CN109920193B - Intelligent detection method, system, equipment and medium for electrical fire hazard - Google Patents
Intelligent detection method, system, equipment and medium for electrical fire hazard Download PDFInfo
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- CN109920193B CN109920193B CN201910228178.4A CN201910228178A CN109920193B CN 109920193 B CN109920193 B CN 109920193B CN 201910228178 A CN201910228178 A CN 201910228178A CN 109920193 B CN109920193 B CN 109920193B
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Abstract
The embodiment of the invention discloses an intelligent detection method, a system, equipment and a medium for electrical fire hidden dangers, wherein the method comprises the following steps: the method comprises the steps of obtaining monitoring data when an electrical system operates, preprocessing the monitoring data to obtain at least one target parameter, inputting the at least one target parameter into a pre-trained neural network model, and calculating the at least one target parameter through the neural network model to obtain the hidden danger category of the potential safety hazard of the electrical system.
Description
Technical Field
The embodiment of the invention relates to the technical field of electrical safety, in particular to an intelligent detection method, system, equipment and medium for electrical fire hazard.
Background
With the development of modern science and technology, electric energy is correspondingly widely developed and utilized. The wide application of the electric energy not only benefits the human society, but also brings huge potential safety hazards to the human society. An electrical fire is a common electrical disaster, and generally refers to a fire caused by the ignition of an electrical body or other combustible materials due to heat released after the failure of electrical lines, electrical equipment and power supply and distribution equipment. Due to the particularity of the electrical fire, the early warning difficulty of the electrical fire is higher because the early warning device cannot be seen and touched before the electrical fire happens.
At present, a commonly used electric fire early warning method is mainly based on that a residual current type electric fire monitoring system continuously monitors the residual current or the temperature of an electric circuit in real time for a long time, and when the residual current or the temperature exceeds a set threshold value, an alarm is given immediately, otherwise, the alarm is not given.
Therefore, the electrical fire early warning method cannot actively detect the potential safety hazard of the electrical system, so that the potential safety hazard of the electrical system which does not reach the potential safety hazard causing fire can not be detected in time.
Disclosure of Invention
The embodiment of the invention provides an intelligent detection method, system, equipment and medium for potential electrical fire hazards, and the method can be used for accurately detecting the potential safety hazards of an electrical system.
In a first aspect, an embodiment of the present invention provides an intelligent detection method for an electrical fire hazard, where the method includes:
acquiring monitoring data of an electric system in operation;
preprocessing the monitoring data to obtain at least one target parameter;
and inputting the at least one target parameter into a pre-trained neural network model, and calculating the at least one target parameter through the neural network model to obtain the hidden danger category of the potential safety hazard of the electrical system.
Further, the acquiring monitoring data of the operation of the electrical system includes:
acquiring monitoring data of an electrical system in real time according to a set frequency through a monitoring terminal;
and verifying the monitoring data based on a set rule to obtain the monitoring data when the electrical system operates.
Further, the preprocessing the monitoring data to obtain at least one target parameter includes:
classifying the monitoring data based on a data type;
and calculating the classified monitoring data according to a preset formula to obtain at least one target parameter.
Further, before inputting the at least one target parameter to the trained neural network model, the method further comprises:
and judging whether the electrical system has potential safety hazards or not according to the at least one target parameter, if so, continuing to perform the operation of inputting the at least one target parameter to the trained neural network model, and otherwise, ending the process.
Further, judging whether the electrical system has potential safety hazard according to the at least one target parameter includes:
and judging whether the set key value in the at least one target parameter exceeds a set threshold value, and if so, determining that potential safety hazards exist in the electrical system.
Further, the target parameters include: phase temperature, phase current, reactive power, power factor, or ratio of phase temperature increment to phase current increment;
the hidden danger categories include: at least one of contact failure, short circuit, leakage, overload, and circuit aging.
Further, the method further comprises:
and calculating the at least one target parameter through the neural network model to obtain the hidden danger level and the processing mode corresponding to the hidden danger type of the electrical system.
In a second aspect, an embodiment of the present invention provides an intelligent detection system for electrical fire hazards, where the system includes:
the acquisition module is used for acquiring monitoring data during the operation of the electrical system;
the preprocessing module is used for preprocessing the monitoring data to obtain at least one target parameter;
and the detection module is used for inputting the at least one target parameter to the trained neural network model so as to calculate the at least one target parameter through the neural network model to obtain the hidden danger category of the potential safety hazard of the electrical system.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the intelligent method for detecting an electrical fire hazard according to the first aspect.
