Power consumption data jump anomaly detection method, system, device and medium

Abstract

The application relates to the technical field of electric power, and particularly provides a power consumption collection data jump abnormality detection method, system, device and medium, aiming to solve the problem that an existing method is inaccurate in jump abnormality detection, especially cannot detect accumulated quantity data abnormality of a remote pulse type, and is prone to false positives and false negatives. To this end, the power consumption collection data jump abnormality detection method provided by the application comprises the following steps: receiving current time monitoring point data reported by a device; determining a data signal type based on the current time monitoring point data, wherein the data signal type comprises a telemetry signal, a remote signal, a remote control signal, a remote adjustment signal and a remote pulse signal; and performing data jump abnormality detection on the current time monitoring point data based on the data signal type. According to the application, different jump abnormality detection is adopted for different types of data signals, the detection accuracy is high, the false positive rate and the false negative rate can be reduced, and the accumulated quantity data abnormality of the remote pulse type can be accurately detected.

Description

Technical Field

[0001] This application relates to the field of power technology, specifically to a method, system, device, and medium for detecting abnormal fluctuations in power consumption data. Background Technology

[0002] In Advanced Metering Infrastructure (AMI) systems, various electricity consumption data acquisition devices report monitored electricity consumption data in real time through data collectors or gateways. Due to sensor failures, communication link interference, and other reasons, "jump" anomalies frequently occur in the data reported by the devices, that is, the data values ​​change drastically and without conforming to physical laws within a short period of time.

[0003] Existing technologies for detecting abrupt changes are mostly based on static threshold-based anomaly detection methods: upper and lower thresholds are configured for each monitoring point, and the data reported by the device is compared with the threshold; if the data exceeds the range, an alarm is generated.

[0004] The method has the following problems in practical applications: First, it cannot distinguish between different signal types: The signal types of data collected by devices in the AMI system include five types: telemetry (YC), remote signaling (YX), remote control (YK), remote adjustment (YT), and remote pulse (YM), collectively referred to as "five remotes." Each signal type has a different physical meaning and change pattern, and using a fixed threshold for judgment is inaccurate. Especially for the cumulative data of the remote pulse (YM) type, its value itself increases monotonically with time, and a single threshold comparison cannot identify the abnormal growth rate. Second, it lacks a dimension for detecting the rate of change: For data such as cumulative amount and pulse amount that change over time, the anomaly is often reflected in the change amplitude (i.e., rate of change or derivative) per unit time, rather than the absolute value itself. Existing methods cannot detect such anomalies. Third, the granularity of rule configuration is coarse: Anomaly detection rules are usually configured globally or by device type, which cannot be refined to each data item of each device, resulting in a high false alarm rate and a high false negative rate. Fourth, there is a lack of traceability mechanism for abnormal data: Existing methods often directly discard or correct abnormal data after detection, lacking complete abnormal data log records, which is not conducive to subsequent problem analysis and equipment maintenance.

[0005] Therefore, existing methods are inaccurate in detecting jump anomalies, especially in identifying the growth rate of abnormal cumulative data of remote pulse (YM) type, and are prone to false alarms and false negatives.

[0006] Accordingly, there is a need in the field for a new detection scheme for abnormal fluctuations in power consumption data to solve the above problems. Summary of the Invention

[0007] In order to overcome the above-mentioned shortcomings, this application is proposed to solve or at least partially solve the technical problems of existing methods being inaccurate in detecting jump anomalies, especially inability to detect cumulative data anomalies of remote pulse type, and being prone to false alarms and false negatives.

[0008] In a first aspect, a method for detecting abnormal fluctuations in power consumption data is provided, the method comprising:

[0009] Receive monitoring point data reported by the device at the current time;

[0010] The data signal type is determined based on the monitoring point data at the current time. The data signal type includes: telemetry, remote signaling, remote control, remote adjustment, and remote pulse signal.

