Clothes drying machine fault handling system based on smart speaker

Abstract

The present invention discloses a clothes drying machine fault processing system based on a smart speaker, comprising: an analysis module, which obtains the wear value Zm of each detection segment; i , and construct the wear value set A{Zm1, Zm2, ..., Zm i}; According to the subset in the set A, the average wear value ZJm is calculated; the wear difference ZCm is calculated; and the wear standard value ZBS of the clothesline is calculated, wherein a1 and a2 are both proportional coefficients; the obtained clothesline wear standard value ZBS is compared with the wear standard threshold; if it is greater than, a clothesline abnormal signal is generated; if it is less than, a clothesline normal signal is generated. The present invention analyzes and judges the wear condition of the clothesline, and makes a prediction, so that the use of the clothesline can be safer and its service life is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of clothes drying machines, and in particular to a clothes drying machine fault processing system based on a smart speaker. Background Art

[0002] Chinese patent CN115208710A discloses a method, device, and storage medium for handling faults in a clothes drying machine based on a smart speaker. The method comprises: obtaining a detection request issued by a user through the voice interaction function of the smart speaker, sending a first detection instruction to the clothes drying machine to trigger the clothes drying machine to perform a fault detection operation; generating a maintenance work order based on the detection result fed back by the clothes drying machine performing the fault detection operation, and pushing the work order to a designated terminal for maintenance reminder; determining the maintenance status of the clothes drying machine based on the voice interaction function of the smart speaker, and updating the maintenance work order according to the maintenance status of the clothes drying machine;

[0003] The intelligence level of existing clothes drying machines still remains at the level of being controlled according to manual instructions issued by the user. They cannot monitor and judge the status of the clothesline in the clothes drying machine in real time, provide timely feedback when a fault occurs, and improve the safety of the clothesline. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems of the above-mentioned background technology and to propose a clothes drying machine fault handling system based on a smart speaker.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The clothes drying machine fault handling system based on smart speakers includes:

[0007] Analysis module, obtains the wear value Zm of each detection segment i , and construct the wear value set A{Zm1, Zm2, ..., Zm i};

[0008] According to the subset in set A, by formula Calculate the average wear value ZJm; by the formula ZCm=|Zm1-Zm2|+|Zm2-Zm3|+...+|Zm n-1 -Zm n |, calculate the wear difference ZCm;

[0009] The wear standard value ZBS of the clothesline is calculated by ZBS=a1*ZJm+a2*ZCm, where a1 and a2 are proportional coefficients;

[0010] Compare the obtained clothesline wear standard value ZBS with the wear standard threshold;

[0011] If it is greater, a clothesline anomaly signal is generated;

[0012] If it is less than, a clothesline normal signal is generated;

[0013] The prediction module constructs a rectangular coordinate system with the time in the collection period as the X-axis and the wear standard value of the clothesline as the Y-axis. The wear standard value ZBS corresponding to the obtained time is substituted into the coordinate system to draw the wear standard curve and calculate the slope value Km of the wear standard curve;

[0014] By formula The remaining life Ms is calculated; where ZBSb is the minimum limit of clothesline wear;

[0015] Compare the remaining life Ms obtained with the remaining life threshold;

[0016] If it is greater, a usage signal is generated;

[0017] If less, an unused signal is generated.

[0018] As a further solution of the present invention: the wear value Zm of each detection segment i , which is obtained by dividing the clothesline on the clothes drying machine into multiple i detection segments.

[0019] As a further solution of the present invention: also include:

[0020] When the fault module receives the usage signal from the prediction module, it obtains the wear standard value ZBS of the clothesline within the collection time period T and performs fault analysis.

