Power distribution monitoring method, device and apparatus, and computer device

Abstract

The application relates to a power distribution and utilization monitoring method, device and apparatus and a computer device. The method comprises the following steps: acquiring first operation data of a meter box collected by a sensor in a tth period and calculating the variance of the first operation data; when the variance is greater than a first threshold, determining that a power distribution and utilization fault exists, and calculating the average deviation between the first operation data and preset ideal operation data of the meter box; when the average deviation is greater than a second threshold, acquiring second operation data of the meter box in a t+1th period to a t+pth period and calculating a comprehensive deviation value of the second operation data; and when the comprehensive deviation value is greater than a third threshold, determining that the fault cause of the power distribution and utilization is the sensor fault. The method can improve the accuracy of determining whether a power distribution and utilization fault exists and the specific fault cause, is beneficial to improving the efficiency of power distribution and utilization equipment operation and maintenance and data processing, and realizes real-time fine monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network, in particular to a power distribution monitoring method, device and apparatus, and computer equipment. BACKGROUND

[0002] As an important part of the power system directly connected with the dispersed users, the power distribution network plays an important role in maintaining the normal operation of the power grid and is an important foundation for building the energy internet. Due to the large number of devices on the distribution network side, the wide application range and the involvement of many aspects, and the complex environment, the operation of the power distribution equipment is easily affected by various adverse weather, human damage, construction and other factors, which greatly increases the difficulty of management and maintenance of the power distribution equipment in the operation process. Therefore, it is necessary to monitor the power distribution to determine whether there is a fault in the power distribution and the specific cause of the fault. The traditional power distribution monitoring method mainly relies on manual inspection, but due to the large number of power distribution equipment and the wide deployment range, manual inspection requires a large number of human resources, and more importantly, it is difficult to accurately determine whether there is a fault in the power distribution and the specific cause of the fault by relying on manual inspection. SUMMARY

[0003] Therefore, it is necessary to provide a power distribution monitoring method, device and apparatus, and computer equipment capable of improving the accuracy of determining whether there is a fault in the power distribution and the specific cause of the fault.

[0004] In a first aspect, the present application provides a power distribution monitoring method. The power distribution monitoring method comprises:

[0005] obtaining first operation data of a meter box collected by a sensor in a tth period and calculating a variance of the first operation data;

[0006] when the variance is greater than a first threshold value, determining that there is a fault in the power distribution, and calculating an average deviation between the first operation data and preset ideal operation data of the meter box;

[0007] when the average deviation is greater than a second threshold value, obtaining second operation data of the meter box in a t+1th period to a t+pth period and calculating a comprehensive deviation value of the second operation data;

[0008] when the comprehensive deviation value is greater than a third threshold value, determining that the cause of the fault in the power distribution is a sensor fault.

[0009] In one of the embodiments, the power distribution monitoring method further comprises:

[0010] when the average deviation is less than the second threshold value, determining that the cause of the fault is a meter box fault.

[0011] In one of the embodiments, the power distribution monitoring method further comprises:

[0012] When the comprehensive deviation value is less than the third threshold value, it is determined that the fault cause is abnormal data collected by the sensor.

[0013] In one embodiment, the power utilization monitoring method further comprises:

[0014] The first operation data of the distribution box collected by the sensor in different periods is repeatedly acquired, and the variance is calculated to repeatedly determine the fault cause of the power distribution network.

[0015] In one embodiment, the power utilization monitoring method further comprises:

[0016] The first threshold value, the second threshold value and the third threshold value are respectively corrected according to the fault causes determined in the plurality of different periods and the preset fault causes.

[0017] In one embodiment, the first threshold value, the second threshold value and the third threshold value are respectively corrected according to the fault causes determined in the plurality of different periods and the preset fault causes, comprising:

[0018] The monitoring error times corresponding to the first threshold value, the second threshold value and the third threshold value are respectively obtained according to the fault causes determined in the plurality of different periods and the preset fault causes;

[0019] The adjustment amplitude values corresponding to the first threshold value, the second threshold value and the third threshold value are respectively obtained according to the monitoring error times corresponding to the first threshold value, the second threshold value and the third threshold value and the total monitoring times;

[0020] The first threshold value, the second threshold value and the third threshold value are respectively corrected according to the monitoring error times corresponding to the first threshold value, the second threshold value and the third threshold value, the total monitoring times, the adjustment amplitude values and a preset correction model;

[0021] The adjustment amplitude values corresponding to the first threshold value, the second threshold value and the third threshold value are respectively:

[0022]

[0023] In the formula, and are the monitoring error times corresponding to the first threshold value, the second threshold value and the third threshold value, and γ1, γ2, γ3 are the adjustment factors corresponding to the first threshold value, the second threshold value and the third threshold value.

[0024] In one embodiment, the preset correction model corresponding to each threshold value comprises:

[0025]

[0026]

[0027]

[0028] In the formula, and respectively are a first threshold value, a second threshold value and a third threshold value, and respectively are the monitoring error times corresponding to the first threshold value, the second threshold value and the third threshold value, and respectively are the adjustment amplitude values corresponding to the first threshold value, the second threshold value and the third threshold value, and respectively are the total monitoring times corresponding to the first threshold value, the second threshold value and the third threshold value, is a second threshold value determination result indication variable, is a third threshold value determination result indication variable.

