CN116067690B - Intelligent electric cooker operation fault prediction system based on big data - Google Patents
Intelligent electric cooker operation fault prediction system based on big data Download PDFInfo
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Abstract
The invention belongs to the technical field of electric cooker supervision, in particular to an intelligent electric cooker operation fault prediction system based on big data, which comprises a processor, wherein the processor is in communication connection with a data storage module, a power-time monitoring and analyzing module, a power consumption monitoring and analyzing module, a temperature distribution detecting module and a fault prediction module, and the fault prediction module is in communication connection with a fault early warning module; according to the invention, temperature distribution detection analysis is carried out through the temperature distribution detection module, operation analysis and electric heating pot power analysis are carried out on the electric heating pot through the power time monitoring analysis module, operation consumption analysis is carried out on the electric heating pot through the operation consumption monitoring analysis module, the fault prediction module predicts operation faults and generates fault high-probability signals or fault low-probability signals, the current time and subsequent operation fault prediction of the electric heating pot are realized, the prediction analysis process is more comprehensive, the prediction analysis result is more accurate, and the operation risk of the electric heating pot is obviously reduced.
Description
Technical Field
The invention relates to the technical field of electric cooker supervision, in particular to an intelligent electric cooker operation fault prediction system based on big data.
Background
The electric heating cooker is mainly used for cooking food, has high heat efficiency and long service life, can cook the food, can preserve heat, is clean and sanitary to use, has no radiation, and is one of the tools essential for modernization of household labor; however, the existing electric cooker has a simple structure and a single function, the temperature is regulated and controlled through the knob to cook food in the using process, the operation condition of the electric cooker is difficult to monitor and evaluate and judge, the current moment and the subsequent fault prediction of the electric cooker cannot be comprehensively and accurately carried out according to the operation condition of the electric cooker, the user cannot know the operation fault probability of the electric cooker in time, and the risk degree of the operation process of the electric cooker is increased;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide an intelligent electric cooker operation fault prediction system based on big data, which solves the problems that the current moment and the subsequent faults of an electric cooker cannot be comprehensively and accurately predicted according to the operation condition of the electric cooker in the prior art, a user cannot know the operation fault probability of the electric cooker in time, and the operation risk degree of the electric cooker is increased.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the intelligent prediction system for the operation faults of the electric cooker based on big data comprises a processor, a data storage module, a power time monitoring and analyzing module, a transportation consumption monitoring and analyzing module, a temperature distribution detecting module, a fault prediction module and a fault early warning module, wherein the processor is in communication connection with the data storage module, the power time monitoring and analyzing module, the transportation consumption monitoring and analyzing module, the temperature distribution detecting module and the fault prediction module, and the fault prediction module is in communication connection with the fault early warning module; the processor monitors the analysis signal and the temperature distribution detection signal when generating successfully, transmits the power-time monitoring analysis signal to the power-time monitoring analysis signal and transmits the temperature distribution detection signal to the temperature distribution detection module;
the time-of-operation monitoring analysis module is used for performing time-of-operation analysis on the next operation of the electric cooker and generating a time-of-operation qualified signal or a time-of-operation unqualified signal, sending the time-of-operation unqualified signal to the fault prediction module through the processor when the time-of-operation unqualified signal is generated, generating the time-of-operation qualified signal or the time-of-operation unqualified signal through the power analysis of the electric cooker when the time-of-operation qualified signal is generated, and sending the time-of-operation unqualified signal to the fault prediction module through the processor when the time-of-operation unqualified signal is generated;
when the operation qualified signal is generated, the processor generates an operation consumption analysis signal and sends the operation consumption analysis signal to the operation consumption monitoring analysis module, the operation consumption monitoring analysis module receives the operation consumption analysis signal and then analyzes the operation consumption of the electric heating cooker and generates an operation consumption qualified signal or an operation consumption unqualified signal, and when the operation consumption unqualified signal is generated, the processor sends the operation consumption unqualified signal to the fault prediction module;
the temperature distribution detection module is used for carrying out temperature distribution detection analysis after receiving the temperature distribution detection signal, generating a temperature qualified signal or a temperature unqualified signal, and sending the temperature unqualified signal to the fault prediction module through the processor; the fault prediction module is used for carrying out fault prediction analysis on the electric cooker, generating a fault high-probability signal if a time-of-operation unqualified signal or a time-of-operation consumption unqualified signal and a temperature unqualified signal are received, otherwise generating a fault low-probability signal, sending an early warning instruction to the fault early warning module when the fault high-probability signal is generated, and sending early warning to remind a corresponding user when the fault early warning module receives the early warning instruction.
