CN116384980A - Repair reporting method and system - Google Patents
Repair reporting method and system Download PDFInfo
- Publication number
- CN116384980A CN116384980A CN202310594903.6A CN202310594903A CN116384980A CN 116384980 A CN116384980 A CN 116384980A CN 202310594903 A CN202310594903 A CN 202310594903A CN 116384980 A CN116384980 A CN 116384980A
- Authority
- CN
- China
- Prior art keywords
- repair
- intelligent equipment
- intelligent
- accumulated
- fault
- 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
- 230000008439 repair process Effects 0.000 title claims abstract description 121
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000002159 abnormal effect Effects 0.000 claims abstract description 34
- 238000012545 processing Methods 0.000 claims abstract description 14
- 238000004422 calculation algorithm Methods 0.000 claims description 26
- 238000011156 evaluation Methods 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 10
- 238000010801 machine learning Methods 0.000 claims description 10
- 238000005265 energy consumption Methods 0.000 claims description 4
- 238000011269 treatment regimen Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 abstract 1
- 238000000605 extraction Methods 0.000 abstract 1
- 230000006870 function Effects 0.000 description 16
- 238000012423 maintenance Methods 0.000 description 11
- 238000012360 testing method Methods 0.000 description 10
- 238000012549 training Methods 0.000 description 10
- 238000013528 artificial neural network Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 239000013598 vector Substances 0.000 description 5
- 238000010276 construction Methods 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0499—Feedforward networks
-
- 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)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Debugging And Monitoring (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
The invention provides a repair method and a repair system, which belong to the technical field of data processing, and specifically comprise the following steps: when the intelligent equipment does not have unprocessed repair and the accumulated service time is not more than the set time, and the accumulated repair times of the intelligent equipment of the same type are not more than the preset times, determining the acquisition frequency of the operation data based on the accumulated service time, the accumulated operation time and the accumulated repair times; judging the running state of the intelligent equipment based on the running data, outputting a repair warning signal to a user when the running state is in a suspected abnormal state, after the user performs repair, carrying out keyword extraction based on repair description of the user to obtain repair keywords, determining the fault type of the intelligent equipment based on the repair keywords and the running data by adopting a fault judging model, and adopting different repair treatment strategies according to different fault types, thereby further improving the running reliability of the intelligent equipment and reducing unnecessary detection.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a repair method and system.
Background
In order to realize automatic repair of intelligent household appliances, the invention patent publication No. CN109063864A (a method for repairing faults of intelligent equipment and equipment thereof) is disclosed by acquiring the fault type of the corresponding fault parameter when the intelligent equipment fails; reporting the fault type and the repair application information input by a user to a server; and receiving a progress query instruction, displaying maintenance progress on a maintenance progress interface according to maintenance progress information fed back by the server, wherein a user can see current maintenance progress information in real time according to the displayed maintenance progress, so that maintenance service is more convenient and quick, but the following technical problems exist:
1. the frequency of acquiring the operation parameters of the intelligent device is not considered to be set based on the accumulated use time, the accumulated operation time, the historical report repair data and the like of the intelligent device, and the intelligent device, particularly the intelligent household appliance, is generally in a normal working state, and if the operation parameters are acquired in real time, unnecessary electric energy consumption can be caused.
2. The fault type of the intelligent equipment is judged based on the operation parameters of the intelligent equipment and the repair description of the user, repair is carried out according to the judging result of the fault type, the problem can be found in the first time for repair caused by external factors such as normal working state or unreliable power supply electric energy, the accuracy of the final fault type judgment cannot be guaranteed only by means of a certain mode, and meanwhile, the repair processing efficiency is reduced.
The invention provides a repair method and a repair system aiming at the technical problems.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
according to one aspect of the present invention, a repair method is provided.
The repair method is characterized by comprising the following steps:
s11, judging whether the intelligent equipment has unprocessed repair or not based on the repair report condition of the user of the intelligent equipment, if so, acquiring the operation data of the intelligent equipment in real time, and entering a step S15, otherwise, entering a step S12;
s12, judging whether the accumulated use time of the intelligent equipment is larger than a set time amount, if so, acquiring the operation data of the intelligent equipment in real time, entering a step S14, and if not, entering a step S13;
s13, judging whether the accumulated repair times of the intelligent equipment of the same type are larger than preset times, if so, acquiring the operation data of the intelligent equipment in real time, entering into step S14, if not, determining the acquisition frequency for acquiring the operation data of the intelligent equipment based on the accumulated use time, the accumulated operation time and the accumulated repair times, and acquiring the operation data of the intelligent equipment at the acquisition frequency;
s14, judging the operation state of the intelligent equipment based on the operation data of the intelligent equipment, outputting a repair early warning signal to the user when the operation state of the intelligent equipment is in a suspected abnormal state, judging whether the user performs repair, if so, entering a step S15, otherwise, returning to the step S11;
s15, extracting keywords based on the report and repair description of the user to obtain report and repair keywords, determining the fault type of the intelligent equipment by adopting a fault judgment model based on a machine learning algorithm based on the report and repair keywords and the operation data of the intelligent equipment, and adopting different report and repair processing strategies according to different fault types.
The intelligent equipment with the maintenance reporting function is capable of acquiring operation data at the first time by determining the maintenance reporting condition based on the intelligent equipment, so that the processing efficiency is further improved, the operation stability of the whole system is ensured, and meanwhile, the failure type judging efficiency of the intelligent equipment is improved to a certain extent.
