CN112084220A - Method and device for diagnosing abnormity of electric energy metering device and readable storage medium - Google Patents
Method and device for diagnosing abnormity of electric energy metering device and readable storage medium Download PDFInfo
- Publication number
- CN112084220A CN112084220A CN202010995333.8A CN202010995333A CN112084220A CN 112084220 A CN112084220 A CN 112084220A CN 202010995333 A CN202010995333 A CN 202010995333A CN 112084220 A CN112084220 A CN 112084220A
- Authority
- CN
- China
- Prior art keywords
- abnormal
- electric energy
- metering device
- energy metering
- abnormality
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000003860 storage Methods 0.000 title claims abstract description 16
- 230000002159 abnormal effect Effects 0.000 claims abstract description 314
- 238000003745 diagnosis Methods 0.000 claims abstract description 41
- 238000012216 screening Methods 0.000 claims abstract description 23
- 230000005856 abnormality Effects 0.000 claims description 48
- 238000004458 analytical method Methods 0.000 claims description 24
- 238000012545 processing Methods 0.000 claims description 14
- 238000010801 machine learning Methods 0.000 claims description 13
- 238000009826 distribution Methods 0.000 claims description 11
- 238000012360 testing method Methods 0.000 claims description 10
- 238000012549 training Methods 0.000 claims description 10
- 238000012790 confirmation Methods 0.000 claims description 5
- 238000012546 transfer Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 230000003993 interaction Effects 0.000 description 6
- 230000008901 benefit Effects 0.000 description 5
- 238000007621 cluster analysis Methods 0.000 description 5
- 238000003066 decision tree Methods 0.000 description 5
- 230000005611 electricity Effects 0.000 description 5
- 238000005259 measurement Methods 0.000 description 5
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 4
- 229910052782 aluminium Inorganic materials 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000012937 correction Methods 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 208000025274 Lightning injury Diseases 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000008450 motivation Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24564—Applying rules; Deductive queries
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
- G01R35/04—Testing or calibrating of apparatus covered by the other groups of this subclass of instruments for measuring time integral of power or current
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an abnormity diagnosis method and device for an electric energy metering device and a readable storage medium, wherein the method comprises the following steps: screening abnormal data of the current electric energy metering device according to the obtained operating parameters of the current electric energy metering device; identifying based on the abnormal data through an abnormal case base and an abnormal rule base which are constructed in advance to obtain corresponding abnormal events and abnormal types; performing correlation matching on the abnormal event and the abnormal type; and determining the abnormal information of the current electric energy metering device according to the correlation matching result. The method identifies the acquired abnormal data through an abnormal case base and an abnormal rule base which are constructed in advance, so that corresponding abnormal events and abnormal types are acquired; and performing correlation matching on the abnormal event and the abnormal type so as to obtain the abnormal information of the current electric energy metering device and reduce the dependence on skilled technicians.
Description
Technical Field
The invention relates to the technical field of electric energy metering devices, in particular to an electric energy metering device abnormity diagnosis method and device and a readable storage medium.
Background
At present, the abnormity diagnosis of the electric energy metering device highly depends on site investigation analysis, and in a large period of time from the abnormity discovery to the full test analysis on the site, the overhaul, operation and maintenance personnel cannot effectively deduce and analyze the operation condition and the fault type of the metering device, and a considerable proportion of the abnormity of the electric energy metering device can be properly treated by three times of site work through analysis, rectification and confirmation, so that the work pertinence is poor.
The general flow of the abnormality diagnosis of the existing electric energy metering device is as follows:
the operation fault condition of the electric energy metering device is found through the ways of customer complaints, patrol finding and the like.
Dispatching workers to go to the site to analyze the fault condition, completing the acquisition and measurement of data such as the operating environment, state parameters and the like of the electric energy metering device on the site, and completing the fault type judgment and the adjustment scheme formulation (the first site work) on the site or after the completion of the on-site operation;
after the rectification scheme is determined, the maintainers carry corresponding tools and equipment to go to a fault site for content work such as replacement, maintenance, debugging and the like, and the fault rectification (second site work) of the metering device is completed;
and (4) after the rectification is finished, the electric energy metering device goes to the site under the normal power utilization state of the load, the running state of the rectified electric energy metering device is rechecked, whether the electric energy metering device is recovered to normally run or not is confirmed, and if a problem is found, a worker is repeatedly dispatched (the third time of site work).
The prior art has the following defects:
the abnormal discovery of the metering device is slow, the existing management mode and state exploration means are difficult to realize whether the electric energy metering device is abnormal or not, the abnormal discovery speed of the metering device is objectively reduced, and adverse effects such as abnormal result deterioration and range expansion can be caused under certain conditions.
The abnormal rectification efficiency is low, under normal conditions, only measurement analysis and abnormal diagnosis can be carried out when the electric energy metering device is moved to the site for the first time, although the rectification scheme can be established on site under most conditions according to general rules and work flows, the detection and analysis of the abnormal conditions of the electric energy metering device cannot be realized in the first time, necessary instrument equipment and tool materials are difficult to prepare in advance, the rectification can be finished when the electric energy metering device is moved to the site again in the follow-up process, and the efficiency of the abnormal rectification of the electric energy metering device is slowed down.
