Scrubber water volume intelligent control method and system based on cleaning feedback

Abstract

The invention relates to the technical field of cleaning equipment, in particular to a scrubber water volume intelligent control method and system based on cleaning feedback, and the method comprises the steps: confirming a water volume control database by using a data simulation unit, obtaining a cleaning confirmation parameter set based on the water volume control database, receiving a target cleaning parameter set selected by a user based on the cleaning confirmation parameter set, when the target scrubber detects a detection area with detection stains in the initial cleaning ground, a detection stain image and detection stain humidity of the detection area are obtained, target cleaning water quantity is retrieved from the water quantity control database by using the target cleaning parameter set and the detection stain humidity, and the detection area is cleaned by using the target cleaning water quantity. And the sewage gray value of the sewage after cleaning and the cleaning stain image of the detection area after cleaning are obtained, and after it is confirmed that the detection area is clean according to the detection stain image, the cleaning stain image and the sewage gray value, intelligent control over the water amount of the target floor washing machine is achieved. According to the invention, intelligent control of the water quantity of the scrubber can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of cleaning equipment, and in particular to a method and system for intelligently controlling water volume of a floor scrubber based on cleaning feedback. Background Art

[0002] With the development of intelligent equipment, more and more floor washers have entered people's daily lives. Correspondingly, how to achieve intelligent control of the water volume of floor washers has become an urgent problem to be solved.

[0003] At present, traditional floor scrubbers mostly use a fixed amount of water to clean the floor. When using a fixed amount of water to clean the floor, the water volume cannot be adjusted according to the cleaning needs, which may lead to problems such as waste of water resources or low cleaning efficiency. Therefore, realizing intelligent control of water volume in combination with cleaning needs has become an urgent problem to be solved. Summary of the invention

[0004] The present invention provides a method for intelligently controlling the water volume of a floor scrubber based on cleaning feedback and a computer-readable storage medium, the main purpose of which is to realize intelligent control of the water volume of the floor scrubber.

[0005] To achieve the above object, the present invention provides a method for intelligently controlling water volume of a floor scrubber based on cleaning feedback, comprising:

[0006] Receive a water volume intelligent control instruction, and identify a target floor scrubber, an initial floor cleaning surface, and a water volume control system based on the water volume intelligent control instruction, wherein the water volume control system includes: a data simulation unit and an adjustment feedback unit, and the target floor scrubber includes: a humidity detection unit, a single sewage collection unit, and an image acquisition unit;

[0007] Using the data simulation unit to confirm a water output control database, and obtaining a cleaning confirmation parameter set based on the water output control database, wherein the cleaning confirmation parameter set includes a floor washing type set and a detergent type set;

[0008] The cleaning confirmation parameter set is sent to the initiator of the water volume intelligent control instruction, and the adjustment feedback unit is used to receive the target cleaning parameter set selected by the user based on the cleaning confirmation parameter set, wherein the target cleaning parameter set includes the target floor type and the target detergent;

[0009] When the target floor scrubber detects the presence of a detection area of ​​a detection stain in the initial cleaning of the floor, the detection stain image and the detection stain humidity are respectively acquired by using the humidity detection unit, the image acquisition unit and the detection area;

[0010] Based on the target cleaning parameter set, the detection stain image and the detection stain humidity, a target cleaning water volume is retrieved from a water volume control database, the target cleaning water volume and the target floor scrubber are used to perform a cleaning operation on the detection stain in the detection area to obtain a target cleaning area, and the single-time sewage collection unit is used to collect sewage for cleaning the detection stain to obtain target detection sewage;

[0011] Acquire a cleaning stain image using the image acquisition unit and the target cleaning area, and acquire a sewage grayscale value based on the target sewage detection;

[0012] After confirming that the target cleaning area is unclean according to the stain detection image, the stain cleaning image and the sewage grayscale value, the target cleaning area is used as the detection area, and the process returns to the steps of respectively acquiring the stain detection image and the stain detection humidity using the humidity detection unit, the image acquisition unit and the detection area until it is confirmed that the target cleaning area is clean, thereby realizing intelligent control of the water volume of the target floor scrubber.

[0013] Optionally, the confirming the water output control database by using the data simulation unit includes:

[0014] confirming receipt of a data simulation instruction from a data simulation unit, and acquiring a washing floor type set, a stain capacity gradient set, a washing humidity range, and a detergent type set of a target floor scrubber based on the data simulation instruction, wherein the washing floor type set includes a plurality of washing floor types, the detergent type set includes a plurality of detergents, and the stain capacity gradient set includes a plurality of stain capacities;

[0015] Dividing the washing humidity range by using a preset first division value to obtain a plurality of washing humidity values;

[0016] Acquire an experimental combination set by using the washing floor type set, the plurality of washing humidity values ​​and the detergent type set in a combined manner, wherein the experimental combination set includes a plurality of experimental combinations, and each experimental combination includes a washing floor type, a washing humidity value and a detergent;

[0017] For each experimental combination in the experimental combination set, perform the following operations:

[0018] Associating the experimental combination with the stain capacity gradient set to obtain an initial experimental combination set, wherein the initial experimental combination set includes multiple initial experimental combinations, and the initial experimental combination includes a washing surface type, a washing humidity value, a stain capacity and a detergent, and the initial experimental combination corresponds to the stain capacity one by one;

[0019] Summarizing the initial experimental combination sets to obtain multiple initial experimental combination sets;

[0020] A water quantity control database is obtained using the multiple initial experimental combination sets.

[0021] Optionally, the acquiring a water quantity control database by using the multiple initial experimental combination sets includes:

[0022] The following operations are performed for each of the multiple initial experimental combination sets:

[0023] The following operations are performed for each initial experimental combination in the initial experimental combination set:

[0024] Use the initial experimental combination to build an initial testing environment;

[0025] Acquire a reference cleaning image based on the type of floor being washed corresponding to the initial experimental combination, and acquire an experimental water volume gradient set of a target floor washer, wherein the experimental water volume gradient set includes multiple experimental water volumes;

[0026] The following operations are performed for each experimental water volume in the experimental water volume gradient set:

[0027] Use the experimental water volume to drive the target floor scrubber to clean the initial test environment, photograph the initial test environment after cleaning, and obtain a cleaning test image;

[0028] Acquire a cleaning evaluation value based on the cleaning experiment image and the reference cleaning image, summarize the cleaning evaluation values ​​to obtain a cleaning evaluation value set, and use the cleaning evaluation value set to acquire a target cleaning evaluation value;

[0029] The target cleaning evaluation values ​​are summarized to obtain a target cleaning evaluation value set, a target cleaning curve is fitted using the target cleaning evaluation value set, and a water volume control database is constructed using the target cleaning curve.

[0030] Optionally, obtaining a cleaning evaluation value based on the cleaning experiment image and the reference cleaning image includes:

[0031] Performing a grayscale operation on the reference clean image to obtain a clean grayscale image, and using a pre-built region growing algorithm to retrieve one or more target clean images from the clean grayscale image;

[0032] Acquire the number of cleaning pixels based on the one or more target cleaning images, wherein the number of cleaning pixels is the sum of the pixels corresponding to the one or more target cleaning images;

[0033] Retrieving a target stain image from a cleaning experiment image using the region growing algorithm;

[0034] A target pixel number is obtained based on the target stain image, and a cleaning evaluation value is obtained according to the target pixel number and the cleaning pixel number, wherein the cleaning evaluation value is a ratio of the target pixel number to the cleaning pixel number.

[0035] Optionally, the obtaining a target cleaning evaluation value by using the cleaning evaluation value set includes:

[0036] Performing a sorting operation on the cleaning evaluation values ​​in the cleaning evaluation value set in descending order of the cleaning evaluation values ​​to obtain an initial evaluation sequence;

[0037] Acquiring a target evaluation sequence based on the initial evaluation sequence, wherein the cleaning evaluation values ​​in the target evaluation sequence are all less than or equal to a preset evaluation threshold;

[0038] Extracting evaluation cleaning value groups in sequence in a target evaluation sequence using a pre-constructed cleaning evaluation window, wherein the evaluation cleaning value groups include a plurality of reference cleaning values;

[0039] Acquire a reference cleaning mean and a reference cleaning variance based on the evaluation cleaning value group, wherein the reference cleaning mean and the reference cleaning variance are respectively the mean and variance of a plurality of reference cleaning values ​​in the evaluation cleaning value group;

[0040] The comprehensive evaluation value is calculated using the reference cleaning mean, reference cleaning variance and a pre-constructed cleaning evaluation relationship, wherein the cleaning evaluation relationship is as follows:

[0041] Z=αμ+βσ

[0042] Wherein, Z represents the comprehensive evaluation value, α and β are both preset coefficients, μ represents the reference cleaning mean, and σ represents the reference cleaning variance;

[0043] The comprehensive evaluation value is compared with a preset evaluation threshold, and when the comprehensive evaluation value is less than or equal to the evaluation threshold, the first cleaning evaluation value in the evaluation cleaning value group is used as the target cleaning evaluation value.

