Early warning system for power grid engineering construction material supply chain operation

Abstract

The invention relates to the technical field of data processing, and discloses an early warning system for power grid engineering construction material supply chain operation, and the system comprises a data acquisition module, a dynamic construction module, a data processing module, a data processing module and a data processing module, dynamically constructing a disturbance coefficient of the construction progress node by combining the space interference capability of the progress deviation with the inventory dynamic data; the node acquisition module is used for analyzing a material conduction path of the material supply node through the disturbance coefficient to obtain a key fragile node set of the material supply node; the screening module is used for extracting material demand characteristics of the construction progress nodes; according to the invention, the problem that most early warning systems in the prior art only independently collect inventory data of the construction progress node or the material supply node, and thus the potential risk of mismatching between supply and demand cannot be recognized in advance can be solved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an early warning system for the operation of the power grid engineering construction material supply chain. Background Technology

[0002] In the field of power grid construction, early warning management of the material supply chain is crucial for ensuring the timely progress of projects. However, most existing early warning systems only collect inventory data at individual construction progress nodes or material supply nodes, making it difficult to accurately capture the coupled impact between schedule deviations and inventory fluctuations, and thus failing to identify potential risks of supply-demand mismatches in advance. Furthermore, existing systems employ relatively simplistic methods for assessing project disturbances, often considering only the magnitude of schedule or inventory deviations at a single node, without incorporating the spatial contagion of schedule deviations, thus failing to provide a reliable basis for risk identification.

[0003] In addition, existing technologies lack verification of the continuity of material transmission paths when identifying vulnerable nodes in the material supply chain. They are prone to misjudging non-critical fluctuation nodes as vulnerable nodes, making it difficult to accurately identify key nodes that may cause supply and demand timeline disruptions. This results in insufficient targeted early warning, low response efficiency, and an inability to effectively prevent the chain reaction of risks, which may cause delays in the construction progress of power grid projects. Summary of the Invention

[0004] This invention provides an early warning system for the operation of the power grid engineering construction material supply chain, the main purpose of which is to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an early warning system for the operation of the power grid engineering construction material supply chain, comprising: The data acquisition module is used to synchronously acquire dynamic inventory data of the construction progress nodes and corresponding material supply nodes of the project. The dynamic simulation module is used to calculate the progress deviation between the construction progress nodes, and dynamically simulate the disturbance coefficient of the construction progress node by combining the spatial interference capability of the progress deviation with the dynamic inventory data. The node acquisition module is used to analyze the material transmission path of the material supply node through the disturbance coefficient, and obtain the set of key vulnerable nodes of the material supply node; The filtering module is used to extract the material demand characteristics of the construction progress nodes and filter out the blocking nodes in the set of critical vulnerable nodes whose material demand characteristics conflict with the supply timing. The heatmap drawing module is used to determine the link vulnerability of the blocking node by combining the interruption propagation pattern in the historical risk event database, and draw a risk chain diffusion heatmap based on the link vulnerability; The early warning instruction generation module is used to generate engineering early warning instructions based on the risk chain diffusion heat map.

[0006] Preferably, the synchronous acquisition of dynamic inventory data for construction progress nodes and corresponding material supply nodes includes: Connect to the real-time progress management interface of the project construction and collect construction progress nodes; Continuously record the sequence of inventory level changes at the aforementioned material supply nodes; The progress node and the inventory level change sequence are time-series aligned to obtain the dynamic inventory data of the material supply node.

[0007] Preferably, the formula for calculating the disturbance coefficient is as follows:

[0008] in: The disturbance coefficient is... The total number of the construction progress nodes. for index, For nodes The magnitude of the schedule deviation, For nodes The instability amplitude of the aforementioned inventory dynamic data, The spatial contagion factor for the aforementioned schedule deviation. The system damping strength for inventory response, It is a constant.

[0009] Preferably, the step of analyzing the material transmission path of the material supply node through the disturbance coefficient to obtain the set of critical vulnerable nodes of the material supply node includes: Determine the difference in disturbance levels between the disturbance coefficient and the construction progress node; The fluctuation range of the inventory dynamic data is determined based on the difference in the disturbance level, and potential vulnerabilities of the material supply nodes are marked based on the fluctuation range. By mapping the potential vulnerabilities to the material transmission path, the potential risk propagation path is obtained; The continuity of the risk propagation path is verified, and the results of the verification are identified as a set of critical vulnerable nodes.

[0010] Preferably, the extraction of material demand characteristics for the construction progress nodes includes: Construct a construction timeline for the construction progress nodes, and identify the material categories for the construction progress nodes based on the construction timeline and the dynamic inventory data; Based on the resource list of the material category and the construction progress node, analyze the material demand characteristics of the construction progress node.

[0011] Preferably, the step of screening for blocking nodes in the set of critical vulnerable nodes that have supply timing conflicts with the material demand characteristics includes: Set the demand time-series peak window for the material demand characteristics, and determine the supply response lag of the set of key vulnerable nodes based on the inventory dynamic data; Based on the demand time series peak window and the supply response lag, identify the set of time series conflict points of the set of key vulnerable nodes; Based on the set of time-series conflict points, the blocking nodes of the critical vulnerable nodes are selected.

[0012] Preferably, the step of determining the link vulnerability of the blocking node by combining the interruption propagation patterns in the historical risk event database includes: Based on the interruption propagation pattern, the historical interruption cascade path of the blocking node is extracted; Identify the overlapping risk nodes between the historical interruption cascade path and the supply chain topology points corresponding to the blocking nodes; Verify the transmission consistency between the overlapping risk nodes and the dynamic inventory data, and determine the result of the verification as the risk transmission chain; The link vulnerability of the risk transmission chain is determined to obtain the link vulnerability of the blocking node.

[0013] Preferably, the step of drawing a risk cascading diffusion heatmap based on the link vulnerability includes: Isolate the core points of risk transmission in the transmission strength map corresponding to the link vulnerability; By combining the dynamic inventory data with the neighborhood of the risk transmission core point, a diffusion domain spectrum is obtained; The diffusion intensity of the diffusion domain spectrum is quantified based on the inventory response delay in the inventory dynamic data to obtain a diffusion intensity map. The diffusion intensity spectrum is subjected to thermodynamic gradient transformation to obtain a risk cascading diffusion thermogram.

[0014] Preferably, the step of generating an engineering early warning instruction through the risk cascading diffusion heatmap includes: Define the core risk diffusion axis of the aforementioned risk cascading diffusion heatmap; Based on the inventory response delay, the risk core diffusion axis is topologically diffused to obtain a cascaded response domain. The engineering response lag of each node in the cascaded response basin is determined as the early warning pressure value; The aforementioned warning pressure value is converted into an engineering warning command.

[0015] Preferably, the step of converting the warning pressure value into an engineering warning command includes: A differential analysis is performed on the warning pressure values ​​to obtain the response priority of the warning pressure values; The response priority is converted and encapsulated into an engineering early warning command. Beneficial effects

[0016] 1. By incorporating key parameters such as the spatial contagion factor of schedule deviation and the system damping strength of inventory response when calculating the disturbance coefficient, this method can more comprehensively reflect the actual impact of engineering disturbances compared to existing single-dimensional deviation assessments. Furthermore, by verifying the continuity of material transmission paths to determine the set of critical vulnerable nodes, it effectively avoids misjudging non-critical fluctuation nodes and significantly improves the accuracy of vulnerable node identification.

[0017] 2. By extracting the material demand characteristics of construction progress nodes and combining the construction timeline with the resource list, and by associating the peak demand window and the supply response lag when screening blocking nodes, the key nodes that cause supply and demand timeline conflicts can be accurately identified, avoiding the problem of ambiguous timeline conflict identification in existing technologies. Attached Figure Description

[0018] Figure 1 A functional module diagram of an early warning system for the operation of the power grid engineering construction material supply chain is provided in an embodiment of the present invention. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] like Figure 1 The diagram shown is a functional module diagram of an early warning system for the operation of the power grid engineering construction material supply chain, provided by an embodiment of the present invention.

[0021] The early warning system 100 for the operation of the power grid construction material supply chain, as described in this invention, can be installed in an electronic device. Depending on the functions implemented, the early warning system 100 may include a data acquisition module 101, a dynamic simulation module 102, a node acquisition module 103, a filtering module 104, a heat map drawing module 105, and an early warning command generation module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0022] In this embodiment, the functions of each module / unit are as follows: Data acquisition module 101 is used to synchronously acquire dynamic inventory data of construction progress nodes and corresponding material supply nodes of the project.

[0023] In this embodiment, the synchronous acquisition of construction progress nodes and corresponding material supply node inventory dynamic data includes: Connect to the real-time progress management interface of the project construction and collect construction progress nodes; Continuously record the sequence of inventory level changes at the aforementioned material supply nodes; The progress node and the inventory level change sequence are time-series aligned to obtain the dynamic inventory data of the material supply node.