Further, the operation analysis process of the power monitoring analysis module is as follows:
acquiring the starting operation time and the current operation time of the electric heating cooker, respectively marking the starting operation time and the current operation time of the electric heating cooker as a time value and a real-time value, and calculating the difference value between the real-time value and the time value to acquire a time-of-operation feedback value; and the data storage module is used for calling a preset time-of-operation feedback threshold value, comparing the time-of-operation feedback value with the preset time-of-operation feedback threshold value, generating a time-of-operation disqualification signal if the time-of-operation feedback value is greater than or equal to the preset time-of-operation feedback threshold value, and generating a time-of-operation qualification signal if the time-of-operation feedback value is smaller than the preset time-of-operation feedback threshold value.
Further, the analysis process of the electric pan power analysis is as follows:
establishing a rectangular coordinate system by taking time as an X axis and the operating power of the electric cooker as a Y axis, acquiring the operating power of a plurality of detection time points in the current operation process of the electric cooker, wherein the time intervals of two adjacent groups of detection time points are the same, and correspondingly making analysis coordinate points in the rectangular coordinate system based on the time corresponding to the detection time points and the operating power;
acquiring an offside analysis value through analysis, calling a preset offside analysis threshold through a data storage module, comparing the offside analysis value with the offside analysis threshold, and generating a work disqualification signal if the offside analysis value is greater than or equal to the offside analysis threshold;
if the offside analysis value is smaller than the offside analysis threshold value, acquiring a power analysis value through analysis, calling a preset power analysis threshold value through a data storage module, comparing the power analysis value with the preset power analysis threshold value, generating a work qualified signal if the power analysis value is larger than or equal to the preset power analysis threshold value, and generating a work disqualification signal if the power analysis value is smaller than the preset power analysis threshold value.
Further, the method for obtaining the offside analysis value comprises the following steps:
the method comprises the steps of obtaining a preset power value, marking the value as Pz, taking (0, pz) as an endpoint to make a power reference ray parallel to an X axis, marking an analysis coordinate point above the power reference ray as an offside analysis point, and marking an analysis coordinate point below the power reference ray as a qualified analysis point; counting the number of offside analysis points and the number of qualified analysis points, carrying out numerical calculation on the number of offside analysis points and the number of qualified analysis points to obtain an offside occupation ratio, and carrying out numerical calculation on the offside occupation ratio and a time feedback value to obtain an offside analysis value.
Further, the method for analyzing and acquiring the power analysis value is as follows:
taking the offside analysis point as an endpoint, downward making a line segment vertical to the power reference ray and marking the line segment as an offside line segment, and upward making a line segment vertical to the power reference ray by taking the qualified analysis point as an endpoint and marking the line segment as a qualified line segment; acquiring the length of an offside line segment corresponding to each group of offside analysis points and marking the offside line segment as an offside distance value, and acquiring the length of a qualified line segment corresponding to each combination lattice analysis point and marking the length as a qualified distance value; establishing an offside value set for all offside distance values, establishing a qualified value set for all qualified distance values, and respectively carrying out summation calculation on the offside value set and the qualified value set to obtain an offside sum value and a qualified sum value; and carrying out numerical calculation on the offside sum value, the qualification sum value and the time-of-operation feedback value to obtain a power analysis value.
Further, the specific process of the operation consumption monitoring and analyzing module is as follows:
connecting two adjacent groups of analysis coordinate points one by one in a rectangular coordinate system through a smooth curve to obtain a power time curve of the electric heating cooker in the rectangular coordinate system, wherein the initial end point of the power time curve is positioned on a Y axis, a vertical line segment perpendicular to an X axis is downwards made along the tail end point of the power time curve, an area surrounded by the X axis, the Y axis, the power time curve and the corresponding vertical line segment is marked as an analysis area, and the area of the analysis area is calculated and marked as an operation analysis face value;
the electric energy consumed by the electric cooker from the starting operation time to the current operation time is obtained and marked as an electric consumption value, and the electric consumption value and the operation area value are subjected to numerical calculation to obtain an operation consumption analysis value; and the data storage module is used for calling a preset operation consumption analysis threshold value, comparing the operation consumption analysis value with the preset operation consumption analysis threshold value, generating an operation consumption disqualification signal if the operation consumption analysis value is greater than or equal to the preset operation consumption analysis threshold value, and generating an operation consumption qualification signal if the operation consumption analysis value is less than the preset operation consumption analysis threshold value.
Further, the specific analysis process of the temperature distribution detection analysis is as follows:
the temperature of a plurality of positions inside the current operation time of the electric cooker is obtained and marked as real Wen Liangzhi, a preset real temperature threshold value is called through a data storage module, the real Wen Liangzhi is compared with the real temperature threshold value, if each set of real Wen Liangzhi is smaller than the real temperature threshold value, temperature difference analysis is carried out on each set of real temperature values, and otherwise, a temperature disqualification signal is generated.
Further, the specific analysis process of the temperature difference analysis is as follows:
each group of real temperature values is obtained Wen Liangzhi, a real temperature set is established, variance calculation is carried out on the real temperature set to obtain a real temperature difference value, a preset real temperature difference threshold value is called through a data storage module, if the real temperature difference value is larger than or equal to the preset real temperature difference threshold value, a temperature disqualification signal is generated, and if the real temperature difference value is smaller than the preset real temperature difference threshold value, a temperature qualification signal is generated.
