CN116577552A - Method and device for diagnosing electricity larceny of intelligent measuring switch - Google Patents
Method and device for diagnosing electricity larceny of intelligent measuring switch Download PDFInfo
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Abstract
The application relates to a method and a device for diagnosing electricity larceny of an intelligent measuring switch. The method comprises the following steps: acquiring electric quantity data information of a switch, wherein the electric quantity data information comprises current I, voltage U, power factor F and metering time T; determining electric quantity characteristic data information according to electric quantity data information of a switch and a preset integral algorithm model, wherein the electric quantity characteristic data information comprises C integral characteristic data, and C is a positive integer greater than zero; determining dimension-changing characteristic data information according to the electric quantity characteristic data information and a preset dimension-changing model, wherein the dimension-changing characteristic data information comprises H dimension-changing characteristic data, and H is a positive integer which is not more than C and is more than zero; and determining the electricity stealing state of the intelligent measuring switch according to the variable-dimension characteristic data information and a preset electricity stealing diagnosis model. By adopting the method, the electricity stealing behavior can be automatically detected through the intelligent measuring switch, the electricity stealing identification efficiency is improved, and the labor cost is effectively reduced.
Description
Technical Field
The application relates to the technical field of intelligent measuring switches, in particular to a method and a device for diagnosing electricity larceny of an intelligent measuring switch.
Background
In the prior art, in the electricity larceny investigation work of users, mainly manual investigation is performed, and the method is time-consuming and labor-consuming, and has the defects of large labor quantity, long construction period, low efficiency and the like. With the continuous increase of the number of electric equipment of a power grid, the electric energy consumption is higher and higher, the difficulty of the traditional manual investigation method is higher and higher, the accuracy of subjective judgment is reduced, the electricity stealing behavior is difficult to effectively manage and control, the loss of the power grid is difficult to compensate, and an intelligent and automatic electricity stealing analysis and identification system is urgently needed.
Disclosure of Invention
In view of the foregoing, there is a need for an electricity theft diagnosis method, apparatus, computer device, computer readable storage medium, and computer program product for an intelligent metering switch that can automatically detect electricity theft behavior of the intelligent metering switch, and improve electricity theft recognition efficiency and reduce labor costs.
In a first aspect, the present application provides a method for diagnosing theft of an intelligent metering switch. The method comprises the following steps:
acquiring electric quantity data information of a switch, wherein the electric quantity data information comprises current I, voltage U, power factor F and metering time T;
determining electric quantity characteristic data information W according to electric quantity data information of a switch and a preset integral algorithm model, wherein the electric quantity characteristic data information W comprises C pieces of integral characteristic data, and C is a positive integer greater than zero;
determining dimension-changing characteristic data information according to the electric quantity characteristic data information W and a preset dimension-changing model, wherein the dimension-changing characteristic data information comprises H dimension-changing characteristic data, and H is a positive integer which is not more than C and is more than zero;
and determining the electricity stealing state of the intelligent measuring switch according to the variable-dimension characteristic data information and a preset electricity stealing diagnosis model.
In one embodiment, determining the power characteristic data information W according to the switching power data information and a preset integration algorithm model includes:
the current I, i= { I of the electrical line is recorded over n sampling periods 1 ,...I k ,...I n K is more than or equal to 1 and n is more than or equal to C;
recording the voltage U, u= { U, of the electrical line over n sampling periods 1 ,...U k ,...U n K is more than or equal to 1 and n is more than or equal to C;
recording the power factor F, f= { F of the electrical line over n sampling periods 1 ,...F k ,...F n K is more than or equal to 1 and n is more than or equal to C;
according to the formulaAnd calculating to obtain electric quantity characteristic data W.