[0021] As a further solution of the present invention: the specific working process of the fault module is as follows:

[0022] With the time in the collection period as the X-axis and the wear standard value of the clothesline as the Y-axis, a rectangular coordinate system is constructed to draw the wear standard curve, and the slope value Km of the wear standard curve is calculated;

[0023] The wear standard curve is derived to obtain the wear derivative curve; the points where the derivative is 0 in the wear derivative curve are marked as stationary points, and the time value t of each stationary point is obtained; the time values of two adjacent stationary points are calculated as the difference to obtain the stationary point duration Ct, and the stationary point duration set B{Ct1, Ct2, ..., Ct j}, where j represents the number of stationary durations;

[0024] Get the station time set B{Ct1, Ct2, ..., Ct j}, extract the subset that is greater than the stagnation time threshold, and obtain the number of times CS and Ct that are greater than the stagnation time threshold j; Using the formula ZY=b1*CS+b2*Ct j , calculate the fault impact value ZY of the clothesline, where b1 and b2 are proportional coefficients.

[0025] As a further solution of the present invention: also include:

[0026] Processing module, through the formula The influence coefficient XY of the clothesline is calculated; where c1 and c2 are proportional coefficients;

[0027] Substitute the influence coefficient XY of the clothesline into the formula ZC=XY*ZB to calculate the processing value ZC, where ZB represents the standard value of the influence of the clothesline failure.

[0028] As a further solution of the present invention: according to the processing value ZC, a processing coefficient is set and marked as KCw; j = 1, 2, ..., w; each processing coefficient corresponds to a processing range value, respectively (Wc1, Wc2], (Wc2, Wc3], ..., (Wcw, Wcw+1]); and Wc1 < Wc2 < ... < Wcw < Wcw+1;

[0029] When the processing value ZC∈(Wcw, Wcw+1]), the processing coefficient is KCw.

[0030] As a further solution of the present invention: the load weight value and the processing coefficient KCw of the clothesline in the historical time are obtained, and substituted into the formula Zx=Zz*KCw to calculate the online load capacity Zx of the clothesline.

[0031] As a further solution of the present invention: also include:

[0032] The voice module receives the normal clothesline signal from the analysis module and converts the normal clothesline signal into qualified clothesline voice for playback.

[0033] As a further solution of the present invention, a non-use signal from the prediction module is received, and the non-use signal is converted into a voice message for changing the clothesline for playback.

[0034] As a further solution of the present invention: the online load capacity Zx of the clothesline is received and processed by the module, and when the actual weight is greater than the online load capacity Zx of the clothesline, an overweight voice is issued.

[0035] Beneficial effects of the present invention:

[0036] The analysis module of the present invention processes the average and difference values of the clothesline wear value Zm, thereby comprehensively judging the overall wear of the clothesline and the wear uniformity of each detection section, and numerically processing the wear condition of the clothesline;

[0037] The prediction module of the present invention further judges and processes the abnormal signal of the clothesline from the analysis module, and predicts and analyzes whether the clothesline in the clothes drying machine can continue to be used and the safety of continued use, thereby effectively extending the service life of the clothes drying machine;

[0038] The fault module and processing module of the present invention analyze the fault of the clothesline based on the signal that the prediction module can continue to use, calculate the factors that cause the wear of the clothesline, and thus provide a corresponding reasonable load capacity to ensure reasonable use within the remaining time and further extend its service life;

[0039] The voice module of the present invention is connected to the signal of the clothes drying machine fault processing system, and can provide voice feedback on the weight working node of the clothes drying machine, making the use of the clothes drying machine safer;

[0040] In summary, the present invention makes the use of the clothes drying machine safer and increases its service life by analyzing, judging and predicting the wear condition of the clothesline. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below with reference to the accompanying drawings.