[0024] Specifically, the real-time progress management interface refers to the standardized interface used in power grid engineering construction projects for storing, updating, and transmitting progress data. It is usually connected to project management systems such as ERP and PMIS to support real-time data interaction. The interface protocol often adopts RESTful or SOAP to ensure the stability and timeliness of data transmission.

[0025] Construction progress milestones are key progress milestones unique to power grid projects. They cover milestones such as the completion of substation civil construction, the erection of transmission line towers, cable laying, and equipment installation and commissioning. Each milestone must include attributes such as milestone ID, planned completion time, actual completion time, responsible department, and associated material list ID to clarify the correspondence between progress and material requirements.

[0026] In detail, firstly, maintenance personnel configure interface parameters, such as IP address, port number, and access token, through the system backend to connect to the power grid company's internal progress management platform and complete identity authentication, such as OAuth2.0 authorization, to ensure that the system has the permission to read progress data and avoid the risk of data leakage or tampering caused by unauthorized access.

[0027] Then, based on the preset power grid project progress template, such as the standard progress node library for 220kV substation construction, the system automatically filters core nodes that are strongly related to material supply and excludes non-critical auxiliary nodes, such as project kick-off meetings. It pulls node data in real time through the interface, with the collection frequency set to once every 15 minutes, which can be adjusted according to the complexity of the project, to ensure timely capture of progress deviations and that the actual completion time lags behind the planned time.

[0028] Finally, the collected progress data is converted into a unified JSON format. Abnormal data, such as missing node IDs or incorrect time formats, is cleaned. For example, the timestamp in the format of 2024-05-20T14:30 is uniformly converted to YYYY-MM-DDHH:MM:SS to facilitate subsequent alignment with inventory data time series.

[0029] Furthermore, in the power grid engineering environment, the progress management interface needs to be compatible with the system differences of different construction units, such as the project management platforms of State Grid and China Southern Power Grid. At the same time, it needs to cope with network fluctuations in field construction scenarios, such as the construction of transmission lines in remote areas. It needs to support offline caching function, temporarily storing local progress data when the network is interrupted, and automatically re-transmitting it after the network is restored, so as to avoid data loss affecting subsequent early warning analysis.

[0030] Specifically, material supply nodes refer to key links in the power grid engineering material supply chain, including material manufacturer warehouses, regional distribution centers, and temporary warehouses at construction sites. Each node corresponds to a unique warehouse code and is associated with the type of material, such as transformers, cables, insulators, and tower components. Basic information such as warehouse location coordinates, inventory capacity, and maximum replenishment cycle must be recorded.

[0031] The inventory level change sequence records the fluctuation data of the inventory quantity of a certain type of material at the supply node with time as the axis. It includes fields such as timestamp, material code, current inventory quantity, quantity received, quantity shipped, inventory warning threshold, and inventory status. For example, 2024-05-20 14:30, B001 (220kV transformer), 5 units in inventory, 0 units received, 2 units shipped, warning threshold of 3 units, status normal.

[0032] In detail, IoT sensing devices, such as RFID readers, smart weighbridges, and inventory management terminals, are deployed at each material supply node to collect data on material entry into the warehouse in real time, such as goods delivered by manufacturers to the distribution center and goods leaving the warehouse, such as goods shipped by the distribution center to the construction site. The data is transmitted to the system database via 5G / 4G network, with the collection frequency set to once every 5 minutes to ensure accurate capture of inventory dynamics, such as a sudden large number of outbound shipments that cause a sharp drop in inventory.

[0033] Then, the system automatically generates an inventory level change sequence based on the dimensions of material code and supply node code, using a time-series database such as InfluxDB for storage. It supports fast querying by time range, such as querying the inventory fluctuation of iron tower components in the North China regional distribution center over the past 7 days. At the same time, it records abnormal change events, such as the daily outbound volume exceeding twice the historical average, which are marked as data to be verified.

[0034] Finally, in accordance with the progress requirements of power grid projects, such as ensuring sufficient cable inventory 10 days before the cable laying node, the system automatically adjusts the inventory warning threshold. For example, when the tower erection node of transmission lines starts 3 days in advance, the safety inventory limit of tower components is increased from 50 sets to 80 sets to avoid material shortages caused by the advance schedule.

[0035] Specifically, time alignment is based on the association logic between progress node timestamps and inventory change timestamps, and sets the alignment granularity, such as by hour or by day. In power grid projects, daily alignment is preferred to match the daily management cycle of project progress. In special scenarios, such as the equipment commissioning phase, hourly alignment is used.

[0036] The alignment rules must include a time window from N days before to M days after the progress node. For example, for a cable laying node, the time window for the corresponding inventory data is set from May 25, 2024 to June 5, 2024, covering the entire cycle of advance stocking and replenishment after use.

[0037] The dynamic inventory data is structured data generated after time-series alignment. It includes fields such as progress node ID, progress node time, corresponding supply node code, material code, average inventory within the time window, inventory fluctuation range, and inventory matching degree with progress, such as whether the actual inventory meets the material requirements of the node. It is used to intuitively present the coupling relationship between progress and inventory.

[0038] In detail, the system automatically matches the corresponding inventory change time window of the materials based on the planned / actual time of the progress node. For example, for the substation equipment installation node, the actual completion time is 2024-05-15. It is associated with the inventory data of transformers in the temporary warehouse of the substation, with a time window from 2024-05-10 to 2024-05-20. The progress node attributes are bound to the inventory change sequence through SQL relational queries.

[0039] Then, the accuracy of the associated data is verified. If there is a time deviation, such as the progress node time being 2024-05-15 and the inventory data timestamp being 2024-05-16, the system automatically determines the cause of the deviation. If the inventory update is delayed due to the delay in material transportation, the deviation time is recorded as the supply response lag and included in the subsequent blocking node screening criteria. If the delay is due to data collection error, the inventory timestamp is automatically corrected to the time window corresponding to the progress node.

[0040] Finally, the aligned inventory dynamic data is used to generate trend charts, such as a progress node time-inventory quantity line chart, with key information marked, such as the time when the inventory is below the warning threshold and the inventory change corresponding to the progress lag. For example, the chart is marked in red that on May 12, 2024, there were 2 transformers in stock, the warning threshold was 3, and the progress node was 1 day behind, which makes it easier for maintenance personnel to quickly identify the risk of supply and demand mismatch.

[0041] The dynamic simulation module 102 is used to calculate the progress deviation between the construction progress nodes, and dynamically simulate the disturbance coefficient of the construction progress node by combining the spatial interference capability of the progress deviation with the dynamic inventory data.

[0042] In this embodiment, the formula for calculating the disturbance coefficient is as follows:

[0043] in: The disturbance coefficient is... The total number of the construction progress nodes. for index, For nodes The magnitude of the schedule deviation, For nodes The instability amplitude of the aforementioned inventory dynamic data, The spatial contagion factor for the aforementioned schedule deviation. The system damping strength for inventory response, It is a constant.

[0044] Specifically, This is the disturbance coefficient, and its value range is... A higher value indicates a higher overall risk of disruption to the engineering supply chain, for example... This can be considered a high-risk threshold.

[0045] It is used to quantify the degree of disturbance risk caused by factors such as schedule deviation and dynamic fluctuations in inventory during the operation of the power grid construction material supply chain. It is the core indicator for the early warning system to judge whether the supply chain is stable.

[0046] In detail, in the context of power grid engineering, the supply chain involves multiple stages, including material production, transportation, warehousing, and on-site use. It can reflect the possibility of problems in the coordination of these links. The higher the value, the more likely there are problems in the connection between material supply and project construction. For example, there may be risks such as material shortages that delay the construction period, or inventory backlog that ties up funds, or materials that expire.

[0047] The total number of construction progress nodes refers to the number of all key progress nodes in the construction of a power grid project, from project initiation to completion, such as the total number of nodes in different stages such as substation civil engineering, equipment installation, and line erection.

[0048] In detail, the larger the scale and the more complex the structure of a power grid project, such as ultra-high voltage inter-regional projects, the better. The larger the value, the more interconnected these nodes are; a problem at any node can ripple through and affect the entire supply chain. (Statistics) This serves as the basis for subsequent calculations of the overall disturbance.

[0049] For nodes The magnitude of the schedule deviation is the node The absolute value of the difference between actual progress and planned progress reflects the degree to which the progress deviates from the plan. If the actual progress lags behind the plan, It is a positive difference; if it is earlier, the absolute value is also taken to represent the deviation range.

[0050] In detail, power grid projects are prone to schedule deviations due to natural environmental factors, such as extreme weather affecting pole erection and material supply, and equipment delivery delays affecting installation. Quantifying these deviations allows us to measure their impact on the supply chain rhythm; for example, delays may lead to stockpiling of materials or disruptions in the supply of subsequent materials.

[0051] For nodes The unstable amplitude of the aforementioned inventory dynamic data is a node The corresponding material inventory fluctuates around the normal level, reflecting the drastic changes in inventory quantity, status, etc., such as a large increase or decrease in inventory in a short period of time, or a sudden change in available inventory due to unqualified quality inspection of inventory materials.