In a fourth aspect, embodiments of the present invention provide a storage medium containing computer-executable instructions, which when executed by a computer processor, implement the intelligent method for detecting an electrical fire hazard according to the first aspect.
According to the intelligent detection method for the potential safety hazard of the electrical fire, provided by the embodiment of the invention, through the technical means that the monitoring data during the operation of the electrical system is obtained, at least one target parameter is determined according to the monitoring data, and the at least one target parameter is input into the pre-trained neural network model, so that the potential safety hazard type of the potential safety hazard of the electrical system is obtained through the operation of the neural network model, the problem that the potential safety hazard of the electrical system cannot be actively detected by the electrical fire early warning method in the prior art is solved, the timely detection of the potential safety hazard of the electrical system which does not cause the fire is realized, and the purpose of accurately detecting the potential safety hazard of the electrical system is achieved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the contents of the embodiments of the present invention and the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an intelligent detection method for electrical fire hazards according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of another intelligent detection method for electrical fire hazards according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of an intelligent detection method for electrical fire hazards according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an intelligent detection system for electrical fire hazards according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order to make the technical problems solved, technical solutions adopted and technical effects achieved by the present invention clearer, the technical solutions of the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Fig. 1 is a schematic flow chart of an intelligent detection method for electrical fire hazards according to an embodiment of the present invention. The intelligent detection method for the electrical fire hazard disclosed in this embodiment is suitable for detecting potential safety hazards of an electrical system in various scenes, for example, a mall, a hospital, a factory, a hotel, a residence, an office building, an entertainment place, a warehouse, a school, or the like. Referring specifically to fig. 1, the method may include the steps of:
and S102, acquiring monitoring data during the operation of the electrical system.
The monitoring data during the operation of the electrical system is the monitoring data during the operation of the electrical system. In the power supply process of the electrical system, a large amount of heat is released due to faults of an electrical circuit, electrical equipment and power supply and distribution equipment, so that a fire disaster is caused by ignition of an electrical body or other combustible substances. The monitoring data includes, but is not limited to, data such as current data, temperature data, power data, and the like, and new monitoring data may also appear with the continuous innovation of the electrical system.
Specifically, the monitoring data may be obtained by a sensor disposed at a corresponding position of the electrical system, for example, temperature data of the monitored line is obtained by a temperature sensor disposed in the electrical line, or remaining circuit data of the monitored line is obtained by a detector disposed in the electrical line, and the like.
S104, preprocessing the monitoring data to obtain at least one target parameter.
The monitoring data refers to monitored original data of the electric system in operation, the target parameter refers to data used for analyzing whether a potential safety hazard exists in the electric system and the potential safety hazard category corresponding to the potential safety hazard, and the target parameter may be the monitoring data itself, for example, the monitoring data is a temperature value of an electric circuit, and meanwhile, the target parameter is a temperature value of the electric circuit; the target parameter can also be obtained by performing certain operation processing on the monitoring data.
Illustratively, preprocessing the monitoring data to obtain at least one target parameter includes:
and calculating the monitoring data according to a preset formula to determine at least one target parameter.
For example, when the monitoring data includes a line current, the target parameter may be a phase current of a certain phase calculated according to a conversion formula between the line current and the phase current based on the line current, or the target parameter may be an average value of the line current; when the monitoring data includes current and voltage, the target parameter may be power data obtained by a calculation formula of power based on the current and the voltage. Preferably, the target parameters include: at least one of phase temperature, phase current, reactive power, power factor, or a ratio of phase temperature increment to phase current increment.
Illustratively, if the acquired monitoring data of the electric system during operation includes reactive power P1 and real power P2, a ratio K1 of the reactive power P1 and the real power P2 may be calculated according to a preset formula (K1 — P1/P2), and then K1 is the target parameter. Or, if the acquired monitoring data further includes: the phase temperature T and the phase current I may be calculated according to a preset formula (K2 ═ T/I) by using a ratio K2 of the phase temperature T and the phase current I, and K2 is the target parameter. Still alternatively, if the obtained monitoring data further includes an active power increment Δ P1 and a reactive power increment Δ P2, a ratio K3 of the active power increment Δ P1 and the reactive power increment Δ P2 may be calculated according to a preset formula (K3 ═ Δ P1/Δ P2), and at this time, K3 is the target parameter.
And S106, inputting the at least one target parameter into a pre-trained neural network model, and calculating the at least one target parameter through the neural network model to obtain the hidden danger category of the potential safety hazard of the electrical system.
