An integrated project management information system and method

Abstract

The application relates to the technical field of management and decision support information system, and discloses a comprehensive plan management information system and method, which comprises the following steps: acquiring time sequence dependent graph structure data of each project and plan time period and resource demand identification of each bidding task, generating a cross-project resource association graph as a resource layer of a double-layer graph atlas; receiving resource scheduling change event data, calculating time offset of each affected task, and generating a resource layer affected task set; mapping an indirect risk value of a supplier layer to an associated bidding task, superimposing the time offset influence of the resource layer, and identifying a high-risk bidding task with a composite risk index exceeding a threshold value. The application solves the technical problems that resource conflict analysis and supplier risk analysis are independently operated, cross-system interaction is ignored, and composite risk cannot be timely warned.

Description

Technical Field

[0001] This invention relates to the field of management and decision support information systems, and more specifically, to a comprehensive planning management information system and method. Background Technology

[0002] In the multi-project bidding management scenario of a group enterprise, the bidding plans for multiple engineering projects need to be managed simultaneously. These projects have shared constraints at the resource level, such as sharing the same expert pool for the review process of multiple projects; and there are related network relationships at the supplier level, such as potential winning suppliers for each project having equity ties or historical business cooperation relationships.

[0003] In existing technologies, resource conflict analysis systems and supplier risk management systems operate independently. Resource conflict analysis systems focus on the timing impact of review schedule adjustments on tasks within and across projects, while supplier risk management systems focus on the risk transmission of changes in supplier status to related bidding processes. However, when a project's bidding process is delayed due to expert resource conflicts, this delay may compress the preparation time of related suppliers, while affiliated companies of that supplier are also bidding on another project. This creates a complex, cross-dimensional risk. Because both systems lack a cross-system correlation identification mechanism, the cascading risks generated at the supplier level due to time changes caused by resource scheduling decisions cannot be identified. The interactive effects of resource conflicts and supplier risks are ignored, and complex risks cannot be promptly warned of. Summary of the Invention

[0004] This invention provides a comprehensive planning management information system and method, which solves the technical problems in related technologies where resource conflict analysis and supplier risk analysis operate independently, resulting in the neglect of cross-system interaction effects and the inability to provide timely early warning of complex risks.

[0005] This invention provides a method for integrated planning management information, comprising: Obtain the time-series dependency graph structure data of each project and the planned time period and resource requirement identifier of each bidding task. Connect the time-series dependency graphs of multiple projects through virtual resource nodes to generate a cross-project resource association graph as the resource layer of the two-layer graph. Obtain supplier master data, generate supplier association undirected graph based on equity and cooperation relationships, obtain supplier binding information for each bidding task to establish bidding-supplier coupling association graph, and use it together with the supplier association undirected graph as the supplier layer of the two-layer graph; Establish cross-layer connection edges for each bidding task node to generate a resource-supplier two-layer heterogeneous graph structure; Receive resource scheduling change event data, traverse successor nodes in the resource layer starting from the changed task node, calculate the time offset of each affected task, and generate a set of affected tasks in the resource layer. By connecting cross layers, the set of tasks affected by the resource layer is mapped to the supplier layer. The supplier association undirected graph is traversed starting from the associated supplier, and the indirect risk value of each associated supplier is calculated according to the attenuation of the association strength coefficient. The indirect risk values ​​at the supplier level are mapped to the associated bidding tasks, and then superimposed with the time offset impact at the resource level to calculate the composite risk index for each task. High-risk bidding tasks with composite risk indices exceeding the threshold are identified.

[0006] Furthermore, the connection via virtual resource nodes includes: for bidding tasks that have the same resource requirement identifier, creating virtual resource nodes, and establishing connection edges between bidding task nodes that require the same resources and the corresponding virtual resource nodes.

[0007] Furthermore, the edge attributes of the supplier association undirected graph include association type and association strength coefficient, wherein the association type includes equity association and cooperation association.

[0008] Furthermore, the step of traversing successor nodes at the resource layer starting from the changed task node includes: using a breadth-first search algorithm to traverse all reachable successor nodes of the changed task node in the cross-project resource association graph, and the traversal range includes cross-project task nodes connected through virtual resource nodes.

