Power grid intelligent power failure management and control method and system based on cloud computing

Abstract

The invention discloses a power grid intelligent power failure management and control system based on cloud computing, and relates to the technical field of power failure management and control. According to the power grid intelligent power failure management and control system based on cloud computing, the execution progress is displayed in real time through the power failure maintenance progress display module, and transparency is improved; the power failure maintenance progress prompting module can evaluate unexecuted management and control measures in time and give an alarm to field maintenance personnel, so that the execution efficiency is improved; and the power failure maintenance area risk early warning module ensures that a plan is adjusted in time and potential safety hazards and economic losses are reduced under the condition of potential risks through environmental risk assessment, so that more efficient and safer power grid management is realized, and the problems of information asymmetry and response lag in power failure management can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power outage control, and more specifically, relates to a method and system for intelligent power grid outage control based on cloud computing. Background Art

[0002] At present, the proportion of distributed energy and adjustable loads in the energy system has increased. When there is a power outage demand, the power supply and consumption entities include power grid companies, industrial and commercial enterprises, load aggregators, operation and maintenance service providers, residential community users, etc., which have the characteristics of a wide range of types and a large number. Therefore, the power grid outage control business faces problems such as high frequency, decentralized entities, low execution efficiency of centralized systems, insufficient transparency of processes, and high operation and maintenance costs, which pose severe challenges to traditional outage control using a centralized mode.

[0003] Prior art document 1 (CN114118693B) discloses an intelligent power outage control method and device based on a blockchain consensus algorithm, including: obtaining the power outage demand of the power grid, and screening out the blockchain nodes corresponding to the power supply and consumption entities related to the power outage demand; forming groups of the screened blockchain nodes according to a preset rule, calculating the Shapley value of each blockchain node in each group, and selecting a group proxy node according to the Shapley value; classifying the group proxy nodes into type I nodes and type II nodes according to the service type, and independently executing the consensus algorithm for the type I nodes and the type II nodes respectively; if both the type I nodes and the type II nodes complete the consensus, then publish the power outage demand on the blockchain, and the screened blockchain executes the power outage operation according to the published power outage demand.

[0004] The disadvantages of prior art document 1 are that there are problems of information asymmetry and response lag in power outage management, which may make it difficult to adjust the plan in a timely manner in case of risks and unable to achieve more efficient and safe power grid management. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method and system for intelligent power grid outage control based on cloud computing, which solves the problems of information asymmetry and response lag in power outage management.

[0006] To achieve the above object, the present invention is realized through the following technical solutions:

[0007] The present invention adopts the following technical solutions.

[0008] The first aspect of the present invention provides a method for intelligent power grid outage control based on cloud computing, including the following steps:

[0009] Receive the task data uploaded by on-site maintenance personnel through the cloud computing platform. The task data includes the task start time, task status, and task completion status. Perform real-time verification, duplicate removal, and formatting processing on the received task data to generate task status data;

[0010] According to the task status data, calculate the ratio of the number of completed tasks to the total number of tasks, obtain the execution progress of the power outage control plan, and update the execution progress to the terminal device in real time through the cloud computing platform;

[0011] Extract the progress of unfinished tasks from the task status data, calculate the difference between the current progress of the unfinished tasks and the target progress of the unfinished tasks, compare it with the set threshold value, and identify tasks with lagging progress;

[0012] Collect the environmental data of the power outage control area, including real-time temperature, real-time wind speed, and real-time precipitation. Compare the environmental data with the preset safety threshold values of temperature, wind speed, and precipitation respectively to generate the environmental risk status of the power outage control area;

[0013] Combine the comparison results of tasks with lagging progress and the environmental risk status, adjust the execution order of the task plan through the cloud computing platform to generate an optimized task instruction, and send it to on-site maintenance personnel in real time to execute the power outage control task according to the optimized task instruction.

[0014] Preferably, the generation of task status data includes:

[0015] Through the task data parser, parse the uploaded task data into structured data, extract and store the fields of the task start timestamp, task status, and task completion status; among them, the unique identifier generated in the task data is the task ID;

[0016] Apply regular expressions to the task start timestamp field and task status field in the task data for format verification, check whether the task start timestamp conforms to the ISO 8601 standard format, and judge whether the task status is within the allowed range, including not executed is recorded as 0, in execution is recorded as 1, and completed is recorded as 2;

[0017] Perform hash processing on the task ID in the task data to generate a unique hash value of the task ID; store the hash value in the hash table, compare the task ID hash values, and delete duplicate records with the same hash value; generate task status data.

[0018] Preferably, the obtaining of the execution progress of the power outage control plan includes:

[0019] Traverse the task status data, count the number of tasks with the task status field being "completed", and use a counter to accumulate the task completion status to obtain the number of completed tasks;

[0020] Obtain the total number of tasks by counting all task records in the task status data; the total number of tasks includes all tasks that are completed, in execution, and not yet executed.

[0021] Update the execution progress to the cloud computing platform in real time, and synchronize the progress information to each terminal device through the cloud computing platform.

[0022] Preferably, extracting the progress of unfinished tasks from the task status data, calculating the difference between the current progress of the unfinished tasks and the target progress of the unfinished tasks, and comparing it with a set threshold to identify tasks with lagging progress; including:

[0023] Calculate the tasks with lagging power outage control progress as follows:

[0024]

[0025] In the formula, Dp is the task with lagging power outage control progress; i is the number of the power outage control plan, i = 1, 2, 3, …, n, and n is the total number of power outage control plans; D i is the execution progress of the i-th power outage control plan; γ i is the proportionality factor corresponding to the i-th power outage control plan, and e is the natural constant;

[0026] Preferably, calculate the difference between the current progress of the unfinished power outage control tasks corresponding to the task start timestamp field and the target progress of the unfinished power outage control tasks stored in the database to obtain the difference in unexecuted progress, and identify tasks with lagging progress;

[0027] If the difference in unexecuted progress is greater than the target progress of the unfinished power outage control tasks corresponding to the task start timestamp field stored in the database, it indicates lagging progress, and a progress lagging prompt is sent to the on-site maintenance personnel of the power outage control plan corresponding to the difference in unexecuted progress;

[0028] If the difference in unexecuted progress is not greater than the target progress of the unfinished power outage control tasks corresponding to the task start timestamp field stored in the database, it indicates normal progress, and a progress normal prompt is sent to the on-site maintenance personnel of the power outage control plan corresponding to the difference in unexecuted progress.