The acquisition frequency of acquiring the operation data of the intelligent equipment and the setting of the preset times and the set time amount are determined based on the accumulated use time, the accumulated operation time and the accumulated repair times, so that the acquisition frequencies of the operation data of different intelligent equipment are distinguished, the waste of electric energy caused by unnecessary acquisition is further reduced, and meanwhile, the reliability of operation of the intelligent equipment with longer operation or more historical fault conditions is also ensured.
The fault type of the intelligent equipment is determined by adopting a fault judging model based on a machine learning algorithm based on the repair keywords and the operation data of the intelligent equipment, so that the fault judgment of the intelligent equipment is realized from multiple aspects, and the technical problems of poor accuracy and comprehensiveness of the judgment caused by the original judgment of the fault type by only adopting a single mode are avoided.
In another aspect, embodiments of the present application provide a computer system, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor runs the computer program to obtain the repair method.
In another aspect, the present invention provides a computer storage medium having a computer program stored thereon, which when executed in a computer causes the computer to perform a repair method as described above.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings;
FIG. 1 is a flow chart of a repair method according to embodiment 1;
FIG. 2 is a flowchart of specific steps of acquisition frequency determination of operational data of a smart device according to embodiment 1;
fig. 3 is a flowchart of specific steps for determining that the operation state of the smart device is in a suspected abnormal state according to embodiment 1;
FIG. 4 is a flowchart of specific steps for determining the fault type of the smart device according to embodiment 1;
fig. 5 is a frame diagram of a computer storage medium in embodiment 3.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus detailed descriptions thereof will be omitted.
The terms "a," "an," "the," and "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.
Analysis of prior art problems:
at present, along with the construction of sharing economy and digital campus, sharing air conditioner, dormitory air conditioner, leasing household electrical appliances and the like gradually become a trend, so that the operation condition of the intelligent household electrical appliances is obtained in real time through the setting of the monitoring terminal, and the judgment of faults according to the operation condition of the intelligent household electrical appliances becomes more and more the conventional setting of sharing and leasing household electrical appliances, but at the same time, the following problems exist:
1. the frequency of acquiring the operation parameters of the intelligent device is not considered to be set based on the accumulated use time, the accumulated operation time, the historical report repair data and the like of the intelligent device, and the intelligent device, particularly the intelligent household appliance, is generally in a normal working state, and if the operation parameters are acquired in real time, unnecessary electric energy consumption can be caused.
2. The fault type of the intelligent equipment is judged based on the operation parameters of the intelligent equipment and the repair description of the user, repair is carried out according to the judging result of the fault type, the problem can be found in the first time for repair caused by external factors such as normal working state or unreliable power supply electric energy, the accuracy of the final fault type judgment cannot be guaranteed only by means of a certain mode, and meanwhile, the repair processing efficiency is reduced.
Example 1
In order to solve the above problem, according to one aspect of the present invention, as shown in fig. 1, a repair method is characterized by comprising:
s11, judging whether the intelligent equipment has unprocessed repair or not based on the repair report condition of the user of the intelligent equipment, if so, acquiring the operation data of the intelligent equipment in real time, and entering a step S15, otherwise, entering a step S12;
specifically, the report repair situation of the user of the intelligent device determines the user bound with the intelligent device according to the device unique identifier of the intelligent device, and determines the report repair situation of the user according to the report repair record of the user, specifically including processed and unprocessed two types.
For example, if the intelligent device is an air conditioner, the binding between the user of the intelligent device and the air conditioner can be realized by installing a phone or purchasing a phone according to the unique identifier of the air conditioner in the system, and the user can be guided to bind the air conditioner through the mobile phone APP, which belong to the prior art and are not repeated one by one.
Specifically, the operation data of the intelligent device comprises an operation current and an operation voltage, and some intelligent devices also comprise a signal monitor of a temperature sensor, a real-time monitor of wind speed, an operation current of a compressor, an operation voltage of the compressor, a current and a voltage of a display screen, a historical abnormal restarting frequency, a forced restarting frequency, an operation current and an operation voltage of a motor, an operation temperature of the motor and the like.
The intelligent equipment with the maintenance reporting function is capable of acquiring operation data at the first time by determining the maintenance reporting condition based on the intelligent equipment, so that the processing efficiency is further improved, the operation stability of the whole system is ensured, and meanwhile, the failure type judging efficiency of the intelligent equipment is improved to a certain extent.
S12, judging whether the accumulated use time of the intelligent equipment is larger than a set time amount, if so, acquiring the operation data of the intelligent equipment in real time, entering a step S14, and if not, entering a step S13;
specifically, the accumulated use time of the intelligent device can be obtained from the installation or purchase of the intelligent device to the current accumulated working time, and the accumulated opening or working time of the intelligent device can be obtained according to the operation monitoring of the intelligent device.
For example, if the installation time of the air conditioner is 20201209, the integrated service time is 23 months by 2022, 11 months and 9 months, and the set time is 36 months, the operation data of the intelligent device does not need to be acquired in real time.
Through carrying out the screening of intelligent device according to the setting amount of time at first to the quantity of intelligent device that carries out cumulative warranty number of times and obtains the frequency judgement has further promoted holistic efficiency can be very big reduced.
