Dishwasher and control method for a dishwasher

Abstract

The application relates to the technical field of dishwashers, in particular to a dishwasher and a control method of the dishwasher. The control method of the dishwasher comprises the following steps: acquiring a plurality of turbidity offsets corresponding to a plurality of target sampling points of a turbidity sensor, wherein the plurality of turbidity offsets are offsets between a plurality of turbidity values collected at the plurality of target sampling points and a turbidity baseline value; determining a first turbidity change parameter and a second turbidity change parameter according to the plurality of turbidity offsets, wherein the first turbidity change parameter is used for representing fluctuation intensity and confusion degree of the turbidity value in a pre-washing stage, and the second turbidity value change parameter is used for representing change viscosity of the turbidity value in the pre-washing stage; and determining a target dirt type of tableware according to the first turbidity change parameter and the second turbidity change parameter. According to the application, the dishwasher can analyze change characteristics of a turbidity signal in a pre-washing stage by using an existing turbidity sensor, so as to distinguish the dirt type corresponding to the tableware.

Description

Technical Field

[0001] This application relates to the field of dishwasher technology, and more particularly to a dishwasher and a method for controlling a dishwasher. Background Technology

[0002] Currently, dishwashers are typically equipped with turbidity sensors to detect the turbidity of the wash water, indirectly assessing the amount of dirt on the dishes and adjusting the wash time or number of water intakes accordingly. In addition, some high-end models are also incorporating visual sensors to identify the material and arrangement of the dishes.

[0003] However, existing turbidity sensors can only provide overall water turbidity and cannot analyze the specific composition of dirt. Visual sensors primarily serve for tableware identification and loading detection, but they struggle to penetrate water flow and foam to accurately determine the type of dirt on tableware. Therefore, dishwashers cannot determine the specific type of dirt during the washing process. Because different types of dirt have different optimal washing temperatures, durations, and program logic, dishwashers cannot effectively wash tableware according to the specific type of dirt. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, this application provides a dishwasher and a method for controlling the dishwasher.

[0005] This application provides a dishwasher, comprising: a housing with an installation cavity inside; an inner tub disposed within the installation cavity, the inner tub having a washing chamber inside; a turbidity sensor disposed in a circulation path connected to the washing chamber, the turbidity sensor being used to collect turbidity values ​​of the circulation path; a washing assembly disposed within the installation cavity, the washing assembly being used to wash tableware within the washing chamber; and a controller connected to the turbidity sensor and the washing assembly, the controller being configured to: acquire multiple turbidity offsets of the turbidity sensor at multiple target sampling points, wherein the multiple target sampling points are the washing assembly... The tableware is sampled at multiple turbidity sampling points during the pre-wash stage. Multiple turbidity offsets are the offsets between multiple turbidity values ​​collected at multiple target sampling points and the turbidity baseline value. The turbidity baseline value is used to characterize the average value of multiple turbidity values. A first turbidity variation parameter and a second turbidity variation parameter are determined based on the multiple turbidity offsets. The first turbidity variation parameter is used to characterize the fluctuation intensity and disorder of turbidity values ​​during the pre-wash stage, and the second turbidity variation parameter is used to characterize the viscosity of turbidity value changes during the pre-wash stage. The target dirt type of the tableware is determined based on the first turbidity variation parameter and the second turbidity variation parameter.

[0006] This application also provides a control method for a dishwasher, applicable to any of the dishwashers described above. The control method includes: acquiring multiple turbidity offsets corresponding to multiple target sampling points of a turbidity sensor, wherein the multiple target sampling points are turbidity sampling points during the pre-wash stage of the washing assembly, and the multiple turbidity offsets are offsets between multiple turbidity values ​​collected at the multiple target sampling points and a turbidity baseline value, where the turbidity baseline value is the average value used to characterize the multiple turbidity values; determining a first turbidity change parameter and a second turbidity change parameter based on the multiple turbidity offsets, wherein the first turbidity change parameter is used to characterize the fluctuation intensity and disorder of the turbidity value during the pre-wash stage, and the second turbidity change parameter is used to characterize the viscosity of the change in the turbidity value during the pre-wash stage; and determining the target soiling type of the tableware based on the first turbidity change parameter and the second turbidity change parameter.

[0007] The dishwasher and its control method provided in this application acquire multiple turbidity values ​​collected by a turbidity sensor during the pre-wash stage, and determine multiple corresponding turbidity offsets based on these values. These offsets characterize the degree of turbidity value variation at multiple target sampling points. Then, a first turbidity change parameter characterizing the fluctuation intensity and disorder of turbidity changes, and a second turbidity change parameter characterizing the viscosity of turbidity changes, are determined using these offsets. By analyzing the first and second turbidity change parameters, the characteristics of the physical changes in the washing liquid caused by dirt on the tableware can be determined, thereby identifying the target dirt type on the tableware in the washing chamber. This allows the dishwasher to analyze the variation characteristics of the turbidity signal using only the existing turbidity sensor during the pre-wash stage, thereby distinguishing the corresponding dirt type on the tableware. Based on the identified dirt type, the dishwasher can then adjust its operating parameters in a targeted manner during subsequent washing stages, achieving targeted washing of different types of dirt and improving cleaning effect and energy efficiency. Attached Figure Description

[0008] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This application provides a schematic diagram of the structure of one of the embodiments of a dishwasher. Figure 2The present application provides a structural block diagram of a dishwasher according to some embodiments; Figure 3 The diagram shows a schematic representation of the controller in some embodiments of this application; Figure 4 The following are turbidity variation curves of oily dirt provided in some embodiments of this application; Figure 5 The following are turbidity variation curves of starchy contaminants provided in some embodiments of this application; Figure 6 The following are turbidity change curves of protein-based contaminants provided in some embodiments of this application; Figure 7 A schematic diagram of a dirt feature plane provided in some embodiments of this application is shown; Figure 8 A schematic diagram of the display interface of a dishwasher display panel provided in some embodiments of this application is shown; Figure 9 Flowcharts of dishwasher control methods in some embodiments of this application are shown; Figure 10 A structural block diagram of a dishwasher control device according to some embodiments of this application is shown.

[0011] Among them, 100 is the dishwasher, 110 is the housing, 111 is the cabinet, 112 is the door, 120 is the inner liner, 121 is the washing chamber, 130 is the turbidity sensor, 140 is the washing assembly, 150 is the controller, 151 is the processor, 152 is the memory, 153 is the transceiver, and 154 is the bus. Detailed Implementation

[0012] To make the objectives and implementation methods of this application clearer, the exemplary implementation methods of this application will be clearly and completely described below with reference to the accompanying drawings of the exemplary embodiments of this application. Obviously, the exemplary embodiments described are only some embodiments of this application, and not all embodiments.

[0013] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0014] The terms "first," "second," "third," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar or related objects or entities, and do not necessarily imply a specific order or sequence, unless otherwise specified. It should be understood that such terms are interchangeable where appropriate.