[0029] Preferably, collecting the environmental data of the power outage control area, including real-time temperature, real-time wind speed, and real-time precipitation, comparing the environmental data with the preset safety thresholds of temperature, wind speed, and precipitation respectively, and generating the environmental risk status of the power outage control area; including:

[0030] According to the tasks with lagging progress, collect the environmental data of the power outage control area in real time;

[0031] Extract the environmental definition data related to the power outage control area from the database, including the pre-set safety thresholds covering environmental temperature, wind speed, and precipitation, as the reference standard for whether the current environmental data meets the safety requirements;

[0032] Compare the real-time collected environmental data with the pre-set environmental definition data, and analyze and fuse the data through the cloud to identify whether the current environmental conditions meet the safety requirements and generate an environmental risk status.

[0033] Preferably, calculate the environmental risk status according to the following formula:

[0034]

[0035] In the formula, Hp is the environmental risk status, Hsw is the environmental temperature, Hsf is the environmental wind speed, Hsy is the environmental precipitation, Hcw is the defined environmental temperature, Hcf is the defined environmental wind speed, and Hcy is the predicted defined environmental precipitation.

[0036] Preferably, the cloud computing platform adjusts the execution order of the task plan to generate an optimized task instruction, including:

[0037] Calculate the comprehensive risk factor of each task according to the progress-lagging task and the environmental risk status, and adjust the execution priority of the task according to the risk factor of the task;

[0038] Calculate the risk factor corresponding to the power outage control plan according to the following formula:

[0039]

[0040] In the formula, i is the number of the power outage control plan on the current day, β i is the risk factor corresponding to the i-th power outage control plan, Sm i is the power outage area in the power outage impact parameter corresponding to the i-th power outage control plan, Sh i is the number of users in the power outage area in the power outage impact parameter corresponding to the i-th power outage control plan, Sf i is the total load of the power outage area in the power outage impact parameter corresponding to the i-th power outage control plan, Cm is the defined area of the power outage area, Ch is the defined number of users in the power outage area, Cf is the defined total load of the power outage area, and e is the natural constant.

[0041] Preferably, sort the power outage control tasks according to the risk factor; generate an optimized task instruction according to the adjusted task priority; the task instruction includes the execution order of the task, the expected completion time of the task, and the task resource allocation;

[0042] The optimized task instructions are sent to the terminal devices of on-site maintenance personnel in real time through the cloud computing platform. The on-site maintenance personnel execute the power outage control tasks according to the issued task instructions in the optimized order.

[0043] The second aspect of the present invention provides a power grid intelligent power outage control system based on cloud computing, including: a cloud computing platform, a progress calculation module, a lagging task identification module, an environmental data collection module, a task plan adjustment module, and a task instruction issuing module;

[0044] The cloud computing platform is used to receive the task data uploaded by on-site maintenance personnel. The task data includes the task start time, task status, and task completion status, and performs real-time verification, duplicate removal, and formatting processing on the received task data to generate task status data;

[0045] The progress calculation module is used to calculate the ratio of the number of completed tasks to the total number of tasks according to the task status data, obtain the execution progress of the power outage control plan, and update the execution progress to the terminal device in real time through the cloud computing platform;

[0046] The lagging task identification module is used to extract the information of uncompleted tasks from the task status data, calculate the difference between the current progress and the target progress of the uncompleted tasks, and compare it with the set threshold to identify the tasks with lagging progress;

[0047] The environmental data collection module is used to collect the environmental data of the power outage control area, including the real-time temperature, real-time wind speed, and real-time precipitation, compare the environmental data with the preset safety thresholds of temperature, wind speed, and precipitation respectively, and generate the environmental risk status of the power outage control area;

[0048] The task plan adjustment module is used to combine the comparison results of the lagging tasks and the environmental risk status, and adjust the execution order of the task plan through the cloud computing platform to generate optimized task instructions;

[0049] The task instruction issuing module is used to send the optimized task instructions to the terminal devices of on-site maintenance personnel in real time, and the on-site maintenance personnel execute the power outage control tasks according to the optimized task instructions.

[0050] Compared with the prior art, the beneficial effects of the present invention at least include:

[0051] (1) The cloud computing-based intelligent power grid outage control system can improve transparency by showing the progress of outage maintenance in real time through the outage maintenance progress display module; the outage maintenance progress reminder module can evaluate the unexecuted control measures in a timely manner and send a warning to on-site maintenance personnel, thereby improving execution efficiency; while the outage maintenance area risk warning module can ensure timely adjustment of the plan in the case of potential risks through environmental risk assessment, reducing potential safety hazards and economic losses, so as to achieve more efficient and safe power grid management, and can effectively solve the problems of information asymmetry and response lag in outage management.

[0052] (2) By integrating different types of environmental factors, a unified environmental risk status is obtained, which reflects the impact of the overall environment on the plan execution. When a certain parameter exceeds its defined value (such as too high temperature, too strong wind or abnormal precipitation), the environmental risk status will increase significantly, indicating that there may be higher risks, so as to determine whether to extend the outage control plan.