The neural network model is obtained by training a large number of training samples in advance, wherein the training samples comprise target parameters and hidden danger types existing in the electrical system when the target parameters exist. For example, when the target parameters include: for the temperatures of the a-phase, the currents of the a-phase, the ratio of the temperature increment of the a-phase to the phase current increment, the reactive power, and the power factor, the corresponding training sample examples are shown in table 1:
table 1: training sample example
It is understood that the neural network model may be a deep neural network model or a convolutional neural network model, etc. The input to the neural network model may be one or more target parameters. Table 1 above only serves as an example, and does not limit the present embodiment, and as application scenes are continuously increased, new types of hidden danger categories may appear in the future, and the method for detecting a fire safety hidden danger provided by the present embodiment only needs to continuously train a neural network model for the new application scenes or the new hidden danger categories, so that the method for intelligently detecting an electrical fire hidden danger provided by the present embodiment can be applied to detecting an electrical system in any application scenes (for example, schools, markets, or hospitals, etc.), is applicable to detecting any types of hidden dangers, and has extremely strong inclusion and expandability.
Further, the neural network model is used for calculating the at least one target parameter, so that the hidden danger level and the processing mode corresponding to the hidden danger type of the electric system can be obtained, and the hidden danger level and the processing mode can be labeled on a training sample. For example, the hidden danger levels corresponding to poor contact may include: level 1 (corresponding phenomenon is increased on-line impedance), level 2 (corresponding phenomenon is increased active power), level 3 (corresponding phenomenon is arc signal), level 4 (corresponding phenomenon is temperature and current exceeding alarm threshold), level 5 (corresponding phenomenon is fire can be triggered), and the like; the hidden danger level corresponding to the short circuit may include: level 1 (leakage current occurs), level 2 (higher than harmonic residual current), level 3 (higher than alarm threshold), level 4 (fire may be triggered and hurt people), and the like. For example, when the hidden danger category is 'circuit aging', the corresponding processing mode is 'timely line wire replacement' and the like, the neural network model can also give the hidden danger level corresponding to the hidden danger category, for example, the electric leakage level is 'serious', and at the moment, an alarm can be further triggered to perform early warning so as to improve the attention of related personnel.
According to the intelligent detection method for the potential safety hazard of the electrical fire, provided by the embodiment of the invention, through the technical means that the monitoring data during the operation of the electrical system is obtained, at least one target parameter is determined according to the monitoring data, and the at least one target parameter is input into the pre-trained neural network model, so that the potential safety hazard type of the potential safety hazard of the electrical system is obtained through the operation of the neural network model, the problem that the potential safety hazard of the electrical system cannot be actively detected by the electrical fire early warning method in the prior art is solved, the timely detection of the potential safety hazard of the electrical system which does not cause the fire is realized, and the purpose of accurately detecting the potential safety hazard of the electrical system is achieved.
Further, on the basis of the technical solution of the above embodiment, in order to improve the detection efficiency and reduce the computation amount of the system, after the target parameters are obtained, before the at least one target parameter is input to the trained neural network model, it may be preferentially determined whether the electrical system has a potential safety hazard according to the target parameters, if so, the operation of inputting the at least one target parameter to the trained neural network model is continuously performed, otherwise, the process is ended. Specifically, referring to a schematic flow chart of another intelligent detection method for electrical fire hazards shown in fig. 2, the method includes:
s201, acquiring monitoring data during the operation of the electrical system.
S202, preprocessing the monitoring data to obtain at least one target parameter.
S203, judging whether the electrical system has potential safety hazards or not according to the at least one target parameter; if yes, continuing to execute S204; if not, S205 is performed.
Specifically, whether a set key value in the at least one target parameter exceeds a set threshold value or not can be judged, and if yes, the potential safety hazard of the electrical system is determined. The setting key value may be a parameter, such as the temperature value in the above example, or may be a combination of several parameters. For example, if the set key value in the target parameter is the temperature-current ratio K2, and the corresponding set threshold range is 6-10, if the obtained temperature-current ratio K2 is 5, since the temperature-current ratio K2 does not exceed the corresponding set threshold range (6-10), it is determined that the electrical system does not have a potential safety hazard; if the obtained temperature current ratio K2 is 11, the potential safety hazard of the electrical system is determined to exist because the temperature current ratio K2 exceeds the corresponding set threshold range, and if the temperature reaches 30 degrees and the phase current reaches 100mA, the potential safety hazard of the current electrical system is determined to exist. The setting key value and the corresponding threshold value can be set according to engineering experience.