In one embodiment, the power data information of the switch comprises self-metering power data information and receiving power data information, and the power characteristic data information W comprises reference power characteristic data information W C And target electric quantity characteristic data information W m The method comprises the steps of carrying out a first treatment on the surface of the Determining reference electric quantity characteristic data information W according to self-metering electric quantity data information and a preset integral algorithm model C Determining target electric quantity characteristic data information W according to the received electric quantity data information and a preset integral algorithm model m 。
In one embodiment, determining the variable-dimension characteristic data information according to the electric quantity characteristic data information W and a preset variable-dimension model includes:
s1, setting an initialized value a, a value b, a value E and a value epsilon, wherein a represents a weight, b represents a bias value, E represents an expected error, and epsilon represents a Galileo distance;
s2, comparing the electric quantity characteristic data information W C And target electric quantity characteristic data information W m Sequentially selecting feature points (W Ci ,W mi ) According to the formula a× (W Ci -W mi ) +b meterCalculating a predicted value; wherein i is more than or equal to 1 and less than or equal to H;
s3, if the predicted value is less than or equal to E, changing the values of a and b, and continuing to predict;
s4, repeating the steps S2 and S3 until the predicted value is greater than E, and comparing the current feature point (W Ci ,W mi ) Recording in a power stealing transition feature library;
s5, according to the formulaComputing the Galileo distance epsilon i If epsilon i Not less than 0, the current feature point (W Ci ,W mi ) And the data is recorded in a power stealing final feature library as variable dimension feature data.
In one embodiment, determining the electricity larceny state of the intelligent measurement switch according to the variable dimension characteristic data information and a preset electricity larceny diagnosis model comprises:
all feature points (W) Ci ,W mi ) Calculating Jacquard similarity coefficient with all features in the electricity larceny diagnosis model to obtain Jacquard similarity coefficient J (W Ci ,W mi )；
If Jacquard similarity coefficient J (W Ci ,W mi ) If the threshold value G is exceeded, determining that electricity stealing behavior occurs; otherwise, no theft occurs.
In one embodiment, when it is determined that an electricity theft has occurred, the method further comprises:
and determining the specific electricity larceny type and the corresponding electricity larceny amount through an electricity larceny diagnosis model according to the current I, the voltage U and the power factor F in the received electricity data information and the current I, the voltage U and the power factor F in the self-metering electricity data information.
In a second aspect, the application also provides an electricity larceny diagnosis device of the intelligent measuring switch. The device comprises:
the acquisition module is used for acquiring electric quantity data information of the switch, wherein the electric quantity data information comprises current I, voltage U, power factor F and metering time T;
the first determining module is used for determining electric quantity characteristic data information W according to electric quantity data information of the switch and a preset integral algorithm model, wherein the electric quantity characteristic data information W comprises C pieces of integral characteristic data, and C is a positive integer larger than zero;
the second determining module is used for determining dimension-changing characteristic data information according to the electric quantity characteristic data information W and a preset dimension-changing model, wherein the dimension-changing characteristic data information comprises H dimension-changing characteristic data, and H is a positive integer which is not more than C and is more than zero;
and the third determining module is used for determining the electricity stealing state of the intelligent measuring switch according to the variable-dimension characteristic data information and a preset electricity stealing diagnosis model.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the electricity larceny diagnosis method of the intelligent measuring switch when executing the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described method for diagnosing theft of an intelligent metering switch.
In a fifth aspect, the present application also provides a computer program product. A computer program product comprising a computer program which, when executed by a processor, implements the steps of the above-described method of diagnosing theft of a smart metering switch.
According to the electricity larceny diagnosis method, device, computer equipment, storage medium and computer program product of the intelligent measuring switch, the electricity larceny state of the intelligent measuring switch is automatically diagnosed through the obtained switch electric quantity data information, the preset integral algorithm model, the dimension-changing model and the electricity larceny diagnosis model.
Drawings
FIG. 1 is a flow chart of a method for diagnosing theft of an intelligent measuring switch in one embodiment;
FIG. 2 is a flow diagram of a method for determining variable dimension characteristic data information in one embodiment;
FIG. 3 is a block diagram of a power theft diagnostic device with a smart metering switch in one embodiment;
fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, as shown in fig. 1, there is provided a method for diagnosing electricity theft of an intelligent measuring switch, which includes the steps of:
step 102, acquiring electric quantity data information of the switch, wherein the electric quantity data information comprises current I, voltage U, power factor F and metering time T.
Specifically, the electric quantity data information of the switch comprises self-metering electric quantity data information and receiving electric quantity data information, wherein the receiving electric quantity data information is electric quantity data information uploaded by the electric energy meter; optionally, the received power data information is obtained by a communication module using current as a signal carrier. The self-metering electric quantity data information and the receiving electric quantity data information respectively comprise a current I, a voltage U, a power factor F and a metering time T.
And 104, determining electric quantity characteristic data information W according to the electric quantity data information of the switch and a preset integral algorithm model, wherein the electric quantity characteristic data information W comprises C pieces of integral characteristic data, and C is a positive integer greater than zero.