[0042] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] See also Figure 1 As shown, the present invention is a clothes drying machine fault processing system based on a smart speaker, comprising:

[0045] The acquisition module divides the clothesline on the clothes drying machine into multiple i detection segments, where i = 1, 2, ..., n, n is a positive integer, and the length of each detection segment is equal. The wear value of each detection segment is obtained and marked as Zm i ;

[0046] Analysis module, obtains the wear value Zm of each detection segment i , and analyze the wear values obtained;

[0047] The specific working process of the analysis module is as follows:

[0048] Step 1: Get the wear value Zm of each detection segment i, and construct the wear value set A{Zm1, Zm2, ..., Zm i};

[0049] According to the subset in set A, by formula Calculate the average wear value ZJm; by the formula ZCm=|Zm1-Zm2|+|Zm2-Zm3|+...+|Zm n-1 -Zm n |, calculate the wear difference ZCm;

[0050] Step 2: Substitute the average wear value ZJm and the wear difference ZCm obtained into the formula ZBS = a1*ZJm + a2*ZCm to calculate the wear standard value ZBS of the clothesline, where a1 and a2 are proportional coefficients, a1 is 0.52, and a2 is 0.74;

[0051] Step 3: Compare the obtained clothesline wear standard value ZBS with the wear standard threshold;

[0052] If the clothesline wear standard value ZBS is greater than the wear standard threshold, it means that the clothesline of the clothes drying machine is highly worn, and a clothesline abnormality signal is generated;

[0053] If the clothesline wear standard value ZBS is less than the wear standard threshold, it means that the clothesline wear of the clothes drying machine is low, and a clothesline normal signal is generated;

[0054] The analysis module of the present invention processes the average and difference values of the clothesline wear value Zm, thereby comprehensively judging the overall wear of the clothesline and the wear uniformity of each detection section, and numerically processing the wear condition of the clothesline;

[0055] The prediction module obtains the abnormal signal of the clothesline from the analysis module, obtains the wear standard value ZBS of the clothesline during the collection time period T, and predicts the service life of the clothesline;

[0056] The specific working process of the prediction module is as follows:

[0057] Step 1: Construct a rectangular coordinate system with the time in the collection period as the X-axis and the wear standard value of the clothesline as the Y-axis. Substitute the wear standard value ZBS corresponding to the obtained time into the coordinate system, draw the wear standard curve, and calculate the slope value Km of the wear standard curve;

[0058] Step 2: Get the slope value Km of the wear standard curve and the current wear standard value ZBSd, and use the formula The remaining life Ms is calculated; where ZBSb is the minimum limit of clothesline wear;

[0059] Step 3: Compare the remaining life Ms with the remaining life threshold;

[0060] If the remaining life Ms is greater than the remaining life threshold, it means that the clothesline can still be used and a usage signal is generated;

[0061] If the remaining life Ms is less than the remaining life threshold, it means that the clothesline cannot be used any more and a non-use signal is generated;

[0062] The prediction module of the present invention further judges and processes the abnormal signal of the clothesline from the analysis module, and predicts and analyzes whether the clothesline in the clothes drying machine can continue to be used and the safety of continued use, thereby effectively extending the service life of the clothes drying machine;

[0063] The fault module, upon receiving the usage signal from the prediction module, obtains the standard wear value ZBS of the clothesline within the collection time period T and performs fault analysis;

[0064] The specific working process of the fault module is as follows:

[0065] Step 1: Construct a rectangular coordinate system with the time in the collection period as the X-axis and the wear standard value of the clothesline as the Y-axis. Substitute the wear standard value ZBS corresponding to the obtained time into the coordinate system, draw the wear standard curve, and calculate the slope value Km of the wear standard curve;

[0066] Step 2: Derivate the wear standard curve to obtain the wear derivative curve; mark the point where the derivative is 0 in the wear derivative curve as a stationary point, and obtain the time value t of each stationary point; calculate the difference between the time values of two adjacent stationary points to obtain the stationary point duration Ct, and construct the stationary point duration set B{Ct1, Ct2, ..., Ct j}, where j represents the number of stationary durations;

[0067] Step 3: Get the set of stationary time lengths B {Ct1, Ct2, ..., Ct j}, extract the subset that is greater than the stagnation time threshold, and obtain the number of times CS and Ct that are greater than the stagnation time threshold j ; Using the formula ZY=b1*CS+b2*Ct j , the fault impact value ZY of the clothesline is calculated, where b1 and b2 are proportional coefficients, b1 is 0.49, and b2 is 0.65;