S13, judging whether the accumulated repair times of the intelligent equipment of the same type are larger than preset times, if so, acquiring the operation data of the intelligent equipment in real time, entering into step S14, if not, determining the acquisition frequency for acquiring the operation data of the intelligent equipment based on the accumulated use time, the accumulated operation time and the accumulated repair times, and acquiring the operation data of the intelligent equipment at the acquisition frequency;
specifically, the accumulated repair number is counted by using various repair data such as clients, mobile phone APP, intelligent devices, customer service phones and the like, and generally, the value of the preset number is more than 1000 times.
Specifically, the same type of intelligent device is the same type of intelligent device of the same manufacturer.
Specifically, the preset times are determined according to the time to market, the sales amount and the use frequency of the intelligent equipment, wherein the longer the time to market, the more the sales amount and the more frequent the use frequency of the intelligent equipment are, the larger the preset times are.
Specifically, as shown in fig. 2, the specific steps for determining the acquisition frequency of the operation data of the intelligent device are as follows:
s21, constructing a frequency input set based on the accumulated use time, the accumulated running time and the accumulated repair times;
for a specific example, the frequency input set is x= { T, T }; wherein T and T are respectively accumulated using time and accumulated repair times.
S22, based on the frequency input set, determining basic acquisition frequency of operation data of the intelligent equipment by adopting a prediction model based on an RBF algorithm;
specific examples of the specific steps of the RBF algorithm-based prediction model construction are as follows:
the method comprises the steps of carrying out normalization processing on original data, dividing the processed data into a training data set and a test data set according to a certain proportion, inputting the training data set into an improved RBF neural network for learning and training, then inputting the test data set into the trained RBF neural network for fault diagnosis and classification testing, and finally outputting a diagnosis result. In a specific example, the structure of the device generally comprises three layers: the input layer is used for transmitting the input set to the next layer, the hidden layer is used for performing nonlinear conversion on the input set transmitted by the input layer into nonlinear sequence vectors, the output layer is also called as a linear layer, and the function of the input layer is used for performing linear conversion on the nonlinear sequence vectors transmitted by the hidden layer into output of an overall result.
The processing function in the hidden layer of the RBF neural network consists of the following formula:
the invention adopts the Gaussian function radial basis function as a kernel function, and the output of the hidden layer of the radial basis function neural network is as follows:
Output y at output layer kth neural network k The expression is:
S23, acquiring the generation times and the repair times of the repair early-warning signals of the intelligent equipment, and correcting the basic acquisition frequency of the intelligent equipment based on the generation times and the repair times of the repair early-warning signals of the intelligent equipment to acquire the acquisition frequency of the operation data of the intelligent equipment.
Specifically, a calculation formula of the obtaining frequency of the operation data of the intelligent device is as follows:
The acquisition frequency of acquiring the operation data of the intelligent equipment and the setting of the preset times and the set time amount are determined based on the accumulated use time, the accumulated operation time and the accumulated repair times, so that the acquisition frequencies of the operation data of different intelligent equipment are distinguished, the waste of electric energy caused by unnecessary acquisition is further reduced, and meanwhile, the reliability of operation of the intelligent equipment with longer operation or more historical fault conditions is also ensured.
S14, judging the operation state of the intelligent equipment based on the operation data of the intelligent equipment, outputting a repair early warning signal to the user when the operation state of the intelligent equipment is in a suspected abnormal state, judging whether the user performs repair, if so, entering a step S15, otherwise, returning to the step S11;
specifically, as shown in fig. 3, the specific steps for determining that the operation state of the intelligent device is in a suspected abnormal state are as follows:
s31, judging whether the operation current or the operation voltage of the intelligent equipment is in an abnormal state or not based on the operation current or the operation voltage of the intelligent equipment, if so, judging that the intelligent equipment is in a suspected abnormal state, and if not, entering step S32;
s32, determining a fault evaluation value of the intelligent equipment based on abnormal shutdown times and forced restarting times of the intelligent equipment, judging whether the intelligent equipment is in a suspected abnormal state or not based on the fault evaluation value, if so, entering a step S34, and if not, entering a step S33;
s33, determining an intelligent operation evaluation value based on the operation energy consumption, the accumulated use time and the accumulated operation time of the intelligent equipment, judging whether the intelligent equipment is in a suspected abnormal state or not based on the operation evaluation value, if so, entering a step S34, and if not, determining that the operation state of the intelligent equipment is not in the suspected abnormal state;
s34, based on the fault evaluation value and the operation evaluation value of the intelligent equipment, combining the number of times of reporting and repairing of the intelligent equipment in a preset time, adopting a prediction model based on a machine learning algorithm to obtain a reliability evaluation value of the intelligent equipment, and judging the operation state of the intelligent equipment according to the reliability evaluation value of the intelligent equipment.
Specifically, when the operation current or the operation voltage of the intelligent device is smaller than a certain threshold value or larger than a certain threshold value, the intelligent device can be judged to be in a suspected abnormal state.
For example, if the operating current of the intelligent device is 50A and the rated current of the intelligent device is 20A, it is determined that the intelligent device is in a suspected abnormal state.
Specifically, the abnormal shutdown times can be determined by reading the chip or according to the times of forced restarting by the user.
Specifically, for example, if the number of abnormal shutdown times of the intelligent device is 10, it is determined that the intelligent device is in a suspected abnormal state.
Specifically, the prediction model based on the machine learning algorithm adopts the prediction model based on the RBF algorithm, and a specific construction process is not described again.
Specifically, the reliability scoring value of the intelligent device ranges from 0 to 1, and when the reliability scoring value of the intelligent device is smaller than a set value, the intelligent device is judged to be in a suspected abnormal state.
