A big data-driven power grid load refined management method and system

Abstract

The present invention discloses a method for fine-grained management of power grid load driven by big data, which relates to the technical field of intelligent management of power grid load, including: obtaining multiple existing power distribution equipment; obtaining multiple different set rules, and forming a set model based on the set rules; obtaining first load disturbance information of a first power distribution equipment, obtaining second load disturbance information of a second power distribution equipment, and obtaining the similarity between the first load disturbance information and the second load disturbance information based on a comprehensive calculation model; if the similarity is greater than a first preset value, determining whether there is a set that simultaneously summarizes the first power distribution equipment and the second power distribution equipment, and if so, forming the set into a marked set; determining whether the target management device is in the marked set, and if so, issuing a disturbance hidden danger notification. A fine-grained management system for power grid load driven by big data is also provided. The present invention has the advantages of high data transmission security, traceability, and efficiency optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power grid load management, and in particular to a method and system for refined power grid load management driven by big data. Background Art

[0002] With the rapid development of smart grids and the continuous accumulation of power data, the scale of distribution networks has gradually increased. The increasing complexity of loads has posed significant challenges to the safe and stable operation of power systems. Grid operators are faced with the need to process and analyze massive amounts of data. This data includes not only traditional grid operating parameters such as voltage, current, and power, but also multi-dimensional information such as various equipment status, environmental monitoring, and user electricity usage behavior. How to effectively utilize this data to improve the refinement and intelligence of grid load management has become a major challenge facing the power industry.

[0003] At present, refined management of power grid load refers to the precise monitoring, analysis and prediction of the load conditions of each distribution equipment in the power grid to achieve safe, stable and efficient power grid operation. Under the traditional power grid management model, the power grid can only be managed in an extensive manner, making it difficult to detect potential load disturbances and safety hazards. Summary of the Invention

[0004] In response to the deficiencies in the prior art, the present invention provides a method and system for refined power grid load management driven by big data.

[0005] A big data-driven method for refined management of power grid loads, comprising: S1, obtaining a plurality of existing power distribution devices, the plurality of existing power distribution devices including a first power distribution device, a second power distribution device and a target management device; S2, obtaining a plurality of different set rules, and respectively grouping the plurality of existing power distribution devices into a plurality of sets based on the different set rules, thereby forming a set model, wherein each set rule contains at least two sets; S3, obtaining first load disturbance information of the first power distribution device, obtaining second load disturbance information of the second power distribution device, and obtaining the similarity between the first load disturbance information and the second load disturbance information based on a comprehensive calculation model; if the similarity is greater than a first preset value, judging whether there is a set that simultaneously groups the first power distribution device and the second power distribution device based on the set model; if so, forming a marked set with the set that simultaneously groups the first power distribution device and the second power distribution device; S4, judging whether the target management device is in the marked set; if so, issuing a disturbance hidden danger notification.

[0006] Preferably, in obtaining the similarity between the first load disturbance information and the second load disturbance information based on the comprehensive calculation model, the comprehensive calculation model includes: ; wherein, is the similarity between the first load disturbance information and the second load disturbance information, is the load disturbance of the first distribution equipment at time t, is the load disturbance of the second distribution equipment at time t, and They are and The average value within the preset time series.

[0007] Preferably, the S4 includes: S41, judging whether the target management device is in the tag set, and if so, proceeding to S42; S42, obtaining the real-time load change value of the target management device; S43, processing the real-time load change value based on the comprehensive calculation model and obtaining a comprehensive index, judging whether the comprehensive index exceeds a second preset value, and if so, issuing a disturbance hidden danger notification.

[0008] Preferably, in said S43, the comprehensive calculation model includes: ;in, As a comprehensive indicator, is the real-time load change value, for The average value within the preset time series, is the correction factor.

[0009] Preferably, the S42 also includes: S421, selecting the first time period T1 and the second time period T2; S422, obtaining the current time point t0 of the target management device; S423, drawing a load value change curve of the target management device from t0-T1 to t0+T2, and obtaining the maximum load difference according to the load value change curve, and the real-time load change value is the maximum load difference.