[0029] In a second aspect, the application further provides a power distribution and utilization monitoring device, which comprises:

[0030] a sensor, configured to collect first operation data of a meter box;

[0031] an edge intelligent monitoring terminal, configured to acquire the first operation data of the meter box collected by the sensor in a tth period and calculate a variance of the first operation data, determine that a fault exists in power distribution and utilization when the variance is greater than a first threshold value, and calculate an average deviation between the first operation data and preset ideal operation data of the meter box, acquire second operation data of the meter box in a t+1th period to a t+pth period and calculate a comprehensive deviation value of the second operation data when the average deviation is greater than a second threshold value, and determine that a sensor fault is the fault cause of the power distribution and utilization when the comprehensive deviation value is greater than a third threshold value.

[0032] In a third aspect, the application further provides a power distribution and utilization monitoring device, which comprises:

[0033] a first data acquisition module, configured to acquire first operation data of a meter box collected by a sensor in a tth period and calculate a variance of the first operation data;

[0034] a second data acquisition module, configured to determine that a fault exists in power distribution and utilization when the variance is greater than a first threshold value, and calculate an average deviation between the first operation data and preset ideal operation data of the meter box;

[0035] a third data acquisition module, configured to acquire second operation data of the meter box in a t+1th period to a t+pth period and calculate a comprehensive deviation value of the second operation data when the average deviation is greater than a second threshold value;

[0036] a first cause determination module, configured to determine that a sensor fault is the fault cause of the power distribution and utilization when the comprehensive deviation value is greater than a third threshold value.

[0037] In a fourth aspect, the present application further provides a computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the above method when executing the computer program.

[0038] The above power distribution and utilization monitoring method, device and apparatus and computer device acquire first operation data of the meter box collected by the sensor in the tth period and calculate the variance of the first operation data, when the variance is greater than the first threshold value, it indicates that the first operation data is abnormal, and then it can be determined that the power distribution and utilization has a fault. At this time, the average deviation between the first operation data and the preset ideal operation data of the meter box is further calculated, when the average deviation is greater than the second threshold value, the second operation data of the meter box in the t+1th period to the t+pth period is acquired and the comprehensive deviation value of the second operation data is calculated, when the comprehensive deviation value is greater than the third threshold value, it is determined that the fault reason of the power distribution and utilization is the sensor fault. The power distribution and utilization monitoring method of the present application is adopted, the first operation data is identified based on the first threshold value, it is determined that the power distribution and utilization has a fault, and the specific fault reason is further determined based on the second threshold value and the third threshold value, the abnormal data is identified based on the multi-threshold value linkage to determine, the accuracy of determining whether the power distribution and utilization has a fault and the specific fault reason is improved, which is beneficial to improving the efficiency of operation and maintenance and data processing of the power distribution and utilization equipment, and realizes real-time and refined monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 It is one of application environment diagrams of the power distribution and utilization monitoring method in an embodiment;

[0040] Figure 2 It is another of application environment diagrams of the power distribution and utilization monitoring method in an embodiment;

[0041] Figure 3 It is a flowchart of the power distribution and utilization monitoring method in an embodiment;

[0042] Figure 4 It is a flowchart of the power distribution and utilization monitoring method in an embodiment;

[0043] Figure 5 It is a structural block diagram of the power distribution and utilization monitoring device in an embodiment;

[0044] Figure 6 It is a structural block diagram of the power distribution and utilization monitoring device in an embodiment;

[0045] Figure 7 It is an internal structure diagram of the computer device in an embodiment.

[0046] BRIEF DESCRIPTION OF DRAWINGS

[0047] The power distribution and utilization monitoring device comprises a power distribution and utilization monitoring equipment 10, a sensor 11, an edge intelligent monitoring terminal 12, and a power distribution and utilization monitoring apparatus 20. DETAILED DESCRIPTION

[0048] In order to facilitate the understanding of the embodiments of the present application, the embodiments of the present application will be described more fully below with reference to the accompanying drawings. The preferred embodiments of the present application are shown in the drawings. However, the embodiments of the present application can be realized in many different forms and are not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the embodiments of the present application more thorough and comprehensive.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of the present application belong. The terminology used in the specification of the embodiments of the present application herein is only for the purpose of describing specific embodiments and is not intended to limit the embodiments of the present application.

[0050] It can be understood that the term "comprising / including" specifies the existence of the stated features, integers, steps, operations, components, parts or combinations thereof, but does not exclude the possibility of existence or addition of one or more other features, integers, steps, operations, components, parts or combinations thereof. The terms "first", "second" and the like can be used herein to describe various parameters, but these parameters are not limited by these terms. These terms are only used to distinguish the first parameter from another parameter. For example, without departing from the scope of the present application, the first running data can be referred to as the second running data, and the second running data can also be referred to as the first running data.

