A method, device, and medium for detecting rate period anomalies based on time-sharing users.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU POWER SUPPLY BUREAU GUANGDONG POWER GRID CO LTD
- Filing Date
- 2025-12-19
- Publication Date
- 2026-05-26
Smart Images
Figure CN122087632A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system technology, and more specifically, to a method, device, and medium for verifying rate period anomalies based on time-of-use users. Background Technology
[0002] In time-of-use metering management of power systems, anomaly verification during different tariff periods is a crucial step in ensuring the accuracy of electricity billing and protecting user rights. However, existing technologies have the following significant shortcomings in practical applications: 1) Insufficient accuracy of parameter recall: Traditional verification techniques rely solely on the results of a single parameter recall, failing to consider false alarms caused by factors such as communication interference and terminal malfunctions. For example, when the concentrator and the meter experience communication abnormalities, incorrect rate period settings may be obtained, leading to discrepancies in electricity bill calculations.
[0003] 2) Limited power consumption verification dimensions: The existing solution only performs a simple comparison based on the daily frozen power consumption, lacking correlation analysis between the time-of-use load curve and the tariff period. It cannot detect hidden anomalies such as "incorrect tariff period settings leading to miscalculation of peak and valley power consumption" or "power consumption jumps at the time of time period switching".
[0004] 3) Lack of dynamic verification mechanism: There is no normalized time-of-use parameter and power consumption linkage verification mechanism. Abnormal problems are often only discovered during monthly electricity bill settlement, with a long lag time (average 30 days) and great difficulty in tracing.
[0005] 4) Inefficient anomaly localization: There is a lack of a visualized rate period anomaly distribution model. Operation and maintenance personnel need to manually check massive amounts of data. The anomaly investigation time for a single customer exceeds 2 hours, which cannot meet the real-time operation and maintenance needs.
[0006] Therefore, there is an urgent need for a rate period anomaly verification method that can integrate multi-source data, achieve dynamic and accurate verification, and improve operation and maintenance efficiency. Summary of the Invention
[0007] To overcome the problems of insufficient accuracy of parameter recall, single dimension of power consumption verification, lack of dynamic verification mechanism and low efficiency of anomaly location in existing rate period anomaly verification technologies, this invention provides a rate period anomaly verification method, device and medium based on time-sharing users.
[0008] The technical solution of this invention is as follows: In a first aspect, the present invention provides a method for verifying anomalies in rate periods based on time-sharing users, comprising the following steps: Step S1: Obtain user rate period agreement parameters and user profile information from the marketing system; obtain meter rate period parameter recall data, daily frozen electricity data and load curve data from the metering automation system; and perform data preprocessing on the collected data. Step S2: Construct parameter verification matrix P and power verification matrix E; the parameter verification matrix P is used to store and compare marketing agreement rate periods, meter call periods, and parameter verification results; the power verification matrix E is used to store and compare daily frozen power consumption for each rate period, power consumption calculated based on load curves, and power verification results. Step S3: According to the preset timed triggering conditions and event triggering parameter anomaly verification and power anomaly verification, the parameter anomaly verification is performed based on the parameter verification matrix P, and the power anomaly verification is performed based on the power verification matrix E; Step S4: Based on the verification results, classify the anomaly types and visualize them through the rate period topology map and anomaly statistics dashboard; Step S5: Generate a processing work order based on the anomaly type and push it to the operation and maintenance terminal. After the processing result is fed back, update the parameter verification matrix P and the power verification matrix E to complete the anomaly closed-loop management.
[0009] As a preferred embodiment of the present invention, in step S1, the metering automation system periodically calls the meter rate time period parameter call data at 0:00, 8:00 and 16:00 every day. The meter rate time period parameter call data includes the time period start time, the number of time periods and the electricity price code. The metering automation system acquires the daily frozen electricity data for each rate period at 24:00 every day. The daily frozen electricity data includes the daily frozen peak electricity, the daily frozen valley electricity, and the daily frozen flat electricity. The automated metering system collects real-time power data every 15 minutes and generates a load curve; The data preprocessing includes performing CRC verification on the meter rate period parameter recall data, and unifying the timestamp format and aligning the load curve with the rate period.