[0011] Based on the data signal type, perform data jump anomaly detection on the monitoring point data at the current moment;

[0012] in,

[0013] When the data signal type is a remote pulse signal and the current monitoring point data is not the first time the monitoring point data is acquired, the previous monitoring point data is acquired, the data difference between the current monitoring point data and the previous monitoring point data and the corresponding time interval are determined, the data change rate is determined based on the data difference and the time interval, and if the data change rate is less than a first preset change rate threshold or greater than a second preset change rate threshold, it is determined that the current monitoring point data has a data jump abnormality, and the first preset change rate threshold is less than the second preset change rate threshold;

[0014] When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, determine whether the current monitoring point data is less than a first preset data threshold or greater than a second preset data threshold. If the current monitoring point data is less than the first preset data threshold or greater than the second preset data threshold, determine that the current monitoring point data has a data jump anomaly, and the first preset data threshold is less than the second preset data threshold.

[0015] In one technical solution of the above-mentioned method for detecting abnormal fluctuations in electricity consumption data, determining the data signal type based on the monitoring point data at the current moment includes:

[0016] Based on the monitoring point data at the current moment, determine the device information and the monitoring point information monitored by the device;

[0017] The data signal type is determined based on the monitoring point information monitored by the device.

[0018] In one technical solution of the above-mentioned method for detecting abnormal fluctuations in electricity consumption data, the method for obtaining the first preset rate of change threshold, the second preset rate of change threshold, the first preset data threshold, and the second preset data threshold includes:

[0019] The device filtering rule table is determined based on the device information of the device. The device filtering rule table includes threshold rules, which include a first preset rate of change threshold, a second preset rate of change threshold, a first preset data threshold, and a second preset data threshold.

[0020] In one technical solution of the above-mentioned method for detecting abnormal fluctuations in electricity consumption data, the method further includes:

[0021] When it is determined that the data jump anomaly occurs in the current monitoring point data, an abnormal data log object is recorded. The abnormal data log object includes the device ID, data item code, current monitoring point data, previous monitoring point data, and anomaly type.

[0022] Write the abnormal data log object to the abnormal data log table, but do not write the current monitoring point data to the database.

[0023] In one technical solution of the above-mentioned method for detecting abnormal fluctuations in electricity consumption data, the method further includes:

[0024] When the data signal type is a remote pulse signal, if the data change rate is greater than or equal to the first preset change rate threshold and less than or equal to the second preset change rate threshold, the monitoring point data at the current time is determined to be normal data.

[0025] When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, if the current monitoring point data is greater than or equal to the first preset data threshold and less than or equal to the second preset data threshold, the current monitoring point data is determined to be normal data.

[0026] In one technical solution of the above-mentioned method for detecting abnormal fluctuations in electricity consumption data, the method further includes:

[0027] When it is determined that the monitoring point data at the current time is normal data, the monitoring point data at the current time is written into the database;

[0028] And / or, when the data signal type is a remote pulse signal and the current time monitoring point data is the first monitoring point data acquired, the current time monitoring point data is written into the database.

[0029] In one technical solution of the above-mentioned method for detecting abnormal fluctuations in electricity consumption data, after receiving the monitoring point data reported by the receiving device at the current time, the method further includes:

[0030] The monitoring point data reported by the device at the current time is converted into standard data with a unified format, wherein the standard data includes the device communication address, data item code and monitoring point data value.

[0031] In a second aspect, a power consumption data fluctuation anomaly detection system is provided, the system comprising:

[0032] The data receiving module is used to receive the monitoring point data reported by the device at the current time;

[0033] The data signal type determination module is used to determine the data signal type based on the monitoring point data at the current time. The data signal type includes: telemetry, remote signaling, remote control, remote adjustment, and remote pulse signal.

[0034] The jump detection module is used to detect abnormal data jumps in the monitoring point data at the current time based on the data signal type.

[0035] in,

[0036] When the data signal type is a remote pulse signal, the monitoring point data of the previous moment is acquired, the data difference between the monitoring point data of the current moment and the monitoring point data of the previous moment and the corresponding time interval are determined, the data change rate is determined based on the data difference and the time interval, and if the data change rate is less than a first preset change rate threshold or greater than a second preset change rate threshold, it is determined that the monitoring point data of the current moment has a data jump abnormality, and the first preset change rate threshold is less than the second preset change rate threshold.

