CN111178645A - Power equipment abnormality detection method and device, control equipment and storage medium - Google Patents

Power equipment abnormality detection method and device, control equipment and storage medium Download PDF

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Publication number
CN111178645A
CN111178645A CN202010288404.0A CN202010288404A CN111178645A CN 111178645 A CN111178645 A CN 111178645A CN 202010288404 A CN202010288404 A CN 202010288404A CN 111178645 A CN111178645 A CN 111178645A
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target
data
adjustment amount
power
prediction
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CN111178645B (en
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周泰岳
李英熔
闫科
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Shenzhen Zhaoyanghui Electrical Equipment Co ltd
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Shenzhen Zhaoyanghui Electrical Equipment Co ltd
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/903Querying
    • G06F16/90335Query processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0639Performance analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply

Abstract

The application provides a method and a device for detecting the abnormality of electric power equipment, control equipment and a storage medium, which relate to the technical field of abnormality detection of electric power systems, and are characterized in that a plurality of historical electric power data corresponding to target electric power equipment at a target moment are utilized to obtain first prediction data corresponding to the target electric power equipment; then, acquiring a predicted adjustment amount corresponding to the target electric equipment by using the electric power data of the target electric equipment at the last moment of the target moment; the first prediction data are updated by using the set target prediction data and the prediction adjustment amount to obtain updated second prediction data, so that when the difference value between the target power data and the second prediction data exceeds a set threshold value, the target power utilization equipment can be determined to have power utilization abnormity; compared with the prior art, the target electric equipment can be dynamically combined to detect the current working state of the target electric equipment according to the actual electric information in the historical time period, so that the detection precision is improved.

Description

Power equipment abnormality detection method and device, control equipment and storage medium
Technical Field
The present disclosure relates to the field of power system anomaly detection technologies, and in particular, to a method and an apparatus for detecting an anomaly of a power device, a control device, and a storage medium.
Background
In some manufacturing enterprises, with the increase of the manufacturing equipment, the working state of the manufacturing equipment generally needs to be monitored so as to ensure that the manufacturing equipment can work stably.
In some common monitoring modes, the power data of the production equipment can be detected, so that whether the production equipment works abnormally or not can be judged according to the respective power data of each production equipment.
However, some current detection methods are not flexible enough, and do not consider the actual working state of the production equipment, thereby resulting in low detection accuracy.
Disclosure of Invention
The application aims to provide a power equipment abnormality detection method, a power equipment abnormality detection device, a control device and a storage medium, which can dynamically detect the current working state of a target electric equipment by combining actual electric information of the target electric equipment in a historical time period, so that the detection precision is improved.
In order to achieve the purpose, the technical scheme adopted by the application is as follows:
in a first aspect, the present application provides a method for detecting an abnormality of an electrical device, which is applied to a control device in an electrical operating system, where the electrical operating system further includes a plurality of electrical devices that respectively establish communication with the control device; the method comprises the following steps:
receiving target power data sent by target electric equipment at a target moment; the target electric equipment is any one of the plurality of electric equipment;
obtaining first prediction data corresponding to the target electric equipment according to first historical electric power data corresponding to the target electric equipment; the first historical power data is a plurality of historical power data corresponding to the target electric equipment in the past days at the target time;
obtaining a predicted adjustment amount corresponding to the target electric equipment according to second historical electric power data corresponding to the target electric equipment; the second historical power data is power data of the target electric equipment at a time before the target time on the current day;
updating the first prediction data by using the set target prediction data and the prediction adjustment amount to obtain updated second prediction data;
and when the difference value between the target power data and the second prediction data exceeds a set threshold value, determining that the target electric equipment has power utilization abnormity.