[0015] The terms “comprising” and “having”, and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device that includes a range of components is not necessarily limited to all of the components that are clearly listed, but may include other components that are not clearly listed or that are inherent to such product or device.

[0016] Dishwashers in related technologies are typically equipped with turbidity sensors and vision sensors. Turbidity sensors can only report the overall turbidity of the water and cannot analyze the specific composition of the dirt. Vision sensors primarily serve for tableware identification and loading detection, but they struggle to penetrate water flow and foam to accurately determine the type of dirt on the tableware. Therefore, dishwashers in these technologies can only automatically control the washing process based on the material and degree of dirt on the tableware, making it difficult to set washing parameters for different types of dirt, thus hindering targeted washing of tableware with different types of dirt.

[0017] To solve the above-mentioned technical problems, this application combines Figures 1 to 10 A dishwasher and its control method are proposed.

[0018] Figure 1 The present application provides a schematic diagram of the structure of a dishwasher according to some embodiments. Figure 2 The present application provides a structural block diagram of a dishwasher according to some embodiments, such as... Figure 1 and Figure 2 As shown, some embodiments of this application provide a dishwasher 100, including: a housing 110, an inner tub 120, a turbidity sensor 130, a washing assembly 140, and a controller 150.

[0019] The housing 110 has an installation cavity inside; the inner liner 120 is disposed in the installation cavity, and the inner liner 120 has a washing chamber 121 inside; the turbidity sensor 130 is disposed in the circulation path, which is connected to the washing chamber 121, and the turbidity sensor 130 is used to collect the turbidity value of the circulation path; the washing assembly 140 is disposed in the installation cavity, and the washing assembly 140 is used to wash the tableware in the washing chamber 121; the controller 150 is connected to the turbidity sensor 130 and the washing assembly 140.

[0020] In this embodiment, the dishwasher 100 includes a housing 110, with an installation cavity formed inside the housing 110. An inner tub 120 is disposed within the installation cavity, and the internal space of the inner tub 120 forms a washing chamber 121 for accommodating tableware to be washed. The housing 110 and the inner tub 120 together constitute the main structure and washing space of the dishwasher 100.

[0021] The housing 110 includes a cabinet 111 and a door 112. The door 112 is connected to the cabinet 111 via hinges and can be opened and closed manually or electrically. When the door 112 of the dishwasher 100 is open, the washing chamber 121 is in the open state, at which time the user can take and put dishes in the washing chamber 121; when the door 112 of the dishwasher 100 is closed, the washing chamber 121 is in the closed state, at which time the washing assembly 140 can wash the dishes in the washing chamber 121.

[0022] The circulation path is connected to the washing chamber 121, allowing the washing water to circulate. A turbidity sensor 130 is installed on the circulation path, which can detect the turbidity of the water flowing through it in real time and output the corresponding turbidity value.

[0023] The washing assembly 140 is disposed within the installation cavity. The washing assembly 140 performs a series of actions, including water intake into the washing cavity 121, heating, circulating spraying, and drainage, thereby completing the washing of the tableware. The controller 150 is electrically connected to the turbidity sensor 130 and the washing assembly 140. The controller 150 receives signals from the turbidity sensor 130, processes and makes decisions according to a preset program, and controls the washing assembly 140 to perform corresponding actions.

[0024] Specifically, the washing assembly 140 includes a water pump, a spray arm, a detergent dispensing mechanism, and a heater. The water pump is located in the circulation path and drives the washing water to circulate within the system. The spray arm is connected to the water pump and sprays the pressurized water onto the tableware surface in a jet form. The detergent dispensing mechanism dispenses detergent into the washing chamber 121. The heater heats the washing liquid in the washing chamber 121 to improve the washing effect. The circulation path connects the washing chamber 121, the water pump, the filter, and other components, enabling the washing liquid to be circulated and flow through key detection points. The turbidity sensor 130 is directly installed in the circulation path, typically located near the water pump outlet or in a section of the water flow where the flow is stable.

[0025] In this embodiment, the controller 150 is connected to the turbidity sensor 130 and the washing assembly 140. The controller 150 can acquire the turbidity value collected by the turbidity sensor 130. The controller 150 can also control the operating parameters of the washing assembly 140, including but not limited to: washing temperature, spray flow rate, etc.

[0026] Figure 3 The following are schematic diagrams of the controller structure in some embodiments of this application, such as... Figure 3 As shown, the controller 150 includes a processor 151 and a memory 152 storing computer program instructions.

[0027] Specifically, the processor 151 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this disclosure.

[0028] Memory 152 may include a large-capacity storage for information or instructions. For example, and not limitingly, memory 152 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 152 may include removable or non-removable (or fixed) media. Where appropriate, memory 152 may be internal or external to the integrated gateway device. In a particular embodiment, memory 152 is a non-volatile solid-state memory. In a particular embodiment, memory 152 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0029] The processor 151 reads and executes computer program instructions stored in the memory 152 to perform the steps of the dishwasher control method provided in this embodiment of the present disclosure.

[0030] In one example, the controller 150 may also include a transceiver 153 and a bus 154. Wherein, as... Figure 3 As shown, the processor 151, memory 152 and transceiver 153 are connected via bus 154 and communicate with each other.

[0031] Bus 154 includes peripheral component interconnect (PCI) lines or extended industry standard architecture (EISA) buses. Bus 154 can be divided into address bus, data bus, control bus, etc.

[0032] The following explanation uses the controller 150 executing the control method as an example to illustrate the operation process of the dishwasher 100: The controller 150 is configured to: acquire multiple turbidity offsets corresponding to multiple target sampling points of the turbidity sensor 130, wherein the multiple target sampling points are turbidity sampling points of the washing assembly 140 during the pre-wash stage, the multiple turbidity offsets are the offsets between multiple turbidity values ​​collected at the multiple target sampling points and the turbidity baseline value, and the turbidity baseline value is used to characterize the average value of the multiple turbidity values; determine a first turbidity change parameter and a second turbidity change parameter based on the multiple turbidity offsets, wherein the first turbidity change parameter is used to characterize the fluctuation intensity and disorder of the turbidity value during the pre-wash stage, and the second turbidity change parameter is used to characterize the viscosity of the change of the turbidity value during the pre-wash stage; and determine the target soiling type of the tableware based on the first turbidity change parameter and the second turbidity change parameter.

[0033] In this embodiment, the target sampling point is a specific time point during the pre-washing stage, which can be set to be collected at certain time intervals. Therefore, the interval between two adjacent target sampling points is the same.

[0034] The turbidity offset is the difference between the turbidity value collected at the target sampling point and the turbidity baseline value, used to characterize the degree of deviation of the turbidity value at that target sampling point. The turbidity baseline value is a dynamically calculated reference value used to characterize the average level of multiple turbidity values ​​over a period of time.

[0035] In this embodiment, the pre-wash stage is the operating stage of the washing assembly 140 before the main wash stage. The pre-wash stage is mainly used to rinse away most of the loose stains on the tableware. Since the detergent is usually added during the main wash stage, when the washing assembly 140 is operating in the pre-wash stage, the change in turbidity value collected by the turbidity sensor 130 is directly related to the physical properties of the dirt when it is rinsed. The physical properties include the form and degree of dissolution of the dirt when it is rinsed, and the physical properties of the dirt when it is rinsed can reflect the type of dirt on the tableware to a certain extent.