[0052] In detail, power grid materials have their own characteristics. For example, the inventory changes of large equipment such as transformers are relatively small but have a large impact, while the inventory of materials such as cables may fluctuate frequently due to the rhythm of construction requisition. These inventory fluctuations need to be monitored, as inventory instability can directly disrupt the supply chain's ability to ensure the availability of supplies.

[0053] The spatial contagion factor of the schedule deviation is a coefficient that measures the ability of schedule deviation to spread between different construction nodes and different regions of a power grid project, reflecting the intensity of the spread of schedule problems from one node or region to other nodes or regions.

[0054]

[0055] in: The spatial contagion factor for the aforementioned schedule deviation. For the standard deviation operator, For nodes The magnitude of the schedule deviation, The total number of the construction progress nodes. for The index.

[0056] In detail, power grid engineering is a system engineering project with a large spatial span. For example, if a transmission line is being built from province A to province B, and the progress of a certain section in province A is lagging behind, it may be due to changes in the allocation of construction teams or the order of material allocation, which could affect the progress of the section in province B. To quantify this spatial transmission risk, we need to consider the role of environmental factors such as the geographical distribution of the project and the coordination of construction organization in the transmission of deviations.

[0057] The system damping strength for inventory response represents the power grid material supply chain system's ability to buffer and regulate inventory fluctuations. It is similar to the damping effect of damping on vibration in a physical system. The larger the value, the stronger the system's ability to offset the impact of inventory fluctuations and maintain stability.

[0058] In detail, power grid companies typically have material reserve strategies and emergency allocation mechanisms, which act as dampers for inventory response. For example, if regional material allocation centers have sufficient reserves and efficient allocation processes, they can better cope with inventory fluctuations. It reflects the supply chain management system, including warehouse layout, logistics capabilities, and contingency plans, and its ability to withstand inventory fluctuations.

[0059] The constant is used to avoid the logarithm being undefined in the formula, such as... The minimum positive value set for the case of 0 ensures the rationality of mathematical calculations.

[0060] In the complex environment of power grid engineering, the calculation of various parameters may encounter boundary value problems due to data accuracy and extreme conditions, such as extremely low inventory response damping approaching 0. Ensuring that formulas can be calculated correctly when actual engineering data is substituted is a small trick for adapting mathematical processing to engineering applications.

[0061] Furthermore,

[0062] in: For nodes The instability amplitude of the aforementioned inventory dynamic data, This represents the actual inventory level. This represents the planned inventory level.

[0063]

[0064] in: The system damping strength for inventory response, For time intervals, Time point, The total number of the construction progress nodes. for index, For nodes exist The unstable amplitude of the inventory dynamic data at any given time.

[0065] More specifically, traverse the nodes to calculate, corresponding to From 1 to Select each construction progress node in sequence Extract the node By comparing actual and planned progress through the progress management system, and The system calculates inventory fluctuations from inventory management systems, IoT monitoring data, and other sources, and then substitutes these values ​​into the corresponding positions in the formula.

[0066] Specifically, power grid projects have a large number of information management systems, such as ERP systems for managing progress and inventory. Data is collected through system interfaces to adapt to the collaborative environment of multiple nodes and systems in the project, ensuring that accurate data from each node can be obtained for calculations.

[0067] Furthermore, first for each node The first step is to calculate the product of the absolute values ​​of the schedule deviation amplitude and the inventory instability amplitude, which is to initially couple the disturbance effects of schedule and inventory. Then, the logarithmic term is calculated to integrate factors such as the spatial contagion of schedule deviation and the damping of inventory response into a correction coefficient, which reflects the impact of environmental factors, such as schedule transmission and system adjustment capabilities, on the overall disturbance.

[0068] Specifically, in power grid projects, progress and inventory influence each other; the product term reflects the strength of their combined disturbance, while the logarithmic term... Considering the large spatial span and the ease with which deviations can be transmitted in engineering projects, By combining enterprise supply chain management capabilities, this computational approach is adapted to complex power grid engineering scenarios with environmental characteristics such as space and management systems, integrating environmental impacts into disturbance quantification.

[0069] Summation and averaging will be applied to each node. Calculated Add the results together and then divide by the total number of nodes. This yields the average contribution of each node to the overall disturbance, ultimately leading to...

[0070] In detail, power grid engineering is a complex system composed of multiple nodes. By summing and averaging, the disturbance of all nodes can be comprehensively considered, avoiding extreme values ​​of a single node. For example, if a node is severely affected by force majeure, such as serious deviation in schedule or excessive fluctuation in inventory, it will have an excessive impact on the result. This adapts to the needs of overall project risk assessment and provides a global quantitative value of supply chain operation disturbance.

[0071] The node acquisition module 103 is used to analyze the material transmission path of the material supply node through the disturbance coefficient to obtain the set of key vulnerable nodes of the material supply node.

[0072] In this embodiment, the step of analyzing the material transmission path of the material supply node through the perturbation coefficient to obtain the set of critical vulnerable nodes of the material supply node includes: Determine the difference in disturbance levels between the disturbance coefficient and the construction progress node; The fluctuation range of the inventory dynamic data is determined based on the difference in the disturbance level, and potential vulnerabilities of the material supply nodes are marked based on the fluctuation range. By mapping the potential vulnerabilities to the material transmission path, the potential risk propagation path is obtained; The continuity of the risk propagation path is verified, and the results of the verification are identified as a set of critical vulnerable nodes.

[0073] Specifically, the disturbance level difference value is the disturbance performance of a single construction progress node, such as the difference / ratio between the local disturbance intensity caused by the progress deviation or inventory fluctuation of that node and the overall disturbance coefficient. It is used to measure the deviation of the node disturbance from the global situation and to identify abnormal fluctuation nodes.

[0074] In detail, the calculated disturbance coefficients are retrieved from the system database, covering the comprehensive disturbance value across all nodes of the supply chain. Simultaneously, local disturbance data for each construction progress node is extracted, such as node... Schedule deviation Inventory fluctuations The single-node disturbance contribution value of the node is calculated locally using the formula.

[0075] Then, using algorithms such as the difference method and the ratio method, the difference between each construction progress node and the global disturbance is calculated, and nodes with excessively large difference values, such as exceeding the threshold of 20%, are marked to initially screen out disturbance anomalies.

[0076] Furthermore, in the context of power grid engineering, construction progress milestones have clear professional attributes, such as substation civil engineering and transmission line erection. Different milestones are affected by the environment differently. For example, outdoor line milestones are prone to progress deviations due to weather, resulting in large differences in disturbances.

[0077] By calculating the difference value, we can accurately locate coastal line nodes affected by extreme environments, such as typhoons, and abnormal nodes impacted by warehouse nodes with large fluctuations in large transformer inventory, laying the foundation for subsequent vulnerability analysis.

[0078] Specifically, the fluctuation range is the normal fluctuation range of the quantity and status of power grid material inventory over time, such as the upper and lower limits of safety inventory. After combining the difference in disturbance level, it needs to be adjusted to the fluctuation range affected by the disturbance. For example, at nodes with large disturbances, the tolerance for inventory fluctuations is reduced and the range is narrowed.

[0079] Potential vulnerabilities are nodes where inventory dynamics exceed the fluctuation range after the adjustment due to disturbances, or supply nodes where the inventory guarantee capacity drops sharply due to disturbances, such as warehouses and distribution centers, which manifest as risks of inventory shortages, backlogs, and supply disruptions.

[0080] In detail, based on the difference in disturbance levels, the inventory fluctuation range for each construction progress node associated with the material supply node is dynamically adjusted. For example, if the disturbance difference at a node is large, such as due to a delay caused by heavy rain, and the disturbance exceeds the threshold, the inventory fluctuation range is tightened, the lower limit of the safety stock is raised, and the tolerance for normal inventory levels is reduced.

[0081] Vulnerability identification involves comparing real-time / historical inventory dynamics with the adjusted fluctuation range and marking nodes where inventory data exceeds the range, such as inventory falling below the new lower limit or backlog exceeding the new upper limit, which are identified as potential vulnerabilities. For example, if a construction site warehouse experiences a delay in progress, the cable inventory may change from normal fluctuations to a shortage risk.

[0082] In detail, the supply nodes of power grid engineering materials, such as regional distribution centers and construction site warehouses, are greatly affected by geographical environment, such as logistics delays in remote mountain warehouses, and the properties of materials, such as precision equipment, which are sensitive to the storage environment.

[0083] By adjusting the inventory range through disturbance correlation, it is possible to adapt to engineering environmental risks. For example, the transportation cycle of materials in high-altitude areas is long, and more buffer needs to be reserved in the inventory fluctuation range under disturbance. It can accurately mark the inventory vulnerability points caused by environmental disturbances, such as the decline in battery inventory performance due to low temperature, which triggers vulnerability point marking.

[0084] Specifically, the material transmission path is the flow path of power grid materials from manufacturers, storage centers, distribution nodes, and construction sites. For example, for a transformer: factory A, regional warehouse B, substation C, it is a physical link + information link for material supply.