Further, the processor is in communication connection with the user terminal, the fault prediction module generates a fault high-probability signal and then sends an early warning instruction to the processor, the processor sends the early warning instruction to the user terminal, and the user terminal sends early warning.
Compared with the prior art, the invention has the beneficial effects that:
the invention carries out temperature distribution detection analysis to generate a temperature qualified signal or a temperature unqualified signal through a temperature distribution detection module, carries out operation analysis to the electric heating cooker through a power-time monitoring analysis module, generates an operation time qualified signal or an operation time unqualified signal, generates an operation power qualified signal or an operation power unqualified signal through electric heating cooker power analysis when generating the operation time qualified signal, carries out operation consumption analysis to the electric heating cooker through an operation consumption monitoring analysis module when generating the operation power qualified signal, and generates an operation consumption qualified signal or an operation consumption unqualified signal; when the fault prediction module receives the operation failure signal or the operation consumption failure signal or the temperature failure signal, a fault high-probability signal is generated, the current time and the subsequent operation faults of the electric heating cooker are predicted, the prediction analysis process is more comprehensive, the prediction analysis result is more accurate, and early warning is sent out through the fault early warning module when the fault high-probability signal is generated, so that the operation risk of the electric heating cooker is obviously reduced.
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For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
FIG. 2 is a system block diagram of a second embodiment of the present invention;
fig. 3 is a system block diagram of a third embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
as shown in FIG. 1, the intelligent prediction system for the operation faults of the electric cooker based on big data comprises a processor, wherein the processor is in communication connection with a data storage module, a power-time monitoring and analyzing module, a power consumption monitoring and analyzing module and a fault prediction module, and the fault prediction module is in communication connection with a fault early warning module; the processor generates a monitoring analysis signal when successful and sends the power-time monitoring analysis signal to the power-time monitoring analysis signal; after the power-time monitoring analysis module receives the power-time monitoring analysis signal, the operation analysis is carried out on the electric heating cooker when the electric heating cooker is operated for the next time, and a time-of-operation qualified signal or a time-of-operation unqualified signal is generated, wherein the time-of-operation analysis process is as follows:
acquiring the starting operation time and the current operation time of the electric cooker, respectively marking the starting operation time and the current operation time of the electric cooker as a time value and a real-time value, and calculating the difference between the real-time value and the time value to acquire a time-of-operation feedback value YF; the method comprises the steps that a preset time-of-operation feedback threshold value is called through a data storage module, the preset time-of-operation feedback threshold value is preset by a user or an electric cooker manufacturer and stored in the data storage module, and the preset time-of-operation feedback threshold value represents proper duration of single continuous operation of the electric cooker;
comparing the time-of-operation feedback value YF with a preset time-of-operation feedback threshold, if the time-of-operation feedback value YF is larger than or equal to the preset time-of-operation feedback threshold, the current continuous operation duration of the electric heating cooker is overlong, faults are easy to occur and safety risks are brought, a time-of-operation disqualification signal is generated, and if the time-of-operation feedback value YF is smaller than the preset time-of-operation feedback threshold, the current continuous operation duration of the electric heating cooker is within a proper range, and the time-of-operation qualification signal is generated.
When the operation time unqualified signal is generated, the operation time unqualified signal is sent to the fault prediction module through the processor, and when the operation time unqualified signal is generated, the operation work qualified signal or the operation work unqualified signal is generated through electric pan power analysis, and the analysis process of the electric pan power analysis is as follows:
s1, establishing a rectangular coordinate system by taking time as an X axis and the running power of an electric pan as a Y axis;
step S2, setting a plurality of detection time points in the current operation time period, and acquiring the operation power of the detection time points in the current operation process of the electric cooker, wherein the operation power is a data value for representing the output power of the electric cooker, the time intervals of two adjacent groups of detection time points are the same, and analysis coordinate points are correspondingly made in a rectangular coordinate system based on the time corresponding to the detection time points and the operation power;
step S3, obtaining an offside analysis value WF through analysis, wherein the method for obtaining the offside analysis value WF comprises the following steps:
step S31, obtaining a preset power value and marking the value as Pz, wherein the preset power value is preset by a user or a electric cooker producer, and when the actual running power of the electric cooker is larger than the preset power value, the electric cooker is greatly damaged; taking (0, PZ) as an endpoint to make rays parallel to an X axis and marking the rays as power reference rays, marking an analysis coordinate point above the power reference rays as an offside analysis point, and marking an analysis coordinate point below the power reference rays as a qualified analysis point;
s2, counting the number of offside analysis points and the number of qualified analysis points, respectively marking the number as YS and HS, and passing through a formulaCarrying out numerical calculation on the number YS of offside analysis points and the number HS of qualified analysis points to obtain an offside occupation ratio YZ; wherein e is a preset correction factor, and the value of e is 1.328; it should be noted that, the larger the value of the offside ratio YZ is, the larger the damage to the electric heating pan in the operation period from the starting operation time to the current operation time is;
