JPH0595881A - Control device of vacuum cleaner - Google Patents

Abstract

(57) [Summary] [Purpose] The floor texture is recognized by the unevenness of the floor surface, and the input control of the electric blower is performed finely according to the floor surface to improve usability. [Structure] A floor sensor 9 is configured to detect a concave / convex state of a floor by a light emitting element 10 and a light receiving element 11 provided in a floor suction tool 7, and an analog output of the floor sensor 9 is A / D converted. The means 13 converts it into a digital signal, the light quantity absolute value detection means 15 averages it within a unit time to calculate the light quantity absolute value, and the light quantity change amount detection means 16 averages the amount of change in the light quantity within a unit time. Calculate the quantity. The fuzzy reasoner 17 sequentially learns the floor quality from the floor quality information obtained from both the light intensity absolute value information and the light intensity change amount information and the storage floor quality information stored in the storage means 18 that can be stored even when the power is off.
The input of the electric blower 2 is determined, and the output of the electric blower 2 controls the input of the electric blower 2.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention identifies a floor quality to be cleaned by using a floor sensor, stores the floor quality information, an input control method of an electric blower, and a rotation speed of a rotary brush of a floor suction tool. The present invention relates to an electric vacuum cleaner that learns control methods.

[0002]

2. Description of the Related Art In recent years, electric vacuum cleaners are required to automatically control the input of an electric blower according to the floor surface to be cleaned, the amount of dust, etc. to control the suction force.

Conventionally, this type of electric vacuum cleaner generally has a structure as shown in FIG. The configuration will be described below.

As shown in the figure, a cleaner body (hereinafter referred to as a body) 1 has a built-in electric blower 2 and a floor suction tool 7 having a hose 4, an extension tube 5 and a rotating brush 6 at a suction port 3. Are connected. A hand switch 8 is provided at the tip of the hose 4, and the rotation speed of the electric blower 2 arranged in the main body 1 is controlled by operating the hand switch 8.

[0005]

In such a conventional electric vacuum cleaner, the user judges the floor surface to be cleaned, and manually operates the hand switch 8 according to the floor surface (rotation speed).
It is necessary to change the suction force by changing the pressure, which is a troublesome operation.

The present invention solves the above-mentioned conventional problems by recognizing the quality of the floor by the unevenness of the floor surface, finely controlling the input of the electric blower according to the floor surface, and the rotational speed of the rotary brush of the floor suction tool. The purpose is to control and improve usability.

[0007]

In order to achieve the above object, the present invention detects an uneven condition of a floor from an electric blower for suction and a light emitting element and a light receiving element provided in a floor suction tool. A floor sensor configured as described above, A / D conversion means for converting an analog output of the floor sensor into a digital signal, and digital amounts from the A / D conversion means are averaged within a unit time to calculate an absolute light amount value. Absolute light amount detection means, light amount change amount detection means for averaging the change amount of the light amount within a unit time to obtain the light amount change amount, storage means capable of storing even when the power is off, and determining the input of the electric blower And a fuzzy inference device for sequentially learning the floor quality from the floor quality information obtained from both the light intensity absolute value information and the light intensity change amount information and the memory floor quality information stored in the storage means. ,
First, the input of the electric blower is determined.
Is used as a means for solving the problem.

In addition to the electric blower of the first problem solving means, a rotary brush is provided in the floor suction tool, and the number of rotations of the rotary brush is determined by a fuzzy reasoner. Is used as a means for solving the problem.

Further, the fuzzy reasoner of the first problem solving means optimizes various parameters of the fuzzy reasoning by a learning rule such as the steepest descent method of the neuro technique, and uses as a parameter a membership function of the antecedent part. The third problem solving means is to optimize the shape of the consequent part membership function and the number of inference rules.

[0010]

According to the first means for solving the above problems, the present invention provides:
From the output of the floor sensor to the floor quality of the cleaning surface (wood floor, carpet, etc.)
It is possible to indirectly obtain information, cumulatively store the information, and control the stored floor quality information and real-time floor quality information so that the electric blower can be appropriately input according to the floor surface.

Further, according to the second means for solving the problem, the number of rotations of the rotary brush of the floor suction tool can be appropriately controlled according to the quality of the floor, and the usability can be improved.

Further, by the third means for solving the problem, qualitative information such as intuition and tips for cleaning is optimally tuned and incorporated in the fuzzy reasoner, so that cleaning suitable for the user can be performed. Become.

[0013]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to FIGS. The same components as those in the conventional example are designated by the same reference numerals and the description thereof will be omitted.