[0037] When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, determine whether the current monitoring point data is less than a first preset data threshold or greater than a second preset data threshold. If the current monitoring point data is less than the first preset data threshold or greater than the second preset data threshold, determine that the current monitoring point data has a data jump anomaly, and the first preset data threshold is less than the second preset data threshold.

[0038] In a third aspect, an electronic device is provided, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, which, when executed by the at least one processor, implements the method described in any of the above-described technical solutions for detecting abnormal fluctuations in power consumption data acquisition.

[0039] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, the program codes being adapted to be loaded and run by a processor to perform the method described in any of the technical solutions of the above-described method for detecting abnormal fluctuations in power consumption data collection.

[0040] The above-described technical solutions of this application have at least one or more of the following beneficial effects:

[0041] In implementing the power consumption data jump anomaly detection method provided in this application, the method involves receiving monitoring point data reported by the receiving device at the current moment; determining the data signal type based on the monitoring point data at the current moment, where the data signal type includes: telemetry, remote signaling, remote control, remote adjustment, and remote pulse signals; performing data jump anomaly detection on the monitoring point data at the current moment based on the data signal type; wherein, when the data signal type is a remote pulse signal and the monitoring point data at the current moment is not the first time the monitoring point data is acquired, the monitoring point data at the previous moment is acquired, the data difference between the monitoring point data at the current moment and the monitoring point data at the previous moment and the corresponding time interval are determined, and the data change rate is determined based on the data difference and the time interval; if the data change rate is less than a first preset change rate threshold or greater than a certain threshold, the method proceeds accordingly. When the data changes at the second preset rate of change threshold, it is determined that the data at the current monitoring point has an abnormal data jump, and the first preset rate of change threshold is less than the second preset rate of change threshold. When the data signal type is a telemetry, remote signaling, remote control, or remote adjustment signal, it is determined whether the data at the current monitoring point is less than the first preset data threshold or greater than the second preset data threshold. If the data at the current monitoring point is less than the first preset data threshold or greater than the second preset data threshold, it is determined that the data at the current monitoring point has an abnormal data jump, and the first preset data threshold is less than the second preset data threshold. Based on this application, different jump anomaly detection methods are used for different types of data signals, resulting in high detection accuracy, reduced false alarm rate and false negative rate, and accurate detection of cumulative data anomalies of the remote pulse type. Attached Figure Description

[0042] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Wherein:

[0043] Figure 1 This is a schematic flowchart of the main steps of a power consumption data jump anomaly detection method according to an embodiment of this application;

[0044] Figure 2 This is a detailed flowchart illustrating the steps of a method for detecting abnormal fluctuations in power consumption data collection according to an embodiment of this application.

[0045] Figure 3This is a schematic diagram of the main modules of a power consumption data jump anomaly detection system according to an embodiment of this application;

[0046] Figure 4 This is a schematic diagram of the main structure of an electronic device according to an embodiment of this application.

[0047] Figure label:

[0048] 31: Data receiving module; 32: Data signal type determination module; 33: Transition detection module; 41: Memory; 42: Processor. Detailed Implementation

[0049] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0050] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and may also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0051] In the embodiments of this application, it should be noted that, in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0052] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0053] In the description of the embodiments of this application, the words "example" or "for example" are used to indicate exemplification, illustration, or description. Any embodiment or design described as "example" or "for example" in the embodiments of this application is not to be construed as being more preferred or having more advantages than another embodiment or design. The use of the words "example" or "for example" is intended to present relative concepts in a clear manner.

[0054] Here we will first explain some of the terms used in this application.

[0055] Remote signaling (YX) refers to remotely acquiring switch status signals; remote measurement (YC) refers to remotely measuring analog signals; remote control (YK) refers to remotely controlling switch signals; remote adjustment (YT) refers to remotely adjusting analog signals; and remote pulse (YM), as a special form of telemetry, is mainly used for pulse counting (such as electrical energy accumulation), and its numerical conversion rules may follow the integer or floating-point type of telemetry.

