CN113419207A - Measurement chip anomaly detection and diagnosis system based on Internet of things - Google Patents

Measurement chip anomaly detection and diagnosis system based on Internet of things Download PDF

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CN113419207A
CN113419207A CN202110974998.5A CN202110974998A CN113419207A CN 113419207 A CN113419207 A CN 113419207A CN 202110974998 A CN202110974998 A CN 202110974998A CN 113419207 A CN113419207 A CN 113419207A
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CN113419207B (en
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刘良江
李庆先
柏文琦
向德
王晋威
朱宪宇
刘青
左从瑞
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Hunan Institute of Metrology and Test
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Abstract

The invention provides a metering chip abnormity detection and diagnosis system based on the Internet of things, which comprises: the system comprises a communication module, an acquisition module, an operation processing module and a micro control module; the communication module is used for connecting the electric equipment to an internet of things; the acquisition module is used for acquiring the state change condition of the power utilization terminal; the micro control module is used for controlling the working process of the metering chip and communicating with the outside; the operation module is used for operating the acquired data and providing a result to the micro control module for making control input. According to the invention, an intelligent dynamic detection technical scheme is formed by utilizing the multi-equipment interconnection characteristic of the Internet of things and using big data and a machine learning mode, so that the adaptability and efficiency of the detection and diagnosis technology of the metering chip are greatly improved.

Description

Measurement chip anomaly detection and diagnosis system based on Internet of things
Technical Field
The invention relates to the technical field of electric energy metering. Particularly, the invention relates to a metering chip abnormity detection and diagnosis system based on the Internet of things.
Background
With the rapid development of chip technology, the technical precision of chip design and manufacture is continuously improved, the manufacturing cost is greatly optimized, and a large number of application scenes can eliminate the control system which simply uses a low-operation-capability singlechip in the past and adopt a modern chip with high integration level and high operation capability. Meanwhile, as the concept and technology of the internet of things are continuously popularized, more living equipment and production equipment in human life are also incorporated into the network, and the high-tech modern environment of interconnection of everything is gradually realized. Along with the great use of chips and the complexity and multilayering of the design and organization functions of the chips, the reliability requirements of the chips are more emphasized, and particularly, the accuracy and the reliability of the electric power metering chips which are concerned more by people are concerned, and the electric power metering chips are closely related to commercial reputation and personal interests. Therefore, it is very important to detect and diagnose the abnormality of the power metering chip.
The conventional chip abnormity detection is generally carried out when the chip works obviously abnormally at present. The detection method mainly comprises the detection of human hands, namely, technicians hold detection equipment such as a universal meter, an oscilloscope and the like by hands and detect functional pins of the chip. The reaction is relatively lagged, equipment is required to be shut down to wait for detection to be completed, the detection efficiency is correspondingly low, and a technician is required to perform a series of sequential detection on pins to determine the final abnormal reason.
Disclosure of Invention
The invention aims to provide a technical scheme aiming at the problems that the detection efficiency of a measurement chip is not high and the detection preparation work is long at present. The invention adopts the following technical scheme:
a measurement chip anomaly detection and diagnosis system based on the Internet of things comprises: the system comprises a communication module, an acquisition module, an operation processing module and a micro control module; the communication module is used for connecting the electric equipment to an internet of things; the acquisition module is used for acquiring the state change condition of each power utilization terminal; the micro control module is used for controlling the working process of the metering chip and communicating with the outside; the operation processing module is used for further operating the acquired data and providing a judgment result to the micro control module for sending a control instruction to the metering chip; the communication module, the acquisition module, the operation processing module and the micro control module are connected with each other through a circuit or a wireless network;
the communication module can communicate by utilizing one or more of wired communication, short-distance wireless communication and long-distance wireless communication; the communication module can use a single-terminal mode or a multi-node gateway mode for communication;
the acquisition module is configured in the electric equipment or the power supply socket; the acquisition module is used for judging the work shutdown and startup state of the electric equipment; further, the acquisition module is used for judging the working mode of the electric equipment; the acquisition module is connected with the communication module and is used for uploading the data acquired by the acquisition module to the Internet of things;
