Smart laundry washing control method and device based on si sensor and washing machine

Abstract

The application discloses a clothes intelligent washing control method and device based on an SI sensor and a washing machine. The method comprises the following steps: storing real-time stain data of washing water in time sequence, wherein the real-time stain data is high-frequency and high-density data of a washing process collected by the SI sensor in real time; continuously reading the real-time stain data in a storage area, calculating corresponding credible SI values; storing the calculated credible SI values in time sequence; continuously reading the credible SI values in the storage area, calculating relative dirtiness; comparing the relative dirtiness with a preset dirtiness threshold, and adjusting the washing process according to the comparison result. The application can obtain a better real-time clothes dirtiness and cleanliness representation index, uses the obtained representation index to guide the washing machine to wash, and achieves the effect of stopping after washing, thereby saving energy and reducing consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of washing machines, in particular to a clothes intelligent washing control method and device based on SI sensor and a washing machine. BACKGROUND

[0002] Most of the existing washing machines judge the washing degree of the washing objects by turbidity during the washing process of the washing objects. For example, the application No. CN200710084288.5 named clothes washing and stopping method and washing and stopping full-automatic washing machine applying the method, the application No. CN201310305616.5 named washing machine circulating water washing control method and washing machine thereof, and the application No. CN201710392999.2 named turbidity detection module, washing machine and control method of washing machine all judge whether the clothes are washed by detecting the light transmittance value of the rinsing water in the washing machine through the turbidity sensor. However, the turbidity includes the scattering of the suspended matter to the light, which can only reflect the influence of the suspended particulate matter or the emulsion in the washing machine on the water transmittance. It has no response to other non-suspended particulate matter or emulsion stains and cannot be used to judge whether the stains on the clothes are washed. Therefore, the turbidity sensor cannot really achieve the washing and stopping effect. In addition, the turbidity sensor has many interference substances, large fluctuation and small signal-to-noise ratio. SUMMARY

[0003] Technical purpose: in view of the above technical problems, the present application provides a clothes intelligent washing control method and device based on SI sensor and a washing machine. The method obtains a better real-time clothes dirty and clean degree index, uses the obtained index to guide the washing machine to wash, and achieves the effect of washing and stopping, thereby saving energy and reducing consumption.

[0004] Technical scheme: in order to achieve the above technical purpose, the present application adopts the following technical scheme:

[0005] A clothes intelligent washing control method based on SI sensor, characterized in that it comprises the following steps:

[0006] Obtain and store the real-time stain data of the washing water in chronological order, wherein the real-time stain data is the high-frequency and high-density data collected by the SI sensor at a set interval after the washing machine enters the washing process;

[0007] When a new real-time stain data is added in the storage area, read the first preset number of real-time stain data in the storage area, calculate the current time credible SI value according to the read first preset number of real-time stain data and store it;

[0008] After the trusted SI value of the current time is calculated, a second preset number of trusted SI values in the storage area are read, including the trusted SI values of the current time and historical times, and a relative dirtiness is calculated according to the read second preset number of trusted SI values;

[0009] The relative dirtiness is compared with a preset dirtiness threshold to obtain a comparison result;

[0010] According to the comparison result, the washing process is adjusted.

[0011] Preferably, the real-time stain data is stored and the trusted SI value of the current time is calculated in the following method:

[0012] The real-time stain data of the washing water is stored in chronological order and placed in a time sequence originV, and the real-time stain data collected later is located at the end of the time sequence originV;

[0013] When a new real-time stain data is added in the storage area, the first preset number of real-time stain data smoothLength is read from the end of the time sequence originV;

[0014] From the read first preset number of real-time stain data smoothLength, a third preset number of values arranged in the middle position in ascending order are selected, and the arithmetic mean of the selected values in the middle position is calculated, and the arithmetic mean is taken as the trusted SI value of the current time.

[0015] Preferably, the trusted SI value is stored and the relative dirtiness is calculated in the following method:

[0016] The calculated trusted SI value is stored in chronological order and placed in a time sequence smoothSet, and the trusted SI value obtained later is located at the end of the time sequence smoothSet;

[0017] After the trusted SI value of the current time is calculated, the second preset number of trusted SI values seqLength is read from the end of the time sequence smoothSet;

[0018] The standard deviation sdV of the read second preset number of trusted SI values seqLength is calculated, and a fourth preset number of values arranged in the middle position in ascending order are selected from the read second preset number of trusted SI values, and the arithmetic mean meanV of the selected values in the middle position is calculated.