[0056] Data change rate: refers to a parameter used to characterize the magnitude of change of cumulative data such as remote pulse signals per unit time; in a physical sense, it reflects the growth rate of the cumulative amount over a period of time; it can be determined by dividing the data difference between the monitoring point data at the current moment and the monitoring point data at the previous moment by the corresponding time interval; by monitoring the data change rate, abnormal sudden increases or stagnation of data values ​​that do not conform to physical laws can be effectively identified.

[0057] An abnormal data log object is a data structure used to record abnormal data change events. When an abnormal data change is detected at a monitoring point, an abnormal data log object is created. It can include fields such as device ID, data item code, current monitoring point data, previous monitoring point data, and abnormality type (e.g., data change rate is less than a first preset change rate threshold or greater than a second preset change rate threshold, data is less than a first preset data threshold or greater than a second preset data threshold). It can also include data type. This facilitates subsequent tracing, statistics, and analysis of abnormal data changes, and provides a basis for equipment fault diagnosis and system operation and maintenance.

[0058] Abnormal data log table: includes multiple abnormal data log objects.

[0059] Database: Used to store monitoring point data at different times. When detecting anomalies in subsequent data changes, the monitoring point data from the previous time can be retrieved from the database.

[0060] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a power consumption data abrupt change anomaly detection method according to an embodiment of this application. Figure 1 As shown, the power consumption data jump anomaly detection method in this application embodiment mainly includes the following steps S101 to S103.

[0061] Step S101: Receive the monitoring point data reported by the device at the current time;

[0062] Step S102: Determine the data signal type based on the monitoring point data at the current time. The data signal types include: telemetry, remote signaling, remote control, remote adjustment, and remote pulse signal.

[0063] Step S103: Detect data jump anomalies in the monitoring point data at the current moment based on the data signal type;

[0064] in,

[0065] When the data signal type is a remote pulse signal and the current monitoring point data is not the first time the monitoring point data is acquired, the monitoring point data of the previous time is acquired, the data difference between the current monitoring point data and the previous monitoring point data and the corresponding time interval are determined, and the data change rate is determined based on the data difference and the time interval. If the data change rate is less than the first preset change rate threshold or greater than the second preset change rate threshold, it is determined that the current monitoring point data has a data jump abnormality. The first preset change rate threshold is less than the second preset change rate threshold.

[0066] When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, determine whether the current monitoring point data is less than the first preset data threshold or greater than the second preset data threshold. If the current monitoring point data is less than the first preset data threshold or greater than the second preset data threshold, determine that the current monitoring point data has a data jump abnormality, and the first preset data threshold is less than the second preset data threshold.

[0067] Based on the methods described in steps S101 to S103 above, different jump anomaly detection strategies are adopted for different types of data signals. For remote pulse signals, rate of change detection is used, and for other signals, threshold detection is used. This can more accurately identify data jump anomalies of different data signal types, with high detection accuracy and significantly reduced false alarm and false negative rates. At the same time, for cumulative data such as remote pulse signals that monotonically increase over time, by calculating the data rate of change and comparing it with the rate of change threshold, abnormal jumps (abnormal increases or decreases) in the growth rate can be effectively and accurately detected, making up for the deficiency of existing technologies that cannot detect such anomalies by relying solely on absolute value thresholds.

[0068] The following describes an embodiment of the power consumption data jump anomaly detection method provided in this application, specifically step S101.

[0069] In one embodiment of step S101 above, in the Advanced Metering Infrastructure (AMI) system, various electricity consumption data acquisition devices are set up at different monitoring points to monitor the system and report the electricity consumption data at the monitoring points in real time at preset time intervals. The monitoring point data is the electricity consumption data at the monitoring point. For example, the various electricity consumption data acquisition devices may include smart meters, data collectors, or various sensors, and the preset time interval may be 1 second.

[0070] In one embodiment of step S101 above, the data structures reported by devices from different manufacturers and models are different. Therefore, after receiving the current monitoring point data reported by the device, the method further includes:

[0071] The monitoring point data reported by the device at the current time is converted into standard data with a unified format. The standard data includes the device communication address, data item code, and monitoring point data value.