[0010] As a preferred embodiment of the present invention, the parameter verification matrix P has a dimension of m×4, where m is the number of users in the time period of the station. The four columns of data in the parameter verification matrix P are marketing agreement rate period p[0], first meter call period p[1], second meter call period p[2], and parameter comprehensive verification result p[3]. Among them, p[3]=0 indicates that the parameter is normal; p[3]=1 indicates that the parameter is abnormal.
[0011] As a preferred embodiment of the present invention, the dimension of the power verification matrix E is m×5, and the five columns of the power verification matrix E are daily frozen peak power e[0], daily frozen valley power e[1], daily frozen flat power e[2], peak power calculated based on the load curve e[3], and power comprehensive verification result e[4]; where e[4]=0 indicates that the power is normal; e[4]=1 indicates that the power is abnormal.
[0012] As a preferred embodiment of the present invention, in step S3, the parameter anomaly verification includes consistency verification, stability verification and logic verification, and the power anomaly verification includes time period power matching degree verification, time period switching anomaly verification and line loss association verification.
[0013] As a preferred embodiment of the present invention, the consistency verification rule is: if p[1]≠p[0] and p[2]≠p[0], then p[3]=1; The stability check rule is: if p[1]≠p[2], then p[3]=1; The rule for the logical verification is: check whether the time period setting conforms to the preset specification; if it does not conform, determine p[3]=1; The rule for verifying the power matching degree of the time period is as follows: calculate the power Eload-peak of the peak segment of the load curve. If (e[0]- Eload-peak) / e[0] > 10%, then determine e[4]=1; The rule for the time period switching anomaly verification is: detect the slope of the load curve within the preset time window before and after the peak and valley time period switching. If the slope changes abruptly and exceeds the preset threshold and there is no record of starting or stopping electrical equipment, then determine e[4]=1; The rule for line loss correlation verification is as follows: if the deviation between the peak line loss rate of the transformer area and the total peak power consumption of users exceeds a preset threshold, a batch power consumption anomaly review will be triggered.
[0014] As a preferred embodiment of the present invention, in step S4, the abnormality types are divided into parameter abnormalities and power abnormalities. The parameter abnormalities include type A: rate period settings do not match the protocol, type B: parameter recall is unstable, and type C: period logic error. The power abnormalities include type D: peak and valley power consumption deviates from the load curve by more than a threshold, type E: power consumption jumps at the time of period switching, and type F: line loss correlation abnormality.
[0015] As a preferred embodiment of the present invention, in step S4, the rate period topology map displays the rate period configuration according to the transformer area, branch, and user level, the node color of the rate period topology map marks the abnormal type, and the curve comparison map of the rate period topology map overlays the marketing agreement period, the meter call period, and the load curve. The anomaly statistics dashboard displays the number and distribution of each type of anomaly in real time, and generates heat maps according to the anomaly type to locate high-risk transformer areas / routes.
[0016] In a second aspect, the present invention provides an electronic device, comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the above-described method for checking rate period anomalies based on time-sharing users.
[0017] Thirdly, the present invention provides a computer-readable medium storing computer-executable instructions for performing the above-described method for verifying rate period anomalies based on time-sharing users.
[0018] According to the above-described solution, the beneficial effects of this invention are as follows: 1. Through multi-source data fusion and multi-round verification, the accuracy of parameter recall and the comprehensiveness of anomaly identification are significantly improved, effectively reducing the error in electricity bill calculation; 2. By combining in-depth analysis of load curves, refined identification of power anomalies was achieved, and the time for anomaly detection was shortened from monthly to near real-time; 3. Through dynamic triggering mechanisms and visualization models, the efficiency of operation and maintenance troubleshooting has been greatly improved, and the time for handling anomalies for a single household has been shortened. 4. It has achieved closed-loop management of anomaly handling and can effectively link with line loss analysis, thereby improving the level of precision in power grid operation and management. Attached Figure Description
[0019] Figure 1 This is a flowchart illustrating a method for verifying rate period anomalies based on time-sharing users in one embodiment of the present invention. Figure 2 This is a schematic diagram of a rate period anomaly verification device based on time-sharing users in one embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation
[0020] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.