[0044] Optionally, fitting a target cleaning curve using the target cleaning evaluation value set includes:

[0045] The following operations are performed on the target cleaning evaluation values ​​in the target cleaning evaluation value set:

[0046] Acquire a stain grayscale image using an initial detection environment corresponding to a target cleaning evaluation value, and divide the stain grayscale image into one or more target stain images according to regions of one or more target cleaning images corresponding to the target cleaning evaluation value, wherein the target stain images correspond one to one to the target cleaning images;

[0047] The reference stain evaluation value is calculated based on one or more target clean images and one or more target stain images. The calculation formula is as follows:

[0048]

[0049] Where W represents the reference stain evaluation value, n represents one or more target clean images or one or more target stain images, and h i (X,Y) represents the grayscale value of the Ith target cleaning image at the (X,Y)th pixel in one or more target cleaning images, g i (X, y) represents the grayscale value of the i-th target stain image at the (x, y)-th pixel in one or more target stain images;

[0050] Summarizing the reference stain assessment values ​​to obtain a reference stain assessment value set, and performing a sorting operation on the reference stain assessment values ​​in the reference stain assessment value set in ascending order to obtain a reference stain assessment sequence;

[0051] Acquire a stain cleaning node sequence based on a reference stain evaluation sequence, wherein the stain cleaning node sequence includes a plurality of stain cleaning nodes, and the stain cleaning nodes include: a reference stain evaluation value and an experimental water volume corresponding to the reference stain evaluation value;

[0052] Mapping the stain cleaning nodes in the stain cleaning node sequence to a pre-constructed reference coordinate system to obtain a reference point set, wherein the horizontal axis of the reference coordinate system is the reference stain evaluation value, and the vertical axis is the experimental water volume;

[0053] A target cleaning curve is obtained based on the reference point set.

[0054] Optionally, acquiring a target cleaning curve based on the reference point set includes:

[0055] Obtain a fitting model set for fitting a curve, wherein the fitting model set includes multiple fitting models, and perform the following operations on each of the fitting models in the fitting model set:

[0056] An initial cleaning curve is obtained based on the fitting model and the reference point set, a plurality of stain capacity ranges are obtained using the stain capacity gradient set, and the following operations are performed on each of the stain capacity ranges in the plurality of stain capacity ranges:

[0057] Dividing the stain capacity range by using a preset second division value to obtain a plurality of verification stain values;

[0058] Using a preset extraction value, randomly extract one or more verification stain values ​​from a plurality of check stain values, wherein the number corresponding to the one or more verification stain values ​​is the same as the extraction value;

[0059] Acquire one or more verification cleaning nodes based on the one or more verification stain values, and map the one or more verification cleaning nodes to the reference coordinate system to obtain a verification point set;

[0060] Extracting a verification point set from an initial cleaning curve using the verification point set, wherein the verification point set includes one or more verification cleaning nodes, and the verification cleaning nodes correspond one to one with the verification cleaning nodes;

[0061] Acquire an evaluation point group set based on the verification point set and the check point set, wherein the evaluation point group set includes one or more evaluation point groups, and the evaluation point group includes a verification cleaning node and a check cleaning node, and the horizontal coordinate corresponding to the verification cleaning node is equal to the horizontal coordinate corresponding to the check cleaning node;

[0062] Calculating an evaluation fitting value using the evaluation point set and a pre-constructed comprehensive evaluation relationship;

[0063] The evaluation fitting values ​​are summarized to obtain an evaluation fitting value set, and a target cleaning curve is determined based on the evaluation fitting value set, wherein the target cleaning curve is an initial cleaning curve corresponding to the minimum evaluation fitting value in the evaluation fitting value set.

[0064] Optionally, the comprehensive evaluation relationship is as follows:

[0065]

[0066] Wherein, P represents the evaluation fitting value, m represents that there are m evaluation point groups in the evaluation point group set, and a k represents the horizontal coordinate corresponding to the kth evaluation point group in the evaluation point group set, a j represents the horizontal coordinate corresponding to the jth evaluation point group in the evaluation point group set, b 1k represents the verification clean node corresponding to the kth evaluation point group in the evaluation point group set, b 2k Represents the verification cleaning node corresponding to the kth evaluation point group in the evaluation point group set.

[0067] Optionally, the step of confirming that the target cleaning area is unclean based on the stain detection image, the stain cleaning image and the sewage gray value includes:

[0068] The detection stain image and the cleaning stain image are used to obtain the detection grayscale mean and the cleaning grayscale mean, respectively. The cleaning evaluation value is obtained based on the detection grayscale mean, the cleaning grayscale mean, the sewage grayscale value and the pre-constructed cleaning evaluation relationship, wherein the cleaning evaluation relationship is as follows:

[0069]

[0070] Wherein, Q represents the cleaning evaluation value, J1 represents the detection grayscale mean, J2 represents the cleaning grayscale mean, w1 represents the sewage grayscale value, and w2 represents the preset reference grayscale value. ω are all preset coefficients;

[0071] The cleaning evaluation value is compared with a preset clean evaluation threshold value, and if the cleaning evaluation value is greater than the clean evaluation threshold value, it is determined that the target cleaning area is not clean.

[0072] To achieve the above object, the present invention also provides a floor scrubber water volume intelligent control system based on cleaning feedback, comprising:

[0073] A water volume control system confirmation module is used to receive a water volume intelligent control instruction, and confirm a target floor scrubber, an initial cleaning floor, and a water volume control system based on the water volume intelligent control instruction, wherein the water volume control system includes: a data simulation unit and an adjustment feedback unit, and the target floor scrubber includes: a humidity detection unit, a single sewage collection unit, and an image acquisition unit;

[0074] A floor scrubber cleaning parameter confirmation module, used to confirm a water output control database using the data simulation unit, and obtain a cleaning confirmation parameter set based on the water output control database, wherein the cleaning confirmation parameter set includes a floor scrubbing type set and a detergent type set;

[0075] The cleaning confirmation parameter set is sent to the initiator of the water volume intelligent control instruction, and the adjustment feedback unit is used to receive the target cleaning parameter set selected by the user based on the cleaning confirmation parameter set, wherein the target cleaning parameter set includes the target floor type and the target detergent;

[0076] A stain parameter confirmation module, which is used to obtain a detection stain image and a detection stain humidity respectively by using the humidity detection unit, the image acquisition unit and the detection area when the target floor scrubber detects the presence of a detection stain in the detection area during the initial cleaning of the floor;

[0077] a stain area cleaning module, for retrieving a target cleaning water volume from a water volume control database based on a target cleaning parameter set, a detected stain image, and a detected stain humidity, performing a cleaning operation on the detected stain in the detection area using the target cleaning water volume and a target floor scrubber to obtain a target cleaning area, and collecting sewage for cleaning the detected stain using a single sewage collection unit to obtain target detected sewage;

[0078] The image acquisition unit and the target cleaning area are used to obtain a cleaning stain image, and the sewage grayscale value is obtained based on the target detected sewage. After confirming that the target cleaning area is unclean according to the detected stain image, the cleaning stain image and the sewage grayscale value, the target cleaning area is used as the detection area, and the process returns to the steps of respectively obtaining the detected stain image and detecting the stain humidity using the humidity detection unit, the image acquisition unit and the detection area until it is confirmed that the target cleaning area is clean, thereby realizing intelligent control of the water volume of the target floor scrubber.

[0079] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0080] a memory storing at least one instruction; and

[0081] The processor executes the instructions stored in the memory to implement the above-mentioned intelligent water volume control method for a floor scrubber based on cleaning feedback.

[0082] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned intelligent water volume control method for a floor scrubber based on cleaning feedback.