[0085] Potential risk propagation paths are risk transmission links triggered by potential vulnerabilities in the material transmission path, such as inventory shortages in warehouse B, delivery delays, and construction site C stagnation, with risks spreading along the path.

[0086] In detail, by calling the system's built-in material transmission path model, based on the power grid engineering material supply chain topology, including node connection relationships, material flow direction, logistics timeliness, etc., potential vulnerable points, such as warehouse B, are substituted into the model.

[0087] Through simulation algorithms, such as breadth-first search, the impact of vulnerabilities on upstream and downstream nodes is simulated. For example, if warehouse B is short of supplies, upstream factory A is rushing to produce, and downstream construction site C is waiting for materials, the nodes through which the risk is transmitted are marked, potential risk propagation paths are generated, and the path is visualized as a chain from the vulnerability to the affected nodes.

[0088] In detail, the transmission paths of power grid materials are influenced by the geographical layout of the project, such as material transfer nodes in inter-regional transmission projects and administrative jurisdiction, such as restrictions on inter-provincial material allocation authority. Risk propagation simulations need to adapt to environmental rules, such as the need for approval for inter-provincial deliveries, which slows down risk transmission due to the process. Accurate simulations of real-world scenarios are crucial, such as the vulnerability of mountainous delivery nodes and the delayed spread of risk due to transportation difficulties, ensuring that path analysis aligns with the project's implementation logic.

[0089] Specifically, transmission continuity refers to the characteristic that risks, starting from vulnerable points in the material transmission path, can continuously affect downstream nodes, and the impact logic conforms to the supply rules of power grid engineering materials. For example, inventory shortages caused by logistics disruptions can indeed lead to construction stagnation, rather than just accidental data fluctuations.

[0090] The set of critical and vulnerable nodes has been continuously verified. These are nodes that play a key supporting role in the supply chain of materials and whose risk transmission is irreplaceable, such as the only material transit warehouse in the region. Risk transmission can paralyze the entire path.

[0091] In detail, based on the power grid engineering material supply rules, such as logistics timeliness standards, material substitution strategies, and emergency allocation procedures, the rationality of potential risk propagation paths is verified.

[0092] For example, if there is a shortage of warehouses and construction site stagnation in the path, it is necessary to verify whether there are emergency material reserves, such as whether there is a backup inventory at the construction site, whether cross-regional allocation can be triggered, whether the rules allow it, and to determine whether the risk is really continuously transmitted.

[0093] Then, paths with discontinuous transmission are eliminated, such as those where risks are blocked by emergency mechanisms, while retaining the true transmission paths. Next, key nodes in the paths are marked, such as the only inter-provincial material distribution center or special material warehouses with no alternatives. These nodes are then aggregated into a set of key vulnerable nodes and output to the early warning system for key monitoring.

[0094] Furthermore, power grid engineering has strict material support standards, such as dual backup emergency nodes for major projects. Verification of transmission continuity must conform to actual rules. For example, in mountainous construction nodes, if inconvenient transportation prevents the rapid replenishment of materials, the risk will inevitably be transmitted.

[0095] By verifying the rules, we can distinguish between real risks, such as material warehouses in remote converter stations that are geographically restricted and have no alternatives, and false risks, such as urban distribution nodes that can be quickly replenished from surrounding warehouses, ensuring that the set of key vulnerable nodes accurately reflects the critical supply chain risks in the engineering environment.

[0096] The filtering module 104 is used to extract the material demand characteristics of the construction progress nodes and filter out the blocking nodes in the set of critical vulnerable nodes whose material demand characteristics conflict with the supply timing.

[0097] In this embodiment, extracting the material demand characteristics of the construction progress node includes: Construct a construction timeline for the construction progress nodes, and identify the material categories for the construction progress nodes based on the construction timeline and the dynamic inventory data; Based on the resource list of the material category and the construction progress node, analyze the material demand characteristics of the construction progress node.

[0098] Specifically, the construction timeline is a time-series axis that records the planned start / end time and actual start / end time of each construction progress node of the power grid project. For example, the civil engineering node of the 500kV substation is planned from January 1, 2025 to February 15, 2025, and actually from January 3, 2025 to February 20, 2025. This is the time dimension baseline of the project progress.

[0099] Material categories are classifications of materials required for corresponding construction progress nodes. For example, substation civil engineering requires steel and cement; equipment installation requires transformers and switchgear. These categories are identified by linking the timeline with inventory data.

[0100] In detail, the planned / actual time parameters of each construction progress node are extracted from the power grid project progress management system, such as P6 and Primavera, arranged in chronological order, and a construction timeline covering the entire project cycle is constructed. This is then visualized as a Gantt chart structure, with the time boundaries of key nodes marked.

[0101] For example, for the construction of power transmission lines, a timeline can be drawn up for the following nodes: foundation construction (March 1, 2025 - March 31, 2025), tower erection (April 1, 2025 - April 20, 2025), conductor erection (April 21, 2025 - May 10, 2025).

[0102] Furthermore, the system calls upon the dynamic inventory data from the material supply chain inventory management module, aligning it with the construction timeline by timestamp, such as filtering inventory inbound and outbound records from April 1, 2025 to April 20, 2025.

[0103] Through algorithms, such as association rule mining: if a large number of tower components are shipped out of the warehouse during a certain period of time, and the corresponding time axis node is tower assembly, then the material category of the node is identified as including tower components, the correspondence between inventory changes and progress nodes is matched, and the material category associated with the node is marked.

[0104] More specifically, power grid projects are affected by geographical environments, such as the long material transportation cycle at pole erection nodes in mountainous areas. Dynamic inventory data requires advance stockpiling of corresponding types of materials. Climate conditions also play a role; for example, rainy seasons can affect foundation construction, potentially delaying the timeline and necessitating dynamic adjustments to inventory correlation logic. When constructing the timeline, the impact of environmental factors on progress must be considered, such as adjusting coastal project milestones during typhoon season. When correlating inventory data, it's crucial to adapt to material characteristics; for instance, the slow transportation of large transformers necessitates identifying the requirements of their corresponding progress milestones in advance, ensuring accurate identification of the time-material-environment relationship.

[0105] Specifically, the resource list is a detailed list of all resources required to support the implementation of construction progress nodes. In addition to material categories, it also includes material quantities, technical standards, such as cables needing to meet the 110kV withstand voltage standard, supply priority, main transformers being critical path materials, supporting services, and parameters such as the need for manufacturer's technical personnel to be on-site for equipment installation. It is a complete description of material requirements.

[0106] Material demand characteristics: The structured extraction of material demand at construction progress nodes includes demand time windows, such as the need for 10 transformers from April 1 to April 10, 2025; demand intensity, such as the need for 50 tons of steel per day during peak periods; demand constraints, such as the need for materials to meet the weather resistance standards for high-altitude environments; risk associations, such as the impact of customs policies on the supply of imported equipment, etc., which are used for subsequent conflict screening.

[0107] In detail, the resource list for each construction progress node is extracted from the engineering BIM system and material management platform. For example, the resource list for the 500kV circuit breaker installation node includes 10 circuit breakers, 5 sets of installation accessories, and support from 2 technical personnel from the manufacturer.

[0108] The identified material categories are used to filter out material resources from the list, and non-material items such as manpower and technical services are removed, focusing on material demand analysis.

[0109] Based on the construction timeline, determine the earliest demand time, latest demand time, and duration of demand for materials. For example, the conductor erection node requires continuous supply of conductors from April 21, 2025 to May 10, 2025.

[0110] The quantity of materials in the resource list is statistically analyzed, and the average daily demand and peak demand are calculated in combination with the time window. For example, the basic construction node requires 1,000 tons of cement, the average daily demand during the construction period in April is 25 tons, and the average daily demand is 50 tons in the first 3 days of the rainy season due to rushing to complete the work.

[0111] Extract technical standards from the resource list, such as high-altitude equipment and environmental adaptability requirements, such as the need for salt spray protection materials for coastal projects, and combine them with the environment of power grid projects, such as the need to consider the low-temperature performance of materials for projects in Tibetan areas. At the same time, link them with the supply chain risk database, such as customs policies for imported materials and geological risks of transportation routes, to identify demand risk characteristics.

[0112] The final output is a structured material demand feature, such as in JSON format: {Node: Tower erection, Material category: Tower component, Time window: [2025.04.01, 2025.04.20], Quantity: 500 sets, Intensity: 25 sets per day, Constraint: High-altitude low-temperature resistance, Risk: Delay in permafrost sections of the transportation route}.

[0113] More specifically, the resource list for power grid projects needs to be adapted to the characteristics of the project environment, such as the customs codes and international standards for materials in cross-border projects. When analyzing demand characteristics, environmental constraints should be incorporated, such as the sand prevention requirements for materials in desert projects and the anti-freezing requirements for materials in cold region projects.