In a second aspect, the present application provides an apparatus for detecting an abnormality of an electrical device, which is applied to a control device in an electrical operating system, where the electrical operating system further includes a plurality of electrical devices that respectively establish communication with the control device; the device comprises:
the receiving module is used for receiving target power data sent by the target power utilization equipment at a target moment; the target electric equipment is any one of the plurality of electric equipment;
the processing module is used for obtaining first prediction data corresponding to the target electric equipment according to first historical electric power data corresponding to the target electric equipment; the first historical power data is a plurality of historical power data corresponding to the target electric equipment in the past days at the target time;
the processing module is further used for obtaining a predicted adjustment amount corresponding to the target electric equipment according to second historical electric power data corresponding to the target electric equipment; the second historical power data is power data of the target electric equipment at a time before the target time on the current day;
the processing module is further used for updating the first prediction data by using set target prediction data and the prediction adjustment amount to obtain updated second prediction data;
the detection module is used for determining that the target electric equipment has power utilization abnormity when the difference value between the target power data and the second prediction data exceeds a set threshold value.
In a third aspect, the present application provides a control apparatus comprising a memory for storing one or more programs; a processor; the one or more programs, when executed by the processor, implement the power equipment anomaly detection method described above.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described power equipment abnormality detection method.
According to the method, the device, the control equipment and the storage medium for detecting the abnormity of the power equipment, a plurality of historical power data corresponding to the target power equipment at the target moment are utilized, so that first prediction data corresponding to the target power equipment are obtained; then, acquiring a predicted adjustment amount corresponding to the target electric equipment by using the electric power data of the target electric equipment at the last moment of the target moment; the first prediction data are updated by using the set target prediction data and the prediction adjustment amount to obtain updated second prediction data, so that when the difference value between the target power data and the second prediction data exceeds a set threshold value, the target power utilization equipment can be determined to have power utilization abnormity; compared with the prior art, the target electric equipment can be dynamically combined to detect the current working state of the target electric equipment according to the actual electric information in the historical time period, so that the detection precision is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly explain the technical solutions of the present application, the drawings needed for the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also derive other related drawings from these drawings without inventive effort.
Fig. 1 illustrates a schematic application scenario diagram of the anomaly detection method for electrical equipment provided by the present application;
FIG. 2 is a schematic block diagram of a control device provided in the present application;
FIG. 3 illustrates a schematic flow chart diagram of a power equipment anomaly detection method provided by the present application;
FIG. 4 shows a schematic flow diagram of sub-steps of step 207 of FIG. 3;
FIG. 5 shows a schematic flow diagram of sub-steps of step 207-1 in FIG. 4;
FIG. 6 shows a schematic flow diagram of sub-steps of step 205 of FIG. 3;
FIG. 7 shows a schematic block flow diagram of sub-steps of step 205-2 of FIG. 6;
FIG. 8 illustrates another schematic flow diagram of a power device anomaly detection method provided herein;
fig. 9 shows a schematic block diagram of an electrical equipment abnormality detection apparatus provided in the present application.
In the figure: 100-a control device; 101-a memory; 102-a processor; 103-a communication interface; 300-electrical equipment abnormality detection means; 301-a receiving module; 302-a processing module; 303-detection module.
Detailed Description
To make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in some embodiments of the present application, and it is obvious that the described embodiments are some, but not all embodiments of the present application. The components of the present application, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as presented in the figures, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments obtained by a person of ordinary skill in the art based on a part of the embodiments in the present application without any creative effort belong to the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In the above scenario of detecting the power data of the production equipment, some common monitoring methods are to fixedly set an abnormal threshold, and compare the actual power data of the production equipment with the abnormal threshold, so as to determine whether the production equipment is abnormal in operation.
However, this detection method merely compares the power data of the production equipment with the abnormal threshold value mechanically, and ignores the actual operating state of the production equipment in the historical time period, so that the detection accuracy is low, and false detection often occurs.
Therefore, based on the above drawbacks, the present application provides a possible implementation manner as follows: obtaining first prediction data corresponding to the target electric equipment by utilizing a plurality of historical electric power data corresponding to the target electric equipment at the target moment; then, acquiring a predicted adjustment amount corresponding to the target electric equipment by using the electric power data of the target electric equipment at the last moment of the target moment; the first prediction data are updated by using the set target prediction data and the prediction adjustment amount to obtain updated second prediction data, so that when the difference value between the target power data and the second prediction data exceeds a set threshold value, the target power utilization equipment can be determined to have power utilization abnormity; the current working state of the target electric equipment is detected by dynamically combining the actual electric information of the target electric equipment in the historical time period, so that the detection precision of the working state of the target electric equipment can be improved.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic application scenario diagram illustrating an abnormal detection method for an electrical device according to the present application. In some embodiments of the present application, the control device may be located in a wireless network or a wired network with the plurality of electric terminals, and the control device may establish communication with the plurality of electric devices for data interaction through the wireless network or the wired network; for example, the electric device may upload its own power data to the control device, and the control device may send a control instruction to the electric device to control the electric device to perform a corresponding operation.