The abnormal analysis and exploration of the electric energy metering device needs higher professional foundation and work experience, and is often implemented by skilled personnel with certain technical skill level and analysis capability, particularly for part of difficult and complicated abnormal conditions, and professional field expert implementers can accurately judge the abnormal cause and properly make a disposal scheme.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, it is an object of the present invention to provide an abnormality diagnosis method, apparatus and readable storage medium for an electric energy metering device, which can improve the dependency of the prior art on skilled technicians and improve the abnormality rectification rate and abnormality processing efficiency of the metering device.
One of the objects of the present invention is achieved by the above technical solution, in which an abnormality diagnosis method for an electric energy metering device includes:
screening abnormal data of the current electric energy metering device according to the obtained operating parameters of the current electric energy metering device;
identifying based on the abnormal data through an abnormal case base and an abnormal rule base which are constructed in advance to obtain corresponding abnormal events and abnormal types;
performing correlation matching on the abnormal event and the abnormal type;
and determining the abnormal information of the current electric energy metering device according to the correlation matching result.
Optionally, the pre-constructing an abnormal case library includes:
acquiring historical abnormal event related data of the electric energy metering device and a corresponding metering scheme;
and reconstructing the historical abnormal event related data and the corresponding metering scheme to construct the abnormal case library.
Optionally, the pre-constructing an exception rule base includes:
determining sample data according to the abnormal case library;
and dividing the sample data into a training set and a test set according to a preset proportion for machine learning so as to construct an abnormal rule base.
Optionally, screening abnormal data of the current electric energy metering device according to the obtained operating parameters of the current electric energy metering device includes:
and screening the current operating parameters of the electric energy metering device according to the normal operating parameters of the electric energy metering device so as to determine the abnormal operating parameters and the corresponding metering scheme of the current electric energy metering device.
Optionally, identifying based on the abnormal data according to a pre-constructed abnormal case library includes:
determining a clustering index according to the metering scheme of the current electric energy metering device;
and performing clustering analysis on the abnormal case library through the clustering indexes to obtain the abnormal event probability distribution of the current electric energy metering device and a corresponding abnormal event handling scheme.
Optionally, identifying based on the abnormal data according to a pre-constructed abnormal rule base includes:
and determining an abnormal type and a corresponding abnormal handling scheme which conform to the abnormal operation parameters based on the abnormal rule base and the normal operation parameters and the specification requirements of the electric energy metering device.
Optionally, determining the abnormal information of the current electric energy metering device according to the correlation matching result includes:
and under the condition that the correlation matching results are inconsistent, updating the abnormal case base according to the abnormal information confirmed on site.
Another object of the present invention is achieved by the above technical solution, in which an abnormality diagnosis device for an electric energy metering device includes:
the screening unit is used for screening the abnormal data of the current electric energy metering device according to the acquired operation parameters of the current electric energy metering device;
the identification unit is used for identifying based on the abnormal data according to a pre-constructed abnormal case base and an abnormal rule base so as to obtain corresponding abnormal events and abnormal types;
the matching unit is used for carrying out association matching on the abnormal event and the abnormal type;
and the diagnosis unit is used for determining the abnormal information of the current electric energy metering device according to the correlation matching result.
Optionally, the method further includes:
the data acquisition unit is used for acquiring historical abnormal event related data of the electric energy metering device and a corresponding metering scheme;
and the data processing unit is used for reconstructing the historical abnormal event related data and the corresponding metering scheme so as to construct the abnormal case library.
Optionally, the method further includes:
the sample selection unit is used for determining sample data according to the abnormal case library;
and the machine learning unit is used for dividing the sample data into a training set and a test set according to a preset proportion for machine learning so as to construct an abnormal rule base.
Optionally, the screening unit is specifically configured to screen the current operating parameters of the electric energy metering device according to the normal operating parameters of the electric energy metering device, so as to determine the abnormal operating parameters of the current electric energy metering device and a corresponding metering scheme.
Optionally, the identification unit includes:
the index confirmation module is used for determining a clustering index according to the metering scheme of the current electric energy metering device;
and the case reasoning module is used for carrying out clustering analysis on the abnormal case library through the clustering indexes so as to obtain the abnormal event probability distribution of the current electric energy metering device and a corresponding abnormal event handling scheme.
Optionally, the identification unit further includes:
and the abnormality determination module is used for determining the abnormality type and the corresponding abnormality treatment scheme which are consistent with the abnormal operation parameters based on the abnormal rule base and the normal operation parameters and the specification requirements of the electric energy metering device.
Optionally, the method further includes:
and the correcting unit is used for updating the abnormal case base according to the abnormal information confirmed on site under the condition that the correlation matching results are inconsistent.
The third object of the present invention is achieved by the means of the above-mentioned technical solution, and a computer-readable storage medium having stored thereon an information transfer implementation program which, when executed by a processor, implements the aforementioned electric energy metering device abnormality diagnosis method.
Due to the adoption of the technical scheme, the invention has the following advantages: the method identifies the acquired abnormal data through an abnormal case base and an abnormal rule base which are constructed in advance, so that corresponding abnormal events and abnormal types are acquired; and performing correlation matching on the abnormal event and the abnormal type so as to obtain the abnormal information of the current electric energy metering device, reduce the dependence on skilled technicians and improve the abnormal correction rate and the abnormal processing efficiency of the metering device.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
The drawings of the invention are illustrated as follows:
FIG. 1 is a flow chart of a first embodiment of the present invention;
FIG. 2 is a flow chart of a third embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
Example one
Rule base
The rule base is a set of knowledge and rules generated in a certain professional field, and the rule base technology refers to an intelligent technology which carries out semantic recognition and decomposition on established knowledge, experience and judgment rules, splits and recombines information contained in natural language into a database which can be read and called by a computer, and further forms intelligent analysis, comparison and examination capabilities.