Specifically, the current I, i= { I of the electric circuit is recorded for n calculation periods 1 ,...I k ,...I n K is more than or equal to 1 and n is more than or equal to C;
recording the voltage U, u= { U, of the electrical line over n calculation cycles 1 ,...U k ,...U n K is more than or equal to 1 and n is more than or equal to C;
recording the power factor F, f= { F of the electrical line over n calculation cycles 1 ,...F k ,...F n K is more than or equal to 1 and n is more than or equal to C;
according to the formulaAnd calculating to obtain electric quantity characteristic data W.
Specifically, a calculation period t=1/f, n=t/t=t×f, where f is the frequency of the electrical circuit, preferably f is 50Hz; t is a metering time, preferably 1min < T < 10min; n is a positive integer greater than 0.
Optionally, the electrical quantity characteristic data information W includes reference electrical quantity characteristic data information W C And target electric quantity characteristic data information W m The method comprises the steps of carrying out a first treatment on the surface of the Reference electrical quantity characteristic data information W C And target electric quantity characteristic data information W m Each comprises C integral characteristic data, wherein C is a positive integer greater than zero; determining reference electric quantity characteristic data information W according to self-metering electric quantity data information and a preset integral algorithm model C Determining target electric quantity characteristic data information W according to the received electric quantity data information and a preset integral algorithm model m 。
And 106, determining dimension-changing characteristic data information according to the electric quantity characteristic data information W and a preset dimension-changing model, wherein the dimension-changing characteristic data information comprises H dimension-changing characteristic data, and H is a positive integer which is not more than C and is more than zero.
And step 108, determining the electricity stealing state of the intelligent measuring switch according to the variable-dimension characteristic data information and a preset electricity stealing diagnosis model.
Optionally, when it is determined that the electricity theft behavior occurs, the method further comprises:
step 110, determining specific electricity stealing type and corresponding electricity stealing amount through an electricity stealing diagnosis model according to the current I, the voltage U and the power factor F in the received electricity data information and the current I, the voltage U and the power factor F in the self-metering electricity data information.
Optionally, the electricity stealing types comprise undercurrent electricity stealing, undervoltage electricity stealing, phase-shifting electricity stealing and surface electricity stealing.
In one embodiment, as shown in fig. 2, there is provided a method for determining variable dimension characteristic data information, which specifically includes:
step 202, setting initialized a value, b value, E value and epsilon value, wherein a represents weight, b represents offset value, E represents expected error, epsilon represents Galileo distance.
Optionally, the weight a has a value ranging from 0 to 1, and the bias b has a value ranging from 0 to 10. Specifically, in the present embodiment, the initial weight a=0.1, the initial bias b=5, and the expected error e=10.
Step 204, from the reference electrical quantity characteristic data information W C And target electric quantity characteristic data information W m Sequentially selecting feature points (W Ci ,W mi ) According to the formula a× (W Ci -W mi ) +b calculating a predicted value; wherein i is more than or equal to 1 and H is more than or equal to H.
Specifically, the electric quantity characteristic data information W is referenced C And target electric quantity characteristic data information W m According to step 104 in the previous embodiment, the reference power characteristic data information W is calculated C And target electric quantity characteristic data information W m Each includes C integral feature data, C being a positive integer greater than zero.
And 206, if the predicted value is less than or equal to E, changing the values of a and b, and continuing to predict.
Specifically, in the present embodiment, if the expected error e=10, and the predicted value is equal to or less than 10, the value of the modification weight a is traversed in the range of 0 to 1, the value of the modification bias b is traversed in the range of 0 to 10, and the prediction is continued.
Step 208, repeat steps 204 and 206 until the predicted value > E, and compare the current feature point (W Ci ,W mi ) And recording in a power stealing transition feature library.
Step 210, according to the formulaComputing the Galileo distance epsilon i If epsilon i Not less than 0, the current feature point (W Ci ,W mi ) And the data is recorded in a power stealing final feature library as variable dimension feature data.