In some embodiments of the present application, the control device may be a mobile terminal device, which may include, for example, a smart phone, a Personal Computer (PC), a tablet computer, a handheld controller, and the like; of course, the control device may also be a server.
The method for detecting the abnormality of the electrical equipment can be applied to the control equipment shown in fig. 1, an application program can be installed in the control equipment, corresponds to an electricity utilization terminal and is used for providing services for users, and the method for detecting the abnormality of the electrical equipment can be realized through the application program installed in the control equipment.
Referring to fig. 2, fig. 2 shows a schematic block diagram of a control device 100 provided in the present application, and in an embodiment, the control device 100 may include a memory 101, a processor 102, and a communication interface 103, where the memory 101, the processor 102, and the communication interface 103 are electrically connected to each other directly or indirectly to implement data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
The memory 101 may be used to store software programs and modules, such as program instructions/modules corresponding to the power equipment abnormality detection apparatus provided in the present application, and the processor 102 executes the software programs and modules stored in the memory 101 to execute various functional applications and data processing, thereby executing the steps of the power equipment abnormality detection method provided in the present application. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Programmable Read-Only Memory (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The processor 102 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
It will be appreciated that the configuration shown in fig. 2 is merely illustrative and that the control device 100 may also include more or fewer components than shown in fig. 2 or have a different configuration than shown in fig. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof, which is not limited in this application.
As such, the power equipment abnormality detection method provided by the present application is exemplarily described below with the control apparatus 100 shown in fig. 2 as a schematically executing subject.
Referring to fig. 3, fig. 3 shows a schematic flow chart of an electrical equipment abnormality detection method provided in the present application, where the electrical equipment abnormality detection method may include the following steps:
step 201, receiving target power data sent by target electric equipment at a target time;
step 203, obtaining first prediction data corresponding to the target electric equipment according to the first historical electric power data corresponding to the target electric equipment;
step 205, obtaining a predicted adjustment amount corresponding to the target electric equipment according to the second historical electric power data corresponding to the target electric equipment;
step 207, updating the first prediction data by using the set target prediction data and the prediction adjustment amount to obtain updated second prediction data;
and step 209, when the difference value between the target power data and the second prediction data exceeds a set threshold value, determining that the target electric equipment has power utilization abnormity.
In an embodiment, the electric device may report its own power data, such as current data, voltage data, electric power data, etc., to the control device at a set time interval; accordingly, the control device may record the power data reported by each of the power consumption devices in units of date and time.
For example, assuming that the electrical equipment reports the power data every other hour, the control device may record 24 power data corresponding to each electrical equipment every day for each electrical equipment. Taking 100 electric devices connected to the control device as an example, the control device may utilize the array ak,i,jRecording each power data; wherein the content of the first and second substances,ican represent 100 electric devicesFirst, theiThe power utilization equipment is used for supplying power to the power utilization equipment,iis any positive integer less than 100;jcan represent the firstjThe number of days is,jis any positive integer;kcan represent the firstkThe data of the electricity consumption is stored in the storage device,kis any natural number; thus, Ak,i,jCan indicate thatiThe electric equipment is arranged atjThe first daykAnd electricity consumption data.
Therefore, taking any one of the plurality of electric devices connected to the control device as an example of the target electric device, when determining whether or not the target electric device has an electric abnormality, the control device may receive the target electric power data transmitted by the target electric device at the target time with the current time as the target time when receiving the electric power data transmitted by each electric device.
Then, the control device may use a plurality of historical power data corresponding to the target electrical device at the target time as first historical power data according to the stored power data of all the electrical devices, so as to obtain first prediction data corresponding to the target electrical device according to the first historical power data corresponding to the target electrical device, where the first prediction data represents the power data of the target electrical device at the target time predicted by the control device.