[0009] Furthermore, the calculation of the indirect risk value of each associated supplier based on the attenuation of the association strength coefficient includes: for an associated supplier reached from the starting supplier via a path, the product of the direct risk value of the starting supplier and the association strength coefficient of each edge on the path is used as the indirect risk value of that associated supplier.

[0010] Furthermore, when the traversal depth exceeds a preset depth threshold, or the product of the accumulated correlation strength coefficients is lower than a preset decay threshold, traversal in that direction is stopped.

[0011] Furthermore, the superposition operation includes: weighting and summing the resource layer risk contribution value corresponding to the time offset with the supplier layer indirect risk value to obtain a composite risk index.

[0012] Furthermore, it also includes the following steps: for the identified high-risk bidding tasks, trace back their risk transmission path in the two-layer graph, and output a composite risk identification result. The composite risk identification result includes a high-risk task identifier, a resource layer risk contribution value, a supplier layer risk contribution value, a cross-layer transmission path description, and a risk source task identifier.

[0013] Furthermore, the backtracking of the risk transmission path in the two-layer graph includes: backtracking in the opposite direction of the time-dependent edge at the resource layer to the source task node of the change event, jumping to the supplier layer through the cross-layer connection edge, and backtracking along the supplier association edge to the directly affected supplier node and its associated source bidding task.

[0014] This invention provides a comprehensive planning management information system, comprising: The resource layer graph generation module is used to obtain the time-series dependency graph structure data of each project and the planned time period and resource requirement identifier of each bidding task. It connects the time-series dependency graphs of multiple projects through virtual resource nodes to generate a cross-project resource association graph. The supplier layer graph generation module is used to generate an undirected graph of supplier associations based on supplier master data, and to establish a bidding-supplier coupling association graph based on the supplier binding information of the bidding task. The dual-layer graph fusion module is used to establish cross-layer connection edges for each bidding task node and generate a resource-supplier dual-layer heterogeneous graph structure. The resource layer impact analysis module is used to receive resource scheduling change event data, traverse the successor nodes in the resource layer to calculate the time offset, and generate a set of tasks affected by the resource layer. The supplier-layer risk propagation module is used to map the set of affected tasks in the resource layer to the supplier layer through cross-layer connection edges and calculate the indirect risk value of each associated supplier. The composite risk identification module is used to calculate the composite risk index by superimposing the two layers of influence, and to identify high-risk bidding tasks.

[0015] The beneficial effects of this invention are as follows: This invention integrates a cross-project resource association graph and a supplier association undirected graph into a resource-supplier two-layer heterogeneous graph structure. This allows the impact of resource scheduling and supplier risk transmission, previously handled separately by independent systems, to be expressed within a unified graph framework. By establishing cross-layer connecting edges, a mapping relationship is created between the resource layer and the supplier layer for bidding tasks. This enables the time offset impact of the resource layer to be transmitted across layers to the supplier layer, triggering risk propagation calculations in the supplier association network. Through the superposition calculation of composite risk indices, high-risk bidding tasks simultaneously affected by the resource layer and the supplier layer can be identified, thus uncovering interactive risks that cannot be identified by independent single-layer analysis. Cross-layer path backtracking in the two-layer graph clarifies the formation path and source of composite risks, providing a basis for risk management decisions. Therefore, this invention solves the technical problem that the independent operation of resource conflict analysis and supplier risk analysis leads to the neglect of cross-system interactive impacts and the inability to provide timely early warnings of composite risks. Attached Figure Description

[0016] Figure 1This is a flowchart of a comprehensive planning management information method according to the present invention; Figure 2 This is the cross-project resource association map of the present invention; Figure 3 This is the supplier association network diagram of the present invention; Figure 4 This is a bar chart illustrating the propagation of the impact of resource scheduling changes in this invention; Figure 5 This is a scatter plot of the supplier risk transmission and attenuation analysis of the present invention; Figure 6 This is a bar chart of the composite risk index decomposition analysis of the present invention; Figure 7 The objective of this invention is a heatmap of supplier risk transmission. Detailed Implementation

[0017] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0018] At least one embodiment of the present invention discloses a method for integrated planning management information, such as... Figure 1 As shown, it includes the following steps: Step 100: Obtain time-series dependency data and resource requirement data for multiple projects, connect the time-series dependency graphs of each project through virtual resource nodes, and generate a cross-project resource association graph as the resource layer of the two-layer graph.