[0021] It should be noted that the terms "comprising" and "having," and any variations thereof, in the specification and claims of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.
[0022] Please refer to Figure 1 This embodiment provides a method for verifying anomalies in rate periods based on time-sharing users, including the following steps: Step S1: Obtain user rate period agreement parameters and user profile information from the marketing system; obtain meter rate period parameter recall data, daily frozen electricity data, and load curve data from the metering automation system; and perform data preprocessing on the collected data.
[0023] Specifically, the metering automation system periodically collects meter rate-period parameter data at 0:00, 8:00, and 16:00 daily. This data includes the start time of the period, the number of periods, and the electricity price code. At 24:00 daily, the system acquires daily frozen electricity data for each rate period, including frozen peak, valley, and flat period electricity. The system also collects real-time power data every 15 minutes and generates load curves. This periodic collection and data acquisition frequency setting ensures data timeliness while reasonably controlling the amount of data collected, avoiding excessive data redundancy.
[0024] Specifically, data preprocessing includes performing CRC checks on the meter rate period parameter recall data to ensure the accuracy and integrity of the data, as well as unifying the timestamp format and aligning the load curve with the rate period, such as the peak period of 7:00-11:00 corresponding to the load curve data of 7:15-10:45, making different data comparable and providing accurate data support for subsequent matrix construction and verification work.
[0025] Step S2: Construct parameter verification matrix P and power verification matrix E; parameter verification matrix P is used to store and compare marketing agreement rate periods, meter call periods and parameter verification results; power verification matrix E is used to store and compare daily frozen power consumption for each rate period, power consumption calculated based on load curve and power verification results.
[0026] Specifically, the parameter verification matrix P has a dimension of m×4, where m is the number of users in each time period of the station. The four columns of the parameter verification matrix P are the marketing agreement rate period (standard period) p[0], the first meter call period p[1], the second meter call period p[2], and the parameter comprehensive verification result p[3]. Among them, p[3]=0 indicates that the parameter is normal; p[3]=1 indicates that the parameter is abnormal. By displaying the parameter status of each user in the above matrix form, it is convenient to perform consistency, stability and logic verification, and quickly and accurately discover parameter abnormalities.
[0027] Specifically, the power verification matrix E has a dimension of m×5. The five columns of power verification matrix E are daily frozen peak power e[0], daily frozen valley power e[1], daily frozen flat power e[2], peak power calculated based on load curve (power integral) e[3], and power comprehensive verification result e[4]. Among them, e[4]=0 indicates that the power is normal; e[4]=1 indicates that the power is abnormal. The above power verification matrix E can comprehensively consider power data from different sources, discover power abnormalities through multiple verification methods, and improve the accuracy and comprehensiveness of power verification.
[0028] Step S3: Based on the preset timed trigger conditions and event trigger parameter anomaly verification and power anomaly verification, parameter anomaly verification is performed based on parameter verification matrix P, and power anomaly verification is performed based on power verification matrix E.
[0029] Specifically, the preset timed trigger conditions are: parameter anomaly checks and power consumption anomaly checks are performed at 8:00 AM, 4:00 PM, and 12:00 AM daily. These timed trigger conditions establish a normalized time-of-use parameter and power consumption linkage verification mechanism, enabling timely detection of anomalies and preventing them from being discovered only during monthly electricity billing, thus shortening the lag time for anomaly detection.
[0030] Specifically, the preset events include: a sudden increase / decrease in user electricity consumption exceeding 30%, receiving a user's electricity bill dispute report, and successful time synchronization of the meter clock (to avoid errors caused by clock deviations during certain time periods). These preset events further enhance the timeliness and specificity of the verification, enabling rapid response to potential anomalies.
