Risk monitoring method and system based on power grid material supply chain operation

Abstract

The invention provides a risk monitoring method and system based on power grid material supply chain operation. The supply chain management efficiency is remarkably improved through an intelligent risk identification process. The method comprises the following steps: firstly, acquiring power grid material consumption data from a historical database, and calculating the consumption speed and consumption of various materials according to a bill of materials; and key monitoring materials are screened out through the preset consumption monitoring threshold value, so that tedious data processing on all the materials is avoided. And for the screened monitored materials, the system further collects storage, production and logistics data of the screened monitored materials, and evaluates the risk degree by using a pre-trained supply chain risk identification model. And when the material supply chain risk degree exceeds a preset threshold value, defining the material as a supply risk material. The accurate risk monitoring method not only reduces the calculation overhead of data processing, but also can identify potential supply chain risks in time, and provides a more efficient and intelligent solution for power grid material management.

Description

Technical Field

[0001] The present application relates to the field of power supply, and in particular to a risk monitoring method and system based on power grid material supply chain operations. Background Art

[0002] In the operation of power grid material supply chain, risk monitoring is a key link to ensure the stability, security and efficiency of the supply chain. The necessity of supply chain risk monitoring stems from the particularity and complexity of the power industry. The power grid material supply chain involves multiple links from raw material procurement to final equipment delivery, and each link may have potential risks, which may have a significant impact on the normal operation of the power system.

[0003] Traditional supply chain risk monitoring methods mainly include static risk assessment and regular review. Static risk assessment usually relies on historical data and expert experience, and identifies and quantifies potential risks by establishing a risk indicator system and scoring model. The advantage of this method is that it can systematically sort out the sources of risk, but its most significant drawback is its lag and static nature. Due to the ever-changing environment of the power grid material supply chain, static assessment is difficult to capture dynamic risk factors in a timely manner.

[0004] Another common method is regular audit, which is to evaluate the supplier's performance and risk level through periodic on-site inspections and document reviews. This method can intuitively understand the actual operating status of the supplier, but its frequency is low and continuous monitoring cannot be achieved. More importantly, regular audits often have sampling selection bias and may miss some important risk points. Summary of the invention

[0005] The present application provides a risk monitoring method based on power grid material supply chain operation, comprising the following steps: A1, obtaining corresponding historical power grid material consumption data from a preset historical power grid material database according to a preset detection cycle; A2, determine the material consumption data of each power grid based on the historical power grid material consumption data and the preset power grid material list; A3, calculating the material consumption rate of each corresponding power grid according to the material consumption data of each power grid; A4, calculating the corresponding time window material consumption according to the material consumption speed of each power grid and the preset consumption monitoring time window; A5, determining the corresponding monitoring materials according to the material consumption in each time window and the preset consumption monitoring threshold; A6, obtain the corresponding material storage data, material production data and material logistics data according to each monitored material; A7, based on the material storage data, material production data and material logistics data, the corresponding material supply chain risk is generated through the pre-trained supply chain risk identification model; A8: If the material supply chain risk of the monitored material is greater than the preset supply chain risk threshold, the monitored material is defined as a supply risk material.

[0006] By adopting the above technical solution, the risk monitoring method based on the power grid material supply chain operation can screen out power grid materials that may have excessive consumption by counting the consumption of each power grid material, and obtain the storage, production and material-related data of the power grid materials with excessive consumption, and then identify the corresponding risk level through a pre-trained risk identification model, and then determine the power grid materials with supply chain risks, avoiding comprehensive data acquisition and risk identification of all power grid materials, reducing the computational overhead of data processing, and improving the efficiency of supply chain risk monitoring.

[0007] Optionally, the risk monitoring method based on power grid material supply chain operation further comprises the following steps: B1, generate the corresponding material storage risk level based on the material storage data of each supply risk material using the pre-trained storage risk assessment model; B2, generating the corresponding material production risk degree based on the material production data of each supply risk material using the pre-trained production risk assessment model; B3, generating the corresponding material logistics risk level according to the material logistics data of each supply risk material using the pre-trained logistics risk assessment model; B4, add the supply risk materials whose material storage risk is greater than the preset storage risk threshold to the storage risk material list; B5, add the supply risk materials whose production risk is greater than the preset production risk threshold to the production risk material list; B6, add the supply risk materials whose material production risk is greater than the preset logistics risk threshold to the logistics risk material list; B7, determine the risk level of each supply risk material based on the warehousing risk material list, production risk material list and logistics risk material list.

[0008] By adopting the above-mentioned technical scheme, the risk monitoring method based on the operation of the power grid material supply chain can use multiple independent identification models to specifically evaluate the specific risk levels of power grid materials that may have supply chain risks in each link of warehousing, production and logistics, and conduct a comprehensive assessment of the risk levels of power grid materials that may have supply chain risks. On the one hand, it can determine the departments with supply risks for power grid materials that may have supply chain risks, and on the other hand, it can determine the corresponding risk levels, which helps managers to formulate targeted risk response strategies, thereby significantly improving the stability of the power grid material supply chain.

[0009] Optionally, step B7 includes the following steps: B701, combine the warehousing risk material list, production risk material list and logistics risk material list to generate a comprehensive risk material list; B702, count the number of times each supply risk material appears in the comprehensive list of risk materials and define it as the risk number; B703, generates the corresponding risk level based on the sum of the risk times of each supply risk material and the corresponding material supply chain risk degree.

[0010] By adopting the above technical solution, the risk monitoring method based on the operation of the power grid material supply chain can cross-count and comprehensively analyze the risk lists in the three dimensions of warehousing, production and logistics. It not only takes into account the number of times the power grid materials appear in the list, but also combines the material supply chain risk corresponding to the power grid materials. It can more comprehensively reflect the overall risk characteristics of supply risk materials and provide a refined risk grading strategy for power grid material supply chain management.

