and resource allocation information of each task unit are updated, and the cross-project constraint satisfaction is verified again, thus forming a multi-project collaborative scheduling scheme that can be directly executed.

[0071] S3 triggers multi-role collaborative execution of supervision tasks according to the execution order, and synchronously manages the execution status of supervision tasks;

[0072] In this embodiment, multiple roles are triggered to collaboratively execute supervision tasks according to the execution order, and the execution status of the supervision tasks is managed synchronously, as follows:

[0073] Each task unit is treated as a system element, each task execution entity is treated as an execution resource, and the task unit's dependencies, critical path status, resource requirements, priority, task group, and project status are treated as node attributes. Dependencies and resource conflicts are represented as edges, forming a task dependency network.

[0074] In the supervision task unit generation stage, the logical sequence of each supervision task unit is obtained by analyzing the historical supervision execution behavior sequence, and the logical sequence is written into the task dependency network as a prerequisite dependency. The supervision task unit generation stage is as follows: the obtained supervision items are automatically split into a set of supervision task units for subsequent parallel scheduling.

[0075] Each task unit determines whether it has the conditions to execute based on its own node attributes and the completion status of its predecessors in the task dependency network, and broadcasts its execution intention in its local neighborhood. At the same time, it senses the resource consumption and execution status of adjacent task units in the neighborhood, forming a local information interaction network.

[0076] The local information interaction network is iterated. If resource conflicts or dependency conflicts are detected in task units locally, the local conflicts are adaptively adjusted according to the request for neighborhood cooperation, such as task rearrangement or temporary resource allocation.

[0077] As local iterations continue, the data accumulation and self-organizing behavior of the local information interaction network gradually emerge globally, naturally aggregating parallel task units into task groups. Within each group, there are no conflicts, key task units are initiated first, and cross-project dependency conflicts are automatically adjusted through local coordination within the network, thus forming a globally executable task sequence.

[0078] Based on the task groups and global executable task sequences formed by global emergence, the system senses state changes through a local information interaction network during execution and makes dynamic adjustments through local self-organizing rules. These local self-organizing rules include: if a preceding task is not completed or resources are insufficient, the task unit delays its start or rearranges its execution order; when resource conflicts or changes in available resources are detected, the system requests idle execution entities in the neighborhood or reallocates resources; and in the case of cross-task group conflicts or dependency blocking, the system coordinates through local communication to migrate some actions to other executable entities for execution.

[0079] In this embodiment, the logical sequence relationship is written into the task dependency network as a prerequisite dependency, as follows:

[0080] During the generation phase of the supervision task unit, the system performs sequence association analysis on each supervision task unit based on the historical supervision execution behavior sequence, identifies the logical sequence relationship of tasks that is stably presented in the historical execution, and verifies the validity of the sequence relationship in combination with the supervision business rules.

[0081] When it is determined that a certain supervision task unit can only be executed after another task unit is completed, the corresponding task unit is used as a node in the task dependency network. A directed dependency edge is established before the node representing the subsequent task unit, and the dependency type, triggering condition and dependency strength are recorded in the directed dependency edge. In this way, the logical sequence relationship is written into the task dependency network in a structured way, forming a pre-dependency that can be directly called by subsequent scheduling judgment, conflict detection and dynamic adjustment.

[0082] In this embodiment, each task unit determines whether it meets the execution conditions based on its own node attributes and the completion status of its prerequisites, and broadcasts its execution intention in its local neighborhood. At the same time, it senses the resource usage and execution status of other task units in the neighborhood, forming a local information interaction network, as follows:

[0083] Based on the set of supervision operations corresponding to the supervision items and their execution order, the sequential constraints between each supervision task unit are analyzed, and task unit identifiers with sequential constraints or completion dependencies are associated to form a task unit dependency record.

[0084] Based on the task unit dependency record, a corresponding set of predecessor dependent task unit identifiers is generated for each supervision task unit. The set of predecessor dependent task unit identifiers is used to represent other task units that must be completed before the current task unit starts.

[0085] During the scheduling phase, each task unit obtains its own node attributes and loads the set of identifiers of the preceding dependent task units corresponding to that task unit as the basis for execution determination;

[0086] The execution status of each preceding dependent task unit in the preceding dependent task unit identifier set is detected. When all preceding dependent task units in the set are detected to be in the completed state, it is determined that the task unit has the conditions for execution. Otherwise, its own state is marked as waiting and the dependent state is continuously monitored for changes.