[0019] The ratio of the standard deviation sdV and the arithmetic mean meanV is calculated, and the ratio is taken as the relative dirtiness.

[0020] Preferably, the third preset number or the fourth preset number of intermediate positions are selected in the following way:

[0021] The corresponding real-time stain data or the trusted SI values are arranged in ascending order to obtain a corresponding sequence. After discarding the same number of deviating values at both ends of the corresponding sequence, the third preset number or the fourth preset number of values in the intermediate positions are selected from the remaining values.

[0022] Preferably, the third preset number or the fourth preset number is one-third of the length of the corresponding sequence. If one-third of the length of the corresponding sequence is not an integer, the integer is rounded up, and the number of smaller numbers not taken is ensured to be the same as the number of larger numbers not taken.

[0023] Preferably, according to the comparison result, the washing process is adjusted, specifically including:

[0024] If the comparison result is that the relative dirtiness is less than the dirtiness threshold criteriaV, the washing process is ended;

[0025] If the comparison result is that the relative dirtiness is greater than or equal to the dirtiness threshold criteriaV, the washing process is continued until the relative dirtiness is less than the preset dirtiness threshold criteriaV.

[0026] A clothes intelligent washing control device based on an SI sensor, characterized in that it comprises a stain data real-time acquisition device and a controller. The stain data real-time acquisition device is used to acquire stain data of washing water in a washing chamber at a set interval through an SI sensor. The stain data is high-frequency and high-density data.

[0027] The controller comprises the following functional modules:

[0028] A first data module is used to acquire and store real-time stain data of washing water in chronological order. The real-time stain data is high-frequency and high-density data acquired through an SI sensor at a set interval after a washing machine enters a washing process.

[0029] A second data module is used to read a first preset number of real-time stain data in a storage area after a new real-time stain data is added to the storage area. A trusted SI value at a current time is calculated based on the read first preset number of real-time stain data and is stored.

[0030] A third data module is used to read a second preset number of trusted SI values in the storage area after the trusted SI value at the current time is calculated. The trusted SI values include those at the current time and historical times. A relative dirtiness is calculated based on the read second preset number of trusted SI values.

[0031] a comparison module configured to compare the relative soil with a preset soil threshold to obtain a comparison result;

[0032] an execution module configured to adjust a washing procedure according to the comparison result.

[0033] Preferably, the first data module is configured to store the real-time soil data of the washing water in a time sequence originV, and the real-time soil data collected later is located at the end of the time sequence originV.

[0034] The second data module comprises:

[0035] a second data calculation module configured to, after a real-time soil data is added to the storage area, read the first preset number of smoothLength soil data from the end of the time sequence originV, select a third preset number of values arranged in the middle position from small to large from the first preset number of real-time soil data, calculate an arithmetic mean of the selected values arranged in the middle position, and take the arithmetic mean as a credible SI value, i.e., a credible SI value at the current time.

[0036] a second data storage module configured to store the calculated credible SI value in a time sequence smoothSet, and the obtained credible SI value is located at the end of the time sequence smoothSet.

[0037] The third data module is configured to, after the credible SI value at the current time is calculated, read the second preset number of seqLength credible SI values from the end of the time sequence smoothSet, including the credible SI values at the current time and historical times, calculate a standard deviation sdV of the read second preset number of credible SI values, select a fourth preset number of values arranged in the middle position from small to large from the read second preset number of credible SI values, calculate an arithmetic mean meanV of the selected values arranged in the middle position, calculate a standard deviation sdV of the read credible SI values, calculate a ratio of the standard deviation sdV to the arithmetic mean meanV, and take the ratio as the relative soil.

[0038] Preferably, the soil data real-time collection device comprises an SI sensor arranged at the entrance of a drain pipe of the washing machine and communicating with the washing cabin.

[0039] A washing machine characterized in that the washing machine is provided with the clothes intelligent washing control device based on the SI sensor.

[0040] Beneficial effects: due to the adoption of the above technical solutions, the present application has the following beneficial effects:

[0041] The present application detects real-time stain data of high frequency and high density of washing water by SI sensor in real time, then stores in time sequence, obtains the reliable SI value at the current time based on the stored real-time stain data, obtains the better representation index of real-time clothing dirtiness degree based on the reliable SI value at the current time and the previous several historical reliable SI values, uses the obtained representation index to guide the washing of the washing machine, and can achieve the effect of stopping after washing, saving energy and reducing consumption. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 Flow chart of the clothing intelligent washing control method based on SI sensor;

[0043] Figure 2 Comparison chart of the clothing intelligent washing process based on SI sensor and the traditional washing process;

[0044] Figure 3 Clothing intelligent washing method based on SI sensor. DETAILED DESCRIPTION

[0045] The embodiments of the present application will be described in detail below.

