JP3003240B2 - Electric vacuum cleaner - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an electric cleaning device for identifying a floor surface to be cleaned by accumulating a count result of unit time of a dust amount, storing the floor surface information, and learning an input control method of an electric blower. About the machine.

[0002]

2. Description of the Related Art In recent years, there has been a demand for a vacuum cleaner which automatically controls an input of an electric blower according to a floor condition to be cleaned or an amount of dust.

Conventionally, this type of vacuum cleaner generally has a configuration as shown in FIG. Hereinafter, the configuration will be described. As shown in the figure, a cleaner body (hereinafter, referred to as a body) 1 has a built-in electric blower 2, a hose 4 is connected to a suction port 3, and the hose 4 has an extension tube 5 and a rotating brush 6. The floor-type suction device 7 is connected. A hand switch 8 is provided at the tip of the hose 4, and the rotation speed of the electric blower 2 disposed in the main body 1 is controlled by operating the hand switch 8.

[0004]

In such a conventional vacuum cleaner, the user determines the floor surface to be cleaned, and manually operates the hand switch 8 according to the floor surface to adjust the rotation speed of the electric blower 2. Since the control is performed, the operation is troublesome, and there is a problem that an appropriate input cannot be obtained.

An object of the present invention is to solve the above-mentioned conventional problems, and to control the input of an electric blower and the number of rotations of a rotating brush of a suction tool for an electric floor in detail according to the condition of the floor surface and the amount of dust. It is an object of the present invention to provide a vacuum cleaner capable of performing the following.

[0006]

According to the present invention, there is provided an electric blower for suction, an electric floor suction device having a rotating brush, and a sensor for detecting dust passing through a dust passage. A pulse converting means for converting the output of the sensor into a pulse signal, a counting means for counting the number of pulses from the pulse converting means within a unit time, and a counting result of the counting means for accumulating the result of the debris from the floor. Easy to get
Garbage change rate calculation means for obtaining floor surface information represented by the following, and cumulative storage of floor surface information obtained by the garbage change rate calculation means
A floor information storage means for storing floor information of the entire cleaned floor , and a neuro / optimizing various parameters of fuzzy inference by a learning rule such as a steepest descent method to determine the input of the electric blower. A fuzzy inference device, wherein the neuro-fuzzy inference device determines the input of the electric blower and the number of revolutions of the rotary brush by the output of the counting means and the floor information storage means.

[0007]

With the above arrangement, the sensor detects and accumulates dust to obtain floor information represented by the ease with which dust can be removed from the floor, and accumulates and stores this floor information to store and clean the entire floor. Floor
Based on the information , the real-time floor information, and the amount of dust, the input control of the electric blower and the number of rotations of the rotating brush of the electric floor suction tool are controlled according to the floor and the amount of dust.

[0008]

1 to 3 show an embodiment of the present invention.
This will be described with reference to FIG. The same components as those in the conventional example are denoted by the same reference numerals, and description thereof will be omitted. As shown in the figure,
The sensor 9 for detecting dust includes a light emitting diode 10 and a light receiving unit 11. The sensor 9 is provided opposite to the dust passage 12, and detects a change in the amount of light due to dust 13 passing through the dust passage 12. The signal from the light receiving unit 11 is amplified by the amplifying unit 14, the waveform is shaped by the pulse converting unit 15, converted into a pulse signal, and input to the microcomputer 16. The microcomputer 16 counts the number of pulses from the pulse conversion means 15 within a unit time, and accumulates the count results of the counting means 17 to remove dust from the floor.
Garbage change rate calculating means 18 for obtaining floor surface information represented by ease
And a floor information storage unit 19 for storing the floor information obtained by the dust change rate calculation unit 18, and a neuro-fuzzy inference unit 20. The output of the microcomputer 16 is input to the phase control means 21 and 22, and the electric blower 2
Input control and the number of rotations of the rotating brush 6 are controlled. The neuro-fuzzy inference unit 20 infers the input of the electric blower 2 and the rotation speed of the rotary brush 6 based on the floor surface information obtained by the refuse change rate calculation unit 18, the output of the counting unit 17, and the output of the floor surface information storage unit 19. I do. The neuro-fuzzy inference unit 20 has a configuration as shown in FIG. That is,
The antecedent membership function storage means 23 stores membership functions relating to the amount of waste, floor information, storage floor information, and the like . The garbage amount suitability calculating means 24, the floor information suitability calculating means 25, and the storage floor information suitability calculating means 26
The relevance between the garbage amount, the floor information, and the membership function relating to the storage floor information and the input garbage, floor information, and storage floor information stored in the antecedent membership function storage means 23, respectively. Calculate. The antecedent minimum computing means 27 takes the minimum of the three conformances output from the garbage amount conformity computing means 24, the floor information conformity computing means 25, and the storage floor information conformity computing means 26 and takes the antecedent minimum. The conclusion of the calculation means 27 is made. Suction force inference rule storage means 28
Stores the inference rules regarding the suction force. The suction force membership function storage means 29 stores a membership function relating to the suction force of the consequent part minimum operation means 30. According to the inference rules stored in the suction force inference rule storage means 28 in the consequent part minimum operation means 30, the conclusion of the antecedent part minimum operation means 27 and the consequent part stored in the suction force membership function storage means 29. The minimum of the suction force membership function of the minimum calculation means 30 is taken as the conclusion of the rule. After the conclusions are obtained for all the rules by the center-of-gravity calculating means 31, the maximum of all the conclusions is obtained, and the center of gravity is calculated to finally obtain the suction force. Further, a rotating brush rotation number inference rule storage unit 32 storing inference rules for inferring the rotation number of the rotating brush 6, and a rotating brush rotation number storing a membership function related to the rotation number of the rotating brush 6. Storage means 33 is also included.
The neuro-fuzzy inference unit 20 can be easily realized by the microcomputer 16. Antecedent membership function storage means 2 included in neuro-fuzzy inference unit 20
3, the suction force inference rule storing means 28, the suction force membership function storage means 2-9, the rotation brush rotation speed inference rule storing means 32, membership stored in each of the rotary brush rotational speed membership function storing means 3 3 functions and inference rules, taking into account the operational feeling at the time of cleaning waste amount and the floor information and the data storage floor information
The rotational speed of the data input and the rotating brush 6 set Teisu should electric blower 2, previously steepest descent method (in one learning rule used in the neural network, a method for the error function to a minimum) learning such It is set optimally by the law. The phase control means 21 and 22 calculate and control the amount of phase control of the electric blower 2 and the rotating brush 6 based on the determined input and the rotation speed of the rotating brush 6.