step S33, through the formulaCalculating the numerical value of the offside occupation ratio YZ and the time-of-operation feedback value YF to obtain an offside analysis value WF; wherein, ag1 and ag2 are preset weight coefficients, the values of ag1 and ag2 are both larger than zero, and ag1 is larger than ag2;
s4, calling a preset offside analysis threshold through a data storage module, presetting the preset offside analysis threshold by a user or a electric cooker manufacturer, storing the preset offside analysis threshold into the data storage module to serve as a judgment value of an offside analysis value, comparing the offside analysis value WF with the offside analysis threshold, and generating a movement disqualification signal if the offside analysis value WF is greater than or equal to the offside analysis threshold; if the offside analysis value WF is smaller than the offside analysis threshold, the power analysis value GF is obtained through analysis, and the analysis and obtaining method of the power analysis value GF is as follows:
s41, taking an offside analysis point as an endpoint, downward making a line segment vertical to the power reference ray and marking the line segment as an offside line segment, and upward making a line segment vertical to the power reference ray by taking a qualified analysis point as an endpoint and marking the line segment as a qualified line segment;
step S42, obtaining the length of an offside line segment corresponding to each group of offside analysis points and marking the offside line segment as an offside distance value YJ, wherein the length of the offside line segment reflects the deviation degree of the running power of the corresponding offside analysis point compared with a preset power value, and obtaining the length of a qualified line segment corresponding to each combination lattice analysis point and marking the length of the qualified line segment as a qualified distance value HJ, and the length of the qualified line segment reflects the deviation degree of the running power of the corresponding qualified analysis point compared with the preset power value;
step S43, establishing an offside value set for all offside distance values, establishing a qualified value set for all qualified distance values, carrying out summation calculation on the offside value set to obtain an offside sum value YH, and carrying out summation calculation on a qualified value set to obtain a qualified sum value HH;
step S44, through normalization formulaSubstituting the offside sum value YH, the qualification sum value HH and the time-of-operation feedback value YF to perform numerical calculation, and obtaining a power analysis value GF after the numerical calculation; wherein tp1, tp2 and tp3 are preset proportionality coefficients, the values of tp1, tp2 and tp3 are all larger than zero, and tp1 is larger than tp2 and tp3; the larger the value of the power analysis value GF, the larger the damage to the electric heating pan in the operation period from the start operation time to the current operation time;
and S5, a preset power analysis threshold value is called through the data storage module, the preset power analysis threshold value is preset by a user or a electric cooker manufacturer and is stored in the data storage module to serve as a judging value of an offside analysis value, the power analysis value GF is compared with the preset power analysis threshold value, if the power analysis value GF is greater than or equal to the preset power analysis threshold value, a work qualified signal is generated, and if the power analysis value GF is smaller than the preset power analysis threshold value, a work disqualification signal is generated.
When the operation failure signal is generated, the operation failure signal is sent to the fault prediction module through the processor, when the operation failure signal is generated, the processor generates an operation consumption analysis signal and sends the operation consumption analysis signal to the operation consumption monitoring analysis module, the operation consumption monitoring analysis module receives the operation consumption analysis signal and then carries out operation consumption analysis on the electric heating cooker to generate an operation consumption failure signal or an operation consumption failure signal, and when the operation failure signal is generated, the operation consumption failure signal is sent to the fault prediction module through the processor; the specific process of the operation consumption monitoring and analyzing module is as follows:
step T1, connecting two adjacent groups of analysis coordinate points one by one through a smooth curve in a rectangular coordinate system to obtain a power time curve of the electric cooker in the rectangular coordinate system, wherein the initial end point of the power time curve is positioned on a Y axis;
step T2, making a vertical line segment vertical to the X axis downwards along the end point of the power time curve, marking an area surrounded by the X axis, the Y axis, the power time curve and the corresponding vertical line segment as an analysis area, calculating the area of the analysis area and marking the area as a transport and division face value FM;
step T3, obtaining the electric energy consumed by the electric cooker from the starting operation time to the current operation time, marking the electric energy as an electric consumption value DL, and passing through the formulaCarrying out numerical calculation on an electricity consumption value DL and a transport and distribution face value FM to obtain a transport and consumption analysis value YX; wherein u is a preset correction factor, and the value of u is 0.914; the operation consumption analysis value YX reflects the electric energy utilization condition of the electric heating pan in the operation period from the start operation time to the current operation time, and the operation consumption analysis value YXThe larger the value of the electric energy is, the worse the electric energy utilization condition of the electric heating pot is;
and T4, calling a preset operation consumption analysis threshold through the data storage module, presetting the preset operation consumption analysis threshold by a user or an electric cooker producer, storing the preset operation consumption analysis threshold into the data storage module, comparing the operation consumption analysis value YX with the preset operation consumption analysis threshold, if the operation consumption analysis value YX is larger than or equal to the preset operation consumption analysis threshold, judging that the electric energy utilization condition of the electric cooker in the operation period is poor, generating an operation consumption disqualification signal, and if the operation consumption analysis value YX is smaller than the preset operation consumption analysis threshold, indicating that the electric energy utilization condition of the electric cooker in the operation period is good, and generating an operation consumption qualification signal.