[0036] In this embodiment, during the pre-wash stage, the controller 150 drives the turbidity sensor 130 to sample at a fixed frequency, obtaining a series of turbidity values, each corresponding to a target sampling point. Then, the controller dynamically calculates the average turbidity value within a recent sampling window and uses this average as the current turbidity baseline value. This turbidity baseline value characterizes the average turbidity value, thereby offsetting the interference and influence caused by the zero-point drift of the turbidity sensor 130 itself. Next, the controller 150 calculates the difference between the turbidity value at the target sampling point and the turbidity baseline value, thus obtaining the calibrated turbidity offset. This turbidity offset characterizes the degree of change in turbidity value at each target sampling point, reflecting the dynamic change in turbidity caused by dirt on the tableware entering the water flow during rinsing. By analyzing this dynamic change, the physical properties of the dirt when rinsed can be determined, thereby identifying the type of dirt on the tableware.

[0037] Specifically, the expression for the turbidity shift (1) is as follows: S(t) = R(t) - B(t); (1) Where S(t) is the turbidity offset, R(t) is the turbidity value, and B(t) is the turbidity baseline value.

[0038] In this embodiment, after acquiring multiple turbidity offsets, the controller 150 constructs a turbidity offset sequence, in which the turbidity offsets are arranged in a predetermined temporal order. Then, based on the turbidity offset sequence, a first turbidity change parameter and a second turbidity change parameter are determined. The first turbidity change parameter characterizes the intensity and degree of temporal fluctuation of the turbidity value, thus reflecting the degree of chaos in the impact of dirt flushing on water quality. The second turbidity change parameter characterizes the viscosity of turbidity value changes, that is, the inertia of turbidity value changes.

[0039] For example, when grease is rinsed, because grease is not easily dissolved, it tends to form an unstable emulsion. Its turbidity curve exhibits high-frequency, large-amplitude fluctuations, leading to large and irregular jumps in turbidity values; that is, both the first and second turbidity change parameters are high. When starch is rinsed, because starch dissolves in water to form a relatively stable colloidal solution, its turbidity curve is smooth and stable, and the change in turbidity value has a tailing effect. This may manifest as a certain change in velocity but a small change in acceleration, with the signal approximating a slowly changing straight line; that is, both the first and second turbidity change parameters are low.

[0040] In this embodiment, after determining the first turbidity change parameter and the second turbidity change parameter, the controller 150 can analyze the first turbidity parameter and the second turbidity parameter to determine the fluctuation intensity and disorder of the turbidity change of the washing liquid in the washing chamber 121, as well as the viscosity of the turbidity change. Furthermore, by analyzing the fluctuation intensity, disorder and viscosity of the turbidity change, the target dirt type of the tableware can be determined.

[0041] In this embodiment, multiple turbidity values ​​collected by the turbidity sensor 130 during the pre-wash stage are acquired, and multiple turbidity offsets are determined based on these values. These offsets characterize the degree of turbidity value variation at multiple target sampling points. Then, a first turbidity change parameter characterizing the fluctuation intensity and disorder of turbidity changes, and a second turbidity change parameter characterizing the viscosity of turbidity changes, are determined using these offsets. By analyzing the first and second turbidity change parameters, the characteristics of the physical changes in the washing liquid caused by dirt on the tableware can be determined. This allows the target dirt type on the tableware in the washing chamber 121 to be identified. This enables the dishwasher 100 to analyze the turbidity signal variation characteristics using only the existing turbidity sensor 130 during the pre-wash stage, thereby distinguishing the corresponding dirt type on the tableware. Based on the identified dirt type, the dishwasher 100 can then adjust its operating parameters in a targeted manner during the subsequent washing stage, achieving targeted washing of different types of dirt and improving cleaning effect and energy efficiency.

[0042] In some embodiments of this application, when determining the first turbidity change parameter and the second turbidity change parameter based on multiple turbidity offsets, the controller 150 is configured to: The first turbidity change parameter is determined based on the standard deviation of the offset and the approximate entropy of the offset of multiple turbidity offsets; the second turbidity change parameter is determined based on the multiple first-order difference values ​​and multiple second-order difference values ​​corresponding to the multiple turbidity offsets.

[0043] In this embodiment, the offset standard deviation is the standard deviation of multiple turbidity offsets, which can quantify the fluctuation amplitude of multiple turbidity offsets. The offset approximate entropy is the approximate entropy (ApEn) of multiple turbidity offsets, which can characterize the complexity and irregularity of the changes of multiple turbidity offsets.

[0044] Specifically, the controller 150 needs to perform two different calculations based on multiple turbidity offsets, one of which is to calculate the standard deviation of the offsets of the multiple turbidity offsets. The calculated standard deviation of the offsets can quantify the degree of fluctuation of the multiple collected turbidity values ​​around the turbidity baseline value, that is, the fluctuation intensity of the turbidity value offsets. The larger the standard deviation of the offsets, the more drastic the turbidity change; the smaller the standard deviation of the offsets, the more stable the turbidity change. The expression (2) for the standard deviation of the offsets is as follows: (2) in, 1 represents the standard deviation of the offset, μ represents the average of multiple turbidity offsets, N represents the number of turbidity offsets, and S(t) represents the turbidity offset.

[0045] Another calculation involves calculating the approximate entropy of multiple turbidity offsets. The calculated approximate entropy can assess the pattern complexity and unpredictability of a sequence composed of multiple turbidity offsets over time, i.e., the degree of disorder in the changes of turbidity offsets. The higher the approximate entropy value, the more random and irregular the turbidity changes; the lower the approximate entropy value, the more regular the turbidity changes.

[0046] The controller 150 then combines these two parameters according to a predetermined rule to generate a first turbidity variation parameter. The offset standard deviation provides a measure of the magnitude variation of the turbidity offset, while the offset approximate entropy provides a measure of the degree of disorder in the variation. The first turbidity variation parameter obtained by combining the two can provide a comprehensive description of the degree of chaos in a turbidity value variation, which includes information from two dimensions: the intensity of the fluctuation and the degree of disorder in the regularity of the time series pattern.

[0047] In this embodiment, the multiple first-order difference values ​​are obtained by subtracting two adjacent turbidity offsets among multiple turbidity offsets. Each first-order difference value represents the instantaneous change of turbidity offset between adjacent sampling points, which can be regarded as the instantaneous change rate.

[0048] The second-order difference value is obtained by subtracting two adjacent first-order difference values ​​from multiple first-order difference values. Each second-order difference value represents the change of the first-order difference value between adjacent sampling points, that is, the change of the instantaneous rate of change between adjacent sampling points, which can be regarded as the instantaneous acceleration.