[0010] Preferably, S3 further includes: if there is a set that simultaneously summarizes the first power distribution equipment and the second power distribution equipment, forming an associated tag based on the comprehensive calculation model, the first load disturbance information and the second load disturbance information, and associating the associated tag with the tag set.

[0011] Preferably, in forming the association tag based on the comprehensive calculation model, the first load disturbance information, and the second load disturbance information, the comprehensive calculation model includes: ;in, is the association tag, is the serialized string of the first load disturbance information, is the serialized string of the second load disturbance information, is a hash function.

[0012] Preferably, a big data-driven power grid load refined management system is also provided, which is used to implement the above-mentioned big data-driven power grid load refined management method, and the system includes: a first acquisition module, used to acquire multiple existing power distribution equipment, the multiple existing power distribution equipment including a first power distribution equipment, a second power distribution equipment and a target management equipment; a second acquisition module, used to acquire multiple different set rules, and based on different set rules, summarize the multiple existing power distribution equipment into multiple sets to form a set model, wherein each set rule contains at least two sets; a calculation and processing module, used to acquire first load disturbance information of the first power distribution equipment, acquire second load disturbance information of the second power distribution equipment, and obtain the similarity between the first load disturbance information and the second load disturbance information based on the comprehensive calculation model; the calculation and processing module is also used to determine whether there is a set that simultaneously summarizes the first power distribution equipment and the second power distribution equipment based on the set model when the similarity is greater than a first preset value, and if so, the set that simultaneously summarizes the first power distribution equipment and the second power distribution equipment is formed into a marked set; a judgment and notification module, used to determine whether the target management equipment is in the marked set, and if so, issue a disturbance hidden danger notification.

[0013] Preferably, a non-temporary computer-readable storage medium is also provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned big data-driven power grid load refined management method is implemented.

[0014] Preferably, an electronic device is also provided, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the above-mentioned big data-driven refined management method for power grid loads.

[0015] The beneficial effects of the present invention are embodied in:

[0016] By comprehensively collecting information on distribution equipment through the power grid management system or database and classifying and summarizing it according to clear collection rules, power grid operators can more systematically manage equipment of different types, different geographical locations, different voltage levels and different operating years. This classification management method not only improves management efficiency, but also makes it possible to conduct targeted analysis of equipment of specific types or regions, thereby improving the accuracy and effectiveness of management decisions; further, by real-time monitoring of load disturbance information of distribution equipment and using similarity algorithms to analyze the similarity of load disturbances between different equipment, the technical solution can detect potential load disturbance risks at an early stage. When similar load disturbance patterns are identified, the system can quickly mark the relevant equipment collection and issue disturbance hidden danger notifications in a timely manner, thereby realizing risk warnings, which enables power grid operators to Preventive maintenance measures are taken to avoid equipment failures and improve the stability and reliability of the power grid. Furthermore, by accurately identifying sets of equipment facing similar load disturbance risks, power grid operators can more reasonably allocate maintenance resources, give priority to equipment with higher risks, and avoid waste of resources. At the same time, the implementation of preventive maintenance strategies also reduces power outages and repair costs caused by equipment failures, thereby effectively saving operating costs. Furthermore, this technical solution uses big data analysis and intelligent algorithms to achieve refined management of power grid loads. By automatically collecting, analyzing and processing data, the system can intelligently identify risks and respond, thereby improving the intelligence level of power grid management. This not only enhances the security and stability of the power grid system, but also lays a solid foundation for the future development of smart grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0018] Figure 1 This is a schematic diagram of the steps of the big data driven power grid load refined management method of the present invention;

[0019] Figure 2 This is a schematic diagram of step S4 of the present invention;

[0020] Figure 3 This is a schematic diagram of step S42 of the present invention;

[0021] Figure 4 The figure is a block diagram of an electronic device according to the present invention.