[0053] (3) Based on the difference between the tasks with lagging outage control progress on the current day and the preset evaluation threshold, the progress lag can be identified in a timely manner. Maintenance personnel can take measures quickly to avoid further deterioration of the problem, thereby improving the overall execution efficiency. After receiving the progress reminder, on-site maintenance personnel will pay more attention to their own work progress, enhancing their sense of responsibility and urgency for the tasks. This mechanism realizes the real-time monitoring of the outage control progress, and maintenance personnel can obtain feedback in a timely manner, which is convenient for adjusting work strategies and arrangements. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flowchart of the cloud computing-based intelligent power grid outage control system provided according to an embodiment of the present invention;

[0055] Figure 2 is a schematic diagram of the module structure of the cloud computing-based intelligent power grid outage control system provided according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0057] As Figure 1 shown, Example 1 of the present invention provides a cloud computing-based intelligent power grid outage control method, including the following steps:

[0058] Receive the task data uploaded by on-site maintenance personnel through the cloud computing platform. The task data includes the task start time, task status, and task completion status. Perform real-time verification, duplicate removal, and formatting on the received task data to generate task status data;

[0059] Preferably, the generation of task status data includes:

[0060] Through a task data parser, parse the uploaded task data into structured data, extract and store the fields of the task start timestamp, task status, and task completion status; among them, the unique identifier generated in the task data is the task ID;

[0061] Apply regular expressions to the task start timestamp field and task status field in the task data for format verification. Check whether the task start timestamp conforms to the ISO 8601 standard format, and determine whether the task status is within the allowed range, including not executed is recorded as 0, in execution is recorded as 1, and completed is recorded as 2;

[0062] Perform a hash process on the task ID in the task data to generate a unique hash value of the task ID; store the hash value in a hash table, compare the task ID hash values, and delete duplicate records with the same hash value; generate task status data.

[0063] According to the task status data, calculate the ratio of the number of completed tasks to the total number of tasks, obtain the execution progress of the power outage control plan, and update the execution progress to the terminal device in real time through the cloud computing platform;

[0064] Preferably, the obtaining of the execution progress of the power outage control plan includes:

[0065] Traverse the task status data, count the number of tasks with the task status field being "completed", use a counter to accumulate the task completion status, and obtain the number of completed tasks;

[0066] Obtain the total number of tasks by counting all task records in the task status data; the total number of tasks includes all tasks that are completed, in execution, and not executed;

[0067] Update the execution progress to the cloud computing platform in real time, and synchronize the progress information to each terminal device through the cloud computing platform.

[0068] Extract the progress of unfinished tasks from the task status data, calculate the difference between the current progress and the target progress of the unfinished tasks, and compare it with the set threshold to identify tasks with lagging progress;

[0069] Preferably, extracting the progress of unfinished tasks from the task status data, calculating the difference between the current progress of the unfinished tasks and the target progress of the unfinished tasks, and comparing it with a set threshold to identify tasks with lagging progress; including:

[0070] Calculating the tasks with lagging power outage control progress by the following formula:

[0071]

[0072] In the formula, Dp is the task with lagging power outage control progress; i is the number of the power outage control plan, i = 1, 2, 3, …, n, and n is the total number of power outage control plans; D i is the execution progress of the i-th power outage control plan; γ i is the proportionality factor corresponding to the i-th power outage control plan, and e is the natural constant;

[0073] Preferably, calculating the difference between the current progress of the unfinished power outage control tasks corresponding to the task start timestamp field and the target progress of the unfinished power outage control tasks stored in the database to obtain the difference in unexecuted progress, and identifying tasks with lagging progress;

[0074] If the difference in unexecuted progress is greater than the target progress of the unfinished power outage control tasks corresponding to the task start timestamp field stored in the database, it indicates lagging progress, and a progress lagging prompt is sent to the on-site maintenance personnel of the power outage control plan corresponding to the difference in unexecuted progress;

[0075] If the difference in unexecuted progress is not greater than the target progress of the unfinished power outage control tasks corresponding to the task start timestamp field stored in the database, it indicates normal progress, and a progress normal prompt is sent to the on-site maintenance personnel of the power outage control plan corresponding to the difference in unexecuted progress.

[0076] Collecting the environmental data of the power outage control area, including the real-time temperature, real-time wind speed, and real-time precipitation, comparing the environmental data with the preset safety thresholds of temperature, wind speed, and precipitation respectively, and generating the environmental risk status of the power outage control area;

[0077] Preferably, the collecting the environmental data of the power outage control area, including the real-time temperature, real-time wind speed, and real-time precipitation, comparing the environmental data with the preset safety thresholds of temperature, wind speed, and precipitation respectively, and generating the environmental risk status of the power outage control area; including:

[0078] According to the tasks with lagging progress, collecting the environmental data of the power outage control area in real time;

[0079] Extract the environmental definition data of the power outage control area from the database, including pre-set safety thresholds covering environmental temperature, wind speed, and precipitation, as the reference standard for whether the current environmental data meets the safety requirements;

[0080] Compare the real-time collected environmental data with the pre-set environmental definition data, and analyze and fuse the data through the cloud to identify whether the current environmental conditions meet the safety requirements and generate an environmental risk status.

[0081] Preferably, calculate the environmental risk status with the following formula:

[0082]

[0083] In the formula, Hp is the environmental risk status, Hsw is the environmental temperature, Hsf is the environmental wind speed, Hsy is the environmental precipitation, Hcw is the defined environmental temperature, Hcf is the defined environmental wind speed, and Hcy is the expected defined environmental precipitation.

[0084] Combine the comparison results of the tasks with lagging progress and the environmental risk status, and adjust the execution order of the task plan through the cloud computing platform to generate an optimized task instruction, which is sent to the on-site maintenance personnel in real time, and the power outage control task is executed according to the optimized task instruction.