[0019] Specifically, the temporal dependency graph structure data for each project is obtained. This structure data includes bidding task nodes and their sequential dependencies. Simultaneously, the planned time period and resource requirement identifier for each bidding task are acquired. The planned time period includes the planned start and end times, and the resource requirement identifier represents the type of shared resource required by the bidding task. For bidding tasks with the same resource requirement identifier, virtual resource nodes are created. Connection edges are established between bidding task nodes requiring the same resource and their corresponding virtual resource nodes. This connects the temporal dependency graphs of multiple independent projects through virtual resource nodes, generating a cross-project resource association graph. ,in It is a collection of bidding task nodes and virtual resource nodes. This is a set of temporally dependent edges and resource-related edges. This cross-project resource association graph serves as the resource layer of a two-layer graph.

[0020] Furthermore, the aforementioned sequential dependency relationship refers to the execution order constraint relationship between bidding tasks. Specifically, if the planned start time of bidding task A depends on the planned end time of bidding task B, then a directed sequential dependency edge from task B to task A is established in the temporal dependency graph, indicating that task B is the predecessor task and task A is the successor task, and the execution of task A needs to wait for task B to complete.

[0021] A construction group needs to manage the bidding process for three ongoing construction projects: Project A (office building construction), Project B (residential community construction), and Project C (commercial complex construction). Each project involves multiple bidding tasks, such as pre-qualification, technical review, commercial review, and contract signing. The technical review tasks for all three projects require the use of the group's unified "Construction Engineering Review Expert Database," thus creating resource sharing constraints.

[0022] Table 1: Time Dependencies and Resource Requirements for Each Project's Bidding Tasks: Based on the data in Table 1, create virtual resource nodes VR1 for tasks A2, B2, and C2 that require the "Expert Library R1" resource, and establish resource association edges between task nodes and virtual resource nodes.

[0023] Regarding temporal dependencies, establish edges , , , , , , Regarding resource association, establishing edges , , The generated cross-project resource association graph contains 10 task nodes, 1 virtual resource node, and 10 edges: 7 temporal dependency edges and 3 resource association edges.

[0024] Figure 2 This demonstrates the relationships between multiple project bidding tasks through virtual resource nodes.

[0025] Step 200: Obtain supplier master data and generate an undirected graph of supplier relationships based on equity and cooperation relationships.

[0026] Specifically, supplier master data is obtained, which includes supplier identifiers, a list of suppliers with equity affiliations, and a list of historical subcontracting partners. Based on equity affiliations, a first type of association edge is established between supplier nodes with equity affiliations, with the edge attributes including an association type label of "equity affiliation" and a corresponding association strength coefficient. Based on historical subcontracting partnerships, a second type of association edge is established between supplier nodes with partnership partnerships, with the edge attributes including an association type label of "partnership partnership" and a corresponding association strength coefficient.

[0027] Generate an undirected graph of supplier associations ,in For the set of supplier nodes, For the set of associated edges, each edge Has associated type attribute and correlation strength coefficient attribute ,in and This represents the node index of the edge connection.

[0028] It should be noted that the value range of the above correlation strength coefficient is [missing information]. For equity-related relationships, shareholding ratio data is converted into a relationship strength coefficient through normalization; for cooperative relationships, the frequency or amount of historical cooperation is converted into a relationship strength coefficient through normalization, ensuring that all relationship strength coefficients are normalized to a certain level. Within the range.

[0029] The group's supplier pool includes six potential suppliers. Among them, supplier S1 and S2 have an equity relationship (S1 holds 30% equity in S2), supplier S2 and S3 have a historical cooperation relationship (they have subcontracted in five projects in the past three years), supplier S4 and S5 have an equity relationship (S4 holds 45% equity in S5), and supplier S5 and S6 have a cooperation relationship (they have subcontracted in three projects in the past two years).