[0022] Reference numerals:

[0023] 700 - electronic device, 701 - processor, 702 - memory, 703 - multimedia component, 704 - input / output (I / O) interface, 705 - communication component. DETAILED DESCRIPTION

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0026] It should be noted that similar reference numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not require further definition or explanation in subsequent figures. Furthermore, the terms "first," "second," etc. are used solely to distinguish between descriptions and should not be construed as indicating or implying relative importance.

[0027] like Figure 1 As shown, a big data-driven refined management method for power grid loads is provided, including:

[0028] S1. Acquire multiple existing power distribution devices, where the multiple existing power distribution devices include a first power distribution device, a second power distribution device, and a target management device;

[0029] S2. Obtain multiple different set rules, and classify multiple existing power distribution devices into multiple sets based on the different set rules to form a set model, wherein each set rule includes at least two sets;

[0030] S3. Obtain first load disturbance information of the first power distribution device, obtain second load disturbance information of the second power distribution device, and obtain similarity between the first load disturbance information and the second load disturbance information based on a comprehensive calculation model;

[0031] If the similarity is greater than a first preset value, determining whether there is a set that includes both the first power distribution device and the second power distribution device based on the set model; if so, forming a tag set with the set that includes both the first power distribution device and the second power distribution device;

[0032] S4. Determine whether the target management device is in the tag set. If so, issue a disturbance risk notification.

[0033] In this embodiment, it should be noted that in S1 and S2, multiple existing distribution equipment are obtained and classified and summarized. First, basic information and operating data of all existing distribution equipment are collected through the power grid management system or relevant database. This information may include equipment type, geographical location, capacity, operating status, historical load data, etc.; according to the needs and objectives of power grid management, appropriate aggregation rules are determined. These aggregation rules can be based on the equipment type (transformer, switch, capacitor, etc.), geographical location (city A, city B, suburbs, etc.), voltage level (high voltage, medium voltage, low voltage), and operating age (new equipment, 5-10 years, more than 10 years), etc. of the distribution equipment; according to the determined aggregation rules, the collected distribution equipment is summarized into different sets. For example, transformers, switches, capacitors, etc. can be summarized into different sets according to equipment type; equipment can also be summarized into different city or regional sets according to geographical location; the summarized equipment sets are represented in the form of a data model to facilitate subsequent data analysis and processing. This model can be a data structure, such as a tree structure or a graph structure, used to describe the relationships and attributes between devices.

[0034] In S3, load disturbance information of the distribution equipment is obtained. First and second load disturbance information of the first and second distribution equipment are collected using real-time monitoring devices such as sensors or smart meters installed on the distribution equipment. The first and second load disturbance information both represent the occurrence of a load disturbance event that has caused abnormal conditions such as voltage fluctuation and current mutation. Based on the characteristics of the load disturbance information, a suitable similarity algorithm is selected, such as cosine similarity or Pearson correlation coefficient. These algorithms can quantify the degree of similarity between the two load disturbance events. Using the selected similarity algorithm, a similarity value between the first and second load disturbance information is calculated. This value serves as an important basis for determining whether the two devices are associated. If the similarity is greater than a first preset value, it means that the first and second load disturbance information are traced back to the same fault, such as a capacitor fault. Then, it is determined whether there is a set that includes both the first and second distribution equipment. If such a set exists, it means that at least two load disturbance phenomena with the same source have occurred in the set, and therefore all distribution equipment in the set are at excessive risk of facing similar load disturbances.

[0035] In S4, the target management device can be a strictly monitored distribution device, such as a distribution device with a long maximum load operation duration. If it is in the tag set, it means that it may face similar load disturbance risks. At this time, the system should automatically issue a disturbance hidden danger notification to remind relevant personnel to pay attention and take corresponding preventive measures. The notification can be sent via email, SMS or other instant messaging methods to ensure that relevant personnel can respond and deal with potential risks in a timely manner.

