Decision-making processing method for cleaning device, and cleaning device, storage medium and device

Abstract

A decision-making processing method for a cleaning device, and a cleaning device, a computer storage medium and an electronic device. The decision-making processing method for a cleaning device comprises: when the current cleaning cycle in the current cleaning task is executed on a cleaning member, acquiring soiling data of the cleaning member that corresponds to the current cleaning cycle; on the basis of the soiling data of the current cleaning cycle, determining a soiling level of the cleaning member; on the basis of a difference value between the soiling data of the current cleaning cycle and soiling data of a previous cleaning cycle, determining a cleaning effect quantization value of the cleaning member; and on the basis of at least one of the cycle number of the current cleaning cycle, the soiling level of the cleaning member and the cleaning effect quantization value of the cleaning member, determining a subsequent cleaning strategy with respect to the cleaning member.

Description

Decision processing method of cleaning device, cleaning device, storage medium and device

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] The present disclosure claims priority to Chinese Patent Application No. 202410383021.X, filed March 29, 2024, which is incorporated by reference herein in its entirety. TECHNICAL FIELD

[0003] The present disclosure relates to the technical field of smart home, and in particular, relates to a decision processing method of a cleaning device, the cleaning device, a computer storage medium and an electronic device. BACKGROUND

[0004] With the rapid development and progress of computers and Internet technologies, related smart home devices are constantly innovating and breaking through, and various cleaning devices have emerged.

[0005] The cleaning device in the related art can only execute a cleaning task of a cleaning element according to a fixed flow set in advance, and has a low degree of intelligence.

[0006] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure. SUMMARY

[0007] The present disclosure aims to provide a decision processing method of a cleaning device, the cleaning device, a computer storage medium and an electronic device, thereby at least partially overcoming the technical problem of a low degree of intelligence due to the limitations of the related art.

[0008] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.

[0009] According to a first aspect of the present disclosure, a decision processing method of a cleaning device is provided, the cleaning device being provided with a cleaning element for mopping, and the method comprising:

[0010] obtaining dirt data of the cleaning element corresponding to a current round of cleaning flow in a current cleaning task of the cleaning element;

[0011] determining a dirt degree of the cleaning element according to the dirt data of the current round of cleaning flow;

[0012] determining a cleaning effect quantification value of the cleaning element according to a difference between the dirt data of the current round of cleaning flow and dirt data of a previous round of cleaning flow; and

[0013] determine a subsequent cleaning strategy for the cleaning piece according to at least one of the round number of the current round cleaning process, the dirtiness degree of the cleaning piece, and the cleaning effect quantified value of the cleaning piece.

[0014] In an example embodiment of the present disclosure, each round cleaning process comprises a water-out cleaning link;

[0015] The obtaining of the dirtiness data corresponding to the current round cleaning process of the cleaning piece comprises:

[0016] The obtaining of the dirtiness data corresponding to the water-out cleaning link of the current round cleaning process of the cleaning piece comprises:

[0017] In an example embodiment of the present disclosure, the water-out cleaning link comprises a plurality of continuous preset time periods, and each of the preset time periods comprises a plurality of continuous unit time periods.

[0018] The obtaining of the dirtiness data corresponding to the water-out cleaning link of the current round cleaning process of the cleaning piece comprises:

[0019] After entering the water-out cleaning link of the current round cleaning process, a plurality of first dirtiness detection values of the cleaning piece in a first preset time period are obtained, and a first fluctuation condition representation value corresponding to the plurality of first dirtiness detection values is determined.

[0020] In response to determining that the first fluctuation condition representation value is less than or equal to a first fluctuation threshold, the dirtiness data corresponding to the water-out cleaning link is determined based on an average value of the plurality of first dirtiness detection values.

[0021] In an example embodiment of the present disclosure, the determination of the first fluctuation condition representation value corresponding to the plurality of first dirtiness detection values comprises:

[0022] The maximum value and the minimum value of the plurality of first dirtiness detection values are obtained, and the first fluctuation condition representation value is determined according to a difference between the maximum value and the minimum value.

[0023] Alternatively, a variance or a standard deviation of the plurality of first dirtiness detection values is obtained, and the first fluctuation condition representation value is determined based on the variance or the standard deviation.

[0024] In an example embodiment of the present disclosure, the method further comprises:

[0025] In response to determining that the dirtiness data is not within a preset normal value range, a first abnormal event of the current round cleaning process is recorded.

[0026] The first abnormal event is used to represent that the dirt data of the current cleaning process is an abnormal value.

[0027] In the example embodiments of the present disclosure, the method further comprises:

[0028] In response to determining that the first fluctuation condition representation value is greater than the first fluctuation threshold, a plurality of second dirt detection values of the cleaning piece in a second preset time period are obtained, and a second fluctuation condition representation value corresponding to the plurality of second dirt detection values is determined;

[0029] In response to determining that the second fluctuation condition representation value is still greater than the first fluctuation threshold, a fluctuation condition representation value corresponding to a plurality of dirt detection values in a next preset time period is obtained until the next preset time period is the last preset time period, and in response to determining that a last fluctuation condition representation value corresponding to the last preset time period is greater than the first fluctuation threshold, a second abnormal event of the current cleaning process is recorded;

[0030] The second abnormal event is used to represent that the current cleaning process has a dirt fluctuation abnormality.

[0031] In the example embodiments of the present disclosure, each cleaning process further comprises a water pumping segment, the water pumping segment is located after the water outlet cleaning segment, and the method further comprises:

[0032] Obtaining reference dirt data of the cleaning piece corresponding to the water pumping segment of the current cleaning process; and

[0033] According to the reference dirt data, or according to the reference dirt data combined with the dirt data of the water outlet cleaning segment, an abnormal event in the current cleaning process is identified.

[0034] In the example embodiments of the present disclosure, the reference dirt data of the cleaning piece corresponding to the water pumping segment of the current cleaning process is obtained by:

[0035] After entering the water pumping segment of the current cleaning process, a plurality of third dirt detection values of the cleaning piece in a specified time period are obtained;

[0036] According to a difference between a maximum value and a minimum value in the plurality of third dirt detection values, the reference dirt data is determined;

[0037] Alternatively, according to a variance or a standard deviation of the plurality of third dirt detection values, the reference dirt data is determined.

[0038] In the example embodiments of the present disclosure, the dirt data comprises a first dimension value and a second dimension value;

[0039] The method further includes:

[0040] In response to determining that the reference dirtiness data is greater than or equal to a first preset threshold, determining that no abnormal event is identified.

[0041] In response to determining that the reference dirtiness data is less than the first preset threshold, and the first dimension value of the dirtiness data is greater than or equal to a second preset threshold, and the second dimension value is greater than or equal to a third preset threshold, recording that a third abnormal event occurs in the current round of cleaning process; the third abnormal event is used to represent that an abnormality occurs in the cleaning piece.

[0042] In response to determining that the reference dirtiness data is less than the first preset threshold, and the first dimension value of the dirtiness data is less than the second preset threshold or the second dimension value is less than the third preset threshold, recording that a fourth abnormal event occurs in the current round of cleaning process; the fourth abnormal event is used to represent that a wall-hanging abnormality of dirt occurs.

[0043] In the example embodiment of the present disclosure, the method further includes:

[0044] In response to determining that the round number of the current round of cleaning process is less than a specified round number, determining to perform a next round of cleaning process on the cleaning piece.

[0045] In response to determining that the round number of the current round of cleaning process is greater than or equal to the specified round number, determining whether to perform a next round of cleaning process on the cleaning piece according to the dirtiness degree of the cleaning piece and the cleaning effect quantification value of the cleaning piece.

[0046] In the example embodiment of the present disclosure, the method further includes:

[0047] In response to determining that the cleaning effect quantification value is greater than or equal to a preset quantification threshold, or the dirtiness degree is greater than or equal to a preset dirtiness degree threshold, or the number of data loss events recorded for the current round of cleaning process is less than a preset number threshold, determining to perform a next round of cleaning process on the cleaning piece.

[0048] In the example embodiment of the present disclosure, the method further includes:

[0049] in response to determining that the maximum limit number of rounds is reached, not performing a next round of cleaning procedure on the cleaning piece; the maximum limit number of rounds is determined according to a device type of the cleaning device;

[0050] in response to determining that the maximum limit number of rounds is not reached, obtaining a dirtiness degree and a cleaning effect quantified value of the cleaning piece corresponding to the next round of cleaning procedure; and

[0051] determining whether to perform a further next round of cleaning procedure on the cleaning piece according to at least one of a round number of the next round of cleaning procedure, the dirtiness degree and the cleaning effect quantified value of the cleaning piece corresponding to the next round of cleaning procedure.

[0052] In an example embodiment of the present disclosure, the determining whether to perform the next round of cleaning procedure on the cleaning piece according to the dirtiness degree of the cleaning piece and the cleaning effect quantified value of the cleaning piece further comprises:

[0053] in response to determining that the cleaning effect quantified value is less than the preset quantified threshold value and the dirtiness degree is less than the preset dirtiness degree threshold value, not performing the next round of cleaning procedure on the cleaning piece.

[0054] In an example embodiment of the present disclosure, the dirtiness degree of the cleaning piece is determined according to a difference between the dirtiness data of the current round of cleaning procedure and preset reference dirtiness data, and the method further comprises:

[0055] after the current cleaning task is completed, obtaining target dirtiness data corresponding to a last round of cleaning procedure of the current cleaning task; and

[0056] determining whether to update the preset reference dirtiness data according to the target dirtiness data.

[0057] In an example embodiment of the present disclosure, the determining whether to update the preset reference dirtiness data according to the target dirtiness data comprises:

[0058] in response to determining that the target dirtiness data is within a preset normal value range and a difference between the target dirtiness data and the preset reference dirtiness data is less than a preset difference threshold value, updating the preset reference dirtiness data by using the target dirtiness data.