Optionally, in one embodiment, determining the electricity theft state of the intelligent measurement switch according to the variable dimension characteristic data information and the preset electricity theft diagnosis model in the step 108 includes:
all feature points (W) Ci ,W mi ) Calculating Jacquard similarity coefficient with all features in the electricity larceny diagnosis model to obtain Jacquard similarity coefficient J (W Ci ,W mi )；
If Jacquard similarity coefficient J (W Ci ,W mi ) If the threshold value G is exceeded, determining that electricity stealing behavior occurs; otherwise, no theft occurs.
Specifically, the ratio of the numbers of intersection elements of the two sets a and B in A, B is called the jaccard similarity coefficient of the two sets, and is represented by symbol J (a, B), and the calculation formula is as follows:
the Jacquard similarity coefficient J (A, B) ε (0, 1), which is used to measure the similarity between two sets, the closer J (A, B) is to 1, the higher the representative similarity.
In particular in this embodiment, the threshold g=0.8, i.e. when the jekcal similarity coefficient J (W Ci ,W mi ) Above 0.8, it is determined that electricity theft has occurred.
According to the embodiment, the electricity stealing state of the intelligent measuring switch is automatically diagnosed through the obtained switch electric quantity data information, the preset integral algorithm model, the dimension-changing model and the electricity stealing diagnosis model, and the electricity stealing behavior of the intelligent measuring switch can be automatically detected, so that the electricity stealing identification efficiency is improved, and the labor cost is effectively reduced.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an electricity larceny diagnosis device of the intelligent measuring switch, which is used for realizing the electricity larceny diagnosis method of the intelligent measuring switch. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the electricity larceny diagnosis device provided by the following one or more intelligent measurement switches may be referred to the limitation of the electricity larceny diagnosis method of the intelligent measurement switch hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 3, there is provided an electricity theft diagnosis device of an intelligent measuring switch, comprising:
the acquisition module is used for acquiring electric quantity data information of the switch, wherein the electric quantity data information comprises current I, voltage U, power factor F and metering time T.
Specifically, the electric quantity data information of the switch comprises self-metering electric quantity data information and receiving electric quantity data information; the self-metering electric quantity data information and the receiving electric quantity data information respectively comprise a current I, a voltage U, a power factor F and a metering time T.
The first determining module is used for determining electric quantity characteristic data information W according to electric quantity data information of the switch and a preset integral algorithm model, wherein the electric quantity characteristic data information W comprises C integral characteristic data, and C is a positive integer larger than zero.
Optionally, the first determining module includes a recording unit and a calculating unit. The recording unit is used for recording the current I, the voltage U and the power factor F of the electric circuit in n sampling periods, wherein I= { I 1 ,...I k ,...I n }，U＝{U 1 ,...U k ,...U n }，F＝{F 1 ,...F k ,...F n K is more than or equal to 1 and n is more than or equal to C. The calculating unit is used for calculating according to the formulaAnd calculating to obtain electric quantity characteristic data W.
Specifically, the electrical quantity characteristic data information W includes reference electrical quantity characteristic data information W C And target electric quantity characteristic data information W m The method comprises the steps of carrying out a first treatment on the surface of the Reference electrical quantity characteristic data information W C And target electric quantity characteristic data information W m Each comprises C integral characteristic data, wherein C is a positive integer greater than zero; determining reference electric quantity characteristic data information W according to self-metering electric quantity data information and a preset integral algorithm model C Determining target electric quantity characteristic data information W according to the received electric quantity data information and a preset integral algorithm model m 。
The second determining module is used for determining dimension-changing characteristic data information according to the electric quantity characteristic data information W and a preset dimension-changing model, wherein the dimension-changing characteristic data information comprises H dimension-changing characteristic data, and H is a positive integer which is not more than C and is more than zero.
Optionally, the second determining module includes an initializing unit, a predicting unit, and a data confirming unit. The initialization unit is used for setting an initialized value a, a value b, an E value and an epsilon value, wherein a represents a weight, b represents a bias value, E represents an expected error, and epsilon represents a gamma distance. The prediction unit is used for comparing the electric quantity characteristic data information W C And target electric quantity characteristic data information W m Randomly selected feature points (W) Ci ,W mi ) According to the formula a× (W Ci -W mi ) +b calculating a predicted value; wherein i is more than or equal to 1 and less than or equal to H; if the predicted value is less than or equal to E, changing the values of a and b, and continuing to predict; repeating the above steps until the predicted value > E, and adding the current feature point (W Ci ,W mi ) And recording in a power stealing transition feature library. The data confirmation unit is used for confirming the data according to the formulaComputing the Galileo distance epsilon i If epsilon i Not less than 0, the current feature point (W Ci ,W mi ) And the data is recorded in a power stealing final feature library as variable dimension feature data.