The operation of the above configuration will be described. That is, the dust 13 detected by the sensor 9 by the counting means 17 is integrated for a predetermined time (for example, 0.1 second). By integrating, the amount of waste on the floor at that time can be determined. This amount of waste is used as input for fuzzy inference. FIG. 4 shows the degree of change in the integrated value of the amount of dust when cleaning is continued. The dust is reduced at a stretch until T1 from the start of cleaning, but this indicates that the dust on the floor surface has been removed. Is shown. Further, after T1, the dust is largely separated as shown in (a), (b) and (c) depending on how the waste is removed. In the case of (c), the integrated value of the garbage is almost 0, indicating that almost all of the garbage has been removed before T1. This is the case where the floor to be cleaned is a wooden floor, a cushion floor, a tatami mat, or the like. Moreover, if the floor surface is a carpet, buried garbage during haired, generally the amount of relatively dust many easily take difficulty fry compared to wood floors or tatami, (a), (b )like,
The characteristic shows that the integrated value of the amount of waste gradually decreases.
When the change rate of the dust amount is calculated by the dust change rate calculating means 18 in this manner, it is possible to estimate what the characteristics of the floor surface currently being cleaned are. Here, an indexed value (for example, 0 to 7) is set as the floor surface information. This index is used as input for fuzzy inference. Because garbage amount of the rate of change is small, dust is hard to take the difficulty stomach, such as carpet
It indicates that it is a floor, because dust amount change rate is large, which indicates that dust is floor such as take easy-wood floors.

FIG. 5 shows a method of storing floor information. The floor information obtained from the dust change rate calculating means 18 is periodically stored in the floor information storing means 19. This floor surface information storage means 19 uses a non-volatile memory or the like that retains its stored contents even when the power is turned off. Each time the main body 1 is used, a frequency is added for each index of the floor surface information, and a vacuum cleaner is used. Information on all floors can be obtained. As pre-processing for obtaining the degree of conformity by the memory floor information conformity calculating means 26, for example, average floor information is obtained from an index of the memory floor information and the frequency thereof, and is input as fuzzy inference. The optimum suction force for cleaning is determined by the amount of dust on the floor, the characteristics of the floor, and the like. Inference is performed by the fuzzy inference unit 20.

Next, the process of inferring the suction force will be described. Inference rules of the fuzzy inference of this embodiment, a larger amount of "garbage amount, in take hard floor surface of the dust (a small amount of waste of the rate of change), have any Mino take hard to the floor on the floor that was past cleaning The more you draw, the more you draw. ""Large" waste
Toka, Toka refuse the amount of the rate of change is "small", the qualitative concept your Mino take hard to the floor on the floor that was past cleaning is or "often", the suction force such as "very large" 6
It is quantitatively expressed by membership functions as shown in (A), (B), (C), (D), and (E). new
In Lo fuzzy inference device 20 of the dust amount adaptation calculating means 24, the degree of conformity membership functions concerning waste amount stored in the input and the antecedent membership function memory means 23 from the counting means 1 7 in the calculation good Ri seek. The floor information adaptation calculating means 25 includes a dust change rate calculating means 18.
And the membership function of the refuse change rate stored in the antecedent part membership function storage means 23 is similarly determined for the degree of conformity. The storage floor information adaptability calculating means 26 similarly obtains the adaptability of the input from the floor information storage means 19 and the membership function of the storage floor information stored in the antecedent membership function storage means 23. . The antecedent part minimum calculation means 27 takes the minimum of the three degrees of conformity as the degree of conformity of the antecedent part. In the consequent part minimum operation means 30, the suction force inference rule storage means 2
In accordance with 8 to rules that have been stored, and the conclusion of the rule to take <br/> the goodness-of-fit of Rume membership function has been stored in the conclusion and the suction force membership function storage means 29 of the antecedent minimum calculation means 27 I do.