[0059] In an example embodiment of the present disclosure, the determining whether to update the preset reference dirtiness data according to the target dirtiness data further comprises:

[0060] in response to determining that the target dirtiness data is within the preset normal value range and a difference between the target dirtiness data and the preset reference dirtiness data is greater than or equal to the preset difference threshold, recording a pending update event and target dirtiness data associated with the pending update event;

[0061] in response to determining that the pending update event occurs continuously for a specified number of times, updating the preset reference dirtiness data according to a specified number of target dirtiness data associated with the specified number of pending update events.

[0062] In an example embodiment of the present disclosure, the determining whether to update the preset reference dirtiness data according to the target dirtiness data further includes:

[0063] in response to determining that the target dirtiness data is not within the preset normal value range, not updating the preset initial reference value.

[0064] In an example embodiment of the present disclosure, the method further includes:

[0065] obtaining dirtiness data of a specified round of cleaning process in a current cleaning task;

[0066] determining a ground dirtiness degree of a target cleaning area according to dirtiness data of a last round of cleaning process in a previous cleaning task and the dirtiness data of the specified round of cleaning process in the current cleaning task; the target cleaning area is an area cleaned during an interval from completion of the last round of cleaning process to start of the specified round of cleaning process; and

[0067] determining whether to perform re-cleaning on the cleaning area according to the ground dirtiness degree.

[0068] In an example embodiment of the present disclosure, the determining whether to perform re-cleaning on the cleaning area according to the ground dirtiness degree includes:

[0069] obtaining an area of the target cleaning area;

[0070] determining a ground dirtiness density according to a ratio of the ground dirtiness degree and the area; and

[0071] determining whether to perform re-cleaning on the target cleaning area according to the ground dirtiness density.

[0072] In an example embodiment of the present disclosure, the determining whether to perform re-cleaning on the target cleaning area according to the ground dirtiness density includes:

[0073] in response to determining that the ground dirtiness density is greater than a preset dirtiness density threshold, performing re-cleaning on the target cleaning area.

[0074] According to a second aspect of the present disclosure, a cleaning device is provided, the cleaning device is provided with a cleaning piece for mopping, and the cleaning device comprises:

[0075] a data acquisition module, configured to acquire dirt data corresponding to a current round of cleaning process of the cleaning piece when a current round of cleaning process in a current cleaning task is performed on the cleaning piece;

[0076] a data processing module, configured to determine a dirt degree of the cleaning piece according to the dirt data of the current round of cleaning process, and determine a cleaning effect quantification value of the cleaning piece according to a difference between the dirt data of the current round of cleaning process and dirt data of a previous round of cleaning process; and

[0077] a decision module, configured to determine a subsequent cleaning strategy for the cleaning piece according to at least one of a round number of the current round of cleaning process, the dirt degree of the cleaning piece, and the cleaning effect quantification value of the cleaning piece.

[0078] According to a third aspect of the present disclosure, a computer storage medium is provided, and the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the decision processing method of the cleaning device according to the first aspect.

[0079] According to a fourth aspect of the present disclosure, an electronic device is provided, and the electronic device comprises a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the decision processing method of the cleaning device according to the first aspect by executing the executable instructions.

[0080] According to the above technical solutions, the decision processing method of the cleaning device, the cleaning device, the computer storage medium and the electronic device in the exemplary embodiments of the present disclosure at least have the following advantages and positive effects:

[0081] In the technical solution provided by some embodiments of the present disclosure, in one aspect, by obtaining the dirt data of the cleaning piece corresponding to the current round cleaning process in the current cleaning task, determining the dirt degree of the cleaning piece according to the dirt data of the current round cleaning process, the dirt degree of the cleaning piece can be obtained in real time, and an effective cleaning strategy for the cleaning piece can be determined according to the dirt degree. Further, by determining the cleaning effect quantitative value of the cleaning piece according to the difference between the dirt data of the current round cleaning process and the dirt data of the last round cleaning process, the cleaning effect can be intuitively quantified, so that an effective cleaning strategy for the cleaning piece can be determined according to the quantitative value. In another aspect, by determining the subsequent cleaning strategy for the cleaning piece according to at least one of the round number of the current round cleaning process, the dirt degree of the cleaning piece and the cleaning effect quantitative value of the cleaning piece, the subsequent cleaning strategy suitable for various scenarios can be determined more accurately by comprehensively considering various factors, so that the cleaning piece can be effectively cleaned in an extreme dirty environment, thereby ensuring the cleanliness of the extreme dirty environment, and the unnecessary cleaning process for the cleaning piece can be reduced in a general dirty environment, thereby prolonging the service life of the cleaning piece, improving the working efficiency of the cleaning equipment, and enabling the cleaning equipment to perform the cleaning task more accurately and efficiently.

[0082] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0083] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0084] FIG. 1 shows a flowchart of a decision-making method of a cleaning equipment according to an embodiment of the present disclosure;

[0085] FIG. 2 shows a flowchart of how to obtain the dirt data of the cleaning piece corresponding to the water-out cleaning link in the current round cleaning process according to an embodiment of the present disclosure;

[0086] FIG. 3 shows a flowchart of how to identify the abnormal event in the current round cleaning process according to an embodiment of the present disclosure;

[0087] FIG. 4 shows a flowchart of how to identify the abnormal event in the current round cleaning process according to the reference dirt data, or the reference dirt data combined with the dirt data of the water-out cleaning link according to an embodiment of the present disclosure;

[0088] FIG. 5 shows a flowchart illustrating how to determine a subsequent cleaning strategy for a cleaning element according to at least one of the number of the current cleaning cycle, the dirt level of the cleaning element, and the cleaning effect quantification value of the cleaning element in an embodiment of the present disclosure;

[0089] FIG. 6 shows a diagram illustrating how the dirt data corresponding to N cleaning cycles included in a cleaning task varies in an embodiment of the present disclosure;

[0090] FIG. 7 shows a flowchart illustrating how to determine whether to update the preset reference dirt data in an embodiment of the present disclosure;

[0091] FIG. 8 shows a flowchart illustrating how to determine whether to perform area re-dragging in an embodiment of the present disclosure;

[0092] FIG. 9 shows a flowchart illustrating how to determine whether to perform re-cleaning on a cleaning area according to the ground dirt level in an embodiment of the present disclosure;

[0093] FIG. 10 shows a diagram illustrating the structure of a cleaning device in an exemplary embodiment of the present disclosure;

[0094] FIG. 11 shows a diagram illustrating the structure of an electronic device in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0095] Example implementations are now described with reference to the drawings; however, these implementations are merely examples of implementations and are not intended to be limiting on the scope of what can be claimed. Rather, these implementations are intended to demonstrate example implementations considered by the inventors to be most useful, the scope of what can be claimed is intended to include one or more aspects of the example implementations as described herein, and one or more such aspects are described herein. It should be noted that the use of particular terminology when describing certain features or aspects of the example implementations should not be taken to indicate that such terminology is being redefined herein to be restricted to include any specific characteristics of the examples with that terminology. The example implementations described herein are used to enable a person of ordinary skill in the art to make and use certain features and aspects of the implementations. It will be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the principles of the example implementations and, as such, have valuable aspects associated with them that are within the scope of the example implementations. Those skilled in the art will further recognize the exemplary nature of the examples presented herein; the intent is to present examples only and not a limitation of the scope of the example implementations. There is no intention to exclude any additional features from the scope of the example implementations; rather, the intent is to cover all included features and alternatives of the examples described herein, as well as equivalents thereof, in the example implementations. It is also to be understood that features and / or aspects of the examples described herein can be interchanged, substituted, and / or combined with features and / or aspects of other examples described herein, and that such variations are contemplated as being within the scope of the example implementations. Various aspects of the example implementations are described in sections of the specification for ease of delivery; however, it is to be understood that the example implementations can include one or more aspects from any section of the specification.

[0096] The terms "one", "an", "the" and "said" are used to indicate that there are one or more of the specified elements / components / etc.; the terms "include" and "has" are used to indicate an open-ended inclusion of one or more elements / components / etc. in the description of features, advantages, and aspects of the examples; the terms "first" and "second" are used to label various elements / components / etc. and are not meant to be limiting on the number of such elements / components / etc.

[0097] In addition, the accompanying drawings are only schematic and are non-limiting, each identical, or similar components that are denoted by the same reference numerals throughout the figures and block diagrams can be omitted or can be described without reference to the others. This division into blocks is thus only for clarity and illustration in connection with the disclosure.

[0098] The present inventors have found that the cleaning device in the related art has at least the following to be improved:

[0099] First, only the cleaning effect of the mop (i.e., the difference in the degree of dirtiness of the water used for washing the mop between two adjacent times) is used to determine the gear and water output when the mop is cleaned. This scheme has too single a judgment condition and is not accurate in some scenarios, for example, when the mop is still very dirty after mopping wet garbage with abnormal dirt such as old soybean paste, a small amount of water and a low gear may be used, resulting in the mop not being cleaned thoroughly.

[0100] Second, there is no abnormality recognition mechanism, so in the case of an abnormality, the wrong detection value is still used for calculation and processing. This scheme can lead to some extreme situations, for example, when the mop is very dirty, only a small amount of water is used to clean it.

[0101] Third, the cleaning effect of the mop is used to determine the dirtiness of the ground to determine whether to mop again. In this scheme, the cleaning effect of the mop may not represent the dirtiness of the ground, resulting in inaccurate decision making for mopping again.