[0031] Specifically, parameter anomaly verification includes consistency verification, stability verification, and logic verification, while power anomaly verification includes time period power matching degree verification, time period switching anomaly verification, and line loss correlation verification.
[0032] The consistency verification rule is: by comparing the marketing agreement rate period with the meter call period, if p[1]≠p[0] and p[2]≠p[0], then p[3]=1, that is, the parameter setting is in conflict with the agreement, and the problem of the rate period setting not conforming to the agreement can be detected in time. The stability verification rule is: compare the results of two meter calls. If p[1]≠p[2], then p[3]=1, that is, the parameter call is unstable and can detect the parameter false alarm problem caused by factors such as communication interference and terminal failure. The rules for logical verification are: check whether the time period setting conforms to the preset specifications, such as peak duration ≥ 4 hours and time periods not overlapping. If it does not conform, p[3] = 1, that is, the parameter is abnormal, to ensure the rationality of the time period setting; The rule for verifying the matching degree of power consumption during a period is: calculate the peak power consumption Eload-peak of the load curve. If (e[0] -Eload-peak) / e[0] > 10%, then determine e[4]=1, that is, the power consumption is abnormal, and the problem of the peak and valley power consumption deviating from the load curve by more than the threshold can be found. The rule for checking the abnormality of time period switching is: detect the slope of the load curve within the preset time window (such as 30 minutes) before and after the peak and valley time period switching. If the slope changes abruptly and exceeds the preset threshold and there is no record of the start and stop of the electrical equipment, then it is determined that e[4]=1, that is, the power is abnormal, and the problem of power jump at the time period switching can be found. The rule for line loss correlation verification is as follows: if the deviation between the peak line loss rate of the transformer area and the total peak electricity consumption of users exceeds a preset threshold (such as 15%), a batch electricity consumption anomaly review will be triggered, which can effectively correlate line loss analysis and discover the problem of mismatch between user electricity consumption and transformer area line loss.
[0033] Step S4: Based on the verification results, classify the anomaly types and visualize them through the rate period topology map and anomaly statistics dashboard.
[0034] Specifically, the abnormality types are divided into parameter abnormalities and power abnormalities.
[0035] Among them, parameter anomalies include: Type A: Rate period settings do not match the agreement, such as incorrect peak period start time; Type B: Unstable parameter recall, such as inconsistent results from multiple recalls; Type C: Time period logic error, such as overlapping time periods or insufficient duration; Abnormal battery levels include: Type D: The deviation between peak and off-peak electricity consumption and the load curve exceeds the threshold; Type E: Battery level changes during time period switching; Type F: Line loss correlation discrepancy, such as a mismatch between user power consumption and line loss in the distribution area.
[0036] By classifying anomalies into parameter anomalies and power anomalies, and further subdividing parameter anomalies into type A, type B, and type C; and power anomalies into type D, type E, and type F, maintenance personnel can more accurately understand the anomaly situation and take targeted measures.
[0037] Specifically, the rate-period topology diagram displays rate-period configurations by transformer area, branch, and user level. Node colors in the topology diagram indicate anomaly types, such as red for parameter anomalies and yellow for power consumption anomalies. The topology diagram also overlays marketing agreement periods, meter call periods, and load curves, visually demonstrating the match between time-period settings and electricity consumption behavior. Through this visualization method of the rate-period topology diagram, maintenance personnel can quickly locate anomalies, understand the anomalies, and improve maintenance troubleshooting efficiency.
[0038] Specifically, the anomaly statistics dashboard displays the number and distribution of each type of anomaly in real time, and generates heat maps by anomaly type to locate high-risk areas / routes. Through this anomaly statistics dashboard, operations and maintenance personnel can rationally allocate operational resources and prioritize handling anomalies in high-risk areas, further improving operational efficiency.
[0039] Step S5: Generate a processing work order based on the anomaly type and push it to the operation and maintenance end. After the processing result is fed back, update the parameter verification matrix P and the power verification matrix E to complete the anomaly closed-loop management.