[0051] Embodiment one

[0052] The power distribution and utilization fine monitoring sensor collects a large amount of data of various types, and the sensor is deployed at a position far away from the main station. The time delay caused by the transmission of a large amount of collected data increases, which easily leads to a delay in the fine monitoring result. At the same time, with the intelligent construction of the power distribution network, in the process of monitoring abnormal data collected by the sensor, the fault of the meter box, the fault of the sensor or the abnormal data collected by the sensor will all cause data anomalies, resulting in inaccurate fault positioning, fault omission and other phenomena, affecting the decision of the power distribution and utilization system and disturbing the management and operation process of the power distribution and utilization system. The traditional monitoring technology uses a single threshold to identify abnormal data and the specific reason for the fault. Therefore, how to perform fine monitoring of power distribution and utilization, design a terminal and a system, identify abnormal data near the sensor side to determine that there is a fault in the power distribution network, and determine the specific reason for the fault to achieve fine monitoring is a challenge.

[0053] The data collection fluctuates due to the electromagnetic environment, temperature change and other reasons in the meter box, leading to misjudgment of the meter box failure, sensor failure and the like, and the static abnormal data identification method cannot adjust the determination threshold according to the result. Therefore, how to use the feedback of the determination result of the failure cause to dynamically adjust the determination threshold for the meter box failure, sensor failure or abnormal data collected by the sensor is also a challenge.

[0054] In view of the above problems, it is urgent to carry out fine monitoring of distribution and utilization of electricity and design corresponding terminals and systems, so as to comprehensively collect device state monitoring data in the operation process of the distribution and utilization of electricity equipment and timely transmit the data to the metering automation master station, analyze the terminal operation state or locate the fault position by the metering automation master station, improve the maintenance efficiency, realize fine monitoring of distribution and utilization of electricity, and timely find and eliminate safety hazards.

[0055] 5G edge computing technology combines the characteristics of edge computing and 5G communication, integrates the innovative capabilities of network transmission, computing, storage and application, and improves the feedback and efficiency of data processing. The embodiments of the present application provide a distribution and utilization of electricity monitoring method based on 5G edge computing, which identifies abnormal data to determine that there is a fault in the power distribution network and further determines the specific cause of the fault, realizes real-time monitoring of edge intelligent terminals close to the user side, and improves the accuracy of determining whether there is a fault in the distribution and utilization of electricity and the specific cause of the fault.

[0056] The embodiments of the present application provide a distribution and utilization of electricity monitoring method based on 5G edge computing, Figure 1 and Figure 2 are application environment diagrams of the distribution and utilization of electricity monitoring method. Specifically, as shown in Figure 1 , the application environment of the distribution and utilization of electricity monitoring method is defined as a distribution and utilization of electricity monitoring system, which is divided into three parts of an application layer, a communication layer and a perception layer.

[0057] Application layer: The application layer is composed of a metering automation master station and a device state monitoring module. The device state monitoring module connects an edge intelligent monitoring terminal through 5G communication, actively sends environment data collection instructions to check the state of the device at a specified location, and realizes four functions of device state monitoring, fault / abnormal alarm, fault / abnormal positioning, and device operation and maintenance monitoring. Among them, the device state monitoring function collects working condition data of power distribution and utilization metering equipment through sensors, such as terminal temperature, meter box incoming line current and voltage, etc., so as to monitor the running environment and state of the equipment, and provide a data source for rapid positioning and timely alarm of equipment failure. When it is necessary to check the running state of the equipment in a specified area, the monitoring system can actively request the state data of the specified area; the fault / abnormal alarm function judges the occurrence of faults / abnormalities according to the monitoring data, and reports in real time the occurrence of faults / abnormalities such as line loss anomaly, switch loosening, abnormal operation of meter box, electricity stealing, etc., to improve the efficiency of operation and maintenance; the fault / abnormal positioning function maps the files between the monitoring points and the meters to locate the metering equipment and meter box with faults, helping the operation and maintenance personnel to quickly find the fault location and shorten the operation and maintenance time; the device operation and maintenance monitoring function can remotely monitor the operation and maintenance through the monitoring system, and keep the operation records.

[0058] Communication layer: The communication layer is composed of a concentrator and an edge intelligent monitoring terminal. The concentrator supports power collection of power distribution and utilization metering equipment, and is responsible for returning power data to the metering automation system of the application layer. The edge intelligent monitoring terminal is deployed at the network edge to collect data such as meter box incoming line current and voltage, terminal temperature, etc. in the sensing layer. At the same time, the intelligent sensing terminal uses the edge computing capability provided by the edge server to analyze and process the monitored fine information of power distribution and utilization, realizes the pre-judgment of the running state of the equipment and the fault, and uploads the pre-judgment result to the device state monitoring system.

[0059] Sensing layer: The sensing layer is configured with various sensors, including electrical quantity and non-electrical quantity collection points, to complete the collection of various monitoring quantities related to metering equipment and the running state data of each meter box. The specific types of sensors include: an environmental camera for completing abnormal switch operation of the meter box and electricity stealing behavior investigation function; a meter box incoming line collection device installed on the meter box incoming line side for collecting line current and voltage of the incoming line, which can quickly locate the line loss fault point by comparing with the meter data; a terminal temperature measuring device installed in the metering compartment of the meter for collecting the temperature of the meter terminal, which can judge the looseness, aging, etc. of the meter wiring and maintain in time. Among them, various sensors of the sensing layer and edge intelligent monitoring terminals of the communication layer can be arranged in the meter box.