[0072] The following describes an embodiment of the power consumption data jump anomaly detection method provided in this application, specifically step S102.

[0073] In one embodiment of step S102 above, determining the data signal type based on the monitoring point data at the current time includes:

[0074] The equipment information and the monitoring point information are determined based on the monitoring point data at the current moment;

[0075] The data signal type is determined based on the monitoring point information from the equipment monitoring.

[0076] Specifically, the process involves querying the equipment information table to obtain equipment metadata (including equipment ID, model, etc.) and querying the equipment category code. Based on the equipment communication address and data item code in the standard data, the equipment information is obtained, including equipment ID, equipment model, equipment category, etc. The process then determines whether the equipment information matches the equipment metadata and equipment category code. If they match, it indicates that the equipment reporting the monitoring point data is a device in the Advanced Metering Infrastructure (AMI) system. After confirming the match, the monitoring point information corresponding to the equipment is determined based on the equipment communication address. The monitoring point information includes the monitoring point name, data signal type, etc. At the same time, a mapping relationship between the equipment ID and the monitoring point can be constructed. Based on the mapping relationship, the monitoring point information corresponding to the equipment can be quickly found after obtaining the equipment ID. Finally, the data signal type is determined based on the monitoring point information monitored by the equipment.

[0077] In one embodiment of step S102 above, after obtaining the device information, the method further includes:

[0078] The device filtering rule table is determined based on the device information. The device filtering rule table includes threshold rules, which include a first preset rate of change threshold, a second preset rate of change threshold, a first preset data threshold, and a second preset data threshold.

[0079] Based on this, the signal type of each monitoring point data of each device can be determined, and the data change rate and upper and lower limit thresholds of the data can be configured. The configuration is flexible and highly adaptable, and it can more accurately identify data jump anomalies of different data signal types. The detection accuracy is high, and the false alarm rate and false alarm rate are significantly reduced.

[0080] The following describes an embodiment of the power consumption data jump anomaly detection method provided in this application, specifically step S103.

[0081] In one embodiment of step S103 above, the standard data includes monitoring point data values. Therefore, abnormal data jumps in the monitoring point data values ​​can be detected based on the data signal type.

[0082] In one embodiment of step S103 above, when the data signal type is a remote pulse signal and the current monitoring point data is the first monitoring point data obtained, the current monitoring point data is written into the database. When the monitoring point data reported by the device is received at the next moment and the data signal type is a remote pulse signal, the first monitoring point data obtained can be retrieved from the database, the rate of change between the two data is obtained and compared with the first preset rate of change threshold and the second preset rate of change threshold to determine whether there is a data jump abnormality.

[0083] When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, and the current monitoring point data is the first monitoring point data acquired, it can be directly compared with the first preset data threshold and the second preset data threshold to determine whether there is an abnormal data jump.

[0084] For example, when the data signal type is a remote pulse signal and the current monitoring point data is not the first time the monitoring point data has been acquired, if the current meter pulse count is 5060 (i.e., the current monitoring point data), the previous meter pulse count was 5000, the time interval between the previous and current times is 30 minutes, the first preset change rate threshold is 10 pulses / hour, and the second preset change rate threshold is 30 pulses / hour, the data change rate at this time is 120 pulses / hour, which is greater than the second preset change rate threshold, thus causing a data jump anomaly; when the data signal type is a telemetry, remote signaling, remote control, or remote adjustment signal, if the current temperature is 85℃, the first preset data threshold is 30℃, and the second preset data threshold is 80℃, the current temperature is greater than the second preset data threshold, thus causing a data jump anomaly.

[0085] In one embodiment of step S103 above, the method further includes:

[0086] When it is determined that there is an abnormal data jump in the current monitoring point data, an abnormal data log object is recorded. The abnormal data log object includes the device ID, data item code, current monitoring point data, previous monitoring point data, and abnormality type (e.g., data change rate is less than the first preset change rate threshold or greater than the second preset change rate threshold, data is less than the first preset data threshold or greater than the second preset data threshold). The data item code is the data item code included in the standard data.