S15, extracting keywords based on the report and repair description of the user to obtain report and repair keywords, determining the fault type of the intelligent equipment by adopting a fault judgment model based on a machine learning algorithm based on the report and repair keywords and the operation data of the intelligent equipment, and adopting different report and repair processing strategies according to different fault types.
Specifically, as shown in fig. 4, the specific steps for determining the fault type of the intelligent device are as follows:
s41, judging whether the fault type of the intelligent equipment is an abnormal fault type or not based on a matching result of the repair keyword of the intelligent equipment and the keyword database, if so, entering a step S43, and if not, entering a step S42;
s42, judging whether the reliability score value of the intelligent equipment is smaller than a reliability preset value, wherein the reliability preset value is larger than a set value, if so, entering a step S43, and if not, determining that the intelligent equipment is not in a fault state;
s43, determining a suspected fault type based on a matching result of the repair keyword of the intelligent device and the keyword database, and obtaining the fault type of the intelligent device by adopting a predictive model based on an IGWO-RBF algorithm based on the suspected fault type and the operation data of the intelligent device.
Specifically, if the repair keyword is sudden shutdown or automatic shutdown, judging that the intelligent equipment is likely to have internal faults, and suggesting that the sudden shutdown or automatic shutdown output internal faults in the keyword database are suspected faults of the main board.
Specifically, if the reliability score value is 0.6, the set value is 0.7, and the reliability preset value is 0.5, it is determined that the intelligent device is not in a fault state.
Specifically, the specific steps of constructing the predictive model based on the IGWO-RBF algorithm are as follows:
step1: firstly, historical data are obtained, then data preprocessing is carried out on sample data sets with different levels, then the selection of output indexes and input indexes of a model is carried out, then the sample data are divided, 80% of the selected data sets are used as training sets (x, y), and the remaining 20% are used as test sets (x, y);
step2: and inputting a training sample set data pair (x, y), and training the learning parameters of the training sample set through an IGWO-RBF algorithm. According to the nearest neighbor clustering algorithm, the central clustering parameters of the RBF network are learned, and then the IGWO optimization weight parameters are utilized to continuously learn the learning parameters of the RBF neural network; then by judging whether the convergence condition is satisfied or the maximum iteration number t is reached max If yes, finishing iteration, and constructing a stable IGWO-RBF prediction model; otherwise, training the learning parameters by using the IGWO-RBF algorithm;
step3: and inputting test sample set data pairs (x, y), performing test verification of a training model, and performing prediction error analysis by different algorithms through evaluation indexes (MAE, MRE, RMSE and the like) to verify the generalization capability of the prediction model. And inputting test data x 'into a trained prediction model to obtain a predicted output y' of the test set, performing inverse normalization processing on the y 'through a data processing module, and performing error analysis and comparison on the y' and a y value of the test set. Meanwhile, 10 data pairs (x, y) are selected, and the model provided by the invention has good generalization capability by performing prediction verification error analysis by using three different algorithms and the same normalization processing and experimental environments as the training data set and the test data set.
Specifically, for the IGWO algorithm, the convergence factor a of the formula can achieve nonlinear variation, but the interval and nonlinear variation of the hunting are not ideal, and the variation of a is also affected, and the global searching capability is affected. Aiming at the defect that the above-mentioned deficiency is inspired by PSO algorithm, the sin function is non-linearly reduced in [0, pi/2 ] interval, the invention provides a sin-based convergence factor alpha iterative formula, namely
The improved factor a dynamically and nonlinearly decreases from 2 to 0 along with the increase of the iteration times. The nonlinear variation of a is improved by using the sin function, the convergence is reduced more rapidly on 2-0, and the exploration and development capabilities are effectively balanced in the initial and middle stages of the algorithm. In the later period of optimizing, a keeps 1 small value, namely, a parameter evolves towards the direction of utilizing global optimum, so that the algorithm is not easy to sink into local optimum, and can converge faster. Moreover, the nonlinear reduction of the search interval can be realized, and the change is firstly slow and then fast, so that the distance between A and 1 is influenced, the range of the individual of the sirius can be expanded to search the global optimal solution, and the global exploration and the local exploration of GWO are effectively balanced.
Specifically, the fault types of the intelligent equipment comprise abnormal fault types, improper operation fault types and external factor fault types.
Specifically, the abnormal fault type is the fault of the equipment, the user cannot remove the fault, the improper operation fault type is the fault caused by improper operation of the user, and the external factor fault type is the external factors such as mismatching of input voltage and current, poor network signal and the like, so that the user can be guided to complete the fault removal.
Specifically, when the fault type of the intelligent device is an abnormal fault type, the service maintenance of going to the door is performed, and other users are guided to try to remove the fault.
The fault type of the intelligent equipment is determined by adopting a fault judging model based on a machine learning algorithm based on the repair keywords and the operation data of the intelligent equipment, so that the fault judgment of the intelligent equipment is realized from multiple aspects, and the technical problems of poor accuracy and comprehensiveness of the judgment caused by the original judgment of the fault type by only adopting a single mode are avoided.
Example 2
In an embodiment of the present application, a computer system is provided, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor runs the computer program to obtain the repair method.
The repair method specifically comprises the following steps:
based on the repair report condition of the user of the intelligent equipment, when judging that the intelligent equipment has unprocessed repair report, acquiring the operation data of the intelligent equipment in real time, and entering the next step;
and extracting keywords based on the report and repair description of the user to obtain report and repair keywords, determining the fault type of the intelligent equipment by adopting a fault judgment model based on a machine learning algorithm based on the report and repair keywords and the operation data of the intelligent equipment, and adopting different report and repair treatment strategies according to different fault types.