[0083] The present invention is to solve the problem described in the background technology. An embodiment of the present invention receives a water volume intelligent control instruction, and based on the water volume intelligent control instruction, confirms a target floor scrubber, an initial cleaning floor, and a water volume control system, wherein the water volume control system includes: a data simulation unit and an adjustment feedback unit, and the target floor scrubber includes: a humidity detection unit, a single sewage collection unit, and an image acquisition unit. It can be seen that the embodiment of the present invention considers combining the sewage situation during a single cleaning to determine whether the stained area is cleaned, thereby improving the accuracy of water volume control. The embodiment of the present invention uses the data simulation unit to confirm a water volume control database, and obtains a cleaning confirmation parameter set based on the water volume control database, wherein the cleaning confirmation parameter set includes a type of floor washing. The cleaning confirmation parameter set is sent to the initiator of the water volume intelligent control instruction, and the adjustment feedback unit is used to receive the target cleaning parameter set selected by the user based on the cleaning confirmation parameter set, wherein the target cleaning parameter set includes the target floor type and the target detergent. When the target floor scrubber detects the presence of the detection stain in the detection area during the initial cleaning of the floor, the humidity detection unit, the image acquisition unit and the detection area are used to respectively obtain the detection stain image and the detection stain humidity. Based on the target cleaning parameter set, the detection stain image and the detection stain humidity, the target cleaning water volume is retrieved from the water volume control database, and the target cleaning water volume and the target floor scrubber are used to perform a cleaning operation on the detection stain in the detection area to obtain the target The target cleaning area is determined, and the sewage for cleaning the detection stains is collected by a single sewage collection unit to obtain the target detection sewage. It can be seen that the embodiment of the present invention also takes into account that the type of detergent and the target ground type may have an impact on cleaning stains. Therefore, by obtaining the target ground type and the target detergent, the accuracy of water volume control is improved. The embodiment of the present invention realizes the guidance of the required water volume for the ground to be cleaned by constructing a water volume control database, and the initial cleaning curve is verified when constructing the water volume control database, so that the verified target cleaning curve can more accurately characterize the relationship between the required water volume for cleaning and the stains on the ground, thereby improving the accuracy of retrieving the target cleaning water volume in the water volume control database. The embodiment of the invention uses the image acquisition unit and the target cleaning area to obtain the cleaning stain image, obtains the sewage gray value based on the target detection sewage, and confirms that the target cleaning area is not clean according to the detection stain image, the cleaning stain image and the sewage gray value. Then, the target cleaning area is used as the detection area, and the steps of using the humidity detection unit, the image acquisition unit and the detection area to respectively obtain the detection stain image and detect the stain humidity are returned until the target cleaning area is confirmed to be clean. It can be seen that the embodiment of the present invention also takes into account the stubbornness of the stain, that is, the detection area may not be cleaned by one cleaning. Therefore, the water volume control data and the target cleaning area are combined to retrieve the water volume used to clean the target cleaning area until the target cleaning area is cleaned. Therefore, the present invention can realize intelligent control of the water volume of the floor scrubber. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 A schematic flow chart of a method for intelligently controlling water volume of a floor scrubber based on cleaning feedback provided in one embodiment of the present invention;

[0085] Figure 2 A functional module diagram of an intelligent water volume control system for a floor scrubber based on cleaning feedback provided by an embodiment of the present invention;

[0086] Figure 3 A schematic diagram of the structure of an electronic device for implementing the intelligent water volume control method for a floor scrubber based on cleaning feedback provided in one embodiment of the present invention.

[0087] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0088] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0089] The embodiment of the present application provides a method for intelligently controlling the water volume of a floor scrubber based on cleaning feedback. The execution subject of the method for intelligently controlling the water volume of a floor scrubber based on cleaning feedback includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for intelligently controlling the water volume of a floor scrubber based on cleaning feedback can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0090] Reference Figure 1 FIG. 1 is a flow chart of a method for intelligently controlling water volume of a floor scrubber based on cleaning feedback provided by an embodiment of the present invention. In this embodiment, the method for intelligently controlling water volume of a floor scrubber based on cleaning feedback includes:

[0091] S1. Receive a water volume intelligent control instruction, and identify a target floor scrubber, an initial floor cleaning unit, and a water volume control system based on the water volume intelligent control instruction, wherein the water volume control system includes: a data simulation unit and an adjustment feedback unit, and the target floor scrubber includes: a humidity detection unit, a single sewage collection unit, and an image acquisition unit.

[0092] It should be explained that the intelligent water volume control instruction refers to an instruction issued by the user of the floor scrubber to the target floor scrubber, and the instruction is used to realize the intelligent control of the water volume of the target floor scrubber. The target floor scrubber refers to a floor scrubber that needs to realize intelligent water volume control when cleaning the floor. The target floor scrubber includes: a humidity detection unit, a single sewage collection unit and an image acquisition unit. Please refer to the subsequent embodiments for the application of specific units. The water volume control system refers to a small program or APP for intelligent water volume control of the floor scrubber, and the water volume control system includes: a data simulation unit and an adjustment feedback unit. Please refer to the subsequent embodiments for the application of specific units. The initial cleaning floor refers to the floor that needs to be cleaned by the target floor scrubber. The present invention mainly solves the problem of waste of resources caused by the inability to realize intelligent control of water volume when the floor scrubber is cleaning the floor.

[0093] For example, Xiao Zhang uses the target floor scrubber to clean the house, and in order to save water resources as much as possible, Xiao Zhang issues the water volume intelligent control instruction to the target floor scrubber.

[0094] S2. Confirm a water volume control database using the data simulation unit, and obtain a cleaning confirmation parameter set based on the water volume control database, wherein the cleaning confirmation parameter set includes a floor washing type set and a detergent type set.

[0095] It is understandable that the use of the data simulation unit to confirm the water output control database includes:

[0096] confirming receipt of a data simulation instruction from a data simulation unit, and acquiring a washing floor type set, a stain capacity gradient set, a washing humidity range, and a detergent type set of a target floor scrubber based on the data simulation instruction, wherein the washing floor type set includes a plurality of washing floor types, the detergent type set includes a plurality of detergents, and the stain capacity gradient set includes a plurality of stain capacities;

[0097] Dividing the washing humidity range by using a preset first division value to obtain a plurality of washing humidity values;

[0098] Acquire an experimental combination set by using the washing floor type set, the plurality of washing humidity values ​​and the detergent type set in a combined manner, wherein the experimental combination set includes a plurality of experimental combinations, and each experimental combination includes a washing floor type, a washing humidity value and a detergent;

[0099] For each experimental combination in the experimental combination set, perform the following operations:

[0100] Associating the experimental combination with the stain capacity gradient set to obtain an initial experimental combination set, wherein the initial experimental combination set includes multiple initial experimental combinations, and the initial experimental combination includes a washing surface type, a washing humidity value, a stain capacity and a detergent, and the initial experimental combination corresponds to the stain capacity one by one;

[0101] Summarizing the initial experimental combination sets to obtain multiple initial experimental combination sets;

[0102] A water quantity control database is obtained using the multiple initial experimental combination sets.

[0103] It should be explained that the type of floor to be washed refers to the type of floor that can be washed by a floor scrubber. The types of floor to be washed include but are not limited to: cement floor, PVC floor, epoxy floor, marble floor, tile floor, floor tile floor and epoxy resin floor. The type of detergent refers to the type of detergent used to clean the floor. The types of detergent include but are not limited to: neutral detergent, acidic detergent, low foam degreaser and rubber stamp detergent. The washing humidity range refers to the humidity range of the floor or stains when the floor is washed by a floor scrubber. Optionally, the first division value is obtained by artificial setting. For example, the washing humidity range is: 10% to 60%, and the preset first division value is 6, then the washing humidity range can be divided into: 10%, 20%, 30%, 40%, 50% and 60% by using the first division value. The purpose of dividing the washing humidity range is to improve the accuracy of the evaluation of the washing effect of the floor scrubber at a certain washing humidity value. Generally speaking, the washing humidity value, the type of floor to be washed and the detergent will affect the washing effect of the floor. Therefore, obtaining the experimental combination by combination can improve the accuracy of intelligent water volume control. The stain capacity refers to the capacity of the stain in the washing floor. For example: if there is 1 ml of grease on the floor, then 1 ml is the stain capacity.

[0104] Exemplarily, the washing floor type set includes two washing floor types, the detergent type set includes two detergents, there are two washing humidity values, and a total of 8 different experimental combinations can be obtained in the form of combinations. There are 5 stain capacities in the stain capacity gradient set. After associating each of the 8 different experimental combinations with the stain capacity gradient set, the obtained initial experimental combination set includes 5 initial experimental combinations, and each initial experimental combination includes a washing floor type, a washing humidity value, a stain capacity and a detergent.

[0105] It should be understood that there is only one variable in each initial experimental combination set, which is the stain capacity. Generally speaking, under the same other conditions, the more stain capacity, the more water is required for cleaning.

[0106] It should be explained that the use of the multiple initial experimental combination sets to obtain a water volume control database includes:

[0107] The following operations are performed for each of the multiple initial experimental combination sets:

[0108] The following operations are performed for each initial experimental combination in the initial experimental combination set:

[0109] Use the initial experimental combination to build an initial testing environment;

[0110] Acquire a reference cleaning image based on the type of floor being washed corresponding to the initial experimental combination, and acquire an experimental water volume gradient set of a target floor washer, wherein the experimental water volume gradient set includes multiple experimental water volumes;

[0111] The following operations are performed for each experimental water volume in the experimental water volume gradient set:

[0112] Use the experimental water volume to drive the target floor scrubber to clean the initial test environment, photograph the initial test environment after cleaning, and obtain a cleaning test image;

[0113] Acquire a cleaning evaluation value based on the cleaning experiment image and the reference cleaning image, summarize the cleaning evaluation values ​​to obtain a cleaning evaluation value set, and use the cleaning evaluation value set to acquire a target cleaning evaluation value;

[0114] The target cleaning evaluation values ​​are summarized to obtain a target cleaning evaluation value set, a target cleaning curve is fitted using the target cleaning evaluation value set, and a water volume control database is constructed using the target cleaning curve.