[0049] Specifically, in determining the second turbidity change parameter, the controller 150 performs three calculation processes. First, it performs a first-order difference operation on multiple turbidity offset sequences to obtain multiple first-order difference values. The calculated multiple first-order difference values ​​can convert the absolute quantity sequence of turbidity offsets into a change rate sequence. The expression (3) for the first-order difference value is as follows: V(t) = S(t+1) - S(t); (3) Where V(t) is the first-order difference value, S(t) is the first turbidity deviation among two adjacent turbidity deviations, and S(t+1) is the second turbidity deviation among two adjacent turbidity deviations.

[0050] The sequence of multiple first-order difference values ​​is subjected to second-order difference operation to obtain multiple second-order difference values. The calculated multiple second-order difference values ​​further convert the rate of change sequence into a rate of change sequence. The expression (4) for the second-order difference value is as follows: A(t) = V(t+1) - V(t); (4) Where A(t) is the second-order difference value, V(t) is the first first-order difference value among two adjacent first-order difference values, and V(t+1) is the second first-order difference value among two adjacent first-order difference values.

[0051] The controller 150 calculates a second turbidity change parameter based on these two difference sequences. Multiple first-order difference values ​​describe the distribution of the signal change rate. Multiple second-order difference values ​​describe the distribution of the signal change acceleration. The second turbidity change parameter, determined based on the first-order and second-order difference values, measures the magnitude of viscosity during turbidity change. When the signal change is smooth and has inertia, the acceleration change is relatively small, and the second turbidity change parameter tends to be moderate or small; when the signal change is rapid and has no inertia, the acceleration change may be significant, and the second turbidity change parameter may increase.

[0052] In this embodiment, a first turbidity variation parameter reflecting both signal fluctuation amplitude and temporal pattern complexity is generated by calculating the offset standard deviation and the offset approximate entropy. This avoids misjudgments that may occur with a single indicator in characterizing signal chaos, thereby improving the accuracy of characterizing fluctuation intensity and disorder. A second turbidity variation parameter reflecting the dynamic characteristics of the signal change process is generated by analyzing the first and second difference values ​​of the turbidity offset. This allows the characterization of turbidity variation viscosity to be based on the relative relationship between signal change rate and acceleration, better distinguishing between smoothly changing signals and rapidly changing signals with different patterns, thus improving the accuracy of characterizing variation viscosity.

[0053] In some embodiments of this application, when determining the first turbidity change parameter based on the offset standard deviation and offset approximate entropy of multiple turbidity offsets, the controller 150 is configured to: The standard deviation of the offset and the approximate entropy of the offset are determined based on multiple turbidity offsets; the product of the standard deviation of the offset and the first sum is determined as the first turbidity change parameter, wherein the first sum is the sum of the approximate entropy of the offset and the first insurance constant.

[0054] In this embodiment, if the turbidity value changes smoothly, there is a possibility that the approximate entropy of the offset may be 0. In this case, if the standard deviation of the offset is directly multiplied by the approximate entropy of the offset, it may cause the first degree of change parameter to be 0, and the fluctuation intensity represented by the standard deviation of the offset will also disappear. Therefore, a first insurance constant is introduced to avoid the first turbidity change parameter being 0 in the final calculation.

[0055] Specifically, after calculating the offset standard deviation and offset approximate entropy of multiple turbidity offsets, firstly, the calculated offset approximate entropy is added to a preset first insurance constant to obtain a first sum. Secondly, the calculated offset standard deviation is multiplied by the first sum, and the product is determined as the first turbidity change parameter. The first turbidity change parameter in the form of a product can couple the information of the two dimensions: the fluctuation intensity represented by the offset standard deviation and the degree of disorder represented by the offset approximate entropy. The expression (5) of the first turbidity change parameter is as follows: F1 = σ 1× (Approx Entropy(S(t)) + 1); (5) Where is the first turbidity change parameter of F1, σ1 is the standard deviation of the offset, Approx Entropy(S(t)) is the approximate entropy of the offset, and 1 is the first insurance constant.

[0056] The first turbidity change parameter determined by the above expression (5) makes the approximate entropy value increase when the degree of disorder of turbidity change is high, and the first sum value increases accordingly, thereby amplifying the contribution of the fluctuation intensity represented by the standard deviation, making the first turbidity change parameter respond more significantly to the "drastic and disorderly" change trend; when the degree of signal disorder is low, the approximate entropy value is small, but due to the existence of the first insurance constant, the first sum value is not less than 1. At this time, the first turbidity change parameter mainly reflects the fluctuation intensity represented by the standard deviation, which can effectively distinguish between "stable" signals and "regularly fluctuating" signals, and improves the robustness of the calculated first turbidity change parameter.

[0057] In the embodiments of this application, the offset standard deviation and offset approximate entropy are calculated by multiplication, which not only improves the sensitivity of the first turbidity change parameter to turbidity changes with high fluctuation and high disorder, but also improves the sensitivity of the first turbidity change parameter to turbidity changes with low fluctuation and low disorder. Furthermore, a first insurance constant is introduced to reduce the possibility of information loss in the first turbidity change parameter.

[0058] In some embodiments of this application, when determining the second turbidity change parameter based on multiple first-order difference values ​​and multiple second-order difference values ​​corresponding to multiple turbidity offsets, the controller 150 is configured to: Multiple first-order difference values ​​are determined based on multiple turbidity offsets, and multiple second-order difference values ​​are determined based on multiple first-order difference values; the standard deviation of the first difference value of the multiple first-order difference values ​​is determined, and the standard deviation of the second difference value of the multiple second-order difference values ​​is determined; the ratio of the standard deviation of the second difference value to the second sum value is determined as the second turbidity change parameter, wherein the second sum value is the sum of the standard deviation of the first difference value and the second insurance constant.

[0059] In this embodiment, multiple first-order difference values ​​are used to characterize the instantaneous rate of turbidity change. In practical applications, the first-order difference values ​​may remain stable. In this case, the standard deviation of the first difference value corresponding to the first-order difference value may be 0. Therefore, by introducing a second insurance constant, the division-to-zero error in the ratio calculation is avoided.

[0060] Specifically, after calculating multiple first-order difference values ​​and multiple second-order difference values, the standard deviations of the multiple first-order difference values ​​and multiple second-order difference values ​​are calculated respectively, thereby obtaining the standard deviations of the first difference value and the second difference value.

[0061] Calculate the standard deviation of the first difference of multiple first-order differences. The standard deviation of the first difference quantifies the fluctuation range of the rate of change of turbidity. A larger standard deviation of the first difference indicates that the turbidity change is sometimes fast and sometimes slow, with significant differences; a smaller standard deviation of the first difference indicates that the rate of change of turbidity is relatively stable. Calculate the standard deviation of the second difference of multiple second-order differences. The standard deviation of the second difference quantifies the fluctuation range of the acceleration of change of turbidity. A larger standard deviation of the second difference indicates that the rate of change of turbidity is inherently unstable, with drastic acceleration or deceleration; a smaller standard deviation of the second difference indicates that the rate of change of turbidity is relatively stable, with strong inertia.