[0102] Based on the above problems, the present disclosure provides a decision processing method for a cleaning device, which can use green light and infrared light dirt detection sensors to provide a reasonable abnormality recognition mechanism, a cleaning strategy for a cleaning element, and a decision on whether to mop again, and proposes a method for determining a ground dirt threshold value through a big data model, so that the self-cleaning robot can more accurately and effectively clean the cleaning element and perform the mopping task.

[0103] In an embodiment of the present disclosure, a decision processing method for a cleaning device is first provided, which at least partially overcomes the low degree of intelligence in the related art.

[0104] FIG. 1 shows a flowchart of a decision processing method for a cleaning device in an embodiment of the present disclosure. The execution subject of the decision processing method for the cleaning device can be the cleaning device.

[0105] Referring to FIG. 1, the decision processing method for a cleaning device according to one embodiment of the present disclosure includes the following steps:

[0106] In step S110, when performing a current round of cleaning process in a current cleaning task for a cleaning element, the dirt data corresponding to the current round of cleaning process of the cleaning element is obtained.

[0107] Step S120, determining the dirtiness degree of the cleaning piece according to the dirt data of the current round cleaning process;

[0108] Step S130, determining the cleaning effect quantitative value of the cleaning piece according to the difference between the dirt data of the current round cleaning process and the dirt data of the last round cleaning process;

[0109] Step S140, determining the subsequent cleaning strategy for the cleaning piece according to at least one of the round number of the current round cleaning process, the dirtiness degree of the cleaning piece and the cleaning effect quantitative value of the cleaning piece.

[0110] In the technical scheme provided by the embodiment shown in FIG. 1, on the one hand, when the current round cleaning process in the current cleaning task is performed on the cleaning piece, the dirt data corresponding to the current round cleaning process of the cleaning piece is obtained, and the dirtiness degree of the cleaning piece is determined according to the dirt data of the current round cleaning process, so that the dirtiness degree of the cleaning piece can be obtained in real time, and an effective cleaning piece cleaning strategy can be decided according to the dirtiness degree. Further, the cleaning effect quantitative value of the cleaning piece is determined according to the difference between the dirt data of the current round cleaning process and the dirt data of the last round cleaning process, which can intuitively quantify the cleaning effect, so that an effective cleaning piece cleaning strategy can be decided according to the quantitative value. On the other hand, the subsequent cleaning strategy for the cleaning piece is determined according to at least one of the round number of the current round cleaning process, the dirtiness degree of the cleaning piece and the cleaning effect quantitative value of the cleaning piece, which can comprehensively consider multiple factors to more accurately decide the subsequent cleaning strategy suitable for multiple different scenarios, so that the cleaning piece can be effectively cleaned in an extreme dirty environment, thereby ensuring the cleanliness of the extreme dirty environment, and unnecessary cleaning process for the cleaning piece can be reduced in a general dirty environment, thereby prolonging the service life of the cleaning piece, improving the working efficiency of the cleaning equipment, and enabling the cleaning equipment to more accurately and efficiently perform the cleaning task.

[0111] The specific implementation process of each step in FIG. 1 is described in detail as follows:

[0112] Before step S110, it should be noted that the cleaning equipment in the present disclosure can be a self-moving equipment with mopping function, such as a mopping robot or a sweeping and mopping robot. The above-mentioned cleaning equipment is provided with green light and infrared light dirt sensors at the inlet of the sewage pipe. Through the green light and infrared light dirt sensors, the dirt detection value can be obtained.

[0113] The above-mentioned cleaning equipment is provided with a cleaning piece for mopping, such as a mop or other flexible sponge that can be used for wiping the floor, which can be set according to the actual situation, and the present disclosure does not make special limitation thereto.

[0114] The cleaning device also has a "wash and scrape" self-cleaning base station for cleaning the brush module. After receiving each cleaning task, the self-cleaning base station can detect the sewage in the suction pipe / drain pipe using a dual-light source detection method (i.e., green light detection and infrared light detection), for example, detecting once every 50 ms (which can be set according to actual conditions, and the present disclosure does not make special limitations thereon). The self-cleaning base station can continuously record the detection values of the green light / infrared light and transmit them to the cleaning device, so that the cleaning device obtains the relevant dirt detection values.

[0115] In addition, each cleaning task in the present disclosure can include N rounds of cleaning processes + a scraping and drying process (i.e., scraping and drying without water). If the cleaning device has a water tank version base station, the value of N is in the range of 3≤N≤6. If the cleaning device has an automatic water supply and drainage version base station, the value of N is in the range of 3≤N≤8. The specific value of N is determined by the cleaning device according to the actual situation when each round of cleaning process is performed. Different values of N correspond to different cleaning gears. Specifically, N=3 is the first gear, N=4 is the second gear, N=5 is the third gear, N=6 is the fourth gear, N=7 is the fifth gear, and N=8 is the sixth gear.

[0116] Each round of cleaning process can include a water washing link and a water suction link. Specifically, the water washing link can include water washing and scraping + dry scraping + water washing and scraping + dry scraping; wherein water washing and scraping means scraping while water is flowing out, that is, when water flows out of the cleaning brush module, the scraping brush works at the same time to remove stains on the cloth; dry scraping means scraping without water, which is usually used to remove residual water on the cloth and difficult-to-clean stains. Through the cleaning process provided in the present disclosure, it can ensure that the mop can be washed by water to remove most of the stains, and can be deeply cleaned by the scraping brush, and finally the inside of the module is dried through water suction to avoid adverse effects of residual water on the mop or other components.

[0117] Referring to FIG. 1, in step S110, when the cleaning piece is performing the current round of cleaning process in the current cleaning task, the dirt data corresponding to the current round of cleaning process of the cleaning piece is obtained.

[0118] In this step, the dirt data of the cleaning piece in the water washing link of the current round of cleaning process (which can be referred to as the i-th round of cleaning process) can be obtained. Based on the dirt data of the water washing link, the dirt data corresponding to the current round of cleaning process of the cleaning piece is determined. For example, the dirt data of the water washing link can be determined as the dirt data corresponding to the current round of cleaning process of the cleaning piece.

[0119] For example, the water washing link can be a stage from 7s after the end of the water suction link of the previous round of cleaning process to before the water suction link of the current round of cleaning process.

[0120] It should be noted that the water-out cleaning link can include a plurality of continuous preset time periods, and each preset time period includes a plurality of continuous unit time periods. For example, the preset time period can be 3s, and the unit time period can be 1s.

[0121] For example, assuming that the timestamp at which the water-out cleaning link of the previous cleaning process ends is t0, and the water-out cleaning link includes three continuous preset time periods, the plurality of continuous preset time periods can be represented as: [t0+7s, t0+10s], [t0+10s, t0+13s], and [t0+13s, t0+16s], where [t0+7s, t0+10s] is the first preset time period, [t0+10s, t0+13s] is the second time period, and [t0+13s, t0+16s] is the third preset time period.

[0122] Specifically, referring to FIG. 2, FIG. 2 shows a flowchart of how to obtain the dirt data of the cleaning piece corresponding to the water-out cleaning link in the current round of cleaning process according to an embodiment of the present disclosure, including steps S201-S202:

[0123] In step S201, after entering the water-out cleaning link of the current round of cleaning process, a plurality of first dirt detection values of the cleaning piece in the first preset time period are obtained, and a first fluctuation condition representation value corresponding to the plurality of first dirt detection values is determined.

[0124] In this step, after entering the water-out cleaning link of the current round of cleaning process, a plurality of first dirt detection values of the cleaning piece in the first preset time period can be obtained. Since the green light / infrared light sensor is provided in the present disclosure, the dirt detection values involved in the present disclosure include two dimensions of detection values, the first dimension being the green light detection value and the second dimension being the infrared light detection value.

[0125] After obtaining the plurality of first dirt detection values of the cleaning piece in the first preset time period, in an optional embodiment, the maximum value and the minimum value of the plurality of first dirt detection values can be obtained, and the first fluctuation condition representation value can be determined according to the difference between the maximum value and the minimum value. Since the first dirt detection value includes the green light detection value and the infrared light detection value, the first fluctuation condition representation value also includes two dimensions of representation values (for example, for the convenience of description, the fluctuation condition representation value corresponding to the green light dimension can be denoted as data1, and the fluctuation condition representation value corresponding to the infrared light dimension can be denoted as data2). Specifically, the maximum value and the minimum value of the plurality of green light detection values can be obtained, and the data1 can be determined according to the difference between the maximum value and the minimum value. In addition, the maximum value and the minimum value of the plurality of infrared light detection values can be obtained, and the data2 can be determined according to the difference between the maximum value and the minimum value.

[0126] In the second optional embodiment, the variance or standard deviation of the plurality of first dirt detection values can be obtained. Based on the calculated variance / standard deviation, the first fluctuation condition representation value is determined. For example, the calculated variance or standard deviation can be determined as the first fluctuation condition representation value. Since the first dirt detection values include green light detection values and infrared light detection values, the variance or standard deviation of the plurality of green light detection values can be obtained, and the data 1 is determined according to the variance or standard deviation of the plurality of green light detection values, and the variance or standard deviation of the plurality of infrared light detection values can be obtained, and the data 2 is determined according to the variance or standard deviation of the plurality of infrared light detection values.

[0127] In step S202, in the case that the first fluctuation condition representation value is less than or equal to the first fluctuation threshold value, the dirt data corresponding to the water outlet cleaning link is determined based on the average value of the plurality of first dirt detection values. For example, the average value of the plurality of first dirt detection values can be determined as the dirt data corresponding to the water outlet cleaning link.

[0128] In this step, the first fluctuation threshold value in the disclosure corresponding to the first dirt detection value and the first fluctuation condition representation value also includes two-dimensional threshold values, i.e. the threshold value W corresponding to the green light and the threshold value w corresponding to the infrared light. Therefore, the first fluctuation threshold value (which can also be referred to as the non-pumping jitter threshold value) can be represented as W, w.