For example, in connection with the above example, assuming that the 50 th electric device of the 100 electric devices is used as the target electric device, the current statistical number of days is the 100 th day, and the target time is the 10 th time of the 100 th day, the target power data received by the control device may be denoted as a10,50,100And the first historical power data may include a10,50,95、A10,50,96、A10,50,97、A10,50,98、A10,50,99(ii) a Thus, the control device may utilize A10,50,95、A10,50,96、A10,50,97、A10,50,98、A10,50,99Performing data processing to obtain first predicted data of the 50 th electric device at the 10 th time on the 100 th day, for example, the first predicted data may be denoted as P10,50,100
In addition, as a possible implementation manner, in executing step 203, the control apparatus may perform numerical fitting using a plurality of historical power data included in the first historical power data, for example, fitting using a least squares algorithm, to obtain the first prediction data.
Therefore, through continuous fitting iteration, the obtained first prediction data can be closer to actual power data, and the detection precision is improved.
Then, the control device may store the power data a of all the electric devicesk,i,jThe power data of the target electric device at the time before the target time is taken as the second historical power data, for example, in the foregoing example, the target electric device corresponds to the target electric device with the target power data a10,50,100And A, recording the second historical power data corresponding to the target electric equipment at the previous moment on the same day9,50,100(ii) a In this way, the control device may obtain a predicted adjustment amount corresponding to the target electrical device according to the second historical power data corresponding to the target electrical device, where the predicted adjustment amount represents a magnitude of adjustment performed by the control device on the first predicted data.
In addition, in order to combine the production practice of the production enterprise, in one embodiment, target forecast data representing forecast data expected at a target time can be set for the target time; the target prediction data may be input by a user or a default value, and the obtaining mode of the specific numerical value of the target prediction data is not limited in the present application.
Therefore, after the control device obtains the first prediction data and the prediction adjustment amount, the control device can update the first prediction data by using the set target prediction data and the prediction adjustment amount to obtain updated second prediction data, so that the abnormality detection of the electric equipment is more accurate; wherein the second prediction data may be represented as Q10,50,100
In this way, after obtaining the second prediction data, the control device may determine whether the target electrical device is abnormal in power consumption by using the second prediction data, for example, perform difference calculation on the second prediction data and the target power data, and when the difference between the second prediction data and the target power data does not exceed the set threshold, the control device may determine that the target electrical device is normal in power consumption; on the contrary, when the difference between the second prediction data and the target power data exceeds the set threshold, the control device may determine that the target power consumption device has power consumption abnormality, and at this time, the control device may record information that the target power consumption device has power consumption abnormality, or send alarm information to a monitoring device, or the like.
Based on the design, the power equipment abnormality detection method provided by the application obtains first prediction data corresponding to a target power equipment by using a plurality of historical power data corresponding to the target power equipment at a target time; then, acquiring a predicted adjustment amount corresponding to the target electric equipment by using the electric power data of the target electric equipment at the last moment of the target moment; the first prediction data are updated by using the set target prediction data and the prediction adjustment amount to obtain updated second prediction data, so that when the difference value between the target power data and the second prediction data exceeds a set threshold value, the target power utilization equipment can be determined to have power utilization abnormity; compared with the prior art, the target electric equipment can be dynamically combined to detect the current working state of the target electric equipment according to the actual electric information in the historical time period, so that the detection precision is improved.
As a possible implementation manner, referring to fig. 4 on the basis of fig. 3, fig. 4 shows a schematic flow chart of the sub-step of step 207 in fig. 3, and in an embodiment, step 207 may include the following sub-steps:
step 207-1, processing the first prediction data by using the prediction adjustment amount to obtain intermediate prediction data;
step 207-2, judging whether the intermediate prediction data is smaller than the target prediction data; if so, perform step 207-3; when not, go to step 207-4;
step 207-3, determining the intermediate prediction data as second prediction data;
in step 207-4, the target prediction data is determined as the second prediction data.
In an embodiment, the set target prediction data may be used to represent an upper limit of power data of the target electric device at the target time, and the predicted value of the control device for the target electric device at the target time cannot be higher than the target prediction data.