[0011] Optionally, the risk monitoring method based on power grid material supply chain operation further comprises the following steps: C1, define the risk level of each supply risk material as the supply priority; C2, remove duplicate supply risk materials in the risk material comprehensive list and assign supply priority to the corresponding supply risk materials to generate a risk material supply priority list; C3, sorting the risk material supply priority list from large to small according to the supply priority of each supply risk material to generate a risk material supply priority sequence; C4, obtain the supply risk materials in the risk material supply priority sequence and define them as demand materials; C5, generates the risk distribution data of the required materials according to the required materials and the corresponding supply priority, material storage risk, material production risk and material logistics risk; C6, sends the risk distribution data of the required materials to the warehousing department, production department and logistics department corresponding to the required materials.

[0012] By adopting the above technical solution, the risk monitoring method based on the power grid material supply chain operation can realize the priority management of risk materials through deduplication and sorting, and can also generate comprehensive demand material risk distribution data based on supply priority, warehousing, production and logistics multi-dimensional risk data. It can convert complex supply chain risk assessment results into actionable risk distribution information and transmit it to relevant departments, so that the power grid material supply chain management is transformed from passive response to active prevention, which greatly improves the risk management capability of the supply chain, resource allocation efficiency and overall operational stability.

[0013] Optionally, the risk monitoring method based on power grid material supply chain operation further includes the following steps for training the supply chain risk identification model: D1, obtain the historical material demand information from the historical power grid material database; D2, determine the corresponding historical material demand time and historical material arrival time based on the historical material demand information; D3, according to the historical material demand time of each historical material demand information and the historical material arrival time, calculate the corresponding historical material arrival time by taking the difference; D4, classifying the historical material arrival time consumption according to the material category information in each historical material demand information to generate corresponding historical material arrival time consumption data of each category; D5, determining the abnormal historical material arrival time consumption according to the classified historical material arrival time consumption data and using a preset abnormal time consumption judgment algorithm; D6, determine the corresponding historical material demand moment according to the historical material arrival time corresponding to the abnormal historical material arrival time and define it as the historical supply chain abnormal moment; D7, according to the historical supply chain abnormality moment, obtain the corresponding historical material storage data, historical material production data and historical material logistics data from the historical power grid material database and combine them to generate historical supply chain abnormality data; D8, combines all historical supply chain anomaly data to generate a supply chain anomaly training dataset and trains a preset machine learning model to generate a supply chain risk identification model.

[0014] By adopting the above technical solution, the risk monitoring method based on the operation of the power grid material supply chain can identify abnormal moments of the supply chain through the historical demand time and arrival time of power grid materials and the abnormal time-consuming judgment algorithm. It can not only systematically capture potential risk signals in the historical supply chain operation, but also convert these risk data into valuable machine learning training data sets. By mining historical data, the prediction accuracy and generalization ability of the supply chain risk identification model can be effectively improved.

[0015] Optionally, the abnormal time-consuming judgment algorithm includes the following steps: E1, calculate the corresponding confidence interval of the material arrival time according to the classified historical material arrival time data and the preset selection confidence level; E2, determine the corresponding upper limit of the confidence interval based on the confidence interval of the material arrival time; E3: If the historical material arrival time in the classified historical material arrival time data is greater than the upper limit of the confidence interval, the historical material arrival time is defined as the abnormal historical material arrival time.

[0016] By adopting the above technical scheme, the risk monitoring method based on the power grid material supply chain operation can systematically identify the material arrival time consumption that significantly deviates from the normal mode in the supply chain operation, and can be used to further determine the corresponding supply chain abnormal training data set according to the abnormal time consumption. The data volume and data range of the supply chain abnormal training data set can be adjusted by selecting the confidence level, thereby adjusting the sensitivity and accuracy of the supply chain risk identification model to be suitable for different electricity consumption occasions and electricity consumption departments.

[0017] Optionally, the risk monitoring method based on power grid material supply chain operation further comprises the following steps: F1, according to the preset material consumption detection time interval, the historical material arrival time data of the classified material arrival time is obtained, and the historical material arrival time of the corresponding historical material arrival time is combined to generate the detection interval classified material consumption time data; F2, generating a sequence of the consumption time of the classified materials in the detection interval according to the sorting of the consumption time data of the classified materials in the detection interval from small to large; F3, divide the material consumption detection time interval into multiple classified material consumption sub-intervals according to the consumption monitoring time window; F4, respectively count the number of historical material arrival times in each classified material consumption sub-interval in the detection interval classified material consumption time sequence and define it as the classified material sub-interval consumption; F5, generate the corresponding linear regression equation according to the sub-interval consumption of all classified materials and determine the corresponding regression equation slope; F6: If the slope of the regression equation is greater than the preset threshold of the slope of classified material consumption, the classified historical material corresponding to the classified historical material arrival time data is defined as an abnormally consumed material.

[0018] By adopting the above technical solution, the risk monitoring method based on the operation of the power grid material supply chain can accurately identify materials that deviate from the normal consumption pattern by systematically dividing, sorting and counting the material consumption moments and using a linear regression model to capture consumption trends. It can not only objectively evaluate the changing characteristics of material consumption, but also judge abnormal consumption, providing a forward-looking risk monitoring solution for potential abnormalities and failures in power grid operation.