As shown in the figure, in the floor surface sensor 9, a light emitting element 10 and a light receiving element 11 are installed in the floor suction tool 7 so as to face each other at an angle with respect to the floor surface. And the amount of change is detected. The signal from the floor sensor 9 is amplified by the amplifier 12, converted into a digital signal by the A / D conversion means 13, and input to the microcomputer 14.
The microcomputer 14 averages the digital amount from the A / D conversion unit 13 within a unit time to calculate an absolute value of the light amount, and an absolute light amount detection unit 15 and averages a change amount of the light amount within a unit time to change the light amount. Light amount change amount detecting means 16 for obtaining the amount
And a fuzzy reasoner 17. Storage means 18
Is a non-volatile memory in which information is not erased even when the power supply of the control system is turned off. Information such as the cumulative value and average value of is stored. The output of the microcomputer 14 is input to the phase control means 19 to control the input of the electric blower 2.
The fuzzy inference device 17 uses the floor quality information obtained from the light intensity absolute value calculated by the light intensity absolute value detection means 15 and the light intensity change amount calculated by the light intensity change amount detection means 16, and the storage floor quality stored in the storage means 18. The input of the electric blower 2 is inferred from the information and determined.

The fuzzy reasoner 17 has a structure as shown in FIG. 2, and the antecedent part membership function storage means 2
0 stores the membership function regarding the floor quality information and the stored floor quality information. The floor quality information adaptability calculating means 21 and the memory floor quality information adaptability calculating means 22 respectively input the floor quality information stored in the antecedent part membership function storage means 20 and the membership function relating to the storage floor quality information. The degree of compatibility with certain floor quality information and memory floor quality information is calculated. The antecedent part minimum calculating means 23 is the floor quality information compatibility calculating means 2
1. The MINs of the two fitness levels which are the outputs of the storage floor quality information fitness level computing means 22 are taken to be the conclusion of the antecedent part. The suction force inference rule storage means 24 stores the inference rule regarding the suction force. The suction force membership function storage means 25 stores the membership function regarding the suction force of the consequent part. In accordance with the inference rules stored in the consequent part minimum computing means 26 and the suction force inference rule storage means 24, the antecedent part conclusion and the suction force membership function of the consequent part stored in the suction force membership function storage means 25 The MIN of the function is taken to conclude the rule. The center-of-gravity means 27 finally obtains the suction force by obtaining the respective conclusions for all the rules, then taking the MAX of all the conclusions, and calculating the center of gravity thereof.

The fuzzy reasoner 17 is composed of a neuro-fuzzy reasoner that optimizes various parameters of fuzzy reasoning by a learning rule such as the steepest descent method of the neuro technique. This neuro-fuzzy reasoner can be easily realized by the microcomputer 14. The membership function and the inference rule stored in the antecedent part membership function storage means 20, the suction force inference rule storage means 24, and the suction force membership function storage means 25 included in the neuro-fuzzy reasoner are the floor quality information. From the data of the memory floor quality information and the input data of the electric blower 2 which should be set in consideration of the operation feeling when cleaning, the steepest descent method (one of the learning rules used in the neural network is used to minimize the error function. It is set by the learning rule such as).

To explain the operation in the above configuration, the light intensity absolute value detection means 15 integrates the light intensity detected by the floor sensor 9 for a unit time to obtain an average value, and the light intensity change amount detection means 16 calculates the light intensity per unit time. The amount of change in light is averaged to obtain the amount of change in light amount. From this information, the quality of the floor surface at that time can be known. These two information amounts are input to the fuzzy reasoner 17.

FIG. 3 shows the degree of change in the amount of light when cleaning is continuously performed. In the figure, on a relatively flat floor such as a wooden floor or a cushion floor, the absolute value of the light amount increases because the irregular reflection is small, and the change amount is small. When the floor surface is a carpet, the unevenness of the carpet surface becomes large and the absolute value of the light amount is small due to the influence of diffuse reflection,
There is a relation that the change in light amount becomes large. In this way, by detecting the absolute value of light intensity and the amount of change in light intensity, it is possible to estimate what the quality of the floor surface currently being cleaned is. Here, an indexed value (for example, 0 to 7) is set as the floor information. This index is used as one input of the fuzzy reasoner 17. A small amount of change in light quantity indicates a flat floor surface such as a wooden floor, and a large amount of change in light quantity indicates a rugged carpet surface with long hair.