[0129] After obtaining the first fluctuation condition representation value, the fluctuation condition representation value can be compared with the first fluctuation threshold value. If the comparison result is that the first fluctuation condition representation value is less than or equal to the first fluctuation threshold value (i.e. data 1 is less than or equal to W, and data 2 is less than or equal to w), then the average value of the plurality of first dirt detection values (i.e. the average value of the plurality of green light detection values B i , and the average value of the plurality of infrared light detection values b i ) can be calculated, and the dirt data of the cleaning part corresponding to the water outlet cleaning link (i.e. the green light dimension dirt data B i , the infrared light dimension dirt data b i , which can also be referred to as stable detection data) is obtained.

[0130] It should be noted that after determining the dirt data corresponding to the water outlet cleaning link based on the average value of the plurality of first dirt detection values, it can also be judged whether the dirt data is within a preset normal value range (for example, the preset normal value range can be set to B i ≤ 3000, b i≤3800). If the dirty data is not within the preset normal value range, it can be recorded that the first abnormal event occurs in the current round of cleaning process, and the first abnormal event is used to represent that the dirty data is an abnormal value. For example, the first abnormal event can be recorded as: dirty detection abnormality-stable detection data abnormality.

[0131] After step S202, if the comparison result is that the first fluctuation condition value is greater than the first fluctuation threshold, the first preset time period can be shifted back by 3s, the second plurality of dirty detection values of the cleaning element in the second preset time period are obtained, and the second fluctuation condition value corresponding to the second plurality of dirty detection values is determined. If the second fluctuation condition value is less than or equal to the first fluctuation threshold, the average value of the second plurality of dirty detection values can be directly calculated to obtain the dirty data of the cleaning element corresponding to the water outlet cleaning link. If the second fluctuation condition value is still greater than the first fluctuation threshold, the second preset time period can be shifted back by 3s, and then the fluctuation condition value corresponding to the plurality of dirty detection values in the next preset time period is obtained, until the fluctuation condition value corresponding to the plurality of dirty detection values in the next preset time period is less than the first fluctuation threshold to determine the dirty data of the cleaning element corresponding to the water outlet cleaning link.

[0132] If the next preset time period obtained is the last preset time period, and the last fluctuation condition value corresponding thereto is still greater than the first fluctuation threshold, it can be recorded that the second abnormal event occurs in the current round of cleaning process, and the second abnormal event is used to represent that the dirty shaking is abnormal. For example, the second abnormal event can be recorded as: dirty detection abnormality-dirty shaking abnormality (for example, abnormal dirty adhesion, unable to clean).

[0133] Each round of cleaning process in the present disclosure also includes a water pumping link after the water outlet cleaning link. Based on the water pumping link, the present disclosure also provides a process for identifying abnormal events. Specifically, referring to FIG. 3, FIG. 3 shows a flowchart of how to identify abnormal events in the current round of cleaning process in the embodiment of the present disclosure, including steps S301-S302:

[0134] In step S301, the reference dirty data of the cleaning element corresponding to the water pumping link of the current round of cleaning process is obtained.

[0135] In this step, for example, the water pumping link can refer to the stage from 1s after the current round of cleaning process enters the water pumping link to before the water pumping link of the current round of cleaning process ends.

[0136] After the water pumping link of the current cleaning procedure is entered, a plurality of third dirt detection values of the cleaning piece in a specified time period can be obtained. For example, the specified time period can be 3s. Thus, assuming that the timestamp when the water pumping link of the current cleaning procedure is entered is t1, the specified time period can be represented as: [t1+1s, t1+4s].

[0137] As can be known from the above related explanations, the third dirt detection value still contains two-dimensional detection values, i.e., green light detection values and infrared light detection values. Thus, the reference dirt data determined according to the third dirt detection value also contains reference dirt data corresponding to the green light dimension (denoted as data3) and reference dirt data corresponding to the infrared light dimension (denoted as data4).

[0138] For example, the above reference dirt data can be determined based on the following embodiments:

[0139] In an optional embodiment, the maximum value and the minimum value of the plurality of green light detection values can be obtained, and the data3 can be determined according to the difference between the maximum value and the minimum value; and the maximum value and the minimum value of the plurality of infrared light detection values can be obtained, and the data4 can be determined according to the difference between the maximum value and the minimum value.

[0140] In another optional embodiment, the variance or standard deviation of the plurality of green light detection values can be obtained, and the data3 can be determined according to the calculated variance or standard deviation; and the variance or standard deviation of the plurality of infrared light detection values can be obtained, and the data4 can be determined according to the calculated variance or standard deviation.

[0141] In step S302, according to the reference dirt data, or the reference dirt data combined with the dirt data of the water outlet cleaning link, an abnormal event in the current cleaning procedure is identified.

[0142] In this step, referring to FIG. 4, FIG. 4 shows a flowchart of how to identify an abnormal event in the current cleaning procedure according to the reference dirt data, or the reference dirt data combined with the dirt data of the water outlet cleaning link, in the embodiments of the present disclosure, which contains steps S401-S403:

[0143] In step S401, if the reference dirt data is greater than or equal to a first preset threshold, it is determined that no abnormal event is identified.

[0144] In this step, the first preset threshold value can include the water pumping judgment threshold value K corresponding to the green light dimension and the water pumping judgment threshold value k corresponding to the infrared light dimension. Thus, if the data 3 corresponding to the green light dimension in the reference dirt data is greater than or equal to the water pumping judgment threshold value K, and the data 4 corresponding to the infrared light dimension is greater than or equal to the water pumping judgment threshold value k, it can be determined that no abnormal event is identified.

[0145] In step S402, if the reference dirt data is less than the first preset threshold value, and the first dimension value of the dirt data is greater than or equal to the second preset threshold value, and the second dimension value is greater than or equal to the third preset threshold value, a third abnormal event in the current round of cleaning process is recorded.

[0146] In this step, if the value of any one dimension in the reference dirt data is less than the first preset threshold value, i.e., (data 3 is less than K, or data 4 is less than k), and the dirt data corresponding to the green light dimension in the water outlet cleaning link is greater than or equal to the second preset threshold value E, and the dirt data corresponding to the infrared light dimension in the water outlet cleaning link is greater than or equal to the third preset threshold value e, the third abnormal event in the current round of cleaning process can be recorded. The third abnormal event is used to represent that the cleaning element is abnormal. For example, the third abnormal event can be recorded as: dirt detection abnormality-no water washing cloth abnormality (for example, the washing cloth is not normally placed, the washing cloth is missing or does not work normally, etc.).

[0147] In step S403, if the reference dirt data is less than the first preset threshold value, and the first dimension value of the dirt data is less than the second preset threshold value or the second dimension value is less than the third preset threshold value, a fourth abnormal event in the current round of cleaning process is recorded.

[0148] In this step, if the value of any one dimension in the reference dirt data is less than the first preset threshold value, i.e., (data 3 is less than K, or data 4 is less than k), and the dirt data corresponding to the green light dimension in the water outlet cleaning link is less than the second preset threshold value E, and the dirt data corresponding to the infrared light dimension in the water outlet cleaning link is less than the third preset threshold value e, the fourth abnormal event in the current round of cleaning process can be recorded. The fourth abnormal event is used to represent that the dirt is wall-hung abnormal. For example, the fourth abnormal event can be recorded as: dirt detection abnormality-dirt wall-hung abnormality.

[0149] In addition, it should be noted that during the execution of each round of cleaning process, the disclosure also records a data loss event. The data loss event represents that the dirt detection value reported by the green light / infrared light sensor is lost, resulting in that the dirt detection value is not received. For example, due to network disconnection or other problems, the dirt detection value may be lost. In the case of loss of dirt detection value, a data loss event can be recorded.

[0150] Based on the above processing mode, after the current round of cleaning process ends, the present disclosure may record the following three kinds of information for the current round of cleaning process:

[0151] ① The dirt data of the water outlet cleaning link of the current round of cleaning process;

[0152] ② Various abnormal events occurring in the current round of cleaning process;

[0153] ③ The number of times of not receiving the dirt detection value in the current round of cleaning process (i.e. the number of times of loss of the dirt detection value, i.e. the number of times of occurrence of the data loss event), if the loss number is greater than 3, it can be determined that the current round of cleaning process has a loss of dirt detection value abnormality.

[0154] Next, referring to FIG. 1, in step S120, the dirt degree of the cleaning piece is determined according to the dirt data of the current round of cleaning process.

[0155] In this step, the dirt degree of the cleaning piece can be determined according to the dirt data of the current round of cleaning process. For example, the dirt degree of the cleaning piece can be determined according to the difference between the above-mentioned dirt data of the current round of cleaning process and the preset reference dirt data.

[0156] Wherein, the initial fixed dirt data (which can be the clean water detection value) will be written before the equipment leaves the factory, for example: A0=2200, a0=2650 (which can be set according to actual conditions, and the present disclosure does not make special limitations thereon), and when the factory settings are restored, the equipment will be restored to the above-mentioned initial fixed dirt data.

[0157] After receiving each cleaning task, the equipment can receive the reference dirt data transmitted by the base station, for example: A, a. If the reference dirt data is received, the above-mentioned initial fixed dirt data can be updated to the reference dirt data A, a, and if the reference dirt data is not received, the above-mentioned initial fixed dirt data can be determined as the reference dirt data A, a.

[0158] Thus, in view of the fact that the dirt data of the current round of cleaning process includes the green light dimension dirt data B i and the infrared light dimension dirt data b i , the dirt degree also includes the dirt degree corresponding to the green light dimension (denoted as C i ) and the dirt degree corresponding to the infrared light dimension (denoted as c i ). The difference between A and B i can be used to determine the above-mentioned C i , and the difference between a and b i can be used to determine the above-mentioned c i .