the operation processing module comprises a memory and a central processing unit; the memory comprises a random access memory and an erasable memory; the random access memory is used as a cache of input data and waits for data to be written into the erasable memory; the erasable memory is used for storing an executable program and a database; the central processing unit can call the executable program in the memory and call the data of the database for operation;
the micro control module is connected with the operation processing module and can perform bottom layer communication with the metering chip and perform control; the micro control module can perform operations including restarting, switching and communication cutting on the metering chip aiming at the output result of the operation processing module, and waits for the execution feedback of the chip after an operation instruction is sent out;
the acquisition module is installed on a power supply input circuit or a control circuit of the electric equipment by a manufacturer of the electric equipment before delivery in a pre-installation mode; or the acquisition module is mounted on a power input port or a power supply socket of the electric equipment by using a secondary mounting mode aiming at the electric equipment without the pre-mounted acquisition module, so that a power supply firstly passes through the acquisition module and then enters the electric equipment;
executable programs stored by the operation processing module comprise programs for machine learning and data samples of power utilization parameters of electric equipment provided by manufacturers and the Internet of things, and meanwhile, the operation processing module establishes a dynamic database in the memory;
the acquisition module is used for recording the daily working time and the working cycle of the electric equipment or the power supply socket under the normal working condition and acquiring at least one of the following power consumption parameters under the normal working condition: phase voltage, phase current and power change curves to form a series of recorded values, and the recorded time and values are given to a label and uploaded to the operation processing module; the operation processing module forms an electricity utilization parameter model for the electric equipment or the power supply socket label through machine learning according to the statistical result of the big data; the acquisition module identifies various working states of the electric equipment, gives the various working states to a label, records the change conditions of the electric parameters under the various working states, and uploads the electric parameters to the operation processing module; the operation processing module identifies the discrete degree and the change rule of the power utilization parameters in various working state changes through machine learning and big data analysis algorithms, and predicts and calculates the theoretical value of the future power utilization parameters; the operation processing module forms one or more dynamic thresholds according to the discrete degree and the change rule of the electricity utilization parameters; according to one or more dynamic thresholds of the electricity reference model, the operation processing module compares the difference between the actual reading value and the ideal value in the metering chip so as to detect whether the metering chip has working abnormity and errors;
the operation processing module and the micro control module send diagnosis results and control the metering chip to perform at least one of the following operations: restarting, closing, cutting off communication and stopping metering.
The beneficial effects obtained by the invention are as follows:
1. through a remote system based on the Internet of things, more barriers in space and time can be spanned by monitoring and controlling the metering chip;
2. updating power consumption parameter characteristic databases of various power consumption equipment at regular time based on the Internet of things, comparing the obtained big data with the measurement value of the actual metering chip, and adjusting the abnormity diagnosis basis and strategy in real time;
3. the working modes and states of a plurality of electric equipment are measured by using the Internet of things, and the support of big data is fully utilized as the basis for detection and diagnosis;
4. by adopting a machine deep learning mode, the method is suitable for the continuously changing circuit characteristics in the power grid, so that the threshold value for diagnosis is flexibly adjusted;
5. introducing a weight coefficient for calculating comparison of multiple input data so as to balance the dispersion degree caused by large fluctuation variables;
6. the database record is adopted and the computer program is added for cooperation, so that a large amount of continuous collection of data is realized, and the data is used for providing data analysis and research in the future; and can organize the data to produce the graphical form sheet or picture, make the technician more explicit and more intuitive analysis test result.
Drawings
The invention will be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. Like reference numerals designate corresponding parts throughout the different views.
FIG. 1 is a schematic diagram of the layout of the apparatus of the present invention.
Fig. 2 is a schematic workflow diagram of example 1.
Fig. 3 is a schematic workflow diagram of example 2.
Fig. 4 is a schematic workflow diagram of example 3.
FIG. 5 is a schematic diagram of the dynamic threshold diagnostic curve.
The reference numbers illustrate: 101-a metering chip; 102-a micro control module; 103-an operation processing module; 104-receiving end of internet of things; 105-an acquisition module; 106-a communication module; 107-transmission network; 108 — subordinate grid; 109-a power consumer; 701: actually measuring the power value; 702: an upper power initial threshold; 703: an upper power dynamic threshold; 704: a lower power initial threshold; 705: power dynamics lower threshold.