[0087] Write the abnormal data log object to the abnormal data log table, and do not write the current monitoring point data to the database.

[0088] For example, the database can be a time-series database InfluxDB or Redis (Remote DictionaryServer, an open-source, network-enabled, in-memory or persistent log-structured key-value database written in ANSI C).

[0089] Based on this, abnormal data is automatically recorded in a complete log while being isolated from the database. This prevents abnormal data from contaminating the normal database, ensuring the purity and high quality of data in the main database, and providing a complete traceability basis for subsequent fault analysis and system maintenance.

[0090] In one embodiment of step S103 above, the method further includes:

[0091] When the data signal type is a remote pulse signal, if the data change rate is greater than or equal to the first preset change rate threshold and less than or equal to the second preset change rate threshold, the data at the monitoring point at the current time is judged to be normal data.

[0092] When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, if the current monitoring point data is greater than or equal to the first preset data threshold and less than or equal to the second preset data threshold, the current monitoring point data is judged to be normal data.

[0093] For example, when the data signal type is a remote pulse signal and the current monitoring point data is not the first time the monitoring point data has been acquired, if the current meter pulse count is 5008 (i.e., the current monitoring point data), the previous meter pulse count was 5000, the time interval between the previous and current times is 30 minutes, the first preset change rate threshold is 10 pulses / hour, the second preset change rate threshold is 30 pulses / hour, and the data change rate at this time is 16 pulses / hour, which is greater than the first preset change rate threshold and less than the second preset change rate threshold. Therefore, the current monitoring point data is normal data. When the data signal type is a telemetry, remote signaling, remote control, or remote adjustment signal, if the current temperature is 60℃, the first preset data threshold is 30℃, the second preset data threshold is 80℃, the current temperature is greater than the first preset data threshold and less than the second preset data threshold. Therefore, the current monitoring point data is normal data.

[0094] Furthermore, the method also includes:

[0095] When the monitoring point data at the current moment is determined to be normal, the monitoring point data at the current moment is written into the database. When the monitoring point data reported by the device is received at the next moment and the data signal type is remote pulse signal, the monitoring point data at the current moment can be retrieved from the database to detect data jump anomalies.

[0096] In one application scenario according to an embodiment of this application, see appendix. Figure 2 , Figure 2 This is a detailed flowchart illustrating the steps of a method for detecting abnormal fluctuations in power consumption data collection according to an embodiment of this application. Figure 2 As shown, the method for detecting abnormal fluctuations in power consumption data in this embodiment includes the following steps:

[0097] Step S201: Receive the monitoring point data reported by the device at the current time.

[0098] Step S202: Convert the current monitoring point data reported by the device into standard data with a unified format. The standard data includes the device communication address, data item code, and monitoring point data value.

[0099] Step S203: Determine the equipment information and the monitoring point information of the equipment based on the monitoring point data at the current time.

[0100] Step S204: Determine the data signal type based on the monitoring point information of the device monitoring, and determine the device filtering rule table based on the device information. The device filtering rule table includes threshold rules, which include a first preset rate of change threshold, a second preset rate of change threshold, a first preset data threshold, and a second preset data threshold.

[0101] Step S205: Detect data jump anomalies in the monitoring point data at the current time based on the data signal type, and determine whether the data signal type is a remote pulse signal;

[0102] in,

[0103] When the data signal type is a remote pulse signal and the current monitoring point data is not the first time the monitoring point data has been acquired, the monitoring point data of the previous time is acquired, the data difference between the current monitoring point data and the previous monitoring point data and the corresponding time interval are determined, and the data change rate is determined based on the data difference and the time interval. If the data change rate is less than the first preset change rate threshold or greater than the second preset change rate threshold, it is determined that the current monitoring point data has a data jump abnormality, and the first preset change rate threshold is less than the second preset change rate threshold; if the data change rate is greater than or equal to the first preset change rate threshold and less than or equal to the second preset change rate threshold, it is determined that the current monitoring point data is normal data.