[0115] It should be explained that constructing the initial detection environment using the initial experimental combination means: constructing an environment that meets the initial experimental combination according to a type of washing floor, a washing humidity value, a stain capacity and a detergent corresponding to the initial experimental combination. The reference cleaning image refers to the image of the type of washing floor corresponding to the initial experimental combination during cleaning. The experimental water volume refers to the amount of water used when the target floor washer cleans the floor in the simulation experiment. The cleaning experimental image refers to the image of the initial detection environment after the initial detection environment is cleaned using the experimental water volume and the target floor washer. The water volume control database is composed of multiple different target cleaning curves.

[0116] Exemplarily, the initial experimental combination corresponds to: the type of floor to be washed is PVC floor, the washing humidity value is 50 percent, the detergent is a neutral detergent, the type of stain is grease, and the stain capacity is 1 ml. Using the initial experimental combination to construct the initial detection environment means: spraying 1 ml of grease on the PVC floor, and controlling the humidity of the PVC floor after spraying the grease to 50 percent. The image of the clean PVC floor is the reference cleaning image. After cleaning the grease in the initial detection environment using the experimental water volume and the target floor scrubber, the image of the PVC floor in the initial detection environment is taken as the cleaning experimental image.

[0117] It should be understood that the obtaining of the cleaning evaluation value based on the cleaning experiment image and the reference cleaning image includes:

[0118] Performing a grayscale operation on the reference clean image to obtain a clean grayscale image, and using a pre-built region growing algorithm to retrieve one or more target clean images from the clean grayscale image;

[0119] Acquire the number of cleaning pixels based on the one or more target cleaning images, wherein the number of cleaning pixels is the sum of the pixels corresponding to the one or more target cleaning images;

[0120] Retrieving a target stain image from a cleaning experiment image using the region growing algorithm;

[0121] A target pixel number is obtained based on the target stain image, and a cleaning evaluation value is obtained according to the target pixel number and the cleaning pixel number, wherein the cleaning evaluation value is a ratio of the target pixel number to the cleaning pixel number.

[0122] It should be explained that the grayscale operation refers to the operation of converting a color image into a grayscale image, and the technology of the grayscale operation is a prior art and will not be described in detail here. It is well known that after converting a color image into a grayscale image, each pixel in the grayscale image corresponds to a grayscale value, so the cleaned grayscale mean can be obtained by cleaning the grayscale image. The region growing algorithm is a prior art and will not be described in detail here.

[0123] It is understandable that there may be different target clean images in the clean grayscale image, so the region growing algorithm can be used to retrieve one or more target clean images in the clean grayscale image. For example, the region corresponding to the clean grayscale image is the region at the intersection of the floor tile floor, that is, there may be a target clean image corresponding to the floor tile region and a target clean image corresponding to the floor seam in the clean grayscale image. Optionally, by selecting the grayscale value corresponding to the floor tile region in the clean grayscale image as the starting point of the region growing algorithm, selecting one or more target clean images corresponding to the floor tile region in the clean grayscale image, and selecting the grayscale value corresponding to the floor seam region in the clean grayscale image as the starting point of the region growing algorithm, selecting the target clean image corresponding to the floor seam region in the clean grayscale image, and summing up all the target clean images to obtain one or more target clean images. The number of clean pixels is both the sum of the pixels corresponding to one or more target clean images and the number of pixels corresponding to the clean grayscale image. The purpose of dividing the cleaning grayscale image into one or more target cleaning images is to analyze each target cleaning image and thereby improve the accuracy of water volume control for the target floor scrubber.

[0124] It should be understood that in the embodiment of the present invention, the area corresponding to the target stain image refers to the area that has not been cleaned, and the target stain image refers to the image of the area that has not been cleaned. The target pixel number refers to the total number of pixels in the target stain image.

[0125] Further, the obtaining a target cleaning evaluation value by using the cleaning evaluation value set includes:

[0126] Performing a sorting operation on the cleaning evaluation values ​​in the cleaning evaluation value set in descending order of the cleaning evaluation values ​​to obtain an initial evaluation sequence;

[0127] Acquiring a target evaluation sequence based on the initial evaluation sequence, wherein the cleaning evaluation values ​​in the target evaluation sequence are all less than or equal to a preset evaluation threshold;

[0128] Extracting evaluation cleaning value groups in sequence in a target evaluation sequence using a pre-constructed cleaning evaluation window, wherein the evaluation cleaning value groups include a plurality of reference cleaning values;

[0129] Acquire a reference cleaning mean and a reference cleaning variance based on the evaluation cleaning value group, wherein the reference cleaning mean and the reference cleaning variance are respectively the mean and variance of a plurality of reference cleaning values ​​in the evaluation cleaning value group;

[0130] The comprehensive evaluation value is calculated using the reference cleaning mean, reference cleaning variance and a pre-constructed cleaning evaluation relationship, wherein the cleaning evaluation relationship is as follows:

[0131] Z=αμ+βσ

[0132] Wherein, Z represents the comprehensive evaluation value, α and β are both preset coefficients, μ represents the reference cleaning mean, and σ represents the reference cleaning variance;

[0133] The comprehensive evaluation value is compared with a preset evaluation threshold, and when the comprehensive evaluation value is less than or equal to the evaluation threshold, the first cleaning evaluation value in the evaluation cleaning value group is used as the target cleaning evaluation value.

[0134] It should be explained that the smaller the cleaning evaluation value, the cleaner the initial test environment is after being cleaned by the floor scrubber. The purpose of sorting the cleaning evaluation values ​​is to confirm the experimental water volume that can clean the initial test environment under the initial experimental combination corresponding to the initial test environment in the initial evaluation sequence, and when using this experimental water volume, it can achieve the cleaning of the initial test environment without causing waste of experimental water.

[0135] It is understandable that when the cleaning evaluation value is greater than or equal to the evaluation threshold, it indicates that the floor scrubber cannot clean the initial detection environment when the target cleaning parameters corresponding to the cleaning evaluation value are used. Therefore, obtaining the target evaluation sequence through the initial evaluation sequence can save the resources required for calculation when confirming the target cleaning evaluation value. Optionally, a sliding window with one specification is used as the cleaning evaluation window. For example: the target evaluation sequence is: 0.8, 0.78, 0.6, 0.4, 0.3 and 0.2, and the clean evaluation window is a sliding window with a specification of three. Then, the clean evaluation window is used to extract evaluation clean value groups in the target evaluation sequence in sequence. The first evaluation clean value group extracted is: {0.8, 0.78, 0.6}, wherein 0.8, 0.78 and 0.6 are all reference clean values. The second evaluation clean value group extracted is: {0.78, 0.6, 0.4}, ..., and the last evaluation clean value group extracted is: {0.4, 0.3, 0.2}, wherein the comprehensive evaluation value corresponding to the last evaluation clean value group is less than or equal to the evaluation threshold, and 0.4 is used as the target clean evaluation value.

[0136] It is understandable that the step of fitting a target cleaning curve using the target cleaning evaluation value set includes:

[0137] The following operations are performed on the target cleaning evaluation values ​​in the target cleaning evaluation value set:

[0138] Acquire a stain grayscale image using an initial detection environment corresponding to a target cleaning evaluation value, and divide the stain grayscale image into one or more target stain images according to regions of one or more target cleaning images corresponding to the target cleaning evaluation value, wherein the target stain images correspond one to one to the target cleaning images;

[0139] The reference stain evaluation value is calculated based on one or more target clean images and one or more target stain images. The calculation formula is as follows:

[0140]

[0141] Where W represents the reference stain evaluation value, n represents one or more target clean images or one or more target stain images, and h i (x, y) represents the grayscale value of the i-th target clean image at the (x, y) pixel in one or more target clean images, g i (x, y) represents the gray value of the i-th target stain image at the (x, y)-th pixel in one or more target stain images;

[0142] Summarizing the reference stain assessment values ​​to obtain a reference stain assessment value set, and performing a sorting operation on the reference stain assessment values ​​in the reference stain assessment value set in ascending order to obtain a reference stain assessment sequence;

[0143] Acquire a stain cleaning node sequence based on a reference stain evaluation sequence, wherein the stain cleaning node sequence includes a plurality of stain cleaning nodes, and the stain cleaning nodes include: a reference stain evaluation value and an experimental water volume corresponding to the reference stain evaluation value;

[0144] Mapping the stain cleaning nodes in the stain cleaning node sequence to a pre-constructed reference coordinate system to obtain a reference point set, wherein the horizontal axis of the reference coordinate system is the reference stain evaluation value, and the vertical axis is the experimental water volume;

[0145] A target cleaning curve is obtained based on the reference point set.