Further, the floor quality information obtained from the light intensity absolute value detecting means 15 and the light intensity change amount detecting means 16 is periodically stored in the storage means 19. This storage means 19 uses a non-volatile memory or the like that retains the stored contents even when the power is turned off. Every time the cleaner body is used, a frequency is added for each index of the floor surface information, and all the cleaners used. Information on the floor can be obtained. As a pre-process for obtaining the goodness of fit by the storage floor surface information goodness-of-fit calculation means 22, for example, average floor quality information is obtained from the index of the storage floor surface information and its frequency and used as another input to the fuzzy reasoner 17. The optimum suction force for cleaning is determined by the characteristics of the floor surface, etc., and the outputs of the light intensity absolute value detection means 15 and the light intensity change amount detection means 16 and the storage means 1
The fuzzy reasoner 17 infers from the stored contents of 8.

Next, the process of inferring the suction force will be described. The inference rule of the fuzzy inference according to the present embodiment is "a floor with a large absolute value of light intensity (a flat floor with little unevenness) and a small amount of change in light intensity (a flat floor such as a wooden floor or a cushion floor) that can be used for past cleaning. The floor surface is made on the basis of a general judgment such that the suction power is set to be very low if there is much floor quality with a relatively small change in light intensity. Absolute value of light intensity is "more", change amount of light intensity is "smaller",
The qualitative concept that the suction force is "very high" is quantitatively expressed by the membership function as shown in Fig. 4 (a), (b), (c). In the floor quality information adaptability calculation means 21, the output of the light quantity absolute value detection means 15, the input from the light quantity change amount detection means 16, and the membership of the waste change rate stored in the antecedent part membership function storage means 20. Find the goodness of fit for a function. The memory floor quality information fitness degree calculating means 22 similarly obtains the fitness degree with respect to the input from the memory means 18 and the membership function of the memory floor surface information stored in the antecedent part membership function memory means 20. The antecedent part minimum computing means 23 takes the MINs of these suitability values and makes the conclusion of the antecedent part. In the consequent part minimum calculating means 26, in accordance with the rule stored in the suction force inference rule storage means 24, the antecedent part conclusion and the suction force membership of the consequent part stored in the suction force membership function storage means 25 The MIN of the function is taken to conclude the rule. After obtaining the respective conclusions for all the rules, the centroid calculating means 27 takes MAX of all the conclusions and calculates the centroids thereof to finally obtain the suction force. The phase control means 19 calculates and controls the phase control amount of the electric blower 2 based on the determined suction force.

Next, another embodiment of the present invention will be described with reference to FIGS. The same components as those in the above-described embodiment are designated by the same reference numerals and the description thereof will be omitted.

As shown in FIG.
Reference numeral 8 denotes a light quantity absolute value detection means 1 for averaging the digital quantities from the A / D conversion means 13 within a unit time to calculate a light quantity absolute value.
5, a light amount change amount detecting means 16 for averaging the light amount change amount within a unit time to obtain a light amount change amount, and a fuzzy reasoner 29.
It has and. The output of the microcomputer 28 is input to the phase control means 19 to control the input of the electric blower 2,
Further, it is input to the phase control means 30 to control the rotation speed of the rotary brush 6 of the floor suction tool. Fuzzy reasoner 29
Is an electric blower based on the floor quality information obtained from the light intensity absolute value calculated by the light intensity absolute value detection unit 15 and the light intensity change amount calculated by the light intensity change amount detection unit 16 and the storage floor quality information stored in the storage unit 18. The input of 2 and the rotation speed of the rotary brush 6 are inferred and determined.

In the fuzzy reasoner 29, the rotating brush rotation speed inference rule storage means 31 stores inference rules regarding the rotation speed of the rotating brush 6. The rotating brush rotation speed membership function storage means 32 stores a membership function relating to the rotation speed of the rotating brush 6. The other structure is the same as that of the above-mentioned embodiment.

The fuzzy reasoner 29 is composed of a neuro-fuzzy reasoner that optimizes various parameters of fuzzy reasoning by a learning rule such as the steepest descent method of neuro technology. The membership functions and the inference rules stored in the antecedent part membership function storage means 20, the rotating brush rotation speed inference rule storage means 31, and the rotating brush rotation speed membership function storage means 32 included in the neuro-fuzzy reasoner are From the data of the floor quality information and the memory floor quality information and the data of the number of rotations of the rotary brush 6 that should be set in consideration of the operation feeling when cleaning, the steepest descent method (one of the learning rules used in the neural network, It is a method of minimizing the error function) and is optimally set by a learning rule such as.

The rotation speed of the rotary brush 6 is determined by calculating the conclusion of the antecedent part in the same manner as the process of determining the suction force in the above embodiment, and the rotary brush rotation speed inference rule storage means 31.
The rotational speed of the rotating brush 6 is determined from the rotational brush rotational speed membership function storage means 32. Phase control means 1
In 9 and 30, the phase control amounts of the electric blower 2 and the rotary brush 31 are calculated and controlled based on the determined input of the electric blower 2 and the rotational speed of the rotary brush 31.