Example 3
As shown in fig. 5, the present invention provides a computer storage medium having a computer program stored thereon, which when executed in a computer causes the computer to perform a repair method as described above.
The repair method specifically comprises the following steps:
based on the repair report condition of the user of the intelligent equipment, when judging that the intelligent equipment does not have the unprocessed repair report, entering the next step;
when the accumulated use time of the intelligent equipment is judged to be larger than the set time, acquiring the operation data of the intelligent equipment in real time, and entering the next step;
judging the operation state of the intelligent equipment based on the operation data of the intelligent equipment, outputting a repair early warning signal to the user when the operation state of the intelligent equipment is in a suspected abnormal state, judging that the user performs repair, extracting keywords based on the repair description of the user to obtain repair keywords, determining the fault type of the intelligent equipment based on the repair keywords and the operation data of the intelligent equipment by adopting a fault judgment model based on a machine learning algorithm, and adopting different repair treatment strategies according to different fault types.
In the several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways as well. The system embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (10)
1. The repair method is characterized by comprising the following steps:
s11, judging whether the intelligent equipment has unprocessed repair or not based on the repair report condition of the user of the intelligent equipment, if so, acquiring the operation data of the intelligent equipment in real time, and entering a step S15, otherwise, entering a step S12;
s12, judging whether the accumulated use time of the intelligent equipment is larger than a set time amount, if so, acquiring the operation data of the intelligent equipment in real time, entering a step S14, and if not, entering a step S13;
s13, judging whether the accumulated repair times of the intelligent equipment of the same type are larger than preset times, if so, acquiring the operation data of the intelligent equipment in real time, entering into step S14, if not, determining the acquisition frequency for acquiring the operation data of the intelligent equipment based on the accumulated use time, the accumulated operation time and the accumulated repair times, and acquiring the operation data of the intelligent equipment at the acquisition frequency;
s14, judging the operation state of the intelligent equipment based on the operation data of the intelligent equipment, outputting a repair early warning signal to the user when the operation state of the intelligent equipment is in a suspected abnormal state, judging whether the user performs repair, if so, entering a step S15, otherwise, returning to the step S11;
s15, extracting keywords based on the report and repair description of the user to obtain report and repair keywords, determining the fault type of the intelligent equipment by adopting a fault judgment model based on a machine learning algorithm based on the report and repair keywords and the operation data of the intelligent equipment, and adopting different report and repair processing strategies according to different fault types.
2. The repair method of claim 1, wherein the repair situation of the user of the intelligent device determines the user bound to the intelligent device according to the device unique identifier of the intelligent device, and determines the repair situation of the user according to the repair record of the user, specifically including both processed and unprocessed.
3. The repair reporting method of claim 1, wherein the preset number of times is determined according to a time to market, a sales volume, and a frequency of use of the smart device, wherein the longer the time to market, the more sales volume, and the more frequent the frequency of use of the smart device, the larger the preset number of times.
4. The repair reporting method of claim 1, wherein the specific step of determining the acquisition frequency of the operation data of the intelligent device is:
s21, constructing a frequency input set based on the accumulated use time, the accumulated running time and the accumulated repair times;
s22, based on the frequency input set, determining basic acquisition frequency of operation data of the intelligent equipment by adopting a prediction model based on an RBF algorithm;
s23, acquiring the generation times and the repair times of the repair early-warning signals of the intelligent equipment, and correcting the basic acquisition frequency of the intelligent equipment based on the generation times and the repair times of the repair early-warning signals of the intelligent equipment to acquire the acquisition frequency of the operation data of the intelligent equipment.
5. The repair reporting method of claim 1, wherein the specific step of determining that the operation state of the intelligent device is in a suspected abnormal state comprises:
s31, judging whether the operation current or the operation voltage of the intelligent equipment is in an abnormal state or not based on the operation current or the operation voltage of the intelligent equipment, if so, judging that the intelligent equipment is in a suspected abnormal state, and if not, entering step S32;
s32, determining a fault evaluation value of the intelligent equipment based on abnormal shutdown times and forced restarting times of the intelligent equipment, judging whether the intelligent equipment is in a suspected abnormal state or not based on the fault evaluation value, if so, entering a step S34, and if not, entering a step S33;
s33, determining an intelligent operation evaluation value based on the operation energy consumption, the accumulated use time and the accumulated operation time of the intelligent equipment, judging whether the intelligent equipment is in a suspected abnormal state or not based on the operation evaluation value, if so, entering a step S34, and if not, determining that the operation state of the intelligent equipment is not in the suspected abnormal state;
s34, based on the fault evaluation value and the operation evaluation value of the intelligent equipment, combining the number of times of reporting and repairing of the intelligent equipment in a preset time, adopting a prediction model based on a machine learning algorithm to obtain a reliability evaluation value of the intelligent equipment, and judging the operation state of the intelligent equipment according to the reliability evaluation value of the intelligent equipment.
6. The repair reporting method of claim 5, wherein the reliability score of the smart device ranges from 0 to 1, and the smart device is determined to be in a suspected abnormal state when the reliability score of the smart device is less than a set value.