[0085] Preferably, the adjustment of the execution order of the task plan through the cloud computing platform to generate an optimized task instruction includes:

[0086] Calculate the comprehensive risk factor of each task according to the tasks with lagging progress and the environmental risk status, and adjust the execution priority of the task according to the risk factor of the task;

[0087] Calculate the risk factor corresponding to the power outage control plan with the following formula:

[0088]

[0089] In the formula, i is the number of the power outage control plan on the current day, β i is the risk factor corresponding to the i-th power outage control plan, Sm i is the power outage area in the power outage impact parameter corresponding to the i-th power outage control plan, Sh i is the number of users in the power outage area in the power outage impact parameter corresponding to the i-th power outage control plan, Sf i is the total load of the power outage area in the power outage impact parameter corresponding to the i-th power outage control plan, Cm is the defined area of the power outage area, Ch is the defined number of users in the power outage area, Cf is the defined total load of the power outage area, and e is the natural constant.

[0090] Preferably, the power outage control tasks are sorted according to risk factors; an optimized task instruction is generated according to the adjusted task priorities; the task instruction includes the execution order of the task, the estimated completion time of the task, and the task resource allocation;

[0091] The optimized task instruction is sent in real time to the terminal devices of on-site maintenance personnel through the cloud computing platform, and the on-site maintenance personnel execute the power outage control tasks in the optimized order according to the issued task instruction.

[0092] Specifically, obtaining the tasks with lagging power outage control progress on the current day includes the following steps: obtaining the execution progress of the power outage control plan; obtaining the proportionality factor corresponding to the power outage control plan; comprehensively analyzing the execution progress of the power outage control plan and the proportionality factor corresponding to the power outage control plan to obtain the tasks with lagging power outage control progress on the current day.

[0093] The calculation formula for the tasks with lagging power outage control progress on the current day is as follows:

[0094]

[0095] In the formula, Dp is the task with lagging power outage control progress on the current day, i is the number of the power outage control plan on the current day, i = 1, 2, 3, …, n, n is the total number of power outage control plans on the current day, D i is the execution progress of the power outage control plan of the i-th power outage control plan, γ i is the proportionality factor corresponding to the i-th power outage control plan, and e is the natural constant.

[0096] In this implementation plan, first, the execution progress of the current power outage control plan is obtained, which indicates the completion situation of each planned task within a day. At the same time, the proportionality factors related to these plans are obtained, and these factors reflect the urgency of each task, and calculations are performed using the formula. This process combines the execution status of each task with its importance, and evaluates the overall unexecuted situation by comparing the executed progress with its corresponding proportionality factor.

[0097] By calculating the execution progress of each power outage control plan, the tasks with lagging power outage control progress on the current day are comprehensively obtained. This value reflects how much work remains unfinished among the various planned tasks and takes into account the relative importance of each piece of work. Based on the calculated tasks with lagging power outage control progress on the current day, the system will generate a prompt message for on-site maintenance personnel to remind them of the unfinished tasks and the measures to be taken.

[0098] Based on the combined analysis of specific execution values and scale factors, it can more accurately reflect the work progress, help the management understand the overall execution effect of power outage control, provide quantified tasks with progress lags, provide a basis for the management's decision-making, enable timely grasping of the completion situation of the power outage control plan, and thus make reasonable decisions.

[0099] Specifically, obtaining the scale factor corresponding to the power outage control plan includes the following steps: Obtaining the power outage impact parameters corresponding to the power outage control plan, where the power outage impact parameters include the area of the power outage area, the number of users in the power outage area, and the total load in the power outage area. Among them, the area of the power outage area can obtain the boundary and terrain data of the power outage area through Geographic Information System (GIS) data, which can help calculate the area of the power outage area. The number of users in the power outage area can obtain the power user information in this area through the user management database and count the number of users. The total load in the power outage area collects the load data in the power outage area from the power monitoring system, and the total load information can be obtained through the electricity consumption records of users, metering equipment, etc.

[0100] Obtaining the defined parameters of power outage impact stored in the database. The defined parameters of power outage impact stored in the database include the defined area of the power outage area, the defined number of users in the power outage area, and the defined total load in the power outage area. The defined area of the power outage area, the defined number of users in the power outage area, and the defined total load in the power outage area are obtained based on historical data and stored in the database. By analyzing historical power outage situations, the median values of the historical power outage area, the number of users in the power outage area, and the total load in the power outage area are determined as the defined area of the power outage area, the defined number of users in the power outage area, and the defined total load in the power outage area.

[0101] Respectively perform fusion processing on the power outage impact parameters corresponding to the power outage control plan and the defined parameters of power outage impact stored in the database to obtain the risk factor corresponding to the power outage control plan; perform normalization processing on the risk factor corresponding to the power outage control plan to obtain the scale factor corresponding to the power outage control plan. The calculation formula is as follows:

[0102]

[0103] In the formula, i is the number of the power outage control plan on the current day, i = 1, 2, 3, …, n, where n is the total number of power outage control plans on the current day, γ i is the scale factor corresponding to the i-th power outage control plan, and β i is the risk factor corresponding to the i-th power outage control plan.

[0104] By obtaining and fusing risk factors through systematic steps, it can more accurately identify potential risks in the power outage control plan, provide a reliable basis for decision-making, and use the fused risk factors and normalized scale factors to avoid subjective biases and ensure a more objective and scientific assessment of the power outage impact.

[0105] By determining the proportionality factor for each plan, resources can be allocated more effectively, high-risk and high-priority tasks can be prioritized, work efficiency and emergency response capabilities can be enhanced, and more scientific power outage management can reduce the impact on users. Timely and accurate handling of plans can improve user satisfaction and enhance the public image.