Therefore, when executing step 207, the control device may first perform adjustment processing on the first prediction data by using the prediction adjustment amount, thereby obtaining intermediate prediction data; then comparing the intermediate prediction data with the target prediction data, and when the intermediate prediction data is smaller than the target prediction data, determining the intermediate prediction data as second prediction data by the control equipment; on the contrary, when the intermediate prediction data is greater than or equal to the target prediction data, the control device determines the target prediction data as the second prediction data.
That is, after the step 207-1 is executed to process the first prediction data by using the prediction adjustment amount to obtain the intermediate prediction data, the control device may compare the intermediate prediction data with the target prediction data, and determine the smaller of the intermediate prediction data and the target prediction data as the second prediction data, so that the second prediction data determined by the control device meets the actual power demand of the enterprise.
In addition, as a possible implementation manner, referring to fig. 5 on the basis of fig. 4, fig. 5 shows a schematic flow chart of the sub-step of step 207-1 in fig. 4, and in an embodiment, step 207-1 may include the following sub-steps:
step 207-1a, obtaining a target adjusting proportion coefficient corresponding to a target time according to a set time adjusting strategy;
and step 207-1b, adjusting the prediction adjustment amount by using the target adjustment proportion coefficient, and performing weighted summation on the adjusted prediction adjustment amount and the first prediction data to obtain intermediate prediction data.
In some possible application scenarios, the working states of the electric devices may be different at different times within the same day; therefore, in an embodiment, different scale coefficients can be set for different times, so that the predicted adjustment amount at different times can be pre-adjusted in different magnitudes according to the actual electricity utilization scene.
Therefore, in some possible implementations, the control device may record a time adjustment policy, where the time adjustment policy records a correspondence relationship between a plurality of times and a plurality of adjustment scale factors, for example, the time adjustment policy may be recorded in a time adjustment table.
Then, when the control device executes step 207-1, the control device may first query the time adjustment table according to the recorded time adjustment policy, for example, to obtain the target adjustment scaling factor corresponding to the target time.
Then, the control device may adjust the predicted adjustment amount in combination with the target adjustment ratio coefficient to scale the predicted adjustment amount by a corresponding ratio; and then the adjusted prediction adjustment amount and the first prediction data are subjected to weighted summation, so that intermediate prediction data are obtained, and the second prediction data at the target moment can be more accurate.
In step 207-1b, the weighting coefficients of the prediction adjustment amount and the first prediction data may be set by the control device as a default, or may be coefficients of the control device receiving input from another device or a user.
In addition, as a possible implementation manner, referring to fig. 6 on the basis of fig. 3, fig. 6 shows a schematic flow chart of the sub-steps of step 205 in fig. 3, and in an embodiment, step 205 may include the following sub-steps:
step 205-1, obtaining the difference between the historical actual power data and the historical predicted power data to obtain an initial adjustment amount;
step 205-2, the initial adjustment amount and the set adjustment amount threshold are weighted and summed to obtain the predicted adjustment amount.
In an embodiment, the second historical power data corresponding to the target electrical device may include historical actual power data and historical predicted power data of the target electrical device at a time before the target time; the historical actual power data isThe power data is the actual power data of the target electric device at the previous time, and the historical predicted power data is the second predicted data corresponding to the target electric device at the previous time9,50,100And historical predicted power data is expressed as Q9,50,100
Then, the control device may first find the difference between the historical actual power data and the predicted power data when executing step 205, that is, find a according to the foregoing example9,50,100And Q9,50,100The difference is obtained, thereby obtaining the initial adjustment amount.
Then, the control equipment carries out weighted summation on the initial adjustment amount and a set adjustment amount threshold value to obtain a predicted adjustment amount; wherein the set adjustment threshold is an adjustment parameter of the predicted adjustment, thereby reducing the sensitivity of the initial adjustment to the predicted adjustment.
In addition, when step 205-2 is executed, the weighting coefficients of the initial adjustment amount and the adjustment amount threshold may be values set by the control device as a default, values that the control device receives from other devices or user inputs, and the present application is not limited thereto.