[0146] It should be understood that the stain grayscale image is an image obtained by graying the image of the initial detection environment corresponding to the target clean evaluation value, and the graying operation is a prior art and will not be described in detail here. The pixel area of ​​the target stain image is consistent with the target clean image corresponding to the target stain image. Generally, the larger the reference stain evaluation value, the dirtier the initial detection environment is, and therefore, the more experimental water is required to clean the initial detection environment.

[0147] Exemplarily, the stain cleaning node sequence includes three stain cleaning nodes, wherein the three stain cleaning nodes are: (10, 5), (15, 7) and (20, 9), wherein 10, 15 and 20 are the reference stain assessment values, 5, 7 and 9 are the experimental water volumes, and the three stain cleaning nodes in the stain cleaning node sequence are marked in the reference coordinate system, wherein the three stain cleaning nodes marked in the reference coordinate system constitute the reference point set.

[0148] Further, acquiring a target cleaning curve based on the reference point set includes:

[0149] Obtain a fitting model set for fitting a curve, wherein the fitting model set includes multiple fitting models, and perform the following operations on each of the fitting models in the fitting model set:

[0150] An initial cleaning curve is obtained based on the fitting model and the reference point set, a plurality of stain capacity ranges are obtained using the stain capacity gradient set, and the following operations are performed on each of the stain capacity ranges in the plurality of stain capacity ranges:

[0151] Dividing the stain capacity range by using a preset second division value to obtain a plurality of verification stain values;

[0152] Using a preset extraction value, randomly extract one or more verification stain values ​​from a plurality of check stain values, wherein the number corresponding to the one or more verification stain values ​​is the same as the extraction value;

[0153] Acquire one or more verification cleaning nodes based on the one or more verification stain values, and map the one or more verification cleaning nodes to the reference coordinate system to obtain a verification point set;

[0154] Extracting a verification point set from an initial cleaning curve using the verification point set, wherein the verification point set includes one or more verification cleaning nodes, and the verification cleaning nodes correspond one to one with the verification cleaning nodes;

[0155] Acquire an evaluation point group set based on the verification point set and the check point set, wherein the evaluation point group set includes one or more evaluation point groups, and the evaluation point group includes a verification cleaning node and a check cleaning node, and the horizontal coordinate corresponding to the verification cleaning node is equal to the horizontal coordinate corresponding to the check cleaning node;

[0156] Calculating an evaluation fitting value using the evaluation point set and a pre-constructed comprehensive evaluation relationship;

[0157] The evaluation fitting values ​​are summarized to obtain an evaluation fitting value set, and a target cleaning curve is determined based on the evaluation fitting value set, wherein the target cleaning curve is an initial cleaning curve corresponding to the minimum evaluation fitting value in the evaluation fitting value set.

[0158] It should be explained that the fitting model refers to a model that can fit the reference point set into a curve. Optionally, a polynomial model is used as the fitting model, that is, by constructing a polynomial, the reference point set is fitted into a curve, and the curve is the initial cleaning curve. The stain capacity range refers to the range formed by two adjacent stain capacities in the stain capacity gradient set. For example: the stain capacity gradient set is: 1ml, 3ml and 5ml, then two stain capacity ranges can be obtained through the stain capacity gradient set, wherein the two stain capacity ranges are: 1ml to 3ml and 3ml to 5ml respectively. The method of dividing the stain capacity range using the preset second division value is the same as the method of dividing the washing humidity range using the preset first division value, and can achieve the same effect, which will not be repeated here.

[0159] Further, the method of obtaining one or more verification cleaning nodes based on the one or more verification stain values ​​is the same as the method of obtaining stain cleaning nodes using stain capacity, which is not repeated here. The definition of the verification point set is similar to that of the reference point set, which is not repeated here.

[0160] It can be understood that the check point set is the real value obtained through the experiment, and the verification point set is the predicted value extracted from the initial cleaning curve. By calculating and evaluating the fitting value, it can be determined which fitting model is used to fit the initial cleaning curve more in line with the actual situation.

[0161] Exemplarily, the reference point set is (10, 5), (15, 7) and (20, 9), the check point set is: (11, 6), (12, 6), (17, 7) and (18, 8), and the verification cleaning nodes extracted from the initial cleaning curve fitted by the reference point set using the horizontal coordinates corresponding to the verification cleaning nodes in the check point set are: (11, 7), (12, 8), (17, 8) and (18, 10), wherein the evaluation point group set composed of the verification point set and the check point set includes 4 evaluation point groups, and the 4 evaluation point groups are: (11, 6) and (11, 7), (12, 6) and (12, 8), (17, 7) and (17, 8) and (18, 8) and (18, 10).

[0162] It should be understood that the comprehensive evaluation relationship is as follows:

[0163]

[0164] Wherein, P represents the evaluation fitting value, m represents that there are m evaluation point groups in the evaluation point group set, and a k represents the horizontal coordinate corresponding to the kth evaluation point group in the evaluation point group set, a j represents the horizontal coordinate corresponding to the jth evaluation point group in the evaluation point group set, b 1krepresents the verification clean node corresponding to the kth evaluation point group in the evaluation point group set, b 2k Represents the verification cleaning node corresponding to the kth evaluation point group in the evaluation point group set.

[0165] It is understandable that the larger the horizontal coordinate corresponding to the evaluation point group is, the more water is required for cleaning. Therefore, by setting the weight coefficient using the horizontal coordinate corresponding to the evaluation point group, it is possible to save water resources. The weight coefficient is:

[0166] S3. Send the cleaning confirmation parameter set to the initiator of the water volume intelligent control instruction, and use the adjustment feedback unit to receive the target cleaning parameter set selected by the user based on the cleaning confirmation parameter set, wherein the target cleaning parameter set includes a target floor type and a target detergent.

[0167] Exemplarily, before Xiao Zhao uses the floor scrubber to clean the floor, the water volume control system sends the cleaning confirmation parameter set to Xiao Zhao, and Xiao Zhao selects a suitable floor washing type and detergent based on the cleaning confirmation reference set, wherein the suitable floor washing type and detergent are the target floor type and target detergent, respectively.

[0168] S4. When the target floor scrubber detects the presence of a detection area for detection stains during the initial cleaning of the floor, the humidity detection unit, the image acquisition unit and the detection area are used to respectively acquire a detection stain image and a detection stain humidity.

[0169] It is understandable that the technology of detecting the presence of detection stains in the initial cleaning of the floor using the target floor scrubber is a prior art and will not be described in detail here. The detection stain refers to the stain present in the initial cleaning of the floor. The detection stain image is an image of the detection area where the detection stain exists. The detection stain humidity is the humidity of the detection area detected by the humidity detection unit. Optionally, an electrochemical humidity sensor is selected as the humidity detection unit. The same effect can be achieved by using other technologies and will not be described in detail here.

[0170] S5. Based on the target cleaning parameter set, the detection stain image and the detection stain humidity, the target cleaning water volume is retrieved from the water volume control database, and the target cleaning water volume and the target floor scrubber are used to perform a cleaning operation on the detection stain in the detection area to obtain the target cleaning area, and the single sewage collection unit is used to collect sewage for cleaning the detection stain to obtain the target detection sewage.

[0171] It should be explained that retrieving the target cleaning water volume in the water volume control database based on the target cleaning parameter set, the detected stain image and the detected stain humidity means: first, according to the target cleaning parameter set and the monitored stain humidity, confirming the target cleaning curve that is the same as the target floor type, monitored stain humidity and target detergent in the water volume control database, and then using the detected stain image to obtain the target stain assessment value for retrieving the target cleaning water volume in the target cleaning curve, wherein the target stain assessment value is obtained in the same way as the reference stain assessment value and can achieve the same effect, which will not be repeated here, and the target cleaning water volume is retrieved from the target cleaning curve using the target stain assessment value.

[0172] Furthermore, the cleaning operation refers to the operation of cleaning the floor using the target floor scrubber. The target detection wastewater refers to the wastewater collected by the target floor scrubber after the detection area is cleaned using the target cleaning water volume.

[0173] S6. Acquire a cleaning stain image using the image acquisition unit and the target cleaning area, and acquire a sewage grayscale value based on the target sewage detection.

[0174] It is understandable that the clean stain image refers to the image of the target clean area. The sewage gray value refers to the average gray value in the grayscale image corresponding to the target detected sewage. The technology of obtaining the sewage gray value by using the target detected sewage is an existing technology and will not be repeated here.

[0175] S7. After confirming that the target cleaning area is unclean based on the stain detection image, the stain cleaning image and the sewage grayscale value, take the target cleaning area as the detection area, and return to the steps of respectively acquiring the stain detection image and detecting the stain humidity using the humidity detection unit, the image acquisition unit and the detection area until it is confirmed that the target cleaning area is clean, thereby realizing intelligent control of the water volume of the target floor scrubber.