Detailed Description
In order to make the objects and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the following embodiments; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Other systems, methods, and/or features of the present embodiments will become apparent to those skilled in the art upon review of the following detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims. Additional features of the disclosed embodiments are described in, and will be apparent from, the detailed description that follows.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it is to be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", etc. based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not intended to indicate or imply that the device or assembly referred to must have a specific orientation.
The first embodiment is as follows:
as shown in fig. 1, an anomaly detection and diagnosis system for a metering chip based on the internet of things includes: the system comprises a communication module, an acquisition module, an operation processing module and a micro control module; the communication module is used for connecting each piece of electric equipment to the Internet of things; the acquisition module is used for acquiring the state change condition of each power utilization terminal; the micro control module is used for controlling the working process of the metering chip and communicating with the outside; the operation module is used for further operating the acquired data and providing a judgment result to the micro control module for sending a control instruction to the metering chip;
the communication module can communicate by utilizing one or more of various communication technologies based on wired communication, short-distance wireless communication, long-distance wireless communication and the like; the communication module can use a single-terminal mode or a multi-node gateway mode for communication;
the acquisition module is configured in each piece of electric equipment or each power supply socket; the acquisition module is used for judging the work shutdown and startup state of the electric equipment; the acquisition module is further used for judging the working mode of the electric equipment; the acquisition module is connected with the communication module and is used for uploading the data acquired by the acquisition module to the Internet of things;
the operation processing module comprises a memory and a central processing unit; the memory comprises a random access memory and an erasable memory; the random access memory is used as a cache of input data and waits for data to be written into the erasable memory; the erasable memory is used for storing an executable program and a database; the central processing unit can call the executable program in the memory and call the data of the database for operation;
the micro control module is connected with the operation processing module and can perform bottom layer communication with the metering chip and perform control; the micro control module can perform operations including restarting, switching on and switching off communication and the like on the metering chip aiming at the output result of the operation processing module, and waits for the execution feedback of the chip after an operation instruction is sent out;
the acquisition module is installed on a power supply input circuit or a control circuit of the electric equipment by a manufacturer of the electric equipment before delivery in a pre-installation mode; or the acquisition module is mounted on a power input port or a power supply socket of the electric equipment by using a secondary mounting mode aiming at the electric equipment without the pre-mounted acquisition module, and a power supply firstly passes through the acquisition module and then enters the electric equipment; the acquisition module is used for judging whether the connected electric equipment is in a power-on working state or not; the acquisition module records the acquired state data according to time and uploads the state data periodically;
executable programs stored by the operation processing module comprise programs and algorithms for machine learning; and measuring the corresponding value of the running time and the power of the product A and the product B in various working states aiming at specific products and models, such as the product A and the product B, by a manufacturer or a compliance detection mechanism, and recording the corresponding value as standard data [ A ]s]、[Bs]… …, etc., the time-power corresponding value includes a plurality of [ time-power ]]Data sets of nodes, for example:
Figure 555122DEST_PATH_IMAGE001
wherein:
[As]=[
Figure 499944DEST_PATH_IMAGE002
]for product a;
[Bs]=[
Figure 914745DEST_PATH_IMAGE003
]for product B;
the plurality of standard data are stored in the erasable memory in a pre-storage or Internet of things real-time communication mode, and a standard database is established; when the acquisition module judges that a certain electric device A is started, the acquisition module sends information to the operation processing module, and the operation processing module is set to start adopting standard data [ A ]s]As the basis for detection, and detecting the measurement chip at multiple time nodes
Figure 919610DEST_PATH_IMAGE004
Measured value of power
Figure 357545DEST_PATH_IMAGE005
And standard data [ A ]s]Theoretical value of power of
Figure 138681DEST_PATH_IMAGE006
The difference between
Figure 673568DEST_PATH_IMAGE007
、
Figure 583755DEST_PATH_IMAGE008
And calculating:
Figure 804258DEST_PATH_IMAGE009
；
wherein
Figure 294146DEST_PATH_IMAGE010
Is a power discrete threshold value according to actual measurement; the operation processing module detects whether the operation result of the stage is multiple
Figure 417959DEST_PATH_IMAGE011
All values are less than
Figure 764627DEST_PATH_IMAGE010
If so, determining that the metering chip is not abnormal at the current stage; if the present order appearsSegment operation result appears n times
Figure 177154DEST_PATH_IMAGE011
If the current value exceeds the threshold value range, prompting a detector to check the electric equipment A and simultaneously checking whether the metering chip is abnormal or not; further, the operation processing module and the micro control module send a judgment result and control further behaviors of the metering chip; according to the power difference value
Figure 798628DEST_PATH_IMAGE011
The micro control module restarts the micro circuit or calls a standby detection circuit for the metering chip; if the abnormality is serious, the metering chip is restarted, even the circuit is cut off, so that the safety of the electricity environment is protected.