Also, as a possible implementation manner, referring to fig. 7 on the basis of fig. 6, fig. 7 shows a schematic flow chart of the sub-steps of step 205-2 in fig. 6, and in an embodiment, step 205-2 may include the following sub-steps:
step 205-2a, judging whether the initial adjustment amount is smaller than the adjustment amount threshold value; when yes, perform step 205-2 b; when no, perform step 205-2 c;
step 205-2b, performing weighted summation on the initial adjustment amount and the adjustment amount threshold value by using the first weighting parameter to obtain a predicted adjustment amount;
step 205-2c, performing weighted summation on the initial adjustment amount and the adjustment amount threshold by using a second weighting parameter to obtain a predicted adjustment amount;
in one embodiment, the control device may record at least two sets of weighting parameters to perform step 205-2, such as a first weighting parameter and a second weighting parameter, wherein the specific gravity of the initial adjustment amount in the second weighting parameter is greater than the specific gravity of the initial adjustment amount in the first weighting parameter; for example, the weighting parameters of the initial adjustment amount and the adjustment amount threshold in the first weighting parameter may be 0.7 and 0.3, respectively, and the weighting parameters of the initial adjustment amount and the adjustment amount threshold in the second weighting parameter may be 0.4 and 0.6, respectively.
Therefore, in executing step 205-2, the control apparatus may first determine the magnitudes of both the initial adjustment amount and the adjustment amount threshold; when the initial adjustment amount is smaller than the adjustment amount threshold, the control device may perform weighted summation on the initial adjustment amount and the adjustment amount threshold by using a first weighting parameter to obtain a predicted adjustment amount, for example, perform step 205-2 by using 0.7 and 0.3 in the above example; conversely, when the initial adjustment amount is greater than or equal to the adjustment amount threshold, the control device may perform a weighted summation of the initial adjustment amount and the adjustment amount threshold by using a second weighting parameter to obtain a predicted adjustment amount, such as performing step 205-2 by using 0.4 and 0.6 in the above example; thus, when the predicted adjustment amount is obtained by weighting, the predicted adjustment amount can be closer to the adjustment amount threshold value, and the predicted adjustment amount is prevented from greatly fluctuating.
In addition, in some possible implementation manners, the adjustment amount threshold may be continuously iterated, so that the adjustment amount threshold is closer to the actual adjustment amount.
For example, as a possible implementation manner, on the basis of fig. 6, please refer to fig. 8, and fig. 8 shows another schematic flow chart of the electrical equipment abnormality detection method provided in the present application, where the electrical equipment abnormality detection method may further include the following steps:
step 210, when the initial adjustment amount is greater than or equal to the adjustment amount threshold, updating the adjustment amount threshold according to the predicted adjustment amount.
In an embodiment, when step 205-2 is executed, if the initial adjustment amount is greater than or equal to the set adjustment amount threshold, the characteristic control device may calculate an initial adjustment amount that exceeds the set adjustment amount upper limit, and at this time, the adjustment amount threshold may be updated according to the obtained predicted adjustment amount; for example, the predicted adjustment amount is used as a new adjustment amount threshold; or, after the prediction adjustment amount is scaled according to the set scaling factor, the scaled prediction adjustment amount is summed with the adjustment amount threshold value, so as to obtain an updated adjustment amount threshold value. By conditionally updating the adjustment threshold in this manner, the calculated predicted adjustment can be made closer to the actually calculated initial adjustment.
Based on the same inventive concept as the aforementioned power equipment abnormality detection method provided in the present application, please refer to fig. 9, fig. 9 shows a schematic block diagram of a power equipment abnormality detection apparatus 300 provided in the present application, where the power equipment abnormality detection apparatus 300 may include a receiving module 301, a processing module 302, and a detecting module 303; wherein:
a receiving module 301, configured to receive target power data sent by a target electrical device at a target time; the target electric equipment is any one of a plurality of electric equipment;
the processing module 302 is configured to obtain first prediction data corresponding to the target electrical device according to first historical power data corresponding to the target electrical device; the first historical power data are a plurality of historical power data corresponding to the target electric equipment at the target moment;
the processing module 302 is further configured to obtain a predicted adjustment amount corresponding to the target electrical device according to the second historical power data corresponding to the target electrical device; the second historical power data is power data of the target electric equipment at a time before the target time;
the processing module 302 is further configured to update the first prediction data by using the set target prediction data and the prediction adjustment amount to obtain updated second prediction data;
the detecting module 303 is configured to determine that the target electrical device has an electrical anomaly when a difference between the target electrical power data and the second prediction data exceeds a set threshold.