[0176] It should be explained that the step of confirming that the target cleaning area is unclean based on the stain detection image, the stain cleaning image and the sewage gray value includes:

[0177] The detection stain image and the cleaning stain image are used to obtain the detection grayscale mean and the cleaning grayscale mean, respectively. The cleaning evaluation value is obtained based on the detection grayscale mean, the cleaning grayscale mean, the sewage grayscale value and the pre-constructed cleaning evaluation relationship, wherein the cleaning evaluation relationship is as follows:

[0178]

[0179] Wherein, Q represents the cleaning evaluation value, J1 represents the detection grayscale mean, J2 represents the cleaning grayscale mean, w1 represents the sewage grayscale value, and w2 represents the preset reference grayscale value. ω are all preset coefficients;

[0180] The cleaning evaluation value is compared with a preset clean evaluation threshold value, and if the cleaning evaluation value is greater than the clean evaluation threshold value, it is determined that the target cleaning area is not clean.

[0181] Furthermore, the detection grayscale mean is the mean of the grayscale values ​​in the grayscale image corresponding to the detection stain image. The method for obtaining the cleaning grayscale mean is the same as the method for obtaining the detection grayscale mean, and can achieve the same effect, which will not be repeated here. The purpose of setting the cleanliness assessment threshold is to prevent the problem of water waste caused by excessive cleaning of defective locations due to defects in the ground.

[0182] The present invention is to solve the problem described in the background technology. An embodiment of the present invention receives a water volume intelligent control instruction, and based on the water volume intelligent control instruction, confirms a target floor scrubber, an initial cleaning floor, and a water volume control system, wherein the water volume control system includes: a data simulation unit and an adjustment feedback unit, and the target floor scrubber includes: a humidity detection unit, a single sewage collection unit, and an image acquisition unit. It can be seen that the embodiment of the present invention considers combining the sewage situation during a single cleaning to determine whether the stained area is cleaned, thereby improving the accuracy of water volume control. The embodiment of the present invention uses the data simulation unit to confirm a water volume control database, and obtains a cleaning confirmation parameter set based on the water volume control database, wherein the cleaning confirmation parameter set includes a type of floor washing. The cleaning confirmation parameter set is sent to the initiator of the water volume intelligent control instruction, and the adjustment feedback unit is used to receive the target cleaning parameter set selected by the user based on the cleaning confirmation parameter set, wherein the target cleaning parameter set includes the target floor type and the target detergent. When the target floor scrubber detects the presence of the detection stain in the detection area during the initial cleaning of the floor, the humidity detection unit, the image acquisition unit and the detection area are used to respectively obtain the detection stain image and the detection stain humidity. Based on the target cleaning parameter set, the detection stain image and the detection stain humidity, the target cleaning water volume is retrieved from the water volume control database, and the target cleaning water volume and the target floor scrubber are used to perform a cleaning operation on the detection stain in the detection area to obtain the target The target cleaning area is determined, and the sewage for cleaning the detection stains is collected by a single sewage collection unit to obtain the target detection sewage. It can be seen that the embodiment of the present invention also takes into account that the type of detergent and the target ground type may have an impact on cleaning stains. Therefore, by obtaining the target ground type and the target detergent, the accuracy of water volume control is improved. The embodiment of the present invention realizes the guidance of the required water volume for the ground to be cleaned by constructing a water volume control database, and the initial cleaning curve is verified when constructing the water volume control database, so that the verified target cleaning curve can more accurately characterize the relationship between the required water volume for cleaning and the stains on the ground, thereby improving the accuracy of retrieving the target cleaning water volume in the water volume control database. The embodiment of the invention uses the image acquisition unit and the target cleaning area to obtain the cleaning stain image, obtains the sewage gray value based on the target detection sewage, and confirms that the target cleaning area is not clean according to the detection stain image, the cleaning stain image and the sewage gray value. Then, the target cleaning area is used as the detection area, and the steps of using the humidity detection unit, the image acquisition unit and the detection area to respectively obtain the detection stain image and detect the stain humidity are returned until the target cleaning area is confirmed to be clean. It can be seen that the embodiment of the present invention also takes into account the stubbornness of the stain, that is, the detection area may not be cleaned by one cleaning. Therefore, the water volume control data and the target cleaning area are combined to retrieve the water volume used to clean the target cleaning area until the target cleaning area is cleaned. Therefore, the present invention can realize intelligent control of the water volume of the floor scrubber.

[0183] like Figure 2 , which is a functional module diagram of a floor scrubber water volume intelligent control system based on cleaning feedback provided by an embodiment of the present invention.

[0184] The floor scrubber water volume intelligent control system 100 based on cleaning feedback of the present invention can be installed in an electronic device. According to the functions implemented, the floor scrubber water volume intelligent control system 100 based on cleaning feedback can include a water volume control system confirmation module 101, a floor scrubber cleaning parameter confirmation module 102, a stain parameter confirmation module 103 and a stain area cleaning module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0185] The water volume control system confirmation module 101 is used to receive the water volume intelligent control instruction, and confirm the target floor scrubber, the initial cleaning floor and the water volume control system based on the water volume intelligent control instruction, wherein the water volume control system includes: a data simulation unit and an adjustment feedback unit, and the target floor scrubber includes: a humidity detection unit, a single sewage collection unit and an image acquisition unit;

[0186] The floor scrubber cleaning parameter confirmation module 102 is used to confirm the water output control database using the data simulation unit, and obtain a cleaning confirmation parameter set based on the water output control database, wherein the cleaning confirmation parameter set includes a floor type set and a detergent type set;

[0187] The cleaning confirmation parameter set is sent to the initiator of the water volume intelligent control instruction, and the adjustment feedback unit is used to receive the target cleaning parameter set selected by the user based on the cleaning confirmation parameter set, wherein the target cleaning parameter set includes the target floor type and the target detergent;

[0188] The stain parameter confirmation module 103 is used to obtain the detection stain image and the detection stain humidity respectively by using the humidity detection unit, the image acquisition unit and the detection area after the target floor scrubber detects the presence of the detection stain in the detection area during the initial cleaning of the floor;

[0189] The stain area cleaning module 104 is used to retrieve a target cleaning water volume from a water volume control database based on a target cleaning parameter set, a detected stain image, and a detected stain humidity, perform a cleaning operation on the detected stain in the detection area using the target cleaning water volume and a target floor scrubber to obtain a target cleaning area, and collect sewage for cleaning the detected stain using a single sewage collection unit to obtain target detected sewage;

[0190] The image acquisition unit and the target cleaning area are used to obtain a cleaning stain image, and the sewage grayscale value is obtained based on the target detected sewage. After confirming that the target cleaning area is unclean according to the detected stain image, the cleaning stain image and the sewage grayscale value, the target cleaning area is used as the detection area, and the process returns to the steps of respectively obtaining the detected stain image and detecting the stain humidity using the humidity detection unit, the image acquisition unit and the detection area until it is confirmed that the target cleaning area is clean, thereby realizing intelligent control of the water volume of the target floor scrubber.

[0191] In detail, each module in the intelligent control system 100 of the water volume of the floor scrubber based on cleaning feedback in the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means are used as the intelligent control method of water volume of a floor scrubber based on cleaning feedback described in the text, and can produce the same technical effects, so they will not be repeated here.

[0192] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing an intelligent water volume control method for a floor scrubber based on cleaning feedback provided by an embodiment of the present invention.

[0193] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for an intelligent water volume control method for a floor scrubber based on cleaning feedback.

[0194] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the program of the water volume intelligent control method of the floor scrubber based on cleaning feedback, but also can be used to temporarily store data that has been output or is to be output.

[0195] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a program for intelligent water volume control method of a floor scrubber based on cleaning feedback, etc.), and calls data stored in the memory 11, so as to execute various functions of the electronic device 1 and process data.