[0106] The calculation formula for the risk factor corresponding to the power outage management plan is as follows:

[0107]

[0108] In the formula, i is the number of the power outage management plan on the current day, and β i is the risk factor corresponding to the i-th power outage management plan, Sm i is the area of the power outage area in the power outage impact parameter corresponding to the i-th power outage management plan, Sh i is the number of users in the power outage area in the power outage impact parameter corresponding to the i-th power outage management plan, Sf i is the total load of the power outage area in the power outage impact parameter corresponding to the i-th power outage management plan, Cm is the specified area of the power outage area, Ch is the specified number of users in the power outage area, Cf is the specified total load of the power outage area, and e is the natural constant.

[0109] In this implementation plan, reflects the impact of the area and number of users in the power outage area on the risk. As the area and number of users in the power outage area increase, the risk factor grows exponentially, indicating the non-linear characteristics of the impact. reflects the impact of the total load in the power outage area on the risk. The tanh function can effectively balance the impact of the load on the risk and avoid calculation instability caused by excessive values. By calculating the risk factor, the risks of different power outage management plans can be quantitatively evaluated, providing a basis for decision-making.

[0110] The area of the power outage area usually determines the geographical scope of the impact. Larger areas may contain more users and higher loads, so when a power outage occurs, the breadth of the impact is greater. The number of users in the power outage area directly reflects the number of affected people. The more users there are, the greater the potential social impact (such as inconvenience, economic losses, etc.) that the power outage may cause. The total load in the power outage area represents the electricity demand in that area under normal circumstances. The greater the load, the greater the potential economic losses (such as the suspension of business activities) and safety hazards (such as the impact on important services like hospitals and traffic signals) caused by the power supply interruption during the power outage.

[0111] Combined, these three parameters can more comprehensively characterize the impact of a power outage: the size of the power outage area and the number of users may cause the affected areas of the power outage to be interconnected. Especially in an urban environment, a larger power outage area may lead to more complex consequences. This relationship means that a power outage in one area may affect a wider area in terms of time and space. The combination of these three parameters can relatively comprehensively reflect the economic losses caused by the power outage. For example, a power outage in an area with a large area, a large number of users, and a large total load often means a longer recovery time, which may lead to higher user complaints and economic losses. Since risks and impacts usually have non-linear characteristics, when calculating the risk factor, by introducing exponential and hyperbolic tangent functions, the impact of incremental changes can be captured more effectively. In many cases, the risk of a power outage is not simply the sum of the area, the number of users, and the load, but the interaction and superposition effects between these parameters need to be considered.

[0112] Specifically, based on the tasks with lagging power outage control progress on the current day, prompt the on-site maintenance personnel of the power outage control plan, including the following steps: Calculate the difference between the tasks with lagging power outage control progress corresponding to the time period on the current day and the unexecuted evaluation threshold of the power outage control plan corresponding to the time period stored in the database to obtain the unexecuted progress difference; Compare the unexecuted progress difference with the unexecuted progress reference value corresponding to the time period stored in the database (the unexecuted progress reference value corresponding to the time period stored in the database is usually obtained through historical data analysis and actual execution situation statistics. Combining the execution situation of the power outage control plan in past similar time periods, a reasonable reference value is obtained using statistical methods (such as mean, standard deviation, etc.) to facilitate the subsequent evaluation and comparison of the current progress):

[0113] If the unexecuted progress difference is greater than the unexecuted progress reference value corresponding to the time period stored in the database, it indicates that the progress is lagging, and prompt the on-site maintenance personnel of the power outage control plan corresponding to the unexecuted progress difference about the lagging progress; If the unexecuted progress difference is not greater than the unexecuted progress reference value corresponding to the time period stored in the database, it indicates that the progress is normal, and prompt the on-site maintenance personnel of the power outage control plan corresponding to the unexecuted progress difference about the normal progress.

[0114] In this implementation plan, first of all, the system needs to collect in real time the tasks with lagging power outage control progress on the current day and store them in the database to ensure the accuracy and timeliness of the data. For each time period, calculate the difference between the tasks with lagging power outage control progress on the current day and the preset evaluation threshold. This difference reflects the deviation between the actual execution situation and the expected goal. Compare the calculated difference in unexecuted progress with the corresponding unexecuted progress reference value stored in the database for this time period. If the difference is greater than the reference value, it indicates that the execution progress is lagging; otherwise, it indicates that the execution progress is normal. According to the comparison result, send a prompt message to the corresponding on-site maintenance personnel: if the difference is greater than the reference value, remind the maintenance personnel to pay attention to the progress problem and take necessary measures to improve the execution efficiency. If the difference is not greater than the reference value, confirm that the progress is normal and give positive feedback to the maintenance personnel.

[0115] By promptly identifying the lagging progress, the maintenance personnel can quickly take measures to prevent the problem from deteriorating further, thereby improving the overall execution efficiency. After receiving the progress prompt, the on-site maintenance personnel will pay more attention to their own work progress, enhancing their sense of responsibility and urgency for the tasks. This mechanism realizes the real-time monitoring of the power outage control progress, enabling the maintenance personnel to obtain feedback in a timely manner, which is convenient for adjusting work strategies and arrangements.

[0116] The risk warning module for the power outage maintenance area is used to conduct an environmental risk assessment on the execution area of the power outage control plan, determine whether the power outage control plan needs to be postponed and display it.

[0117] Specifically, the environmental risk assessment of the execution area of the power outage control plan includes the following steps: Obtain the environmental data of the execution area of the power outage control plan. The environmental data of the execution area of the power outage control plan includes environmental temperature, environmental wind force, and expected environmental precipitation; Obtain the environmental definition data stored in the database. The environmental definition data stored in the database includes defined environmental temperature, defined environmental wind force, and expected defined environmental precipitation; Perform a fusion analysis on the environmental data of the execution area of the power outage control plan and the environmental definition data stored in the database to obtain the environmental risk status. The environmental risk status serves as the basis for the environmental risk assessment of the execution area of the power outage control plan.