And the third determining module is used for determining the electricity stealing state of the intelligent measuring switch according to the variable-dimension characteristic data information and a preset electricity stealing diagnosis model.
Optionally, the third determining module includes a calculating unit and a judging unit. The computing unit is used for determining all characteristic points (W Ci ,W mi ) Calculating Jacquard similarity coefficient with all features in the electricity larceny diagnosis model to obtain Jacquard similarity coefficient J (W Ci ,W mi ). The judging unit is used for judging the Jacquard similarity coefficient J (W Ci ,W mi ) Whether the threshold G is exceeded or not, if yes, the electricity stealing behavior is determined to occur, otherwise, no electricity stealing behavior occurs.
Optionally, the device further includes a fourth determining module, when the third determining module determines that the electricity stealing state of the intelligent measuring switch is that electricity stealing behavior occurs, the fourth determining module is configured to determine a specific electricity stealing type and a corresponding electricity stealing amount through the electricity stealing diagnosis model according to the current I, the voltage U, the power factor F in the received electricity quantity data information and the current I, the voltage U and the power factor F in the self-measured electricity quantity data information.
Optionally, the electricity stealing types comprise undercurrent electricity stealing, undervoltage electricity stealing, phase-shifting electricity stealing and surface electricity stealing.
The above-mentioned various modules in the electricity larceny diagnosis device of the intelligent measuring switch can be implemented in whole or in part by software, hardware and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server or a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing the acquired freezing data of the electric energy meter. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method for preserving electric energy meter data based on a message queue.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring electric quantity data information of a switch, wherein the electric quantity data information comprises current I, voltage U, power factor F and metering time T;
determining electric quantity characteristic data information W according to electric quantity data information of a switch and a preset integral algorithm model, wherein the electric quantity characteristic data information W comprises C pieces of integral characteristic data, and C is a positive integer greater than zero;
determining dimension-changing characteristic data information according to the electric quantity characteristic data information W and a preset dimension-changing model, wherein the dimension-changing characteristic data information comprises H dimension-changing characteristic data, and H is a positive integer which is not more than C and is more than zero;
and determining the electricity stealing state of the intelligent measuring switch according to the variable-dimension characteristic data information and a preset electricity stealing diagnosis model.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring electric quantity data information of a switch, wherein the electric quantity data information comprises current I, voltage U, power factor F and metering time T;
determining electric quantity characteristic data information W according to electric quantity data information of a switch and a preset integral algorithm model, wherein the electric quantity characteristic data information W comprises C pieces of integral characteristic data, and C is a positive integer greater than zero;
determining dimension-changing characteristic data information according to the electric quantity characteristic data information W and a preset dimension-changing model, wherein the dimension-changing characteristic data information comprises H dimension-changing characteristic data, and H is a positive integer which is not more than C and is more than zero;
and determining the electricity stealing state of the intelligent measuring switch according to the variable-dimension characteristic data information and a preset electricity stealing diagnosis model.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
acquiring electric quantity data information of a switch, wherein the electric quantity data information comprises current I, voltage U, power factor F and metering time T;
determining electric quantity characteristic data information W according to electric quantity data information of a switch and a preset integral algorithm model, wherein the electric quantity characteristic data information W comprises C pieces of integral characteristic data, and C is a positive integer greater than zero;
determining dimension-changing characteristic data information according to the electric quantity characteristic data information W and a preset dimension-changing model, wherein the dimension-changing characteristic data information comprises H dimension-changing characteristic data, and H is a positive integer which is not more than C and is more than zero;
and determining the electricity stealing state of the intelligent measuring switch according to the variable-dimension characteristic data information and a preset electricity stealing diagnosis model.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A method for diagnosing theft of an intelligent metering switch, the method comprising:
acquiring electric quantity data information of a switch, wherein the electric quantity data information comprises current I, voltage U, power factor F and metering time T;
determining electric quantity characteristic data information W according to the electric quantity data information of the switch and a preset integral algorithm model, wherein the electric quantity characteristic data information W comprises C pieces of integral characteristic data, and C is a positive integer greater than zero;
determining dimension-changing characteristic data information according to the electric quantity characteristic data information W and a preset dimension-changing model, wherein the dimension-changing characteristic data information comprises H dimension-changing characteristic data, and H is a positive integer which is not more than C and is more than zero;
and determining the electricity stealing state of the intelligent measuring switch according to the dimension-variable characteristic data information and a preset electricity stealing diagnosis model.