[0159] In step S130, the cleaning effect quantification value of the cleaning item is determined based on the difference between the dirt data of the current cleaning process and the dirt data of the previous cleaning process.

[0160] In this step, referring to the above explanation, the dirt data for this round of cleaning can be represented as B. i b i Assume the dirt data from the previous cleaning process is represented as B. i-1 b i-1 The cleaning effect quantification value also includes a quantification value corresponding to the green light dimension (denoted as D). i ) and the quantization value corresponding to the infrared light dimension (denoted as d) i Therefore, it is possible to determine the order based on B. i and B i-1 The difference determines D i According to b i and b i-1 The difference between them is determined by d. i .

[0161] In step S140, a subsequent cleaning strategy for the cleaning part is determined based on at least one of the following: the round number of the current cleaning process, the degree of dirtiness of the cleaning part, and the quantitative value of the cleaning effect of the cleaning part.

[0162] In this step, referring to Figure 5, Figure 5 shows a flowchart illustrating how, in this embodiment of the present disclosure, a subsequent cleaning strategy for the cleaning component is determined based on at least one of the following: the round number of the current cleaning process, the degree of dirtiness of the cleaning component, and the quantitative value of the cleaning effect of the cleaning component. The flowchart includes steps S501-S502:

[0163] In step S501, if the round number of the current cleaning process is less than the specified round number, it is determined that the next round of cleaning process will be performed on the cleaned parts.

[0164] In this step, the current cleaning cycle number i can be compared with a specified cycle number (which can be a minimum number of cleaning cycles, such as 3). If the current cycle number i is less than the specified cycle number, then it can be determined to continue the next cleaning cycle for the cleaned parts.

[0165] In step S502, if the round number of the current cleaning process is greater than or equal to the specified round number, it is determined whether to perform the next cleaning process on the cleaning part based on the degree of dirtiness of the cleaning part and the quantitative value of the cleaning effect of the cleaning part.

[0166] In this step, if the current cleaning cycle number i is greater than or equal to the specified cycle number (i.e., 3), then the cleaning process can be adjusted according to the degree of soiling of the parts to be cleaned (i.e., the aforementioned C). i c i) and the cleaning effect quantitative value of the cleaning piece (i.e., the above D i , d i ), whether to perform the next cleaning process on the cleaning piece. Specifically, there are the following cases:

[0167] ① If the cleaning effect quantitative value is greater than or equal to a preset quantitative threshold (indicating that the cleaning effect of the mop is obvious, i.e., D i ≥ H, or, d i ≥ h), or the dirt degree is greater than or equal to a preset dirt degree threshold (i.e., C i ≥ Q, or, c i ≥ q), or the number of data loss events recorded for the current cleaning process is less than a preset number threshold (for example: 3 times), the next cleaning process can be performed on the cleaning piece.

[0168] After the next cleaning process ends, it can be determined whether the maximum limit number of rounds is reached (determined according to the type of cleaning equipment, for example, the maximum limit number of rounds of the basic version water tank is 6 or the maximum limit number of rounds of the automatic water up and down version is 8). If the maximum limit number of rounds is reached, the next cleaning process does not need to be performed on the cleaning piece, and the no-water scraping drying process can be entered. If the maximum limit number of rounds is not reached, the dirt degree and the cleaning effect quantitative value of the cleaning piece corresponding to the next cleaning process can be obtained. According to the round number of the next cleaning process, at least one of the dirt degree and the cleaning effect quantitative value of the cleaning piece corresponding to the next cleaning process, it is determined again whether to perform the next cleaning process on the cleaning piece.

[0169] ② If the cleaning effect quantitative value is less than the preset quantitative threshold (i.e., D i < H, and d i < h) and the dirt degree is less than the preset dirt degree threshold (i.e., C i < Q, or, c i < q), the next cleaning process does not need to be performed on the cleaning piece.

[0170] In addition, it should be noted that if it is determined that any abnormal event (i.e., any one of the first to fourth abnormal events) is recorded in the current round of cleaning process, or the number of times of the data loss event is greater than the preset number threshold (for example, 3), the dirty detection result (i.e., the determined cleaning effect quantitative value and the dirt level) corresponding to the current round of cleaning process can be discarded, and the dirty detection result is no longer used to determine whether to execute the next round of cleaning process. In this case, the numerical relationship between the round number i of the current cleaning process and the target round (for example, 5) can be determined. If i is less than or equal to 5, the total number of cleaning processes included in the current cleaning task can be set to the target round 5, that is, the third gear is used when the current cleaning task is executed. If i is greater than 5, it can be determined that the current cleaning process is the last round of cleaning process of the current cleaning task, and after the end, the waterless wiping process can be directly entered.

[0171] Based on the processing flow in step S140, it is determined whether to execute the next round of cleaning process on the cleaning piece. The present disclosure can dynamically determine and adjust the cleaning strategy according to various factors. Since the total amount of water used for cleaning the cleaning piece is different when the total number of cleaning processes performed on the cleaning piece is different, the present disclosure can more accurately determine the subsequent cleaning strategy suitable for various scenarios by comprehensively considering various factors. For extremely dirty environments (high dirt level), the cleaning piece can be effectively cleaned through multiple rounds of cleaning process and a large amount of water, thereby ensuring the cleanliness of the extremely dirty environment. For general dirty environments (low dirt level), unnecessary cleaning processes for the cleaning piece can be reduced through fewer rounds of cleaning process and less water, thereby prolonging the service life of the cleaning piece, saving water resources, and improving the working efficiency of the cleaning equipment, so that the cleaning equipment can more accurately and efficiently perform the cleaning task.

[0172] Referring to FIG. 6, FIG. 6 shows a variation diagram of N pieces of dirt data corresponding to N cleaning processes included in one cleaning task according to an embodiment of the present disclosure. As shown in FIG. 6:

[0173] Each round of cleaning process includes a water washing link and a water pumping link. The dirt data obtained by the water washing link in the first round of cleaning process can be represented as B1, b1, the dirt data obtained by the water washing link in the second round of cleaning process can be represented as B2, b2, the dirt data obtained by the water washing link in the third round of cleaning process can be represented as B3, b3, …, and the dirt data obtained by the water washing link in the Nth round of cleaning process can be represented as BN, bn. N N As the round number of the cleaning process gradually increases, the value of the dirt data gradually approaches the clean water detection value.

[0174] ​After all N rounds of cleaning processes included in the current cleaning task are completed (i.e., the current cleaning task is completed), referring to FIG. 7, FIG. 7 shows a schematic flowchart of how to determine whether to update the above-mentioned preset reference dirt data in an embodiment of the present disclosure, including steps S701 - step S702:

[0175] In step S701, obtain the target dirt data of the last round of the cleaning process of the current cleaning task.

[0176] In this step, the target dirt data (taking B N1 and b N2 as an example) of the last round of the cleaning process of the current cleaning task can be obtained.

[0177] In step S702, determine whether to update the preset reference dirt data according to the target dirt data.

[0178] Specifically, in this step, determining whether to update the preset reference data according to the target dirt data includes the following situations:

[0179] ① If the target dirt data is within the preset normal value range, and the difference between the target dirt data and the preset reference dirt data is less than the preset difference threshold (i.e., A - B N1 <H, and a - b N1 <h), update the preset reference dirt data with the target dirt data, that is: update the preset reference dirt data A and a to B N1 and b N1 .

[0180] ② If the target dirt data is within the preset normal value range, and the difference between the target dirt data and the preset reference dirt data is greater than or equal to the preset difference threshold (i.e., A - B N1 ≥H, and a - b N1 ≥h), record a pending update event (the pending update event represents that the current situation does not yet meet the conditions for updating the preset reference dirt data, and a counter can be used to count it, and determine whether to update the preset reference dirt data according to the counting situation) and the target dirt data associated with the pending update event. When the above pending update event appears continuously for a specified number of times (for example: 10 times), the preset reference dirt data can be updated according to the specified number of target dirt data (for example: B N1 and b N1 , B N2 and b N2 , ……, B N10 and b N10 ) associated with the specified number of pending update events. For example: the above B N1 , B N2 , ……, BN10 Select the maximum value (e.g., B) N10 ), update A to B N10 , and, from the above b N1 b N2 , ..., b N10 Select the maximum value (e.g., b) N1 ), update a to b N10 This is to update the preset baseline dirt data.

[0181] It should be noted that if the number of consecutive occurrences of the aforementioned pending update events does not reach the specified number, there is no need to update the preset baseline dirty data or reset the counter. If the number of consecutive occurrences of the aforementioned pending update events does not reach the specified number, but situation ① occurs, the preset baseline dirty data can be updated based on the processing scheme in situation ①. At the same time, the value of the counter is set to the initial value, for example, 0.

[0182] ③ If the target dirt data is not within the preset normal value range, the preset initial baseline value will not be updated, and the counter can be reset to the above initial value.

[0183] In an optional implementation, this disclosure also provides a processing scheme for deciding whether to perform regional re-drafting. Referring to FIG8, FIG8 shows a schematic flowchart of how to decide whether to perform regional re-drafting in an embodiment of this disclosure, including steps S801-S803:

[0184] In step S801, the dirt data of a specified cleaning cycle in the current cleaning task is obtained.

[0185] In this step, the dirt data of a specified round of cleaning in the current cleaning task (let's say it's the t-th time) can be obtained. For example, this specified round of cleaning could be the dirt data of the second round of cleaning (denoted as B). 2t b 2t It can also be the dirt data from the first round of cleaning. It can be set according to the actual situation. This disclosure does not impose any special restrictions on it.