[0036] In summary, by comprehensively collecting information on distribution equipment through the power grid management system or database and classifying and summarizing it according to clear collection rules, power grid operators can more systematically manage equipment of different types, different geographical locations, different voltage levels and different operating years. This classification management method not only improves management efficiency, but also makes it possible to conduct targeted analysis of equipment of specific types or regions, thereby improving the accuracy and effectiveness of management decisions; further, by real-time monitoring of load disturbance information of distribution equipment and using similarity algorithms to analyze the similarity of load disturbances between different equipment, the technical solution can detect potential load disturbance risks at an early stage. When similar load disturbance patterns are identified, the system can quickly mark the relevant equipment collection and issue disturbance hidden danger notifications in a timely manner, thereby realizing risk warnings, which enables power grid operators to Preventive maintenance measures can be taken to avoid equipment failures, thereby improving the stability and reliability of the power grid. Furthermore, by accurately identifying sets of equipment facing similar load disturbance risks, power grid operators can more reasonably allocate maintenance resources, give priority to equipment with higher risks, and avoid waste of resources. At the same time, the implementation of preventive maintenance strategies also reduces power outages and repair costs caused by equipment failures, thereby effectively saving operating costs. Furthermore, this technical solution uses big data analysis and intelligent algorithms to achieve refined management of power grid loads. By automatically collecting, analyzing and processing data, the system can intelligently identify risks and respond, thereby improving the intelligence level of power grid management. This not only enhances the security and stability of the power grid system, but also lays a solid foundation for the future development of smart grids.

[0037] Specifically, in obtaining the similarity between the first load disturbance information and the second load disturbance information based on the comprehensive calculation model, the comprehensive calculation model includes: ; wherein, is the similarity between the first load disturbance information and the second load disturbance information, is the load disturbance of the first distribution equipment at time t, is the load disturbance of the second distribution equipment at time t, and They are and The average value within the preset time series.

[0038] In this embodiment, it should be noted that and They are and The average value within a preset time series. Specifically, if we have a time series such as , which is at different time points t1, t2, ..., t N If there are different values ​​on , then the average value of this time series is It is the arithmetic mean of all these values, where N is the total number of time points. Setting such a similarity function is to quantify the two time series, namely the first load disturbance information and the second load disturbance information This quantitative method helps to understand and compare the similarity of load disturbances of two distribution equipment. In the defined similarity function: the numerator part The dot product of the two time series after removing the mean (i.e., centering) is calculated, which reflects the product of the degree to which the load disturbances of the two time series at the same time point deviate from their mean values, thereby capturing the co-variation between them; the denominator part The product of the standard deviations of the two time series is used to normalize the numerator, so that the output value of the similarity function is between -1 and 1. The standard deviation measures the degree to which each point in the time series deviates from the mean, so the denominator reflects the degree of fluctuation of the two time series. In practice, if the load disturbance information of two distribution devices is highly similar, then their similarity function value will be close to 1; if their load disturbance information is completely dissimilar, then the similarity function value will be close to 0; if their load disturbance information changes in opposite trends, then the similarity function value will be negative.

[0039] like Figure 2 As shown, specifically, S4 includes:

[0040] S41, determine whether the target management device is in the tag set, if yes, proceed to S42;

[0041] S42. Obtain the real-time load change value of the target management device;

[0042] S43. Process the real-time load change value based on the comprehensive calculation model and obtain a comprehensive index, determine whether the comprehensive index exceeds a second preset value, and if so, issue a disturbance hidden danger notification.

[0043] In this embodiment, it should be noted that in S41, the target management devices that need to be monitored are determined. These devices may be selected based on specific risk assessments, business importance or other criteria; then, the previously formed tag set (i.e., the set selected due to similar load disturbance risks in step S3) is queried; it is determined whether the target management device is included in these tag sets. If so, the next step S42 is performed; if not, the status of other devices is monitored. In S42, for the target management devices in the tag set, the real-time load data of the device is collected through real-time monitoring tools such as sensors or smart meters installed on the device; the real-time load change value is obtained using the real-time load data. In S43, a comprehensive indicator is calculated through the model, which comprehensively reflects the load disturbance risk of the device; it is determined whether this comprehensive indicator exceeds a preset second preset value. If it exceeds, it means that the load disturbance risk of the device has reached a high level and immediate action is required.