[0060] The edge intelligent monitoring terminal and the sensor in the application environment power distribution and utilization monitoring system are as follows Figure 2As shown, each acquisition module represents a different type of sensor. The edge intelligent monitoring terminal consists of uplink and downlink communication modules, a multi-threshold adjustment module, and an anomaly data identification module. The downlink communication includes power line carrier, RS485, and low-power wireless communication modules to monitor, acquire, and issue commands to the sensors in the sensing layer of the power distribution monitoring system. The uplink communication uses a 5G transmission module to listen to, receive, and respond to commands from the metering automation master station in the application layer of the power distribution monitoring system. The multi-threshold adjustment module and the anomaly data identification module are connected to the processor, utilizing edge computing capabilities to process the sensor data, identify anomalies, and upload them to the master station. This completes the anomaly data identification for refined power distribution monitoring based on multi-threshold linkage, and the multi-threshold linkage adjustment based on the meter box and sensor fault judgment results fed back from the anomaly data identification. A cache module stores the data results, acquired data, and intermediate process data.

[0061] First, this application's embodiments include a method for refined monitoring of power distribution based on multi-threshold linkage to identify abnormal data. In this application's embodiments, the identification of abnormal data based on multi-threshold linkage is divided into three processes: first-level threshold determination, second-level threshold determination, and third-level threshold determination. Initialization is performed on the number of sensors and meter boxes, the collection of current, voltage, and temperature status information, operating environment image information, and sensor acquisition cycle during the meter box's operation. The meter box is set as b, and N sensors are connected to meter box b to collect current, voltage, and temperature status information and operating environment image information during the meter box's operation. The acquisition cycle is t, and the sensor set is represented as M = {m1,...,m}. n ,...,m N}.like Figure 3 As shown, the power distribution monitoring method includes steps 310 to 340.

[0062] Step 310: Obtain the first operating data of the meter box collected by the sensor within the t-th period and calculate the variance of the first operating data. In this embodiment, at the end of each acquisition period, the sensor uploads the operating data of the meter box collected in a single period to the edge intelligent monitoring terminal. The edge intelligent monitoring terminal obtains this first operating data and calculates the variance of the first operating data. Sensor m n The variance of the first set of running data collected in period t can be calculated using the following formula:

[0063]

[0064] In the formula, For sensor m n The first running data collected at time i within period t.

[0065] Step 320: When the variance exceeds a first-level threshold, a power distribution fault is determined, and the average deviation between the first operating data and the preset ideal operating data of the meter box is calculated. In this embodiment, the determination of the fault's authenticity is based on a first-level threshold. Specifically, a first-level threshold is set. To determine if there is a fault in the power distribution system. If the variance of the first set of operating data is greater than the first-level threshold. Right now If the data changes within period t are abnormal, it indicates a power distribution fault. The specific cause of this fault could be a meter box malfunction, a sensor malfunction, or abnormal data acquisition by the sensor. If the variance of the first operating data is less than the first-level threshold... Right now This indicates that there is no fault in the power distribution.

[0066] In this embodiment, specifically, if all the first operating data collected by the sensors connected to the meter box b shows abnormalities, that is, if sensors m1 to m... n All exist The fault is determined to be caused by a meter box malfunction, specifically, a fire in the meter box or aging of terminals. Furthermore, the edge intelligent monitoring terminal promptly uploads the determination that there is a power distribution fault and the specific cause is a meter box malfunction to the metering automation main station for timely repair; if only the sensor m... n The first set of operational data collected showed an anomaly, while other sensors did not exhibit any anomalies; that is, only sensor m showed an anomaly. n exist This only indicates a power distribution fault but not the specific cause. In this case, it's necessary to calculate the average deviation between the initial operating data and the preset ideal operating data for the meter box, to further determine the specific cause of the fault based on a secondary threshold. Sensor m n The average deviation between the first set of operational data and the ideal operational data collected in period t can be calculated using the following formula:

[0067]

[0068] In the formula, This is the ideal operating data for the preset meter box.

[0069] Step 330: When the average deviation is greater than the secondary threshold, the second operating data of the meter box from period t+1 to period t+p is obtained respectively, and the comprehensive deviation value of the second operating data is calculated. In this embodiment, the specific cause of the fault is determined based on the secondary threshold. Specifically, a secondary threshold is set. To further determine whether the specific cause of the fault is a meter box malfunction. If the average deviation is greater than the secondary threshold. Right now If the first operation data exceeds the normal range collected by the sensor, i.e., the specific cause of the fault is not related to the meter box fault, the second operation data of the meter box in the next t+1 period to the t+p period is obtained, and the comprehensive deviation value of the second operation data is calculated, so as to further determine the specific cause of the fault based on the third threshold value. The comprehensive deviation value of the second operation data in the next P periods can be calculated according to the following formula:

[0070]