In each of the above embodiments, the MAX-MIN composition method and the center of gravity method are used as the inference method, but other methods are also possible, and the suction force, which is the consequent part, can be expressed by the membership function. Although it is expressed, it goes without saying that it can also be expressed in a real number or linear form. Further, although the floor surface sensor 9 directs the optical axis directly to the floor surface, the floor surface sensor 9 may indirectly detect the optical axis, or may have any configuration as long as it optically detects the unevenness of the floor surface.

[0027]

As is apparent from the above embodiments, according to the present invention, the electric blower for suction and the light emitting element and the light receiving element provided in the floor suction tool detect the unevenness of the floor surface. A floor sensor configured as described above, A / D conversion means for converting an analog output of the floor sensor into a digital signal, and digital amounts from the A / D conversion means are averaged within a unit time to calculate an absolute light amount value. Absolute light amount detection means, light amount change amount detection means for averaging the change amount of the light amount within a unit time to obtain the light amount change amount, storage means capable of storing even when the power is off, and determining the input of the electric blower And a fuzzy inference device for sequentially learning the floor quality from the floor quality information obtained from both the light intensity absolute value information and the light intensity change amount information and the memory floor quality information stored in the storage means. , Turn on the electric blower Therefore, the optimum suction force can be determined finely by fuzzy reasoning from the floor surface information obtained from the absolute light intensity value and the light intensity change amount and the stored floor quality information, and the efficiency can be determined regardless of the floor surface to be cleaned. It is easy to remove dust and has a very comfortable operation, which improves usability.

In addition to the electric blower, the floor suction device is provided with a rotating brush, and the number of rotations of the rotating brush is determined by a fuzzy reasoner. Therefore, the number of rotations of the rotating brush is finely determined by fuzzy inference. it can.

Further, the fuzzy reasoner optimizes various parameters of the fuzzy inference by learning rules such as the steepest descent method of the neuro technique, and uses the membership functions of the antecedent part and the consequent part as various parameters. Since the shape and the number of inference rules are optimized, if the number of inputs and outputs in fuzzy inference increases, it becomes difficult for humans to optimize the inference rules and their configuration between them, but as a result, it is fine and optimal. The suction force and the rotation speed of the rotating brush can be determined, and the suction force and the rotation speed of the rotating brush are optimized more and more by the learning function depending on the environment in which the vacuum cleaner is used (uniqueness of the floor quality). Can be improved.

[Brief description of drawings]

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

FIG. 2 is a block diagram of a fuzzy reasoner of the controller of the electric vacuum cleaner.

FIG. 3 is a diagram showing a change in output of a floor surface sensor of the control device of the electric vacuum cleaner.

4A to 4C are diagrams showing membership functions of a fuzzy reasoner of the control device of the vacuum cleaner.

FIG. 5 is a block diagram of a control device for an electric vacuum cleaner according to another embodiment of the present invention.

FIG. 6 is a block diagram of a fuzzy reasoner of the control device of the electric vacuum cleaner.

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

[Explanation of symbols]

 2 Electric blower 7 Floor suction device 9 Floor sensor 10 Light emitting element 11 Light receiving element 13 A / D conversion means 15 Light intensity absolute value detection means 16 Light intensity variation detection means 17 Fuzzy reasoner 18 Storage means

Claims (3)

[Claims] 1. An electric blower for suction, a floor sensor provided in a floor suction tool configured to detect a concave and convex state of a floor from a light emitting element and a light receiving element, and an analog output of the floor sensor. A / D to convert digital to digital signal
A conversion means, a light quantity absolute value detection means for averaging the digital quantities from the A / D conversion means within a unit time to calculate a light quantity absolute value, and a change quantity of the light quantity over a unit time for averaging the light quantity change quantity. A light amount change amount detecting means for obtaining, a storage means capable of storing even when the power is off, and a fuzzy inference device for determining an input of the electric blower, and the fuzzy inference device includes both the absolute light amount value and the light amount change amount. A controller for an electric vacuum cleaner, which sequentially learns a floor quality from the floor quality information obtained from the information and the stored floor quality information stored in the storage means, and determines the input of the electric blower. 2. The control device for the electric vacuum cleaner according to claim 1, wherein a rotary brush is provided in the floor suction tool in addition to the electric blower, and the number of rotations of the rotary brush is determined by a fuzzy reasoner. 3. A fuzzy reasoner optimizes various parameters of fuzzy inference by a learning rule such as the steepest descent method of neuro technology, and uses the membership functions of the antecedent part and the consequent part as various parameters. The control device for the electric vacuum cleaner according to claim 1, wherein the shape and the number of inference rules are optimized.