[0019] The present application also provides a risk monitoring system based on power grid material supply chain operations, including: Supply chain data collection module; Data processing module; Risk assessment module; Information feedback module; The supply chain data collection module, the risk assessment module and the information feedback module are respectively data-connected to the data processing module; The supply chain data collection module includes a warehouse data collection module, a production data collection module and a logistics data collection module, and the warehouse data collection module, the production data collection module and the logistics data collection module are respectively connected to the data processing module; Wherein, the risk assessment module includes a supply chain risk identification module, a warehousing risk identification module, a production risk identification module and a logistics risk identification module, and the supply chain risk identification module, the warehousing risk identification module, the production risk identification module and the logistics risk identification module are respectively data-connected to the data processing module; The risk monitoring system based on the operation of the power grid material supply chain further includes a supply chain risk identification strategy, including the following steps: G1, obtaining corresponding historical power grid material consumption data from a preset historical power grid material database according to a preset detection cycle through the supply chain data collection module; G2, determining the consumption data of each power grid material according to the historical power grid material consumption data and the preset power grid material list through the data processing module; G3, calculate the material consumption rate of each corresponding power grid according to the material consumption data of each power grid; G4, calculate the corresponding time window material consumption according to the material consumption speed of each power grid and the preset consumption monitoring time window; G5, determining the corresponding monitoring materials according to the material consumption in each time window and the preset consumption monitoring threshold; G6, obtaining corresponding material storage data, material production data and material logistics data according to each monitored material through the supply chain data collection module; G7, generating a corresponding material supply chain risk degree through the risk assessment module according to the material warehousing data, material production data and material logistics data through a pre-trained supply chain risk identification model; G8. If the material supply chain risk of the monitored material is greater than a preset supply chain risk threshold, the monitored material is defined as a supply risk material, and the supply risk material is fed back through the information feedback module.

[0020] By adopting the above technical solution, the risk monitoring system based on the power grid material supply chain operation can screen out power grid materials that may have excessive consumption by counting the consumption of each power grid material, and obtain the storage, production and material-related data of the power grid materials with excessive consumption, and then identify the corresponding risk level through the pre-trained risk identification model, and then determine the power grid materials with supply chain risks, avoiding comprehensive data acquisition and risk identification of all power grid materials, reducing the computational overhead of data processing, and improving the efficiency of supply chain risk monitoring.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. By counting the consumption of each power grid material, we can screen out power grid materials that may have excessive consumption, and obtain the storage, production and material-related data of power grid materials with excessive consumption, and then identify the corresponding risk level through the pre-trained risk identification model, and then determine the power grid materials with supply chain risks, avoiding comprehensive data acquisition and risk identification for all power grid materials, reducing the computational overhead of data processing, and improving the efficiency of supply chain risk monitoring.

[0022] 2. Multiple independent identification models can be used to specifically evaluate the specific risk levels of power grid materials that may have supply chain risks in each link of warehousing, production and logistics, and conduct a comprehensive assessment of the risk levels of power grid materials that may have supply chain risks. On the one hand, it can determine the departments with supply risks for power grid materials that may have supply chain risks, and on the other hand, it can determine the corresponding risk levels, which helps managers to formulate targeted risk response strategies, thereby significantly improving the stability of the power grid material supply chain.

[0023] 3. By cross-counting and comprehensively analyzing the risk lists of the three dimensions of warehousing, production and logistics, not only the number of times the power grid materials appear in the list is taken into account, but also the material supply chain risk corresponding to the power grid materials is combined. This can more comprehensively reflect the overall risk characteristics of supply risk materials and provide a refined risk grading strategy for power grid material supply chain management. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a process schematic diagram of a risk monitoring method based on power grid material supply chain operation of the present invention.

[0025] Figure 2 It is a schematic diagram of the principle of a risk monitoring system based on power grid material supply chain operation of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0028] refer to Figure 1 The present invention provides a risk monitoring method based on power grid material supply chain operation, comprising the following steps: A1, obtaining corresponding historical power grid material consumption data from a preset historical power grid material database according to a preset detection cycle; The detection period is a pre-set duration used to determine the time range for obtaining data; The historical power grid material database is a pre-set database that stores historical data related to power grid materials, which may include the name information, classification information, consumption time and consumption amount of power grid materials, and other related information and data; The historical power grid material consumption data is historical data including the consumption of various types of power grid materials, for example, the time when the power grid material consumption occurs, the name information, classification information and consumption amount of the consumed power grid materials.

[0029] A2, determine the material consumption data of each power grid based on the historical power grid material consumption data and the preset power grid material list; The power grid material list is a pre-set list used to determine and count various power grid materials; The power grid material consumption data is the consumption data corresponding to various types of power grid materials in the historical power grid material consumption data; According to the grid material list, the consumption data of various grid materials can be classified from the historical grid material consumption data to obtain the grid material consumption data of various grid materials.

[0030] A3, calculating the material consumption rate of each corresponding power grid according to the material consumption data of each power grid; The power grid material consumption rate is the consumption rate of various power grid materials. Based on the consumption in the power grid material consumption data and the historical time span involved in the data, the consumption rate of the corresponding power grid material can be calculated.

[0031] A4, calculating the corresponding time window material consumption according to the material consumption speed of each power grid and the preset consumption monitoring time window; The consumption monitoring time window is a pre-set time window used to determine a specific time interval; The time window material consumption is the estimated possible consumption quantity of the power grid material within the consumption monitoring time window.

[0032] A5, determining the corresponding monitoring materials according to the material consumption in each time window and the preset consumption monitoring threshold; The consumption monitoring threshold is a pre-set reference value used to determine whether the material consumption in the time window has reached a certain level. Different grid materials have different consumption monitoring thresholds, which can be set by staff based on experience or determined based on historical data statistics; The monitored materials are grid materials whose consumption in the time window reaches the consumption monitoring threshold, and further monitoring and analysis is required.