[0068] The processing module obtains the fault impact value ZY of the clothesline from the fault module and performs corresponding processing according to the fault impact value ZY;

[0069] The specific working process of the processing module is as follows:

[0070] Step 1: Obtain the historical load weight and ambient humidity values of the clothesline, and mark them as Zz and Zs respectively; as well as the fault impact value ZY of the clothesline;

[0071] By formula The influence coefficient XY of the clothesline is calculated; c1 and c2 are both proportional coefficients, c1 is 2.7, and c2 is 1.3;

[0072] Step 2: Substitute the influence coefficient XY of the clothesline into the formula ZC=XY*ZB to calculate the treatment value ZC, where ZB represents the standard value of the influence of the clothesline failure; the standard value of the influence of the clothesline failure is obtained by technicians based on experiments;

[0073] Step 3: Set the processing coefficients according to the processing value ZC and mark them as KCw; j = 1, 2, ..., w; each processing coefficient corresponds to the processing range value, which is (Wc1, Wc2], (Wc2, Wc3], ..., (Wcw, Wcw+1]; and Wc1 < Wc2 < ... < Wcw < Wcw+1;

[0074] When the processing value ZC∈(Wcw, Wcw+1], the processing coefficient is KCw;

[0075] Step 4: Obtain the historical load weight value and processing coefficient KCw of the clothesline, substitute them into the formula Zx = Zz * KCw, and calculate the online load capacity Zx of the clothesline;

[0076] The fault module and processing module of the present invention analyze the fault of the clothesline based on the signal that the prediction module can continue to use, calculate the factors that cause the wear of the clothesline, and thus provide a corresponding reasonable load capacity to ensure reasonable use within the remaining time and further extend its service life;

[0077] The voice module receives the normal clothesline signal from the analysis module and converts it into a qualified clothesline voice for playback; receives the unused signal from the prediction module and converts it into a clothesline replacement voice for playback; receives the clothesline online load capacity Zx from the processing module and issues an overweight voice when the actual weight exceeds the clothesline online load capacity Zx;

[0078] The voice module of the present invention is connected to the signal of the clothes drying machine fault processing system, and can provide voice feedback on the weight working node of the clothes drying machine, making the clothes drying machine safer to use.

[0079] Working principle of the present invention: The analysis module of the present invention processes the average and difference of the wear value Zm of the clothesline, so as to comprehensively judge the overall wear of the clothesline and the wear uniformity of each detection section, and numerically process the wear condition of the clothesline;

[0080] The prediction module of the present invention further judges and processes the abnormal signal of the clothesline from the analysis module, and predicts and analyzes whether the clothesline in the clothes drying machine can continue to be used and the safety of continued use, thereby effectively extending the service life of the clothes drying machine;

[0081] The fault module and processing module of the present invention analyze the fault of the clothesline based on the signal that the prediction module can continue to use, calculate the factors that cause the wear of the clothesline, and thus provide a corresponding reasonable load capacity to ensure reasonable use within the remaining time and further extend its service life;

[0082] The voice module of the present invention is connected to the signal of the clothes drying machine fault processing system, and can provide voice feedback on the weight working node of the clothes drying machine, making the clothes drying machine safer to use.

[0083] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The clothes drying machine fault handling system based on smart speakers is characterized by: include: Analysis module, obtains the wear value Zm of each detection segment i , and construct the wear value set A{Zm1, Zm2, ..., Zm n }; According to the subset in set A, by formula Calculate the average wear value ZJm; by the formula ZCm=|Zm1-Zm2|+|Zm2-Zm3|+...+|Zm n-1 -Zm n |, calculate the wear difference ZCm; The wear standard value ZBS of the clothesline is calculated by ZBS=a1*ZJm+a2*ZCm, where a1 and a2 are proportional coefficients; Compare the obtained clothesline wear standard value ZBS with the wear standard threshold; If it is greater, a clothesline anomaly signal is generated; If it is less than, a clothesline normal signal is generated; The prediction module constructs a rectangular coordinate system with the time in the collection period as the X-axis and the wear standard value of the clothesline as the Y-axis. The wear standard value ZBS corresponding to the obtained time is substituted into the coordinate system to draw the wear standard curve and calculate the slope value Km of the wear standard curve; By formula The remaining life Ms is calculated; where ZBSb is the minimum limit of clothesline wear; and ZBSd is the current wear standard value. Compare the remaining life Ms obtained with the remaining life threshold; If it is greater, a usage signal is generated; If less, an unused signal is generated.