After obtaining the respective conclusions for all the rules, the center-of-gravity calculating means 31 takes the maximum of all the conclusions and calculates the center of gravity to finally obtain the suction force. The phase control means 21 calculates and controls the amount of phase control of the electric blower 2 based on the determined suction force. The rotation speed of the rotary brush 6 is determined by calculating the conclusion of the antecedent minimum operation unit 27 in the same manner as in the process of determining the suction force, and the rotary brush rotation number inference rule storage unit 32 and the rotation brush rotation number membership function storage. The rotation speed of the rotating brush 6 is determined from the means 33. In the present embodiment, the maximum-minimum combining method and the center of gravity method are used in the inference method, but other methods are also possible, and the consequent suction force is expressed by a membership function. Needless to say, it can also be represented by a numerical value or a line format.

[0013]

As is apparent from the above embodiments, the vacuum cleaner of the present invention has an electric blower for suction, an electric floor suction device having a rotating brush, and a sensor for detecting dust passing through the dust passage. Pulse conversion means for converting the output of the sensor into a pulse signal; counting means for counting the number of pulses from the pulse conversion means within a unit time; and counting the counting results of the counting means to collect dust from the floor. Easy to remove
Garbage change rate calculation means for obtaining floor surface information represented by the following, and cumulative storage of floor surface information obtained by the garbage change rate calculation means
A floor information storage means for storing floor information of the entire cleaned floor , and a neuro / optimizing various parameters of fuzzy inference by a learning rule such as a steepest descent method to determine the input of the electric blower. A fuzzy inference device, wherein the neuro-fuzzy inference device determines the input of the electric blower and the number of rotations of the rotating brush by the output of the counting means and the floor information storage means, and has this configuration. By doing so, it is possible to control the input of the electric blower and the rotation speed of the rotating brush of the electric floor suction device finely according to the condition of the floor surface and the amount of dust, regardless of the condition of the floor surface to be cleaned. Cleaning can be performed efficiently and operability can be improved.

[Brief description of the drawings]

FIG. 1 is a block circuit diagram of a vacuum cleaner according to an embodiment of the present invention.

FIG. 2 is a block circuit diagram of a neuro-fuzzy inference device of the vacuum cleaner.

FIG. 3 is a sectional view of a dust sensor mounting portion of the vacuum cleaner.

FIG. 4 is a characteristic diagram showing a change in the amount of waste of the vacuum cleaner.

FIG. 5 is a diagram showing a method of storing floor information of the electric vacuum cleaner.

FIG. 6 is a view showing a membership function of the vacuum cleaner.

FIG. 7 is an external perspective view of a conventional vacuum cleaner.

[Explanation of symbols]

 Reference Signs List 2 electric blower 6 rotating brush 7 electric floor suction tool 9 sensor 12 dust passage 13 dust 15 pulse conversion means 17 counting means 18 dust change rate calculating means 19 floor information storage means 20 neuro-fuzzy inference device

────────────────────────────────────────────────── ─── Continued on the front page (72) Inventor Shinji Kondo 1006 Kazuma Kadoma, Kadoma, Osaka Prefecture Inside Matsushita Electric Industrial Co., Ltd. (56) References JP-A-4-170927 (JP, A) JP-A-2- 131734 (JP, A) JP-A-2-152427 (JP, A) (58) Fields investigated (Int. Cl. ⁷ , DB name) A47L 9/28

Claims (1)

(57) [Claims] 1. An electric blower for suction, an electric floor suction tool having a rotating brush, a sensor for detecting dust passing through a dust passage, and pulse conversion means for converting an output of the sensor into a pulse signal. A counting means for counting the number of pulses from the pulse converting means within a unit time; and a counting result of the counting means for removing dust from the floor.
A refuse change rate calculating means for obtaining floor surface information represented by ease ,
Cumulative storage of floor surface information obtained by this waste change rate calculation means
A floor information storage means for storing floor information of the entire cleaned floor , and a neuro / optimizing various parameters of fuzzy inference by a learning rule such as a steepest descent method to determine the input of the electric blower. A vacuum cleaner comprising a fuzzy inferencer, wherein the neuro-fuzzy inferencer determines the input of the electric blower and the number of rotations of the rotating brush by the output of the counting means and the floor information storage means.