[0062] By calculating the standard deviation of the first difference value and the standard deviation of the second difference value, the turbidity change rate represented by the first difference value and the turbidity change acceleration represented by the second difference value are extracted into two scalar features that characterize the overall fluctuation intensity, providing a data basis for the subsequent calculation of the second turbidity change parameter.

[0063] In this embodiment, a second turbidity change parameter is generated by performing a ratio calculation on the standard deviation of the first difference value and the standard deviation of the second difference value.

[0064] Specifically, the calculated standard deviation of the first difference value is added to a preset second insurance constant to obtain the second sum. Introducing the second insurance constant ensures that the denominator of the ratio operation is not zero, avoiding mathematical calculation errors when the turbidity signal is relatively stable and the first difference standard deviation is 0. Next, the calculated standard deviation of the second difference value is divided by the second sum, and the quotient is determined as the second turbidity change parameter, so that the second turbidity change parameter can measure the proportion of the "fluctuation intensity of acceleration" to the "fluctuation intensity of velocity" during the turbidity change process. This second turbidity change parameter can characterize the viscosity or inertia of the change. The expression (6) of the second turbidity change parameter is as follows: F2 =σ_a / (σ_v +ε); (6) Where F2 is the second turbidity change parameter, σ_v is the standard deviation of the first difference value, σ_a is the standard deviation of the second difference value, and ε is the second insurance constant.

[0065] In this embodiment, the second turbidity change parameter is not obtained solely from the standard deviation of the first difference value used to characterize the rate of turbidity change or the standard deviation of the second difference value used to characterize the acceleration of turbidity change. Instead, it reflects the relative relationship between the two by the ratio of the second difference standard deviation to the first difference standard deviation. This more fundamentally captures the differences in inertia or viscosity changes exhibited during the dissolution and diffusion of different substances. Furthermore, the introduction of a second safety constant improves the effectiveness and numerical stability of the second turbidity change parameter under limiting conditions, thereby enhancing the accuracy of the second turbidity change parameter in characterizing the viscosity of turbidity changes.

[0066] In some embodiments of this application, when determining the first turbidity change parameter and the second turbidity change parameter based on multiple turbidity offsets, the controller 150 is configured to: When the washing assembly 140 is running in the pre-wash stage, and each time a first preset number of turbidity offsets are obtained, a first sub-change parameter and a second sub-change parameter are determined based on the first preset number of turbidity offsets. The first sub-change parameter is used to characterize the fluctuation intensity and disorder of the first preset number of turbidity values, and the second sub-change parameter is used to characterize the change viscosity of the first preset number of turbidity values. When the washing unit 140 exits the pre-wash stage, and the number of both the first sub-variable parameter and the second sub-variable parameter is at least two, the average value of the at least two first sub-variable parameters is determined as the first turbidity variation parameter, and the average value of the at least two second sub-variable parameters is determined as the second turbidity variation parameter.

[0067] In this embodiment, when the pre-wash program of the dishwasher 100 is started, the controller 150 continuously counts the turbidity offset while collecting and calculating it. Whenever the accumulated turbidity offset reaches a first preset number, the controller 150 immediately uses this first preset number of turbidity offsets as input to independently calculate the first sub-variation parameter and the second sub-variation parameter. After the calculation is complete, the controller 150 of the dishwasher 100 only needs to save the first and second sub-variation parameters and can release or overwrite the memory used for calculating the turbidity offset, thereby avoiding the accumulation and storage of all raw data or intermediate process data during the lengthy pre-wash stage and reducing memory usage.

[0068] It should be noted that the calculation method for the first sub-variation parameter is the same as that for the first turbidity variation parameter, and the calculation method for the second sub-variation parameter is the same as that for the second turbidity variation parameter.

[0069] For example, the first preset quantity ranges from 10 to 50.

[0070] In this embodiment, when the pre-wash stage is completed, the controller 150 checks the number of stored first and second sub-variable parameters. If there are at least two of each, the controller 150 takes the arithmetic mean of all the first sub-variable parameters and determines this average as the final first turbidity variation parameter. Similarly, the controller takes the arithmetic mean of all the second sub-variable parameters and determines this average as the final second turbidity variation parameter. This integrates the first and second sub-variable parameters from different time periods throughout the pre-wash process, thereby obtaining the first and second turbidity variation parameters that characterize the overall dynamic characteristics of the turbidity signal during the entire pre-wash stage.

[0071] In this embodiment, by calculating the first sub-variation parameter and the second sub-variation parameter in segments, and taking the average of at least two first sub-variation parameters and at least two second sub-variation parameters respectively, the system only needs to retain a first preset number of original data for calculation, and stores a number of first sub-variation parameters and second sub-variation parameters that are far fewer than the number of original data points. This helps to save the memory resources of the controller 150 and improve the stability of the final feature parameters. This allows the solution to run efficiently in a resource-constrained hardware environment, thus improving the applicability of the solution.

[0072] In some embodiments of this application, when acquiring multiple turbidity offsets of the turbidity sensor 130 at multiple target sampling points, the controller 150 is configured to: When the washing unit 140 is running in the pre-wash stage, and each time a second preset number of turbidity values ​​are obtained, the turbidity baseline value corresponding to the second preset number of turbidity values ​​is determined according to the average value of the second preset number of turbidity values; the difference between the second preset number of turbidity values ​​and the turbidity baseline value is calculated to determine the second preset number of turbidity offsets; the above steps are repeated until the washing unit 140 exits the pre-wash stage.

[0073] In this embodiment, the controller 150 collects turbidity values ​​at a fixed frequency during the pre-washing phase. The controller 150 continuously maintains a data buffer of a second preset length to store several recently collected turbidity values. Whenever a new turbidity value is collected and stored in the buffer, when the amount of data in the buffer reaches the second preset length, the controller 150 calculates the arithmetic mean of all turbidity values ​​in the buffer and determines this average value as the current turbidity baseline value. This allows the turbidity baseline value to be dynamically adjusted according to the slow drift of sensor status and environmental factors such as water temperature, avoiding errors caused by using a fixed initial value as the baseline.

[0074] In this embodiment, after calculating the turbidity baseline value corresponding to the second preset number of turbidity values, the difference operation is performed on the turbidity baseline value and the second preset number of turbidity values ​​corresponding to the turbidity baseline value one by one, thereby generating a batch of calibrated turbidity offset data sequence that can more directly reflect the dynamics of dirt cleaning.

[0075] In this embodiment, whenever the buffer is filled with new data and a batch of turbidity offsets is calculated, the buffer is updated in a sliding window manner, preparing to calculate the next turbidity baseline value and the next batch of turbidity offsets. This cyclical process continues until the washing assembly 140 completes the pre-wash stage, thereby obtaining the turbidity offsets for the entire pre-wash stage.

[0076] In this embodiment, the system does not need to store all turbidity values ​​collected during the entire pre-washing stage to calculate a static turbidity baseline value. Instead, it only needs to maintain a sliding window of data with a fixed length of a second preset number, reducing the memory usage required to calculate the turbidity offset. Simultaneously, the dynamically updated turbidity baseline value can compensate for sensor drift more in real time. Compared to methods using a fixed baseline or a global average baseline during the initial pre-washing stage, the calculated turbidity offset more accurately represents instantaneous turbidity changes.