[0046] Most washing machines will have slight vibration during normal operation, which will affect the accuracy of sensor readings. The present application uses a special filtering algorithm to obtain more stable readings, and then better performs subsequent automatic judgment. Here, the algorithm of discarding the large values at both ends and then averaging is used, because in the actual research and development process, it is found that the periodic motion of the washing machine will produce a low data density (much lower than the data density of SI readings) but high influence (large change amplitude) noise data. Through the existing filtering algorithm, better filtering effect can be obtained under the premise of the law of large numbers.

[0047] Embodiment one

[0048] The present embodiment proposes a clothing intelligent washing control method based on SI sensor, as shown in Figure 1 The steps include:

[0049] The real-time stain data of washing water is obtained and stored in time sequence, the real-time stain data is high frequency and high density data collected by SI sensor at a set interval after the washing machine enters the washing process;

[0050] When a real-time stain data is added in the storage area, read a first preset number of real-time stain data in the storage area, calculate a credible SI value at the current time according to the read first preset number of real-time stain data, and store the credible SI value;

[0051] After the credible SI value at the current time is calculated, read a second preset number of credible SI values in the storage area, including the credible SI values at the current time and historical times, and calculate a relative dirtiness according to the read second preset number of credible SI values;

[0052] Compare the relative dirtiness with a preset dirtiness threshold to obtain a comparison result;

[0053] Adjust a washing process according to the comparison result.

[0054] Specifically, the real-time stain data is stored and the credible SI value at the current time is calculated in the following method:

[0055] The real-time stain data of the washing water is stored in chronological order, and is placed in a time sequence originV, and the real-time stain data collected later is located at the end of the time sequence originV;

[0056] When a real-time stain data is added in the storage area, read a first preset number of real-time stain data from the end of the time sequence originV;

[0057] From the read first preset number of real-time stain data, select a third preset number of values arranged in the middle position in ascending order, calculate the arithmetic mean of the selected values in the middle position, and the arithmetic mean is taken as the credible SI value at the current time.

[0058] Specifically, the credible SI value is stored and the relative dirtiness is calculated in the following method:

[0059] The calculated credible SI value is stored in chronological order, and is placed in a time sequence smoothSet, and the obtained credible SI value is located at the end of the time sequence smoothSet;

[0060] After the credible SI value at the current time is calculated, read a second preset number of credible SI values from the end of the time sequence smoothSet;

[0061] Calculate the standard deviation sdV of the second preset number seqLength of the read trusted SI values, and from the second preset number seqLength of the read trusted SI values, select the fourth preset number of values arranged in the middle position from small to large, and calculate the arithmetic mean meanV of the selected values in the middle position;

[0062] Calculate the ratio of the standard deviation sdV and the arithmetic mean meanV, and the ratio as the relative dirt.

[0063] Wherein, the selected values in the middle position are all values in the middle position after discarding the same number of deviating values at both ends after arranging the values from small to large. Since the SI sensor needs to use water as the medium, from the completion of water filling after the clothes are weighed, the high-frequency real-time data provided by the SI sensor can be repeatedly used to form data with time sequence characteristics with high data density. According to the filtering algorithm with real-time SI reading and previous historical SI reading as input, the real-time trusted SI value with good predictability is obtained. Here, the algorithm for calculating the average after discarding the large deviating values at both ends is used, because it is found in the actual research and development process that the washing machine will generate a low data density (much lower than the data density of the SI reading) but high influence (large change range) noise data due to its periodic motion. Through the existing filtering algorithm, good filtering effect can be obtained under the premise of the law of large numbers.

[0064] Based on the ratio of the trusted SI value and the previous number of historical trusted SI values based on the fluctuation of the historical trusted SI values, the threshold value is judged, and it is determined whether the clothes still have a large amount of washable dissolved dirt, and then it is determined whether the washing machine needs to perform the next stage operation. After the calculation, a relative dirt (i.e. the dirt degree of the water quality in the washing cabin in a period relative to the real-time dirt degree) index can be obtained to guide the subsequent program. This algorithm has good recognition and algorithm robustness for relatively clean and relatively dirty clothes.