The failure prediction module performs failure prediction analysis on the electric cooker, and if the failure prediction module receives a time-of-operation unqualified signal or a work consumption unqualified signal, the failure prediction module indicates that the electric cooker has higher probability of failure at the current moment and when the electric cooker continues to operate, a failure high-probability signal is generated; otherwise, the current time of the electric cooker and the probability of faults occurring when the electric cooker continues to operate are indicated to be lower, a fault low-probability signal is generated, the current time and the subsequent operation faults of the electric cooker are predicted, the prediction analysis process is more comprehensive, the prediction analysis result is more accurate, an early warning instruction is sent to a fault early warning module when the fault high-probability signal is generated, the fault early warning module sends early warning to remind a corresponding user when receiving the early warning instruction, the corresponding user can timely pause the operation of the electric cooker after receiving the early warning to reduce the operation risk, and the electric cooker is overhauled and maintained subsequently.
Embodiment two:
as shown in fig. 2, the difference between the present embodiment and embodiment 1 is that the apparatus further includes a temperature distribution detection module, the processor is in communication connection with the temperature distribution detection module, the processor generates a temperature distribution detection signal and sends the temperature distribution detection signal to the temperature distribution detection module, the temperature distribution detection module performs temperature distribution detection analysis after receiving the temperature distribution detection signal and generates a temperature qualified signal or a temperature unqualified signal, and a specific analysis process of the temperature distribution detection analysis is as follows:
acquiring temperatures of a plurality of positions in the current operation time of the electric cooker, marking the temperatures as real Wen Liangzhi SWi, i= [1,2, …, n }, wherein n represents the number of temperature detection positions and n is a positive integer larger than 1, calling a preset real temperature threshold through a data storage module, presetting the preset real temperature threshold by a user or an electric cooker manufacturer and storing the preset real temperature threshold into the data storage module, comparing the real Wen Liangzhi SWi with the real temperature threshold, and carrying out temperature difference analysis on each group of real Wen Liangzhi SWi if each group of real Wen Liangzhi SWi is smaller than the real temperature threshold, otherwise, generating a temperature disqualification signal. The temperature difference analysis is specifically as follows:
each set of real temperature values is obtained Wen Liangzhi, a real temperature set { SW1, SW2, … and SWn } is established for each set of real temperature values, variance calculation is carried out on the real temperature set { SW1, SW2, … and SWn } to obtain a real temperature difference value and marked as CY, a preset real temperature difference threshold value is called through a data storage module, the preset real temperature difference threshold value is preset by a user or a boiler producer and is stored into the data storage module, the real temperature difference value CY is compared with the preset real temperature difference threshold value, if the real temperature difference value CY is larger than or equal to the preset real temperature difference threshold value, the fact that the temperature distribution inside the boiler is uneven is indicated, the possibility of failure at the current moment or later is larger, a temperature disqualification signal is generated, if the real temperature difference value CY is smaller than the preset real temperature difference threshold value, the possibility of failure at the current moment or later is smaller, and the temperature qualification signal is generated.
The temperature distribution detection module generates a temperature qualified signal or a temperature unqualified signal through temperature distribution detection analysis, and sends the temperature unqualified signal to the fault prediction module through the processor, when the fault prediction module receives the temperature unqualified signal, a fault high-probability signal is generated, the temperature distribution detection analysis plays an auxiliary fault prediction role, so that the fault prediction analysis is more comprehensive, the prediction result is more accurate, the operation risk of the electric cooker is reduced, an early warning instruction is sent to the fault early warning module when the fault high-probability signal is generated, the fault early warning module sends early warning to remind a corresponding user when receiving the early warning instruction, the corresponding user can timely pause the operation of the electric cooker after receiving the early warning to reduce the operation risk, and the electric cooker is overhauled and maintained at the follow-up time.
Embodiment III:
as shown in fig. 3, the difference between this embodiment and embodiments 1 and 2 is that the processor is communicatively connected to the user terminal, and after the fault prediction module generates a fault high probability signal, an early warning instruction is sent to the processor, and the processor sends the early warning instruction to the user terminal, so that the user terminal sends an early warning, and is facilitated to know the operation risk of the electric heating cooker in time, and the early warning sent by the fault early warning module is prevented from not paying attention to the user, so that the use effect is further improved and the operation risk of the electric heating cooker is reduced.