[0030] Table 2 Supplier Master Data and Relationships: Generate an undirected graph of supplier relationships based on the data in Table 2. It includes 6 supplier nodes and 4 related edges. The equity related edges include... "Association strength 0.30" and "Association strength 0.45"; cooperative association edges include "Association strength 0.50" and "Association strength 0.35".

[0031] Figure 3 Show the equity and cooperative relationships between suppliers.

[0032] Step 300: Obtain the supplier binding information for each bidding task, establish a bipartite graph connection between the bidding task node and the supplier node, forming a bidding-supplier coupled relationship graph, which together with the supplier-related undirected graph serves as the supplier layer of the two-layer graph.

[0033] Specifically, the supplier binding information for each bidding task is obtained. This information includes the bidding task identifier and a list of potential supplier identifiers associated with that task. The set of bidding task nodes and the set of supplier nodes are treated as two disjoint vertex sets in a bipartite graph. Bipartite graph edges are then established between each bidding task node and its associated supplier node, generating a bidding-supplier coupling graph. ,in This is a set of bidding task nodes. For the set of supplier nodes, This is the set of binding edges between the bidding task and the suppliers. This bidding-supplier coupling graph, together with the supplier association undirected graph generated in step 200, forms the supplier layer of a two-layer graph.

[0034] Furthermore, the aforementioned list of potential supplier identifiers is determined by matching the qualification requirements of the bidding task with the business scope of the suppliers. Specifically, suppliers whose business scope covers the field to which the bidding task belongs and who have the corresponding qualification certifications are selected from the supplier master data, and the selected supplier identifiers are used to form the list of potential supplier identifiers for the bidding task.

[0035] Based on the technical requirements and business areas of each bidding task, the bidding tasks are linked with qualified potential suppliers. For Project A, the technical review task A2 is linked with suppliers S1 and S2, and the business review task A3 is linked with supplier S1; for Project B, the technical review task B2 is linked with suppliers S2 and S3; for Project C, the technical review task C2 is linked with suppliers S4 and S5, and the business review task C3 is linked with suppliers S5 and S6.

[0036] Table 3: Relationship between Bidding Tasks and Suppliers: Based on Table 3, construct a coupling relationship diagram between the bidding and supplier systems. It includes 10 bidding task nodes, 6 supplier nodes, and 9 binding edges. The specific binding edges include: , , , , , , , , .

[0037] Step 400: Identify the correspondence between bidding task nodes in the resource layer and supplier layer, establish cross-layer connection edges, and generate a resource-supplier two-layer heterogeneous graph structure.

[0038] Specifically, the bidding task nodes in the cross-project resource association graph of the resource layer are traversed. For each bidding task node, a corresponding node with the same task identifier is found in the bidding-supplier coupling association graph of the supplier layer. A cross-layer connection edge is established for each pair of nodes with the same task identifier. This cross-layer connection edge is used to represent the mapping relationship of the same bidding task in the resource layer and the supplier layer. A resource-supplier two-layer heterogeneous graph structure is generated. ,in For the resource layer map, and Constructing a supplier layer map, This is a set of cross-layer connection edges.

[0039] Step 500: Receive resource scheduling change event data, perform a breadth-first traversal in the resource layer starting from the changed task node, calculate the time offset of each affected task, and generate a set of affected tasks in the resource layer.

[0040] Specifically, the system receives resource scheduling change event data, which includes the identifier of the changed bidding task and the changed scheduled time period. The changed task node is located in the cross-project resource association graph as the starting point for traversal. A breadth-first search algorithm is used to traverse all reachable successor nodes of this starting node in the cross-project resource association graph. For each successor node traversed, its time offset is calculated based on its temporal dependency with its predecessor node and the time change amount of the predecessor node. Record all bidding task nodes with non-zero time offsets and their corresponding time offsets as the resource layer affected task set. ,in This indicates a bidding task.

[0041] Furthermore, regarding the time offset of subsequent nodes... The calculation assumes the planned end time after the predecessor node change is... The original planned start time for the successor node was The formula for calculating the time offset of the successor node is: , among which when When the predecessor node's end time is later than the successor node's originally planned start time, the successor node needs to wait for the predecessor node to complete, and the time offset is the time difference between the two. When the time offset is zero, it indicates that the change of the predecessor node was completed before the original planned start time of the successor node, and the successor node does not need to be adjusted.