[0196] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0197] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0198] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0199] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0200] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0201] The program of the method for intelligently controlling the water volume of a floor scrubber based on cleaning feedback stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, the following can be achieved:

[0202] Receive a water volume intelligent control instruction, and identify a target floor scrubber, an initial floor cleaning surface, and a water volume control system based on the water volume intelligent control instruction, wherein the water volume control system includes: a data simulation unit and an adjustment feedback unit, and the target floor scrubber includes: a humidity detection unit, a single sewage collection unit, and an image acquisition unit;

[0203] Using the data simulation unit to confirm a water output control database, and obtaining a cleaning confirmation parameter set based on the water output control database, wherein the cleaning confirmation parameter set includes a floor washing type set and a detergent type set;

[0204] The cleaning confirmation parameter set is sent to the initiator of the water volume intelligent control instruction, and the adjustment feedback unit is used to receive the target cleaning parameter set selected by the user based on the cleaning confirmation parameter set, wherein the target cleaning parameter set includes the target floor type and the target detergent;

[0205] When the target floor scrubber detects the presence of a detection area of ​​a detection stain in the initial cleaning of the floor, the detection stain image and the detection stain humidity are respectively acquired by using the humidity detection unit, the image acquisition unit and the detection area;

[0206] Based on the target cleaning parameter set, the detection stain image and the detection stain humidity, a target cleaning water volume is retrieved from a water volume control database, the target cleaning water volume and the target floor scrubber are used to perform a cleaning operation on the detection stain in the detection area to obtain a target cleaning area, and the single-time sewage collection unit is used to collect sewage for cleaning the detection stain to obtain target detection sewage;

[0207] Acquire a cleaning stain image using the image acquisition unit and the target cleaning area, and acquire a sewage grayscale value based on the target sewage detection;

[0208] After confirming that the target cleaning area is unclean according to the stain detection image, the stain cleaning image and the sewage grayscale value, the target cleaning area is used as the detection area, and the process returns to the steps of respectively acquiring the stain detection image and the stain detection humidity using the humidity detection unit, the image acquisition unit and the detection area until it is confirmed that the target cleaning area is clean, thereby realizing intelligent control of the water volume of the target floor scrubber.

[0209] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0210] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0211] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0212] Receive a water volume intelligent control instruction, and identify a target floor scrubber, an initial floor cleaning surface, and a water volume control system based on the water volume intelligent control instruction, wherein the water volume control system includes: a data simulation unit and an adjustment feedback unit, and the target floor scrubber includes: a humidity detection unit, a single sewage collection unit, and an image acquisition unit;

[0213] Using the data simulation unit to confirm a water output control database, and obtaining a cleaning confirmation parameter set based on the water output control database, wherein the cleaning confirmation parameter set includes a floor washing type set and a detergent type set;

[0214] The cleaning confirmation parameter set is sent to the initiator of the water volume intelligent control instruction, and the adjustment feedback unit is used to receive the target cleaning parameter set selected by the user based on the cleaning confirmation parameter set, wherein the target cleaning parameter set includes the target floor type and the target detergent;

[0215] When the target floor scrubber detects the presence of a detection area of ​​a detection stain in the initial cleaning of the floor, the detection stain image and the detection stain humidity are respectively acquired by using the humidity detection unit, the image acquisition unit and the detection area;

[0216] Based on the target cleaning parameter set, the detection stain image and the detection stain humidity, a target cleaning water volume is retrieved from a water volume control database, the target cleaning water volume and the target floor scrubber are used to perform a cleaning operation on the detection stain in the detection area to obtain a target cleaning area, and the single-time sewage collection unit is used to collect sewage for cleaning the detection stain to obtain target detection sewage;

[0217] Acquire a cleaning stain image using the image acquisition unit and the target cleaning area, and acquire a sewage grayscale value based on the target sewage detection;

[0218] After confirming that the target cleaning area is unclean according to the stain detection image, the stain cleaning image and the sewage grayscale value, the target cleaning area is used as the detection area, and the process returns to the steps of respectively acquiring the stain detection image and the stain detection humidity using the humidity detection unit, the image acquisition unit and the detection area until it is confirmed that the target cleaning area is clean, thereby realizing intelligent control of the water volume of the target floor scrubber.

[0219] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0220] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0221] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0222] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0223] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for intelligently controlling water volume of a floor scrubber based on cleaning feedback, characterized in that: The method comprises: Receive a water volume intelligent control instruction, and identify a target floor scrubber, an initial floor cleaning surface, and a water volume control system based on the water volume intelligent control instruction, wherein the water volume control system includes: a data simulation unit and an adjustment feedback unit, and the target floor scrubber includes: a humidity detection unit, a single sewage collection unit, and an image acquisition unit; Using the data simulation unit to confirm a water output control database, and obtaining a cleaning confirmation parameter set based on the water output control database, wherein the cleaning confirmation parameter set includes a floor washing type set and a detergent type set; The cleaning confirmation parameter set is sent to the initiator of the water volume intelligent control instruction, and the adjustment feedback unit is used to receive the target cleaning parameter set selected by the user based on the cleaning confirmation parameter set, wherein the target cleaning parameter set includes the target floor type and the target detergent; When the target floor scrubber detects the presence of a detection area of ​​a detection stain in the initial cleaning of the floor, the detection stain image and the detection stain humidity are respectively acquired by using the humidity detection unit, the image acquisition unit and the detection area; Based on the target cleaning parameter set, the detection stain image and the detection stain humidity, a target cleaning water volume is retrieved from a water volume control database, the target cleaning water volume and the target floor scrubber are used to perform a cleaning operation on the detection stain in the detection area to obtain a target cleaning area, and the single-time sewage collection unit is used to collect sewage for cleaning the detection stain to obtain target detection sewage; Acquire a cleaning stain image using the image acquisition unit and the target cleaning area, and acquire a sewage grayscale value based on the target sewage detection; After confirming that the target cleaning area is unclean according to the stain detection image, the stain cleaning image and the sewage grayscale value, the target cleaning area is used as the detection area, and the process returns to the steps of respectively acquiring the stain detection image and the stain detection humidity using the humidity detection unit, the image acquisition unit and the detection area until it is confirmed that the target cleaning area is clean, thereby realizing intelligent control of the water volume of the target floor scrubber.

2. The method for intelligently controlling water volume of a floor scrubber based on cleaning feedback according to claim 1, characterized in that: The method of using the data simulation unit to confirm the water output control database includes: confirming receipt of a data simulation instruction from a data simulation unit, and acquiring a washing floor type set, a stain capacity gradient set, a washing humidity range, and a detergent type set of a target floor scrubber based on the data simulation instruction, wherein the washing floor type set includes a plurality of washing floor types, the detergent type set includes a plurality of detergents, and the stain capacity gradient set includes a plurality of stain capacities; Dividing the washing humidity range by using a preset first division value to obtain a plurality of washing humidity values; Acquire an experimental combination set by using the washing floor type set, the plurality of washing humidity values ​​and the detergent type set in a combined manner, wherein the experimental combination set includes a plurality of experimental combinations, and each experimental combination includes a washing floor type, a washing humidity value and a detergent; For each experimental combination in the experimental combination set, perform the following operations: Associating the experimental combination with the stain capacity gradient set to obtain an initial experimental combination set, wherein the initial experimental combination set includes multiple initial experimental combinations, and the initial experimental combination includes a washing surface type, a washing humidity value, a stain capacity and a detergent, and the initial experimental combination corresponds to the stain capacity one by one; Summarizing the initial experimental combination sets to obtain multiple initial experimental combination sets; A water quantity control database is obtained using the multiple initial experimental combination sets.

3. The method for intelligently controlling water volume of a floor scrubber based on cleaning feedback according to claim 2, characterized in that: The step of obtaining a water quantity control database by using the multiple initial experimental combination sets includes: The following operations are performed for each of the multiple initial experimental combination sets: The following operations are performed for each initial experimental combination in the initial experimental combination set: Use the initial experimental combination to build an initial testing environment; Acquire a reference cleaning image based on the type of floor being washed corresponding to the initial experimental combination, and acquire an experimental water volume gradient set of a target floor washer, wherein the experimental water volume gradient set includes multiple experimental water volumes; The following operations are performed for each experimental water volume in the experimental water volume gradient set: Use the experimental water volume to drive the target floor scrubber to clean the initial test environment, photograph the initial test environment after cleaning, and obtain a cleaning test image; Acquire a cleaning evaluation value based on the cleaning experiment image and the reference cleaning image, summarize the cleaning evaluation values ​​to obtain a cleaning evaluation value set, and use the cleaning evaluation value set to acquire a target cleaning evaluation value; The target cleaning evaluation values ​​are summarized to obtain a target cleaning evaluation value set, a target cleaning curve is fitted using the target cleaning evaluation value set, and a water volume control database is constructed using the target cleaning curve.

4. The method for intelligently controlling water volume of a floor scrubber based on cleaning feedback according to claim 3, characterized in that: The obtaining of a cleaning evaluation value based on the cleaning experiment image and the reference cleaning image includes: Performing a grayscale operation on the reference clean image to obtain a clean grayscale image, and using a pre-built region growing algorithm to retrieve one or more target clean images from the clean grayscale image; Acquire the number of cleaning pixels based on the one or more target cleaning images, wherein the number of cleaning pixels is the sum of the pixels corresponding to the one or more target cleaning images; Retrieving a target stain image from a cleaning experiment image using the region growing algorithm; A target pixel number is obtained based on the target stain image, and a cleaning evaluation value is obtained according to the target pixel number and the cleaning pixel number, wherein the cleaning evaluation value is a ratio of the target pixel number to the cleaning pixel number.