[0077] In some embodiments of this application, when determining the target soiling type of the tableware based on a first turbidity change parameter and a second turbidity change parameter, the controller 150 is configured to: Based on the first turbidity change parameter and the second turbidity change parameter, a two-dimensional turbidity change vector is determined; the two-dimensional turbidity change vector is compared with at least two vector ranges; based on the numerical relationship between the two-dimensional turbidity change vector and at least two vector ranges, the target dirt type is determined from at least two preset dirt types, wherein at least two preset dirt types correspond one-to-one with at least two vector ranges.

[0078] In this embodiment, the controller 150 combines the calculated first turbidity change parameter and the second turbidity change parameter in sequence to form a two-dimensional turbidity change vector, which can characterize the characteristics of the current turbidity change of the washing water.

[0079] In this embodiment, at least two vector ranges are parameter ranges pre-stored in the memory of the controller 150, and different vector ranges correspond to different preset dirt types. The controller 150 checks whether the two-dimensional vector of turbidity change falls within any of the preset vector range regions. If the two-dimensional vector of turbidity change falls within a certain vector range, the controller 150 determines the preset dirt type that uniquely corresponds to the vector range it falls within as the target dirt type for this operation.

[0080] In this embodiment, the type of dirt is determined by constructing a two-dimensional vector with the first turbidity change parameter and the second turbidity change parameter and comparing the values ​​with a preset vector range. This allows the controller 150 of the dishwasher 100 to complete the classification of dirt types by performing only a few value comparisons. This improves the efficiency of determining the target dirt type without significantly increasing the computational load of the controller 150.

[0081] For example, the two-dimensional vector of turbidity change can be (F1, F2), where F1 is the first turbidity change parameter and F2 is the second turbidity change parameter. The preset vector range includes three levels: high, medium, and low. The preset vector range for the low level corresponds to the preset dirt type of starch, the preset vector range for the medium level corresponds to the preset dirt type of protein, and the preset vector range for the high level corresponds to the preset dirt type of grease.

[0082] Figure 4 The following are turbidity change curves of oily dirt provided in some embodiments of this application. Figure 5 The following are turbidity change curves of protein-based contaminants provided in some embodiments of this application. Figure 6 The following are turbidity change curves of starchy contaminants provided in some embodiments of this application, such as... Figures 4 to 6As shown, because grease is not easily soluble and tends to form unstable emulsions, the turbidity curve of grease-based contaminants exhibits high-frequency, large-amplitude fluctuations and a large signal variance. Starch, after dissolving in water, forms a relatively stable colloidal solution; the turbidity curve of starch-based contaminants is smooth and stable with minimal fluctuations, and the signal approximates a slowly changing straight line. Proteins fall between these two extremes; they may form relatively stable colloids, but they can denature and aggregate under heat or mechanical action, resulting in low-to-medium frequency, medium-amplitude fluctuations in the turbidity curve. In summary, the first turbidity change parameter corresponding to grease-based contaminants is greater than that corresponding to protein-based contaminants, and the first turbidity change parameter corresponding to protein-based contaminants is greater than that corresponding to starch-based contaminants; the second turbidity change parameter corresponding to grease-based contaminants is greater than that corresponding to protein-based contaminants, and the second turbidity change parameter corresponding to protein-based contaminants is greater than that corresponding to starch-based contaminants.

[0083] In some embodiments of this application, when determining the target soiling type of the tableware based on a first turbidity change parameter and a second turbidity change parameter, the controller 150 is configured to: Obtain a dirt feature plane, wherein the dirt feature plane includes at least two dirt type cluster points, and the at least two dirt type cluster points are used to characterize the turbidity change parameters corresponding to at least two preset dirt types; map the first turbidity change parameter and the second turbidity change parameter to the dirt feature plane to obtain turbidity sample points; determine at least two distance values ​​between the turbidity sample points and the at least two dirt type cluster points; determine the target dirt type based on the at least two distance values ​​and the at least two preset dirt types.

[0084] In this embodiment, the dirt feature plane is a two-dimensional coordinate system plane composed of multiple historical first turbidity variation parameters and second turbidity variation parameters. Reference points representing different pure dirt types, i.e., dirt type cluster points, are marked on this dirt feature plane. Each dirt type cluster point represents a typical or central position of a preset dirt type in the feature space, and the coordinates of this point are obtained based on statistical analysis of a large amount of historical data.

[0085] Specifically, controller 150 accesses pre-stored data that defines the structure of the dirt feature plane. This plane uses a first turbidity variation parameter as the horizontal axis and a second turbidity variation parameter as the vertical axis. At least two dirt type cluster points are marked on the plane. Controller 150 uses the calculated first and second turbidity variation parameters as coordinate positions and places them on the dirt feature plane; these coordinate positions are the turbidity sample points. Controller 150 calculates the distance from each turbidity sample point to each dirt type cluster point; the smaller the distance value, the more similar the sample feature is to the standard feature of that type.

[0086] For example, at least two Euclidean distances are calculated between the turbidity sample point and at least two dirt type cluster points, and the preset dirt type corresponding to the dirt type cluster point with the smallest Euclidean distance is determined as the target dirt type. Specifically, if there are two Euclidean distance values ​​that are small in difference, that is, the difference between the two Euclidean distances is less than the difference threshold, then the target dirt type is determined to be a mixed type of the two preset dirt types.

[0087] Figure 7 The following are schematic diagrams of dirt feature planes provided in some embodiments of this application, such as... Figure 7 As shown, the horizontal axis represents the first turbidity change parameter, and the vertical axis represents the second turbidity change parameter. Dirt type cluster point A represents oily dirt, dirt type cluster point B represents protein dirt, and dirt type cluster point C represents starch dirt. Turbidity sample point P1 is closer to dirt type cluster point C, so the target dirt type of turbidity sample point P1 is determined to be starch. Turbidity sample point P2 is close to both dirt type cluster points A and B, and the difference between the two distances to dirt type cluster points A and B is less than the difference threshold, so the target dirt type of turbidity sample point P2 is determined to be a mixture of oil and protein.

[0088] In this embodiment, the distance from the turbidity sample point to each dirt type cluster point is calculated, and the target dirt type is determined according to the nearest distance principle. Furthermore, by comparing the relative magnitudes of multiple distance values, it is possible to determine whether there are mixed dirt types, thereby improving the comprehensiveness and accuracy of the determination of the target dirt type.

[0089] In some embodiments of this application, after determining the target soiling type of the tableware based on a first turbidity change parameter and a second turbidity change parameter, the controller 150 is further configured to: Output a dirt type prompt based on the target dirt type; and / or When the washing unit 140 is running in the main wash stage, the operating parameters of the washing unit 140 in the main wash stage are adjusted according to the target type of dirt.