Case reasoning
Case reasoning is a new knowledge-based problem solving and learning method, a historical case similar to information to be processed is found, and a new problem is solved by using specific knowledge and a specific case in the existing experience or result.
Case library
The method is characterized in that a database is formed by marking information such as metering device configuration information, environment information, fault expression, abnormal types, processing modes and the like through information mining and data reconstruction according to a history event set (particularly an electric energy metering device fault event set) which has occurred and is subjected to investigation processing.
Learning feedback
The learning feedback means that the relevant information of the result (correctness and appropriateness) of the learning activity is provided for the learner to check and adjust the learning activity, and the learning motivation is strengthened or weakened, thereby realizing the improvement of the learning effect. The method is commonly used in the middle and later periods of machine learning training to improve the optimization of the artificial intelligence learning model.
A first embodiment of the present invention provides an abnormality diagnosis method for an electric energy metering device, as shown in fig. 1, including:
s10, screening abnormal data of the current electric energy metering device according to the obtained operation parameters of the current electric energy metering device;
s20, identifying based on the abnormal data through a pre-constructed abnormal case base and an abnormal rule base to obtain corresponding abnormal events and abnormal types;
s30, performing association matching on the abnormal event and the abnormal type;
and S40, determining the abnormal information of the current electric energy metering device according to the correlation matching result.
The method identifies the acquired abnormal data through an abnormal case base and an abnormal rule base which are constructed in advance, so that corresponding abnormal events and abnormal types are acquired; and performing correlation matching on the abnormal event and the abnormal type so as to obtain the abnormal information of the current electric energy metering device, reduce the dependence on skilled technicians and improve the abnormal correction rate and the abnormal processing efficiency of the metering device.
Optionally, the pre-constructing an abnormal case library includes:
acquiring historical abnormal event related data of the electric energy metering device and a corresponding metering scheme;
and reconstructing the historical abnormal event related data and the corresponding metering scheme to construct the abnormal case library.
In the actual implementation process, the related information of the abnormal events of the historical electric energy metering device can be retrieved and extracted and the metering scheme information of the corresponding customer can be extracted through the database structure of the electricity utilization information acquisition system, wherein the related information of the abnormal events can comprise abnormal data, diagnosis types, treatment modes and the like. And reconstructing information based on the related information of the abnormal event and the metering scheme to form an abnormal case library of the electric energy metering device.
Optionally, the pre-constructing an exception rule base includes:
determining sample data according to the abnormal case library;
and dividing the sample data into a training set and a test set according to a preset proportion for machine learning so as to construct an abnormal rule base.
On the basis of the abnormal case library, sample data is selected from the abnormal case library, in the embodiment, a machine learning mode is adopted to imitate the thinking process and the technical criteria of measurement and analysis of an expert on site, an abnormal rule library of the electric energy metering device is constructed, in the specific implementation process, 70% of samples in the abnormal diagnosis case library can be used as a training set for learning and training, 30% of samples can be used as a test set for supervised learning, and the thinking process and the technical criteria of measurement and analysis of the expert on site are imitated, and the abnormal rule library of the electric energy metering device is constructed according to a quadruple structure, wherein the quadruple can contain description information, abnormal characteristics, abnormal results and solving measures.
Optionally, screening abnormal data of the current electric energy metering device according to the obtained operating parameters of the current electric energy metering device includes:
and screening the current operating parameters of the electric energy metering device according to the normal operating parameters of the electric energy metering device so as to determine the abnormal operating parameters and the corresponding metering scheme of the current electric energy metering device.
Specifically, in this embodiment, the current operation parameters of the electric energy metering device operated by the customer may be obtained by calling a test function of the electricity consumption information collection system through the data interface based on the abnormality detection module of the electric energy metering device, in this embodiment, the operation parameters include voltage, current, power factor, active and reactive power, and after obtaining the operation parameters of the current electric energy metering device, the operation parameters are detected and screened according to the normal operation parameters of the electric energy metering device as a standard, the abnormal operation parameters of the electric energy metering device not meeting the standard and the corresponding metering scheme information are determined, the operation parameters of the electric energy metering device meeting the standard are not processed, and the process is ended.
Optionally, identifying based on the abnormal data according to a pre-constructed abnormal case library includes:
determining a clustering index according to the metering scheme of the current electric energy metering device;
and performing clustering analysis on the abnormal case library through the clustering indexes to obtain the abnormal event probability distribution of the current electric energy metering device and a corresponding abnormal event handling scheme.
Specifically, after determining the abnormal operation parameters and the corresponding metering schemes of the electric energy metering devices which do not meet the standard, the abnormal operation parameters and the corresponding metering schemes are respectively and independently analyzed and processed, in the embodiment, the metering schemes of the current abnormal electric energy metering devices are processed, for example, the electric energy metering devices to be analyzed belong to the electrolytic aluminum industry, then 200 of 1000 cases of the abnormal case library belong to the electrolytic aluminum industry, the capacity of the electrolytic aluminum industry is close to that of the electric energy metering devices to be analyzed, namely, a cluster is formed according to the electrolytic aluminum, and further more than 80% of the electric energy metering devices in the cluster are burnt due to improper operation, so that the electric energy metering devices to be analyzed are also considered to have higher risk due to improper operation of the electric energy metering devices.