2. The clothes drying machine fault handling system based on smart speaker according to claim 1 is characterized in that: Wear value Zm of each detection segment i , which is obtained by dividing the clothesline on the clothes drying machine into i detection segments.

3. The clothes drying machine fault handling system based on smart speaker according to claim 2 is characterized in that: Also includes: When the fault module receives the usage signal from the prediction module, it obtains the wear standard value ZBS of the clothesline within the collection time period T and performs fault analysis.

4. The clothes drying machine fault handling system based on smart speakers according to claim 3 is characterized in that: The specific working process of the fault module is as follows: With the time in the collection period as the X-axis and the wear standard value of the clothesline as the Y-axis, a rectangular coordinate system is constructed to draw the wear standard curve, and the slope value Km of the wear standard curve is calculated; The wear standard curve is derived to obtain the wear derivative curve; the points where the derivative is 0 in the wear derivative curve are marked as stationary points, and the time value t of each stationary point is obtained; the time values of two adjacent stationary points are calculated as the difference to obtain the stationary point duration Ct, and the stationary point duration set B{Ct1, Ct2, ..., Ct j }, where j represents the number of stationary durations; Get the station time set B{Ct1, Ct2, ..., Ct j }, extract the subset that is greater than the stationary time threshold, and obtain the number of times CS that is greater than the stationary time threshold and the element Ct in the corresponding set j ; Using the formula ZY=b1*CS+b2*Ct j , calculate the fault impact value ZY of the clothesline, where b1 and b2 are proportional coefficients.

5. The clothes drying machine fault handling system based on smart speakers according to claim 4 is characterized in that: Also includes: Processing module, through the formula The influence coefficient XY of the clothesline is calculated; c1 and c2 are proportional coefficients; Zz is the load value of the clothesline during the historical period, and Zs is the ambient humidity value during the historical period. Substitute the influence coefficient XY of the clothesline into the formula ZC=XY*ZB to calculate the processing value ZC, where ZB represents the standard value of the influence of the clothesline failure.

6. The clothes drying machine fault handling system based on smart speakers according to claim 5 is characterized in that: According to the processing value ZC, the processing coefficients are set and marked as KCj; j = 1, 2, ..., w; each processing coefficient corresponds to the processing value ZC range of (Wc1, Wc2], (Wc2, Wc3], ..., (Wcw, Wcw+1]; And Wc1<Wc2<…<Wcw<Wcw+1; When the processing value ZC∈(Wcw, Wcw+1]), the processing coefficient is KCw.

7. The clothes drying machine fault handling system based on smart speakers according to claim 6 is characterized in that: Obtain the historical load weight value Zz and the processing coefficient KCj of the clothesline, substitute them into the formula Zx=Zz*KCj, and calculate the online load capacity Zx of the clothesline.

8. The clothes drying machine fault handling system based on smart speakers according to claim 7 is characterized in that: Also includes: The voice module receives the normal clothesline signal from the analysis module and converts the normal clothesline signal into qualified clothesline voice for playback.

9. The clothes drying machine fault handling system based on smart speaker according to claim 8 is characterized in that: Receive the unused signal from the prediction module and convert the unused signal into a clothesline replacement voice for playback.

10. The clothes drying machine fault handling system based on smart speaker according to claim 9 is characterized in that: The receiving and processing module receives the clothesline online load capacity Zx, and when the actual weight is greater than the clothesline online load capacity Zx, an overweight voice is issued.

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