[0087] When a task unit is determined to meet the execution conditions, corresponding execution intention information is generated. The execution intention information includes the task unit identifier, the type and quantity of required resources, the estimated execution time, and the task priority identifier.

[0088] The task unit broadcasts its execution intention information to task units within its local neighborhood, which consists of task units that have a direct dependency relationship, a resource sharing relationship, or a cross-project relationship with the current task unit.

[0089] The task unit receives execution intention information and real-time status information broadcast by other task units in its local neighborhood, and stores the received information as a neighborhood status record to reflect the resource consumption, execution status and dependency completion status of tasks in the neighborhood.

[0090] Based on the neighborhood state record, the association of other task units related to the current task unit is resolved. Dependency edges are established between task units with prior dependencies, and resource conflict edges are established between task units with resource competition or sharing relationships. With the task unit as the node, a corresponding local connection structure is generated. The node state and edge constraints in the local connection structure are updated synchronously, thereby dynamically forming a connection structure with task units as nodes and dependency and resource conflict relationships as edges, thus constituting a local information interaction network.

[0091] In this embodiment, based on the set of supervision operations corresponding to the supervision items and their execution order, the sequential constraints between each supervision task unit are parsed, and task unit identifiers with sequential constraints or completion dependencies are associated to form a task unit dependency record, as follows:

[0092] Based on the set of supervision operations corresponding to the supervision items and their execution order, the supervision operations contained in each supervision task unit are compared to determine whether there are situations where the operation objects are consistent, the execution results are dependent, or the execution order is restricted among different task units.

[0093] When the execution result of a supervision operation contained in a task unit is used as a prerequisite for the execution of another task unit, or when the object of its operation logically requires the completion of the supervision operation of the previous task unit, it is determined that there is a completion dependency relationship between the two task units.

[0094] When multiple task units target the same supervised object or the same inspection stage, and the execution order requirements are predefined in the set of supervised operations, it is determined that there is a sequential constraint relationship between the task units.

[0095] Associate task unit identifiers that have completion dependencies or sequence constraints to form a task unit dependency record.

[0096] In this embodiment, based on the task groups and global executable task sequences formed by global emergence, state changes are perceived through a local information interaction network during execution, and dynamic adjustments are made through local self-organizing rules, as follows:

[0097] After the global emergence of task groups and global executable task sequences, each supervision task unit enters the pending execution state according to the global executable task sequence, and receives status information related to its local neighborhood through the local information interaction network. The status information includes the completion status of the preceding dependent task units, the resource consumption changes of other task units in the neighborhood, and the real-time progress changes of the project to which it belongs.

[0098] When a task unit detects that a preceding dependent task unit has not completed as expected, resource usage has changed, or a conflict has occurred in the task state within the neighborhood before or during execution, it triggers a local self-organizing rule based on the neighborhood state records stored in the local information interaction network to adjust the execution state of the current task unit. The adjustment includes delayed start, temporary pause, reordering of execution, or requesting cooperation from other available execution entities within the neighborhood.

[0099] The adjustment results are broadcast in real time through a local information exchange network and updated synchronously to the relevant task units, enabling the affected task units to re-evaluate the execution conditions based on the updated state. This achieves dynamic adaptive adjustment within and across task groups without violating the constraints of the global executable task sequence.

[0100] S4 collects feedback information during the execution of supervision tasks and dynamically adjusts the supervision collaboration process;

[0101] In this embodiment, feedback information is collected during the execution of the supervision task, and the supervision collaboration process is dynamically adjusted, as follows:

[0102] The system receives feedback information from each task unit based on a local information exchange network, and associates the feedback information with the corresponding task unit identifier, the task group to which it belongs, and the global executable task sequence to form a real-time feedback dataset that reflects the current supervision execution status. The feedback information includes the task unit execution status, actual execution time, resource consumption changes, completion status of prerequisite dependencies, and execution anomaly identifiers.

[0103] Based on the real-time feedback dataset, the planned execution status of the task unit is compared with the actual execution status to identify abnormal states and determine the scope of the abnormal impact. The abnormal states include execution delay, resource over-extraction, blocking of preceding dependent task units, or cross-task group conflicts.

[0104] When an abnormal state is detected, a dynamic adjustment mechanism for the supervision and collaboration process is triggered. This dynamic adjustment mechanism is based on local self-organization rules and only re-determines the execution conditions of task units and their local neighborhoods within the scope of the abnormality's influence.

[0105] During the re-determination of execution conditions, if the preceding dependent task unit is not completed or resources are insufficient, the corresponding task unit will be marked as delayed, and the relevant task units will be notified through the local information exchange network to adjust the execution order synchronously.