[0071] Preferably, the power distribution and utilization monitoring method further comprises: when the average deviation is less than the second threshold value, determining that the fault cause is the meter box fault. In this embodiment, if the average deviation is less than the second threshold value If the first operation data exceeds the normal range collected by the sensor, i.e., the specific cause of the fault is not related to the meter box fault, the second operation data of the meter box in the next t+1 period to the t+p period is obtained, and the comprehensive deviation value of the second operation data is calculated, so as to further determine the specific cause of the fault based on the third threshold value. The comprehensive deviation value of the second operation data in the next P periods can be calculated according to the following formula: If the first operation data exceeds the normal range collected by the sensor, i.e., the specific cause of the fault is not related to the meter box fault, the second operation data of the meter box in the next t+1 period to the t+p period is obtained, and the comprehensive deviation value of the second operation data is calculated, so as to further determine the specific cause of the fault based on the third threshold value. The comprehensive deviation value of the second operation data in the next P periods can be calculated according to the following formula:

[0072] Step 340, when the comprehensive deviation value is greater than the third threshold value, determining that the fault cause of the power distribution and utilization is the sensor fault. In this embodiment, the specific cause of the fault is determined based on the third threshold value. Specifically, the third threshold value is set as to further determine whether the specific cause of the fault is the sensor abnormality or the abnormal data collected by the sensor. If the comprehensive deviation value of the second operation data is greater than the third threshold value If the first operation data exceeds the normal range collected by the sensor, i.e., the specific cause of the fault is not related to the meter box fault, the second operation data of the meter box in the next t+1 period to the t+p period is obtained, and the comprehensive deviation value of the second operation data is calculated, so as to further determine the specific cause of the fault based on the third threshold value. The comprehensive deviation value of the second operation data in the next P periods can be calculated according to the following formula: If the first operation data exceeds the normal range collected by the sensor, i.e., the specific cause of the fault is not related to the meter box fault, the second operation data of the meter box in the next t+1 period to the t+p period is obtained, and the comprehensive deviation value of the second operation data is calculated, so as to further determine the specific cause of the fault based on the third threshold value. The comprehensive deviation value of the second operation data in the next P periods can be calculated according to the following formula:

[0073] Preferably, the power distribution and utilization monitoring method further comprises: when the comprehensive deviation value is less than the third threshold value, determining that the fault cause is the abnormal data collected by the sensor. In this embodiment, if the comprehensive deviation value of the second operation data is less than the third threshold value If the first operation data exceeds the normal range collected by the sensor, i.e., the specific cause of the fault is not related to the meter box fault, the second operation data of the meter box in the next t+1 period to the t+p period is obtained, and the comprehensive deviation value of the second operation data is calculated, so as to further determine the specific cause of the fault based on the third threshold value. The comprehensive deviation value of the second operation data in the next P periods can be calculated according to the following formula: If the first operation data exceeds the normal range collected by the sensor, i.e., the specific cause of the fault is not related to the meter box fault, the second operation data of the meter box in the next t+1 period to the t+p period is obtained, and the comprehensive deviation value of the second operation data is calculated, so as to further determine the specific cause of the fault based on the third threshold value. The comprehensive deviation value of the second operation data in the next P periods can be calculated according to the following formula:

[0074] Preferably, the power distribution and utilization monitoring method further comprises: repeatedly obtaining the first operation data of the meter box collected by the sensor in different periods and calculating the variance to repeatedly determine the fault cause of the power distribution network. In this embodiment, when the edge intelligent monitoring terminal completes the determination of whether the power distribution network has a fault and the specific cause of the fault in one period, it immediately enters the determination of the next period, so as to continuously monitor the power distribution and utilization based on the multi-threshold value linkage to identify the abnormal data.

[0075] Secondly, the embodiment of the present application also includes multi-threshold linkage adjustment based on the specific cause of the fault feedback by identifying abnormal data. The selection of multi-threshold is crucial for the fine monitoring of meter box and sensor faults, and the quality of multi-threshold directly affects the accuracy of fine monitoring of power distribution. In the embodiment of the present application, the multi-threshold linkage adjustment based on the specific cause of the fault includes adjustment of the first threshold for determining the authenticity of the fault, adjustment of the second threshold for determining whether the specific cause of the fault is a meter box fault, and adjustment of the third threshold for determining whether the specific cause of the fault is a sensor fault or abnormal data collected by the sensor.

[0076] Preferably, the power distribution monitoring method further comprises: correcting the first threshold, the second threshold and the third threshold according to the determined fault causes corresponding to a plurality of different periods and preset fault causes respectively. In this embodiment, the preset fault cause refers to the specific cause of the fault corresponding to a plurality of different periods of on-site investigation, and the on-site investigation results are preset in the edge intelligent monitoring terminal.

[0077] Preferably, as shown in Figure 4 the above correction of the first threshold, the second threshold and the third threshold according to the determined fault causes corresponding to a plurality of different periods and preset fault causes respectively includes steps 410 to 430.

[0078] Step 410: obtaining the monitoring error times corresponding to the first threshold, the second threshold and the third threshold according to the determined fault causes corresponding to a plurality of different periods and preset fault causes respectively.