[0044] Specifically, in said S43, the comprehensive calculation model includes:

[0045] ;in, As a comprehensive indicator, is the real-time load change value, for The average value within the preset time series, is the correction factor.

[0046] In this embodiment, it should be noted that the purpose of setting such a comprehensive index function is to combine the real-time load change value of the target management device. Similarity to the previously calculated load disturbances of the first and second distribution equipment , in order to evaluate whether it is necessary to issue a disturbance risk notification. In this comprehensive indicator function: is the similarity calculated previously, which reflects the similarity between the load disturbances of the first and second distribution equipment. This value ranges from -1 to 1. When the load disturbances of the two equipments are exactly the same, the similarity is 1; when the load disturbances of the two equipments are completely opposite, the similarity is -1; when the load disturbances of the two equipments have no linear relationship, the similarity is 0; Calculate the real-time load change value of the target management equipment and its average value The average absolute deviation of the target management equipment load changes, which reflects the degree of fluctuation. If the values ​​at different time points differ greatly from their average value, then this value will be large, indicating that the load change of the target management device has a large fluctuation; Is the correction coefficient, which is used to adjust the sensitivity of the comprehensive index function according to the actual situation. By adjusting the value of , we can make the function more sensitive or more conservative to adapt to different application scenarios and security requirements.

[0047] To sum up, if the load disturbances of the first power distribution equipment and the second power distribution equipment are very similar, and the load changes of the target management equipment also have similar sufficiently large fluctuations, then this may mean that the target management equipment has been affected by disturbance factors similar to those of the first power distribution equipment and the second power distribution equipment, and therefore there is a high probability that there are hidden dangers. By comprehensively evaluating these two factors, we can more accurately determine whether it is necessary to issue a disturbance hidden danger notification.

[0048] like Figure 3 As shown, specifically, the S42 further includes:

[0049] S421, selecting a first time period T1 and a second time period T2;

[0050] S422, obtaining the current time point t0 of the target management device;

[0051] S423. Draw a load value change curve of the target management device from time t0-T1 to time t0+T2, and obtain the maximum load difference according to the load value change curve. The real-time load change value is the maximum load difference.

[0052] In this embodiment, it should be noted that, first, in step S421, the system will select two time periods, namely the first time period T1 and the second time period T2; the selection of these two time periods may be based on the historical data of the power grid load, operating experience or specific monitoring requirements; for example, T1 and T2 may represent a time period in the past and the future, respectively, for analyzing the load changes of the target management device before and after the current time point. Next, in step S422, the system will obtain the current time point t0 of the target management device; this time point can be obtained in real time or a time point specified by the user for subsequent load analysis. Then, in step S423, the system will draw a load value change curve of the target management device from time t0-T1 to time t0+T2 based on the acquired data; this curve can intuitively show the load changes of the target management device in the selected time period, including the peak value, valley value and change trend of the load. Finally, based on this load change curve, the system obtains the maximum load difference—the difference between the highest and lowest points on the curve. This difference represents the load fluctuation of the target management device during the selected time period, or the real-time load change value. This value is crucial for assessing the device's operating status, predicting potential load disturbances, and developing appropriate management measures. This real-time load change value acquisition process leverages the real-time analysis and processing capabilities of big data technology, enabling rapid processing of large amounts of real-time data, accurately mapping the load change curve, and extracting useful information from it.

[0053] Specifically, the S3 further includes:

[0054] If there is a set that includes both the first power distribution device and the second power distribution device, an associated tag is formed based on the comprehensive calculation model, the first load disturbance information, and the second load disturbance information, and the associated tag is associated with the tag set.

[0055] In this embodiment, it should be noted that when the system detects the presence of a set that includes both the first power distribution device and the second power distribution device, this means that the two devices may have similar load characteristics to some extent or be affected by the same external factors; in order to further analyze and confirm this correlation, the system will form an association tag based on the comprehensive calculation model, the first load disturbance information, and the second load disturbance information; once the association tag is generated, the system will associate it with the tag set. The advantage of this is that when the system detects a similar load disturbance pattern again in the future, it can quickly find the related equipment combination through the association tag, thereby accelerating the process of troubleshooting and hidden danger identification.