Example two:
this embodiment should be understood to include at least all of the features of any of the foregoing embodiments and further modifications thereon; a measurement chip anomaly detection and diagnosis system based on the Internet of things comprises: the system comprises a communication module, an acquisition module, an operation processing module and a micro control module; the communication module is used for connecting each piece of electric equipment to the Internet of things; the acquisition module is used for acquiring the state change condition of each power utilization terminal; the micro control module is used for controlling the working process of the metering chip and communicating with the outside; the operation module is used for further operating the acquired data and providing a judgment result to the micro control module for sending a control instruction to the metering chip;
the communication module can communicate by utilizing one or more of various communication technologies based on wired communication, short-distance wireless communication, long-distance wireless communication and the like; the communication module can use a single-terminal mode or a multi-node gateway mode for communication;
the acquisition module is configured in each piece of electric equipment or each power supply socket; the acquisition module is used for judging the work shutdown and startup state of the electric equipment; the acquisition module is further used for judging the working mode of the electric equipment; the acquisition module is connected with the communication module and is used for uploading the data acquired by the acquisition module to the Internet of things;
the operation processing module comprises a memory and a central processing unit; the memory comprises a random access memory and an erasable memory; the random access memory is used as a cache of input data and waits for data to be written into the erasable memory; the erasable memory is used for storing an executable program and a database; the central processing unit can call the executable program in the memory and call the data of the database for operation;
the micro control module is connected with the operation processing module and can perform bottom layer communication with the metering chip and perform control; the micro control module can perform operations including restarting, switching on and switching off communication and the like on the metering chip according to the output result of the operation processing module, and waits for the execution feedback of the chip after the operation instruction is sent out.
The acquisition module is installed on a power supply input circuit or a control circuit of the electric equipment by a manufacturer of the electric equipment before delivery in a pre-installation mode; or the acquisition module is mounted on a power input port or a power supply socket of the electric equipment by using a secondary mounting mode aiming at the electric equipment without the pre-mounted acquisition module, and a power supply firstly passes through the acquisition module and then enters the electric equipment;
the acquisition module is used for acquiring multiple items of data, including input voltage V, input current I and input power P of electric equipment; the acquisition module can record acquired data according to time and periodically upload the data to a memory of the operation processing module; if a certain electricity consumption unit starts the electric equipment A at a daily time of 17 hours, and runs for one hour, the acquisition module records the starting time T, measures the running time-voltage, current and power corresponding values of the product A in the working state, and records the running time-voltage, current and power corresponding values as measured data [ A ]r]And (2) making:
[Ar]=[
Figure 278413DEST_PATH_IMAGE012
];
and establishing data [ A ] in the database stored in the operation processing moduler]The above records are shown in the following table:
Figure 530403DEST_PATH_IMAGE013
the operation processing module is used for processing the measured data [ A ] according to a plurality of groupsr]Combined with standard data [ A ] provided by the manufacturer and the detection institutions]Forming a power utilization model { A } of the power utilization equipment or the power supply socket label as a judgment model through machine learning; using the same method, a power consumption parameter model of a plurality of power consumers is established such that:
Figure 492543DEST_PATH_IMAGE014
；
furthermore, in the process of measuring the power consumption of the power consumption circuit by the measuring chip and in the process of measuring a plurality of values including phase voltage, phase current, phase potential, active power, reactive power and the like, the operation processing module can search one or more power utilization models which accord with the power utilization characteristics of the current power utilization circuit in a database by adopting an algorithm, such as linear regression fitting, so as to predict the model or type of one or more power utilization equipment in the circuit; the operation processing module predicts theoretical parameter values measured by the metering chip in the next period of time by using a machine learning mode, and continuously calculates whether the difference between the actually measured power utilization parameter values and the theoretical parameter values is within a rated threshold range, so as to judge whether the metering chip works abnormally;
because the power consumption unit may have the situation that a plurality of electric devices work simultaneously, the voltage and current values in the power grid can drift and fluctuate due to different loads, so that the actual power consumption parameters of the plurality of electric devices are influenced; therefore, the arithmetic processing module combines a big data analysis method to continuously correct the standard data [ As]And further continuously optimizing the electricity utilization model { A }.