[0079] In this embodiment, based on the abnormal data identification result, the specific cause of the fault determined in a plurality of different periods is taken as the monitoring data, and the specific cause of the fault investigated on-site in a plurality of different periods is taken as the investigation data, and then the monitoring error times corresponding to the first threshold, the second threshold and the third threshold are obtained according to the monitoring data and the investigation data. Specifically, the monitoring error times refer to the number of monitoring errors in the whole process of determining whether there is a fault in power distribution and determining the specific cause of the fault in a plurality of different periods, for example, the specific cause of the fault is determined to be abnormal data collected by the sensor, but the specific cause of the fault investigated on-site is determined to be a sensor fault, which indicates that the third threshold monitoring error, and its monitoring error times is then added by 1.

[0080] Step 420, the corresponding adjustment amplitude value is obtained according to the monitoring error number and the total monitoring number corresponding to each level threshold value. In the embodiment, the total monitoring number refers to the total number of monitoring and judgment in the whole process of determining whether the power utilization exists fault and determining the specific reason of the fault in different periods. If it is determined that the power utilization does not exist fault, the total monitoring number of the first level threshold value is accumulated by 1, and the total monitoring number of the second level threshold value and the third level threshold value is not accumulated because they do not participate in this monitoring and judgment. The total monitoring number corresponding to each level threshold value can be generated based on the identification process of abnormal data and is preset in the edge intelligent monitoring terminal. The adjustment amplitude corresponding to each level threshold value is affected by the monitoring error number and the total monitoring number. The more the monitoring error number is, the larger the adjustment amplitude is. If the proportion of the monitoring error number in the total monitoring number is larger, the adjustment amplitude is larger, and the corresponding threshold value is adjusted to a more accurate direction. The adjustment amplitude values corresponding to the first level threshold value, the second level threshold value and the third level threshold value are respectively:

[0081]

[0082] In the formula, and are the monitoring error numbers corresponding to the first level threshold value, the second level threshold value and the third level threshold value, and γ1, γ2 and γ3 are the adjustment factors corresponding to the first level threshold value, the second level threshold value and the third level threshold value.

[0083] Step 430, the first level threshold value, the second level threshold value and the third level threshold value are corrected according to the monitoring error number, the total monitoring number, the adjustment amplitude value and the preset correction model corresponding to each level threshold value.

[0084] Preferably, in one of the embodiments, the preset correction model corresponding to each level threshold value includes:

[0085]

[0086]

[0087]

[0088] In the formula, and are the first level threshold value, the second level threshold value and the third level threshold value, and are the monitoring error numbers corresponding to the first level threshold value, the second level threshold value and the third level threshold value, and are the adjustment amplitude values corresponding to the first level threshold value, the second level threshold value and the third level threshold value, and are the total monitoring numbers corresponding to the first level threshold value, the second level threshold value and the third level threshold value, is a secondary threshold value determination result indication variable, is a tertiary threshold value determination result indication variable.

[0089] The determination result refers to the specific cause of the fault determined by the power distribution and utilization monitoring method, and the survey result refers to the specific cause of the fault surveyed on site. In the embodiment, the determination result of the tertiary threshold value is used to feedback and adjust the tertiary threshold value and the secondary threshold value, and the determination result of the tertiary threshold value and the secondary threshold value is used to feedback and adjust the primary threshold value.

[0090] is set is a tertiary threshold value determination result indication variable, if the determination result is sensor failure, and the survey result is that the sensor itself is not faulty, then Otherwise If , it indicates that the tertiary threshold value is set too small, and the tertiary threshold value value should be increased; if , it indicates that the tertiary threshold value range can be further narrowed, and the monitoring accuracy of the specific cause of the fault of the sensor failure and the abnormal data collected by the sensor can be improved.

[0091] Further, if multiple survey failures occur, i.e. , it may not be a sensor failure but a meter box failure, indicating that the secondary threshold value is set too large, and the secondary threshold value value can be reduced to improve the monitoring accuracy of the specific cause of the fault of the meter box.

[0092] Further, is set is a secondary threshold value determination result indication variable, if the determination result is sensor failure, and the survey result is that the sensor itself is not faulty, then Otherwise If the determination result is that the power distribution and utilization has a fault, and the survey result is that the meter box and the sensor are both not faulty, then At this time, the primary threshold value is set too small, resulting in a false judgment in the first step, and the primary threshold value value should be increased.

[0093] In the embodiment, the first operating data anomaly is identified based on the primary threshold value, and it is determined that the power distribution and utilization has a fault, and the specific cause of the fault is further determined based on the secondary threshold value and the tertiary threshold value. The accuracy of determining whether the power distribution and utilization has a fault and the specific cause of the fault is improved, which is beneficial to improving the efficiency of operation and maintenance and data processing of the power distribution and utilization equipment, and realizing real-time and refined monitoring. At the same time, the multi-threshold value linkage is adjusted based on the specific cause of the fault identified based on the abnormal data, which avoids errors in power distribution and utilization monitoring caused by unreasonable setting of the threshold values, and is also beneficial to improving the accuracy of refined monitoring of the power distribution and utilization.

[0094] It should be understood that, although the flowcharts involved in each of the embodiments described above Figures 3-4The steps in each of the flowcharts are shown in sequence according to the arrows, but the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the steps are not strictly limited in sequence, and the steps can be executed in other sequences. Moreover, as described above, at least some of the steps in the flowcharts of the embodiments Figures 3-4 The at least some of the steps in each of the flowcharts can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of the steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least some of the other steps or steps or stages in other steps.