[0065] The present application uses a relative threshold algorithm to well represent the degree of residual dissolved dirt of the clothes to guide whether the washing machine needs to continue the washing link. If not, it enters the rinsing link.

[0066] In combination with Figure 2 , the data processing flow of the control method of the embodiment is described in detail:

[0067] After the washing link starts, the data is stored in the sequence in real time, and the sequence is denoted as originV, and the new data is placed at the end of the sequence;

[0068] At each reading, the latest smoothLength values in originV are taken;

[0069] From the obtained smoothLength values, select the 1 / 3 from small to large arrangement in the middle position (the number is rounded up, and ensure that the number of smaller numbers not taken is the same as the number of larger numbers not taken), calculate the arithmetic mean; Here, the algorithm of discarding the larger values at both ends and then averaging is preferred, because in the actual research and development center, it is found that the washing machine will generate a low data density (much lower than the data density of SI reading) but high influence (large change range) noise data due to its periodic motion, using the algorithm of discarding the larger values at both ends and then averaging, under the premise of the law of large numbers, a better filtering effect can be obtained; And this average value is recorded as the reliable SI value at this reading time, and it is put into another sequence, which is recorded as smoothSet; The latest reliable SI value is stored in the end of the smoothSet sequence;

[0070] Each time the SI sensor obtains and stores a reliable SI value, seqLength latest values in smoothSet are obtained;

[0071] Calculate the standard deviation of the obtained seqLength data, denoted as sdV,

[0072] At the same time, calculate the arithmetic mean of the 1 / 3 (the number is rounded up, and ensure that the number of smaller numbers not taken is the same as the number of larger numbers not taken) data in the seqLength data arranged from small to large, denoted as meanV;

[0073] Determine whether sdV / meanV is less than the threshold value criteriaV set in the washing machine, if the former is smaller, it is considered that the residual washable soluble dirt on the clothes is very small, and there is no need to continue washing, the washing machine starts to spin and prepares to enter the rinsing stage, that is, the washing machine stops when it is clean; If the former is greater than or equal to the threshold value, continue the washing stage.

[0074] Here, sdV represents the degree of change of dirt in water in a short period of time, and after calculating meanV representing the absolute degree of dirt in water, a relative dirt index (i.e. the degree of dirt in the water quality change in the washing cabin for a period of time relative to the real-time dirt degree) can be obtained to guide the subsequent program, the overall algorithm and control process, which is simple and effective, low in operation amount, and has good recognition and algorithm robustness for relatively clean and relatively dirty clothes.

[0075] For example Figure 3The effect diagram shows that the abscissa is the washing progress, the ordinate is the reading of the SI sensor, the vertical solid line is the identification mark between the single repeated washing links in the washing stage, the vertical dotted line is that the SI algorithm determines that the clothes are clean and can enter the subsequent rinsing link after the current washing stage ends. The solid line between the scattered points is the trend of the real-time reliable SI value after the filtering algorithm. The traditional washing mode generally washes according to the weight of the clothes, and the test here adopts the most common washing process composed of 6 repeated washing links.

[0076] The application first uses a filtering algorithm to obtain a reliable SI value, that is, the changing solid line between the scattered points in the figure. Then, according to the reliable SI value, the threshold judgment of the relative threshold algorithm is performed after each sensor measurement. In this test, it is determined that the clothes are clean soon after the start of the second washing link. On the washing machine equipped with the algorithm, the washing machine will be guided to perform dehydration after completing the second washing link, and prepare to enter the rinsing process. As can be seen from the figure, if the traditional timed washing mode is used, there is no significantly more different dirt washed out in the subsequent four repeated washing links. That is, the washing machine using the control method of the application can obtain the same washing degree in only 1 / 3 of the washing time.

[0077] The SI sensor referred to in the application can be the sensor provided in the application No. 202020052508.7, entitled "Absorption spectrum sensor monitoring device", or other detection devices that can respond to and detect suspended particulate matter, emulsion, non-suspended particulate matter and emulsion stains in the washing machine.

[0078] Embodiment two

[0079] The embodiment provides a clothes intelligent washing control device based on an SI sensor, which comprises a stain data real-time acquisition device and a controller. The stain data real-time acquisition device is used to acquire stain data of washing water in a washing cabin in real time at a set interval through an SI sensor. The stain data is high-frequency and high-density data.