The working principle of the invention is as follows: when the electric heating cooker is used, temperature distribution detection and analysis are carried out through the temperature distribution detection module, and temperature data at a plurality of positions inside the electric heating cooker in the temperature distribution detection and analysis process are relied on and subjected to data analysis to generate a temperature qualified signal or a temperature unqualified signal; performing operation analysis on the electric cooker through the power time monitoring analysis module, generating an operation time qualified signal or an operation time unqualified signal, and generating an operation time qualified signal or an operation time unqualified signal through electric cooker power analysis when the operation time qualified signal is generated, wherein the operation time analysis and the electric cooker power analysis depend on operation time duration data and massive electric cooker power data and ensure the accuracy of analysis results through data analysis; carrying out operation analysis on the electric heating cooker based on the electric energy consumption data and the power data by using an operation monitoring analysis module after generating an operation qualified signal, and generating an operation qualified signal or an operation unqualified signal by using the operation analysis; the fault prediction module performs fault prediction analysis on the electric cooker, generates a fault high-probability signal if the fault prediction module receives a time-of-operation unqualified signal or a work-consumption unqualified signal or a temperature unqualified signal, and generates a fault low-probability signal if the fault prediction module receives the time-of-operation unqualified signal or the work-of-operation unqualified signal or the temperature unqualified signal;
the big data show that the operation abnormality of the electric heating pot can be effectively reflected by the temperature data, the operation time length data and the mass electric heating pot power data which are depended on the operation time analysis and the electric heating pot power analysis of the electric heating pot and the electric energy consumption data and the corresponding power data which are depended on the operation time of the electric heating pot, and the electric heating pot operation fault prediction method is particularly suitable for the operation fault prediction of the electric heating pot; the electric cooker fault prediction is carried out by selecting the category data through data screening, and the data with huge numbers and scattered sources are collected, stored and associated and analyzed, so that the current time and subsequent operation fault prediction of the electric cooker is realized based on big data analysis, the prediction and analysis process is more comprehensive, the prediction and analysis result is more accurate, and the fault early warning module sends out early warning when generating a fault high probability signal, thereby obviously reducing the operation risk of the electric cooker.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. The intelligent prediction system for the operation faults of the electric cooker based on big data is characterized by comprising a processor, a data storage module, a power-time monitoring and analyzing module, a transportation consumption monitoring and analyzing module, a temperature distribution detecting module, a fault prediction module and a fault early warning module, wherein the processor is in communication connection with the data storage module, the power-time monitoring and analyzing module, the transportation consumption monitoring and analyzing module, the temperature distribution detecting module and the fault prediction module, and the fault prediction module is in communication connection with the fault early warning module; the processor monitors the analysis signal and the temperature distribution detection signal when generating successfully, transmits the power-time monitoring analysis signal to the power-time monitoring analysis signal and transmits the temperature distribution detection signal to the temperature distribution detection module;
the time-of-operation monitoring analysis module is used for performing time-of-operation analysis on the next operation of the electric cooker and generating a time-of-operation qualified signal or a time-of-operation unqualified signal, sending the time-of-operation unqualified signal to the fault prediction module through the processor when the time-of-operation unqualified signal is generated, generating the time-of-operation qualified signal or the time-of-operation unqualified signal through the power analysis of the electric cooker when the time-of-operation qualified signal is generated, and sending the time-of-operation unqualified signal to the fault prediction module through the processor when the time-of-operation unqualified signal is generated; the operation analysis process of the power-time monitoring analysis module is as follows:
acquiring the starting operation time and the current operation time of the electric heating cooker, respectively marking the starting operation time and the current operation time of the electric heating cooker as a time value and a real-time value, and calculating the difference value between the real-time value and the time value to acquire a time-of-operation feedback value; the method comprises the steps of calling a preset time feedback threshold value through a data storage module, comparing the time feedback value with the preset time feedback threshold value, generating a time disqualification signal if the time feedback value is greater than or equal to the preset time feedback threshold value, and generating a time qualification signal if the time feedback value is less than the preset time feedback threshold value;
the analysis process of the electric pan power analysis is as follows:
establishing a rectangular coordinate system by taking time as an X axis and the operating power of the electric cooker as a Y axis, acquiring the operating power of a plurality of detection time points in the current operation process of the electric cooker, wherein the time intervals of two adjacent groups of detection time points are the same, and correspondingly making analysis coordinate points in the rectangular coordinate system based on the time corresponding to the detection time points and the operating power;
the offside analysis value is obtained through analysis, and the method for obtaining the offside analysis value is as follows: acquiring a preset power value and marking the value as Pz, wherein the preset power value is preset by a user or a electric cooker producer, and when the actual running power of the electric cooker is larger than the preset power value, the electric cooker is greatly damaged; taking (0, PZ) as an endpoint to make rays parallel to an X axis and marking the rays as power reference rays, marking an analysis coordinate point above the power reference rays as an offside analysis point, and marking an analysis coordinate point below the power reference rays as a qualified analysis point;