[0114] By associating environmental risks, such as the risk of landslides during the transportation of materials for construction in mountainous areas, we can accurately extract the characteristics of environmentally sensitive demands. For example, we can reserve a buffer for the time window of demand for materials that are easily affected by the environment, providing a basis for subsequent screening of supply-time conflict-prone nodes that are disruptive to the actual scenario.

[0115] In this embodiment, the step of screening for blocking nodes in the set of critical vulnerable nodes that have supply timing conflicts with the material demand characteristics includes: Set the demand time-series peak window for the material demand characteristics, and determine the supply response lag of the set of key vulnerable nodes based on the inventory dynamic data; Based on the demand time series peak window and the supply response lag, identify the set of time series conflict points of the set of key vulnerable nodes; Based on the set of time-series conflict points, the blocking nodes of the critical vulnerable nodes are selected.

[0116] Specifically, the peak demand window is the period of highest demand for materials during key milestones in power grid construction projects. It represents the time interval with the strongest demand intensity per unit time. For example, at a certain transmission line erection milestone, conductor laying needs to be completed intensively before the rainy season. The 10 days before the rainy season until the start of the rainy season constitute the peak demand window for conductors, reflecting the most urgent period of material demand.

[0117] Supply response lag refers to the delay in the actual response of material supply to demand within a set of critical and vulnerable nodes, such as the completion of material release from the warehouse and delivery to the construction site. In other words, it's the time difference between the generation of material demand and the actual commencement of effective supply at the supply node. For example, when the peak demand window begins, materials should be released immediately, but due to the time-consuming inventory count shown in the inventory dynamic data, the actual release is delayed by 3 days. These 3 days represent the supply response lag, reflecting the delay in the supply node's response to demand.

[0118] In detail, by combining environmental factors of power grid construction, such as climate and construction plans, we can identify patterns in the intensity of changes in material demand. For example, considering that construction efficiency is low in winter and spring is the peak season for power grid construction, the peak window for equipment installation demand at substation construction nodes may be set at 15-30 days after the start of spring construction.

[0119] By utilizing historical engineering data and construction progress simulations, the peak demand periods for different materials at corresponding construction nodes can be determined, and the peak demand window can be precisely defined. For example, by analyzing cable demand data from similar power grid projects over the past three years and combining it with the local rainy season, the peak demand window for cable laying nodes in this project can be set.

[0120] Furthermore, the system extracts dynamic inventory data for key vulnerable nodes and analyzes the flow of materials at these nodes. For example, for a key warehousing node, it examines the time difference between materials entering the pending outbound state and actually being outbound, as well as the transportation time from outbound to delivery to the demand node.

[0121] By comparing the start time of the peak demand window, calculate the time delay from when the supply node receives the demand, or when the demand should trigger a supply action, to when the actual supply begins. For example, if materials are needed on day 1 of the peak demand window, but inventory dynamics show that due to the cumbersome inventory counting process, materials will not be released until 2 days later, and considering transportation time, calculate the overall supply response lag to be 4 days: 2 days of lag within inventory + 2 days of lag in transportation.

[0122] The calculation of lag should be adjusted by taking into account environmental constraints of power grid projects, such as traffic conditions and warehouse locations. For example, if a critical vulnerable node is located in a mountain warehouse, and rain or snow makes roads muddy and increases transportation time, the transportation delays caused by the environment should be added to the supply response lag.

[0123] More specifically, power grid projects are greatly affected by the natural environment, project organization environment, construction process, and material management standards. Regarding the natural environment, if the project is located in a high-altitude region, material transportation will be affected by terrain and climate, increasing the material transportation time in inventory dynamic data and leading to a larger supply response lag. In terms of the project organization environment, if the inventory management process at material supply nodes is cumbersome, such as multi-level approval for outbound shipments, it will also cause supply response lags. These environmental factors must be fully considered when setting window values ​​and calculating lag amounts to ensure that parameters closely match the actual engineering scenario.

[0124] Specifically, the set of time-series conflict points is the set of nodes and conflicting time points within the set of critical vulnerable nodes where a lag in the response of material supply clashes with the peak demand window. In other words, during the peak demand window when supply cannot meet demand in a timely manner due to lag, the time contradiction arises. For example, if the peak demand window is from day 5 to 10, and the supply response lags by 3 days, resulting in unmet demand from day 5 to 8, these time points and their corresponding nodes constitute the set of time-series conflict points.

[0125] In detail, compare the supply response lag of each critical vulnerable node with the time range of the peak demand window. For example, if the peak demand window is [Day 5, Day 10], and the supply response lag of a certain node is 3 days, it means that the node should have supplied materials on Day 5, but due to the lag, the actual effective supply starts from Day 8. Therefore, Day 5-Day 7 is the conflict period.

[0126] Mark the nodes and time points corresponding to the conflict periods to form a preliminary list of conflict points, and record which critical vulnerable node each conflict point is and when it conflicts with the peak demand window due to supply lag.

[0127] All conflict points at critical and vulnerable nodes are summarized, deduplicated, and integrated to construct a set of temporal conflict points. For example, if multiple nodes have conflicts between Day 5 and Day 7, these conflict points are merged to clearly show which nodes have temporal supply and demand conflicts during which time periods.

[0128] Optimize the set of conflict points by combining the overall progress of the power grid project and the collaborative supply relationship of materials. If a conflict point can be compensated for by the allocation of materials from other nodes (such as support from adjacent warehouses), then adjust the conflict point judgment to ensure that the set reflects real conflicts that will have a substantial impact on the supply chain.

[0129] More specifically, power grid engineering is a complex systems engineering project with collaborative relationships between material supply nodes, and it is also subject to environmental constraints. When identifying time-series conflict points, the impact of the environment on collaborative supply must be considered. For example, in mountainous projects, conflicts caused by a supply lag at one node cannot be resolved through coordination with other nodes. Due to transportation limitations, materials cannot be quickly transferred, and such conflict points should be prioritized. In contrast, in plains projects with convenient transportation, some conflicts can be mitigated through collaborative coordination, and the impact and judgment of conflict points differ. At the same time, extreme weather, such as typhoons and blizzards, may lead to the emergence of new conflict points or amplify the impact of already identified conflict points, requiring dynamic adjustment of the time-series conflict point set to adapt to environmental changes.

[0130] Specifically, disruptive nodes are critical and vulnerable nodes where the timing of supply and demand conflicts can substantially disrupt the material transmission path of the entire power grid construction material supply chain, causing subsequent construction progress nodes to be unable to advance due to material shortages. If the conflicts at these nodes are not resolved, they will trigger a chain reaction, disrupting the normal supply of materials in the supply chain.

[0131] In detail, analyze the impact of each time-series conflict point on the construction progress of the power grid project. For example, a conflict point at a critical vulnerable node may lead to an interruption in the supply of conductors for transmission line construction, which in turn may prevent the subsequent installation of tower accessories, line commissioning, and other nodes from proceeding. Assess the number and importance of the construction nodes covered by its impact.

[0132] By considering the irreplaceability of materials and the critical path of the project, the impact of the conflict point can be determined. If the material supplied by the conflict node is the only source on the critical path of the project, such as an ultra-high voltage transformer with no alternative equipment, and the conflict leads to a shortage of materials, then the conflict impact of that node is critical.

[0133] In detail, the identification of blocking nodes is achieved by setting blocking criteria, such as conflicts that cause critical materials to be unavailable for three or more consecutive days or affect two or more critical construction progress nodes. If a node conflict meets the criteria, it is identified as a blocking node.

[0134] For nodes identified as blocking nodes, further analysis is conducted to determine the severity of the blocking and possible solutions. This prepares the early warning system for issuing warnings and providing solutions. For example, the blocking of a node may be caused by inventory management or transportation environment, which facilitates targeted handling.

[0135] More specifically, the critical path and material importance of power grid projects are affected by the environment. In power grid projects in remote areas, certain material supply nodes may be the only source of supply in the local area. Due to geographical constraints, these nodes can easily become blocking nodes if timing conflicts occur.

[0136] In urban power grid projects with ample supplies and convenient transportation, the criteria for identifying disruptive nodes are more stringent. Meanwhile, environmental risks, such as natural disasters damaging nodes, may transform nodes that were not originally disruptive into disruptive ones. Therefore, it is necessary to dynamically monitor environmental changes and adjust the screening process for disruptive nodes to ensure that nodes that could truly have a fatal impact on the project's supply chain are identified, allowing the early warning system to function effectively.

[0137] The heatmap drawing module 105 is used to determine the link vulnerability of the blocking node by combining the interruption propagation pattern in the historical risk event database, and draw a risk chain diffusion heatmap based on the link vulnerability.

[0138] In this embodiment, determining the link vulnerability of the blocking node by combining the interruption propagation patterns in the historical risk event database includes: Based on the interruption propagation pattern, the historical interruption cascade path of the blocking node is extracted; Identify the overlapping risk nodes between the historical interruption cascade path and the supply chain topology points corresponding to the blocking nodes; Verify the transmission consistency between the overlapping risk nodes and the dynamic inventory data, and determine the result of the verification as the risk transmission chain; The link vulnerability of the risk transmission chain is determined to obtain the link vulnerability of the blocking node.