[0033] A6, obtain the corresponding material storage data, material production data and material logistics data according to each monitored material; Material storage data refers to data related to the storage department corresponding to the monitored materials, which can be the storage quantity of the monitored materials in each warehouse, the storage volume and weight of the monitored materials, the distance from each warehouse to the demand department, and other related data; Material production data refers to the relevant data of the production department corresponding to the monitored material, which can be the production efficiency, production time, sufficiency of production materials, distance from the production department to the demand department and other related data of the monitored material in the relevant production department; Material logistics data refers to data related to the logistics and transportation departments corresponding to the monitored materials, which may be the idle transportation capacity of the relevant material departments, traffic congestion data of available transportation routes, future weather data and other related data.

[0034] A7, based on the material storage data, material production data and material logistics data, the corresponding material supply chain risk is generated through the pre-trained supply chain risk identification model; The supply chain risk identification model is a pre-trained identification model that can be generated through corresponding historical data training and is used to identify and output corresponding risk values ​​based on existing material storage data, material production data, and material logistics data; The material supply chain risk is a numerical value that reflects the possibility that the monitored material has supply chain risks.

[0035] A8: If the material supply chain risk of the monitored material is greater than the preset supply chain risk threshold, the monitored material is defined as a supply risk material; The supply chain risk threshold is a pre-set reference value used to determine whether the material supply chain risk of the monitored material has reached the level that is considered to be at risk; Supply risk materials are monitored materials that are identified as having supply chain risks; By determining the supply risk logistics, the information can be further fed back to the control background or the competent department, so as to further monitor or respond to the possible supply chain risks in a timely manner.

[0036] Through the above steps, the risk monitoring method based on the power grid material supply chain operation can screen out power grid materials that may have excessive consumption by counting the consumption of each power grid material, and obtain the storage, production and material-related data of the power grid materials with excessive consumption, and then identify the corresponding risk level through the pre-trained risk identification model, and then determine the power grid materials with supply chain risks, avoiding comprehensive data acquisition and risk identification for all power grid materials, reducing the computational overhead of data processing, and improving the efficiency of supply chain risk monitoring.

[0037] Furthermore, the risk monitoring method based on power grid material supply chain operation further includes the following steps: B1, generate the corresponding material storage risk level based on the material storage data of each supply risk material using the pre-trained storage risk assessment model; The warehouse risk assessment model is a pre-trained recognition model that can be used to identify the supply risk level of supply risk materials in warehouse supply; The warehouse risk assessment model can be obtained through targeted training based on the historical data related to warehouse; The material storage risk is a numerical value that reflects the degree of risk that may exist in the storage supply of supply risk materials.

[0038] B2, generating the corresponding material production risk degree based on the material production data of each supply risk material using the pre-trained production risk assessment model; The production risk assessment model is a pre-selected and trained identification model that can be used to identify the supply risk level of supply risk materials in production supply; The production risk assessment model can be obtained through targeted training based on historical data related to production; The material production risk is a numerical value that reflects the degree of risk that may exist in the production and supply of supply risk materials.

[0039] B3, generating the corresponding material logistics risk level according to the material logistics data of each supply risk material using the pre-trained logistics risk assessment model; The logistics risk assessment model is a pre-trained identification model that can be used to identify the supply risk level of supply risk materials in material transportation; The logistics risk assessment model can be obtained through targeted training based on historical data related to logistics and transportation; The material logistics risk level is a numerical value that reflects the degree of risk that may exist in the logistics and transportation of supply risk materials.

[0040] B4, add the supply risk materials whose material storage risk is greater than the preset storage risk threshold to the storage risk material list; The storage risk threshold is a pre-set reference value used to determine whether the material storage risk has reached the level that is considered to be a storage supply risk; The warehouse risk material list is a collection of all supply risk materials that are identified as having warehouse supply risks.

[0041] B5, add the supply risk materials whose production risk is greater than the preset production risk threshold to the production risk material list; The production risk threshold is a pre-set reference value used to determine whether the material production risk has reached the level that is considered to be a production supply risk; The production risk materials list is a collection of all supply risk materials that are identified as having production supply risks.

[0042] B6, add the supply risk materials whose material production risk is greater than the preset logistics risk threshold to the logistics risk material list; The material risk threshold is a pre-set reference value used to determine whether the material logistics risk has reached the level that is considered to be a logistics and transportation risk; The logistics risk material list is a collection of all supply risk materials that are identified as having logistics and transportation risks.

[0043] B7, determine the risk level of each supply risk material based on the warehousing risk material list, production risk material list and logistics risk material list.

[0044] The risk level is a numerical value that reflects the risk level of each supply risk material. It can be calculated and calibrated using different algorithms as needed. For example, the corresponding risk levels of the supply risk materials appearing in the warehousing risk materials list, the production risk materials list, and the logistics risk materials list can be determined by summing up; or it can be determined based on the number of times the supply risk materials appear in the warehousing risk materials list, the production risk materials list, and the logistics risk materials list.

[0045] Through the above steps, the risk monitoring method based on the power grid material supply chain operation can use multiple independent identification models to specifically evaluate the specific risk levels of power grid materials that may have supply chain risks in each link of warehousing, production and logistics, and conduct a comprehensive assessment of the risk levels of power grid materials that may have supply chain risks. On the one hand, it can determine the departments with supply risks for power grid materials that may have supply chain risks, and on the other hand, it can determine the corresponding risk levels, which helps managers to formulate targeted risk response strategies, thereby significantly improving the stability of the power grid material supply chain.

[0046] Furthermore, the step B7 comprises the following steps: B701, combine the warehousing risk material list, production risk material list and logistics risk material list to generate a comprehensive risk material list; The comprehensive list of risk materials is a collection of the warehousing risk material list, the production risk material list and the logistics risk material list without deduplication, that is, the comprehensive list of risk materials may include duplicate supply risk materials.