[0104] When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, determine whether the current monitoring point data is less than the first preset data threshold or greater than the second preset data threshold. If the current monitoring point data is less than the first preset data threshold or greater than the second preset data threshold, it is determined that the current monitoring point data has an abnormal data jump, and the first preset data threshold is less than the second preset data threshold. If the current monitoring point data is greater than or equal to the first preset data threshold and less than or equal to the second preset data threshold, it is determined that the current monitoring point data is normal data.

[0105] Step S206: When it is determined that the monitoring point data at the current moment has a data jump anomaly, an abnormal data log object is recorded. The abnormal data log object includes the device ID, data item code, monitoring point data at the current moment, monitoring point data at the previous moment, and anomaly type. The abnormal data log object is written to the abnormal data log table, and the monitoring point data at the current moment is not written to the database. When it is determined that the monitoring point data at the current moment is normal data, the monitoring point data at the current moment is written to the database. When the monitoring point data reported by the device is received at the next moment and the data signal type is remote pulse signal, the monitoring point data at the current moment can be retrieved from the database to perform data jump anomaly detection.

[0106] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in this application and therefore will also fall within the protection scope of this application.

[0107] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0108] See appendix Figure 3 , Figure 3 This is a schematic diagram of the main modules of a power consumption data fluctuation anomaly detection system according to an embodiment of this application. Figure 3 As shown, another aspect of this application provides a power consumption data fluctuation anomaly detection system, the system comprising:

[0109] Data receiving module 31 is used to receive the monitoring point data reported by the device at the current time;

[0110] The data signal type determination module 32 is used to determine the data signal type based on the monitoring point data at the current time. The data signal types include: telemetry, remote signaling, remote control, remote adjustment, and remote pulse signal.

[0111] Jump detection module 33 is used to detect data jump anomalies in the monitoring point data at the current time based on the data signal type;

[0112] in,

[0113] When the data signal type is a remote pulse signal, the monitoring point data of the previous moment is obtained, the data difference between the monitoring point data of the current moment and the monitoring point data of the previous moment and the corresponding time interval are determined, and the data change rate is determined based on the data difference and the time interval. If the data change rate is less than the first preset change rate threshold or greater than the second preset change rate threshold, it is determined that the monitoring point data of the current moment has a data jump abnormality. The first preset change rate threshold is less than the second preset change rate threshold.

[0114] When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, determine whether the current monitoring point data is less than the first preset data threshold or greater than the second preset data threshold. If the current monitoring point data is less than the first preset data threshold or greater than the second preset data threshold, determine that the current monitoring point data has a data jump abnormality, and the first preset data threshold is less than the second preset data threshold.

[0115] Another aspect of this application provides a computer-readable storage medium.

[0116] In one embodiment of a computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program for performing the power consumption data jump anomaly detection method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described power consumption data jump anomaly detection method. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be a storage device comprising various electronic devices. Optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0117] Another aspect of this application provides an electronic device.

[0118] In one embodiment of an electronic device according to this application, the electronic device may include at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program that, when executed by the at least one processor, implements the methods described in any of the above embodiments. See Appendix Figure 4 , Figure 4 The example shows a memory 41 and a processor 42 connected via a bus communication connection.

[0119] In some embodiments of this application, the electronic device may further include at least one sensor for sensing information. The sensor is communicatively connected to any type of processor mentioned in this application. The processor communicates with the sensor to perform the methods described in any of the above embodiments. The electronic device described in this application may be, but is not limited to, mobile phones, tablets, desktop computers, laptops, handheld computers, notebook computers, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc., and this application does not limit the scope of the application.