[0186] In step S802, the degree of dirt on the ground in the target cleaning area is determined based on the dirt data from the last round of cleaning in the previous cleaning task and the dirt data from the specified round of cleaning in the current cleaning task.

[0187] In this step, the dirt data from the last round of cleaning in the previous cleaning task (let's say it's the lth time) can be represented as (B) Nl b Nl Therefore, based on the above B Nl b Nl With B2t b 2t Determine the degree of dirtiness on the ground in the target cleaning area.

[0188] The target cleaning area refers to the area cleaned during the interval between the completion of the last round of the cleaning process and the start of the specified round of the cleaning process (i.e., the time interval between the two rounds of cleaning processes).

[0189] Specifically, B can be calculated. Nl With B 2t The difference between them, and the calculation of b Nl With b 2t The difference between the two values. The degree of dirtiness (B) of the cleaned area is determined based on the obtained two differences. Nl -B 2t b Nl -b 2t Based on this difference, the cumulative amount of dirt on the floor between two washes can be quantified.

[0190] In step S803, it is determined whether the cleaned area needs to be cleaned again based on the degree of dirt on the ground.

[0191] In this step, referring to Figure 9, which illustrates a flowchart of how to determine whether to clean the area again based on the degree of dirt on the ground in this embodiment of the present disclosure, including steps S901-S903:

[0192] In step S901, the area of ​​the target cleaning area is obtained.

[0193] In this step, the area S of the target cleaning area can be obtained.

[0194] In step S902, the ground dirt density is determined based on the ratio of the degree of ground dirtiness to the area.

[0195] In this step, the ground dirt density can be determined based on the ratio between the degree of ground dirt and the area of ​​the aforementioned region. Ground dirt density reflects the amount of dirt per unit area. Specifically, the values ​​of the two dimensions included in ground dirt density can be expressed as: (B Nl -B 2t ) / S and (b Nl -b 2t ) / S.

[0196] In step S903, it is determined whether the target cleaning area should be cleaned again based on the density of dirt on the ground.

[0197] In this step, it can be determined whether the cleaned area needs to be cleaned again based on the aforementioned ground dirt density. Specifically, the aforementioned ground dirt density can be compared with a preset dirt density threshold (G, g). If the comparison result is: (B Nl -B 2t ) / S>G, or, (b Nl -b 2t If ) / S>g, then it can be determined that the target cleaning area needs to be cleaned again.

[0198] It should be noted that if any of the above-mentioned abnormal events (i.e., any one of the first to fourth abnormal events) are detected during the execution of the previous cleaning task or the current cleaning task, or if the number of the above-mentioned data loss events exceeds the preset number threshold (e.g., 3), the relevant dirt data from the two cleaning tasks can be omitted from the decision on whether to repeat the process, in order to avoid decision-making errors.

[0199] It should be noted that the thresholds (E, e, H, h, K, k and W, w) involved in this disclosure can be determined through user surveys, while the aforementioned dirt density thresholds G and g can be determined through a combination of user surveys and big data ranking models. For example, by collecting dirt density data points from internal testing users each time they wash their mops, a big data ranking model for dirt density values ​​can be created. If the probability of dirt appearing on the user's floor is determined to be x% based on user surveys, and the top x% of all users' dirt density values ​​are defined as dirty floors, then the dirt density thresholds G and g can be taken as the values ​​of the top x% of all users' dirt density values.

[0200] Based on the above technical solutions, this disclosure can achieve at least the following technical effects:

[0201] First, it provides a solution for identifying various abnormal events, including abnormalities such as waterless cloth washing, abnormal dirt clinging to the wall, abnormal dirt shaking, abnormal dirt data, and abnormal loss of detection values.

[0202] Second, it provides an update scheme for preset initial baseline values ​​A and a, and determines the dirt data B corresponding to each cleaning cycle. i b i Methods;

[0203] Third, by combining the current cleaning cycle number, the degree of dirtiness of the cleaning items, and the quantitative value of the cleaning effect of the cleaning items to determine the total number of cleaning cycles and the cloth washing level, the accuracy of the decision can be improved, thereby improving the cleaning quality and cleaning efficiency of the cleaning equipment.

[0204] Fourth, by using the difference in the degree of dirtiness of the mop in the two wash rooms to represent the degree of dirtiness of the floor, and combining the mopping area of ​​the two wash rooms to calculate the dirt density of the floor, the floor dirt density can be used to determine whether a second mopping is needed. This allows for a more accurate quantification of the degree of dirtiness of the floor, thus enabling an accurate decision on whether to re-mop.

[0205] Fifth, by using a big data model of user dirt density to determine the dirt density threshold, the threshold determination can be more closely aligned with the actual usage scenarios of users, thereby improving the accuracy of re-drying decisions.

[0206] This disclosure also provides a cleaning device. Figure 10 shows a schematic diagram of the structure of the cleaning device in an exemplary embodiment of this disclosure. As shown in Figure 10, the cleaning device 1000 may include a data acquisition module 1010, a data processing module 1020, and a decision module 1030. Wherein:

[0207] The data acquisition module 1010 is used to acquire dirt data of the cleaning component corresponding to the current cleaning process when performing the current cleaning process in the current cleaning task on the cleaning component;

[0208] The data processing module 1020 is used to determine the degree of dirtiness of the cleaning component based on the dirt data of the current cleaning process; and to determine the cleaning effect quantification value of the cleaning component based on the difference between the dirt data of the current cleaning process and the dirt data of the previous cleaning process.

[0209] The decision module 1030 is used to determine a subsequent cleaning strategy for the cleaning component based on at least one of the following: the round number of the current cleaning process, the degree of dirtiness of the cleaning component, and the quantitative value of the cleaning effect of the cleaning component.

[0210] In an exemplary embodiment of this disclosure, each cleaning process includes a water rinsing step.

[0211] The data acquisition module 1010 acquires the dirt data of the cleaning component corresponding to the current cleaning process, including:

[0212] The dirt data of the cleaning component corresponding to the effluent cleaning stage in the current cleaning process is obtained. Based on the dirt data of the effluent cleaning stage, the dirt data corresponding to the current cleaning process is determined. For example, the dirt data of the effluent cleaning stage can be determined as the dirt data corresponding to the current cleaning process.

[0213] In an exemplary embodiment of this disclosure, the water rinsing process includes multiple consecutive preset time periods, and each preset time period includes multiple consecutive unit time periods.

[0214] The data acquisition module 1010 acquires the dirt data of the cleaning component corresponding to the effluent cleaning stage of the current cleaning process, including:

[0215] After entering the effluent cleaning stage of the current cleaning process, multiple first dirt detection values ​​of the cleaning component within the first preset time period are obtained, and the first fluctuation characterization value corresponding to the multiple first dirt detection values ​​is determined.

[0216] If the first fluctuation characteristic value is less than or equal to the first fluctuation threshold, the dirt data corresponding to the effluent cleaning stage is determined based on the average of the plurality of first dirt detection values. For example, the average of the plurality of first dirt detection values ​​can be determined as the dirt data corresponding to the effluent cleaning stage.

[0217] In an exemplary embodiment of this disclosure, the data acquisition module 1010 determines the first fluctuation characterization value corresponding to the plurality of first dirt detection values, including:

[0218] Obtain the maximum and minimum values ​​among the plurality of first dirt detection values, and determine the first fluctuation characterization value based on the difference between the maximum and minimum values; or,

[0219] Obtain the variance / standard deviation of the plurality of first dirt detection values, and determine the first fluctuation characteristic value based on the variance / standard deviation. For example, the variance / standard deviation can be determined as the first fluctuation characteristic value.

[0220] In an exemplary embodiment of this disclosure, after determining the dirt data corresponding to the effluent cleaning stage based on the average of the plurality of first dirt detection values, the data processing module 1020 is configured to:

[0221] Determine whether the dirt data is within a preset normal range;

[0222] If the dirt data is not within the preset normal value range, record the first abnormal event in the current cleaning process;

[0223] The first abnormal event is used to characterize the dirt data of the current cleaning process as an abnormal value.

[0224] In an exemplary embodiment of this disclosure, after determining a first fluctuation characterization value for the plurality of first dirt detection values, the data processing module 1020 is configured to:

[0225] When the first fluctuation condition characterization value is greater than the first fluctuation threshold, multiple second dirt detection values ​​of the cleaning component are obtained within a second preset time period, and the second fluctuation condition characterization value corresponding to the multiple second dirt detection values ​​is determined.

[0226] If the second fluctuation condition characterization value is still greater than the first fluctuation threshold, then the fluctuation condition characterization value corresponding to multiple dirt detection values ​​in the next preset time period is obtained until the next preset time period is the last preset time period. In response to the last fluctuation condition characterization value corresponding to the last preset time period being greater than the first fluctuation threshold, the second abnormal event of the current cleaning process is recorded.

[0227] The second abnormal event is used to characterize the dirt shaking abnormality in the current round of cleaning process.

[0228] In an exemplary embodiment of this disclosure, each cleaning process further includes a water pumping stage, which is located after the water effluent cleaning stage. The data processing module 1020 is configured to:

[0229] Obtain reference dirt data for the cleaning component corresponding to the water pumping stage of the current cleaning process;

[0230] Based on the reference dirt data, or the reference dirt data combined with the dirt data from the effluent cleaning stage, abnormal events in the current cleaning process are identified.

[0231] In an exemplary embodiment of this disclosure, the data processing module 1020 acquires reference dirt data of the cleaning component corresponding to the water pumping stage of the current cleaning process, including:

[0232] After entering the water pumping stage of the current cleaning process, multiple third-party dirt detection values ​​of the cleaning component are obtained within a specified time period;

[0233] The reference dirt data is determined based on the difference between the maximum and minimum values ​​among the plurality of third dirt detection values;

[0234] Alternatively, the reference dirt data can be determined based on the variance / standard deviation of the plurality of third dirt detection values.