[0139] Specifically, the interruption propagation pattern is a pattern model in the historical risk event database of how power grid material supply chain risks are transmitted from the initial interruption node to the upstream and downstream, such as the transmission pattern of storage shortage - delivery delay - construction site stagnation. It includes the node association of risk propagation, the temporal logic, and the rules for the attenuation / amplification of impact intensity.

[0140] Historical interruption cascading paths are the node-to-node cascading links formed by the transmission of risk along the material supply chain in historical risk events, starting from or at a disruptive node. Examples include factory shutdowns, regional warehouse shortages, and material shortages at construction sites. These paths record the actual path and scope of risk propagation.

[0141] In detail, events matching the attributes of the current blocking node are retrieved from the historical risk event database. For example, for a 500kV transformer storage node, interruption events caused by this type of node in the past are filtered.

[0142] Extract the interruption propagation pattern in the event, such as insufficient inventory at the warehousing node - delayed restocking at the downstream distribution node - project delay, as a reference model for risk transmission.

[0143] Based on the matching interruption propagation pattern, the risk in historical events is traced from the blocking node to the supply chain nodes affected in sequence, such as node A (blocking) - node B (regional warehouse) - node C (construction site), and the historical interruption cascading path is sorted out.

[0144] More specifically, for example: if the current blocking node is the Northwest Regional Transformer Storage Center, the cascading path of the 2023 inventory shortage event of the center can be extracted from the historical event database as the storage center - Shaanxi distribution branch center - Xi'an substation construction site - 330kV line commissioning node.

[0145] Furthermore, the power grid engineering supply chain has strong geographical correlations. For example, materials for inter-provincial power transmission projects need to be distributed level by level, and the cascading paths of historical interruptions need to be adapted to the geographical environment. For example, distribution nodes in mountainous areas are prone to extending transmission paths due to road interruptions.

[0146] Meanwhile, the interruption propagation modes of projects with different voltage levels, such as ultra-high voltage and distribution networks, vary greatly. When extracting paths, it is necessary to take into account environmental factors such as the project's voltage level and geographical span to ensure the reference value of the paths. For example, in ultra-high voltage projects, materials are scarce, and interruption propagation is more likely to cause global impact.

[0147] Specifically, supply chain topology points are abstract nodes in the power grid material supply chain, such as warehouses, distribution centers, construction sites, and manufacturers. They constitute the basic units of the supply chain topology network and mark the function, location, and relationships of the nodes.

[0148] Overlapping risk nodes are nodes in the historical interruption cascade path that have highly overlapping functions, locations, and relationships with the current blocking node in the supply chain topology network. For example, the Shaanxi distribution sub-center in the historical path overlaps with the Xi'an distribution node in the current project topology, reflecting the common nodes of risk transmission.

[0149] In detail, construct the supply chain topology network of the current project, mark the attributes of each supply chain topology point, such as location coordinates, service voltage level, and material type, and form a visual topology map, such as using Graphviz to draw the hierarchical topology of manufacturer-regional warehouse-provincial warehouse-construction site.

[0150] Example: Construct a topology for the Longdong-Shandong UHV project, including topology points such as Xinjiang Transformer Factory, Lanzhou Regional Warehouse, Xi'an Distribution Center, and Weinan Construction Site.

[0151] Nodes in the historical interrupted cascade path, such as the Shaanxi distribution sub-center, are matched with current engineering supply chain topology points, such as the Xi'an distribution center, based on attributes such as geographical location, service area, and material flow.

[0152] Nodes with an attribute overlap of ≥ 80% are identified as overlapping risk nodes. Their location and association in the topology are recorded. For example, the Xi'an distribution center is an overlapping node, associated with the Lanzhou regional warehouse and the Weinan construction site.

[0153] More specifically, the supply chain topology of power grid projects is affected by administrative regions, voltage levels, and grid planning. For example, ultra-high voltage projects need to rely on provincial hub warehouses for distribution. When identifying overlapping nodes, environmental rules need to be considered. For example, inter-provincial material allocation needs to pass through specific hub warehouses, which are key overlapping points in the topology.

[0154] Meanwhile, topological points in extreme environments are more likely to become overlapping risk nodes. Both historical and current projects are threatened by the environment and need to be marked in a key manner. For example, the delivery nodes in the Sichuan earthquake zone have both historical and current projects that have been interrupted due to geological disasters.

[0155] Specifically, transmission continuity refers to whether the dynamic data of inventory, such as the time of entry and exit from the warehouse and changes in inventory quantity, are continuous, traceable, and conform to the risk transmission logic in the transmission of materials at overlapping risk nodes, such as inventory shortage at node A - passive inventory adjustment at node B - unmet demand at node C.

[0156] The risk transmission chain is a verified, continuous link formed by the transmission of dynamic inventory data between overlapping risk nodes, which triggers, transmits, and impacts risks. For example, a blocking node's inventory shortage leads to overlapping node A adjusting its inventory, which leads to overlapping node B's supply delay, which leads to waiting for materials at the construction site.

[0157] In detail, extract the inventory dynamic data of overlapping risk nodes, such as the inventory entry and exit records and inventory warning logs of the past year, and check the time continuity of the data, such as whether there are data interruptions or abnormal jumps.

[0158] If, in the historical path, node A experiences an inventory shortage while node B experiences a surge in outbound shipments, it is necessary to check whether there is a similar correlation between the inventory data of nodes A and B in the current project. For example, if the inventory of node A is below the safety threshold, does the outbound shipment volume of node B increase significantly?

[0159] If the inventory dynamic data transmission of overlapping risk nodes is continuous, such as the inventory fluctuations of blocking node - node A - node B having a temporal correlation, it is determined to be a risk transmission chain, and the nodes on the chain, the transmission time, and the intensity of the impact are marked.

[0160] More specifically, the dynamics of power grid material inventory are strongly affected by environmental factors. For example, high temperatures can degrade cable insulation performance, triggering inventory spot checks and affecting the continuity of inventory transmission. Verification must eliminate environmental interference, such as distinguishing between inventory fluctuations caused by actual demand transmission and inventory anomalies caused by environmental spot checks. Furthermore, during the construction phase, concentrated material demand during peak construction periods can easily lead to inconsistent transmission, affecting verification results. Therefore, verification standards need to be adjusted based on the project schedule and environment. For instance, during peak periods, an inventory transmission time difference of ≤1 day is allowed, while during off-peak periods, this can be relaxed to 3 days.

[0161] Specifically, link vulnerability refers to the ability of a risk transmission chain to resist interference. It reflects the possibility that risk transmission may be interrupted or amplified due to node failures or environmental disturbances as it propagates along the chain. The higher the vulnerability, the easier it is for the risk to spread along the chain, potentially causing a global supply chain disruption.

[0162] In detail, based on the node attributes of the risk transmission chain, such as whether it is a unique path node, the scarcity of materials, environmental sensitivity, whether it is located in a flood-prone area, a transportation hub, and historical interruption frequency, such as the number of historical interruptions of the nodes on the chain, a vulnerability assessment model is constructed. For example, the analytic hierarchy process is adopted, with the following weight allocation: environmental sensitivity 40%, uniqueness 30%, and historical frequency 30%.

[0163] Calculate the vulnerability score of the risk transmission chain and map the score to low, medium and high vulnerability levels. This score serves as the link vulnerability result for blocking nodes. For example, a score >80 indicates high vulnerability, requiring a high level of warning.

[0164] More specifically, the vulnerability of power grid links is highly dependent on the environment. For example, mountainous links are prone to interruption due to landslides, making them highly vulnerable. The assessment needs to incorporate geographical factors such as altitude and topography, climate factors such as precipitation and high temperatures, and social factors such as traffic control and material control.

[0165] In this embodiment, drawing a risk cascading diffusion heatmap based on the link vulnerability includes: Isolate the core points of risk transmission in the transmission strength map corresponding to the link vulnerability; By combining the dynamic inventory data with the neighborhood of the risk transmission core point, a diffusion domain spectrum is obtained; The diffusion intensity of the diffusion domain spectrum is quantified based on the inventory response delay in the inventory dynamic data to obtain a diffusion intensity map. The diffusion intensity spectrum is subjected to thermodynamic gradient transformation to obtain a risk cascading diffusion thermogram.

[0166] Specifically, the transmission strength map is a visualization map that quantifies the transmission strength of risks between nodes in the supply chain based on the vulnerability of the links. For example, the transmission strength is marked by the intensity of the color, with red indicating high intensity. It includes the transmission capacity of the nodes, the relationship between them, and the range of risk radiation.

[0167] The core of risk transmission is the node with the strongest risk transmission capacity and the greatest impact on the overall situation in the transmission intensity map (such as the regional material distribution center of an ultra-high voltage project, where the risk transmission intensity is much higher than other nodes), which is the source of the chain spread of risk.

