Washing mode control method based on multi-attribute decision model
Through the washing mode control method of the multi-attribute decision-making model, user scores are used to construct the initial evaluation factor data group and calculate the weighted arithmetic average operator, which solves the problem of difficult washing mode selection for washing machines, realizes personalized and intelligent laundry experience, and reduces costs.
Patent Information
- Application Number
- CN202410395182.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-04-02
- Publication Date
- 2025-10-14
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The washing mode selection of existing washing machines is difficult, and users find it difficult to accurately select the appropriate mode according to their personal needs. In addition, the cost of intelligent solutions is high, which limits their popularization.
A washing mode control method based on a multi-attribute decision model is adopted. The initial evaluation factor data group is constructed through user ratings, the weighted arithmetic average operator is calculated, the optimal washing mode is matched, and consistency verification and historical mode storage are performed.
It realizes personalized and intelligent laundry experience, improves washing effect and user satisfaction, reduces costs, and is applicable to more models.
Smart Images
Figure CN120776544A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application belongs to the technical field of household appliances, and in particular, relates to a washing mode control method based on a multi-attribute decision model. BACKGROUND
[0002] Among current various types of washing machines, different washing modes are often provided, such as standard washing, fast washing, strong washing, wool washing, mixed washing, cold water washing, hair removal washing, and intelligent washing, etc. These washing modes often correspond to different operating parameters, such as water quantity, power consumption, washing time, power consumption, and whether to sterilize, etc. Users themselves are not sensitive to these parameters, and more often select according to personal experience, and the understanding difference of each person, which leads to that the actual needs of users are not correctly and properly applied to actual system operation. On the other hand, when the needs of users are more, such as wanting to wash clean and save water and electricity, and wanting to wash quickly, it will appear that it is difficult to select the washing mode and the decision is fuzzy, and it is not clear which type of mode is a more reasonable decision.
[0003] Although current intelligent intelligent washing schemes (which identify clothes and dirt levels by setting intelligent devices including cameras, etc., without user selection, reducing the operation failure and reducing the operation time, and having a high degree of intelligence), but the disadvantage of the scheme is high cost, at least not universal for overall promotion at present, especially for low-end models, the cost will be a big limitation.
[0004] Therefore, the present application provides a washing mode control method based on a multi-attribute decision model, removes the existing various washing mode operation modules on the operation panel, adds keys on the operation panel for users to adjust parameters according to needs and important factors in the washing process, and users only need to score the current actual situation. It aims to change user needs into appropriate and appropriate washing modes, so that user needs can be correctly and accurately applied to washing machine parameter settings through the logic, and a decision that is most beneficial to users can be made, so it is more promotional. And improve user experience. SUMMARY
[0005] The present application aims to overcome the shortcomings of the prior art, and provides a washing mode control method based on a multi-attribute decision model, which changes user needs into appropriate and appropriate washing modes, so that user needs can be correctly and accurately applied to washing machine parameter settings through the logic.
[0006] To solve the above technical problems, the basic idea of the technical solution of the present application is:
[0007] A washing mode control method based on a multi-attribute decision model is proposed to construct an initial evaluation factor data set a according to the washing attributes of the washing machine. ij ,
[0008] For washing attribute scores, the weighted arithmetic average operator WAA is calculated.
[0009] According to the weighted arithmetic average operator WAA and the initial evaluation factor data group a ij The product between them is the attribute value V, and the washing mode is matched according to the attribute value V.
[0010] Furthermore, a judgment matrix Q is constructed based on the scoring results, and the weight w is calculated for the judgment matrix Q. i The matrix Q1 is obtained, and the matrix Q1 is added row by row to obtain the sum vector WAA1. The sum vector WAA1 is then normalized to obtain the weighted arithmetic average operator WAA.
[0011] Furthermore, the types of evaluation factors are divided into cost type and benefit type; when the evaluation factor is cost type, let r ij =mina ij / a ij , calculate the initial mina ij Attribute a corresponding to the cost factor ij The ratio between them is used to get the normalized evaluation factor data set r ij ;
[0012] When the evaluation factor is benefit type, let r ij =a ij / maxa ij ; Calculate the attribute a corresponding to the benefit factor ij With the initial maxa ij The ratio between them is used to get the normalized evaluation factor data set r ij ;
[0013] At least three washing modes are included, and each washing mode corresponds to a set of initial evaluation factor data sets aij.