7. The repair method of claim 1, wherein the specific steps of determining the fault type of the intelligent device are:
s41, judging whether the fault type of the intelligent equipment is an abnormal fault type or not based on a matching result of the repair keyword of the intelligent equipment and the keyword database, if so, entering a step S43, and if not, entering a step S42;
s42, judging whether the reliability score value of the intelligent equipment is smaller than a reliability preset value, wherein the reliability preset value is larger than a set value, if so, entering a step S43, and if not, determining that the intelligent equipment is not in a fault state;
s43, determining a suspected fault type based on a matching result of the repair keyword of the intelligent device and the keyword database, and obtaining the fault type of the intelligent device by adopting a predictive model based on an IGWO-RBF algorithm based on the suspected fault type and the operation data of the intelligent device.
8. The repair method of claim 7, wherein the fault type of the intelligent device comprises an abnormal fault type, an improper operation fault type, and an external factor fault type.
9. A computer system, comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor, when executing the computer program, performs a repair method as claimed in any one of claims 1 to 8.
10. A computer storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a repair method according to any of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310594903.6A CN116384980B (en) | 2023-05-25 | 2023-05-25 | Repair reporting method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310594903.6A CN116384980B (en) | 2023-05-25 | 2023-05-25 | Repair reporting method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116384980A true CN116384980A (en) | 2023-07-04 |
| CN116384980B CN116384980B (en) | 2023-08-25 |
Family
ID=86963641
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310594903.6A Active CN116384980B (en) | 2023-05-25 | 2023-05-25 | Repair reporting method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116384980B (en) |
Citations (35)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001175936A (en) * | 1999-12-20 | 2001-06-29 | Fuji Electric Co Ltd | Vending machine controller, vending machine and vending machine managing device |
| JP2006145053A (en) * | 2004-11-16 | 2006-06-08 | Matsushita Electric Ind Co Ltd | Home electric appliance with consumable management function and its program |
| US20070079082A1 (en) * | 2005-09-30 | 2007-04-05 | Gladwin S C | System for rebuilding dispersed data |
| CN103325072A (en) * | 2013-06-18 | 2013-09-25 | 国家电网公司 | Equipment condition maintenance fuzzy decision-making method of servers in power distribution system and power system |
| US20130325541A1 (en) * | 2012-05-08 | 2013-12-05 | John A. Capriotti | System and method for managing and providing vehicle maintenance |
| CN104102773A (en) * | 2014-07-05 | 2014-10-15 | 山东鲁能软件技术有限公司 | Equipment fault warning and state monitoring method |
| WO2014199410A1 (en) * | 2013-06-11 | 2014-12-18 | 三菱電機株式会社 | Safety system |
| CN104483586A (en) * | 2015-01-07 | 2015-04-01 | 佛山市顺德区美的洗涤电器制造有限公司 | Household electric appliance fault detection method and device |
| CN104635599A (en) * | 2014-12-20 | 2015-05-20 | 蓝星（北京）技术中心有限公司 | Active preventive maintaining method and device for rotating equipment |
| CN105590146A (en) * | 2016-02-29 | 2016-05-18 | 上海带来科技有限公司 | Power plant device intelligent prediction overhaul method and power plant device intelligent prediction overhaul system based on big data |
| CN105737903A (en) * | 2016-04-27 | 2016-07-06 | 於斌 | Intelligent pre-diagnosis and maintenance intelligent early warning method for faults of port machinery |
| CA2981796A1 (en) * | 2015-05-18 | 2016-11-24 | Halliburton Energy Services Inc. | Condition based maintenance program based on life-stress acceleration model and cumulative damage model |
| CN205906842U (en) * | 2016-07-01 | 2017-01-25 | 赵海波 | Elevator reports management system for repairment |
| CN107640672A (en) * | 2017-09-22 | 2018-01-30 | 深圳市正弦电气股份有限公司 | A kind of elevator and its maintenance monitoring system and operation method |
| CN107680520A (en) * | 2017-09-26 | 2018-02-09 | 上海欧美拉光电股份有限公司 | The diagnostic method of Precise Diagnosis LED failure on a kind of intelligent APP lines |
| KR101894697B1 (en) * | 2018-02-27 | 2018-09-04 | 안재봉 | Fault estimated device based on programmable logic controller and method thereof |
| CN109190958A (en) * | 2018-08-23 | 2019-01-11 | 合肥好多帮信息科技有限公司 | A kind of troublshooting Intelligentized regulating and controlling system |
| CN110145838A (en) * | 2019-05-31 | 2019-08-20 | 宁波奥克斯电气股份有限公司 | Fault detection method, processing method and the air conditioner of air conditioner |
| CN110569993A (en) * | 2019-08-21 | 2019-12-13 | 广东技术师范大学天河学院 | Intelligent maintenance and maintenance system based on big data |
| CN110569139A (en) * | 2019-08-02 | 2019-12-13 | 中国船舶工业系统工程研究院 | vitality guarantee system and method for information system |
| CN110895032A (en) * | 2019-12-31 | 2020-03-20 | 福建省南鸿通讯科技有限公司 | Method and device for automatically diagnosing and positioning air conditioner fault of communication machine room |