Optionally, as a possible implementation manner, when the processing module 302 updates the first prediction data by using the set target prediction data and the prediction adjustment amount to obtain the updated second prediction data, specifically:
processing the first prediction data by using the prediction adjustment amount to obtain intermediate prediction data;
when the intermediate prediction data is smaller than the target prediction data, determining the intermediate prediction data as second prediction data;
when the intermediate prediction data is greater than or equal to the target prediction data, the target prediction data is determined as second prediction data.
Optionally, as a possible implementation manner, when the processing module 302 processes the first prediction data by using the prediction adjustment amount to obtain the intermediate prediction data, specifically configured to:
obtaining a target adjusting proportion coefficient corresponding to the target time according to a set time adjusting strategy; the time adjustment strategy records the corresponding relation between a plurality of times and a plurality of adjustment proportionality coefficients;
and adjusting the prediction adjustment amount by using the target adjustment proportion coefficient, and performing weighted summation on the adjusted prediction adjustment amount and the first prediction data to obtain intermediate prediction data.
Optionally, as a possible implementation manner, when obtaining first prediction data corresponding to the target electrical device according to the first historical power data corresponding to the target electrical device, the processing module 302 is specifically configured to:
and performing numerical fitting by using a plurality of historical power data included in the first historical power data to obtain first prediction data.
Optionally, as a possible implementation manner, the second historical power data includes historical actual power data and historical predicted power data of the target electrical device at a time before the target time;
when obtaining the predicted adjustment amount of the target electrical device corresponding to the current time node according to the second historical power data corresponding to the target electrical device, the processing module 302 is specifically configured to:
obtaining the difference between the historical actual power data and the historical predicted power data to obtain an initial adjustment amount;
and carrying out weighted summation on the initial adjustment amount and the set adjustment amount threshold value to obtain a predicted adjustment amount.
Optionally, as a possible implementation manner, when the processing module 302 performs weighted summation on the initial adjustment amount and the set adjustment amount threshold to obtain a predicted adjustment amount, the processing module is specifically configured to:
when the initial adjustment amount is smaller than the adjustment amount threshold value, carrying out weighted summation on the initial adjustment amount and the adjustment amount threshold value by using a first weighting parameter so as to obtain a predicted adjustment amount;
when the initial adjustment amount is larger than or equal to the adjustment amount threshold value, carrying out weighted summation on the initial adjustment amount and the adjustment amount threshold value by using a second weighting parameter so as to obtain a predicted adjustment amount; wherein the specific gravity of the initial adjustment amount in the second weighting parameter is greater than the specific gravity of the initial adjustment amount in the first weighting parameter.
Optionally, as a possible implementation manner, the processing module 302 is further configured to: when the initial adjustment amount is greater than or equal to the adjustment amount threshold, the adjustment amount threshold is updated according to the predicted adjustment amount.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to some embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in some embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to some embodiments of the present application. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic or optical disk, etc. for storing program codes.
The above description is only a few examples of the present application and is not intended to limit the present application, and those skilled in the art will appreciate that various modifications and variations can be made in the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (10)

1. The method for detecting the abnormity of the power equipment is characterized by being applied to control equipment in a power operation system, wherein the power operation system further comprises a plurality of pieces of power equipment which are communicated with the control equipment respectively; the method comprises the following steps:
receiving target power data sent by target electric equipment at a target moment; the target electric equipment is any one of the plurality of electric equipment;
obtaining first prediction data corresponding to the target electric equipment according to first historical electric power data corresponding to the target electric equipment; the first historical power data is a plurality of historical power data corresponding to the target electric equipment in the past days at the target time;
obtaining a predicted adjustment amount corresponding to the target electric equipment according to second historical electric power data corresponding to the target electric equipment; the second historical power data is power data of the target electric equipment at a time before the target time on the current day;
updating the first prediction data by using the set target prediction data and the prediction adjustment amount to obtain updated second prediction data;
and when the difference value between the target power data and the second prediction data exceeds a set threshold value, determining that the target electric equipment has power utilization abnormity.