[0047] B702, count the number of times each supply risk material appears in the comprehensive list of risk materials and define it as the risk number; The risk number refers to the number of times a supply risk material appears in the comprehensive list of risk materials. The more times it appears, the more departments have supply risks for the supply risk material. For example, a supply risk material appears in both the warehousing risk material list and the production risk material list. Its number of appearances in the comprehensive list of risk materials is 2, and its corresponding risk number is 2, reflecting that it has supply chain risks in two departments.

[0048] B703, generates the corresponding risk level based on the sum of the risk times of each supply risk material and the corresponding material supply chain risk degree; The risk level is the sum of the number of risk times of supplying risky materials and the corresponding material supply chain risk level; By summing the number of risk times and the material supply chain risk, the risk level of each supply risk material can be better quantified and distinguished. Because the number of risk times is an integer, when multiple supply risk materials have the same risk number, it is difficult to compare the corresponding risk levels, which is not conducive to subsequent decision-making.

[0049] Through the above steps, the risk monitoring method based on the operation of the power grid material supply chain can cross-count and comprehensively analyze the risk lists in the three dimensions of warehousing, production and logistics. It not only takes into account the number of times the power grid materials appear in the list, but also combines the material supply chain risk corresponding to the power grid materials. It can more comprehensively reflect the overall risk characteristics of supply risk materials and provide a refined risk grading strategy for power grid material supply chain management.

[0050] Furthermore, the risk monitoring method based on power grid material supply chain operation also includes the following steps: C1, define the risk level of each supply risk material as the supply priority; Supply priority refers to the risk level of supply risk materials, which is used to determine the priority of supplying risk materials.

[0051] C2, remove duplicate supply risk materials in the risk material comprehensive list and assign supply priority to the corresponding supply risk materials to generate a risk material supply priority list; The risk material supply priority list is a comparison list of each supply risk material and the corresponding supply priority.

[0052] C3, sorting the risk material supply priority list from large to small according to the supply priority of each supply risk material to generate a risk material supply priority sequence; The risk material supply priority sequence is a production sequence sorted according to the supply priority of each supply risk material, reflecting the supply order of each supply risk material.

[0053] C4, obtain the supply risk materials in the risk material supply priority sequence and define them as demand materials; Demand materials are the supply risk materials currently selected that require supply chain scheduling.

[0054] C5, generates the risk distribution data of the required materials according to the required materials and the corresponding supply priority, material storage risk, material production risk and material logistics risk; The demand material risk distribution data is a collection of data on supply priority, material storage risk, material production risk and material risk, which is used to comprehensively reflect the departments, risk levels and supply priorities of demand materials with supply risks.

[0055] C6, sending the risk distribution data of the required materials to the warehousing department, production department and logistics department corresponding to the required materials; By sending the demand material risk distribution data of the demand materials to the corresponding warehousing department, production department and logistics department, the relevant departments can make judgments based on the data and adjust their own work and collaborate to eliminate the supply chain risks of the demand materials as soon as possible.

[0056] Through the above steps, the risk monitoring method based on the power grid material supply chain operation can realize the priority management of risk materials through deduplication and sorting, and can also generate comprehensive demand material risk distribution data based on supply priority, warehousing, production and logistics multi-dimensional risk data. It can convert complex supply chain risk assessment results into actionable risk distribution information and transmit it to relevant departments, so that the power grid material supply chain management is transformed from passive response to active prevention, which greatly improves the risk management capability of the supply chain, resource allocation efficiency and overall operational stability.

[0057] Furthermore, the risk monitoring method based on power grid material supply chain operation also includes the following steps for training the supply chain risk identification model: D1, obtain the historical material demand information from the historical power grid material database; The historical material demand information is the demand information of all previous power grid materials in the historical power grid material database, which can be obtained through manual records when materials are consumed during power grid maintenance.

[0058] D2, determine the corresponding historical material demand time and historical material arrival time based on the historical material demand information; The historical material demand time is the demand time of the power grid material, that is, the time when the demand for the power grid material is initiated, for example, it can be the time when the material application record of the operation and maintenance personnel is generated; The historical material arrival time is the time when the power grid material arrives at the required location, that is, the time when the demand for power grid materials is resolved. For example, it can be the time when the operation and maintenance personnel generate the receipt record for the required materials.

[0059] D3, according to the historical material demand time of each historical material demand information and the historical material arrival time, calculate the corresponding historical material arrival time by taking the difference; The historical material arrival time is the time difference between the historical material demand time and the historical material arrival time, that is, the time between the historical material demand time and the historical material arrival time, reflecting the time consumed to solve the material demands of each power grid.

[0060] D4, classifying the historical material arrival time consumption according to the material category information in each historical material demand information to generate corresponding historical material arrival time consumption data of each category; Material category information is the classification information of each power grid material, which can be pre-classified and set by the staff; The classified historical material arrival time consumption data is a collection of historical material arrival time consumption data for power grid materials of the same category generated after classification.

[0061] D5, determining the abnormal historical material arrival time consumption according to the classified historical material arrival time consumption data and using a preset abnormal time consumption judgment algorithm; The abnormal time consumption judgment algorithm is a pre-set algorithm used to find abnormal time consumption data from the classified historical material arrival time consumption data; Abnormal historical material arrival time refers to abnormal time consumption data, usually historical material arrival time consumption that is too long. When the time consumption is too long, it means that the supply chain may be interrupted during the material relocation process of the power grid, resulting in abnormal time consumption.

[0062] D6, determine the corresponding historical material demand moment according to the historical material arrival time corresponding to the abnormal historical material arrival time and define it as the historical supply chain abnormal moment; Historical supply chain anomaly moments are moments when supply chain disruptions may occur.