[0090] In this embodiment, the dishwasher 100 also includes a display panel and an audio player. The dirt type prompt information can be image information displayed on the display panel or audio information played by the audio player, thereby increasing the transparency of the washing process and allowing the user to understand the dishwasher 100's determination of the dirt status of its load.

[0091] Figure 8 The following are schematic diagrams illustrating the display interface of a dishwasher display panel provided in some embodiments of this application, such as... Figure 8As shown, the display interface shows a dirt warning message 801 and a cleaning suggestion message 802.

[0092] In this embodiment, the main wash stage is the washing stage performed after the pre-wash stage. During the main wash stage, the washing component 140 needs to perform targeted cleaning on different types of dirt on the tableware, call up an optimized washing strategy that matches the target type of dirt, and adjust the operating parameters of the washing component 140 accordingly.

[0093] For example, if the target dirt type is grease, the controller 150 can instruct the heater to adjust the washing water temperature to a higher value (e.g., 55°C-65°C) to dissolve the grease through high temperature and improve the cleaning effect. If the target dirt type is protein, the controller 150 can appropriately extend the duration of the main wash stage and may maintain a medium water temperature to ensure that the protein is fully decomposed. If the target dirt type is starch, the controller 150 can use a standard or slightly lower washing water temperature and may optimize the water jet pattern to rinse away sticky residue.

[0094] In this embodiment, by outputting prompts to the user indicating the operating parameters that need to be adjusted, the user experience and trust in the device are enhanced. By automatically adjusting the operating parameters of the main wash stage according to the target type of dirt, the washing targeting and effectiveness for dishes with different types of dirt are improved.

[0095] In some embodiments, Figure 9 Flowcharts of dishwasher control methods in some embodiments of this application are shown, such as... Figure 9 As shown, the dishwasher control method is applied to the dishwasher in any of the above embodiments, and the dishwasher control method includes: Step 901: Obtain multiple turbidity offsets corresponding to multiple target sampling points of the turbidity sensor.

[0096] Among them, multiple target sampling points are turbidity sampling points when the washing component is running in the pre-wash stage, multiple turbidity offsets are the offsets between multiple turbidity values ​​collected at multiple target sampling points and the turbidity baseline value, and the turbidity baseline value is the average value used to characterize multiple turbidity values.

[0097] Step 902: Determine the first turbidity change parameter and the second turbidity change parameter based on multiple turbidity offsets.

[0098] The first turbidity change parameter is used to characterize the fluctuation intensity and disorder of turbidity value during the pre-washing stage, while the second turbidity change parameter is used to characterize the viscosity of turbidity value change during the pre-washing stage.

[0099] Step 903: Determine the target type of dirt on the tableware based on the first turbidity change parameter and the second turbidity change parameter.

[0100] In this embodiment, multiple turbidity values ​​collected by a turbidity sensor during the pre-wash stage are acquired, and multiple turbidity offsets are determined based on these values. These offsets characterize the degree of turbidity variation at multiple target sampling points. Then, a first turbidity change parameter characterizing the fluctuation intensity and disorder of turbidity changes, and a second turbidity change parameter characterizing the viscosity of turbidity changes, are determined using these offsets. By analyzing the first and second turbidity change parameters, the characteristics of the physical changes in the washing liquid caused by dirt on the tableware can be determined, thereby identifying the target dirt type on the tableware in the washing chamber. This allows the dishwasher to analyze the variation characteristics of the turbidity signal using only the existing turbidity sensor during the pre-wash stage, thereby distinguishing the corresponding dirt type on the tableware. Based on the identified dirt type, the dishwasher can then adjust its operating parameters in a targeted manner during subsequent washing stages, achieving targeted washing of different types of dirt and improving cleaning effect and energy efficiency.

[0101] It should be noted that when the dishwasher is controlled by the control device of the dishwasher in the embodiment of this application, the specific implementation method is similar to the specific implementation method of the dishwasher in any of the above embodiments of the present invention. Therefore, for a detailed exemplary description of the control process of the dishwasher, please refer to the relevant description of the dishwasher mentioned above. To reduce redundancy, it will not be repeated here.

[0102] In some embodiments, Figure 10 Structural block diagrams of the control device for a dishwasher according to some embodiments of this application are shown, such as... Figure 10 As shown, the dishwasher control device is applied to the dishwasher in any of the above embodiments. The dishwasher control device 1000 includes: an acquisition module 1001 and a determination module 1002.

[0103] The acquisition module 1001 is used to acquire multiple turbidity offsets corresponding to multiple target sampling points of the turbidity sensor. The multiple target sampling points are turbidity sampling points when the washing component is running in the pre-wash stage. The multiple turbidity offsets are the offsets between multiple turbidity values ​​collected at multiple target sampling points and the turbidity baseline value. The turbidity baseline value is the average value used to characterize the multiple turbidity values.

[0104] The determination module 1002 is used to determine a first turbidity change parameter and a second turbidity change parameter based on multiple turbidity offsets. The first turbidity change parameter is used to characterize the fluctuation intensity and disorder of the turbidity value during the prewashing stage, and the second turbidity change parameter is used to characterize the viscosity of the turbidity value during the prewashing stage.

[0105] The determination module 1002 is used to determine the target dirt type of the tableware based on the first turbidity change parameter and the second turbidity change parameter.

[0106] It should be noted that when the dishwasher is controlled by the control device of the dishwasher in the embodiment of this application, the specific implementation method is similar to the specific implementation method of the dishwasher in any of the above embodiments of the present invention. Therefore, for a detailed exemplary description of the control process of the dishwasher, please refer to the relevant description of the dishwasher mentioned above. To reduce redundancy, it will not be repeated here.

[0107] This application discloses a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the methods described in the above embodiments.

[0108] This application discloses a computer program product, including a computer program, which, when executed by a processor, implements the methods described in the above embodiments.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0110] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the exemplary discussion above is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The embodiments were chosen and described to better explain the principles and practical applications, thereby enabling those skilled in the art to better utilize the embodiments and various different variations suitable for specific application considerations.