[0118] Determining whether a power outage control plan needs to be postponed includes the following steps: comparing the environmental risk status with the environmental assessment threshold stored in the database: if the environmental risk status is greater than the environmental assessment threshold stored in the database (the process of determining the environmental assessment threshold includes the collection and analysis of historical environmental data. First, meteorological data (such as temperature, humidity, wind speed, etc.), equipment operating status, and accident records related to power outages in multiple time periods are collected. Then, these data are processed using multivariate regression analysis to identify the relationship between key environmental factors and power outage events. Finally, based on the analysis results and professional standards, an assessment threshold that can effectively reflect the risk of environmental abnormalities is set to ensure the safety and effectiveness of the power outage control plan), then the environment in the execution area of ​​the power outage control plan is abnormal, and it is determined that the power outage control plan needs to be postponed; if the environmental risk status is not greater than the environmental assessment threshold stored in the database, then the environment in the execution area of ​​the power outage control plan is abnormal, and it is determined that the power outage control plan does not need to be postponed.

[0119] First, obtain real-time environmental data of the power outage control plan execution area through sensors, weather stations, etc., including ambient temperature, wind speed, and expected precipitation. Extract environmental definition data from the existing database. These data are pre-set safety thresholds, including environmental definition temperature, wind speed, and expected precipitation. Analyze the acquired environmental data with the environmental definition data, calculate the environmental risk status through the set formula or model, and compare the calculated environmental risk status with the preset risk threshold to determine whether the current environment is suitable for the power outage control plan. If the environmental risk status exceeds the safety range, an alarm will be issued that the execution plan may need to be postponed. The environmental risk assessment results will be presented to relevant managers and on-site maintenance personnel in the form of charts, reports or notifications so that they can respond in a timely manner.

[0120] By assessing environmental risks in advance, we can effectively identify environmental risks that may affect maintenance work, avoid working under unsafe conditions, thereby improving the safety of operators, and provide early warning of environmental factors that may cause delays in power outage control plans. This can significantly reduce the risk of accidents caused by sudden weather and other reasons. By judging whether an extension is needed, we can reasonably arrange human, material and other resources, avoid unnecessary waste and waiting, and improve overall work efficiency.

[0121] The calculation formula for environmental risk status is as follows:

[0122]

[0123] Wherein, Hp is the environmental risk state, Hsw is the ambient temperature, Hsf is the ambient wind force, Hsy is the ambient expected precipitation, Hcw is the ambient limit temperature, Hcf is the ambient limit wind force, and Hcy is the ambient expected limit precipitation.

[0124] Temperature directly affects the operating efficiency and safety of power equipment. High temperature may cause the equipment to overheat, reduce the insulation performance, and even lead to failures or fires. Strong winds may cause trees to fall or other external objects to damage power facilities, increasing the risk of power outages. Wind, especially in elevated transmission lines, may affect the stability of the lines. Excessive wind may cause the lines to swing or break. Excessive precipitation increases the ground moisture, leading to an increased risk of floods, which may affect the safety of power facilities and the accessibility of their surroundings. Together, they constitute the overall picture of assessing the impact of the external environment on power facilities. Temperature, wind, and precipitation reflect different dimensions of weather factors, each of which may have an independent and significant impact on facility safety under certain circumstances and will affect the power outage control plan.

[0125] In this implementation plan, the formula involves three environmental parameters (temperature, wind, and precipitation), which are respectively compared with their corresponding safety thresholds. By integrating different types of environmental factors, a unified environmental risk state Hp is obtained, which reflects the impact of the overall environment on the plan execution. Using the logarithmic function (ln) to calculate the environmental risk state helps to narrow a wide range of environmental changes to a more controllable and relative numerical range. This enables the evaluation value to change more smoothly in the face of large or small changes in environmental parameters, avoiding the influence of extreme values. Through the weighted combination of each environmental parameter (Hsw / Hcw, Hsf / Hcf, Hsy / Hcy), its overall relative hazard is evaluated. When a certain parameter exceeds its defined value (such as too high temperature, too strong wind, or abnormal precipitation), the environmental risk state Hp will increase significantly, indicating that there may be a higher risk.

[0126] As Figure 2 shown, Example 2 of the present invention provides a cloud computing-based intelligent power grid outage control system, including: a cloud computing platform, a progress calculation module, a lagging task identification module, an environmental data collection module, a task plan adjustment module, and a task instruction issuance module;

[0127] The cloud computing platform is used to receive the task data uploaded by on-site maintenance personnel. The task data includes the task start time, task status, and task completion status, and performs real-time verification, duplicate removal, and formatting processing on the received task data to generate task status data;

[0128] The progress calculation module is used to calculate the ratio of the number of completed tasks to the total number of tasks according to the task status data, obtain the execution progress of the power outage control plan, and update the execution progress to the terminal device in real time through the cloud computing platform;

[0129] The lagging task identification module is used to extract information on unfinished tasks from the task status data, calculate the difference between the current progress and the target progress of the unfinished tasks, and compare it with a set threshold to identify tasks with lagging progress.

[0130] The environmental data collection module is used to collect environmental data in the power outage control area, including real-time temperature, real-time wind speed, and real-time precipitation, compare the environmental data with the preset safety thresholds for temperature, wind speed, and precipitation respectively, and generate the environmental risk status of the power outage control area.

[0131] The task plan adjustment module is used to combine the comparison results of the lagging tasks and the environmental risk status, and adjust the execution order of the task plan through the cloud computing platform to generate an optimized task instruction.