Example three:
this embodiment should be understood to include at least all of the features of any of the foregoing embodiments and further modifications thereon; a measurement chip anomaly detection and diagnosis system based on the Internet of things comprises: the system comprises a communication module, an acquisition module, an operation processing module and a micro control module; the communication module is used for connecting the electric equipment to an internet of things; the acquisition module is used for acquiring the state change condition of each power utilization terminal; the micro control module is used for controlling the working process of the metering chip and communicating with the outside; the operation module is used for further operating the acquired data and providing a judgment result to the micro control module for sending a control instruction to the metering chip;
the communication module can communicate by utilizing one or more of various communication technologies based on wired communication, short-distance wireless communication, long-distance wireless communication and the like; the communication module can use a single-terminal mode or a multi-node gateway mode for communication;
the acquisition module is configured in the electric equipment or the power supply socket; the acquisition module is used for judging the work shutdown and startup state of the electric equipment; the acquisition module is further used for judging the working mode of the electric equipment; the acquisition module is connected with the communication module and is used for uploading the data acquired by the acquisition module to the Internet of things;
the operation processing module comprises a memory and a central processing unit; the memory comprises a random access memory and an erasable memory; the random access memory is used as a cache of input data and waits for data to be written into the erasable memory; the erasable memory is used for storing an executable program and a database; the central processing unit can call the executable program in the memory and call the data of the database for operation;
the micro control module is connected with the operation processing module and can perform bottom layer communication with the metering chip and perform control; the micro control module can perform operations including restarting, switching on and switching off communication and the like on the metering chip aiming at the output result of the operation processing module, and waits for the execution feedback of the chip after an operation instruction is sent out;
the acquisition module is installed on a power supply input circuit or a control circuit of the electric equipment by a manufacturer of the electric equipment before delivery in a pre-installation mode; or the acquisition module is mounted on a power input port or a power supply socket of the electric equipment by using a secondary mounting mode aiming at the electric equipment without the pre-mounted acquisition module, so that a power supply firstly passes through the acquisition module and then enters the electric equipment;
executable programs stored by the operation processing module comprise programs and algorithms for machine learning and big data samples of power utilization parameters of electric equipment provided by manufacturers and the Internet of things, and meanwhile, the operation processing module establishes a dynamic database in the memory;
the acquisition module is used for recording the daily working time and the working period of the electric equipment or the power supply socket under the normal working condition, acquiring the change curves of voltage, current, power and other electric parameters under the normal working condition to form a series of recorded numerical values, endowing the recorded time and numerical values with a label and uploading the labeled time and numerical values to the operation processing module; the operation processing module forms a power utilization parameter model for the power utilization equipment or the power supply socket label through machine learning according to the statistical result of the big data; the acquisition module identifies various working states of the electric equipment, gives the various working states to a label, records the change conditions of the electric parameters under the various working states, and uploads the electric parameters to the operation processing module;
the acquisition module records various working states of an electric device A; if A is a washing machine, A has multiple washing modes, and each washing mode corresponds to measured data [ Ar]Has different characteristics; the working state of A of the acquisition module is added into the measured data [ A ]r]And is built in the database;
further, the power utilization parameters of the power utilization equipment have the conditions of large dispersion degree and weak regularity due to the following reasons that 1, a plurality of power utilization equipment work simultaneously in a power utilization unit; 2. the service time and service duration of the electric equipment are not fixed; 3. the power consumption of each working state of the electric equipment is greatly changed, so that the electric parameters of the electric equipment are greatly changed;
therefore, when the deep learning is used for carrying out fitting search on the electricity utilization model, a weight coefficient beta (beta is more than or equal to 0 and less than or equal to 1) is added; the operation processing module carries out actual measurement data [ A ] on each electric equipment in the databaser]And (3) carrying out dispersion judgment:
Figure 58653DEST_PATH_IMAGE015
formula 1;
in the formula 1, the first and second groups of the compound,
Figure 422639DEST_PATH_IMAGE016
is measured data [ A ] of a plurality of electric devices Ar]The average value of each of (a);