[0120] The technical solution of this application has been described above with reference to one embodiment shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A method for detecting abnormal fluctuations in power consumption data, characterized in that, The method includes: Receive monitoring point data reported by the device at the current time; The data signal type is determined based on the monitoring point data at the current time. The data signal type includes: telemetry, remote signaling, remote control, remote adjustment, and remote pulse signal. Based on the data signal type, perform data jump anomaly detection on the monitoring point data at the current moment; in, When the data signal type is a remote pulse signal and the current monitoring point data is not the first time the monitoring point data is acquired, the previous monitoring point data is acquired, the data difference between the current monitoring point data and the previous monitoring point data and the corresponding time interval are determined, the data change rate is determined based on the data difference and the time interval, and if the data change rate is less than a first preset change rate threshold or greater than a second preset change rate threshold, it is determined that the current monitoring point data has a data jump abnormality, and the first preset change rate threshold is less than the second preset change rate threshold; When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, determine whether the current monitoring point data is less than a first preset data threshold or greater than a second preset data threshold. If the current monitoring point data is less than the first preset data threshold or greater than the second preset data threshold, determine that the current monitoring point data has a data jump anomaly, and the first preset data threshold is less than the second preset data threshold.

2. The method according to claim 1, characterized in that, The determination of the data signal type based on the monitoring point data at the current time includes: Based on the monitoring point data at the current moment, determine the device information and the monitoring point information monitored by the device; The data signal type is determined based on the monitoring point information monitored by the device.

3. The method according to claim 2, characterized in that, The methods for obtaining the first preset rate of change threshold, the second preset rate of change threshold, the first preset data threshold, and the second preset data threshold include: The device filtering rule table is determined based on the device information of the device. The device filtering rule table includes threshold rules, which include a first preset rate of change threshold, a second preset rate of change threshold, a first preset data threshold, and a second preset data threshold.

4. The method according to claim 1, characterized in that, The method further includes: When it is determined that the data jump anomaly occurs in the current monitoring point data, an abnormal data log object is recorded. The abnormal data log object includes the device ID, data item code, current monitoring point data, previous monitoring point data, and anomaly type. Write the abnormal data log object to the abnormal data log table, but do not write the current monitoring point data to the database.

5. The method according to claim 1, characterized in that, The method further includes: When the data signal type is a remote pulse signal, if the data change rate is greater than or equal to the first preset change rate threshold and less than or equal to the second preset change rate threshold, the monitoring point data at the current time is determined to be normal data. When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, if the current monitoring point data is greater than or equal to the first preset data threshold and less than or equal to the second preset data threshold, the current monitoring point data is determined to be normal data.

6. The method according to claim 5, characterized in that, The method further includes: When it is determined that the monitoring point data at the current time is normal data, the monitoring point data at the current time is written into the database; And / or, when the data signal type is a remote pulse signal and the current time monitoring point data is the first monitoring point data acquired, the current time monitoring point data is written into the database.

7. The method according to claim 1, characterized in that, After receiving the current monitoring point data reported by the receiving device, the method further includes: The monitoring point data reported by the device at the current time is converted into standard data with a unified format, wherein the standard data includes the device communication address, data item code and monitoring point data value.

8. A power consumption data jump anomaly detection system, characterized in that, The system includes: The data receiving module is used to receive the monitoring point data reported by the device at the current time; The data signal type determination module is used to determine the data signal type based on the monitoring point data at the current time. The data signal type includes: telemetry, remote signaling, remote control, remote adjustment, and remote pulse signal. The jump detection module is used to detect abnormal data jumps in the monitoring point data at the current time based on the data signal type. in, When the data signal type is a remote pulse signal, the monitoring point data of the previous moment is acquired, the data difference between the monitoring point data of the current moment and the monitoring point data of the previous moment and the corresponding time interval are determined, the data change rate is determined based on the data difference and the time interval, and if the data change rate is less than a first preset change rate threshold or greater than a second preset change rate threshold, it is determined that the monitoring point data of the current moment has a data jump abnormality, and the first preset change rate threshold is less than the second preset change rate threshold. When the data signal type is telemetry, telesignaling, remote control, or remote adjustment signal, determine whether the current monitoring point data is less than a first preset data threshold or greater than a second preset data threshold. If the current monitoring point data is less than the first preset data threshold or greater than the second preset data threshold, determine that the current monitoring point data has a data jump anomaly, and the first preset data threshold is less than the second preset data threshold.

9. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores a computer program, which, when executed by the at least one processor, implements the power consumption data jump anomaly detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the power consumption data jump anomaly detection method according to any one of claims 1 to 7.

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