[0056] Specifically, in forming the association tag based on the comprehensive calculation model, the first load disturbance information, and the second load disturbance information, the comprehensive calculation model includes:

[0057] ;in,

[0058] is the association tag, is the serialized string of the first load disturbance information, is the serialized string of the second load disturbance information, is a hash function.

[0059] In this embodiment, it should be noted that is the serialized string of the first load disturbance information, The serialization string of the second load disturbance information is as follows: if the load disturbance information is a series of values ​​arranged in chronological order, the data at each time point can be converted into a string, and then these strings can be connected with a specific separator (such as a comma); for example, the serialization string of the first load disturbance information is { , ,……, If the load disturbance information contains multiple numerical attributes, these numerical values ​​can be converted to strings and concatenated with a delimiter. If the load disturbance information is structured, such as in JSON or XML format, the information can be directly converted to a string representation. For JSON format, a standard JSON serialization library can be used to generate the string.

[0060] A hash function is a function that maps input data to a fixed-length hash value. The hash function (H) should be a secure hash algorithm, such as SHA-256 or SHA-3, to ensure that the generated hash value is sufficiently unique and secure.

[0061] A big data driven power grid load refined management system is also provided, which is used to implement the above-mentioned big data driven power grid load refined management method, and the system includes:

[0062] A first acquisition module is used to acquire multiple existing power distribution devices, where the multiple existing power distribution devices include a first power distribution device, a second power distribution device, and a target management device;

[0063] A second acquisition module is configured to acquire a plurality of different set rules, and classify a plurality of existing power distribution devices into a plurality of sets based on the different set rules, thereby forming a set model, wherein each set rule includes at least two sets;

[0064] a calculation and processing module, configured to obtain first load disturbance information of a first power distribution device, obtain second load disturbance information of a second power distribution device, and obtain similarity between the first load disturbance information and the second load disturbance information based on a comprehensive calculation model;

[0065] The calculation and processing module is further configured to, when the similarity is greater than a first preset value, determine, based on the set model, whether there is a set that simultaneously summarizes the first power distribution device and the second power distribution device; if so, form a tag set with the set that simultaneously summarizes the first power distribution device and the second power distribution device;

[0066] The judgment and notification module is used to judge whether the target management device is in the tag set, and if so, issue a disturbance hidden danger notification.

[0067] Regarding the big data-driven refined management method for power grid loads in the above-mentioned embodiment, the specific manner of performing the operations has been described in detail in the implementation method of the big data-driven refined management method for power grid loads, and will not be elaborated on here.

[0068] Figure 4 FIG is a block diagram of an electronic device shown. Figure 4 As shown, the electronic device 700 may include: a processor 701 , a memory 702 , and may further include one or more of a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .

[0069] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned method for refined power grid load management driven by big data. The memory 702 is used to store various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data, such as contact information, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 702 or transmitted via the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, a mouse, and buttons. These buttons may be virtual or physical. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication may include Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G networks, or a combination thereof, without limitation. Accordingly, the communication component 705 may include a Wi-Fi module, a Bluetooth module, an NFC module, and the like.

[0070] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the above-mentioned big data-driven refined management method for power grid loads.

[0071] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When executed by a processor, the program instructions implement the steps of the above-described method for refined power grid load management driven by big data. For example, the computer-readable storage medium may be the aforementioned memory 702 including the program instructions. The program instructions may be executed by the processor 701 of the electronic device 700 to implement the above-described method for refined power grid load management driven by big data.

[0072] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned big data-driven power grid load refined management method when executed by the programmable device.