Figure 344065DEST_PATH_IMAGE017
equation 2;
using formula 2, n measured data [ A ] are obtainedr]The degree of dispersion P; for the electric equipment with the total P value larger than the preset threshold value, fitting operation can be performed again, so that:
{A’}=β•{A}；
likewise, a certain time period TiIf the dispersion degree P value or the dispersion degree P value under a certain working state is larger than the threshold value, the weight coefficient is also used for calculation, and the weight proportion of the measured data in the corresponding time period is reduced in the calculation, so that the fitting result is more reasonable and stable;
further, for measured data [ Ar]A plurality of measurement items exist, discrete values of the measurement items, such as voltage discrete values or current discrete values, are measured respectively, and more targeted diagnosis is made on corresponding functions of the metering chip;
further, the difference between the predicted value and the measured value is considered for the operation processing moduleWhen different, a dynamic threshold eta can be introduced; the dynamic threshold η is obtained by considering multiple time and working nodes of the discrete degree P, and if the value of the discrete degree P is small, the threshold η is dynamically adjusted to narrow the range of determination, and the upper limit and the lower limit of the threshold η are dynamically narrowed by machine learning, as shown in fig. 5, the upper limit threshold and the lower limit threshold of P are selected from the upper limit threshold and the lower limit threshold of P
Figure 793501DEST_PATH_IMAGE018
And
Figure 960040DEST_PATH_IMAGE019
is adjusted to
Figure 912952DEST_PATH_IMAGE020
And
Figure 8210DEST_PATH_IMAGE021
thereby narrowing the threshold range; according to one or more dynamic thresholds of the power utilization reference model, the operation processing module compares the power utilization parameter change conditions actually read in the metering chip so as to detect whether the metering chip has work abnormity and errors;
the operation processing module and the micro control module send diagnosis results and control the metering chip to be further adjusted.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Although the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the invention. That is, the methods, systems, and devices discussed above are examples. Various configurations may omit, substitute, or add various procedures or components as appropriate. For example, in alternative configurations, the methods may be performed in an order different than that described, and/or various components may be added, omitted, and/or combined. Moreover, features described with respect to certain configurations may be combined in various other configurations, as different aspects and elements of the configurations may be combined in a similar manner. Further, elements therein may be updated as technology evolves, i.e., many elements are examples and do not limit the scope of the disclosure or claims.
Specific details are given in the description to provide a thorough understanding of the exemplary configurations including implementations. However, configurations may be practiced without these specific details, for example, well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring the configurations. This description provides example configurations only, and does not limit the scope, applicability, or configuration of the claims. Rather, the foregoing description of the configurations will provide those skilled in the art with an enabling description for implementing the described techniques. Various changes may be made in the function and arrangement of elements without departing from the spirit or scope of the disclosure.
In conclusion, it is intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that these examples are illustrative only and are not intended to limit the scope of the invention. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.

Claims (9)

1. The utility model provides a measurement chip anomaly detection and diagnostic system based on thing networking which characterized in that, this anomaly detection and diagnostic system includes: the system comprises a communication module, an acquisition module, an operation processing module and a micro control module; the communication module is used for connecting the electric equipment to an internet of things; the acquisition module is used for acquiring the state change condition of each power utilization terminal; the micro control module is used for controlling the working process of the metering chip and communicating with the outside; the operation processing module is used for further operating the acquired data and providing a judgment result to the micro control module for sending a control instruction to the metering chip; the communication module, the acquisition module, the operation processing module and the micro control module are connected with each other through a circuit or a wireless network;
the acquisition module acquires power utilization data of the power utilization equipment within a period of time and uploads the acquired data to the operation processing module; the operation processing module counts the acquired data characteristics, compares the power consumption parameter models of various types of electric equipment which are stored in the operation processing module and verified by manufacturers or laboratories, and comprises a voltage-time curve and a current-time curve of the power consumption parameters, so that the operation processing module judges the types and specifications of the electric equipment connected into a circuit, and uses the power consumption standard data model of the electric equipment of the types and specifications as a basis for judging whether the operation of the metering chip is normal.