[0080] The controller comprises the following functional modules:

[0081] The first data module is used to acquire and store real-time stain data of washing water in chronological order. The real-time stain data is high-frequency and high-density data acquired in real time through the SI sensor at a set interval after the washing machine enters a washing process.

[0082] The second data module is used to read a first preset number of real-time stain data in the storage area after a new real-time stain data is added to the storage area, calculate a reliable SI value at the current time according to the first preset number of real-time stain data, and store the reliable SI value.

[0083] a third data module, configured to read a second preset number of the trusted SI values, including the trusted SI values of the current time and the historical time, in the storage area after the trusted SI value of the current time is calculated, and calculate a relative dirtiness according to the read second preset number of the trusted SI values;

[0084] a comparison module, configured to compare the relative dirtiness with a preset dirtiness threshold to obtain a comparison result;

[0085] an execution module, configured to adjust the washing process according to the comparison result.

[0086] Further, the first data module is configured to store the real-time stain data of the washing water in a time sequence originV in chronological order, and the real-time stain data collected later is located at the end of the time sequence originV.

[0087] The second data module includes:

[0088] a second data calculation module, configured to read the first preset number of the stain data, smoothLength, from the end of the time sequence originV when a new real-time stain data is added to the storage area, and select a third preset number of values arranged in the middle position in ascending order from the read first preset number of the real-time stain data, calculate an arithmetic mean value of the selected values arranged in the middle position, and take the arithmetic mean value as a trusted SI value, i.e., a trusted SI value of the current time;

[0089] a second data storage module, configured to store the calculated trusted SI value in a time sequence smoothSet in chronological order, and take the obtained trusted SI value to the end of the time sequence smoothSet;

[0090] The third data module is configured to read the second preset number of the trusted SI values, seqLength, from the end of the time sequence smoothSet after the trusted SI value of the current time is calculated, including the trusted SI values of the current time and the historical time, and calculate a standard deviation sdV of the read second preset number of the trusted SI values, select a fourth preset number of values arranged in the middle position in ascending order from the read second preset number of the trusted SI values, calculate an arithmetic mean value meanV of the selected values arranged in the middle position, calculate a ratio of the standard deviation sdV and the arithmetic mean value meanV, and take the ratio as a relative dirtiness.

[0091] Specifically, the stain data real-time acquisition device comprises a SI sensor arranged at the entrance of a drain pipe of the washing machine in communication with the washing cabin. Since the SI sensor needs water as medium, the SI sensor is arranged at the entrance of the drain pipe, and since water at the entrance is in communication with the washing cabin and liquid flow is relatively slow, a better test environment can be obtained.

[0092] The present application can provide high-frequency real-time data (acquired once every set seconds) by the SI sensor, forming data with time sequence characteristics with high data density. The high-frequency high-density data will help to more real-time determine whether the clothes have been washed, and will not cause delay of the washing machine operation due to long reading interval.