[0014] Furthermore, the weighted arithmetic average operator WAA is combined with the initial evaluation factor data set a ij The product of gets the attribute value V;
[0015]
[0016] Furthermore, the weighted arithmetic average operator WAA calculated based on the current score is compared with the evaluation factor data set [a1, a2, a3…an] of each mode. T Multiply to obtain the attribute value V of each mode; compare the attribute values V and select the washing mode corresponding to the maximum attribute value V.
[0017] Further, when the difference between the attribute values V of the at least three washing modes is less than or equal to a set value, the attribute value V is calculated according to the product of the weighted arithmetic average operator WAA derived from the current score and the initial evaluation factor data set aij, and a new washing mode is added.
[0018] Further, the judgment matrix Q is subjected to consistency checking, and if the checking is incorrect, the user is prompted to re-score;
[0019] If the checking is correct, the attribute weight w of the judgment matrix Q is calculated i .
[0020] Further, after re-scoring, the judgment matrix Q is constructed again, the attribute value V is recalculated according to the modified score value, and the washing mode is matched;
[0021] Alternatively, the attribute value V is calculated according to the modified score value, and a new washing mode is added, and stored in the historical washing mode list.
[0022] Further, the washing attributes are scored by using the pairwise comparison method.
[0023] Further, the cost-type attributes include energy consumption, water quantity, washing time, and dosage, and the benefit-type attributes include washing ratio, sterilization rate, and dehydration rate; the attributes with different units are processed into decimals between 0 and 1 by using the dimensionless method, to obtain data of the same order of magnitude.
[0024] After the above technical solution is adopted, the present application has the following beneficial effects compared with the prior art.
[0025] 1. The present application provides a more personalized and intelligent laundry experience for the user. By scoring each washing attribute by the user, combining the calculation of the attribute weight by the analytic hierarchy process, and the calculation of the weighted arithmetic average operator, the optimal washing mode can be formed according to the user's requirements and the characteristics of the laundry, thereby improving the washing effect and user satisfaction.
[0026] 2. The user can score the washing attributes according to his own requirements and preferences, select the washing mode that meets the personal preferences, and improve the degree of personalization of the laundry experience.
[0027] 3. The best washing mode can be matched according to the multi-attribute decision model, the intelligent degree of the washing machine is improved, and the user can enjoy a more personalized laundry experience.
[0028] The specific embodiments of the present application will be described in further detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS
[0029] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the principles of the application. It is to be understood that various embodiments can be used, and structural and functional modifications can be made without departing from the scope of the present application disclosed in the appended claims.
[0030] Figure 1 is a washing mode control logic flow chart of the multi-attribute decision-making model of the present application;
[0031] It should be noted that the drawings and the detailed description are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION
[0032] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments will be described clearly and completely below with reference to the drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.
[0033] In the description of the present application, it should be noted that the terms "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.
[0034] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting", "contacting", "communicating" should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium. For those skilled in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.
[0035] For different washing modes: standard washing, fast washing, strong washing, wool washing, mixed washing, cold water washing, and hair removal washing, different washing machine operation parameters are often corresponded, such as water quantity, washing time, power consumption, rotation speed, cleaning agent, sterilizing agent, and fragrance, and the like, and the quantity of the above-mentioned parameters is hidden information for the user, and the user generally does not need to pay attention to. The user's attention points are more: washing degree (washing ratio), whether sterilization is complete (sterilization rate), whether energy saving (energy consumption), whether water saving (water quantity), whether washing fast (washing time), whether the quantity of the cleaning or sterilizing or fragrance is wasted and consumed too much to cause cost increase (dosing quantity), whether the spin-drying is complete (dehydration rate), and the like.
[0036] The application provides a washing mode control method based on a multi-attribute decision model, characterized in that an initial evaluation factor data group a ij ,
[0037] The washing machine is pre-provided with multiple washing modes, and the washing mode is correspondingly provided with an attribute value V;
[0038] The washing attribute is scored, a judgment matrix Q is constructed according to the scoring result, a weighted arithmetic average operator WAA corresponding to the current scoring result is calculated according to the judgment matrix Q,
[0039] The attribute value V is obtained according to the product between the weighted arithmetic average operator WAA and the initial evaluation factor data group a ij , and the washing mode is matched according to the attribute value V calculated at present.