[0063] D7, according to the historical supply chain abnormality moment, obtain the corresponding historical material storage data, historical material production data and historical material logistics data from the historical power grid material database and combine them to generate historical supply chain abnormality data; Historical supply chain exception data is a collection of historical material warehousing data, historical material production data, and historical material logistics data at the time of historical supply chain exceptions. It can be used to comprehensively reflect the status data of each department when a supply chain interruption occurs.

[0064] D8, combining all historical supply chain anomaly data to generate a supply chain anomaly training dataset and training a preset machine learning model to generate a supply chain risk identification model; The supply chain anomaly training dataset is a collection of all historical supply chain anomaly data. By adding corresponding anomaly label values, it can be used to train the corresponding recognition model. The machine learning model is a pre-selected model. Different models can be selected according to the identification requirements and trained with the supply chain anomaly training data set to generate a supply chain risk identification model.

[0065] Through the above steps, the risk monitoring method based on the operation of the power grid material supply chain can identify abnormal moments of the supply chain through the historical demand time and arrival time of power grid materials, and use the abnormal time-consuming judgment algorithm to not only systematically capture potential risk signals in the historical supply chain operation, but also convert these risk data into valuable machine learning training data sets. By mining historical data, the prediction accuracy and generalization ability of the supply chain risk identification model can be effectively improved.

[0066] Furthermore, the abnormal time-consuming judgment algorithm includes the following steps: E1, calculate the corresponding confidence interval of the material arrival time according to the classified historical material arrival time data and the preset selection confidence level; The selected confidence level is the confidence level selected by the staff, for example, 90%, 95%, 99%, etc., which can be set by the staff according to needs or experience. Different selected confidence levels will affect the data volume and data range of the subsequent supply chain anomaly training data set, as well as the sensitivity and accuracy of the corresponding trained supply chain risk identification model. That is, by adjusting the selected confidence level, the subsequent supply chain risk identification model training process can be adjusted to meet different response sensitivity requirements, so as to be suitable for different electricity usage occasions and electricity usage departments; The confidence interval of the time consumption for materials to arrive is the confidence interval corresponding to the classified historical material arrival time consumption data under the selected confidence level.

[0067] E2, determine the corresponding upper limit of the confidence interval based on the confidence interval of the material arrival time; The upper limit of the confidence interval is the upper limit of the confidence interval of the time it takes for materials to arrive.

[0068] E3, if the historical material arrival time in the classified historical material arrival time data is greater than the upper limit of the confidence interval, the historical material arrival time is defined as the abnormal historical material arrival time; Abnormal historical material arrival time is identified as historical material arrival time with supply chain disruption.

[0069] Through the above steps, the risk monitoring method based on the power grid material supply chain operation can systematically identify the material arrival time consumption that significantly deviates from the normal mode in the supply chain operation, and can be used to further determine the corresponding supply chain abnormality training data set according to the abnormal time consumption. The data volume and data range of the supply chain abnormality training data set can be adjusted by selecting the confidence level, thereby adjusting the sensitivity and accuracy of the supply chain risk identification model to be suitable for different electricity consumption occasions and electricity consumption departments.

[0070] Furthermore, the risk monitoring method based on power grid material supply chain operation also includes the following steps: F1, according to the preset material consumption detection time interval, the historical material arrival time data of the classified material arrival time is obtained, and the historical material arrival time of the corresponding historical material arrival time is combined to generate the detection interval classified material consumption time data; The material consumption detection time interval is a pre-set time interval, which is used to select data within the corresponding time interval and can be set according to needs; F2, generating a sequence of the consumption time of the classified materials in the detection interval according to the sorting of the consumption time data of the classified materials in the detection interval from small to large; The detection interval classification material consumption time sequence is the sorting data of the detection interval classification material consumption time data from large to small.

[0071] F3, divide the material consumption detection time interval into multiple classified material consumption sub-intervals according to the consumption monitoring time window; The classified material consumption sub-interval is a time interval of the same length as the consumption monitoring time window, and is used to determine the data within the corresponding time range.

[0072] F4, respectively count the number of historical material arrival times in each classified material consumption sub-interval in the detection interval classified material consumption time sequence and define it as the classified material sub-interval consumption; The classified material sub-interval consumption is the number of historical material arrival times that fall within the classified material consumption sub-interval in the detection interval classified material consumption time sequence, which can be understood as the corresponding power grid material consumption during the classified material consumption sub-interval.

[0073] F5, generate the corresponding linear regression equation according to the sub-interval consumption of all classified materials and determine the corresponding regression equation slope; The slope of the regression equation is the slope of the linear regression equation generated according to the sub-interval consumption of all classified materials, which can reflect the consumption acceleration of the classified power grid material.

[0074] F6: If the slope of the regression equation is greater than the preset threshold of the slope of classified material consumption, the classified historical material corresponding to the classified historical material arrival time data is defined as an abnormally consumed material; The classification material consumption slope threshold is a pre-set reference value used to determine whether the slope of the regression equation has reached a level that requires vigilance, that is, whether the consumption acceleration of the classification material is too large; Abnormally consumable materials are power grid materials with abnormal consumption acceleration. The corresponding abnormally consumable materials can be used to infer whether the corresponding power grid node has abnormal faults and other information, which can be fed back to the operation and maintenance personnel for further inspection and troubleshooting.

[0075] Through the above steps, the risk monitoring method based on the operation of the power grid material supply chain can accurately identify materials that deviate from normal consumption patterns by systematically dividing, sorting and counting the material consumption moments and capturing consumption trends using a linear regression model. It can not only objectively evaluate the changing characteristics of material consumption, but also determine abnormal consumption, providing a forward-looking risk monitoring solution for potential abnormalities and failures in power grid operation.