[0106] If a resource conflict is detected, the execution resources are reallocated within the local neighborhood based on the feedback information, or some supervision operations are migrated to other task execution entities with the conditions to perform them, so as to maintain the continuous execution of the supervision tasks.

[0107] The dynamic adjustment results are broadcast in real time through the local information exchange network and are simultaneously updated to the global executable task sequence and task group status, so that subsequent task scheduling and collaborative execution can continue based on the updated execution status.

[0108] In this embodiment, based on the real-time feedback dataset, the planned execution status of the task unit is compared with the actual execution status to identify abnormal states and determine the scope of the abnormality's impact, as detailed below:

[0109] Based on the real-time feedback dataset formed by the local information interaction network, the actual execution status of each task unit (actual execution time, real-time resource consumption and completion status of the preceding dependent task units) is compared item by item with the planned execution status (planned start time, planned resource quota and planned dependency constraints) of the corresponding task unit in the global executable task sequence.

[0110] When it is detected that the actual execution time exceeds the planned threshold, the resource consumption exceeds the allocated amount, the preceding dependent task unit fails to complete as planned, or there is cross-task group resource competition, the corresponding task unit will be marked as an abnormal task unit.

[0111] Using abnormal task units as the source nodes of influence, and based on task dependencies, resource sharing relationships and cross-project associations, the search is expanded outward in the task dependency network and cross-project association network to include other task units that have direct or indirect influence relationships with abnormal task units into the candidate scope one by one.

[0112] Candidate task units are filtered based on the completion status of their predecessor dependent task units, the correlation of resource consumption, and the overlap of execution time windows. Only task units whose execution status may be actually affected by the delay, resource consumption changes, or execution failure of abnormal task units are retained.

[0113] The selected task unit identifiers are aggregated to form a task unit set, and the task unit set is determined as the scope of the anomaly impact.

[0114] S5 performs correlation analysis based on feedback information from each project and triggers cross-project collaborative actions based on the analysis results;

[0115] In this embodiment, correlation analysis is performed based on feedback information from each project, and cross-project collaborative processing is triggered according to the analysis results, as follows:

[0116] Based on the real-time feedback dataset generated during the execution of supervision tasks in each project, the execution status of task units in each project is extracted, and the execution status is standardized to form a unified set of feedback features across projects. The execution status includes changes in resource consumption, execution anomaly identifiers, scope of anomaly impact, and corresponding project identifiers.

[0117] Based on the set of feedback features, a cross-project association model is constructed. The cross-project association model uses task units and projects as nodes and shared execution resources, overlapping time windows, cross-project pre-dependencies, or reuse relationships of the same execution entity as association edges, thereby mapping feedback information scattered across different projects to a unified cross-project association network.

[0118] Based on the cross-project association model, the abnormal task units and their abnormal impact range of different projects are analyzed for association. When multiple projects are detected to have abnormal states at the same resource node, the same time window or the same association path, it is determined that there is a cross-project collaboration conflict, and the set of projects and task units involved in the cross-project collaboration conflict are determined.

[0119] For identified cross-project collaboration conflicts, the overall impact intensity of the cross-project conflict is assessed by combining the current execution stage, task priority distribution and remaining critical path length of each relevant project, and cross-project collaboration handling trigger conditions are generated. When the assessment result exceeds the preset collaboration threshold, the cross-project collaboration handling mechanism is triggered.

[0120] After the cross-project collaborative handling mechanism is triggered, the task groups and task units of the relevant projects are jointly rearranged based on the cross-project association network. By adjusting the task start sequence between different projects, dynamically reallocating cross-project shared resources, or temporarily migrating the execution position of some supervision operations, cross-project level collaborative conflict resolution is achieved.

[0121] The results of cross-project collaborative handling are synchronously fed back to the real-time feedback dataset of the affected projects and the global executable task sequence through a local information exchange network, so that subsequent task scheduling, local self-organization adjustment and execution status determination are all based on the updated cross-project collaborative status.

[0122] In this embodiment, for the identified cross-project collaboration conflicts, the overall impact intensity of the conflicts is assessed by combining the current execution stage, task priority distribution, and remaining critical path length of each related project. This assessment generates cross-project collaboration handling trigger conditions. When the assessment result exceeds a preset collaboration threshold, the cross-project collaboration handling mechanism is triggered, as follows:

[0123] The scope of the abnormal impact is determined based on cross-project collaboration conflicts, and the supervision task units within the scope of the abnormal impact are identified as the set of abnormal impact task units;

[0124] Based on the set of task units affected by anomalies, the current execution stage of multiple projects associated with them is extracted from the project status data, that is, the overall progress position of each project.