[0042] The aforementioned breadth-first search algorithm takes the changed task node as the starting point of input and uses a cross-project resource association graph. The temporal dependency edges and resource association edges form the traversal path, outputting the sequence of all reachable successor task nodes; during the traversal, cross-project task nodes connected through virtual resource nodes are included in the traversal scope, thereby identifying the cross-project impact propagation path caused by shared resource conflicts.

[0043] On January 8, 2025, due to a temporary reallocation of expert resources, the planned timeframe for technical review task A2 of Project A needs to be postponed. The revised planned start time is January 16, 2025, and the planned end time is January 23, 2025. Starting with task A2, a breadth-first traversal is performed. First, the traversal follows the temporal dependency edges to the subsequent tasks A3 and A4 within Project A; simultaneously, it traverses through the virtual resource node VR1 to tasks B2 of Project B and C2 of Project C, which share the same expert resources, and then continues to B3 and C3.

[0044] Table 4 Resource scheduling change events and the set of affected tasks: Based on Table 4, generate the set of tasks affected by the resource layer: The numerical value represents the time offset (unit: days).

[0045] Since the original planned end time of task C2, 2025-01-20, is earlier than the revised end time of task A2, 2025-01-23, and C2 needs to wait for the release of shared expert resources, the time offset of C2 is [missing information]. sky.

[0046] Figure 4 Demonstrates the propagation of the time offset impact of resource scheduling change events across project tasks.

[0047] Step 600: Map the set of tasks affected by the resource layer to the supplier layer through cross-layer connection edges, obtain the set of associated supplier nodes, perform a breadth-first traversal in the supplier association undirected graph, and calculate the indirect risk value of each associated supplier according to the association strength coefficient decay.

[0048] Specifically, the process iterates through each bidding task in the set of affected tasks at the resource layer, maps these task nodes to corresponding nodes in the bidding-supplier coupling graph at the supplier layer through cross-layer connection edges, and then obtains the set of supplier nodes associated with these tasks as the starting point set for risk propagation. In an undirected graph of supplier associations, starting from the set... Starting from each supplier node in the dataset, a breadth-first traversal is performed. During the traversal, for each supplier node starting from the initial node... Related suppliers reached via the route The indirect risk value of the associated supplier is calculated by attenuating the risk based on the correlation strength coefficient of each edge along the path. in, Indicates related suppliers, Indicates the traversal path, Represents the edges on the path. The direct risk value of the starting supplier is derived from the time offset of its associated bidding task, and the calculation method is as follows: The formula normalizes the time offset. Convert to dimensionless risk value, where This is a preset reference time scale; The correlation strength coefficient of each edge on the path; From arrive All traversal paths. Generate a set of supplier-level risk values. ,in Indicates the supplier node, This represents the corresponding indirect risk value.

[0049] Furthermore, when the starting point supplier When multiple affected bidding tasks are associated, the direct risk value is calculated using the maximum time offset of these tasks. ,in Indicates the task being tendered. This indicates the time offset of the bidding task. In order to cooperate with suppliers A set of linked bidding tasks. For the set of tasks affected at the resource layer, this formula means that the task with the largest time offset among all affected tasks associated with the supplier is taken as the basis for calculating the direct risk value.

[0050] Furthermore, the aforementioned reference time scale The specific timeframe is determined based on the average planned cycle of each bidding task. The value is the average of the planned time period lengths for all bidding tasks.

[0051] Furthermore, the formula for calculating the indirect risk value takes the maximum value for all paths because, in a risk transmission scenario, as long as there is a high-risk transmission path, the associated supplier will face a corresponding degree of risk impact. Therefore, the maximum value strategy is adopted to identify the most severe risk transmission path.

[0052] The aforementioned breadth-first traversal algorithm uses the set of risk propagation starting points. Starting with the supplier nodes in the graph, the input is an undirected graph of supplier associations. The equity-related edges and cooperation-related edges in the algorithm are traversal paths, and the sequence of all reachable related supplier nodes is output. During the traversal, each traversal path and the product of the association strength coefficients on the path are recorded for subsequent calculation of indirect risk values.