Because more than one cause may cause similar parameter abnormal conditions, such as overload, short circuit, and lightning stroke, may be represented as current out-of-limit, weighted statistics may be performed according to the geometric distance between the current metering scheme and the abnormal case base cluster in the cluster analysis process, and then a probability distribution is formed after normalization, for example, a cluster includes 100 short circuits, an average distance is 1, and also includes 50 overload, and a distance is 2, then a short circuit 100/1 is 100 after weighting, an overload is 50/2 is 25, a short circuit is 80% probability after normalization, and an overload is 20% probability.
The method can determine the clustering index according to the metering scheme of the current electric energy metering device, and performs clustering analysis on the abnormal case library through the clustering index, so as to obtain the abnormal event probability distribution of the current electric energy metering device and the corresponding abnormal event handling scheme. After the analysis is finished, the related data of the abnormal event corresponding to the current metering scheme and the corresponding metering scheme can be added into the abnormal case library, so that the accuracy of the cluster analysis is further improved.
Optionally, identifying based on the abnormal data according to a pre-constructed abnormal rule base includes:
and determining an abnormal type and a corresponding abnormal handling scheme which conform to the abnormal operation parameters based on the abnormal rule base and the normal operation parameters and the specification requirements of the electric energy metering device.
Specifically, the abnormal operation parameters can be checked according to the relation between the operation parameters such as voltage, current, power factor, reactive power and the like through the established abnormal rule base according to the general operation rule of the electric energy metering device and the standard specification requirements, and the type of the abnormality corresponding to the current abnormal operation parameters and the corresponding abnormal handling scheme can be judged by adopting a decision tree in the actual operation.
After determining the abnormal event probability distribution and the corresponding abnormal event handling scheme of the current electric energy metering device based on the metering scheme through the abnormal case library, and determining the abnormal type which is consistent with the abnormal operation parameter and corresponds to the abnormal operation parameter and the abnormal handling scheme through the abnormal rule library, performing association matching on the abnormal event and the abnormal type, and if the abnormal event, the abnormal type and the corresponding handling scheme are matched with each other well, determining the abnormal information of the current electric energy metering device according to the abnormal event and the abnormal type, and feeding back the corresponding handling scheme to a user.
Optionally, determining the abnormal information of the current electric energy metering device according to the correlation matching result includes:
and under the condition that the correlation matching results are inconsistent, updating the abnormal case base according to the abnormal information confirmed on site.
Specifically, if the matching results of the abnormal event, the abnormal type and the corresponding handling scheme are inconsistent, the abnormal information fed back by the abnormal case base and the abnormal rule base is fed back to the worker and stored, after the worker conducts on-site exploration to confirm the abnormal information, the abnormal information and the handling scheme are supplemented completely and then sent to the abnormal case base to be stored as a storage case to participate in cluster analysis of the subsequent abnormal event of the electric energy metering device.
Compared with the traditional method for diagnosing the abnormity of the electric energy metering device, the method disclosed by the invention has the advantages that the abnormity diagnosis of the electric energy metering device is carried out by adopting a mode of combining the abnormity rule base and the abnormity case base, the case diagnosis range is reduced, the case diagnosis time is reduced, the diagnosis efficiency and accuracy are improved, and the method has better adaptability.
Example two
A second embodiment of the present invention provides an abnormality diagnosis device for an electric energy metering device, including:
the screening unit is used for screening the abnormal data of the current electric energy metering device according to the acquired operation parameters of the current electric energy metering device;
the identification unit is used for identifying based on the abnormal data according to a pre-constructed abnormal case base and an abnormal rule base so as to obtain corresponding abnormal events and abnormal types;
the matching unit is used for carrying out association matching on the abnormal state and the abnormal type;
and the diagnosis unit is used for determining the abnormal information of the current electric energy metering device according to the correlation matching result.
The method identifies the acquired abnormal data through the pre-constructed abnormal case base and abnormal rule base so as to acquire corresponding abnormal events and abnormal types; and performing correlation matching on the abnormal event and the abnormal type so as to obtain the abnormal information of the current electric energy metering device, reduce the dependence on skilled technicians and improve the abnormal correction rate and the abnormal processing efficiency of the metering device.
Optionally, the method further includes:
the data acquisition unit is used for acquiring historical abnormal event related data of the electric energy metering device and a corresponding metering scheme;
and the data processing unit is used for reconstructing the historical abnormal event related data and the corresponding metering scheme so as to construct the abnormal case library.
Specifically, in this embodiment, the data acquisition unit may retrieve and extract information (including abnormal data, diagnosis types, handling manners, and the like) related to abnormal events of the historical electric energy metering device through a database structure of the electricity consumption information acquisition system, extract metering scheme information of corresponding customers, and perform information reconstruction by the data processing unit to form an abnormal case library of the electric energy metering device.
Optionally, the method further includes:
the sample selection unit is used for determining sample data according to the abnormal case library;
and the machine learning unit is used for dividing the sample data into a training set and a test set according to a preset proportion for machine learning so as to construct an abnormal rule base.
In the embodiment, the sample selection unit selects sample data from the abnormal case library, the machine learning unit adopts a machine learning mode, and an abnormal rule library of the electric energy metering device is constructed according to a quadruple structure by performing learning training on 70% of samples in the abnormal diagnosis case library and performing supervision learning 30% of samples in the abnormal diagnosis case library and imitating the thinking process and the technical criteria of measurement and analysis of an expert on site, wherein the quadruple can contain description information, abnormal characteristics, abnormal results and solving measures.