[0125] Statistical anomalies affect the distribution of task priority in each project and determine the concentration of high-priority task units within the conflict area.

[0126] Based on the current execution phase of each project, and combined with the incomplete critical path task units within the project, the remaining critical path length of the corresponding project is obtained, which serves as the sensitivity of each project to execution delays.

[0127] The current execution stage, task priority distribution, and remaining critical path length are determined separately.

[0128] When a project is in the critical stage or nearing its end, the execution phase will be classified as a high-sensitivity phase; otherwise, it will be classified as a normal phase.

[0129] When the scope of an anomaly's impact contains more than a preset number of high-priority task units, the task priority distribution is determined to be a high-impact distribution; otherwise, it is determined to be a low-impact distribution.

[0130] When the number of remaining unfinished critical path task units in a relevant project exceeds a preset threshold, the remaining critical path length is judged as a high-risk state; otherwise, it is judged as a controllable state.

[0131] Based on the collaborative assessment rules, the results of the sub-item judgments are combined and judged. When at least a preset number of high-impact judgment conditions are met, the overall impact intensity of the cross-project collaborative conflict is determined to be the collaborative handling level, and the cross-project collaborative handling mechanism is triggered; otherwise, it is the regular adjustment level.

[0132] When the cross-project collaborative handling mechanism is triggered, the execution order of the supervision task units, the affiliation of task groups, and the resource allocation strategy are uniformly adjusted based on the set of task units affected by the anomaly and the current execution stage of the project. Specifically, the execution order of task units in the set of task units affected by the anomaly is rearranged to ensure the execution of high-priority task units on the critical path, and the blocked task units are postponed or split. Available execution resources are coordinated and allocated across projects, and some task units are temporarily migrated to projects with lower resource consumption to eliminate cross-project conflicts.

[0133] After cross-project collaborative processing is completed, the task group status, global executable task sequence, and project status data of each relevant project are updated synchronously, and the update results are fed back to the local information exchange network.

[0134] Example 2, Figure 2 The present invention provides a system for collaborative management of information system supervision for multiple parallel projects, characterized by comprising a partitioning module, a parallel scheduling module, a synchronization management module, an adjustment module, and a collaborative handling module, with connections between the modules;

[0135] The segmentation module is used to obtain the supervision items of projects implemented in parallel, divide the supervision items in each project into supervision task units, and obtain the project status data corresponding to the supervision task units.

[0136] The parallel scheduling module is used to perform parallel scheduling of the supervision task units of each project based on project status data, and to determine the execution order of the supervision tasks;

[0137] The synchronization management module is used to trigger multi-role collaborative execution of supervision tasks according to the execution order, and to synchronize the execution status of supervision tasks.

[0138] The adjustment module is used to collect feedback information during the execution of supervision tasks and to dynamically adjust the supervision collaboration process;

[0139] The collaborative handling module is used to perform correlation analysis based on feedback information from each project and trigger cross-project collaborative handling based on the analysis results.

[0140] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0141] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0142] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0143] The above description is merely a specific technical solution of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0144] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A collaborative management method for information system supervision oriented towards multiple parallel projects, characterized in that, include: Obtain the supervision items for projects implemented in parallel, divide the supervision items in each project into supervision task units, and obtain the project status data corresponding to the supervision task units; Based on project status data, the supervision task units of each project are scheduled in parallel to determine the execution order of the supervision tasks; The supervision task is triggered in a multi-role collaborative manner according to the execution order, and the execution status of the supervision task is managed synchronously. Collect feedback information during the execution of supervision tasks and dynamically adjust the supervision collaboration process; Based on feedback information from each project, correlation analysis is performed, and cross-project collaborative actions are triggered according to the analysis results.

2. The information system supervision and collaborative management method for multiple parallel projects as described in claim 1, characterized in that, The process of acquiring the supervision items for parallel implementation projects involves dividing the supervision items in each project into supervision task units and acquiring the project status data corresponding to the supervision task units, as detailed below: Obtain historical supervision execution data generated during the supervision process of completed projects; Process historical supervision execution data to extract the sequence of supervision execution behaviors corresponding to the supervision items; The sequence of supervisory execution behaviors is analyzed to identify repeated execution segments in different projects, and these repeated execution segments are used as candidate supervisory task structures. The candidate supervision task structure is mapped to supervision task units, each supervision task unit corresponds to an independent set of executable supervision operations, and is associated with the corresponding supervision item identifier; In the current projects implemented in parallel, the supervision items are broken down based on the supervision task unit to generate a set of supervision task units; Match the set of supervision task units with the identification of the supervision items to obtain the project and task scope corresponding to the supervision task unit, and obtain the real-time status data of the currently parallel projects; Based on the matching results, the extracted real-time status data is associated with each supervision task unit to form the project status data of the supervision task unit.