[0040] Specifically, after the maintenance personnel complete the on-site processing based on the work order, they upload the processing results and on-site photos through the mobile APP, and the system automatically synchronizes them to the basic data pool. The system updates the parameter verification matrix P and the power verification matrix E according to the feedback results, and updates the node color from the abnormal color to the normal color, marking the abnormal processing as completed, forming a complete processing closed loop, ensuring that the abnormality is thoroughly resolved, and continuously updating the data, thereby improving the stability and reliability of the system.
[0041] The time-of-use (TOU) user-based rate period anomaly verification method provided in this embodiment significantly improves the accuracy of parameter recall and the comprehensiveness of anomaly identification through multi-source data fusion and multi-round verification, effectively reducing electricity bill calculation errors. Combined with in-depth load curve analysis, it achieves refined identification of electricity anomalies and shortens the anomaly discovery time from monthly to near real-time. Through dynamic triggering mechanisms and visualization models, it greatly improves the efficiency of operation and maintenance troubleshooting and shortens the anomaly handling time for a single household. It realizes closed-loop management of anomaly handling and can effectively correlate with line loss analysis, improving the level of refinement of power grid operation and management.
[0042] like Figure 2 As shown, one embodiment of the present invention provides a rate period anomaly verification device based on time-sharing users, comprising: The data acquisition and preprocessing module 1 is used to obtain user rate period agreement parameters and user profile information from the marketing system, obtain electricity meter rate period parameter recall data, daily frozen electricity data and load curve data from the metering automation system, and perform data preprocessing on the acquired data. Matrix construction module 2 is used to construct parameter verification matrix P and power verification matrix E; parameter verification matrix P is used to store and compare marketing agreement rate periods, meter call periods and parameter verification results; power verification matrix E is used to store and compare daily frozen power for each rate period, power calculated based on load curve and power verification results; Dynamic verification module 3 is used to perform abnormal verification and power consumption verification based on preset timed trigger conditions and event trigger parameters. Parameter abnormal verification is performed based on parameter verification matrix P, and power consumption verification is performed based on power consumption verification matrix E. The anomaly analysis and visualization module 4 is used to classify anomaly types based on the verification results and to visualize them through the rate period topology map and anomaly statistics dashboard. The closed-loop processing module 5 is used to generate processing work orders based on the anomaly type and push them to the operation and maintenance end. After the processing results are fed back, the parameter verification matrix P and the power verification matrix E are updated to complete the anomaly closed-loop management.
[0043] The rate period anomaly verification device based on time-sharing users provided in this application embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0044] like Figure 3 The diagram shown is a structural schematic of an electronic device that implements the method for verifying rate-period anomalies based on time-sharing users according to the present invention.
[0045] The electronic device may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a time-sharing user rate period anomaly verification program.
[0046] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing a rate-period anomaly verification program based on time-sharing users) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.
[0047] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, SmartMediaCard (SMC), SecureDigital (SD) card, FlashCard, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as code for a time-sharing user rate period anomaly verification program, but also to temporarily store data that has been output or will be output.
[0048] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.
[0049] Communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.
[0050] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3The structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.
[0051] For example, a power supply, although not shown, may also include a power source (such as a battery) to power various components. Preferably, the power supply can be logically connected to at least one processor 10 via a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power sources, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be elaborated further here.
[0052] It should be understood that the embodiments are for illustrative purposes only and are not limited to this structure in the scope of the patent application.