It can be understood that the grade of the target parameter can be further determined, and whether the potential safety hazard exists in the current electrical system is determined according to the grade determination result, for example, if the target parameter is the temperature of a certain phase and the corresponding temperature value is 50 degrees, the grade determination result obtained through the grade determination is grade 2, and the level of the potential safety hazard is reached, so that the potential safety hazard exists in the current electrical system is determined; if the corresponding temperature value is 10 degrees, the grade discrimination result obtained through grade discrimination is 0 grade, and the grade does not reach the potential safety hazard grade, so that the potential safety hazard does not exist in the current electrical system.
S204, inputting the at least one target parameter into a pre-trained neural network model, and calculating the at least one target parameter through the neural network model to obtain the potential safety hazard category of the electrical system.
And S205, providing prompt information that the current electrical system has no potential safety hazard, and ending the process. If no potential safety hazard exists, sending out prompt information without potential safety hazard, ending the process, and returning to S201 to reacquire monitoring data during the operation of the electrical system.
Example two
Fig. 3 is a schematic flow chart of an intelligent detection method for electrical fire hazards according to a second embodiment of the present invention. The technical solution of this embodiment is further optimized on the basis of the above embodiment, specifically, the monitoring data is preprocessed in S102 "acquiring the monitoring data during the operation of the electrical system" and S104 "to obtain at least one target parameter" and is optimized, and the optimization has the advantage that the monitoring data during the operation of the electrical system can be efficiently acquired. In the method, reference is made to embodiment one for those portions not described in detail. Referring specifically to fig. 3, the method may include the steps of:
and S1021, acquiring the monitoring data of the electrical system in real time according to the set frequency through the monitoring terminal.
The monitoring terminal is a device which is configured at the front end of the electrical system and used for monitoring various electrical characteristic data of the electrical system. The monitoring frequency of the monitoring terminal can be set by itself, and the good performance of the monitoring terminal ensures the diversity and accuracy of monitoring data.
After the monitoring terminal acquires the monitoring data, the monitoring data can be further packaged and uploaded to the cloud server, and the cloud server completes subsequent data verification, target parameter calculation and detection of hidden danger categories; or after the monitoring terminal acquires the monitoring data, the monitoring terminal completes subsequent data verification and target parameter calculation operations, and uploads a calculation result, namely a target parameter, to the cloud server, and the cloud server detects the category of the hidden danger.
And S1022, verifying the monitoring data based on a set rule to obtain the monitoring data during the operation of the electrical system.
The monitoring data of the electrical system collected by the monitoring terminal may be data of the electrical system in operation or data of the electrical system in shutdown due to faults, and the purpose of the verification is to filter the data of the electrical system in shutdown and reserve the data of the electrical system in operation. When the monitoring terminal uploads the collected monitoring data to the cloud server, the attribute identification is usually added to the monitoring data to identify whether the current monitoring data is the monitoring data when the electrical system runs or the monitoring data when the electrical system is shut down, so that the monitoring data when the electrical system is shut down can be filtered according to the attribute identification of the data, and the monitoring data when the electrical system runs is reserved.
S1041, classifying the monitoring data based on the data type.
The acquired monitoring data during the operation of the electrical system may include: the current data, the voltage data, the temperature data, the power data and the like, and the monitoring data can be classified according to data types, such as residual current, insulation resistance, online temperature, single-phase active power, residual current harmonic and the like.
S1042, calculating the classified monitoring data according to a preset formula to obtain at least one target parameter.
The preset formula is a corresponding relation between the classified monitoring data and at least one target parameter. Illustratively, if the acquired monitoring data of the electric system during operation includes reactive power P1 and real power P2, a ratio K1 of the reactive power P1 and the real power P2 may be calculated according to a preset formula (K1 — P1/P2), and then K1 is the target parameter. Or, if the acquired monitoring data further includes: the phase temperature T and the phase current I may be calculated according to a preset formula (K2 ═ T/I) by using a ratio K2 of the phase temperature T and the phase current I, and K2 is the target parameter. Still alternatively, if the obtained monitoring data further includes an active power increment Δ P1 and a reactive power increment Δ P2, a ratio K3 of the active power increment Δ P1 and the reactive power increment Δ P2 may be calculated according to a preset formula (K3 ═ Δ P1/Δ P2), and at this time, K3 is the target parameter.
S306, inputting the at least one target parameter to a pre-trained neural network model, and calculating the at least one target parameter through the neural network model to obtain the potential safety hazard category of the electrical system.