2. The method of claim 1, wherein the step of updating the first prediction data with the set target prediction data and the prediction adjustment amount to obtain updated second prediction data comprises:
processing the first prediction data by using the prediction adjustment amount to obtain intermediate prediction data;
determining the intermediate prediction data as the second prediction data when the intermediate prediction data is smaller than the target prediction data;
determining the target prediction data as the second prediction data when the intermediate prediction data is greater than or equal to the target prediction data.
3. The method of claim 2, wherein the step of processing the first prediction data using the prediction adjustment to obtain intermediate prediction data comprises:
obtaining a target adjusting proportion coefficient corresponding to the target time according to a set time adjusting strategy; the time adjustment strategy records corresponding relations between a plurality of times and a plurality of adjustment proportionality coefficients;
and adjusting the prediction adjustment quantity by using the target adjustment proportion coefficient, and performing weighted summation on the adjusted prediction adjustment quantity and the first prediction data to obtain the intermediate prediction data.
4. The method of claim 1, wherein the step of obtaining first prediction data corresponding to the target electrical device from the first historical power data corresponding to the target electrical device comprises:
performing numerical fitting using a plurality of historical power data included in the first historical power data to obtain the first prediction data.
5. The method of claim 1, wherein the second historical power data comprises historical actual power data and historical predicted power data for the target powered device at a time prior to the target time for the current day;
the step of obtaining a predicted adjustment amount corresponding to the target electric device according to the second historical electric power data corresponding to the target electric device includes:
obtaining the difference between the historical actual power data and the historical predicted power data to obtain an initial adjustment amount;
and carrying out weighted summation on the initial adjustment amount and a set adjustment amount threshold value to obtain the predicted adjustment amount.
6. The method of claim 5, wherein the step of performing a weighted summation of the initial adjustment amount and a set adjustment amount threshold to obtain the predicted adjustment amount comprises:
when the initial adjustment amount is smaller than the adjustment amount threshold value, carrying out weighted summation on the initial adjustment amount and the adjustment amount threshold value by using a first weighting parameter so as to obtain the predicted adjustment amount;
when the initial adjustment amount is greater than or equal to the adjustment amount threshold, performing weighted summation on the initial adjustment amount and the adjustment amount threshold by using a second weighting parameter to obtain the predicted adjustment amount; wherein a specific gravity of the initial adjustment amount in the second weighting parameter is greater than a specific gravity of the initial adjustment amount in the first weighting parameter.
7. The method of claim 5, wherein the method further comprises:
and when the initial adjustment amount is larger than or equal to the adjustment amount threshold, updating the adjustment amount threshold according to the predicted adjustment amount.
8. The power equipment abnormality detection device is applied to control equipment in a power operation system, and the power operation system further comprises a plurality of pieces of power equipment which are communicated with the control equipment respectively; the device comprises:
the receiving module is used for receiving target power data sent by the target power utilization equipment at a target moment; the target electric equipment is any one of the plurality of electric equipment;
the processing module is used for obtaining first prediction data corresponding to the target electric equipment according to first historical electric power data corresponding to the target electric equipment; the first historical power data is a plurality of historical power data corresponding to the target electric equipment in the past days at the target time;
the processing module is further used for obtaining a predicted adjustment amount corresponding to the target electric equipment according to second historical electric power data corresponding to the target electric equipment; the second historical power data is power data of the target electric equipment at a time before the target time on the current day;
the processing module is further used for updating the first prediction data by using set target prediction data and the prediction adjustment amount to obtain updated second prediction data;
the detection module is used for determining that the target electric equipment has power utilization abnormity when the difference value between the target power data and the second prediction data exceeds a set threshold value.
9. A control apparatus, characterized by comprising:
a memory for storing one or more programs;
a processor;
the one or more programs, when executed by the processor, implement the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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