[0076] refer to Figure 2 , the present application also provides a risk monitoring system based on power grid material supply chain operation, including: Supply chain data collection module 10; Data processing module 20; Risk assessment module 30; Information feedback module 40; The supply chain data collection module 10, the risk assessment module 30 and the information feedback module 40 are respectively data-connected to the data processing module 20; The supply chain data collection module 10 includes a warehouse data collection module 11, a production data collection module 12 and a logistics data collection module 13, and the warehouse data collection module 11, the production data collection module 12 and the logistics data collection module 13 are respectively connected to the data processing module 20; The risk assessment module 30 includes a supply chain risk identification module 31, a storage risk identification module 32, a production risk identification module 33 and a logistics risk identification module 34. The supply chain risk identification module 31, the storage risk identification module 32, the production risk identification module 33 and the logistics risk identification module 34 are respectively data-connected to the data processing module 20; The risk monitoring system based on the operation of the power grid material supply chain further includes a supply chain risk identification strategy, including the following steps: G1, obtaining corresponding historical power grid material consumption data from a preset historical power grid material database according to a preset detection cycle through the supply chain data collection module 10; G2, determining the consumption data of each power grid material according to the historical power grid material consumption data and the preset power grid material list through the data processing module 20; G3, calculate the material consumption rate of each corresponding power grid according to the material consumption data of each power grid; G4, calculate the corresponding time window material consumption according to the material consumption speed of each power grid and the preset consumption monitoring time window; G5, determining the corresponding monitoring materials according to the material consumption in each time window and the preset consumption monitoring threshold; G6, obtaining corresponding material storage data, material production data and material logistics data according to each monitored material through the supply chain data collection module 10; G7, generating a corresponding material supply chain risk degree through the risk assessment module 30 according to the material storage data, material production data and material logistics data through a pre-trained supply chain risk identification model; G8, if the material supply chain risk of the monitored material is greater than a preset supply chain risk threshold, the monitored material is defined as a supply risk material, and the supply risk material is fed back through the information feedback module 40.

[0077] The supply chain data collection module 10 is mainly used to obtain supply chain related data corresponding to power grid materials.

[0078] The storage data collection module 11 is mainly used to collect data on power grid materials in the storage department.

[0079] The production data collection module 12 is mainly used to collect data on power grid materials in the production department.

[0080] The logistics data collection module 13 is mainly used to collect data on power grid materials in the logistics department.

[0081] The data processing module 20 is mainly used to process and analyze various data.

[0082] The risk assessment module 30 is mainly used to make corresponding supply chain risk assessment according to the supply chain data of power grid materials.

[0083] The supply chain risk identification module 31 is mainly used to comprehensively identify the supply chain risks of power grid materials based on supply chain data.

[0084] The storage risk identification module 32 is mainly used to specifically identify the supply risk of power grid materials in the storage department according to the storage data in the supply chain data.

[0085] The production risk identification module 33 is mainly used to specifically identify the supply risk of power grid materials to the production department according to the production data in the supply chain data.

[0086] The logistics risk identification module 34 is mainly used to specifically identify the supply risk of power grid materials to the logistics department based on the logistics data in the supply chain data.

[0087] The information feedback module 40 is mainly used to feed back risk information to the preset central control background or staff so that they can make response decisions as soon as possible.

[0088] Through the above technical scheme, the risk monitoring system based on the power grid material supply chain operation can screen out power grid materials that may have excessive consumption by counting the consumption of each power grid material, and obtain the storage, production and material-related data of the power grid materials with excessive consumption, and then identify the corresponding risk level through the pre-trained risk identification model, and then determine the power grid materials with supply chain risks, avoiding comprehensive data acquisition and risk identification of all power grid materials, reducing the computational overhead of data processing, and improving the efficiency of supply chain risk monitoring.

[0089] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A risk monitoring method based on power grid material supply chain operation, characterized in that: The following steps are involved: A1, obtaining corresponding historical power grid material consumption data from a preset historical power grid material database according to a preset detection cycle; A2, determine the material consumption data of each power grid based on the historical power grid material consumption data and the preset power grid material list; A3, calculating the material consumption rate of each corresponding power grid according to the material consumption data of each power grid; A4, calculating the corresponding time window material consumption according to the material consumption speed of each power grid and the preset consumption monitoring time window; A5, determining the corresponding monitoring materials according to the material consumption in each time window and the preset consumption monitoring threshold; A6, obtain the corresponding material storage data, material production data and material logistics data according to each monitored material; A7, based on the material storage data, material production data and material logistics data, the corresponding material supply chain risk is generated through the pre-trained supply chain risk identification model; A8: If the material supply chain risk of the monitored material is greater than the preset supply chain risk threshold, the monitored material is defined as a supply risk material.

2. The risk monitoring method based on power grid material supply chain operation according to claim 1 is characterized in that: The risk monitoring method based on power grid material supply chain operation further includes the following steps: B1, generate the corresponding material storage risk level based on the material storage data of each supply risk material using the pre-trained storage risk assessment model; B2, generating the corresponding material production risk degree based on the material production data of each supply risk material using the pre-trained production risk assessment model; B3, generating the corresponding material logistics risk level according to the material logistics data of each supply risk material using the pre-trained logistics risk assessment model; B4, add the supply risk materials whose material storage risk is greater than the preset storage risk threshold to the storage risk material list; B5, add the supply risk materials whose production risk is greater than the preset production risk threshold to the production risk material list; B6, add the supply risk materials whose material production risk is greater than the preset logistics risk threshold to the logistics risk material list; B7, determine the risk level of each supply risk material based on the warehousing risk material list, production risk material list and logistics risk material list.