[0053] The memory 11 in the electronic device stores a rate period anomaly verification program based on time-sharing users, which is a combination of multiple computer programs. When run in the processor 10, it can achieve the following: The system obtains user rate period agreement parameters and user profile information from the marketing system, and obtains meter rate period parameter recall data, daily frozen electricity data and load curve data from the metering automation system, and performs data preprocessing on the collected data. Construct a parameter verification matrix P and a power verification matrix E; the parameter verification matrix P is used to store and compare the marketing agreement rate period, the meter call period, and the parameter verification results; the power verification matrix E is used to store and compare the daily frozen power, the power calculated based on the load curve, and the power verification results for each rate period. Based on the preset timed triggering conditions and event triggering parameter anomaly verification and power anomaly verification, parameter anomaly verification is performed based on parameter verification matrix P, and power anomaly verification is performed based on power verification matrix E. Based on the verification results, the anomaly types are classified and visualized through the rate period topology map and anomaly statistics dashboard; Based on the anomaly type, a processing work order is generated and pushed to the operation and maintenance end. After the processing result is fed back, the parameter verification matrix P and the power verification matrix E are updated to complete the anomaly closed-loop management.
[0054] Specifically, the specific implementation method of the above computer program by the processor 10 can be found in [reference needed]. Figure 1 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.
[0055] Furthermore, if the modules / units integrated into an electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, a computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).
[0056] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: The system obtains user rate period agreement parameters and user profile information from the marketing system, and obtains meter rate period parameter recall data, daily frozen electricity data and load curve data from the metering automation system, and performs data preprocessing on the collected data. Construct a parameter verification matrix P and a power verification matrix E; the parameter verification matrix P is used to store and compare the marketing agreement rate period, the meter call period, and the parameter verification results; the power verification matrix E is used to store and compare the daily frozen power, the power calculated based on the load curve, and the power verification results for each rate period. Based on the preset timed triggering conditions and event triggering parameter anomaly verification and power anomaly verification, parameter anomaly verification is performed based on parameter verification matrix P, and power anomaly verification is performed based on power verification matrix E. Based on the verification results, the anomaly types are classified and visualized through the rate period topology map and anomaly statistics dashboard; Based on the anomaly type, a processing work order is generated and pushed to the operation and maintenance end. After the processing result is fed back, the parameter verification matrix P and the power verification matrix E are updated to complete the anomaly closed-loop management.
[0057] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.
[0058] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0059] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.
[0060] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0061] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.
[0062] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.
[0063] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.
[0064] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.
[0065] The present invention has been described above with reference to the accompanying drawings. Obviously, the implementation of the present invention is not limited to the above-described manner. Any improvements made using the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution of the present invention to other situations without modification, are all within the protection scope of the present invention.
Claims
1. A time-sharing user-based rate period abnormality checking method, characterized by comprising: The method comprises the following steps: Step S1, obtaining user rate period agreement parameters and user profile information from a marketing system, obtaining electric meter rate period parameter measurement data, daily frozen electric quantity data, and load curve data from a metering automation system, and performing data preprocessing on the collected data; Step S2, constructing a parameter verification matrix P and an electric quantity verification matrix E; The parameter verification matrix P is used to store and compare marketing agreement rate periods, electric meter measurement periods, and parameter verification results; the electric quantity verification matrix E is used to store and compare daily frozen electric quantities of each rate period, electric quantities calculated based on load curves, and electric quantity verification results; Step S3, performing parameter abnormality verification and electric quantity abnormality verification according to preset timing trigger conditions and event trigger parameters, the parameter abnormality verification is performed based on the parameter verification matrix P, and the electric quantity abnormality verification is performed based on the electric quantity verification matrix E; Step S4, according to the verification result, the abnormality type is divided, and the visualization display is performed through the rate period topology graph and the abnormality statistical board; Step S5, generating a processing work order according to the abnormality type and pushing it to an operation and maintenance end, updating the parameter verification matrix P and the electric quantity verification matrix E after a processing result is fed back, and completing abnormality closed-loop management.
2. The time-sharing user-based rate period abnormality checking method of claim 1, wherein, In step S1, the metering automation system measures the electric meter rate period parameter measurement data at 0 o'clock, 8 o'clock, and 16 o'clock every day, and the electric meter rate period parameter measurement data includes a period start time, a period number, and a power price code; The metering automation system obtains the daily frozen electric quantity data of each rate period at 24 o'clock every day, and the daily frozen electric quantity data includes daily frozen peak segment electric quantity, daily frozen valley segment electric quantity, and daily frozen flat segment electric quantity; The metering automation system collects real-time power data at intervals of 15 minutes and generates a load curve; The data preprocessing includes CRC verification on the electric meter rate period parameter measurement data, uniform timestamp format, and alignment of the load curve with the rate period.