[0095] Embodiment Two

[0096] Based on the same inventive concept, the embodiments of the present application also provide a power distribution monitoring device for implementing the power distribution monitoring method described above. The implementation scheme of the device for solving the problem is similar to the implementation scheme described in the above method, so the specific limitations in one or more power distribution monitoring device embodiments provided below can refer to the limitations of the power distribution monitoring method described above, which will not be described here.

[0097] The embodiments of the present application also provide a power distribution monitoring device, as shown in Figure 5 The power distribution monitoring device 10 includes a sensor 11 and an edge intelligent monitoring terminal 12. The sensor 11 is configured to collect first operation data of a meter box. The edge intelligent monitoring terminal 12 is configured to obtain the first operation data of the meter box collected by the sensor 11 in a t-th period and calculate a variance of the first operation data. When the variance is greater than a first threshold value, it is determined that there is a fault in the power distribution, and an average deviation between the first operation data and preset ideal operation data of the meter box is calculated. When the average deviation is greater than a second threshold value, second operation data of the meter box in a t+1-th period to a t+p-th period is obtained and a comprehensive deviation value of the second operation data is calculated. When the comprehensive deviation value is greater than a third threshold value, it is determined that the fault cause of the power distribution is a sensor 11 fault.

[0098] In the present embodiment, the edge intelligent monitoring terminal obtains the first operation data collected by the sensor, identifies the first operation data anomaly based on the first threshold value and determines that there is a fault in the power distribution, and further determines the specific cause of the fault based on the second threshold value and the third threshold value. Based on the multi-threshold linkage, the abnormal data is identified to determine, which improves the accuracy of determining whether there is a fault in the power distribution and the specific cause of the fault, is conducive to improving the efficiency of operation and maintenance and data processing of the power distribution equipment, and realizes real-time fine monitoring.

[0099] Embodiment Three

[0100] Based on the same inventive concept, the embodiments of the present application further provide a power distribution monitoring device for implementing the power distribution monitoring method as described above. The implementation scheme of the device for solving the problem is similar to the implementation scheme described in the above method, so the specific limitations in one or more power distribution monitoring device embodiments provided below can refer to the limitations of the power distribution monitoring method described above, which will not be repeated here.

[0101] The embodiments of the present application further provide a power distribution monitoring device, as shown in Figure 6 The power distribution monitoring device 20 includes a first data acquisition module 21, a second data acquisition module 22, a third data acquisition module 23, and a first cause determination module 24. The first data acquisition module 21 is configured to acquire first operation data of a meter box collected by a sensor in a tthperiod and calculate a variance of the first operation data. The second data acquisition module 22 is configured to determine that a fault exists in the power distribution when the variance is greater than a first threshold value, and calculate an average deviation between the first operation data and preset ideal operation data of the meter box. The third data acquisition module 23 is configured to acquire second operation data of the meter box in a t+1thperiod to a pthperiod and calculate a comprehensive deviation value of the second operation data when the average deviation is greater than a second threshold value. The first cause determination module 24 is configured to determine that the fault cause of the power distribution is a sensor fault when the comprehensive deviation value is greater than a third threshold value.

[0102] Preferably, the power distribution monitoring device 20 further includes a second cause determination module configured to determine that the fault cause is a meter box fault when the average deviation is less than the second threshold value.

[0103] Preferably, the power distribution monitoring device 20 further includes a third cause determination module configured to determine that the fault cause is abnormal data collected by the sensor when the comprehensive deviation value is less than the third threshold value.

[0104] Preferably, the power distribution monitoring device 20 further includes a repetition determination module configured to repeatedly acquire the first operation data of the meter box collected by the sensor in different periods and calculate the variance to repeatedly determine the fault cause of the power distribution network.

[0105] Preferably, the power distribution monitoring device 20 further includes a threshold correction module configured to correct the first threshold value, the second threshold value, and the third threshold value according to the fault causes determined in different periods and preset fault causes, respectively.

[0106] Preferably, the threshold correction module comprises an error number obtaining unit, an adjustment range obtaining unit and a threshold correction unit. The error number obtaining unit is configured to obtain the monitoring error numbers corresponding to the first, second and third thresholds respectively according to the fault causes determined in different periods and the preset fault causes. The adjustment range obtaining unit is configured to obtain the adjustment range values corresponding to the thresholds respectively according to the monitoring error numbers corresponding to the thresholds and the total monitoring number. The threshold correction unit is configured to correct the first, second and third thresholds respectively according to the monitoring error numbers corresponding to the thresholds, the total monitoring number, the adjustment range values and a preset correction model.

[0107] The modules in the power utilization monitoring device 20 can be implemented by software, hardware or a combination thereof. The modules can be embedded in or independent of the processor of the computer device in hardware form, or stored in the memory of the computer device in software form, so as to be called and executed by the processor.

[0108] Embodiment four

[0109] The embodiment of the present application further provides a computer device, which comprises a memory and a processor, and the memory stores a computer program, and the processor implements the steps of the power utilization monitoring method when executing the computer program. Figure 7 As shown in the figure, the computer device comprises a memory and a processor, and the memory stores a computer program, and the processor implements the steps of the power utilization monitoring method when executing the computer program.