3. The collaborative management method for information system supervision oriented towards multiple parallel projects as described in claim 2, characterized in that, The mapping of candidate supervision task structures to supervision task units is as follows: Generate corresponding structure identifiers based on the supervision execution action identifiers and their arrangement order contained in the candidate supervision task structure; Based on historical supervision execution data, determine whether the candidate supervision task structure has been completed without relying on other supervision execution actions outside the structure during the execution process; When the candidate supervision task structure meets the independent executable condition, the execution start action and execution end action corresponding to the structure are obtained, forming the execution boundary; Collect the supervision operation records corresponding to the supervision execution action identifiers located within the execution boundary to form a supervision operation set; Register the collection of supervision operations as a whole as a supervision task unit, and assign a task unit identifier to the supervision task unit; Based on the source information of the candidate supervision task structure, the generated supervision task units are associated and stored with the corresponding supervision item identifiers.

4. The collaborative management method for information system supervision oriented towards multiple parallel projects as described in claim 1, characterized in that, Based on project status data, the supervision task units of each project are scheduled in parallel to determine the execution order of the supervision tasks, as detailed below: Analyze the dependencies and conflicts between each supervision task unit based on project status data; Based on the dependency and conflict analysis results, and combined with the priority of the supervision task units and project status data, the supervision task units are divided into task groups that can be executed in parallel. Based on task priority, project status, and resource availability, the execution order of the supervision task units within each task group is output, forming an executable task sequence list; The sequences of each task group are integrated into the order of execution of the supervision tasks; Based on the execution sequence of supervision tasks, correlation analysis is performed on different project task groups to identify cross-project conflicts and trigger cross-project optimization strategies.

5. A collaborative management method for information system supervision oriented towards multiple parallel projects, as described in claim 4, is characterized in that... Based on dependency and conflict analysis results, and combined with the priority of supervision task units and project status data, the supervision task units are divided into task groups that can be executed in parallel, as follows: Construct a directed acyclic graph based on the supervision task units and their dependencies; Perform topological sorting on the directed acyclic graph to identify the executable order of the supervision task units; Based on the topological sorting results and the weights of the directed acyclic graph, critical task units and parallelizable task units are identified using the critical path method. The state information of each parallelizable task unit is integrated into a multi-dimensional feature vector to form the feature space of the supervision task unit. Cluster the supervision task units in the multidimensional feature space to form task groups that can be executed in parallel.

6. The collaborative management method for information system supervision oriented towards multiple parallel projects as described in claim 1, characterized in that, The process of triggering multi-role collaborative execution of supervision tasks according to the execution order and synchronously managing the execution status of supervision tasks is as follows: Build the task that depends on the network; Analyze the historical sequence of supervisory actions to obtain the logical sequence of each supervisory task unit, and write the logical sequence into the task dependency network as a prerequisite dependency; Each task unit determines whether it has the conditions to execute based on its own node attributes and the completion status of its predecessors in the task dependency network, and broadcasts its execution intention in its local neighborhood. At the same time, it senses the resource consumption and execution status of other task units in the neighborhood, forming a local information interaction network. The local information exchange network is iterated. If a resource conflict or dependency conflict is detected in a local task unit, neighborhood cooperation is carried out according to the request. As local iterations continue, the data accumulation and self-organizing behavior of the local information interaction network gradually emerge globally, naturally aggregating parallel task units into task groups and forming a globally executable task sequence. Based on the task groups and global executable task sequences formed globally, the system senses state changes through local information interaction networks during execution and makes dynamic adjustments through local self-organizing rules.