3. The time-sharing user-based rate period abnormality checking method of claim 1, wherein The dimension of the parameter verification matrix P is m*4, m is the number of substation time-sharing users, and the four columns of data of the parameter verification matrix P are marketing agreement rate periods p[0], first electric meter measurement periods p[1], second electric meter measurement periods p[2], and parameter comprehensive verification results p[3]; wherein p[3]=0 indicates that the parameters are normal, and p[3]=1 indicates that the parameters are abnormal.
4. The time-sharing user-based rate period abnormality checking method of claim 3, wherein, The dimension of the electric quantity verification matrix E is m*5, and the five columns of data of the electric quantity verification matrix E are daily frozen peak segment electric quantity e[0], daily frozen valley segment electric quantity e[1], daily frozen flat segment electric quantity e[2], peak segment electric quantity calculated based on a load curve e[3], and electric quantity comprehensive verification result e[4]; wherein e[4]=0 indicates that the electric quantity is normal, and e[4]=1 indicates that the electric quantity is abnormal.
5. The time-division user based rate period abnormality checking method of claim 4, wherein, In step S3, the parameter abnormality verification includes consistency verification, stability verification, and logic verification, and the electric quantity abnormality verification includes period electric quantity matching degree verification, period switching abnormality verification, and line loss correlation verification.
6. The time-sharing user-based rate period abnormality checking method of claim 5, wherein, The rule of the consistency verification is: if p[1]≠p[0] and p[2]≠p[0], then p[3]=1 is determined. The stability check rule is: if p[1]≠p[2], then p[3]=1; The rule for the logical verification is: check whether the time period setting conforms to the preset specification; if it does not conform, determine p[3]=1; The rule for verifying the matching degree of the power consumption during the specified period is as follows: calculate the peak power consumption Eload-peak of the load curve. If (e[0] - Eload-peak) / e[0] > 10%, then determine that e[4] = 1. The rule for the time period switching anomaly verification is: detect the slope of the load curve within the preset time window before and after the peak and valley time period switching. If the slope changes abruptly and exceeds the preset threshold and there is no record of starting or stopping electrical equipment, then determine e[4]=1; The rule for line loss correlation verification is as follows: if the deviation between the peak line loss rate of the transformer area and the total peak power consumption of users exceeds a preset threshold, a batch power consumption anomaly review will be triggered.
7. The time-division user based rate period anomaly checking method of claim 1, wherein, In step S4, the abnormality types are divided into parameter abnormalities and power abnormalities. The parameter abnormalities include type A: rate period settings do not match the protocol, type B: parameter recall is unstable, and type C: period logic error. The power abnormalities include type D: peak and valley power consumption deviates from the load curve by more than a threshold, type E: power consumption jumps at the time of period switching, and type F: line loss correlation abnormality.
8. The time-division user based rate period anomaly checking method of claim 1, wherein, In step S4, the rate period topology map displays the rate period configuration by transformer area, branch, and user level. The node colors of the rate period topology map mark the abnormal type. The curve comparison chart of the rate period topology map overlays the marketing agreement period, the meter call period, and the load curve. The anomaly statistics dashboard displays the number and distribution of each type of anomaly in real time, and generates heat maps according to the anomaly type to locate high-risk transformer areas / routes.
9. An electronic device, comprising: include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the rate period anomaly verification method based on time-sharing users as described in any one of claims 1 to 7.
10. A computer readable medium storing computer executable instructions, wherein the instructions comprise: It includes a data storage area and a program storage area. The data storage area stores the created data, and the program storage area stores the computer program. When the computer program is executed by the processor, it implements the rate period anomaly verification method based on time-sharing users as described in any one of claims 1 to 8.