5. The method for intelligently controlling water volume of a floor scrubber based on cleaning feedback according to claim 4, characterized in that: The step of obtaining a target cleaning evaluation value by using the cleaning evaluation value set includes: Performing a sorting operation on the cleaning evaluation values ​​in the cleaning evaluation value set in descending order of the cleaning evaluation values ​​to obtain an initial evaluation sequence; Acquiring a target evaluation sequence based on the initial evaluation sequence, wherein the cleaning evaluation values ​​in the target evaluation sequence are all less than or equal to a preset evaluation threshold; Extracting evaluation cleaning value groups in sequence in a target evaluation sequence using a pre-constructed cleaning evaluation window, wherein the evaluation cleaning value groups include a plurality of reference cleaning values; Acquire a reference cleaning mean and a reference cleaning variance based on the evaluation cleaning value group, wherein the reference cleaning mean and the reference cleaning variance are respectively the mean and variance of a plurality of reference cleaning values ​​in the evaluation cleaning value group; The comprehensive evaluation value is calculated using the reference cleaning mean, reference cleaning variance and a pre-constructed cleaning evaluation relationship, wherein the cleaning evaluation relationship is as follows: Z=αμ+βσ Wherein, Z represents the comprehensive evaluation value, α and β are both preset coefficients, μ represents the reference cleaning mean, and σ represents the reference cleaning variance; The comprehensive evaluation value is compared with a preset evaluation threshold, and when the comprehensive evaluation value is less than or equal to the evaluation threshold, the first cleaning evaluation value in the evaluation cleaning value group is used as the target cleaning evaluation value.

6. The method for intelligently controlling water volume of a floor scrubber based on cleaning feedback according to claim 5, characterized in that: The step of fitting a target cleaning curve using the target cleaning evaluation value set includes: The following operations are performed on the target cleaning evaluation values ​​in the target cleaning evaluation value set: Acquire a stain grayscale image using an initial detection environment corresponding to a target cleaning evaluation value, and divide the stain grayscale image into one or more target stain images according to regions of one or more target cleaning images corresponding to the target cleaning evaluation value, wherein the target stain images correspond one to one to the target cleaning images; The reference stain evaluation value is calculated based on one or more target clean images and one or more target stain images. The calculation formula is as follows: Where W represents the reference stain evaluation value, n represents one or more target clean images or one or more target stain images, and h i (x, y) represents the grayscale value of the i-th target clean image at the (x, y) pixel in one or more target clean images, g i (x, y) represents the gray value of the i-th target stain image at the (x, y)-th pixel in one or more target stain images; Summarizing the reference stain assessment values ​​to obtain a reference stain assessment value set, and performing a sorting operation on the reference stain assessment values ​​in the reference stain assessment value set in ascending order to obtain a reference stain assessment sequence; Acquire a stain cleaning node sequence based on a reference stain evaluation sequence, wherein the stain cleaning node sequence includes a plurality of stain cleaning nodes, and the stain cleaning nodes include: a reference stain evaluation value and an experimental water volume corresponding to the reference stain evaluation value; Mapping the stain cleaning nodes in the stain cleaning node sequence to a pre-constructed reference coordinate system to obtain a reference point set, wherein the horizontal axis of the reference coordinate system is the reference stain evaluation value, and the vertical axis is the experimental water volume; A target cleaning curve is obtained based on the reference point set.

7. The method for intelligently controlling water volume of a floor scrubber based on cleaning feedback according to claim 6, characterized in that: The acquiring a target cleaning curve based on the reference point set comprises: Obtain a fitting model set for fitting a curve, wherein the fitting model set includes multiple fitting models, and perform the following operations on each of the fitting models in the fitting model set: An initial cleaning curve is obtained based on the fitting model and the reference point set, a plurality of stain capacity ranges are obtained using the stain capacity gradient set, and the following operations are performed on each of the stain capacity ranges in the plurality of stain capacity ranges: Dividing the stain capacity range by using a preset second division value to obtain a plurality of verification stain values; Using a preset extraction value, randomly extract one or more verification stain values ​​from a plurality of check stain values, wherein the number corresponding to the one or more verification stain values ​​is the same as the extraction value; Acquire one or more verification cleaning nodes based on the one or more verification stain values, and map the one or more verification cleaning nodes to the reference coordinate system to obtain a verification point set; Extracting a verification point set from an initial cleaning curve using the verification point set, wherein the verification point set includes one or more verification cleaning nodes, and the verification cleaning nodes correspond one to one with the verification cleaning nodes; Acquire an evaluation point group set based on the verification point set and the check point set, wherein the evaluation point group set includes one or more evaluation point groups, and the evaluation point group includes a verification cleaning node and a check cleaning node, and the horizontal coordinate corresponding to the verification cleaning node is equal to the horizontal coordinate corresponding to the check cleaning node; Calculating an evaluation fitting value using the evaluation point set and a pre-constructed comprehensive evaluation relationship; The evaluation fitting values ​​are summarized to obtain an evaluation fitting value set, and a target cleaning curve is determined based on the evaluation fitting value set, wherein the target cleaning curve is an initial cleaning curve corresponding to the minimum evaluation fitting value in the evaluation fitting value set.

8. The method for intelligently controlling water volume of a floor scrubber based on cleaning feedback according to claim 7, characterized in that: The comprehensive evaluation relationship is as follows: Wherein, P represents the evaluation fitting value, m represents that there are m evaluation point groups in the evaluation point group set, and a k represents the horizontal coordinate corresponding to the kth evaluation point group in the evaluation point group set, a j represents the horizontal coordinate corresponding to the jth evaluation point group in the evaluation point group set, b 1k represents the verification clean node corresponding to the kth evaluation point group in the evaluation point group set, b 2k Represents the verification cleaning node corresponding to the kth evaluation point group in the evaluation point group set.

9. The method for intelligently controlling water volume of a floor scrubber based on cleaning feedback according to claim 8, characterized in that: The step of confirming that the target cleaning area is unclean based on the stain detection image, the stain cleaning image and the sewage gray value includes: The detection stain image and the cleaning stain image are used to obtain the detection grayscale mean and the cleaning grayscale mean, respectively. The cleaning evaluation value is obtained based on the detection grayscale mean, the cleaning grayscale mean, the sewage grayscale value and the pre-constructed cleaning evaluation relationship, wherein the cleaning evaluation relationship is as follows: Wherein, Q represents the cleaning evaluation value, J1 represents the detection grayscale mean, J2 represents the cleaning grayscale mean, w1 represents the sewage grayscale value, and w2 represents the preset reference grayscale value. ω are all preset coefficients; The cleaning evaluation value is compared with a preset clean evaluation threshold value, and if the cleaning evaluation value is greater than the clean evaluation threshold value, it is determined that the target cleaning area is not clean.

10. An intelligent water volume control system for a floor scrubber based on cleaning feedback, characterized in that: The system comprises: A water volume control system confirmation module is used to receive a water volume intelligent control instruction, and confirm a target floor scrubber, an initial cleaning floor, and a water volume control system based on the water volume intelligent control instruction, wherein the water volume control system includes: a data simulation unit and an adjustment feedback unit, and the target floor scrubber includes: a humidity detection unit, a single sewage collection unit, and an image acquisition unit; A floor scrubber cleaning parameter confirmation module, used to confirm a water output control database using the data simulation unit, and obtain a cleaning confirmation parameter set based on the water output control database, wherein the cleaning confirmation parameter set includes a floor scrubbing type set and a detergent type set; The cleaning confirmation parameter set is sent to the initiator of the water volume intelligent control instruction, and the adjustment feedback unit is used to receive the target cleaning parameter set selected by the user based on the cleaning confirmation parameter set, wherein the target cleaning parameter set includes the target floor type and the target detergent; A stain parameter confirmation module, which is used to obtain a detection stain image and a detection stain humidity respectively by using the humidity detection unit, the image acquisition unit and the detection area when the target floor scrubber detects the presence of a detection stain in the detection area during the initial cleaning of the floor; a stain area cleaning module, for retrieving a target cleaning water volume from a water volume control database based on a target cleaning parameter set, a detected stain image, and a detected stain humidity, performing a cleaning operation on the detected stain in the detection area using the target cleaning water volume and a target floor scrubber to obtain a target cleaning area, and collecting sewage for cleaning the detected stain using a single sewage collection unit to obtain target detected sewage; The image acquisition unit and the target cleaning area are used to obtain a cleaning stain image, and the sewage grayscale value is obtained based on the target detected sewage. After confirming that the target cleaning area is unclean according to the detected stain image, the cleaning stain image and the sewage grayscale value, the target cleaning area is used as the detection area, and the process returns to the steps of respectively obtaining the detected stain image and detecting the stain humidity using the humidity detection unit, the image acquisition unit and the detection area until it is confirmed that the target cleaning area is clean, thereby realizing intelligent control of the water volume of the target floor scrubber.

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