[0110] Those skilled in the art can understand that Figure 7 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can comprise more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0111] Embodiment five

[0112] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to implement the steps of the power utilization monitoring method.

[0113] Embodiment six

[0114] The embodiment of the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by the processor to implement the steps of the power utilization monitoring method.

[0115] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0116] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0117] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method of monitoring utility consumption, characterized by, The method comprises: acquiring first operation data of a meter box collected by a sensor in a tth period and calculating a variance of the first operation data; when the variance is greater than a first-level threshold value, determining that there is a fault in power distribution and utilization, and calculating an average deviation between the first operation data and preset ideal operation data of the meter box; when the average deviation is greater than a second-level threshold value, acquiring second operation data of the meter box in a t+1th period to a pth period respectively and calculating a comprehensive deviation value of the second operation data; wherein the calculation formula of the comprehensive deviation value is: wherein, denotes the overall deviation value, P denotes the span of the period, denotes the average deviation within the kth period, n is a positive integer greater than or equal to 1; when the comprehensive deviation value is greater than a third-level threshold value, determining that the fault cause of power distribution and utilization is the sensor fault.

2. The method of claim 1, wherein, The method further comprises: when the average deviation is less than the second-level threshold value, determining that the fault cause is the meter box fault.

3. The method of claim 1, wherein, The method further comprises: when the comprehensive deviation value is less than the third-level threshold value, determining that the fault cause is that the sensor collects abnormal data.

4. The method of claim 1, wherein, The method further comprises: repeatedly acquiring the first operation data of the meter box collected by the sensor in different periods and calculating the variance to repeatedly determine the fault cause of the power distribution network.

5. The method of claim 4, wherein, The method further comprises: according to the fault causes determined in multiple different periods and preset fault causes, respectively correcting the first-level threshold value, the second-level threshold value and the third-level threshold value.

6. The method of claim 5, wherein, The correction of the first-level threshold value, the second-level threshold value and the third-level threshold value according to the fault causes determined in multiple different periods and preset fault causes comprises: according to the fault causes determined in multiple different periods and preset fault causes, respectively acquiring the number of monitoring errors corresponding to the first-level threshold value, the second-level threshold value and the third-level threshold value; respectively acquiring the adjustment amplitude value corresponding to each level of threshold value according to the number of monitoring errors corresponding to each level of threshold value and the total number of monitoring; respectively correcting the first-level threshold value, the second-level threshold value and the third-level threshold value according to the number of monitoring errors corresponding to each level of threshold value, the total number of monitoring, the adjustment amplitude value and a preset correction model; wherein the adjustment amplitude value corresponding to each level of threshold value is respectively: In the formula, and γ1, γ2, γ3 are the adjustment factors corresponding to the first threshold value, the second threshold value and the third threshold value, respectively.

7. The method of claim 6, wherein, the preset correction model corresponding to each level of threshold value comprises: wherein, and are the first, second and third threshold values, respectively, and are the monitoring error numbers corresponding to the first, second and third threshold values, respectively, and are the adjustment amplitude values corresponding to the first, second and third threshold values, respectively, and are the total monitoring numbers corresponding to the first, second and third threshold values, respectively, is a second threshold value determination result indication variable, is a third threshold value determination result indication variable, is a third threshold value determination result indication variable in the kth period.

8. An electric utility monitoring device, comprising: The device comprises: a sensor for collecting first operation data of a meter box; an edge intelligent monitoring terminal for acquiring the first operation data of the meter box collected by the sensor in a tth period and calculating a variance of the first operation data, when the variance is greater than a first-level threshold value, determining that there is a fault in power distribution and utilization, and calculating an average deviation between the first operation data and preset ideal operation data of the meter box, when the average deviation is greater than a second-level threshold value, acquiring second operation data of the meter box in a t+1th period to a pth period respectively and calculating a comprehensive deviation value of the second operation data, when the comprehensive deviation value is greater than a third-level threshold value, determining that the fault cause of power distribution and utilization is the sensor fault; wherein the calculation formula of the comprehensive deviation value is: wherein, denotes the overall deviation value, P denotes the span of the period, denotes the average deviation within the kth period, n is a positive integer greater than or equal to 1.

9. A power monitoring device, comprising: The device comprises: a first data acquisition module for acquiring first operation data of a meter box collected by a sensor in a tth period and calculating a variance of the first operation data; a second data acquisition module, configured to determine that there is a fault in power distribution and utilization when the variance is greater than a first threshold, and to calculate an average deviation between the first operation data and preset ideal operation data of the meter box; a third data acquisition module, configured to acquire second operation data of the meter box in a t+1th period to a t+pth period and calculate a comprehensive deviation value of the second operation data when the average deviation is greater than a second threshold; wherein the comprehensive deviation value is calculated according to the following formula: wherein, denotes the overall deviation value, P denotes the span of the period, denotes the average deviation within the kth period, n is a positive integer greater than or equal to 1; a first cause determination module, configured to determine that the fault in power distribution and utilization is caused by the sensor fault when the comprehensive deviation value is greater than a third threshold.

10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor implements the steps of the method in any one of claims 1 to 7 when executing the computer program.

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