| CN110925950A (en) * | 2019-11-20 | 2020-03-27 | 广东美的暖通设备有限公司 | Control method and device of air conditioning system, electronic equipment and storage medium |
| CN111144639A (en) * | 2019-12-24 | 2020-05-12 | 国电南京自动化股份有限公司 | Subway equipment fault prediction method and system based on ALLN algorithm |
| CN111352003A (en) * | 2020-05-25 | 2020-06-30 | 北京中航科电测控技术股份有限公司 | Analysis system for electrical equipment faults |
| CN111412579A (en) * | 2020-03-26 | 2020-07-14 | 上海建工四建集团有限公司 | Air conditioning unit fault type diagnosis method and system based on big data |
| CN111428894A (en) * | 2020-03-25 | 2020-07-17 | 蘑菇物联技术（深圳）有限公司 | Equipment maintenance method and system based on cloud computing |
| CN112183780A (en) * | 2020-09-27 | 2021-01-05 | 珠海格力电器股份有限公司 | Fault maintenance guiding method, device and system and storage medium |
| CN113268590A (en) * | 2021-04-06 | 2021-08-17 | 云南电网有限责任公司昆明供电局 | Power grid equipment running state evaluation method based on equipment portrait and integrated learning |
| CN113362083A (en) * | 2021-06-04 | 2021-09-07 | 苏州科达科技股份有限公司 | Repair reporting method and device, electronic equipment and storage medium |
| CN113657221A (en) * | 2021-08-04 | 2021-11-16 | 浙江浙能台州第二发电有限责任公司 | Power plant equipment state monitoring method based on intelligent sensing technology |
| CN114548435A (en) * | 2022-02-14 | 2022-05-27 | 北京精一强远科技有限公司 | Intelligent repair reporting system and method based on equipment monitoring |
| CN114745836A (en) * | 2022-04-09 | 2022-07-12 | 深圳市粤大明智慧科技集团有限公司 | Control method and system of intelligent street lamp |
| CN114814391A (en) * | 2021-01-18 | 2022-07-29 | 国网青海省电力公司西宁供电公司 | Charging pile fault identification method and storage medium |
| CN115508768A (en) * | 2022-09-16 | 2022-12-23 | 安徽南瑞中天电力电子有限公司 | Fault identification method, system and storage medium based on self-checking of intelligent ammeter |
| CN116055900A (en) * | 2023-03-30 | 2023-05-02 | 北京城建智控科技股份有限公司 | Image quality correction method based on image pickup device |
-
2023
- 2023-05-25 CN CN202310594903.6A patent/CN116384980B/en active Active
Patent Citations (35)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001175936A (en) * | 1999-12-20 | 2001-06-29 | Fuji Electric Co Ltd | Vending machine controller, vending machine and vending machine managing device |
| JP2006145053A (en) * | 2004-11-16 | 2006-06-08 | Matsushita Electric Ind Co Ltd | Home electric appliance with consumable management function and its program |
| US20070079082A1 (en) * | 2005-09-30 | 2007-04-05 | Gladwin S C | System for rebuilding dispersed data |
| US20130325541A1 (en) * | 2012-05-08 | 2013-12-05 | John A. Capriotti | System and method for managing and providing vehicle maintenance |
| WO2014199410A1 (en) * | 2013-06-11 | 2014-12-18 | 三菱電機株式会社 | Safety system |
| CN103325072A (en) * | 2013-06-18 | 2013-09-25 | 国家电网公司 | Equipment condition maintenance fuzzy decision-making method of servers in power distribution system and power system |
| CN104102773A (en) * | 2014-07-05 | 2014-10-15 | 山东鲁能软件技术有限公司 | Equipment fault warning and state monitoring method |
| CN104635599A (en) * | 2014-12-20 | 2015-05-20 | 蓝星（北京）技术中心有限公司 | Active preventive maintaining method and device for rotating equipment |
| CN104483586A (en) * | 2015-01-07 | 2015-04-01 | 佛山市顺德区美的洗涤电器制造有限公司 | Household electric appliance fault detection method and device |
| CA2981796A1 (en) * | 2015-05-18 | 2016-11-24 | Halliburton Energy Services Inc. | Condition based maintenance program based on life-stress acceleration model and cumulative damage model |
| CN105590146A (en) * | 2016-02-29 | 2016-05-18 | 上海带来科技有限公司 | Power plant device intelligent prediction overhaul method and power plant device intelligent prediction overhaul system based on big data |
| CN105737903A (en) * | 2016-04-27 | 2016-07-06 | 於斌 | Intelligent pre-diagnosis and maintenance intelligent early warning method for faults of port machinery |
| CN205906842U (en) * | 2016-07-01 | 2017-01-25 | 赵海波 | Elevator reports management system for repairment |
| CN107640672A (en) * | 2017-09-22 | 2018-01-30 | 深圳市正弦电气股份有限公司 | A kind of elevator and its maintenance monitoring system and operation method |
| CN107680520A (en) * | 2017-09-26 | 2018-02-09 | 上海欧美拉光电股份有限公司 | The diagnostic method of Precise Diagnosis LED failure on a kind of intelligent APP lines |
| KR101894697B1 (en) * | 2018-02-27 | 2018-09-04 | 안재봉 | Fault estimated device based on programmable logic controller and method thereof |
| CN109190958A (en) * | 2018-08-23 | 2019-01-11 | 合肥好多帮信息科技有限公司 | A kind of troublshooting Intelligentized regulating and controlling system |
| CN110145838A (en) * | 2019-05-31 | 2019-08-20 | 宁波奥克斯电气股份有限公司 | Fault detection method, processing method and the air conditioner of air conditioner |
| CN110569139A (en) * | 2019-08-02 | 2019-12-13 | 中国船舶工业系统工程研究院 | vitality guarantee system and method for information system |
| CN110569993A (en) * | 2019-08-21 | 2019-12-13 | 广东技术师范大学天河学院 | Intelligent maintenance and maintenance system based on big data |