[0168] In detail, based on link vulnerability data, such as the vulnerability scores of each node, a graph-based algorithm, such as an improved version of PageRank, is used to adapt to the supply chain transmission logic, calculate the transmission strength of the nodes, and construct a transmission strength graph.

[0169] Example: A diagram is constructed for the Zhangbei-Xiong'an UHV project. The size of the nodes indicates vulnerability, and the shade of the color indicates the conduction intensity, highlighting the high-intensity conduction at the Zhangjiakou converter station material warehouse.

[0170] Then, a transmission intensity threshold is set, and the node with the highest intensity in the transmission intensity spectrum is selected and marked as the core point of risk transmission.

[0171] In conjunction with the power grid engineering environment, such as whether the core point is the node with the highest voltage level and the most scarce materials, the rationality of the core point is manually verified. For example, the converter station warehouse of an ultra-high voltage project must be a core point because the materials are highly specialized.

[0172] More specifically, the core points of risk transmission in power grid projects are significantly affected by voltage levels and geographical hub status. For example, the material warehouses of UHV hub substations are prone to becoming core points because they serve multiple projects and the materials are irreplaceable.

[0173] When identifying the core point, it is necessary to take into account factors such as the voltage level of the project (e.g., the core point of a 1000kV project has a greater influence than that of a 500kV project), geographical location (e.g., a distribution center in a transportation hub city), and the wide transmission and radiation range of the project. This will ensure the accuracy of the core point. For example, for a 500kV project along the Beijing-Shanghai High-Speed ​​Railway, the Jinan distribution center became the core point due to its convenient transportation.

[0174] Specifically, the neighborhood is the adjacent node of the core point of risk transmission in the supply chain topology network. For example, if the core point is the Xi'an distribution center, the neighborhood includes the Lanzhou regional warehouse, the Weinan construction site, and the Xianyang backup warehouse, which reflects the direct related nodes of the core point.

[0175] The diffusion domain spectrum is a graph that simulates the range and intensity of risk diffusion, centered on the core point of risk transmission and combined with dynamic inventory data, such as inventory fluctuations and inbound / outbound time sequences of neighboring nodes, marking the initial domain of risk diffusion.

[0176] In detail, in the supply chain topology network, with the core point of risk transmission as the center, the neighboring nodes of the nodes with a distance of ≤N hops (e.g., N=2) are searched to construct a neighborhood list. For example, the neighborhood of the core point Zhengzhou Warehouse Center is: Xinxiang Distribution Station, Kaifeng Construction Site, Wuhan Regional Warehouse.

[0177] Then, extract the dynamic inventory data of neighboring nodes, such as inventory warning time and outbound delay duration. Based on multi-agent simulation algorithms, such as the Swarm model, simulate the diffusion path of risk in the neighborhood and the process of risk diffusion from the core point to the neighborhood.

[0178] Example: If the core point Zhengzhou Warehouse Center triggers a risk due to inventory shortage, and the inventory of the neighboring node Xinxiang Distribution Station can only last for 3 days, simulate the risk spreading to Xinxiang Distribution Station after 3 days, forming a diffusion path from the core point to Xinxiang Station, record the diffusion range and time sequence, and generate a diffusion domain spectrum.

[0179] More specifically, the geographical distribution of neighboring nodes in power grid projects, such as the slow diffusion from mountainous nodes and the rapid diffusion from plains nodes, and inventory strategies, such as emergency reserves in backup depots, significantly affect the diffusion sequence and process. Diffusion simulations must incorporate the geographical environment; for example, diffusion between mountainous nodes requires consideration of transportation delays and inventory conditions. Sufficient backup depot inventory can mitigate diffusion factors, ensuring the diffusion spectrum closely reflects reality. For neighboring nodes in northwestern desert projects, due to long transportation cycles, the diffusion sequence needs to be superimposed with environmental delays.

[0180] Specifically, inventory response latency is the time difference between a neighboring node perceiving a risk, such as a shortage of inventory at a core point, and initiating an inventory response, such as emergency restocking or triggering an early warning. It reflects the node's agility in responding to risks.

[0181] Diffusion intensity is a quantitative value of the ability of risk to spread in the diffusion spectrum as the inventory response time delay changes. The shorter the time delay, the higher the diffusion intensity, because the risk is transmitted quickly. It is used to mark the dynamic intensity of risk diffusion.

[0182] The diffusion intensity map is a visualization of the diffusion field spectrum and diffusion intensity. It uses color gradients to mark the diffusion intensity of different regions, reflecting the dynamic process of risk chain diffusion.

[0183] In detail, the inventory response delay of neighboring nodes is extracted from the inventory dynamic data. For example, the time from when the Xinxiang distribution station receives the risk signal from the core point to when it initiates the transfer of goods: risk trigger time T1, transfer instruction issuance time T2, delay = T2-T1.

[0184] Diffusion intensity = baseline intensity × (1 + α × inventory response delay), where α is the environmental adjustment coefficient. For example, in mountainous areas, α increases, as the time delay has a more significant impact on diffusion.

[0185] Based on the model, the diffusion intensity of each region in the diffusion field spectrum is calculated, and the intensity value of the spectrum node is updated. For example, with a time delay of 1.5 hours, a baseline intensity of 0.8, α=0.2, the diffusion intensity = 0.8×(1+0.2×1.5)=0.96.

[0186] Then use visualization tools, such as Python-Matplotlib, to draw diffusion intensity maps, marking the intensity with light and dark colors, such as dark red for intensity 0.96 and yellow for intensity 0.5, and overlay geographical information, such as terrain icons to mark mountain nodes.

[0187] More specifically, the inventory response time of power grid projects is significantly constrained by the environment. For example, nodes in remote areas experience longer response times due to poor communication and slow logistics. When quantifying the diffusion intensity, environmental factors need to be incorporated. For instance, the α value for projects in Tibet is much greater than that for projects in plains areas. It is important to ensure that the intensity calculation is adapted to the geographical and logistical environment. For example, the average inventory response time for projects in Tibet is 8 hours, while that for projects in plains areas is 2 hours, a difference of up to 3 times in α value.

[0188] Meanwhile, the inventory strategies for projects of different voltage levels differ. For example, ultra-high voltage projects have high inventory redundancy and the time delay has a weak impact on strength. Therefore, it is necessary to adjust the quantitative model parameters to ensure the authenticity of the graph.

[0189] Specifically, the thermal gradient transformation maps the intensity values ​​of the diffusion intensity spectrum to a color gradient, such as red-orange-yellow-green, corresponding to intensities from high to low, forming an intuitive thermal distribution that reflects the spatial trend of risk chain diffusion.

[0190] The risk cascading diffusion heatmap is the final visual output, which uses thermal gradients to show the intensity, scope and timing of risk spreading from the core point to the surrounding area, providing an intuitive basis for decision-making for supply chain risk early warning.

[0191] In detail, thermal gradient rules are set, such as green for intensity 0-0.2, yellow for 0.2-0.5, orange for 0.5-0.8, and red for 0.8-1, mapping the intensity values ​​of the diffusion intensity spectrum to colors.

[0192] Then, a geographic base map of the power grid project, such as OpenStreetMap tiles, is overlaid, and the color-mapped nodes and paths are drawn on the base map to generate a heat map of risk chain diffusion.

[0193] Label key environmental information, such as the mountainous areas and rivers through which the risk spreads, and mark the risk as passing through the Qinling Mountains, with a spread delay of +2 hours, to enhance environmental relevance.

[0194] More specifically, heat maps for power grid projects need to visually represent the impact of the environment on risk spread. For example, when a risk passes through mountainous areas, the thermal gradient becomes more significant due to time delay. Geographical labels, such as mountains, rivers, and major transportation routes, need to be incorporated during the conversion process to allow maintenance personnel to quickly identify environmentally sensitive areas. For instance, if a risk spreads to a flood-prone area, a blue warning band should be marked on the heat map.

[0195] At the same time, by combining the voltage level of the project, such as using a more eye-catching color gradient in the heat map of ultra-high voltage projects, the risk visualization needs of different projects can be adapted to ensure the early warning effect. For example, the heat map of distribution network projects has a smaller risk impact and a gentler heat gradient.

[0196] The early warning instruction generation module 106 is used to generate engineering early warning instructions through the risk chain diffusion heat map.

[0197] In this embodiment, generating engineering early warning instructions through the risk cascading diffusion heatmap includes: Define the core risk diffusion axis of the aforementioned risk cascading diffusion heatmap; Based on the inventory response delay, the risk core diffusion axis is topologically diffused to obtain a cascaded response domain. The engineering response lag of each node in the cascaded response basin is determined as the early warning pressure value; The aforementioned warning pressure value is converted into an engineering warning command.

[0198] Specifically, the risk core diffusion axis is the axis with the highest risk transmission intensity and the most concentrated diffusion path in the risk chain diffusion heat map. For example, the high-intensity transmission path extending from the Zhengzhou warehousing center to the Xinxiang distribution station and the Weinan construction site reflects the main direction and core context of risk diffusion.