[0235] In an exemplary embodiment of this disclosure, the reference dirt data includes a first dimension value and a second dimension value.

[0236] The data processing module 1020 identifies abnormal events in the current cleaning process based on the reference dirt data, or based on the reference dirt data combined with the dirt data, including:

[0237] If the reference dirt data is greater than or equal to the first preset threshold, it is determined that no abnormal event has been identified.

[0238] If the reference dirt data is less than the first preset threshold, and the first dimension value of the dirt data is greater than or equal to the second preset threshold, and the second dimension value is greater than or equal to the third preset threshold, a third abnormal event is recorded in the current cleaning process; the third abnormal event is used to characterize the abnormality of the cleaning component;

[0239] If the reference dirt data is less than the first preset threshold, and the first dimension value of the dirt data is less than the second preset threshold or the second dimension value is less than the third preset threshold, a fourth abnormal event is recorded in the current cleaning process; the fourth abnormal event is used to characterize the abnormal dirt adhesion to the wall.

[0240] In an exemplary embodiment of this disclosure, the decision module 1030 determines a subsequent cleaning strategy for the cleaning component based on at least one of the round number of the current cleaning process, the degree of dirtiness of the cleaning component, and a quantitative value of the cleaning effect of the cleaning component, including:

[0241] If the round number of the current cleaning process is less than the specified round number, then the next cleaning process will be performed on the cleaned part.

[0242] If the current cleaning cycle number is greater than or equal to the specified cycle number, it is determined whether to perform the next cleaning cycle on the cleaning component based on the degree of dirtiness of the cleaning component and the quantitative value of the cleaning effect of the cleaning component.

[0243] In an exemplary embodiment of this disclosure, the decision module 1030 determines whether to perform the next cleaning process on the cleaning component based on the degree of dirtiness of the cleaning component and the quantitative value of the cleaning effect of the cleaning component, including:

[0244] If the cleaning effect quantification value is greater than or equal to a preset quantification threshold, or the degree of dirtiness is greater than or equal to a preset degree of dirtiness threshold, or the number of data loss events recorded in the current cleaning process is less than a preset number threshold, then it is determined to execute the next cleaning process on the cleaning item.

[0245] In an exemplary embodiment of this disclosure, after the next round of cleaning process is completed, the decision module 1030 is configured to:

[0246] Determine if the maximum number of rounds has been reached;

[0247] If the maximum number of cycles is reached, there is no need to perform another cleaning cycle on the cleaning item; the maximum number of cycles is determined based on the type of cleaning equipment.

[0248] If the maximum number of cycles is not reached, obtain the quantitative value of the degree of dirt and cleaning effect of the cleaning component corresponding to the next cleaning cycle;

[0249] Based on at least one of the following: the round number of the next cleaning process, the degree of dirtiness of the cleaning component corresponding to the next cleaning process, and the quantitative value of the cleaning effect, it is determined whether to perform the next cleaning process on the cleaning component.

[0250] In an exemplary embodiment of this disclosure, the decision module 1030 determines whether to perform the next cleaning process on the cleaning component based on the degree of dirtiness of the cleaning component and the quantitative value of the cleaning effect of the cleaning component, and further includes:

[0251] If the cleaning effect quantification value is less than the preset quantification threshold and the degree of dirtiness is less than the preset degree of dirtiness threshold, there is no need to perform the next round of cleaning process on the cleaning component.

[0252] In an exemplary embodiment of this disclosure, the degree of soiling of the cleaning component is determined based on the difference between soiling data from the current cleaning cycle and preset baseline soiling data. The decision module 1030 is configured to:

[0253] After the current cleaning task is completed, obtain the target dirt data corresponding to the last round of cleaning process of the current cleaning task;

[0254] Determine whether to update the preset baseline dirt data based on the target dirt data.

[0255] In an exemplary embodiment of this disclosure, the decision module 1030 determines whether to update the preset baseline dirt data based on the target dirt data, including:

[0256] If the target dirt data is within a preset normal value range, and the difference between the target dirt data and the preset baseline dirt data is less than a preset difference threshold, the preset baseline dirt data is updated using the target dirt data.

[0257] In an exemplary embodiment of this disclosure, the decision module 1030, which determines whether to update the preset baseline dirt data based on the target dirt data, further includes:

[0258] If the target dirt data is within the preset normal value range, and the difference between the target dirt data and the preset baseline dirt data is greater than or equal to the preset difference threshold, record a pending update event and the target dirt data associated with the pending update event.

[0259] When the pending update event occurs a specified number of times consecutively, the preset baseline dirt data is updated based on a specified number of target dirt data associated with the specified number of pending update events.

[0260] In an exemplary embodiment of this disclosure, the decision module 1030, which determines whether to update the preset baseline dirt data based on the target dirt data, further includes:

[0261] If the target dirt data is not within the preset normal value range, the preset initial baseline value will not be updated.

[0262] In an exemplary embodiment of this disclosure, the decision module 1030 is configured to:

[0263] Retrieve dirt data corresponding to a specified round of cleaning in the current cleaning task;

[0264] Based on the dirt data corresponding to the last round of cleaning in the previous cleaning task and the dirt data corresponding to the specified round of cleaning in the current cleaning task, the degree of dirt on the ground in the target cleaning area is determined; the target cleaning area is the area that has been cleaned during the interval from the completion of the last round of cleaning to the start of the specified round of cleaning.

[0265] Based on the degree of dirtiness of the ground, determine whether the cleaned area needs to be cleaned again.

[0266] In an exemplary embodiment of this disclosure, the decision module 1030 determines whether to clean the cleaning area again based on the degree of dirtiness of the ground, including:

[0267] Obtain the area of ​​the target cleaning region;

[0268] The ground dirt density is determined based on the ratio of the degree of ground dirtiness to the area of ​​the region.

[0269] Based on the density of dirt on the ground, determine whether the target cleaning area needs to be cleaned again.

[0270] In an exemplary embodiment of this disclosure, the decision module 1030 determines whether to clean the target cleaning area again based on the ground dirt density, including:

[0271] If the ground dirt density is greater than a preset dirt density threshold, the target cleaning area will be cleaned again.

[0272] The specific details of each module in the above-mentioned cleaning equipment have been described in detail in the decision-making and processing methods of the corresponding cleaning equipment, so they will not be repeated here.

[0273] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0274] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0275] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0276] This application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device.

[0277] Computer-readable storage media can be, for example—but not limited to—electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0278] A computer-readable storage medium can be sent, propagated, or transmitted for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0279] A computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0280] Furthermore, this disclosure also provides an electronic device capable of implementing the above-described method.

[0281] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0282] The electronic device 1100 according to this embodiment of the present disclosure will now be described with reference to FIG11. The electronic device 1100 shown in FIG11 is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present disclosure.

[0283] As shown in Figure 11, the electronic device 1100 is presented in the form of a general-purpose computing device. The components of the electronic device 1100 may include, but are not limited to: at least one processing unit 1110, at least one storage unit 1120, a bus 1130 connecting different system components (including storage unit 1120 and processing unit 1110), and a display unit 1140.

[0284] The storage unit stores program code that can be executed by the processing unit 1110, causing the processing unit 1110 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 1110 can perform the following steps as shown in FIG1: Step S110, when performing the current round of cleaning process in the current cleaning task on the cleaning component, acquiring dirt data of the cleaning component corresponding to the current round of cleaning process; Step S120, determining the degree of dirtiness of the cleaning component based on the dirt data of the current round of cleaning process; Step S130, determining a cleaning effect quantification value of the cleaning component based on the difference between the dirt data of the current round of cleaning process and the dirt data of the previous round of cleaning process; Step S140, determining a subsequent cleaning strategy for the cleaning component based on at least one of the round number of the current round of cleaning process, the degree of dirtiness of the cleaning component, and the cleaning effect quantification value of the cleaning component.

[0285] Storage unit 1120 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 11201 and / or cache memory 11202, and may further include a read-only memory (ROM) 11203.

[0286] Storage unit 1120 may also include a program / utility 11204 having a set (at least one) of program modules 11205, such program modules 11205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0287] Bus 1130 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0288] Electronic device 1100 can also communicate with one or more external devices 1200 (e.g., keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with electronic device 1100, and / or any device that enables electronic device 1100 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 1150. Furthermore, electronic device 1100 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 1160. As shown, network adapter 1160 communicates with other modules of electronic device 1100 via bus 1130. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0289] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A method for determining a cleaning strategy for a cleaning device, wherein the cleaning device is provided with a cleaning element for mopping, the method comprising: obtaining dirt data of the cleaning element corresponding to a current round of cleaning in a current cleaning task performed by the cleaning element; determining a dirt level of the cleaning element based on the dirt data of the current round of cleaning; determining a cleaning effect quantification value of the cleaning element based on a difference between the dirt data of the current round of cleaning and dirt data of a previous round of cleaning; and determining the cleaning strategy for the cleaning element based on at least one of a round number of the current round of cleaning, the dirt level of the cleaning element, and the cleaning effect quantification value of the cleaning element. 2.The method of claim 1, wherein each round of cleaning comprises a water-out cleaning session; and the obtaining the dirt data of the cleaning element corresponding to the current round of cleaning comprises: obtaining the dirt data of the cleaning element corresponding to the water-out cleaning session in the current round of cleaning, and determining the dirt data corresponding to the current round of cleaning based on the dirt data of the water-out cleaning session. 3.The method of claim 2, wherein the water-out cleaning session comprises a plurality of consecutive preset time periods, and each of the preset time periods comprises a plurality of consecutive unit time periods; and the obtaining the dirt data of the cleaning element corresponding to the water-out cleaning session in the current round of cleaning comprises: obtaining a plurality of first dirt detection values of the cleaning element in a first preset time period after entering the water-out cleaning session, and determining a first fluctuation condition representation value corresponding to the plurality of first dirt detection values; and in response to determining that the first fluctuation condition representation value is less than or equal to a first fluctuation threshold, determining the dirt data corresponding to the water-out cleaning session based on an average value of the plurality of first dirt detection values. 4.The method of claim 3, wherein the determining the first fluctuation condition representation value corresponding to the plurality of first dirt detection values comprises: obtaining a maximum value and a minimum value of the plurality of first dirt detection values, and determining the first fluctuation condition representation value based on a difference between the maximum value and the minimum value; or obtaining a variance or a standard deviation of the plurality of first dirt detection values, and determining the first fluctuation condition representation value based on the variance or the standard deviation. 5.The method of claim 3, further comprising: in response to determining that the dirt data is not within a preset normal value range, recording a first abnormal event of the current round of cleaning; wherein the first abnormal event is used to represent that the dirt data of the current round of cleaning is an abnormal value. 6.The method of claim 3, further comprising: in response to determining that the first fluctuation condition representation value is greater than the first fluctuation threshold, obtaining a plurality of second dirt detection values of the cleaning element in a second preset time period, and determining a second fluctuation condition representation value corresponding to the plurality of second dirt detection values; and ​ ​ ​ ​ ​ ​ ​ ​ 4. The method of claim 3, wherein, ​ ​ ​ ​ ​ ​ ​ ​ ​ In response to determining that the second fluctuation condition characteristic value is greater than the first fluctuation threshold value, obtaining fluctuation condition characteristic values corresponding to a plurality of dirt detection values in a next preset time period until the next preset time period is the last preset time period, and in response to determining that the last fluctuation condition characteristic value corresponding to the last preset time period is greater than the first fluctuation threshold value, recording that a second abnormal event occurs in the current round of cleaning process; Wherein, the second abnormal event is used to represent that the current round of cleaning process has a dirtying jittering abnormality.

7. The method of claim 2, wherein each round of cleaning process further comprises a water draining link, the water draining link being located after the water outlet cleaning link, and the method further comprises: obtaining reference dirtying data of the cleaning piece corresponding to the water draining link of the current round of cleaning process; and identifying an abnormal event in the current round of cleaning process according to the reference dirtying data, or according to the reference dirtying data in combination with the dirtying data of the water outlet cleaning link.

8. The method of claim 7, wherein, The obtaining of the reference dirtying data of the cleaning piece corresponding to the water draining link of the current round of cleaning process comprises: after entering the water draining link of the current round of cleaning process, obtaining a plurality of third dirtying detection values of the cleaning piece within a specified time period; determining the reference dirtying data according to a difference between a maximum value and a minimum value in the plurality of third dirtying detection values; or, determining the reference dirtying data according to a variance or a standard deviation of the plurality of third dirtying detection values.

9. The method of claim 7, wherein, The dirtying data comprises a first dimension value and a second dimension value; The identification of an abnormal event in the current round of cleaning process according to the reference dirtying data, or according to the reference dirtying data in combination with the dirtying data, comprises: in response to determining that the reference dirtying data is greater than or equal to a first preset threshold value, determining that no abnormal event is identified; in response to determining that the reference dirtying data is less than the first preset threshold value, and the first dimension value of the dirtying data is greater than or equal to a second preset threshold value, and the second dimension value is greater than or equal to a third preset threshold value, recording that a third abnormal event occurs in the current round of cleaning process; the third abnormal event is used to represent that the cleaning piece has an abnormality; and in response to determining that the reference dirtying data is less than the first preset threshold value, and the first dimension value of the dirtying data is less than the second preset threshold value or the second dimension value is less than the third preset threshold value, recording that a fourth abnormal event occurs in the current round of cleaning process; the fourth abnormal event is used to represent that the dirtying has a wall-hanging abnormality.

10. The method of any preceding claim, wherein, The determination of a subsequent cleaning strategy for the cleaning piece according to at least one of the round number of the current round of cleaning process, the dirtying degree of the cleaning piece, and the cleaning effect quantification value of the cleaning piece comprises: in response to determining that the round number of the current round of cleaning process is less than a specified round number, determining to perform a next round of cleaning process on the cleaning piece; and In response to determining that the round number of the current round of cleaning procedure is greater than or equal to the specified round number, it is determined whether to perform a next round of cleaning procedure on the cleaning article according to the dirtiness level of the cleaning article and the cleaning effect quantification value of the cleaning article.

11. The method of claim 10, wherein, The determining whether to perform a next round of cleaning procedure on the cleaning article according to the dirtiness level of the cleaning article and the cleaning effect quantification value of the cleaning article comprises: In response to determining that the cleaning effect quantification value is greater than or equal to a preset quantification threshold value, or the dirtiness level is greater than or equal to a preset dirtiness level threshold value, or the number of data loss events recorded for the current round of cleaning procedure is less than a preset number threshold value, it is determined to perform a next round of cleaning procedure on the cleaning article.

12. The method of claim 11, further comprising: In response to determining that the maximum limit number of rounds is reached, a next round of cleaning procedure is not performed on the cleaning article; The maximum limit number of rounds is determined according to a device type of the cleaning device; In response to determining that the maximum limit number of rounds is not reached, the dirtiness level and the cleaning effect quantification value of the cleaning article corresponding to the next round of cleaning procedure are obtained; and At least one of the round number of the next round of cleaning procedure, the dirtiness level of the cleaning article corresponding to the next round of cleaning procedure, and the cleaning effect quantification value of the cleaning article corresponding to the next round of cleaning procedure is used to determine whether to perform a further next round of cleaning procedure on the cleaning article.

13. The method of claim 11, wherein, The determining whether to perform a next round of cleaning procedure on the cleaning article according to the dirtiness level of the cleaning article and the cleaning effect quantification value of the cleaning article further comprises: In response to determining that the cleaning effect quantification value is less than the preset quantification threshold value and the dirtiness level is less than the preset dirtiness level threshold value, a next round of cleaning procedure is not performed on the cleaning article.

14. The method of any preceding claim, wherein, The dirtiness level of the cleaning article is determined according to a difference between the dirtiness data of the current round of cleaning procedure and preset reference dirtiness data, and the method further comprises: After the current cleaning task is completed, target dirtiness data corresponding to a last round of cleaning procedure of the current cleaning task is obtained; and Whether to update the preset reference dirtiness data is determined according to the target dirtiness data.

15. The method of claim 14, wherein, The determining whether to update the preset reference dirtiness data according to the target dirtiness data comprises: In response to determining that the target dirtiness data is within a preset normal value range and a difference between the target dirtiness data and the preset reference dirtiness data is less than a preset difference threshold value, the preset reference dirtiness data is updated using the target dirtiness data.

16. The method of claim 15, wherein, The determining whether to update the preset reference dirtiness data according to the target dirtiness data further comprises: In response to determining that the target dirtiness data is within the preset normal value range and the difference between the target dirtiness data and the preset reference dirtiness data is greater than or equal to the preset difference threshold value, a pending update event and target dirtiness data associated with the pending update event are recorded; and In response to determining that the pending update events occur continuously for a specified number of times, updating the preset reference dirty data according to a specified number of target dirty data associated with the specified number of pending update events.

17. The method of claim 16, wherein, The determining whether to update the preset reference dirty data according to the target dirty data further includes: In response to determining that the target dirty data is not within a preset normal value range, not updating the preset initial reference value.

18. The method of any one of the preceding claims, further comprising: obtaining dirty data of a specified round cleaning process in a current cleaning task; determining a ground dirtiness level of a target cleaning area according to dirty data of a last round cleaning process in a previous cleaning task and dirty data of the specified round cleaning process in the current cleaning task; the target cleaning area is an area cleaned during an interval from completion of the last round cleaning process to start of the specified round cleaning process; and and determining whether to re-clean the cleaning area according to the ground dirtiness level.

19. The method of claim 18, wherein, The determining whether to re-clean the cleaning area according to the ground dirtiness level includes: obtaining an area size of the target cleaning area; determining a ground dirtiness density according to a ratio of the ground dirtiness level and the area size; and determining whether to re-clean the target cleaning area according to the ground dirtiness density.

20. The method of claim 19, wherein, The determining whether to re-clean the target cleaning area according to the ground dirtiness density includes: in response to determining that the ground dirtiness density is greater than a preset dirtiness density threshold, re-cleaning the target cleaning area.

21. A cleaning apparatus wherein, The cleaning device is provided with a cleaning element for mopping, and the cleaning device includes: a data obtaining module configured to obtain dirty data of the cleaning element corresponding to a current round cleaning process in a current cleaning task when the cleaning element performs the current round cleaning process in the current cleaning task; a data processing module configured to determine a dirtiness level of the cleaning element according to the dirty data of the current round cleaning process; determine a cleaning effect quantization value of the cleaning element according to a difference between the dirty data of the current round cleaning process and dirty data of a previous round cleaning process; and a decision module configured to determine a subsequent cleaning strategy for the cleaning element according to at least one of a round number of the current round cleaning process, the dirtiness level of the cleaning element, and the cleaning effect quantization value of the cleaning element.

22. A computer storage medium having stored thereon a computer program, characterized in that The computer program, when executed by a processor, implements the decision processing method of the cleaning device of any one of claims 1 to 20.

23. An electronic device, comprising: a processor; and a memory configured to store executable instructions of the processor; wherein the processor is configured to implement the decision processing method of the cleaning device of any one of claims 1 to 20 via execution of the executable instructions.

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