counting the number of offside analysis points and the number of qualified analysis points and marking the count as YS and HS respectively, and passing through the formulaCarrying out numerical calculation on the number YS of offside analysis points and the number HS of qualified analysis points to obtain an offside occupation ratio YZ; wherein e is a preset correction factor, and the value of e is 1.328;
calculating the offside occupation ratio YZ and the time-of-operation feedback value YF by using a formula WF=ag1 x YZ+ag2 x YF to obtain an offside analysis value WF; wherein, ag1 and ag2 are preset weight coefficients, the values of ag1 and ag2 are both larger than zero, and ag1 is larger than ag2; the method comprises the steps that a preset offside analysis threshold value is called through a data storage module, an offside analysis value is compared with the offside analysis threshold value, and if the offside analysis value is larger than or equal to the offside analysis threshold value, a work disqualification signal is generated;
if the offside analysis value is smaller than the offside analysis threshold value, the power analysis value is obtained through analysis, and the analysis and obtaining method of the power analysis value is as follows: taking the offside analysis point as an endpoint, downward making a line segment vertical to the power reference ray and marking the line segment as an offside line segment, and upward making a line segment vertical to the power reference ray by taking the qualified analysis point as an endpoint and marking the line segment as a qualified line segment;
acquiring the length of an offside line segment corresponding to each group of offside analysis points and marking the offside line segment as an offside distance value YJ, wherein the length of the offside line segment reflects the deviation degree of the running power of the corresponding offside analysis point compared with a preset power value, and acquiring the length of a qualified line segment corresponding to each combination lattice analysis point and marking the length of the qualified line segment as a qualified distance value HJ, and the length of the qualified line segment reflects the deviation degree of the running power of the corresponding qualified analysis point compared with the preset power value;
establishing an offside value set for all offside distance values, establishing a qualified value set for all qualified distance values, carrying out summation calculation on the offside value set to obtain an offside sum value YH, and carrying out summation calculation on a qualified value set to obtain a qualified sum value HH;
by normalizing the formulaSubstituting the offside sum value YH, the qualification sum value HH and the time-of-operation feedback value YF to perform numerical calculation, and obtaining a power analysis value GF after the numerical calculation; wherein tp1, tp2 and tp3 are preset proportionality coefficients, the values of tp1, tp2 and tp3 are all larger than zero, and tp1 is larger than tp2 and tp3; the method comprises the steps of calling a preset power analysis threshold value through a data storage module, comparing the power analysis value with the preset power analysis threshold value, generating a work qualified signal if the power analysis value is greater than or equal to the preset power analysis threshold value, and generating a work unqualified signal if the power analysis value is smaller than the preset power analysis threshold value;
when the operation qualified signal is generated, the processor generates an operation consumption analysis signal and sends the operation consumption analysis signal to the operation consumption monitoring analysis module, the operation consumption monitoring analysis module receives the operation consumption analysis signal and then analyzes the operation consumption of the electric heating cooker and generates an operation consumption qualified signal or an operation consumption unqualified signal, and when the operation consumption unqualified signal is generated, the processor sends the operation consumption unqualified signal to the fault prediction module;
the temperature distribution detection module is used for carrying out temperature distribution detection analysis after receiving the temperature distribution detection signal, generating a temperature qualified signal or a temperature unqualified signal, and sending the temperature unqualified signal to the fault prediction module through the processor; the fault prediction module is used for carrying out fault prediction analysis on the electric cooker, generating a fault high-probability signal if a time-of-operation unqualified signal or a time-of-operation consumption unqualified signal and a temperature unqualified signal are received, otherwise generating a fault low-probability signal, sending an early warning instruction to the fault early warning module when the fault high-probability signal is generated, and sending early warning to remind a corresponding user when the fault early warning module receives the early warning instruction.
2. The intelligent prediction system for the operation fault of the electric cooker based on big data according to claim 1, wherein the specific process of the operation consumption monitoring and analyzing module is as follows:
connecting two adjacent groups of analysis coordinate points one by one in a rectangular coordinate system through a smooth curve to obtain a power time curve of the electric heating cooker in the rectangular coordinate system, wherein the initial end point of the power time curve is positioned on a Y axis, a vertical line segment perpendicular to an X axis is downwards made along the tail end point of the power time curve, an area surrounded by the X axis, the Y axis, the power time curve and the corresponding vertical line segment is marked as an analysis area, and the area of the analysis area is calculated and marked as an operation analysis face value;
the electric energy consumed by the electric cooker from the starting operation time to the current operation time is obtained and marked as an electric consumption value, and the electric consumption value and the operation area value are subjected to numerical calculation to obtain an operation consumption analysis value; and the data storage module is used for calling a preset operation consumption analysis threshold value, comparing the operation consumption analysis value with the preset operation consumption analysis threshold value, generating an operation consumption disqualification signal if the operation consumption analysis value is greater than or equal to the preset operation consumption analysis threshold value, and generating an operation consumption qualification signal if the operation consumption analysis value is less than the preset operation consumption analysis threshold value.