2. The method according to claim 1, wherein determining the power characteristic data information W according to the switching power data information and a preset integration algorithm model comprises:
the current I, i= { I of the electrical line is recorded over n sampling periods 1 ,...I k ,...I n K is more than or equal to 1 and n is more than or equal to C;
recording the voltage U, u= { U, of the electrical line over n sampling periods 1 ,...U k ,...U n K is more than or equal to 1 and n is more than or equal to C;
recording the power factor F, f= { F of the electrical line over n sampling periods 1 ,...F k ,...F n K is more than or equal to 1 and n is more than or equal to C;
according to the formulaAnd calculating to obtain the electric quantity characteristic data W.
3. The method according to claim 1 or 2, wherein the power data information of the switch comprises self-metering power data information and receiving power data information, and the power characteristic data information W comprises reference power characteristic data information W C And target electric quantity characteristic data information W m The method comprises the steps of carrying out a first treatment on the surface of the Determining the reference electric quantity characteristic data information W according to the self-metering electric quantity data information and a preset integral algorithm model C Determining the target electric quantity characteristic data information W according to the received electric quantity data information and a preset integral algorithm model m 。
4. A method according to claim 3, wherein said determining the variable-dimension characteristic data information from the electrical quantity characteristic data information W and a preset variable-dimension model comprises:
s1, setting an initialized value a, a value b, a value E and a value epsilon, wherein a represents a weight, b represents a bias value, E represents an expected error, and epsilon represents a Galileo distance;
s2, obtaining the characteristic data information W of the reference electric quantity C And target electric quantity characteristic data information W m Sequentially selecting feature points (W Ci ,W mi ) According to the formula a× (W Ci -W mi ) +b calculating a predicted value; wherein i is more than or equal to 1 and less than or equal to H;
s3, if the predicted value is less than or equal to E, changing the values of a and b, and continuing to predict;
s4, repeating the steps S2 and S3 until the predicted value is greater than E, and comparing the current feature point (W Ci ,W mi ) Recording in a power stealing transition feature library;
s5, according to the formulaComputing the Galileo distance epsilon i If epsilon i Not less than 0, the current feature point (W Ci ,W mi ) Record in the final feature of electricity larceny as variable dimension characteristic dataAnd (5) sign library.
5. The method of claim 4, wherein determining the power theft status of the smart metering switch based on the variable dimension characteristic data information and a pre-determined power theft diagnostic model comprises:
all feature points (W Ci ,W mi ) Calculating Jacquard similarity coefficient with all features in the electricity larceny diagnosis model to obtain Jacquard similarity coefficient J (W Ci ,W mi )；
If the Jacquard similarity coefficient J (W Ci ,W mi ) If the threshold value G is exceeded, determining that electricity stealing behavior occurs; otherwise, no theft occurs.
6. The method of claim 5, wherein when it is determined that an electricity theft has occurred, the method further comprises:
and determining a specific electricity stealing type and corresponding electricity stealing quantity through the electricity stealing diagnosis model according to the current I, the voltage U and the power factor F in the received electricity quantity data information and the current I, the voltage U and the power factor F in the self-metering electricity quantity data information.
7. An electrical theft diagnostic device for an intelligent metering switch, said device comprising:
the device comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring electric quantity data information of a switch, and the electric quantity data information comprises current I, voltage U, power factor F and metering time T;
the first determining module is used for determining electric quantity characteristic data information W according to the electric quantity data information of the switch and a preset integral algorithm model, wherein the electric quantity characteristic data information W comprises C integral characteristic data, and C is a positive integer greater than zero;
the second determining module is used for determining dimension-changing characteristic data information according to the electric quantity characteristic data information W and a preset dimension-changing model, wherein the dimension-changing characteristic data information comprises H dimension-changing characteristic data, and H is a positive integer which is not more than C and is more than zero;
and the third determining module is used for determining the electricity stealing state of the intelligent measuring switch according to the variable dimension characteristic data information and a preset electricity stealing diagnosis model.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method of any of the preceding claims 1 to 6.
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