[0053] By using cross-layer connection edges, affected tasks at the resource layer are mapped to those at the supplier layer. Affected task A2 is bound to suppliers S1 and S2, affected task A3 to supplier S1, affected task B2 to suppliers S2 and S3, affected task C2 to suppliers S4 and S5, and affected task C3 to suppliers S5 and S6. Therefore, the set of risk propagation starting points is... Set a reference time scale. "Based on the average cycle of each bidding task".

[0054] Table 5. Calculation of Direct Risk Value and Indirect Risk Transmission of Suppliers: The direct risk value of supplier S1 is calculated as follows: Supplier S3 is directly bound to the affected task B2, with a direct risk value of 1.000, and is also connected via the path... Receive indirect risk, the value of which is... Supplier S5 is directly linked to affected tasks C2 and C3, with a direct risk value of 1.375, and also through the path Receive indirect risk, the indirect risk value is Supplier S6 is directly bound to the affected task C3, with a direct risk value of 1.375, and is also affected via the path... Receive indirect risk, the indirect risk value is Generate a set of supplier-level risk values ​​based on Table 5. Among them, risk value The direct risk values ​​of each supplier are used.

[0055] In this embodiment, to control the scope of risk propagation, a traversal depth threshold or a cumulative attenuation threshold can be set. When the traversal depth exceeds the preset depth threshold, or the product of the cumulative correlation strength coefficients is lower than the preset attenuation threshold, traversal in that direction is stopped, thereby avoiding distortion of risk values ​​caused by excessive propagation.

[0056] Furthermore, the aforementioned depth threshold ranges from 3 to 5 layers of supplier relationships, and the cumulative attenuation threshold ranges from 0.05 to 0.1. When the cumulative product of the association strength coefficients on the path is lower than the cumulative attenuation threshold, it indicates that the degree of risk transmission has attenuated to a negligible level.

[0057] Figure 5 Show the comparison between the supplier's direct risk value and indirect risk value.

[0058] Step 700: Based on the bidding-supplier coupling relationship diagram, map the indirect risk value of the supplier layer to the associated bidding task, and perform superposition calculation with the time offset effect of the resource layer to calculate the composite risk index of each task and identify high-risk bidding tasks.

[0059] Specifically, based on the binding relationships in the bidding-supplier coupling graph, the indirect risk values ​​of each supplier in the supplier-level risk value set are back-mapped to the bidding task nodes associated with them. For each bidding task... If the task is simultaneously affected by both resource layer time offset and supplier layer indirect risk, the two layers of impact are superimposed to calculate the composite risk index of the task. in, This represents a composite risk index. Indicates the task being tendered. Indicates the supplier node, Time offset The corresponding resource layer risk contribution value is calculated as follows: The formula converts the time offset into a dimensionless risk value through normalization, where This is a preset reference time scale, thereby ensuring that the risk values ​​at the resource layer and the risk values ​​at the supplier layer are consistent. The dimensions are consistent; In order to meet the bidding task The set of suppliers bound together; and The weighting coefficients for two layers of risk and satisfying If the composite risk index exceeds a preset threshold... The bidding tasks are marked as high-risk bidding tasks, and a set of high-risk tasks is generated. .

[0060] Furthermore, the aforementioned weighting coefficients and The settings are based on the degree of importance that enterprises attach to resource conflict risks and supplier association risks. The value range is from 0.4 to 0.6. The value range is from 0.4 to 0.6, and satisfies... Preset threshold The value ranges from 0.3 to 0.5. When the composite risk index exceeds the preset threshold, it indicates that the bidding task is affected by significant risks from both the resource layer and the supplier layer.

[0061] Furthermore, for bidding tasks affected only by a single layer, when the task is only affected by the resource layer but is not tied to a supplier or its tied suppliers have no indirect risks, The risk contribution value at the supplier level is set to zero, i.e. When a task is not affected by resource layer time offset but its bound supplier has indirect risks, the resource layer risk contribution value is zero. At this point, the composite risk index degenerates into a single-layer risk value.