Claims

1. A dishwasher, characterized in that, include: A housing, wherein an installation cavity is provided inside the housing; An inner liner is disposed within the mounting cavity, and a washing chamber is provided inside the inner liner; A turbidity sensor is installed in the circulation path, which is connected to the washing chamber, and the turbidity sensor is used to collect the turbidity value of the circulation path; A washing assembly is disposed within the mounting cavity, and the washing assembly is used to wash the tableware within the washing cavity; A controller, connected to the turbidity sensor and the washing assembly, is configured to: The turbidity sensor acquires multiple turbidity offsets corresponding to multiple target sampling points, wherein the multiple target sampling points are turbidity sampling points when the washing assembly is running in the pre-wash stage, the multiple turbidity offsets are the offsets between multiple turbidity values ​​collected at the multiple target sampling points and the turbidity baseline value, and the turbidity baseline value is the average value used to characterize the multiple turbidity values; A first turbidity change parameter and a second turbidity change parameter are determined based on the plurality of turbidity offsets, wherein the first turbidity change parameter is used to characterize the fluctuation intensity and disorder of the turbidity value in the pre-washing stage, and the second turbidity change parameter is used to characterize the viscosity of the change of the turbidity value in the pre-washing stage. The target type of dirt on the tableware is determined based on the first turbidity change parameter and the second turbidity change parameter; When determining the first turbidity change parameter and the second turbidity change parameter based on the plurality of turbidity offsets, the controller is configured to: The first turbidity change parameter is determined based on the offset standard deviation and offset approximate entropy of the multiple turbidity offsets; The second turbidity change parameter is determined based on the multiple first-order difference values ​​and multiple second-order difference values ​​corresponding to the multiple turbidity offsets; When determining the first turbidity change parameter based on the offset standard deviation and offset approximate entropy of the plurality of turbidity offsets, the controller is configured as follows: The standard deviation of the offset and the approximate entropy of the offset are determined based on the plurality of turbidity offsets; The product of the offset standard deviation and the first sum is determined as the first turbidity change parameter, wherein the first sum is the sum of the offset approximate entropy and the first insurance constant; When determining the second turbidity change parameter based on the multiple first-order difference values ​​and multiple second-order difference values ​​corresponding to the multiple turbidity offsets, the controller is configured as follows: The plurality of first-order difference values ​​are determined based on the plurality of turbidity offsets, and the plurality of second-order difference values ​​are determined based on the plurality of first-order difference values; Determine the first standard deviation of the plurality of first-order difference values, and determine the second standard deviation of the plurality of second-order difference values; The ratio of the standard deviation of the second difference value to the second sum value is determined as the second turbidity change parameter, wherein the second sum value is the sum of the standard deviation of the first difference value and the second insurance constant.

2. The dishwasher according to claim 1, characterized in that, When determining the first turbidity change parameter and the second turbidity change parameter based on the plurality of turbidity offsets, the controller is configured to: When the washing assembly is operating in the pre-wash stage, and each time a first preset number of turbidity offsets are obtained, a first sub-variation parameter and a second sub-variation parameter are determined based on the first preset number of turbidity offsets, wherein the first sub-variation parameter is used to characterize the fluctuation intensity and disorder of the first preset number of turbidity values, and the second sub-variation parameter is used to characterize the viscosity of the change of the first preset number of turbidity values. When the washing assembly exits the pre-wash stage, and the number of both the first sub-variation parameter and the second sub-variation parameter is at least two, the average value of the at least two first sub-variation parameters is determined as the first turbidity variation parameter, and the average value of the at least two second sub-variation parameters is determined as the second turbidity variation parameter.

3. The dishwasher according to claim 1, characterized in that, When acquiring multiple turbidity offsets of the turbidity sensor at multiple target sampling points, the controller is configured to: When the washing assembly is operating in the pre-wash stage, and each time a second preset number of turbidity values ​​are obtained, the turbidity baseline value corresponding to the second preset number of turbidity values ​​is determined based on the average value of the second preset number of turbidity values. The second preset number of turbidity values ​​are all compared with the turbidity baseline value to determine the second preset number of turbidity offsets. Repeat the above steps until the washing assembly exits the pre-wash stage.

4. The dishwasher according to claim 1, characterized in that, When determining the target soiling type of the tableware based on the first turbidity change parameter and the second turbidity change parameter, the controller is configured to: Based on the first turbidity change parameter and the second turbidity change parameter, determine the two-dimensional turbidity change vector; The two-dimensional vector of turbidity change is compared with at least two vector ranges; Based on the numerical relationship between the two-dimensional vector of turbidity change and the at least two vector ranges, the target dirt type is determined from at least two preset dirt types, wherein the at least two preset dirt types correspond one-to-one with the at least two vector ranges.

5. The dishwasher according to claim 1, characterized in that, When determining the target soiling type of the tableware based on the first turbidity change parameter and the second turbidity change parameter, the controller is configured to: Obtain a dirt feature plane, wherein the dirt feature plane includes at least two dirt type cluster points, and the at least two dirt type cluster points are respectively used to characterize the turbidity change parameters corresponding to at least two preset dirt types; The first turbidity change parameter and the second turbidity change parameter are mapped onto the dirt feature plane to obtain turbidity sample points; Determine at least two distance values ​​between the turbidity sample point and the at least two dirt type cluster points, respectively; The target dirt type is determined based on the at least two distance values ​​and the at least two preset dirt types.

6. The dishwasher according to claim 1, characterized in that, After determining the target soiling type of the tableware based on the first turbidity change parameter and the second turbidity change parameter, the controller is further configured to: Output a dirt type prompt based on the target dirt type; and / or When the washing unit reaches the main wash stage, the operating parameters of the washing unit during the main wash stage are adjusted according to the target type of dirt.

7. A method for controlling a dishwasher, characterized in that, The control method, applied to any one of claims 1 to 6, comprises: The turbidity sensor acquires multiple turbidity offsets corresponding to multiple target sampling points, wherein the multiple target sampling points are turbidity sampling points when the washing assembly is running in the pre-wash stage, and the multiple turbidity offsets are the offsets between the multiple turbidity values ​​collected at the multiple target sampling points and the turbidity baseline value, wherein the turbidity baseline value is the average value used to characterize the multiple turbidity values. A first turbidity change parameter and a second turbidity change parameter are determined based on the plurality of turbidity offsets, wherein the first turbidity change parameter is used to characterize the fluctuation intensity and disorder of the turbidity value in the pre-washing stage, and the second turbidity change parameter is used to characterize the viscosity of the change of the turbidity value in the pre-washing stage. Based on the first turbidity change parameter and the second turbidity change parameter, determine the target type of dirt on the tableware; The target soiling type of the tableware is determined based on the first turbidity change parameter and the second turbidity change parameter; the determination of the first turbidity change parameter and the second turbidity change parameter based on the plurality of turbidity offsets includes: The first turbidity change parameter is determined based on the offset standard deviation and offset approximate entropy of the multiple turbidity offsets; The second turbidity change parameter is determined based on the multiple first-order difference values ​​and multiple second-order difference values ​​corresponding to the multiple turbidity offsets; The step of determining the first turbidity change parameter based on the offset standard deviation and offset approximate entropy of the plurality of turbidity offsets includes: The standard deviation of the offset and the approximate entropy of the offset are determined based on the plurality of turbidity offsets; The product of the offset standard deviation and the first sum is determined as the first turbidity change parameter, wherein the first sum is the sum of the offset approximate entropy and the first insurance constant; The step of determining the second turbidity change parameter based on the multiple first-order difference values ​​and multiple second-order difference values ​​corresponding to the multiple turbidity offsets includes: The plurality of first-order difference values ​​are determined based on the plurality of turbidity offsets, and the plurality of second-order difference values ​​are determined based on the plurality of first-order difference values; Determine the first standard deviation of the plurality of first-order difference values, and determine the second standard deviation of the plurality of second-order difference values; The ratio of the standard deviation of the second difference value to the second sum value is determined as the second turbidity change parameter, wherein the second sum value is the sum of the standard deviation of the first difference value and the second insurance constant.

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