[0132] The task instruction issuing module is used to send the optimized task instruction to the terminal device of the on-site maintenance personnel in real time, and the on-site maintenance personnel execute the power outage control task according to the optimized task instruction.

[0133] Specifically, through the cloud computing platform, the system can collect and process power outage maintenance-related data in real time, including progress, environmental conditions, and risk assessment information, and centrally display it to relevant personnel. The system is divided into 6 main modules, and each module is independent and interrelated to better handle complex power outage management tasks. The system automatically evaluates unexecuted tasks and generates corresponding feedback to ensure that on-site maintenance personnel can timely obtain matters that need attention and improve work efficiency. Through the environmental data collection module for environmental risk assessment, the system can dynamically adjust the power outage maintenance plan, thereby reducing potential safety hazards caused by unforeseen factors.

[0134] It can effectively solve the problems of information asymmetry and response lag in power outage management. Through the progress calculation module and the lagging task identification module, the execution progress is displayed in real time, improving transparency; the progress calculation module and the lagging task identification module can timely evaluate unexecuted control measures and issue warnings to on-site maintenance personnel, thereby improving execution efficiency; while the task plan adjustment module ensures timely plan adjustment in the event of potential risks through environmental risk assessment, reducing safety hazards and economic losses, so as to achieve more efficient and safe power grid management.

[0135] The progress calculation module is used to determine and display the execution progress of the power outage control plan. Determining the execution progress of the power outage control plan includes the following steps: obtaining the execution progress of the operation ticket uploaded by on-site maintenance personnel, and determining the number of completed tasks based on the execution progress of the operation ticket; obtaining the total number of tasks in the power outage control plan; calculating the ratio of the number of completed tasks to the total number of tasks, which is recorded as the execution progress of the power outage control plan.

[0136] First, obtain the operation ticket execution progress information uploaded in real time from on-site maintenance personnel. This information usually records the status and completion of each task. According to the obtained operation ticket execution progress, the system counts the number of completed tasks. This process ensures that each task undertakes the corresponding work responsibility and is correctly marked. Extract the total number of tasks from the power outage control plan. These tasks are the steps to ensure the necessary maintenance of the power system during the power outage. By calculating the ratio of the number of completed tasks to the total number of tasks, the execution progress of the power outage control plan is obtained. This ratio can be expressed in the form of a percentage for easy intuitive understanding of the execution situation. Finally, the calculated execution progress of the power outage control plan is displayed through a visual interface, facilitating relevant personnel to monitor and evaluate the execution situation and then make corresponding decisions.

[0137] The lagging task identification module is used to evaluate the execution progress of the power outage control plan, obtain the tasks with lagging power outage control progress on the current day, and prompt the on-site maintenance personnel of the power outage control plan based on the tasks with lagging power outage control progress on the current day.

[0138] In summary, the present application has at least the following effects:

[0139] Through the power outage maintenance progress display module, the execution progress is displayed in real time, improving transparency; the power outage maintenance progress prompt module can timely evaluate the unexecuted control measures and issue warnings to on-site maintenance personnel, thus improving the execution efficiency; while the power outage maintenance area risk warning module ensures timely adjustment of the plan under potential risk situations through environmental risk assessment, reducing potential safety hazards and economic losses, thereby realizing more efficient and safe power grid management and effectively solving the problems of information asymmetry and response lag in power outage management.

[0140] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0141] The present invention is described with reference to the flowcharts and / or block diagrams of systems, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0142] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0144] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0145] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

[0146] The present disclosure can be a system, method, and / or computer program product. The computer program product can include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0147] 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 them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or make equivalent substitutions, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A cloud computing-based intelligent power outage control method for power grids, characterized in that: The following steps are involved: Receive task data uploaded by on-site maintenance personnel through the cloud computing platform. The task data includes task start time, task status and task completion status. Perform real-time verification, deduplication and formatting on the received task data to generate task status data. According to the task status data, the ratio of the number of completed tasks to the total number of tasks is calculated to obtain the execution progress of the power outage control plan, and the execution progress is updated to the terminal device in real time through the cloud computing platform; Extract the progress of unfinished tasks from the task status data, calculate the difference between the current progress of the unfinished tasks and the target progress of the unfinished tasks, compare it with the set threshold, and identify the tasks lagging behind in progress; Collect environmental data of the power outage control area, including real-time temperature, real-time wind speed and real-time precipitation, compare the environmental data with the preset safety thresholds of temperature, wind speed and precipitation, and generate the environmental risk status of the power outage control area; Based on the comparison results of delayed tasks and environmental risk status, the execution order of the task plan is adjusted through the cloud computing platform to generate optimized task instructions, which are sent to on-site maintenance personnel in real time to execute power outage management and control tasks according to the optimized task instructions.

2. The method for intelligent power outage control based on cloud computing according to claim 1 is characterized in that: The generating task status data comprises: The task data parser is used to parse the uploaded task data into structured data, extract and store the task start timestamp, task status, and task completion status fields; the unique identifier generated in the task data is the task ID; Apply regular expressions to the task start timestamp field and task status field in the task data to perform format verification, check whether the task start timestamp conforms to the ISO 8601 standard format, and determine whether the task status is within the allowed range, including not executed as 0, executing as 1, and completed as 2; Hash the task ID in the task data to generate a unique hash value for the task ID; store the hash value in a hash table, compare the task ID hash values, and delete duplicate records with the same hash value; generate task status data.

3. The method for intelligent power outage control based on cloud computing according to claim 1 is characterized in that: The execution progress of obtaining the power outage control plan includes: Traverse the task status data, count the number of tasks whose task status field is "completed", use a counter to accumulate the task completion status, and get the number of completed tasks; Obtain the total number of tasks by counting the number of all task records in the task status data; the total number of tasks includes all tasks that have been completed, are being executed, and have not been executed; The execution progress is updated in real time to the cloud computing platform, and the progress information is synchronized to each terminal device through the cloud computing platform.