[0062] Based on the bidding-supplier coupling diagram, the risk value of the supplier layer is mapped inversely to the bidding task, and a weighting coefficient is set. , Calculate the composite risk index for each affected task. Set a preset threshold. .

[0063] Table 6: Calculation Results of the Composite Risk Index for Bidding Tasks The compound risk index for task A2 is calculated as follows: The compound risk index for task C2 is calculated as follows: Six high-risk bidding tasks were identified, and a set of high-risk tasks was generated. Among them, tasks C2 and C3 have the highest composite risk index, and are affected by both severe delays in the resource layer and high risks in the supplier layer.

[0064] Figure 6 The composite risk index and its composition are displayed for each bidding task.

[0065] Step 800: For high-risk bidding tasks, trace back the risk transmission path in the two-layer graph and output the composite risk identification results.

[0066] Specifically, for each high-risk bidding task in the high-risk task set, a backtracking traversal is performed within the resource-supplier two-layer heterogeneous graph structure. In the resource layer, backtracking is performed along the reverse direction of the temporal dependency edges to the source task node of the change event, recording the resource layer propagation path. Then, jumps to the supplier layer via cross-layer connection edges, and backtracks along supplier association edges to the directly affected supplier nodes and their associated source bidding tasks, recording the supplier layer propagation path. The composite risk identification result is output, which includes a high-risk task identifier and a resource layer risk contribution value. Supplier-level risk contribution value Description of cross-layer transmission paths and identification of risk sources.

[0067] The aforementioned backtracking traversal takes the high-risk bidding task node as the input starting point, and follows the reverse direction of the temporal dependency edge of the resource layer and the reverse direction of the association edge of the supplier layer as the traversal path, outputting the complete transmission path sequence from the high-risk task node to the risk source node.

[0068] For the identified high-risk task C2, the risk transmission path is traced back. At the resource layer, the process traces back from task C2 along the temporal dependency edge to the predecessor task C1, and then connects to the change source task A2 through the virtual resource node VR1. At the supplier layer, task C2 is bound to suppliers S4 and S5, both of which are directly affected by the delay of task C2. At the same time, S5 is connected to S4 through the equity association edge, receiving indirect risk transmission from S4.

[0069] Table 7. Results of composite risk identification for high-risk task C2: The results of this composite risk identification indicate that task C2 is simultaneously affected by cross-project resource conflicts "through expert database VR1" and supplier association risks "through equity associations of S4 and S5". The time pressure caused by the 11-day delay at the resource level and the risk transmission at the supplier level form a composite risk, which requires priority risk management and scheduling optimization.

[0070] Figure 7 Display the risk transmission strength matrix between the bidding task and the supplier.

[0071] This implementation merges a cross-project resource association graph and a supplier association undirected graph into a resource-supplier two-layer heterogeneous graph structure. This allows the impact of resource scheduling and supplier risk transmission, originally handled separately by independent systems, to be expressed within a unified graph framework. Cross-layer connecting edges establish a mapping relationship between bidding tasks in the resource and supplier layers, enabling the time offset impact of the resource layer to propagate across layers to the supplier layer and trigger risk propagation calculations in the supplier association network. Through the superposition calculation of composite risk indices, high-risk bidding tasks simultaneously affected by the resource and supplier layers can be identified, thus uncovering interactive risks that cannot be identified by independent single-layer analysis. Cross-layer path backtracking in the two-layer graph clarifies the formation path and source of composite risks, providing a basis for risk management decisions. Therefore, this implementation solves the technical problem that the independent operation of resource conflict analysis and supplier risk analysis leads to the neglect of cross-system interactive impacts and the inability to provide timely early warnings of composite risks.