Optionally, the screening unit is specifically configured to screen the current operating parameters of the electric energy metering device according to the normal operating parameters of the electric energy metering device, so as to determine the abnormal operating parameters of the current electric energy metering device and a corresponding metering scheme.
The system further comprises an abnormity probing module which is used for calling a calling and testing function of the electricity consumption information acquisition system through a data interface to obtain electric parameters of the electric energy metering device operated by the customer, the screening unit detects and screens the electric parameters such as voltage, current, power factor, active and reactive power and the like of the electric energy metering device operated by the customer on the internet according to the standard normal operation parameters of the electric energy metering device, the state information of the electric energy metering device which does not accord with the standard and the corresponding metering scheme information are sent to the identification unit, and the rest of the information are not processed, so that the flow is ended.
Optionally, the identification unit includes:
the index confirmation module is used for determining a clustering index according to the metering scheme of the current electric energy metering device;
and the case reasoning module is used for carrying out clustering analysis on the abnormal case library through the clustering indexes so as to obtain the abnormal event probability distribution of the current electric energy metering device and a corresponding abnormal event handling scheme.
After receiving the related information of the abnormal electric energy metering device, the identification unit calls the case reasoning module and the abnormality judging module simultaneously to analyze the metering scheme and the running state information of the electric energy metering device respectively and independently.
The case reasoning module is used for carrying out cluster analysis on the metering scheme of the customer to be analyzed in an abnormal case library according to the metering scheme of the current electric energy metering device and the retrieval items configured by the customer industry information, the power consumption capacity, the load property and the metering device, summarizing and analyzing the abnormal cause and solution of the clustering memory quantity case, forming abnormal event probability distribution and a corresponding disposal scheme, and feeding back the abnormal event probability distribution and the corresponding disposal scheme to the identification unit.
Optionally, the identification unit further includes:
and the abnormality determination module is used for determining the abnormality type and the corresponding abnormality treatment scheme which are consistent with the abnormal operation parameters based on the abnormal rule base and the normal operation parameters and the specification requirements of the electric energy metering device.
In this embodiment, the abnormal rule base performs abnormal examination according to the relationship between the voltage, the current, the power factor, the reactive power and other parameter information, and according to the general operation rule of the electric energy metering device and the standard specification requirement, and may adopt a decision tree to judge and infer an abnormal type that matches the current operation condition, and feed back the abnormal type and the disposal scheme to the identification unit.
The identification unit comprehensively matches the information fed back by the case reasoning module and the abnormality judging module, and if the fed back abnormal event is consistent with the abnormal type, the abnormal event is directly output to the interaction interface.
Optionally, the method further includes:
and the correcting unit is used for updating the abnormal case base according to the abnormal information confirmed on site under the condition that the correlation matching results are inconsistent.
In this embodiment, if the feedback abnormal event is inconsistent with the abnormal type, the client metering scheme and the running state information are simultaneously pushed to the case adjusting and storing module, and the abnormal information fed back by the client metering scheme and the running state information is output to prompt the staff to go to the site. After the worker explores on site to confirm the abnormal type, the information supplemented by the abnormal type and the rectification scheme through the man-machine interaction module is sent to the abnormal case library to be stored as a stock case.
The method adopts a mode of combining the rule base and case reasoning to diagnose the abnormity of the electric energy metering device, reduces the case reasoning range, reduces the case reasoning time, improves the diagnosis efficiency and the accuracy, has better adaptability, can help maintainers to quickly and accurately determine the abnormity occurrence position, reason and solution of the electric energy metering device, saves the field overhauling time of the maintainers and further improves the economic benefits of power utilization customers and power supply enterprises.
EXAMPLE III
The third embodiment of the invention provides an implementation case of an abnormality diagnosis device for an electric energy metering device, which is used for carrying out work according to the process of the first technical scheme when monitoring and managing the electric energy metering device operated by a client by applying the conventional method and working mode, and has the defects of strong manpower dependence, slow response, lower efficiency, difficulty in quality guarantee and the like.
When the abnormality diagnosis device for the electric energy metering device of the present invention is used for work, as shown in fig. 2, the abnormality diagnosis device for the electric energy metering device of the present invention includes:
and integrating information data such as fault information, metering scheme information, operating data in abnormal and normal states, abnormal diagnosis conclusion determined after field check, rectification measures and the like of various client operating electric energy metering devices in the stock data of the electricity utilization information acquisition system to form an abnormal case library with a quadruple structure.
According to the function realization mode and the action mechanism of the electric energy meter, the mutual inductor and the secondary loop, the incidence relation of various parameters related to the running state of the electric energy metering device is listed in an equation set mode, a corresponding mathematical model is integrated and established, the tracing from the running parameter information to the possible running state (including the existence of abnormity and the confirmation of the abnormity type) is completed in a decision tree mode, and an abnormity rule base is formed.
The electric energy metering device abnormity probing module calls the real-time calling and historical data query functions of the power utilization information acquisition system through the data interface, obtains the state parameters of the electric energy metering device operated by a customer to be analyzed, sends the state parameters into the electric energy metering device screening unit to complete primary screening, packages the parameter information to the expert diagnosis module (identification unit) under the condition that the parameters do not accord with the preset operation range, and otherwise, ends the flow.
After receiving the data packets, the expert diagnosis module divides the information of the electric energy metering device operated by the client to be analyzed into two data packets, wherein the information of the metering scheme is sent to the case reasoning module, and the operation state parameters and the recent historical data information are sent to the expert diagnosis module.