4. The method for intelligent power outage control based on cloud computing according to claim 3 is characterized in that: The process of extracting the progress of the unfinished task from the task status data, calculating the difference between the current progress of the unfinished task and the target progress of the unfinished task, comparing the difference with the set threshold, and identifying the task with lagging progress includes: The power outage control progress lag task is calculated as follows: Where Dp is the delayed task of power outage control; i is the number of the power outage control plan, i = 1, 2, 3, ..., n, n is the total number of power outage control plans; D i is the execution progress of the power outage control plan of the i-th power outage control plan; γ i is the proportional factor corresponding to the i-th power outage control plan, and e is a natural constant.

5. The method for intelligent power outage control based on cloud computing according to claim 4 is characterized in that: Calculate the difference between the current progress of the unfinished task of power outage control corresponding to the task start timestamp field and the target progress of the unfinished task of power outage control corresponding to the task start timestamp field stored in the database, obtain the unexecuted progress difference, and identify the lagging tasks; If the unexecuted progress difference is greater than the target progress of the uncompleted power outage control task corresponding to the task start timestamp field stored in the database, it indicates that the progress is delayed, and the on-site maintenance personnel of the power outage control plan corresponding to the unexecuted progress difference are prompted with the progress delay; If the unexecuted progress difference is not greater than the target progress of the unfinished power outage control task corresponding to the task start timestamp field stored in the database, it means that the progress is normal, and the on-site maintenance personnel of the power outage control plan corresponding to the unexecuted progress difference will be prompted with the normal progress.

6. The method for intelligent power outage control based on cloud computing according to claim 1 is characterized in that: The environmental data of the power outage control area is collected, including real-time temperature, real-time wind speed and real-time precipitation, and the environmental data is compared with preset safety thresholds of temperature, wind speed and precipitation to generate the environmental risk status of the power outage control area; including: According to the delayed tasks, collect environmental data of the power outage control area in real time; Extract environmental definition data related to the power outage control area from the database, including pre-set safety thresholds, covering ambient temperature, wind speed and precipitation, as a reference standard for whether the current environmental data meets safety requirements; Compare the real-time collected environmental data with the preset environmental definition data, and analyze the integrated data through the cloud to identify whether the current environmental conditions meet the safety requirements and generate the environmental risk status.

7. The method for intelligent power outage control based on cloud computing according to claim 6 is characterized in that: The environmental risk status is calculated as follows: Where Hp is the environmental risk state, Hsw is the ambient temperature, Hsf is the ambient wind speed, Hsy is the ambient precipitation, Hcw is the ambient limit temperature, Hcf is the ambient limit wind speed, and Hcy is the ambient expected limit precipitation.

8. The method for intelligent power outage control based on cloud computing according to claim 1 is characterized in that: The step of adjusting the execution order of the task plan through the cloud computing platform to generate optimized task instructions includes: Calculate the comprehensive risk factor of each task based on the delayed tasks and environmental risk status, and adjust the priority of task execution based on the risk factor of the task; The risk factor corresponding to the power outage control plan is calculated as follows: Where i is the number of the power outage control plan for the day, β i is the risk factor corresponding to the ith power outage control plan, Sm i is the area of ​​the power outage area in the power outage impact parameter corresponding to the ith power outage control plan, Sh i is the number of users in the outage area in the outage impact parameter corresponding to the ith outage control plan, Sf i is the total load of the outage area in the outage impact parameters corresponding to the ith outage control plan, Cm is the parameter area of ​​the outage area, Ch is the parameter number of users in the outage area, Cf is the parameter total load of the outage area, and e is a natural constant.

9. The method for intelligent power outage control based on cloud computing according to claim 8, characterized in that: Sort the power outage control tasks according to the risk factors; generate optimized task instructions according to the adjusted task priorities; the task instructions include the execution order of the tasks, the estimated completion time of the tasks, and the allocation of task resources; The optimized task instructions are sent in real time to the terminal devices of on-site maintenance personnel through the cloud computing platform. The on-site maintenance personnel perform power outage management and control tasks in an optimized sequence based on the issued task instructions.

10. A cloud computing-based intelligent power outage management and control system for power grids, comprising: Cloud computing platform, progress calculation module, delayed task identification module, environmental data collection module, task plan adjustment module and task instruction issuing module; characterized by: The cloud computing platform is used to receive task data uploaded by on-site maintenance personnel. The task data includes task start time, task status and task completion status. The received task data is verified, deduplicated and formatted in real time to generate task status data. The progress calculation module is used to calculate the ratio of the number of completed tasks to the total number of tasks based on the task status data, obtain the execution progress of the power outage control plan, and update the execution progress to the terminal device in real time through the cloud computing platform; The delayed task identification module is used to extract the information of unfinished tasks from the task status data, calculate the difference between the current progress of the unfinished tasks and the target progress, and compare it with the set threshold to identify the delayed tasks; The environmental data collection module is used to collect environmental data of the power outage control area, including real-time temperature, real-time wind speed and real-time precipitation, and compare the environmental data with the preset safety thresholds of temperature, wind speed and precipitation to generate the environmental risk status of the power outage control area; The task plan adjustment module is used to adjust the execution order of the task plan through the cloud computing platform based on the comparison results of the delayed tasks and the environmental risk status, so as to generate optimized task instructions; The task instruction issuing module is used to send the optimization task instructions to the terminal equipment of the on-site maintenance personnel in real time. The on-site maintenance personnel perform the power outage management and control tasks according to the optimization task instructions.

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