2. The system for anomaly detection and diagnosis of a metering chip based on the internet of things of claim 1, wherein the communication module can communicate by using one or more of wired communication, short-distance wireless communication and long-distance wireless communication; the communication module can use a single-terminal mode or a multi-node gateway mode for communication.
3. The internet of things-based metrology chip anomaly detection and diagnosis system of claim 2 wherein said acquisition module is configured in a consumer or in a power outlet; the acquisition module is used for judging the work shutdown and startup state of the electric equipment; further, the acquisition module is used for judging the working mode of the electric equipment; the acquisition module is connected with the communication module and used for uploading the data acquired by the acquisition module to the Internet of things.
4. The system for detecting and diagnosing the abnormality of the metrology chip based on the internet of things as claimed in claim 3, wherein the arithmetic processing module comprises a memory and a central processing unit; the memory comprises a random access memory and an erasable memory; the random access memory is used as a cache of input data and waits for data to be written into the erasable memory; the erasable memory is used for storing an executable program and a database; the central processing unit can call the executable program in the memory and call the data of the database for operation.
5. The system for detecting and diagnosing the abnormality of the metering chip based on the internet of things as claimed in claim 4, wherein the micro control module is connected with the operation processing module and can perform bottom-layer communication and control with the metering chip; the micro control module can perform operations including restarting, switching and communication cutting on the metering chip according to the output result of the operation processing module, and waits for the execution feedback of the chip after the operation instruction is sent out.
6. The system for detecting and diagnosing the abnormality of the metering chip based on the internet of things as claimed in claim 5, wherein the acquisition module is pre-installed in a power input circuit or a control circuit of the electric equipment by a manufacturer of the electric equipment before leaving a factory; or the acquisition module adopts a secondary installation mode, and is installed on a power input port or a power supply socket of the electric equipment aiming at the electric equipment without the pre-installed acquisition module, so that the power firstly passes through the acquisition module and then enters the electric equipment.
7. The Internet of things-based anomaly detection and diagnosis system for the metering chip, according to claim 6, wherein the arithmetic processing module stores executable programs including programs for machine learning and data samples based on power utilization parameters of electric equipment provided by manufacturers and the Internet of things, and meanwhile the arithmetic processing module establishes a dynamic database in the memory.
8. The system for detecting and diagnosing the abnormality of the metering chip based on the internet of things as claimed in claim 7, wherein the collecting module is used for recording the daily working time and the working period of the electric equipment or the power supply socket under the normal working condition, and collecting at least one of the following electric parameters under the normal working condition: phase voltage, phase current and power change curves to form a series of recorded values, and the recorded time and values are given to a label and uploaded to the operation processing module; the operation processing module forms an electricity utilization parameter model for the electric equipment or the power supply socket label through machine learning according to the statistical result of the big data; the acquisition module identifies various working states of the electric equipment, gives the various working states to a label, records the change conditions of the electric parameters under the various working states, and uploads the electric parameters to the operation processing module; the operation processing module identifies the discrete degree and the change rule of the power utilization parameters in each working state change through machine learning and big data analysis algorithms, and predicts and calculates the theoretical value of the future power utilization parameters; the operation processing module forms one or more dynamic thresholds according to the discrete degree and the change rule of the electricity utilization parameters; according to one or more dynamic thresholds of the electricity reference model, the operation processing module compares the difference between the actual reading value and the ideal value in the metering chip so as to detect whether the metering chip has working abnormity and errors.
9. The system for detecting and diagnosing the abnormality of the metrology chip based on the internet of things of claim 8, wherein the arithmetic processing module and the micro control module send the diagnosis result and control the metrology chip to perform at least one of the following operations: restarting, closing, cutting off communication and stopping metering.
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