The case reasoning module is used for calling the abnormal case library module by taking the dimension information of the description information as an index, carrying out multi-dimensional space architecture in the case library, carrying out cluster analysis, counting the description information of all cases in the confidence domain by taking the client operation metering device to be analyzed as a center, calling the information of the rest three tuples, determining the abnormal type, the probability and the adjustment scheme of the client operation metering device to be analyzed by adopting a weight addition mode, and feeding back the abnormal type, the probability and the adjustment scheme to the expert diagnosis module.
And the judgment rule base module (abnormality judgment module) takes the received state parameters of the client running metering device to be analyzed as input, implements backtracking analysis to the running state according to a preset rule base decision tree, similarly determines one or more possible running states of the metering device according to the trend and bifurcation selection conditions of the decision tree, and feeds back the abnormal type, the determined probability and the rectification scheme to the expert diagnosis module.
After receiving the analysis conclusion data packets fed back by the abnormal case library module and the judgment rule library module, the expert diagnosis module compares the analysis conclusions of the abnormal case library module and the judgment rule library module, matches the analysis conclusions, and transmits the possible abnormal type with the determined probability of more than 5%, the determined probability (taking an average value) and the rectification scheme to the human-computer interaction interface under the condition that the consistency is more than a set threshold value; and if the consistency does not meet the requirement of the set threshold, the analysis conclusions of the two parties are not processed and are sent to the human-computer interaction interface, a new storage unit is set by calling a case adjusting and storing module, and the information and the state parameters of the client operation metering device to be analyzed are stored in the storage unit.
The man-machine interaction interface simply and concisely displays the received information, so that the staff can conveniently refer to the information and further analyze the information.
If the case adjusting and storing module is called in the previous step, the system prompts to enter the field checking result of the client operation metering device at the later stage, extracts the result through a human-computer interaction interface input window and then sends the result to the case adjusting and storing module;
after the case adjusting and storing module receives the abnormal type, the abnormal cause, the rectification scheme and the normal operation parameters recovered after rectification after verification and treatment, the information is stored in the corresponding position of the storage unit to form case data which has the same structure as the existing abnormal case library and complete information, and the case data is added into the abnormal case library module for subsequent use.
Example four
The fourth object of the present invention is achieved by the means, and a computer-readable storage medium storing thereon an information transfer implementation program which, when executed by a processor, implements the aforementioned electric energy metering device abnormality diagnosis method.
In one particular embodiment the program when executed by a processor implements:
screening abnormal data of the current electric energy metering device according to the obtained operating parameters of the current electric energy metering device;
identifying based on the abnormal data through an abnormal case base and an abnormal rule base which are constructed in advance to obtain corresponding abnormal events and abnormal types;
performing correlation matching on the abnormal event and the abnormal type;
and determining the abnormal information of the current electric energy metering device according to the correlation matching result.
The method adopts a mode of combining the rule base and case reasoning to diagnose the abnormity of the electric energy metering device, reduces the case reasoning range, reduces the case reasoning time, improves the diagnosis efficiency and the accuracy, has better adaptability, can help maintainers to quickly and accurately determine the abnormity occurrence position, reason and solution of the electric energy metering device, saves the field overhauling time of the maintainers and further improves the economic benefits of power utilization customers and power supply enterprises.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered thereby.
Claims (15)
1. An abnormality diagnosis method for an electric energy metering device, comprising:
screening abnormal data of the current electric energy metering device according to the obtained operating parameters of the current electric energy metering device;
identifying based on the abnormal data through an abnormal case base and an abnormal rule base which are constructed in advance to obtain corresponding abnormal events and abnormal types;
performing correlation matching on the abnormal event and the abnormal type;
and determining the abnormal information of the current electric energy metering device according to the correlation matching result.
2. The method for diagnosing abnormality of an electric energy metering device according to claim 1, wherein the step of constructing an abnormal case library in advance comprises:
acquiring historical abnormal event related data of the electric energy metering device and a corresponding metering scheme;
and reconstructing the historical abnormal event related data and the corresponding metering scheme to construct the abnormal case library.
3. The abnormality diagnosis method for the electric energy metering device according to claim 2, characterized in that an abnormality rule base is previously constructed, including:
determining sample data according to the abnormal case library;
and dividing the sample data into a training set and a test set according to a preset proportion for machine learning so as to construct an abnormal rule base.
4. The abnormality diagnosis method for the electric energy metering device according to any one of claims 1 to 3, wherein the screening of the abnormality data of the current electric energy metering device based on the acquired operation parameters of the current electric energy metering device includes:
and screening the current operating parameters of the electric energy metering device according to the normal operating parameters of the electric energy metering device so as to determine the abnormal operating parameters and the corresponding metering scheme of the current electric energy metering device.
5. The method for diagnosing abnormality of an electric energy metering device according to claim 4, wherein the identifying based on the abnormality data according to a pre-constructed abnormality case library includes:
determining a clustering index according to the metering scheme of the current electric energy metering device;
and performing clustering analysis on the abnormal case library through the clustering indexes to obtain the abnormal event probability distribution of the current electric energy metering device and a corresponding abnormal event handling scheme.
6. The method for diagnosing abnormality of an electric energy metering device according to claim 4, wherein the identification based on the abnormality data according to a pre-constructed abnormality rule base includes:
and determining an abnormal type and a corresponding abnormal handling scheme which conform to the abnormal operation parameters based on the abnormal rule base and the normal operation parameters and the specification requirements of the electric energy metering device.