[0073] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0074] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0075] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for refined power grid load management driven by big data, characterized in that: include: S1. Acquire multiple existing power distribution devices, where the multiple existing power distribution devices include a first power distribution device, a second power distribution device, and a target management device; S2. Obtain multiple different set rules, and classify multiple existing power distribution devices into multiple sets based on the different set rules to form a set model, wherein each set rule includes at least two sets; S3. Obtain first load disturbance information of the first power distribution device, obtain second load disturbance information of the second power distribution device, and obtain similarity between the first load disturbance information and the second load disturbance information based on a comprehensive calculation model; If the similarity is greater than a first preset value, determining whether there is a set that includes both the first power distribution device and the second power distribution device based on the set model; if so, forming a tag set with the set that includes both the first power distribution device and the second power distribution device; S4. Determine whether the target management device is in the tag set. If so, issue a disturbance risk notification.

2. The method for refined power grid load management driven by big data according to claim 1, characterized in that: In obtaining the similarity between the first load disturbance information and the second load disturbance information based on the comprehensive calculation model, the comprehensive calculation model includes: in, The S(L1, L2) is the similarity between the first load disturbance information and the second load disturbance information, L1(t) is the load disturbance of the first distribution equipment at time t, and L2(t) is the load disturbance of the second distribution equipment at time t. and are the average values ​​of L1(t) and L2(t) within the preset time series.

3. The method for refined power grid load management driven by big data according to claim 2, characterized in that: The S4 includes: S41, determine whether the target management device is in the tag set, if yes, proceed to S42; S42. Obtain the real-time load change value of the target management device; S43. Process the real-time load change value based on the comprehensive calculation model and obtain a comprehensive index, determine whether the comprehensive index exceeds a second preset value, and if so, issue a disturbance hidden danger notification.

4. The method for refined power grid load management driven by big data according to claim 3, characterized in that: In said S43, the comprehensive calculation model includes: in, I(L T ) is a comprehensive indicator, L T (t) is the real-time load change value, For L T (t) is the average value within the preset time series, α is the correction coefficient, and N is the total number of time points.

5. The method for refined power grid load management driven by big data according to claim 3, characterized in that: The S42 further includes: S421, selecting a first time period T1 and a second time period T2; S422, obtaining the current time point t0 of the target management device; S423. Draw a load value change curve of the target management device from time t0-T1 to time t0+T2, and obtain the maximum load difference according to the load value change curve. The real-time load change value is the maximum load difference.

6. The method for refined power grid load management driven by big data according to claim 1, characterized in that: Said S3 further comprises: If there is a set that includes both the first power distribution device and the second power distribution device, an associated tag is formed based on the comprehensive calculation model, the first load disturbance information, and the second load disturbance information, and the associated tag is associated with the tag set.

7. The method for refined power grid load management driven by big data according to claim 1, characterized in that: In forming the association mark based on the comprehensive calculation model, the first load disturbance information, and the second load disturbance information, the comprehensive calculation model includes: G=H(S1∥S2); where G is an association tag, S1 is a serialized string of the first load disturbance information, S2 is a serialized string of the second load disturbance information, and H is a hash function.

8. A big data driven power grid load refined management system, characterized by: The system is used to implement the big data-driven refined management method for power grid loads according to any one of claims 1 to 7, and the system includes: A first acquisition module is used to acquire multiple existing power distribution devices, where the multiple existing power distribution devices include a first power distribution device, a second power distribution device, and a target management device; A second acquisition module is configured to acquire a plurality of different set rules, and classify a plurality of existing power distribution devices into a plurality of sets based on the different set rules, thereby forming a set model, wherein each set rule includes at least two sets; a calculation and processing module, configured to obtain first load disturbance information of a first power distribution device, obtain second load disturbance information of a second power distribution device, and obtain similarity between the first load disturbance information and the second load disturbance information based on a comprehensive calculation model; The calculation and processing module is further configured to, when the similarity is greater than a first preset value, determine, based on the set model, whether there is a set that simultaneously summarizes the first power distribution device and the second power distribution device; if so, form a tag set with the set that simultaneously summarizes the first power distribution device and the second power distribution device; The judgment and notification module is used to judge whether the target management device is in the tag set, and if so, issue a disturbance hidden danger notification.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the big data-driven refined management method for power grid loads as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the big data-driven refined management method for power grid loads as described in any one of claims 1 to 7.

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