[0093] The above only describes the preferred embodiments of the present application, and it should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A laundry smart washing control method based on an SI sensor, characterized by, The method comprises the steps of: acquiring and storing real-time stain data of washing water in chronological order, the real-time stain data being high-frequency and high-density data collected by the SI sensor at a set interval after the washing machine enters the washing process; after a new real-time stain data is added to the storage area, reading a first preset number of real-time stain data in the storage area, calculating a trusted SI value at the current time based on the first preset number of real-time stain data read, and storing the trusted SI value; after the trusted SI value at the current time is calculated, reading a second preset number of trusted SI values in the storage area, including trusted SI values at the current time and historical times, and calculating a relative dirtiness based on the second preset number of trusted SI values read; comparing the relative dirtiness with a preset dirtiness threshold to obtain a comparison result; adjusting the washing process according to the comparison result; storing the trusted SI value and calculating the relative dirtiness in the following way: storing the calculated trusted SI value in chronological order into a time sequence smoothSet, and placing the obtained trusted SI value at the end of the time sequence smoothSet; after the trusted SI value at the current time is calculated, reading a second preset number seqLength of trusted SI values from the end of the time sequence smoothSet; calculating the standard deviation sdV of the second preset number seqLength of trusted SI values read, and selecting a fourth preset number of values arranged in the middle position from small to large from the second preset number seqLength of trusted SI values read, and calculating the arithmetic mean meanV of the selected values arranged in the middle position; calculating the ratio of the standard deviation sdV and the arithmetic mean meanV, and taking the ratio as the relative dirtiness. 2.The laundry smart washing control method based on an SI sensor according to claim 1, wherein, storing the real-time stain data and calculating the trusted SI value at the current time in the following way: storing the real-time stain data of washing water in chronological order into a time sequence originV, and placing the obtained real-time stain data at the end of the time sequence originV; after a new real-time stain data is added to the storage area, reading a first preset number smoothLength of real-time stain data from the end of the time sequence originV; selecting a third preset number of values arranged in the middle position from small to large from the first preset number smoothLength of real-time stain data read, and calculating the arithmetic mean of the selected values arranged in the middle position, which is taken as the trusted SI value at the current time. 3.The laundry smart washing control method based on the SI sensor of claim 2, wherein, selecting the third preset number or the fourth preset number of values arranged in the middle position in the following way: arranging the corresponding real-time stain data or trusted SI values in a corresponding sequence from small to large, discarding the same number of deviating values at both ends of the corresponding sequence, and then selecting the third preset number or the fourth preset number from the remaining values arranged in the middle position. 4.The laundry smart washing control method based on the SI sensor according to claim 3, wherein, The third preset number or the fourth preset number is one third of the corresponding number series length, and if one third of the corresponding number series length is not an integer, the integer is rounded up, and the number of smaller numbers not taken in the number series is the same as the number of larger numbers not taken. 5.The laundry smart washing control method based on an SI sensor according to claim 1, wherein, According to the comparison result, the washing process is adjusted, specifically including: If the comparison result is that the relative dirtiness is less than the dirtiness threshold criteriaV, the washing process is ended; If the comparison result is that the relative dirtiness is greater than or equal to the dirtiness threshold criteriaV, the washing process is continued to be executed until the relative dirtiness is less than the preset dirtiness threshold criteriaV. 6.A smart laundry washing control device based on SI sensor, characterized in that, The controller comprises the following functional modules: The first data module is used for acquiring and storing the real-time dirt data of the washing water in chronological order, and the real-time dirt data is high-frequency and high-density data collected by the SI sensor at a set interval after the washing machine enters the washing process; The second data module is used for reading the first preset number of real-time dirt data in the storage area after a new real-time dirt data is added to the storage area, calculating the trusted SI value at the current time according to the first preset number of read real-time dirt data, and storing the trusted SI value; the calculated trusted SI value is stored in chronological order and placed in the time sequence smoothSet, and the obtained trusted SI value is located at the end of the time sequence smoothSet; The third data module is used for reading the second preset number of trusted SI values in the storage area after the trusted SI value at the current time is calculated, including the trusted SI values at the current time and historical times, calculating the relative dirtiness according to the second preset number of read trusted SI values; after the trusted SI value at the current time is calculated, the second preset number seqLength of trusted SI values are read from the end of the time sequence smoothSet, including the trusted SI values at the current time and historical times, and the standard deviation sdV of the second preset number seqLength of read trusted SI values is calculated, and the fourth preset number of values arranged in the middle position from small to large are selected from the second preset number seqLength of read trusted SI values, the arithmetic mean meanV of the selected values in the middle position is calculated, the standard deviation sdV of the read trusted SI values is calculated, and the ratio of the standard deviation sdV to the arithmetic mean meanV is calculated, and the ratio is used as the relative dirtiness; The comparison module is used for comparing the relative dirtiness with the preset dirtiness threshold to obtain a comparison result; The execution module is used for adjusting the washing process according to the comparison result. The first data module is used for storing the real-time dirt data of the washing water in chronological order and placing it in the time sequence originV, and the real-time dirt data collected later is located at the end of the time sequence originV; 7.The smart laundry washing control device based on SI sensor according to claim 6, wherein: ​ The second data module comprises: The second data calculation module is configured to, after a real-time stain data is added in the storage area, read the first preset number of stain data from the end of the time sequence originV sequence, and select the third preset number of values arranged in the middle position from small to large from the first preset number of real-time stain data, calculate the arithmetic mean of the selected values in the middle position, and the arithmetic mean is taken as a credible SI value, i.e. the credible SI value of the current time. 8.The smart laundry washing control device based on SI sensor according to claim 6, wherein: The stain data real-time acquisition device comprises an SI sensor arranged at the entrance of the drain pipe of the washing machine in communication with the washing cabin.

9. A laundry washing machine characterized in that: The laundry intelligent washing control device based on the SI sensor is provided.

Images