[0072] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A comprehensive planning management information method, characterized in that, Includes the following steps: Obtain the time-series dependency graph structure data of each project and the planned time period and resource requirement identifier of each bidding task. Connect the time-series dependency graphs of multiple projects through virtual resource nodes to generate a cross-project resource association graph as the resource layer of the two-layer graph. Obtain supplier master data, generate supplier association undirected graph based on equity and cooperation relationships, obtain supplier binding information for each bidding task to establish bidding-supplier coupling association graph, and use it together with the supplier association undirected graph as the supplier layer of the two-layer graph; Establish cross-layer connection edges for each bidding task node to generate a resource-supplier two-layer heterogeneous graph structure; Receive resource scheduling change event data, traverse successor nodes in the resource layer starting from the changed task node, calculate the time offset of each affected task, and generate a set of affected tasks in the resource layer. By connecting cross layers, the set of tasks affected by the resource layer is mapped to the supplier layer. The supplier association undirected graph is traversed starting from the associated supplier, and the indirect risk value of each associated supplier is calculated according to the attenuation of the association strength coefficient. The indirect risk values ​​at the supplier level are mapped to the associated bidding tasks, and then superimposed with the time offset impact at the resource level to calculate the composite risk index for each task. High-risk bidding tasks with composite risk indices exceeding the threshold are identified.

2. The integrated planning management information method according to claim 1, characterized in that, The connection via virtual resource nodes includes: for bidding tasks that have the same resource requirement identifier, creating virtual resource nodes, and establishing connection edges between bidding task nodes that require the same resources and the corresponding virtual resource nodes.

3. The integrated planning management information method according to claim 1, characterized in that, The edge attributes of the supplier association undirected graph include association type and association strength coefficient. The association type includes equity association and cooperation association.

4. The integrated planning management information method according to claim 1, characterized in that, The step of traversing successor nodes at the resource layer starting from the changed task node includes: using a breadth-first search algorithm to traverse all reachable successor nodes of the changed task node in the cross-project resource association graph, and the traversal range includes cross-project task nodes connected through virtual resource nodes.

5. The integrated planning management information method according to claim 1, characterized in that, The calculation of the indirect risk value of each associated supplier based on the attenuation of the association strength coefficient includes: for associated suppliers reached from the starting supplier via the path, the product of the direct risk value of the starting supplier and the association strength coefficient of each edge on the path is used as the indirect risk value of the associated supplier.

6. The integrated planning management information method according to claim 5, characterized in that, When the traversal depth exceeds the preset depth threshold, or the product of the accumulated correlation strength coefficients is lower than the preset decay threshold, the traversal in that direction is stopped.

7. The integrated planning management information method according to claim 1, characterized in that, The superposition operation includes: weighting and summing the resource layer risk contribution value corresponding to the time offset with the supplier layer indirect risk value to obtain a composite risk index.

8. The integrated planning management information method according to claim 1, characterized in that, It also includes the following steps: For the identified high-risk bidding tasks, the risk transmission path in the two-layer graph is traced back, and a composite risk identification result is output. The composite risk identification result includes the high-risk task identifier, the resource layer risk contribution value, the supplier layer risk contribution value, the cross-layer transmission path description, and the risk source task identifier.

9. The integrated planning management information method according to claim 8, characterized in that, The risk transmission path in the two-layer graph is traced back along the reverse direction of the time-dependent edge in the resource layer to the source task node of the change event, jumps to the supplier layer through the cross-layer connection edge, and traces back along the supplier association edge to the directly affected supplier node and its associated source bidding task.

10. A comprehensive planning management information system, used to execute the comprehensive planning management information method according to any one of claims 1-9, characterized in that, include: The resource layer graph generation module is used to obtain the time-series dependency graph structure data of each project and the planned time period and resource requirement identifier of each bidding task. It connects the time-series dependency graphs of multiple projects through virtual resource nodes to generate a cross-project resource association graph. The supplier layer graph generation module is used to generate an undirected graph of supplier associations based on supplier master data, and to establish a bidding-supplier coupling association graph based on the supplier binding information of the bidding task. The dual-layer graph fusion module is used to establish cross-layer connection edges for each bidding task node and generate a resource-supplier dual-layer heterogeneous graph structure. The resource layer impact analysis module is used to receive resource scheduling change event data, traverse the successor nodes in the resource layer to calculate the time offset, and generate a set of tasks affected by the resource layer. The supplier-layer risk propagation module is used to map the set of affected tasks in the resource layer to the supplier layer through cross-layer connection edges and calculate the indirect risk value of each associated supplier. The composite risk identification module is used to calculate the composite risk index by superimposing the two layers of influence, and to identify high-risk bidding tasks.

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