7. The abnormality diagnosis method for the electric energy metering device according to any one of claims 1 to 3, wherein determining abnormality information of the current electric energy metering device based on the correlation matching result includes:
and under the condition that the correlation matching results are inconsistent, updating the abnormal case base according to the abnormal information confirmed on site.
8. An abnormality diagnostic device for an electric energy metering device, characterized by comprising:
the screening unit is used for screening the abnormal data of the current electric energy metering device according to the acquired operation parameters of the current electric energy metering device;
the identification unit is used for identifying based on the abnormal data according to a pre-constructed abnormal case base and an abnormal rule base so as to obtain corresponding abnormal events and abnormal types;
the matching unit is used for carrying out association matching on the abnormal event and the abnormal type;
and the diagnosis unit is used for determining the abnormal information of the current electric energy metering device according to the correlation matching result.
9. The abnormality diagnostic device according to claim 8, characterized by further comprising:
the data acquisition unit is used for acquiring historical abnormal event related data of the electric energy metering device and a corresponding metering scheme;
and the data processing unit is used for reconstructing the historical abnormal event related data and the corresponding metering scheme so as to construct the abnormal case library.
10. The abnormality diagnostic device according to claim 9, characterized by further comprising:
the sample selection unit is used for determining sample data according to the abnormal case library;
and the machine learning unit is used for dividing the sample data into a training set and a test set according to a preset proportion for machine learning so as to construct an abnormal rule base.
11. The apparatus according to any one of claims 8 to 10, wherein the screening unit is specifically configured to screen the current operating parameters of the electric energy metering device according to the normal operating parameters of the electric energy metering device, so as to determine the abnormal operating parameters of the current electric energy metering device and the corresponding metering scheme.
12. The abnormality diagnostic device according to claim 11, characterized in that the identification unit includes:
the index confirmation module is used for determining a clustering index according to the metering scheme of the current electric energy metering device;
and the case reasoning module is used for carrying out clustering analysis on the abnormal case library through the clustering indexes so as to obtain the abnormal event probability distribution of the current electric energy metering device and a corresponding abnormal event handling scheme.
13. The abnormality diagnostic device according to claim 11, characterized in that the identification unit further includes:
and the abnormality determination module is used for determining the abnormality type and the corresponding abnormality treatment scheme which are consistent with the abnormal operation parameters based on the abnormal rule base and the normal operation parameters and the specification requirements of the electric energy metering device.
14. The abnormality diagnostic device according to any one of claims 8 to 10, characterized by further comprising:
and the correcting unit is used for updating the abnormal case base according to the abnormal information confirmed on site under the condition that the correlation matching results are inconsistent.
15. A computer-readable storage medium, characterized in that an information transfer implementation program is stored on the computer-readable storage medium, and when executed by a processor, implements the electric energy metering device abnormality diagnosis method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010995333.8A CN112084220B (en) | 2020-09-21 | 2020-09-21 | Abnormality diagnosis method and device for electric energy metering device and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010995333.8A CN112084220B (en) | 2020-09-21 | 2020-09-21 | Abnormality diagnosis method and device for electric energy metering device and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112084220A true CN112084220A (en) | 2020-12-15 |
| CN112084220B CN112084220B (en) | 2024-05-14 |
Family
ID=73739298
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010995333.8A Active CN112084220B (en) | 2020-09-21 | 2020-09-21 | Abnormality diagnosis method and device for electric energy metering device and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112084220B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112804114A (en) * | 2021-04-15 | 2021-05-14 | 施维智能计量系统服务(长沙)有限公司 | System and method for automatic communication and configuration of energy metering Internet of things equipment |
| CN113190693A (en) * | 2021-05-31 | 2021-07-30 | 冯凯 | Data processing method and device of energy storage system, electronic equipment and storage medium |
| CN114064711A (en) * | 2021-10-13 | 2022-02-18 | 国家电网公司华中分部 | Rule base-based device exception handling method and device and storage medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050065680A1 (en) * | 2003-08-08 | 2005-03-24 | Hiroshi Kawauchi | Trouble diagnosing device |
| KR20060001923A (en) * | 2005-12-16 | 2006-01-06 | 김두현 | Development of an intelligent electrical fire cause diagnosis system combined case base and rule base using fuzzy logic |
| CN1924734A (en) * | 2006-03-23 | 2007-03-07 | 浙江工业大学 | Control method for online quality detection |
| CN105652232A (en) * | 2015-12-30 | 2016-06-08 | 国家电网公司 | Stream processing-based electric energy metering device online abnormality diagnosis method and system |
| CN106199494A (en) * | 2016-07-25 | 2016-12-07 | 国网上海市电力公司 | A kind of intelligent diagnosis system based on metering device fault |
| CN106291445A (en) * | 2016-10-20 | 2017-01-04 | 国网上海市电力公司 | A kind of Intelligence Diagnosis method that power collection systems is abnormal |