| CN110925950A (en) * | 2019-11-20 | 2020-03-27 | 广东美的暖通设备有限公司 | Control method and device of air conditioning system, electronic equipment and storage medium |
| CN111144639A (en) * | 2019-12-24 | 2020-05-12 | 国电南京自动化股份有限公司 | Subway equipment fault prediction method and system based on ALLN algorithm |
| CN110895032A (en) * | 2019-12-31 | 2020-03-20 | 福建省南鸿通讯科技有限公司 | Method and device for automatically diagnosing and positioning air conditioner fault of communication machine room |
| CN111428894A (en) * | 2020-03-25 | 2020-07-17 | 蘑菇物联技术（深圳）有限公司 | Equipment maintenance method and system based on cloud computing |
| CN111412579A (en) * | 2020-03-26 | 2020-07-14 | 上海建工四建集团有限公司 | Air conditioning unit fault type diagnosis method and system based on big data |
| CN111352003A (en) * | 2020-05-25 | 2020-06-30 | 北京中航科电测控技术股份有限公司 | Analysis system for electrical equipment faults |
| CN112183780A (en) * | 2020-09-27 | 2021-01-05 | 珠海格力电器股份有限公司 | Fault maintenance guiding method, device and system and storage medium |
| CN114814391A (en) * | 2021-01-18 | 2022-07-29 | 国网青海省电力公司西宁供电公司 | Charging pile fault identification method and storage medium |
| CN113268590A (en) * | 2021-04-06 | 2021-08-17 | 云南电网有限责任公司昆明供电局 | Power grid equipment running state evaluation method based on equipment portrait and integrated learning |
| CN113362083A (en) * | 2021-06-04 | 2021-09-07 | 苏州科达科技股份有限公司 | Repair reporting method and device, electronic equipment and storage medium |
| CN113657221A (en) * | 2021-08-04 | 2021-11-16 | 浙江浙能台州第二发电有限责任公司 | Power plant equipment state monitoring method based on intelligent sensing technology |
| CN114548435A (en) * | 2022-02-14 | 2022-05-27 | 北京精一强远科技有限公司 | Intelligent repair reporting system and method based on equipment monitoring |
| CN114745836A (en) * | 2022-04-09 | 2022-07-12 | 深圳市粤大明智慧科技集团有限公司 | Control method and system of intelligent street lamp |
| CN115508768A (en) * | 2022-09-16 | 2022-12-23 | 安徽南瑞中天电力电子有限公司 | Fault identification method, system and storage medium based on self-checking of intelligent ammeter |
| CN116055900A (en) * | 2023-03-30 | 2023-05-02 | 北京城建智控科技股份有限公司 | Image quality correction method based on image pickup device |
Non-Patent Citations (1)
| Title |
|---|
| 肖坚红;赵永红;薛晓茹;孙承露;吴少雄;武文广;: "电能表健康度分析及整体运行状态预测方法", 电网与清洁能源, no. 07 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116384980B (en) | 2023-08-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11927609B2 (en) | Condition monitoring via energy consumption audit in electrical devices and electrical waveform audit in power networks | |
| CN109818942B (en) | User account abnormity detection method and device based on time sequence characteristics | |
| US20060173559A1 (en) | Methods and systems for a prediction model | |
| CN111459700A (en) | Method and apparatus for diagnosing device failure, diagnostic device, and storage medium | |
| CN113935440A (en) | Iterative evaluation method and system for error state of voltage transformer | |
| CN112085111B (en) | Load identification method and device | |
| CN113283491A (en) | Fault diagnosis method of electric vehicle alternating current charging pile based on optimized deep confidence network | |
| Tang et al. | Rolling bearing remaining useful life prediction via weight tracking relevance vector machine | |
| CN115791174B (en) | Rolling bearing abnormality diagnosis method, system, electronic equipment and storage medium | |
| CN113111591A (en) | Automatic diagnosis method, device and equipment based on internal fault of modular power distribution terminal | |
| CN116186603A (en) | Abnormal user identification method and device, computer storage medium and electronic equipment | |
| CN113408804B (en) | Electricity stealing behavior detection method, system, terminal equipment and storage medium | |
| Chen et al. | Convolution-graph attention network with sensor embeddings for remaining useful life prediction of turbofan engines | |
| CN116384980B (en) | Repair reporting method and system | |
| Yusuf et al. | Fault classification improvement in industrial condition monitoring via Hidden Markov Models and Naïve Bayesian modeling | |
| US11334053B2 (en) | Failure prediction model generating apparatus and method thereof | |
| CN114113868A (en) | Transformer fault diagnosis method | |
| US8682817B1 (en) | Product testing process using diploid evolutionary method | |
| Santana et al. | Unsupervised fault detection in refrigeration showcase with single class data using autoencoders | |
| CN117691594B (en) | Energy saving and consumption reduction judging method and system for transformer | |
| Nazeri et al. | Black-Box Stealthy Frequency Spectrum Attack on LSTM-based Power Load Forecasting in an Energy Management System with Islanded Microgrid | |
| CN114528914B (en) | Method, terminal and storage medium for monitoring state of cold water host in loop | |
| CN117763403A (en) | Method for identifying heavy load electricity consumption of residents | |
| Dong et al. | Periodic Segmentation Transformer-Based Internal Short Circuit Detection Method for Battery Packs | |
| Tourpeslis | Data Mining for Smart Cities: Energy Disaggregation and Recommendation System |
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 |