On the basis of the embodiment, the monitoring terminal acquires the monitoring data of the electrical system in real time according to the set frequency, verifies the monitoring data based on the set rule to acquire the monitoring data during the operation of the electrical system, classifies the monitoring data based on the data type, and calculates the classified monitoring data according to the preset formula to obtain at least one target parameter, so that the high-efficiency acquisition and preprocessing of the original data are realized, and the detection efficiency and the detection accuracy of the neural network model on the hidden danger category are improved.
EXAMPLE III
Fig. 4 is a schematic structural diagram of an intelligent detection system for electrical fire hazards, according to a third embodiment of the present invention. Referring to fig. 4, the system comprises: an acquisition module 410, a preprocessing module 420 and a detection module 430;
the acquiring module 410 is used for acquiring monitoring data of the electrical system during operation; a preprocessing module 420, configured to preprocess the monitoring data to obtain at least one target parameter; the detection module 430 is configured to input the at least one target parameter into the trained neural network model, so as to calculate the at least one target parameter through the neural network model, and obtain a category of the potential safety hazard of the electrical system.
On the basis of the above technical solutions, the obtaining module 410 may be further configured to obtain, by the monitoring terminal, monitoring data of the electrical system in real time according to a set frequency;
and verifying the monitoring data based on a set rule to obtain the monitoring data when the electrical system operates.
On the basis of the above technical solutions, the preprocessing module 420 may be further configured to classify the monitoring data based on a data type;
and calculating the classified monitoring data according to a preset formula to obtain at least one target parameter.
On the basis of the above technical solutions, the system further includes: and the judging module is used for judging whether the electrical system has potential safety hazards or not according to the at least one target parameter, if so, continuing to execute the operation of inputting the at least one target parameter to the trained neural network model, and if not, ending the process.
On the basis of the above technical solutions, the determining module may be further configured to determine whether a set key value in the at least one target parameter exceeds a set threshold, and if so, determine that the electrical system has a potential safety hazard.
On the basis of the above technical solutions, the target parameters include: phase temperature, phase current, reactive power, power factor, or ratio of phase temperature increment to phase current increment;
the hidden danger categories include: at least one of contact failure, short circuit, leakage, overload, and circuit aging.
On the basis of the above technical solutions, the system further includes: and the operation module is used for operating the at least one target parameter through the neural network model to obtain the hidden danger level and the processing mode corresponding to the hidden danger type of the electrical system.
According to the intelligent detection system for the potential safety hazard of the electrical fire, provided by the embodiment of the invention, the technical means of obtaining the potential safety hazard category of the electrical system by actively acquiring the monitoring data when the electrical system operates, determining at least one target parameter according to the monitoring data and inputting the at least one target parameter into the pre-trained neural network model so as to calculate the at least one target parameter through the neural network model is adopted, so that the problem that the potential safety hazard of the electrical system cannot be actively detected by the electrical fire early warning method in the prior art is solved, the timely detection of the potential safety hazard of the electrical system which does not cause the fire is realized, and the purpose of accurately detecting the potential safety hazard of the electrical system is achieved.
Example four
Fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary electronic device 12 suitable for use in implementing embodiments of the present invention. The electronic device 12 shown in fig. 5 is only an example and should not bring any limitation to the function and the scope of use of the embodiment of the present invention.
As shown in FIG. 5, electronic device 12 is embodied in the form of a general purpose computing device. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set of program modules (e.g., acquisition module 410, preprocessing module 420, and detection module 430 of an intelligent electrical fire hazard detection system) that are configured to perform the functions of embodiments of the present invention.
A program/utility 40 having a set of program modules 42 (e.g., acquisition module 410, preprocessing module 420, and detection module 430 of an electrical fire hazard intelligent detection system) may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, implementing an intelligent method for detecting an electrical fire hazard provided by an embodiment of the present invention, the method including:
acquiring monitoring data of an electric system in operation;
preprocessing the monitoring data to obtain at least one target parameter;
and inputting the at least one target parameter into a pre-trained neural network model, and calculating the at least one target parameter through the neural network model to obtain the hidden danger category of the potential safety hazard of the electrical system.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, to implement the intelligent detection method for electrical fire hazards provided by the embodiment of the present invention.
Of course, those skilled in the art can understand that the processor may also implement the technical solution of the intelligent detection method for electrical fire hazards provided by any embodiment of the present invention.