3. The intelligent prediction system for the operation fault of the electric cooker based on big data according to claim 1, wherein the specific analysis process of the temperature distribution detection analysis is as follows:
the temperature of a plurality of positions inside the current operation time of the electric cooker is obtained and marked as real Wen Liangzhi, a preset real temperature threshold value is called through a data storage module, the real Wen Liangzhi is compared with the real temperature threshold value, if each set of real Wen Liangzhi is smaller than the real temperature threshold value, temperature difference analysis is carried out on each set of real temperature values, and otherwise, a temperature disqualification signal is generated.
4. The intelligent prediction system for the operation fault of the electric cooker based on big data according to claim 3, wherein the specific analysis process of the temperature difference analysis is as follows:
each group of real temperature values is obtained Wen Liangzhi, a real temperature set is established, variance calculation is carried out on the real temperature set to obtain a real temperature difference value, a preset real temperature difference threshold value is called through a data storage module, if the real temperature difference value is larger than or equal to the preset real temperature difference threshold value, a temperature disqualification signal is generated, and if the real temperature difference value is smaller than the preset real temperature difference threshold value, a temperature qualification signal is generated.
5. The intelligent prediction system for the operation fault of the electric cooker based on big data according to claim 1, wherein the processor is in communication connection with the user terminal, the fault prediction module generates a fault high-probability signal and then sends an early warning instruction to the processor, the processor sends the early warning instruction to the user terminal, and the user terminal sends early warning.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014109928A (en) * | 2012-12-03 | 2014-06-12 | Mitsubishi Electric Corp | Abnormality detection device, abnormality detection method and program |
| CN107424381A (en) * | 2017-07-21 | 2017-12-01 | 河海大学常州校区 | A kind of indoor monitoring system for Empty nest elderly |
| CN107968800A (en) * | 2016-10-19 | 2018-04-27 | 佛山市顺德区美的电热电器制造有限公司 | Electric heating cooking equipment, server, information processing method and system |
| CN108459222A (en) * | 2018-03-22 | 2018-08-28 | 中国海洋大学 | A kind of electric appliance fault detection method and system |
| CN109976273A (en) * | 2019-03-19 | 2019-07-05 | 颜玉玲 | For recording household electrical appliance facility data and electricity consumption detail automated management system |
| CN110432748A (en) * | 2018-05-03 | 2019-11-12 | 佛山市顺德区美的电热电器制造有限公司 | The inspection pot method, apparatus and equipment of electromagnetic heating electric appliance |
| KR20200071584A (en) * | 2018-12-11 | 2020-06-19 | 주식회사 정우계전 | System and method for detection of abnormalities in switchboard |
| CN112665306A (en) * | 2020-12-31 | 2021-04-16 | 合肥美菱物联科技有限公司 | Refrigerator remote fault diagnosis system and diagnosis method thereof |
| CN114995556A (en) * | 2022-07-27 | 2022-09-02 | 山东齐能电器有限公司 | Intelligent control system for electric food warmer operation based on data analysis |
-
2023
- 2023-03-06 CN CN202310200499.XA patent/CN116067690B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014109928A (en) * | 2012-12-03 | 2014-06-12 | Mitsubishi Electric Corp | Abnormality detection device, abnormality detection method and program |
| CN107968800A (en) * | 2016-10-19 | 2018-04-27 | 佛山市顺德区美的电热电器制造有限公司 | Electric heating cooking equipment, server, information processing method and system |
| CN107424381A (en) * | 2017-07-21 | 2017-12-01 | 河海大学常州校区 | A kind of indoor monitoring system for Empty nest elderly |
| CN108459222A (en) * | 2018-03-22 | 2018-08-28 | 中国海洋大学 | A kind of electric appliance fault detection method and system |
| CN110432748A (en) * | 2018-05-03 | 2019-11-12 | 佛山市顺德区美的电热电器制造有限公司 | The inspection pot method, apparatus and equipment of electromagnetic heating electric appliance |
| KR20200071584A (en) * | 2018-12-11 | 2020-06-19 | 주식회사 정우계전 | System and method for detection of abnormalities in switchboard |
| CN109976273A (en) * | 2019-03-19 | 2019-07-05 | 颜玉玲 | For recording household electrical appliance facility data and electricity consumption detail automated management system |
| CN112665306A (en) * | 2020-12-31 | 2021-04-16 | 合肥美菱物联科技有限公司 | Refrigerator remote fault diagnosis system and diagnosis method thereof |
| CN114995556A (en) * | 2022-07-27 | 2022-09-02 | 山东齐能电器有限公司 | Intelligent control system for electric food warmer operation based on data analysis |
Non-Patent Citations (2)
| Title |
|---|
| 基于大数据的智能家电故障检测和诊断模型研究;孙俊佚雄;《中国优秀硕士学位论文全文库》;全文 * |
| 基于电流分析的开关磁阻电机功率变换器故障检测方法;肖丽;范书瑞;王博文;申芳;董昊宇;;电机与控制学报(04);全文 * |
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