3. The risk monitoring method based on power grid material supply chain operation according to claim 2 is characterized in that: The step B7 comprises the following steps: B701, combine the warehousing risk material list, production risk material list and logistics risk material list to generate a comprehensive risk material list; B702, count the number of times each supply risk material appears in the comprehensive list of risk materials and define it as the risk number; B703, generates the corresponding risk level based on the sum of the risk times of each supply risk material and the corresponding material supply chain risk degree.

4. The risk monitoring method based on power grid material supply chain operation according to claim 3 is characterized in that: Further comprising the steps of: C1, define the risk level of each supply risk material as the supply priority; C2, remove duplicate supply risk materials in the risk material comprehensive list and assign supply priority to the corresponding supply risk materials to generate a risk material supply priority list; C3, sorting the risk material supply priority list from large to small according to the supply priority of each supply risk material to generate a risk material supply priority sequence; C4, obtain the supply risk materials in the risk material supply priority sequence and define them as demand materials; C5, generates the risk distribution data of the required materials according to the required materials and the corresponding supply priority, material storage risk, material production risk and material logistics risk; C6, sends the risk distribution data of the required materials to the warehousing department, production department and logistics department corresponding to the required materials.

5. The risk monitoring method based on power grid material supply chain operation according to claim 4 is characterized in that: The following steps are further included for training the supply chain risk identification model: D1, obtain the historical material demand information from the historical power grid material database; D2, determine the corresponding historical material demand time and historical material arrival time based on the historical material demand information; D3, calculating the corresponding historical material arrival time by taking the difference between the historical material demand time and the historical material arrival time of each historical material demand information; D4, classifying the historical material arrival time consumption according to the material category information in each historical material demand information to generate corresponding historical material arrival time consumption data of each category; D5, determining the abnormal historical material arrival time consumption according to the classified historical material arrival time consumption data and using a preset abnormal time consumption judgment algorithm; D6, determine the corresponding historical material demand moment according to the historical material arrival time corresponding to the abnormal historical material arrival time and define it as the historical supply chain abnormal moment; D7, according to the historical supply chain abnormality moment, obtain the corresponding historical material storage data, historical material production data and historical material logistics data from the historical power grid material database and combine them to generate historical supply chain abnormality data; D8, combines all historical supply chain anomaly data to generate a supply chain anomaly training dataset and trains a preset machine learning model to generate a supply chain risk identification model.

6. The risk monitoring method based on power grid material supply chain operation according to claim 5 is characterized in that: The abnormal time-consuming judgment algorithm comprises the following steps: E1, calculate the corresponding confidence interval of the material arrival time according to the classified historical material arrival time data and the preset selection confidence level; E2, determine the corresponding upper limit of the confidence interval based on the confidence interval of the material arrival time; E3: If the historical material arrival time in the classified historical material arrival time data is greater than the upper limit of the confidence interval, the historical material arrival time is defined as the abnormal historical material arrival time.

7. The risk monitoring method based on power grid material supply chain operation according to claim 6 is characterized in that: Further comprising the steps of: F1, according to the preset material consumption detection time interval, the historical material arrival time data of the classified material arrival time is obtained, and the historical material arrival time of the corresponding historical material arrival time is combined to generate the detection interval classified material consumption time data; F2, generating a detection interval classification material consumption time sequence according to the detection interval classification material consumption time data sorted from small to large; F3, divide the material consumption detection time interval into multiple classified material consumption sub-intervals according to the consumption monitoring time window; F4, respectively count the number of historical material arrival times in each classified material consumption sub-interval in the detection interval classified material consumption time sequence and define it as the classified material sub-interval consumption; F5, generate the corresponding linear regression equation according to the sub-interval consumption of all classified materials and determine the corresponding regression equation slope; F6: If the slope of the regression equation is greater than the preset threshold of the slope of classified material consumption, the classified historical material corresponding to the classified historical material arrival time data is defined as an abnormally consumed material.

8. A risk monitoring system based on power grid material supply chain operation, characterized in that: include: Supply chain data collection module; Data processing module; Risk assessment module; Information feedback module; The supply chain data collection module, the risk assessment module and the information feedback module are respectively data-connected to the data processing module; Wherein, the supply chain data collection module includes a warehousing data collection module, a production data collection module and a logistics data collection module, and the warehousing data collection module, the production data collection module and the logistics data collection module are respectively data-connected to the data processing module; Wherein, the risk assessment module includes a supply chain risk identification module, a warehousing risk identification module, a production risk identification module and a logistics risk identification module, and the supply chain risk identification module, the warehousing risk identification module, the production risk identification module and the logistics risk identification module are respectively data-connected to the data processing module; The risk monitoring system based on the operation of the power grid material supply chain further includes a supply chain risk identification strategy, including the following steps: G1, obtaining corresponding historical power grid material consumption data from a preset historical power grid material database according to a preset detection cycle through the supply chain data collection module; G2, determining the consumption data of each power grid material according to the historical power grid material consumption data and the preset power grid material list through the data processing module; G3, calculate the material consumption rate of each corresponding power grid according to the material consumption data of each power grid; G4, calculate the corresponding time window material consumption according to the material consumption speed of each power grid and the preset consumption monitoring time window; G5, determining the corresponding monitoring materials according to the material consumption in each time window and the preset consumption monitoring threshold; G6, obtaining corresponding material storage data, material production data and material logistics data according to each monitored material through the supply chain data collection module; G7, generating a corresponding material supply chain risk degree through the risk assessment module according to the material warehousing data, material production data and material logistics data through a pre-trained supply chain risk identification model; G8. If the material supply chain risk of the monitored material is greater than a preset supply chain risk threshold, the monitored material is defined as a supply risk material, and the supply risk material is fed back through the information feedback module.

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