EXAMPLE five
The fifth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for intelligently detecting an electrical fire hazard provided in the fifth embodiment of the present invention, where the method includes:
acquiring monitoring data of an electric system in operation;
preprocessing the monitoring data to obtain at least one target parameter;
and inputting the at least one target parameter into a pre-trained neural network model, and calculating the at least one target parameter through the neural network model to obtain the hidden danger category of the potential safety hazard of the electrical system.
Of course, the computer program stored on the computer readable storage medium provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the intelligent method for detecting an electrical fire hazard provided by any embodiments of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device.
A computer readable signal medium may be included in which computer readable program code is embodied in the monitoring data, the target parameter, etc. Such propagated monitoring data, target parameters, may take a variety of forms including, but not limited to, data frames, and the like. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (5)
1. An intelligent detection method for electrical fire hidden dangers is characterized by comprising the following steps:
acquiring monitoring data of an electric system in operation;
preprocessing the monitoring data to obtain at least one target parameter;
inputting the at least one target parameter into a pre-trained neural network model, and calculating the at least one target parameter through the neural network model to obtain the hidden danger category of the potential safety hazard of the electrical system;
wherein, the monitoring data when obtaining the electric system operation includes: acquiring monitoring data of an electrical system in real time according to a set frequency through a monitoring terminal, and adding an attribute identifier to the monitoring data; checking the monitoring data based on a set rule, and filtering the monitoring data when the electrical system is shut down according to the attribute identification of the monitoring data so as to obtain the monitoring data when the electrical system is running;
the preprocessing the monitoring data to obtain at least one target parameter includes: classifying the monitoring data based on a data type; calculating the classified monitoring data according to a preset formula to obtain at least one target parameter;
before inputting the at least one target parameter to the trained neural network model, the method further comprises: judging whether the electrical system has potential safety hazards or not according to the at least one target parameter, if so, continuing to perform the operation of inputting the at least one target parameter to the trained neural network model, and if not, ending the process; wherein, judge whether there is the potential safety hazard in the electric system according to the at least one target parameter, include: judging whether a set key value in the at least one target parameter exceeds a set threshold value, if so, determining that potential safety hazards exist in the electrical system; or, grade discrimination is carried out on the at least one target parameter, and whether the potential safety hazard exists in the electrical system is determined according to a grade discrimination result;
the intelligent detection method for the electrical fire hidden danger further comprises the following steps: and calculating the at least one target parameter through the neural network model to obtain the hidden danger level and the processing mode corresponding to the hidden danger type of the electrical system.
2. The method of claim 1, wherein the target parameters comprise: phase temperature, phase current, reactive power, power factor, or ratio of phase temperature increment to phase current increment;
the hidden danger categories include: at least one of contact failure, short circuit, leakage, overload, and circuit aging.
3. An electrical fire hazard intelligent detection system, the system comprising:
the acquisition module is used for acquiring monitoring data during the operation of the electrical system;
the preprocessing module is used for preprocessing the monitoring data to obtain at least one target parameter;
the judging module is used for judging whether the electrical system has potential safety hazards or not according to the at least one target parameter, if so, continuing to execute the operation of inputting the at least one target parameter to the trained neural network model, and if not, ending the process; the judging module is further used for judging whether a set key value in the at least one target parameter exceeds a set threshold value, and if so, determining that potential safety hazards exist in the electrical system;
or, the system judges whether the potential safety hazard exists or not through the following modes: judging the grade of the at least one target parameter, and determining whether the electrical system has potential safety hazard according to the grade judgment result;
the detection module is used for inputting the at least one target parameter to a trained neural network model so as to calculate the at least one target parameter through the neural network model to obtain the hidden danger category of the potential safety hazard of the electrical system;
the operation module is used for operating the at least one target parameter through the neural network model to obtain the hidden danger level and the processing mode corresponding to the hidden danger category of the electrical system;
wherein the obtaining module is further configured to: acquiring monitoring data of an electrical system in real time according to a set frequency through a monitoring terminal, and adding an attribute identifier to the monitoring data; checking the monitoring data based on a set rule, and filtering the monitoring data when the electrical system is shut down according to the attribute identification of the monitoring data so as to obtain the monitoring data when the electrical system is running;
the preprocessing module is further configured to: classifying the monitoring data based on a data type; and calculating the classified monitoring data according to a preset formula to obtain at least one target parameter.
4. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the intelligent method for detecting an electrical fire hazard of any one of claims 1-2 when executing the computer program.
5. A storage medium containing computer executable instructions which, when executed by a computer processor, implement the intelligent method of electrical fire hazard detection of any one of claims 1-2.
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