| US20170097860A1 (en) * | 2015-10-01 | 2017-04-06 | International Business Machines Corporation | System component failure diagnosis |
| KR20170067519A (en) * | 2015-12-08 | 2017-06-16 | 두산중공업 주식회사 | Integrated Diagnostic System and Database based on rules and cases |
| CN106908752A (en) * | 2017-03-17 | 2017-06-30 | 广东电网有限责任公司电力科学研究院 | The electric energy metrical abnormality diagnostic method and system of a kind of feature based packet |
| US20180260266A1 (en) * | 2015-11-10 | 2018-09-13 | Alibaba Group Holding Limited | Identifying potential solutions for abnormal events based on historical data |
| CN110703179A (en) * | 2019-09-05 | 2020-01-17 | 国网新疆电力有限公司电力科学研究院 | Voltage and current metering exception handling method based on knowledge base |
| CN110888101A (en) * | 2019-12-05 | 2020-03-17 | 新奥数能科技有限公司 | Electric energy meter abnormity diagnosis method and device |
-
2020
- 2020-09-21 CN CN202010995333.8A patent/CN112084220B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050065680A1 (en) * | 2003-08-08 | 2005-03-24 | Hiroshi Kawauchi | Trouble diagnosing device |
| KR20060001923A (en) * | 2005-12-16 | 2006-01-06 | 김두현 | Development of an intelligent electrical fire cause diagnosis system combined case base and rule base using fuzzy logic |
| CN1924734A (en) * | 2006-03-23 | 2007-03-07 | 浙江工业大学 | Control method for online quality detection |
| US20170097860A1 (en) * | 2015-10-01 | 2017-04-06 | International Business Machines Corporation | System component failure diagnosis |
| US20180260266A1 (en) * | 2015-11-10 | 2018-09-13 | Alibaba Group Holding Limited | Identifying potential solutions for abnormal events based on historical data |
| KR20170067519A (en) * | 2015-12-08 | 2017-06-16 | 두산중공업 주식회사 | Integrated Diagnostic System and Database based on rules and cases |
| CN105652232A (en) * | 2015-12-30 | 2016-06-08 | 国家电网公司 | Stream processing-based electric energy metering device online abnormality diagnosis method and system |
| CN106199494A (en) * | 2016-07-25 | 2016-12-07 | 国网上海市电力公司 | A kind of intelligent diagnosis system based on metering device fault |
| CN106291445A (en) * | 2016-10-20 | 2017-01-04 | 国网上海市电力公司 | A kind of Intelligence Diagnosis method that power collection systems is abnormal |
| CN106908752A (en) * | 2017-03-17 | 2017-06-30 | 广东电网有限责任公司电力科学研究院 | The electric energy metrical abnormality diagnostic method and system of a kind of feature based packet |
| CN110703179A (en) * | 2019-09-05 | 2020-01-17 | 国网新疆电力有限公司电力科学研究院 | Voltage and current metering exception handling method based on knowledge base |
| CN110888101A (en) * | 2019-12-05 | 2020-03-17 | 新奥数能科技有限公司 | Electric energy meter abnormity diagnosis method and device |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112804114A (en) * | 2021-04-15 | 2021-05-14 | 施维智能计量系统服务(长沙)有限公司 | System and method for automatic communication and configuration of energy metering Internet of things equipment |
| CN113190693A (en) * | 2021-05-31 | 2021-07-30 | 冯凯 | Data processing method and device of energy storage system, electronic equipment and storage medium |
| CN113190693B (en) * | 2021-05-31 | 2023-01-20 | 冯凯 | Data processing method and device of energy storage system, electronic equipment and storage medium |
| CN114064711A (en) * | 2021-10-13 | 2022-02-18 | 国家电网公司华中分部 | Rule base-based device exception handling method and device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112084220B (en) | 2024-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112084220B (en) | Abnormality diagnosis method and device for electric energy metering device and readable storage medium | |
| CN112859822B (en) | Equipment health analysis and fault diagnosis method and system based on artificial intelligence | |
| USRE42858E1 (en) | Method and apparatus for identifying and tracking project trends | |
| CN107967485A (en) | Electro-metering equipment fault analysis method and device | |
| CN112464995A (en) | Power grid distribution transformer fault diagnosis method and system based on decision tree algorithm | |
| CN111124852A (en) | Fault prediction method and system based on BMC health management module | |
| CN116028887B (en) | Analysis method of continuous industrial production data | |
| CN112906764A (en) | Communication safety equipment intelligent diagnosis method and system based on improved BP neural network | |
| CN114255784A (en) | Substation equipment fault diagnosis method based on voiceprint recognition and related device | |
| CN116771576A (en) | Comprehensive fault diagnosis method for hydroelectric generating set | |
| Zhao et al. | Research on international standardization of software quality and software testing | |
| CN115441456A (en) | Fault diagnosis method and device for power grid dispatching support system | |
| CN111612149A (en) | Main network line state detection method, system and medium based on decision tree | |
| CN105117980B (en) | A kind of automatic evaluation method of grid equipment state | |
| CN117520184A (en) | Test system for developing computer software | |
| CN113221937A (en) | Emergency processing system and method based on artificial intelligence judgment | |
| CN116714469A (en) | Charging pile health monitoring method, device, terminal and storage medium | |
| CN117113135A (en) | Carbon emission anomaly monitoring and analyzing system capable of sorting and classifying anomaly data | |
| CN114625627B (en) | User intelligent perception method based on big data multidimensional analysis | |
| WO2024065776A1 (en) | Method for data processing, apparatus for data processing, electronic device, and storage medium | |
| CN116308295A (en) | Industrial production data management method and system | |
| CN115146715A (en) | Power utilization potential safety hazard diagnosis method, device, equipment and storage medium | |
| CN112936342B (en) | Physical robot action evaluation system and method based on human body gesture recognition algorithm | |
| CN115186935A (en) | Electromechanical device nonlinear fault prediction method and system | |
| CN113379314A (en) | Intelligent annual production plan supervision method based on reasoning algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |