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-making processing method for cleaning equipment, cleaning equipment, storage medium, and equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure claims priority to Chinese patent application No. 202410383021.X filed on March 29, 2024, which is incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure relates to the field of smart home technology, and in particular to a decision-making processing method for cleaning equipment, cleaning equipment, computer storage medium, and electronic equipment. Background Art

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

[0005] The cleaning equipment in the related art can only perform the cleaning task of the cleaning parts according to a set fixed process, and its intelligence level is relatively low.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this disclosure. Summary of the Invention

[0007] The purpose of the present disclosure is to provide a decision-making processing method for cleaning equipment, cleaning equipment, computer storage medium and electronic equipment, thereby at least to a certain extent overcoming the technical problem of low intelligence caused by the limitations of related technologies.

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

[0009] According to a first aspect of the present disclosure, a decision-making processing method for a cleaning device is provided, wherein the cleaning device is provided with a cleaning member for mopping a floor, the method comprising:

[0010] When executing a current cleaning process in a current cleaning task on the cleaning element, obtaining dirt data of the cleaning element corresponding to the current cleaning process;

[0011] determining the degree of contamination of the cleaning element according to the contamination data of the current cleaning process;

[0012] Determining a quantitative value of the cleaning effect of the cleaning element according to a difference between the dirt data of the current cleaning process and the dirt data of the previous cleaning process; and

[0013] A subsequent cleaning strategy for the cleaning element is determined according to at least one of the round number of the current cleaning process, the degree of dirtiness of the cleaning element, and the quantitative value of the cleaning effect of the cleaning element.

[0014] In an exemplary embodiment of the present disclosure, each round of cleaning process includes a water outlet cleaning step;

[0015] The obtaining of dirt data of the cleaning component corresponding to the current cleaning process includes:

[0016] Obtain dirt data of the cleaning member corresponding to a water outlet cleaning link in the current cleaning process, and determine dirt data corresponding to the current cleaning process based on the dirt data of the water outlet cleaning link.

[0017] In an exemplary embodiment of the present disclosure, the water outlet cleaning step includes a plurality of consecutive preset time periods, and each of the preset time periods includes a plurality of consecutive unit time periods;

[0018] The obtaining of dirt data of the cleaning component corresponding to the water outlet cleaning step of the current cleaning process includes:

[0019] After entering the water outlet cleaning phase of the current cleaning process, obtaining a plurality of first dirt detection values ​​of the cleaning element within a first preset time period, and determining first fluctuation condition representation values ​​corresponding to the plurality of first dirt detection values;

[0020] In response to determining that the first fluctuation condition characterization value is less than or equal to a first fluctuation threshold, the contamination data corresponding to the water outlet cleaning link is determined based on an average value of the multiple first contamination detection values.

[0021] In an exemplary embodiment of the present disclosure, determining the first fluctuation condition characterization values ​​corresponding to the plurality of first dirt detection values ​​includes:

[0022] Obtaining a maximum value and a minimum value among the plurality of first dirt detection values, and determining the first fluctuation condition characterization value according to a difference between the maximum value and the minimum value;

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

[0024] In an exemplary embodiment of the present disclosure, the method further includes:

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

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

[0027] In an exemplary embodiment of the present disclosure, the method further includes:

[0028] In response to determining that the first fluctuation condition characterization value is greater than the first fluctuation threshold, obtaining a plurality of second dirt detection values ​​of the cleaning member within a second preset time period, and determining second fluctuation condition characterization values ​​corresponding to the plurality of second dirt detection values;

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

[0030] The second abnormal event is used to indicate that the current round of cleaning process has a dirt and jitter abnormality.

[0031] In an exemplary embodiment of the present disclosure, each round of cleaning process further includes a water pumping step, which is located after the water outlet cleaning step. The method further includes:

[0032] Obtaining reference dirt data of the cleaning element corresponding to the pumping step of the current cleaning process; and

[0033] Abnormal events in the current round of cleaning process are identified based on the reference dirt data, or based on the reference dirt data combined with the dirt data of the water outlet cleaning link.

[0034] In an exemplary embodiment of the present disclosure, obtaining reference dirt data of the cleaning component corresponding to the water pumping step of the current cleaning process includes:

[0035] After entering the water pumping phase of the current cleaning process, obtaining a plurality of third dirt detection values ​​of the cleaning element within a specified time period;

[0036] determining the reference soiling data according to a difference between a maximum value and a minimum value among the plurality of third soiling detection values;

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

[0038] In an exemplary embodiment of the present disclosure, the dirt data includes a first dimension value and a second dimension value;

[0039] The identifying of abnormal events in the current round of cleaning process according to the reference contamination data, or according to the reference contamination data combined with the contamination data, 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 has been identified;

[0041] In response to determining that 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, recording that a third abnormal event has occurred in the current cleaning process; the third abnormal event is used to indicate that an abnormality has occurred in the cleaning element;

[0042] In response to determining that 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, the fourth abnormal event occurring in the current round of cleaning process is recorded; the fourth abnormal event is used to characterize the abnormality of dirt sticking to the wall.

[0043] In an exemplary embodiment of the present disclosure, determining a subsequent cleaning strategy for the cleaning element based on at least one of the round number of the current cleaning process, the degree of dirtiness of the cleaning element, and the quantitative value of the cleaning effect of the cleaning element includes:

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

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

[0046] In an exemplary embodiment of the present disclosure, determining whether to perform the next round of cleaning process on the cleaning member according to the degree of dirtiness of the cleaning member and the quantified value of the cleaning effect of the cleaning member includes:

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

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

[0049] In response to determining that the maximum limit number of rounds has been reached, not performing the next round of cleaning process on the cleaning element; the maximum limit number of rounds is determined according to the type of the cleaning device;

[0050] In response to determining that the maximum limit number of rounds has not been reached, obtaining a quantitative value of the degree of dirtiness and cleaning effect of the cleaning member corresponding to the next round of cleaning process; and

[0051] Whether to perform the next round of cleaning process on the cleaning element is determined according to the round number of the next round of cleaning process, at least one of the degree of dirtiness of the cleaning element corresponding to the next round of cleaning process and the quantitative value of the cleaning effect.

[0052] In an exemplary embodiment of the present disclosure, determining whether to perform the next round of cleaning process on the cleaning member according to the degree of dirtiness of the cleaning member and the quantified value of the cleaning effect of the cleaning member further includes:

[0053] In response to determining that the cleaning effect quantification value is less than the preset quantification threshold and the dirtiness is less than the preset dirtiness threshold, the next round of cleaning process is not performed on the cleaning element.

[0054] In an exemplary embodiment of the present disclosure, the degree of contamination of the cleaning element is determined based on the difference between the contamination data of the current cleaning process and a preset reference contamination data, and the method further includes:

[0055] After the current cleaning task is completed, obtaining target dirt data corresponding to the last round of cleaning process of the current cleaning task; and

[0056] Determining whether to update the preset baseline soiling data is determined according to the target soiling data.

[0057] In an exemplary embodiment of the present disclosure, determining whether to update the preset baseline soiling data according to the target soiling data includes:

[0058] In response to determining that the target contamination data is within a preset normal value range and a difference between the target contamination data and the preset baseline contamination data is less than a preset difference threshold, the preset baseline contamination data is updated using the target contamination data.

[0059] In an exemplary embodiment of the present disclosure, the determining whether to update the preset baseline soiling data according to the target soiling data further includes:

[0060] 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 baseline dirtiness data is greater than or equal to the preset difference threshold, recording a pending update event and the 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, the preset baseline soiling data is updated according to a specified number of target soiling data associated with the specified number of pending update events.

[0062] In an exemplary embodiment of the present disclosure, the determining whether to update the preset baseline soiling data according to the target soiling data further includes:

[0063] In response to determining that the target dirt data is not within the preset normal value range, the preset initial reference value is not updated.

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

[0065] Get the dirt data of the specified cleaning process in the current cleaning task;

[0066] Determining the degree of ground contamination in a target cleaning area based on the contamination data of the last cleaning process in the previous cleaning task and the contamination data of the designated cleaning process in the current cleaning task; the target cleaning area is the area cleaned during the interval between the completion of the last cleaning process and the start of the designated cleaning process; and

[0067] According to the dirtiness of the ground, it is determined whether to clean the cleaning area again.

[0068] In an exemplary embodiment of the present disclosure, determining whether to clean the cleaning area again according to the degree of dirtiness of the floor includes:

[0069] Obtaining the area of ​​the target cleaning area;

[0070] Determining the ground dirt density according to the ratio of the ground dirtiness to the area of ​​the region; and

[0071] According to the dirt density of the ground, it is determined whether to clean the target cleaning area again.

[0072] In an exemplary embodiment of the present disclosure, determining whether to re-clean the target cleaning area according to the floor dirt density includes:

[0073] In response to determining that the floor dirt density is greater than a preset dirt density threshold, the target cleaning area is cleaned again.

[0074] According to a second aspect of the present disclosure, there is provided a cleaning device, the cleaning device being provided with a cleaning member for mopping a floor, the cleaning device comprising:

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

[0076] a data processing module, configured to determine the degree of contamination of the cleaning element based on the contamination data of the current cleaning process; and determine a quantitative value of the cleaning effect of the cleaning element based on the difference between the contamination data of the current cleaning process and the contamination data of the previous cleaning process; and

[0077] A decision module is configured to determine a subsequent cleaning strategy for the cleaning element according to at least one of a round number of the current cleaning process, a degree of dirtiness of the cleaning element, and a quantitative value of a cleaning effect of the cleaning element.

[0078] According to a third aspect of the present disclosure, a computer storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the decision-making processing method for the cleaning device described in the first aspect is implemented.

[0079] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the decision-making processing method of the cleaning device described in the first aspect above by executing the executable instructions.

[0080] As can be seen from the above technical solutions, the decision-making processing method for cleaning equipment, cleaning equipment, computer storage medium, and electronic equipment in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:

[0081] In the technical solutions provided by some embodiments of the present disclosure, on the one hand, by obtaining the dirtiness data of the cleaning parts corresponding to the current cleaning process in the current cleaning task, and determining the dirtiness of the cleaning parts based on the dirtiness data of the current cleaning process, the dirtiness of the cleaning parts can be obtained in real time, making it easier to decide on an effective cleaning strategy for the cleaning parts based on the dirtiness. Furthermore, based on the difference between the dirtiness data of the current cleaning process and the dirtiness data of the previous cleaning process, a quantitative value of the cleaning effect of the cleaning parts is determined, which can intuitively quantify the cleaning effect, thereby facilitating the subsequent decision-making of an effective cleaning strategy for the cleaning parts based on the quantitative value. On the other hand, the subsequent cleaning strategy for the cleaning parts is determined based on the round number of the current cleaning process, the degree of dirtiness of the cleaning parts, and at least one of the quantitative values ​​of the cleaning effect of the cleaning parts. This can comprehensively consider multiple factors to more accurately decide on subsequent cleaning strategies suitable for a variety of different scenarios, thereby ensuring that the cleaning parts are effectively cleaned in extremely dirty environments, thereby ensuring the cleanliness of extremely dirty environments, and reducing unnecessary cleaning processes for cleaning parts in generally dirty environments, thereby extending the service life of the cleaning parts, improving the working efficiency of the cleaning equipment, and enabling the cleaning equipment to perform cleaning tasks more accurately and efficiently.

[0082] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0084] FIG1 is a schematic flow chart showing a decision-making processing method for a cleaning device according to an embodiment of the present disclosure;

[0085] FIG2 is a schematic diagram showing a process of obtaining dirt data of a cleaning component corresponding to a water outlet cleaning step in a current cleaning process in an embodiment of the present disclosure;

[0086] FIG3 is a schematic diagram showing a process flow of how to identify abnormal events in the current round of cleaning process in an embodiment of the present disclosure;

[0087] FIG4 is a flow chart showing how to identify abnormal events in the current cleaning process based on reference contamination data, or by combining the reference contamination data with contamination data of the water outlet cleaning process, in an embodiment of the present disclosure;

[0088] FIG5 is a flowchart illustrating how to determine a subsequent cleaning strategy for a cleaning element based on at least one of the round number of the current cleaning process, the degree of dirtiness of the cleaning element, and the quantified value of the cleaning effect of the cleaning element in an embodiment of the present disclosure;

[0089] FIG6 is a schematic diagram showing changes in N dirt data corresponding to N cleaning processes included in executing a cleaning task in an embodiment of the present disclosure;

[0090] FIG7 is a schematic diagram showing a process of determining whether to update preset baseline dirt data in an embodiment of the present disclosure;

[0091] FIG8 is a schematic diagram showing a flow chart of how to decide whether to perform a region re-drag in an embodiment of the present disclosure;

[0092] FIG9 is a flow chart showing how to determine whether to re-clean a clean area according to the degree of dirtiness of the floor in an embodiment of the present disclosure;

[0093] FIG10 is a schematic structural diagram of a cleaning device in an exemplary embodiment of the present disclosure;

[0094] FIG. 11 is a schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0095] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0096] The terms "a", "an", "the" and "said" are used in this specification to indicate the presence of one or more elements / components / etc.; the terms "including" and "having" are used to express open-ended inclusion and mean that additional elements / components / etc. may exist in addition to the listed elements / components / etc.; the terms "first" and "second" etc. are used only as labels and are not intended to limit the quantity of their objects.

[0097] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the drawings represent identical or similar parts, and thus repeated descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically separate entities.

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

[0099] First, the gear and water output setting for cleaning the mop are determined solely based on the cleaning effect of the mop (i.e., the difference in the degree of dirtiness of the water used in two consecutive washes). This solution has too simple a judgment condition and is inaccurate in some scenarios. For example, when mopping extremely dirty wet garbage such as soy sauce, the mop may still be very dirty, but less water output and a lower gear may be used, resulting in an incomplete cleaning of the mop.

[0100] Second, there is no abnormality recognition mechanism. Therefore, when an abnormality occurs, the wrong detection value will still be used for calculation and processing. This solution may lead to some extreme cases, such as using only a small amount of water to clean a very dirty mop.

[0101] Third, the dirtiness of the floor is determined based on the cleaning effect of the mop to determine whether re-mopping is necessary. In this solution, the cleaning effect of the mop may not represent the dirtiness of the floor, resulting in inaccurate re-mopping decisions.

[0102] Based on the above problems, the present disclosure proposes a decision-making and processing method for cleaning equipment, which can use green light and infrared light dirt detection sensors to provide a reasonable abnormality recognition mechanism, cleaning strategies for cleaning parts and decisions on whether to re-mop, and proposes a method for determining the ground dirt threshold through a big data model, so that the self-cleaning sweeping robot can perform cleaning and mopping tasks of cleaning parts more accurately and efficiently.

[0103] In the embodiments of the present disclosure, a decision-making processing method for a cleaning device is first provided, which at least to some extent overcomes the defect of low intelligence level in related technologies.

[0104] FIG1 is a flow chart showing a decision-making processing method for a cleaning device according to an embodiment of the present disclosure. The execution subject of the decision-making processing method for a cleaning device may be a cleaning device.

[0105] 1 , a decision-making method for a cleaning device according to an embodiment of the present disclosure includes the following steps:

[0106] Step S110, when executing the current cleaning process in the current cleaning task on the cleaning element, obtaining dirt data of the cleaning element corresponding to the current cleaning process;

[0107] Step S120, determining the degree of contamination of the cleaning element based on the contamination data of the current cleaning process;

[0108] Step S130, determining a quantitative value of the cleaning effect of the cleaning element according to the difference between the dirt data of the current cleaning process and the dirt data of the previous cleaning process;

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

[0110] In the technical solution provided by the embodiment shown in FIG1 , on the one hand, when executing the current cleaning process in the current cleaning task on a cleaning component, the dirtiness data corresponding to the current cleaning process is obtained for the cleaning component, and the degree of dirtiness of the cleaning component is determined based on the dirtiness data of the current cleaning process. This allows the degree of dirtiness of the cleaning component to be obtained in real time, facilitating the decision-making of an effective cleaning strategy for the cleaning component based on the dirtiness. Furthermore, based on the difference between the dirtiness data of the current cleaning process and the dirtiness data of the previous cleaning process, a quantitative value of the cleaning effect of the cleaning component is determined, enabling intuitive quantification of the cleaning effect, thereby facilitating the subsequent decision-making of an effective cleaning strategy for the cleaning component based on the quantitative value. On the other hand, the subsequent cleaning strategy for the cleaning parts is determined based on the round number of the current cleaning process, the degree of dirtiness of the cleaning parts, and at least one of the quantitative values ​​of the cleaning effect of the cleaning parts. This can comprehensively consider multiple factors to more accurately decide on subsequent cleaning strategies suitable for a variety of different scenarios, thereby ensuring that the cleaning parts are effectively cleaned in extremely dirty environments, thereby ensuring the cleanliness of extremely dirty environments, and reducing unnecessary cleaning processes for cleaning parts in generally dirty environments, thereby extending the service life of the cleaning parts, improving the working efficiency of the cleaning equipment, and enabling the cleaning equipment to perform cleaning tasks more accurately and efficiently.

[0111] The following is a detailed description of the specific implementation process of each step in Figure 1:

[0112] Prior to step S110, it should be noted that the cleaning device in the present disclosure can be a self-propelled device with a mopping function, such as a mopping robot or a sweeping and mopping robot. Green light and infrared light pollution sensors are installed at the sewage pipe inlet of the cleaning device. These green light and infrared light pollution sensors can be used to obtain pollution detection values.

[0113] The above-mentioned cleaning equipment is provided with a cleaning part for mopping the floor, such as a mop or other flexible sponge that can be used to wipe the floor, etc., which can be set according to actual conditions, and the present disclosure does not make any special restrictions on this.

[0114] The cleaning device also includes a "scrubbing and scraping" self-cleaning base station with a cleaning brush module. After receiving each cleaning task, the self-cleaning base station can use a dual-light detection method (i.e., green light detection and infrared light detection) to detect sewage in the suction pipe / drain pipe, for example, once every 50ms (this can be set according to actual conditions and is not specifically limited in this disclosure). The self-cleaning base station can continuously record the green light / infrared light detection values ​​and transmit them to the cleaning device, so that the cleaning device can obtain the relevant dirt detection values.

[0115] In addition, each cleaning task in the present disclosure may include: N rounds of cleaning process + scraping process (i.e. scraping without water). If the cleaning equipment is equipped with a water tank base station, the value range of the above N is: 3≤N≤6. If the cleaning equipment is equipped with an automatic water supply and drainage base station, the value range of the above N is: 3≤N≤8. The specific value of N is decided by the cleaning equipment based on the actual situation when executing each round of cleaning process. Different N values ​​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 outlet cleaning link and a water pumping link. Specifically, the water outlet cleaning link can include: water outlet scraping + dry scraping + water outlet scraping + dry scraping; wherein, water outlet scraping means scraping and washing while water is outlet, that is, when water flows out of the cleaning brush module, the scraping brush will work at the same time to remove stains on the cloth; dry scraping means scraping and washing without water outlet. This step is usually used to remove the remaining water on the cloth and the stains that are difficult to clean. Through the cleaning process set in the present disclosure, it can be ensured that the mop can be washed away by water for most of the stains during the cleaning process, and can also be deeply cleaned by the scraping brush. Finally, the dryness of the module is ensured by pumping out water to avoid the adverse effects of residual moisture on the mop or other components.

[0117] 1 , in step S110 , when a current cleaning process in a current cleaning task is executed on a cleaning member, dirt data of the cleaning member corresponding to the current cleaning process is acquired.

[0118] In this step, contamination data of the cleaning components during the water-outlet cleaning phase of the current cleaning process (which may be referred to as the i-th cleaning process) may be obtained. Based on the contamination data of the water-outlet cleaning phase, the contamination data of the cleaning components corresponding to the current cleaning process may be determined. For example, the contamination data of the water-outlet cleaning phase may be determined as the contamination data of the cleaning components corresponding to the current cleaning process.

[0119] Exemplarily, the above-mentioned water outlet cleaning step can be the stage from 7 seconds after the end of the water pumping step of the previous cleaning process to the beginning of the water pumping step of the current cleaning process.

[0120] It should be noted that the water washing step may include multiple consecutive preset time periods, each of which may include multiple consecutive unit time periods. For example, the preset time period may be 3 seconds, and the unit time period may be 1 second.

[0121] Exemplarily, assuming that the timestamp of the end of the pumping phase of the previous round of cleaning process is t0, and the above-mentioned water outlet cleaning phase includes a total of 3 consecutive preset time periods, then exemplarily, the above-mentioned multiple consecutive preset time periods can be expressed as: [t0+7s, t0+10s], [t0+10s, t0+13s], [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 FIG2 , FIG2 shows a flow chart of how to obtain dirt data of a cleaning component corresponding to the water cleaning step in the current round of cleaning process in an embodiment of the present disclosure, including steps S201 to S202:

[0123] In step S201, after entering the water outlet cleaning phase of the current cleaning process, a plurality of first dirt detection values ​​of the cleaning element within a first preset time period are obtained, and first fluctuation condition characterization values ​​corresponding to the plurality of first dirt detection values ​​are determined.

[0124] In this step, after entering the water outlet cleaning phase of the current cleaning process, multiple first dirt detection values ​​of the cleaning element within the first preset time period can be obtained. Given that the present disclosure is provided with a green light / infrared light sensor, the dirt detection values ​​involved in this disclosure all include detection values ​​of two dimensions: the first dimension is the green light detection value, and the second dimension is the infrared light detection value.

[0125] After obtaining multiple first dirt detection values ​​of the cleaning component within the first preset time period, in an optional embodiment, the maximum and minimum values ​​of the above multiple first dirt detection values ​​can be obtained, and the first fluctuation situation characterization value can be determined based on the difference between the above maximum and minimum values. In view of the fact that the first dirt detection value includes the green light detection value and the infrared light detection value, the first fluctuation situation characterization value also includes characterization values ​​of two dimensions (for example, for the convenience of description, the fluctuation situation characterization value corresponding to the green light dimension can be recorded as data1, and the fluctuation situation characterization value corresponding to the infrared light dimension can be recorded as data2). Specifically, the maximum and minimum values ​​of the multiple green light detection values ​​can be obtained, and the above data1 can be determined based on the difference thereof, and the maximum and minimum values ​​of the multiple infrared light detection values ​​can be obtained, and the above data2 can be determined based on the difference thereof.

[0126] In a second optional embodiment, the variance or standard deviation of multiple first dirt detection values ​​can be obtained. Based on the calculated variance / standard deviation, a first fluctuation condition characterizing value is determined. For example, the calculated variance or standard deviation can be determined as the first fluctuation condition characterizing value. Given that the first dirt detection value includes green light detection values ​​and infrared light detection values, the variances or standard deviations corresponding to the multiple green light detection values ​​can be obtained, and the above-mentioned data1 can be determined based on their variances or standard deviations. Furthermore, the variances or standard deviations of multiple infrared light detection values ​​can be obtained, and the above-mentioned data2 can be determined based on their variances or standard deviations.

[0127] In step S202, when the first fluctuation condition representative value is less than or equal to the first fluctuation threshold, contamination data corresponding to the water outlet cleaning phase is determined based on an average value of the plurality of first contamination detection values. For example, the average value of the plurality of first contamination detection values ​​may be determined as the contamination data corresponding to the water outlet cleaning phase.

[0128] In this step, corresponding to the first dirt detection value and the first fluctuation condition representation value, the first fluctuation threshold in this disclosure also includes two thresholds: a threshold W corresponding to green light and a threshold w corresponding to infrared light. Thus, the first fluctuation threshold (also known as the non-pumping jitter threshold) can be expressed as W and w.

[0129] After obtaining the first fluctuation condition characterization value, the fluctuation condition characterization value can be compared with the first fluctuation threshold. If the comparison result is that the first fluctuation condition characterization value is less than or equal to the first fluctuation threshold (i.e., data1 is less than or equal to W, and data2 is less than or equal to w), then the average value of the plurality of first dirt detection values ​​can be calculated (i.e., the average value of the plurality of green light detection values ​​B is calculated). i , and calculate the average value b of multiple infrared light detection values i ), obtain the dirt data of the cleaning parts corresponding to the water cleaning link (ie, the dirt data B in the green light dimension) i , dirt data b in infrared light dimension i , which can also be called stable detection data).

[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 is also possible to determine 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 iIf the dirt data is not within the above-preset normal value range, a first abnormal event may be recorded as occurring in the current cleaning process. The first abnormal event is used to indicate that the dirt data is an abnormal value. For example, the first abnormal event may be recorded as: dirt detection abnormality - stability detection data abnormality.

[0131] After step S202, if the comparison result is that the first fluctuation condition characterization value is greater than the first fluctuation threshold, the first preset time period may be shifted back by 3 seconds to obtain multiple second dirt detection values ​​of the cleaning component within the second preset time period, and the second fluctuation condition characterization value corresponding to the multiple second dirt detection values ​​may be determined. If the second fluctuation condition characterization value is less than or equal to the first fluctuation threshold, the average value of the multiple second dirt detection values ​​may be directly calculated to obtain the dirt data of the cleaning component corresponding to the water outlet cleaning phase. If the second fluctuation condition characterization value is still greater than the first fluctuation threshold, the second preset time period may be shifted back by 3 seconds, and then the fluctuation condition characterization values ​​corresponding to the multiple dirt detection values ​​within the next preset time period may be obtained, until the fluctuation condition characterization values ​​corresponding to the multiple dirt detection values ​​within the next preset time period obtained are less than the first fluctuation threshold to determine the dirt data of the cleaning component corresponding to the water outlet cleaning phase.

[0132] If the next preset time period obtained is the last preset time period, and the corresponding last fluctuation characteristic value is still greater than the first fluctuation threshold, a second abnormal event may be recorded as occurring in the current cleaning process. The second abnormal event is used to indicate abnormal dirt jitter. For example, the second abnormal event may be recorded as: dirt detection abnormality - dirt jitter abnormality (for example, dirt is abnormally adhered and cannot be cleaned).

[0133] Each cleaning process in this disclosure also includes a pumping step after the water-outlet cleaning step. Based on this pumping step, this disclosure also provides a process for identifying abnormal events. Specifically, referring to Figure 3, Figure 3 shows a schematic flow diagram of how to identify abnormal events in the current cleaning process in an embodiment of this disclosure, including steps S301-S302:

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

[0135] In this step, illustratively, the water pumping phase may refer to the phase from 1 second after the current round of cleaning process enters the water pumping phase to before the water pumping phase of the current round of cleaning process ends.

[0136] After entering the pumping phase of the current cleaning process, multiple third dirt detection values ​​of the cleaning element within a specified time period can be obtained. For example, the specified time period can be 3 seconds. Therefore, assuming that the timestamp of the pumping phase of the current cleaning process is t1, the specified time period can be expressed as: [t1+1s, t1+4s].

[0137] As explained above, the third dirt detection value still includes detection values ​​for two dimensions: green light detection value and infrared light detection value. Therefore, the reference dirt data determined based on the third dirt detection value also includes 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] Exemplarily, the reference dirtiness data may be determined based on the following implementations:

[0139] In an optional embodiment, the maximum value and minimum value among multiple green light detection values ​​can be obtained, and the above-mentioned data3 can be determined based on the difference between the maximum value and the minimum value; and the maximum value and minimum value among multiple infrared light detection values ​​can be obtained, and the above-mentioned data4 can be determined based on the difference between the maximum value and the minimum value.

[0140] In another optional embodiment, the variance or standard deviation of multiple green light detection values ​​can be obtained, and the above-mentioned data3 can be determined based on the calculated variance or standard deviation; and the variance or standard deviation of multiple infrared light detection values ​​can be obtained, and the above-mentioned data4 can be determined based on the calculated variance or standard deviation.

[0141] In step S302, abnormal events in the current round of cleaning process are identified based on the reference dirt data, or the reference dirt data combined with the dirt data of the water outlet cleaning link.

[0142] In this step, referring to FIG4 , FIG4 shows a flow chart of how to identify abnormal events in the current cleaning process based on reference dirt data, or by combining the reference dirt data with dirt data from the water cleaning process, in an embodiment of the present disclosure, including steps S401 to S403:

[0143] In step S401 , if the reference dirtiness 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 may include a water extraction determination threshold K corresponding to the green light dimension and a water extraction determination threshold k corresponding to the infrared light dimension. Thus, if data3 corresponding to the green light dimension in the reference contamination data is found to be greater than or equal to the water extraction determination threshold K, and data4 corresponding to the infrared light dimension is greater than or equal to the water extraction determination threshold k, it can be determined that no abnormal event has been identified.

[0145] In step S402, 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, record that the third abnormal event occurs in the current round of cleaning process.

[0146] In this step, if the value of any dimension in the above-mentioned reference dirt data is less than the above-mentioned first preset threshold value, that is, (data3 is less than K, or data4 is less than k), and the dirt data corresponding to the green light dimension in the water washing 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 washing link is greater than or equal to the third preset threshold value e, then it can be recorded that a third abnormal event has occurred in the current round of cleaning process. The third abnormal event is used to characterize the abnormality of the above-mentioned cleaning parts. Exemplarily, the third abnormal event can be recorded as: dirt detection abnormality-no water washing cloth abnormality (for example: the washing cloth is not placed normally, the washing cloth is missing or fails to work normally, etc.).

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

[0148] In this step, if the value of any dimension in the reference dirt data is less than the first preset threshold (i.e., data3 is less than K, or data4 is less than k), and the dirt data corresponding to the green light dimension during the water-outlet cleaning phase is less than the second preset threshold E, and the dirt data corresponding to the infrared light dimension during the water-outlet cleaning phase is less than the third preset threshold e, a fourth abnormal event can be recorded as occurring during the current cleaning process. This fourth abnormal event indicates that dirt has adhered to the wall. Exemplarily, the fourth abnormal event can be recorded as: dirt detection abnormality - dirt adhered to the wall abnormality.

[0149] Furthermore, it should be noted that during each cleaning process, the present disclosure also records data loss events. A data loss event indicates that the dirt detection value reported by the green light / infrared light sensor is lost, resulting in the non-receipt of the dirt detection value. For example, the dirt detection value may be lost due to an abnormal network disconnection or other issues. In the event of a dirt detection value loss, a data loss event may be recorded.

[0150] Based on the above processing method, after the current cleaning process is completed, the present disclosure may record the following three types of information for the current cleaning process:

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

[0152] ② Various abnormal events that occur during the current cleaning process;

[0153] ③ The number of times the dirt detection value is not received in the current cleaning process (i.e., the number of times the dirt detection value is lost, or the number of times data loss events occur). If the number of losses is greater than 3, it can be determined that an abnormal dirt detection value loss occurs in the current cleaning process.

[0154] 1 , in step S120 , the degree of contamination of the cleaning element is determined based on the contamination data of the current cleaning process.

[0155] In this step, the degree of contamination of the cleaning element can be determined based on the contamination data of the current cleaning process. For example, the degree of contamination of the cleaning element can be determined based on the difference between the contamination data of the current cleaning process and a preset reference contamination data.

[0156] Among them, the initial fixed dirt data (which can be the clean water detection value) will be written into the device before leaving the factory, for example: A0=2200, a0=2650 (can be set according to actual conditions, and this disclosure does not make special restrictions on this). When restoring the factory settings, the device will be restored to the above-mentioned initial fixed dirt data.

[0157] After receiving each cleaning task, the device may receive baseline soiling data, such as A and a, transmitted by the base station. If such baseline soiling data is received, the initial fixed soiling data may be updated to the baseline soiling data A and a. If such baseline soiling data is not received, the initial fixed soiling data may be determined as the baseline soiling data A and a.

[0158] Therefore, given that the dirt data of the current cleaning process includes the dirt data B of the green light dimension i and infrared light dimension dirt data b i , the dirtiness also includes the dirtiness corresponding to the green light dimension (denoted as C i ) and the degree of dirtiness corresponding to the infrared light dimension (denoted as c i ). According to A and B i The difference between the above C i , according to a and b i The difference between the above c i .

[0159] In step S130 , a quantitative value of the cleaning effect of the cleaning element is determined according to 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 of this round of cleaning process can be expressed as B i 、b i ,. Assume that the dirt data of the previous cleaning process is represented by B i-1 、b i-1 , then the cleaning effect quantization value also includes the quantization value corresponding to the green light dimension (denoted as D i ) and the quantized value corresponding to the infrared light dimension (denoted as d i ). Thus, according to B i and B i-1 The difference between the two determines D i , according to b i and b i-1 The difference between them determines d i .

[0161] In step S140 , a subsequent cleaning strategy for the cleaning elements is determined according to at least one of the round number of the current cleaning process, the degree of dirtiness of the cleaning elements, and the quantitative value of the cleaning effect of the cleaning elements.

[0162] In this step, referring to FIG5 , FIG5 shows a flow chart of how to determine a subsequent cleaning strategy for a cleaning member based on at least one of the round number of the current cleaning process, the degree of dirtiness of the cleaning member, and the quantified value of the cleaning effect of the cleaning member in an embodiment of the present disclosure, including steps S501 and S502:

[0163] In step S501, if the round number of the current cleaning process is less than the specified round number, it is determined to perform the next cleaning process on the cleaning element.

[0164] In this step, the round number i of the current cleaning process can be compared with a specified round number (which can be a limited minimum cleaning round number, for example, 3). If the round number i is less than the specified round number, it can be determined to continue the next cleaning process on the cleaning element.

[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 element according to the degree of dirtiness of the cleaning element and the quantitative value of the cleaning effect of the cleaning element.

[0166] In this step, if the round number i of the current cleaning process is greater than or equal to the specified round number (ie 3), the cleaning process can be performed according to the degree of dirtiness of the cleaning piece (ie C i 、c i) and the quantification value of the cleaning effect of the cleaning component (i.e., the above D i , d i ), to determine whether to perform the next round of cleaning process on the above cleaning component. Specifically, there are the following situations:

[0167] ① If the quantification value of the cleaning effect is greater than or equal to the preset quantification threshold (indicating that the cleaning effect of the mop is obvious, i.e., D i ≥H, or, d i ≥h), or, the degree of dirtiness is greater than or equal to the preset dirtiness threshold (C i ≥Q, or, c i ≥q), or, the occurrence frequency of the data loss event recorded for the current cleaning process is less than the preset frequency threshold (for example: 3 times), then the next round of cleaning process can be continued for the cleaning component;

[0168] After the end of the next round of cleaning process, it can be judged whether the maximum limit number of rounds is reached (determined according to the equipment type of the cleaning device, 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 upper and lower water version is 8). If the maximum limit number of rounds is reached, there is no need to perform the next round of cleaning process on the cleaning component, and the process of scraping dry without water can be entered. If the above maximum limit number of rounds is not reached, the degree of dirtiness and the quantification value of the cleaning effect corresponding to the cleaning component for the next round of cleaning process can be obtained. According to at least one of the round number of the next round of cleaning process, the degree of dirtiness and the quantification value of the cleaning effect corresponding to the cleaning component for the next round of cleaning process, it is determined again whether to perform the next round of cleaning process on the cleaning component.

[0169] ② If the quantification value of the cleaning effect is less than the preset quantification threshold (i.e., D i <H, and, d i <h) and the degree of dirtiness is less than the preset dirtiness threshold (i.e., C i <Q, or, c i <q), there is no need to perform the next round of cleaning process on the cleaning component.

[0170] In addition, it should be noted that if it is determined that any abnormal event has been recorded in the current round of cleaning process (i.e., any one of the first to fourth abnormal events mentioned above), or the number of occurrences of the above data loss event is greater than the preset number threshold (e.g., 3), the dirt detection result corresponding to the current round of cleaning process (i.e., the determined quantitative value of the cleaning effect and the degree of dirtiness) can be discarded, and the above dirt detection result will no longer be used to determine whether to execute the next round of cleaning process. In this case, the numerical relationship between the round number i of this round of cleaning process and the target round (e.g., 5) can be determined. If i is less than or equal to 5, the total number of rounds of the cleaning process included in the current cleaning task can be set to the above target round 5, that is, the third gear is used when executing the current cleaning task. If i is greater than 5, it can be determined that this round of cleaning process is the last round of cleaning process of the current cleaning task, and after it ends, the non-water scraping 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 for the cleaning parts. The present disclosure can dynamically determine and adjust the cleaning strategy according to a variety of factors. In view of the fact that the total number of rounds of the cleaning process executed on the cleaning parts is different, the total amount of water output corresponding to the cleaning of the cleaning parts is also different. Therefore, the present disclosure can comprehensively consider a variety of factors and more accurately decide on subsequent cleaning strategies suitable for a variety of different scenarios. For extremely dirty environments (higher dirtiness levels), multiple rounds of cleaning processes and corresponding larger water outputs can be used to ensure that the cleaning parts are effectively cleaned, thereby ensuring the cleanliness of the extremely dirty environments. For generally dirty environments (lower dirtiness levels), fewer rounds of cleaning processes and corresponding smaller water outputs can be used to reduce unnecessary cleaning processes for the cleaning parts, extend the service life of the cleaning parts, save water resources, and improve the working efficiency of the cleaning equipment, so that the cleaning equipment can perform cleaning tasks more accurately and efficiently.

[0172] Referring to FIG6 , FIG6 shows a schematic diagram of changes in N dirt data corresponding to N cleaning processes included in executing a cleaning task in an embodiment of the present disclosure, as shown in FIG6 :

[0173] Each round of cleaning process includes the water cleaning phase and the water pumping phase. The dirt data obtained in the water cleaning phase of the first round of cleaning process can be expressed as B1, b1, the dirt data obtained in the water cleaning phase of the second round of cleaning process can be expressed as B2, b2, the dirt data obtained in the water cleaning phase of the third round of cleaning process can be expressed as B3, b3, ..., the dirt data obtained in the water cleaning phase of the Nth round of cleaning process can be expressed as B N 、b N As the number of cleaning cycles increases, the value of the dirty 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 occurs 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: it is possible to select from the above B N1 , B N2 , ……, BN10 Select the maximum value (for example: B N10 ), update A to B N10 , and, from the above b N1 , b N2 ,……,b N10 Select the maximum value (for example: b N1 ), update a to b N10 , in order to update the preset baseline dirt data.

[0181] It should be noted that if the pending update event does not occur repeatedly for the specified number of times, there is no need to update the preset baseline dirtiness data or reset the counter. If the pending update event does not occur repeatedly for the specified number of times, but the above situation ① occurs, the preset baseline dirtiness data can be updated based on the solution in situation ①. At the same time, the counter value is reset to the initial value, for example, 0.

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

[0183] In an optional embodiment, the present disclosure further provides a solution for determining whether to perform a region re-drag. Referring to FIG8 , FIG8 shows a flow chart of how to determine whether to perform a region re-drag in an embodiment of the present disclosure, including steps S801 to S803:

[0184] In step S801, the dirt data of the cleaning process of the designated wheel in the current cleaning task is obtained.

[0185] In this step, the dirt data of the designated cleaning process in the current cleaning task (assuming it is the tth time) can be obtained. For example, the designated cleaning process can be the dirt data of the second cleaning process (expressed as B 2t 、b 2t ), or it can be the dirt data of the first round of cleaning process, which can be set according to actual conditions, and this disclosure does not make any special restrictions on this.

[0186] In step S802 , the degree of dirtiness of the floor of the target cleaning area is determined based on the dirtiness data of the last cleaning process in the previous cleaning task and the dirtiness data of the specified cleaning process in the current cleaning task.

[0187] In this step, the dirt data of the last cleaning process of the last cleaning task (assuming it is the lth time) can be expressed as (B Nl 、b Nl ), thus, according to the above B Nl 、b Nl With B2t 、b 2t Determine the level of soiling 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 cleaning process and the start of the specified round of cleaning process (ie, the interval between two rounds of cleaning processes).

[0189] Specifically, we can calculate B Nl With B 2t The difference between the two, and the calculation of b Nl with b 2t The difference between the two values ​​is used to determine the degree of dirtiness of the ground in the cleaning area (B Nl -B 2t 、b Nl -b 2t Based on this difference, the amount of dirt accumulated on the floor between washes can be quantified.

[0190] In step S803, it is determined whether to clean the clean area again according to the degree of dirtiness of the ground.

[0191] In this step, referring to FIG9 , FIG9 shows a flow chart of how to determine whether to re-clean the clean area according to the degree of dirtiness of the ground in an embodiment of the present disclosure, including steps S901 to 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 may be obtained.

[0194] In step S902, the ground dirt density is determined according to the ratio of the ground dirtiness and the area of ​​the region.

[0195] In this step, the ground dirt density can be determined based on the ratio between the ground dirtiness and the area of ​​the region. The ground dirt density can reflect the amount of dirt per unit area. Specifically, the values ​​of the two dimensions included in the 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 to clean the target cleaning area again according to the dirt density of the ground.

[0197] In this step, it can be determined whether to clean the clean area again based on the dirt density of the ground. Specifically, the dirt density of the ground can be compared with the preset dirt density threshold (G, g). If the comparison result is: (B Nl -B 2t ) / S>G, or, (b Nl -b 2t ) / S>g, 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 types of abnormal events (i.e., any of the first to fourth abnormal events) is detected during the execution of the previous cleaning task or the current cleaning task, or the number of occurrences of the above-mentioned data loss event is greater than the preset threshold value (for example: 3), then the relevant dirty data of the above-mentioned two cleaning tasks may not be used to decide whether to re-drag, so as to avoid wrong decisions.

[0199] It should be noted that the threshold values ​​(E, e, H, h, K, k and W, w) involved in the present disclosure can be determined by user baseline tests, and the above-mentioned dirt density threshold values ​​G and g can be determined by user baseline tests combined with a big data ranking model. For example: by collecting the dirt density score values ​​of internal test users each time they clean the mop, a big data ranking model for dirt density values ​​is produced. According to the user survey, the probability of the user's floor dirt is determined to be x%, and the top x% of all users' dirt density values ​​are defined as floor dirt. Then the dirt density threshold values ​​G and g can be taken as the values ​​of the x% of all users' dirt density values.

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

[0201] First, it provides solutions for identifying various abnormal events, including abnormal events such as cloth washing without water, dirt sticking to the wall, dirt jitter, dirt data, and detection value loss.

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

[0203] Third, by determining the total number of cleaning rounds and the cloth washing gear based on the current cleaning round number, the degree of dirtiness of the cleaning parts, and the quantified cleaning effect of the cleaning parts, the accuracy of the decision can be improved, thereby improving the cleaning quality and cleaning efficiency of the cleaning equipment.

[0204] Fourth, the difference in mop dirtiness between the two rewashes is used to represent the floor dirtiness. The floor dirt density is calculated based on the mopping area between the two rewashes. This dirt density determines whether remopping is necessary. This allows for more accurate quantification of floor dirtiness, leading to more precise remopping decisions.

[0205] Fifth, by using a statistical user dirt density big data model to determine the dirt density threshold, the threshold can be determined more in line with the user's actual usage scenario, thereby improving the accuracy of repeated drag decisions.

[0206] The present disclosure also provides a cleaning device. FIG10 shows a schematic structural diagram of a cleaning device in an exemplary embodiment of the present disclosure. As shown in FIG10 , the cleaning device 1000 may include a data acquisition module 1010, a data processing module 1020, and a decision module 1030.

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

[0208] The data processing module 1020 is configured to determine the degree of contamination of the cleaning element based on the contamination data of the current cleaning process; and to determine a quantitative value of the cleaning effect of the cleaning element based on the difference between the contamination data of the current cleaning process and the contamination data of the previous cleaning process;

[0209] The decision module 1030 is configured to determine a subsequent cleaning strategy for the cleaning element according to at least one of the round number of the current cleaning process, the degree of dirtiness of the cleaning element, and the quantitative value of the cleaning effect of the cleaning element.

[0210] In an exemplary embodiment of the present disclosure, each round of cleaning process includes a water outlet cleaning step.

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

[0212] Obtaining dirt data of the cleaning element corresponding to the water-outlet cleaning phase of the current cleaning process, and determining dirt data corresponding to the current cleaning process based on the dirt data of the water-outlet cleaning phase. For example, the dirt data of the water-outlet cleaning phase can be determined as the dirt data corresponding to the current cleaning process.

[0213] In an exemplary embodiment of the present disclosure, the water outlet cleaning segment includes a plurality of consecutive preset time periods, and each of the preset time periods includes a plurality of consecutive unit time periods.

[0214] The data acquisition module 1010 acquires the dirtiness data of the cleaning component corresponding to the water outlet cleaning step of the current cleaning process, including:

[0215] After entering the water outlet cleaning phase of the current cleaning process, obtaining a plurality of first dirt detection values ​​of the cleaning element within a first preset time period, and determining first fluctuation condition representation values ​​corresponding to the plurality of first dirt detection values;

[0216] When the first fluctuation condition characterizing value is less than or equal to a first fluctuation threshold, the contamination data corresponding to the water outlet cleaning phase is determined based on an average value of the plurality of first contamination detection values. For example, the average value of the plurality of first contamination detection values ​​may be determined as the contamination data corresponding to the water outlet cleaning phase.

[0217] In an exemplary embodiment of the present disclosure, the data acquisition module 1010 determines the first fluctuation condition representation values ​​corresponding to the plurality of first dirt detection values, including:

[0218] Obtaining a maximum value and a minimum value among the plurality of first dirt detection values, and determining the first fluctuation condition characterization value according to a difference between the maximum value and the minimum value; or

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

[0220] In an exemplary embodiment of the present disclosure, after determining the contamination data corresponding to the water outlet cleaning step based on the average value of the plurality of first contamination detection values, the data processing module 1020 is configured to:

[0221] Determining whether the dirtiness data is within a preset normal value range;

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

[0223] The first abnormal event is used to indicate that the dirtiness data of the current cleaning process is an abnormal value.

[0224] In an exemplary embodiment of the present disclosure, after determining the first fluctuation characterization value of 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, obtaining a plurality of second dirt detection values ​​of the cleaning member within a second preset time period, and determining a second fluctuation condition characterization value corresponding to the plurality of second dirt detection values;

[0226] If the second fluctuation condition characterizing value is still greater than the first fluctuation threshold, then obtaining fluctuation condition characterizing values ​​corresponding to a plurality of dirt detection values ​​within a next preset time period until the next preset time period becomes a last preset time period, and in response to a last fluctuation condition characterizing value corresponding to the last preset time period still being greater than the first fluctuation threshold, recording that a second abnormal event has occurred in the current cleaning process;

[0227] The second abnormal event is used to indicate that the current round of cleaning process has a dirt and jitter abnormality.

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

[0229] Acquiring reference dirt data of the cleaning element corresponding to the water pumping step of the current cleaning process;

[0230] Abnormal events in the current round of cleaning process are identified based on the reference dirt data, or the reference dirt data combined with the dirt data of the water outlet cleaning link.

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

[0232] After entering the water pumping phase of the current cleaning process, obtaining a plurality of third dirt detection values ​​of the cleaning element within a specified time period;

[0233] determining the reference soiling data according to a difference between a maximum value and a minimum value among the plurality of third soiling detection values;

[0234] Alternatively, the reference dirt data is determined according to the variance / standard deviation of the plurality of third dirt detection values.

[0235] In an exemplary embodiment of the present 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 contamination data, or based on the reference contamination data combined with the contamination data, including:

[0237] If the reference dirtiness data is greater than or equal to a first preset threshold, determining that no abnormal event is 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 as occurring in the current cleaning process; the third abnormal event is used to indicate that an abnormality has occurred in the cleaning element;

[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, record the fourth abnormal event in the current cleaning process; the fourth abnormal event is used to characterize the abnormality of dirt sticking to the wall.

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

[0241] If the round number of the current cleaning process is less than the specified round number, determining to perform the next cleaning process on the cleaning element;

[0242] If the round number of the current cleaning process is greater than or equal to the designated round number, whether to perform the next cleaning process on the cleaning element is determined according to the degree of dirtiness of the cleaning element and the quantitative value of the cleaning effect of the cleaning element.

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

[0244] If the quantified value of the cleaning effect is greater than or equal to the preset quantified threshold, or the degree of dirtiness is greater than or equal to the preset degree of dirtiness threshold, or the number of data loss events recorded for the current round of cleaning process is less than the preset number threshold, it is determined to execute the next round of cleaning process on the cleaning part.

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

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

[0247] If the maximum number of rounds is reached, there is no need to perform the next round of cleaning process on the cleaning element; the maximum number of rounds is determined according to the type of the cleaning device;

[0248] If the maximum number of rounds is not reached, obtaining a quantitative value of the degree of contamination and cleaning effect of the cleaning element corresponding to the next round of cleaning process;

[0249] Whether to perform the next round of cleaning process on the cleaning element is determined according to the round number of the next round of cleaning process, at least one of the degree of dirtiness of the cleaning element corresponding to the next round of cleaning process and the quantitative value of the cleaning effect.

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

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

[0252] In an exemplary embodiment of the present disclosure, the degree of contamination of the cleaning element is determined based on the difference between the contamination data of the current cleaning process and the preset baseline contamination data. The decision module 1030 is configured to:

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

[0254] Determining whether to update the preset baseline soiling data is determined according to the target soiling data.

[0255] In an exemplary embodiment of the present disclosure, the decision module 1030 determines whether to update the preset baseline soiling data according to the target soiling data, including:

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

[0257] In an exemplary embodiment of the present disclosure, the decision module 1030 determines whether to update the preset baseline soiling data according to the target soiling data, and further includes:

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

[0259] When the pending update event occurs continuously for a specified number of times, the preset baseline soiling data is updated according to a specified number of target soiling data associated with the specified number of pending update events.

[0260] In an exemplary embodiment of the present disclosure, the decision module 1030 determines whether to update the preset baseline soiling data according to the target soiling data, and further includes:

[0261] If the target dirtiness data is not within the preset normal value range, the preset initial reference value is not updated.

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

[0263] Get the dirt data corresponding to the specified cleaning process in the current cleaning task;

[0264] Determining the degree of ground contamination in a target cleaning area based on the contamination data corresponding to the last cleaning process in the previous cleaning task and the contamination data corresponding to the designated cleaning process in the current cleaning task; the target cleaning area is the area cleaned during the interval between the completion of the last cleaning process and the start of the designated cleaning process;

[0265] According to the dirtiness of the ground, it is determined whether to clean the cleaning area again.

[0266] In an exemplary embodiment of the present disclosure, the decision module 1030 determines whether to clean the cleaning area again according to the degree of dirtiness of the floor, including:

[0267] Obtaining the area of ​​the target cleaning area;

[0268] Determining the ground dirt density according to the ratio of the ground dirtiness to the area of ​​the region;

[0269] According to the dirt density of the ground, it is determined whether to clean the target cleaning area again.

[0270] In an exemplary embodiment of the present disclosure, the decision module 1030 determines whether to re-clean the target cleaning area according to the floor dirt density, including:

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

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

[0273] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0274] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0275] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0276] The present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being 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, devices, or any combination thereof. More specific examples of computer-readable storage media can include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present 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, device, or device.

[0278] Computer-readable storage media can transmit, propagate, or transfer programs for use by or in conjunction with an instruction execution system, apparatus, or device. Program code contained on a computer-readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wireline, optical cable, RF, or any suitable combination thereof.

[0279] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiments.

[0280] In addition, an electronic device capable of implementing the above method is also provided in an embodiment of the present disclosure.

[0281] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods, or program products. Therefore, various aspects of the present disclosure may be implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "systems."

[0282] The electronic device 1100 according to this embodiment of the present disclosure is described below with reference to Figure 11. The electronic device 1100 shown in Figure 11 is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0283] As shown in FIG11 , electronic device 1100 is implemented as a general-purpose computing device. Components of electronic device 1100 may include, but are not limited to, the aforementioned at least one processing unit 1110, the aforementioned at least one storage unit 1120, a bus 1130 connecting various system components (including storage unit 1120 and processing unit 1110), and a display unit 1140.

[0284] The storage unit stores a program code, and the program code can be executed by the processing unit 1110, so that the processing unit 1110 performs the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the processing unit 1110 can perform as shown in Figure 1: Step S110, when executing the current round of cleaning process in the current cleaning task on the cleaning component, obtain the dirtiness data of the cleaning component corresponding to the current round of cleaning process; Step S120, determine the degree of dirtiness of the cleaning component based on the dirtiness data of the current round of cleaning process; Step S130, determine the quantitative value of the cleaning effect of the cleaning component based on the difference between the dirtiness data of the current round of cleaning process and the dirtiness data of the previous round of cleaning process; Step S140, determine the 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 quantitative value of the cleaning effect of the cleaning component.

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

[0286] The 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: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0287] The bus 1130 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0288] The electronic device 1100 can also communicate with one or more external devices 1200 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 1100, and / or any device that enables the electronic device 1100 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 1150. Furthermore, the electronic device 1100 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 1160. As shown, the network adapter 1160 communicates with other modules of the electronic device 1100 via a bus 1130. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the 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 the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

Claims

1. A method for determining a cleaning device, wherein the cleaning device is provided with a cleaning element for mopping the floor, the method comprising: When executing a current cleaning process in a current cleaning task on the cleaning element, obtaining dirt data of the cleaning element corresponding to the current cleaning process; determining the degree of contamination of the cleaning element according to the contamination data of the current cleaning process; determining a quantitative value of a cleaning effect of the cleaning element according to a difference between the dirtiness data of the current cleaning process and the dirtiness data of the previous cleaning process; as well as A subsequent cleaning strategy for the cleaning element is determined according to at least one of the round number of the current cleaning process, the degree of dirtiness of the cleaning element, and the quantitative value of the cleaning effect of the cleaning element.

2. The method according to claim 1, wherein each round of cleaning process includes a water cleaning step; The obtaining of dirt data of the cleaning component corresponding to the current cleaning process includes: Obtain dirt data of the cleaning member corresponding to a water outlet cleaning link in the current cleaning process, and determine dirt data corresponding to the current cleaning process based on the dirt data of the water outlet cleaning link.

3. The method according to claim 2, wherein the water washing step comprises a plurality of consecutive preset time periods, and each of the preset time periods comprises a plurality of consecutive unit time periods; The obtaining of dirt data of the cleaning component corresponding to the water outlet cleaning step of the current cleaning process includes: After entering the water outlet cleaning phase of the current cleaning process, obtaining a plurality of first dirt detection values ​​of the cleaning element within a first preset time period, and determining first fluctuation condition representation values ​​corresponding to the plurality of first dirt detection values; as well as In response to determining that the first fluctuation condition characterization value is less than or equal to a first fluctuation threshold, the contamination data corresponding to the water outlet cleaning link is determined based on an average value of the multiple first contamination detection values.

4. The method according to claim 3, wherein: The determining of first fluctuation condition representation values ​​corresponding to the plurality of first dirt detection values ​​includes: Obtaining a maximum value and a minimum value among the plurality of first dirt detection values, and determining the first fluctuation condition characterization value according to a difference between the maximum value and the minimum value; Alternatively, the variance or standard deviation of the plurality of first dirt detection values ​​is obtained, and the first fluctuation condition characterization value is determined based on the variance or standard deviation.

5. The method according to claim 3, further comprising: In response to determining that the dirtiness data is not within a preset normal value range, recording that a first abnormal event occurs in the current round of cleaning process; The first abnormal event is used to indicate that the dirtiness data of the current cleaning process is an abnormal value.

6. The method according to claim 3, further comprising: In response to determining that the first fluctuation condition characterization value is greater than the first fluctuation threshold, obtaining a plurality of second dirt detection values ​​of the cleaning member within a second preset time period, and determining second fluctuation condition characterization values ​​corresponding to the plurality of second dirt detection values; as well as In response to determining that the second fluctuation condition characterizing value is greater than the first fluctuation threshold, obtaining fluctuation condition characterizing values ​​corresponding to a plurality of dirt detection values ​​within a next preset time period until the next preset time period is a last preset time period, and in response to determining that a last fluctuation condition characterizing value corresponding to the last preset time period is greater than the first fluctuation threshold, recording that a second abnormal event has occurred in the current cleaning process; The second abnormal event is used to indicate that the current round of cleaning process has a dirt and jitter abnormality.

7. The method according to claim 2, wherein each round of cleaning process further comprises a water pumping step, wherein the water pumping step is located after the water outlet cleaning step, and the method further comprises: Acquiring reference dirt data of the cleaning element corresponding to the water pumping step of the current cleaning process; as well as Abnormal events in the current round of cleaning process are identified based on the reference dirt data, or based on the reference dirt data combined with the dirt data of the water outlet cleaning link.

8. The method according to claim 7, wherein: The obtaining of reference dirt data of the cleaning component corresponding to the water pumping step of the current cleaning process includes: After entering the water pumping phase of the current cleaning process, obtaining a plurality of third dirt detection values ​​of the cleaning element within a specified time period; determining the reference soiling data according to a difference between a maximum value and a minimum value among the plurality of third soiling detection values; Alternatively, the reference dirt data is determined according to the variance or standard deviation of the plurality of third dirt detection values.

9. The method according to claim 7, wherein: The dirt data includes a first dimension value and a second dimension value; The identifying of abnormal events in the current round of cleaning process according to the reference contamination data, or according to the reference contamination data combined with the contamination data, includes: In response to determining that the reference dirtiness data is greater than or equal to a first preset threshold, determining that no abnormal event has been identified; In response to determining that 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, recording that a third abnormal event occurs in the current cleaning process; the third abnormal event is used to indicate that an abnormality occurs in the cleaning element; and In response to determining that 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, the fourth abnormal event occurring in the current round of cleaning process is recorded; the fourth abnormal event is used to characterize the abnormality of dirt sticking to the wall.

10. A method according to any preceding claim, wherein: The determining of a subsequent cleaning strategy for the cleaning element according to at least one of the round number of the current cleaning process, the degree of dirtiness of the cleaning element, and the quantitative value of the cleaning effect of the cleaning element includes: In response to determining that the round number of the current cleaning process is less than the specified round number, determining to perform the next cleaning process on the cleaning member; and In response to determining that the round number of the current cleaning process is greater than or equal to the specified round number, determining whether to perform the next cleaning process on the cleaning element according to the degree of dirtiness of the cleaning element and the quantitative value of the cleaning effect of the cleaning element.

11. The method according to claim 10, wherein: The determining whether to perform the next round of cleaning process on the cleaning member according to the degree of dirtiness of the cleaning member and the quantified value of the cleaning effect of the cleaning member includes: In response to determining that the quantified value of the cleaning effect is greater than or equal to a preset quantified threshold, or that the degree of dirtiness is greater than or equal to a preset degree of dirtiness threshold, or that the number of occurrences of data loss events recorded for the current round of cleaning process is less than a preset number threshold, it is determined to perform the next round of cleaning process on the cleaning part.

12. The method according to claim 11, further comprising: In response to determining that the maximum limit of rounds is reached, not performing the next round of cleaning process on the cleaning element; The maximum number of rounds is determined according to the type of the cleaning device; In response to determining that the maximum limit number of rounds has not been reached, obtaining a quantitative value of the degree of contamination and cleaning effect of the cleaning element corresponding to the next round of cleaning process; as well as Whether to perform the next round of cleaning process on the cleaning element is determined according to the round number of the next round of cleaning process, at least one of the degree of dirtiness of the cleaning element corresponding to the next round of cleaning process and the quantitative value of the cleaning effect.

13. The method according to claim 11, wherein The determining whether to perform the next round of cleaning process on the cleaning member according to the degree of dirtiness of the cleaning member and the quantified value of the cleaning effect of the cleaning member further includes: In response to determining that the cleaning effect quantification value is less than the preset quantification threshold and the dirtiness is less than the preset dirtiness threshold, the next round of cleaning process is not performed on the cleaning element.

14. A method according to any preceding claim, wherein: The degree of contamination of the cleaning element is determined based on a difference between contamination data of the current cleaning process and preset reference contamination data, and the method further includes: After the current cleaning task is completed, obtaining target dirt data corresponding to the last round of cleaning process of the current cleaning task; and Determining whether to update the preset baseline soiling data is determined according to the target soiling data.

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

16. The method according to claim 15, wherein The determining whether to update the preset baseline soiling data according to the target soiling data further includes: 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 baseline dirtiness data is greater than or equal to the preset difference threshold, recording a pending update event and the target dirtiness data associated with the pending update event; and In response to determining that the pending update event occurs continuously for a specified number of times, the preset baseline soiling data is updated according to a specified number of target soiling data associated with the specified number of pending update events.

17. The method according to claim 16, wherein The determining whether to update the preset baseline soiling data according to the target soiling data further includes: In response to determining that the target dirt data is not within the preset normal value range, the preset initial reference value is not updated.

18. The method according to any preceding claim, further comprising: Get the dirt data of the specified cleaning process in the current cleaning task; Determining the degree of ground contamination in a target cleaning area based on the contamination data of the last cleaning process in the previous cleaning task and the contamination data of the designated cleaning process in the current cleaning task; the target cleaning area is the area cleaned during the interval between the completion of the last cleaning process and the start of the designated cleaning process; as well as According to the dirtiness of the ground, it is determined whether to clean the cleaning area again.

19. The method according to claim 18, wherein The determining whether to clean the cleaning area again according to the degree of dirtiness of the ground includes: Obtaining the area of ​​the target cleaning area; Determining the ground dirt density according to the ratio of the ground dirtiness to the area of ​​the region; and According to the dirt density of the ground, it is determined whether to clean the target cleaning area again.

20. The method according to claim 19, wherein The determining whether to re-clean the target cleaning area according to the floor dirt density includes: In response to determining that the floor dirt density is greater than a preset dirt density threshold, the target cleaning area is cleaned again.

21. A cleaning device, wherein: The cleaning device is provided with a cleaning member for mopping the floor, and the cleaning device comprises: a data acquisition module, configured to acquire dirt data of the cleaning member corresponding to the current cleaning process when the current cleaning process in the current cleaning task is performed on the cleaning member; a data processing module, configured to determine the degree of contamination of the cleaning element based on the contamination data of the current cleaning process; and determine a quantitative value of the cleaning effect of the cleaning element based on the difference between the contamination data of the current cleaning process and the contamination data of the previous cleaning process; and A decision module is configured to determine a subsequent cleaning strategy for the cleaning element according to at least one of a round number of the current cleaning process, a degree of dirtiness of the cleaning element, and a quantitative value of a cleaning effect of the cleaning element.

22. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the decision-making processing method for a cleaning device according to any one of claims 1 to 20 is implemented.

23. An electronic device comprising: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to execute the decision-making processing method for a cleaning device according to any one of claims 1 to 20 by executing the executable instructions.

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