[0199] In detail, the thermal gradient data of the risk chain diffusion heatmap is analyzed to extract the node sequence with the highest conduction intensity, such as the node chain with an intensity value continuously > 0.8.

[0200] Example: In the heat map of the Longdong-Shandong UHV project, a high-strength transmission chain was identified from Xinjiang Transformer Factory to Lanzhou Regional Warehouse to Xi'an Distribution Center to Weinan Construction Site, which serves as the core risk diffusion axis.

[0201] Specifically, topology diffusion is based on the risk core diffusion axis and combined with inventory response delay to simulate the process of risk spreading to surrounding nodes in the supply chain topology network, forming a cascaded response area of ​​axis-plane.

[0202] The cascading response domain is the set of supply chain nodes and response sequence covered by the risk core diffusion axis after topological diffusion. It reflects the impact range and temporal distribution of the risk chain diffusion. For example, after the risk is triggered at a node on the axis, neighboring nodes respond in sequence due to inventory delays.

[0203] Specifically, the engineering response lag is the time difference between the actual engineering response at a node within a cascaded response basin, such as the time difference between the planned response and the time difference between material supply and construction adjustments. For example, if the plan is to complete material replenishment within 24 hours, but the actual delay is 12 hours due to risk, it reflects the degree of impact of risk on the project progress.

[0204] The early warning pressure value is a quantitative value of the engineering response lag. It is used to measure the urgency of the early warning faced by a node. The greater the lag, the higher the pressure value and the more urgent the early warning level.

[0205] Engineering early warning instructions are operational instructions generated based on early warning pressure values ​​to guide supply chain risk intervention, such as the Xi'an distribution center activating the Level 1 emergency plan and the Weinan construction site adjusting the construction sequence. These instructions include the early warning level, intervention measures, and responsible parties.

[0206] In this embodiment, converting the warning pressure value into an engineering warning command includes: A differential analysis is performed on the warning pressure values ​​to obtain the response priority of the warning pressure values; The response priority is converted and encapsulated into an engineering early warning command.

[0207] Specifically, differential analysis involves statistically analyzing the warning pressure values ​​of multiple nodes, calculating the differences between pressure values, and distinguishing the distribution levels of pressure values, such as high, medium, and low pressure ranges.

[0208] The warning pressure value is a vector composed of the warning pressure values ​​of all nodes in the cascade response basin, such as [25,38,12,45], which reflects the overall distribution of risk pressure in the basin.

[0209] The response priority is based on differential analysis, which sorts the pressure values ​​of each node in the early warning pressure value to determine the order of risk response. For example, node 45 is responded to first and node 12 is responded to last, reflecting the priority logic of resource allocation.

[0210] In detail, the grade difference calculation can be understood as statistically analyzing the warning pressure values, calculating the grade difference between the maximum and minimum values, such as max=45, min=18, grade difference=27, dividing the pressure range, such as 0-20 low pressure, 20-40 medium pressure, and 40-60 high pressure.

[0211] Example: Weinan construction site 45 (high pressure), Xi'an distribution center 38 (medium-high pressure), Shangluo backup warehouse 22 (medium pressure), Linfen transfer station 18 (low pressure).

[0212] Specifically, engineering early warning instructions are operational instructions that guide risk intervention in the power grid engineering material supply chain. They include response priorities, intervention measures, responsible parties, and time limits, and must be accurate and executable.

[0213] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0214] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0215] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0216] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0217] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0218] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An early warning system for the operation of the power grid engineering construction material supply chain, characterized in that, The system includes: The data acquisition module is used to synchronously acquire dynamic inventory data of the construction progress nodes and corresponding material supply nodes of the project. The dynamic simulation module is used to calculate the progress deviation between the construction progress nodes, and dynamically simulate the disturbance coefficient of the construction progress node by combining the spatial interference capability of the progress deviation with the dynamic inventory data. The node acquisition module is used to analyze the material transmission path of the material supply node through the disturbance coefficient, and obtain the set of key vulnerable nodes of the material supply node; The filtering module is used to extract the material demand characteristics of the construction progress nodes and filter out the blocking nodes in the set of critical vulnerable nodes whose material demand characteristics conflict with the supply timing. The heatmap drawing module is used to determine the link vulnerability of the blocking node by combining the interruption propagation pattern in the historical risk event database, and draw a risk chain diffusion heatmap based on the link vulnerability; The early warning instruction generation module is used to generate engineering early warning instructions based on the risk chain diffusion heat map.

2. The early warning system for the operation of the power grid engineering construction material supply chain as described in claim 1, characterized in that, The synchronous acquisition of construction progress nodes and corresponding material supply node inventory dynamic data includes: Connect to the real-time progress management interface of the project construction and collect construction progress nodes; Continuously record the sequence of inventory level changes at the aforementioned material supply nodes; The progress node and the inventory level change sequence are time-series aligned to obtain the dynamic inventory data of the material supply node.

3. The early warning system for the operation of the power grid engineering construction material supply chain as described in claim 1, characterized in that, The formula for calculating the disturbance coefficient is as follows: in: The disturbance coefficient is... The total number of the construction progress nodes. for index, For nodes The magnitude of the schedule deviation, For nodes The instability amplitude of the aforementioned inventory dynamic data, The spatial contagion factor for the aforementioned schedule deviation. The system damping strength for inventory response, It is a constant.

4. The early warning system for the operation of the power grid engineering construction material supply chain as described in claim 3, characterized in that, The process of analyzing the material transmission path of the material supply node using the disturbance coefficient yields a set of critical vulnerable nodes for the material supply node, including: Determine the difference in disturbance levels between the disturbance coefficient and the construction progress node; The fluctuation range of the inventory dynamic data is determined based on the difference in the disturbance level, and potential vulnerabilities of the material supply nodes are marked based on the fluctuation range. By mapping the potential vulnerabilities to the material transmission path, the potential risk propagation path is obtained; The continuity of the risk propagation path is verified, and the results of the verification are identified as a set of critical vulnerable nodes.

5. The early warning system for the operation of the power grid engineering construction material supply chain as described in claim 1, characterized in that, The extraction of material demand characteristics for the construction progress nodes includes: Construct a construction timeline for the construction progress nodes, and identify the material categories for the construction progress nodes based on the construction timeline and the dynamic inventory data; Based on the resource list of the material category and the construction progress node, analyze the material demand characteristics of the construction progress node.

6. The early warning system for the operation of the power grid engineering construction material supply chain as described in claim 1, characterized in that, The process of screening for blocking nodes that have supply timing conflicts with the material demand characteristics and the set of critical vulnerable nodes includes: Set the peak demand window for the material demand characteristics, and determine the supply response lag of the set of key vulnerable nodes based on the inventory dynamic data; Based on the demand time series peak window and the supply response lag, identify the set of time series conflict points of the set of key vulnerable nodes; Based on the set of time-series conflict points, the blocking nodes of the critical vulnerable nodes are selected.

7. The early warning system for the operation of the power grid engineering construction material supply chain as described in claim 1, characterized in that, The method of determining the link vulnerability of the blocking node by combining the interruption propagation patterns in the historical risk event database includes: Based on the interruption propagation pattern, the historical interruption cascade path of the blocking node is extracted; Identify the overlapping risk nodes between the historical interruption cascade path and the supply chain topology points corresponding to the blocking nodes; Verify the transmission consistency between the overlapping risk nodes and the dynamic inventory data, and determine the result of the verification as the risk transmission chain; The link vulnerability of the risk transmission chain is determined to obtain the link vulnerability of the blocking node.

8. The early warning system for the operation of the power grid engineering construction material supply chain as described in claim 7, characterized in that, The process of drawing a risk cascading diffusion heatmap based on the link vulnerability includes: Isolate the core points of risk transmission in the transmission strength map corresponding to the link vulnerability; By combining the dynamic inventory data with the neighborhood of the risk transmission core point, a diffusion domain spectrum is obtained; The diffusion intensity of the diffusion domain spectrum is quantified based on the inventory response delay in the inventory dynamic data to obtain a diffusion intensity map. The diffusion intensity spectrum is subjected to thermodynamic gradient transformation to obtain a risk cascading diffusion thermogram.

9. The early warning system for the operation of the power grid engineering construction material supply chain as described in claim 8, characterized in that, The process of generating engineering early warning instructions through the risk cascading diffusion heatmap includes: Define the core risk diffusion axis of the aforementioned risk cascading diffusion heatmap; Based on the inventory response delay, the risk core diffusion axis is topologically diffused to obtain a cascaded response domain. The engineering response lag of each node in the cascaded response basin is determined as the early warning pressure value; The aforementioned warning pressure value is converted into an engineering warning command.

10. The early warning system for the operation of the power grid engineering construction material supply chain as described in claim 9, characterized in that, The step of converting the early warning pressure value into an engineering early warning command includes: A differential analysis is performed on the warning pressure values ​​to obtain the response priority of the warning pressure values; The response priority is converted and encapsulated into an engineering early warning command.

Images