7. A collaborative management method for information system supervision oriented towards multiple parallel projects, as described in claim 6, is characterized in that... Each task unit determines whether it meets the execution conditions based on its own node attributes and the completion status of its prerequisites, and broadcasts its execution intention in its local neighborhood. At the same time, it senses the resource usage and execution status of other task units in the neighborhood, forming a local information interaction network, as follows: Based on the set of supervision operations corresponding to the supervision items and their execution order, the sequential constraints between each supervision task unit are analyzed, and task unit identifiers with sequential constraints or completion dependencies are associated to form a task unit dependency record. Based on the task unit dependency record, generate a corresponding set of predecessor dependent task unit identifiers for each supervision task unit; During the scheduling phase, the node attributes of each task unit are obtained, and the set of identifiers of the preceding dependent task units corresponding to that task unit is loaded as the basis for execution determination. The execution status of each preceding dependent task unit in the preceding dependent task unit identifier set is detected. When all preceding dependent task units in the set are detected to be in the completed state, it is determined that the task unit has the conditions for execution. Otherwise, its own state is marked as waiting and the dependent state is continuously monitored for changes. When a task unit is determined to meet the execution conditions, corresponding execution intention information is generated; The task unit will broadcast its execution intention information to the task units within its local neighborhood; The task unit receives execution intention information and real-time status information broadcast by other task units in its local neighborhood, and stores the received information as a neighborhood status record; Based on the neighborhood state record, the association of other task units related to the current task unit is resolved. Dependency edges are established between task units with prior dependencies, and resource conflict edges are established between task units with resource competition or sharing relationships. The task unit is used as a node to generate the corresponding local connection structure, thus forming a local information interaction network.

8. The information system supervision and collaborative management method for multiple parallel projects as described in claim 1, characterized in that, The process of collecting feedback information during the execution of supervision tasks and dynamically adjusting the supervision collaboration process is as follows: The system receives feedback information from each task unit based on a local information exchange network, and associates the feedback information with the corresponding task unit identifier, the task group to which it belongs, and the global executable task sequence to form a real-time feedback dataset. Based on real-time feedback datasets, the planned execution status of task units is compared with their actual execution status to identify abnormal states and determine the scope of their impact. When an abnormal state is detected, a dynamic adjustment mechanism for the supervision collaboration process is triggered; During the re-determination of execution conditions, if the preceding dependent task unit has not been completed, the corresponding task unit will be marked as delayed, and the relevant task units will be notified through the local information exchange network to adjust the execution order synchronously. If a resource conflict is detected, execution resources are reallocated within the local neighborhood based on the feedback information; The adjustment results are broadcast in real time through the local information exchange network and are simultaneously updated to the global executable task sequence and task group status.

9. A collaborative management method for information system supervision oriented towards multiple parallel projects, as described in claim 1, is characterized in that... The process involves correlation analysis based on feedback information from each project, and triggering cross-project collaborative actions based on the analysis results, as detailed below: Based on the real-time feedback dataset generated during the execution of supervision tasks in each project, the execution status of task units in each project is extracted and standardized to form a set of feedback features. Based on the set of feedback features, construct a cross-project relationship model; Based on the cross-project correlation model, correlation analysis is performed on abnormal task units and their abnormal impact range in different projects to determine cross-project collaboration conflicts and identify the project set and task unit set involved in the cross-project collaboration conflicts. For identified cross-project collaboration conflicts, the overall impact intensity of the cross-project conflict is assessed by combining the current execution stage, task priority distribution and remaining critical path length of each relevant project, and cross-project collaboration handling trigger conditions are generated. When the assessment result exceeds the preset collaboration threshold, the cross-project collaboration handling mechanism is triggered. After the cross-project collaborative handling mechanism is triggered, the task groups and task units of the relevant projects are jointly rearranged based on the cross-project association network. The results of cross-project collaborative handling are synchronously fed back to the real-time feedback dataset of the affected projects and the global executable task sequence through a local information exchange network.

10. A system using the collaborative management method for information system supervision oriented towards multiple parallel projects as described in any one of claims 1-9, characterized in that, It includes a partitioning module, a parallel scheduling module, a synchronization management module, an adjustment module, and a collaborative processing module, and the modules are interconnected. The segmentation module is used to obtain the supervision items of projects implemented in parallel, divide the supervision items in each project into supervision task units, and obtain the project status data corresponding to the supervision task units. The parallel scheduling module is used to perform parallel scheduling of the supervision task units of each project based on project status data, and to determine the execution order of the supervision tasks; The synchronization management module is used to trigger multi-role collaborative execution of supervision tasks according to the execution order, and to synchronize the execution status of supervision tasks. The adjustment module is used to collect feedback information during the execution of supervision tasks and to dynamically adjust the supervision collaboration process; The collaborative handling module is used to perform correlation analysis based on feedback information from each project and trigger cross-project collaborative handling based on the analysis results.

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