[0040] The washing attribute includes: washing time, the duration of the washing program, and different clothes may need different washing time.
[0041] Water quantity, the water quantity used in the washing process, which influences the washing effect and washing degree.
[0042] Energy consumption, the power or energy consumed in the washing process, which has influence on environmental protection and energy saving.
[0043] Dosing quantity, the usage quantity of cleaning supplies such as washing agent and softening agent.
[0044] Washing ratio, the washing effect of the washing machine, that is, the cleanliness of the clothes after washing.
[0045] Sterilization rate, the sterilization effect on bacteria in the washing process, which is related to the health condition of the clothes.
[0046] Dehydration rate, the dehydration effect after the washing is finished, which influences the humidity of the clothes and the subsequent airing time.
[0047] These washing attributes are important indicators for evaluating the washing effect and performance of a washing machine. By reasonably setting these attribute values V, the best washing effect for different types of clothes can be achieved, and the efficiency and quality of washing can be improved.
[0048] Initial evaluation factor data group a ij , including washing time, water volume, energy consumption, amount of input, cleaning ratio, sterilization rate, dehydration rate, and corresponding initial values; each washing mode includes an evaluation factor data group a ij , the evaluation factor data set a corresponding to the washing mode ij With the initial evaluation factor data group a ij It’s different content.
[0049] The types of evaluation factors are divided into cost type and benefit type; cost type attributes include energy consumption, water volume, washing time, and dosage, and benefit type attributes include cleaning ratio, sterilization rate, and dehydration rate; attributes of different units are processed into decimals between 0 and 1 using the dimensionless method to obtain data of the same order of magnitude.
[0050] When the evaluation factor is cost type, let r ij =mina ij / a ij , calculate the initial value mina ij Attribute a corresponding to the cost factor ij The ratio between them is used to get the normalized evaluation factor data set r ij ;
[0051] When the evaluation factor is benefit type, let r ij =a ij / maxa ij ; Calculate the attribute a corresponding to the benefit factor ij With the initial value maxa ij The ratio between them is used to get the normalized evaluation factor data set r ij ;
[0052] Among them, mina ij and maxa ij is the initial evaluation factor data group a ij The corresponding attributes in are normalized to obtain the evaluation factor data group corresponding to each washing mode.
[0053] The initial evaluation factor data set is [a1, a2, a3…, an] T , maxa ij represents the element in row i and column j where the benefit-based evaluation factor is located; mina ij It represents the element in row i and column j of the cost-type evaluation factor data group.
[0054] Because the cost is less the better, so defined as min, the benefit is higher the better so defined as max.
[0055] The attribute value V corresponding to the preset washing mode of the washing machine is obtained by the product of the set weighted arithmetic average operator WAA and the evaluation factor data set corresponding to each washing mode.
[0056] When the user needs to select the washing mode, the washing machine matches the washing mode according to the user's score result.
[0057] The washing attributes are scored by using the pairwise comparison method; for example, the pairwise comparison method shown in Table 1.
[0058] Table 1
[0059] wash ratio sterilization rate dehydration rate energy consumption water amount washing time dosing amount wash ratio sterilization rate dehydration rate energy consumption water amount washing time dosing amount
[0060] The scale includes: 1 represents the same importance, 3 represents slightly important, 5 represents obviously important, 7 represents strong importance, 9 represents extreme importance, and 2, 4, 6, and 8 represent the above-mentioned middle values; for example, 2 represents a value between 1 and 3; the inverse, if the scale is 3, then B and A are 1 / 3 compared to A.
[0061] A judgment matrix Q is constructed according to the score result, and a weighted arithmetic average operator WAA corresponding to the current score result is calculated according to the judgment matrix Q,
[0062]
[0063] The weight w of the judgment matrix Q is calculated i The matrix Q1 is obtained, the sum vector WAA1 is obtained by adding the matrix Q1 by row, and the weighted arithmetic average operator WAA is obtained by normalizing the sum vector WAA1.
[0064] The weight is calculated by using the "sum product method". The matrix Q1 is obtained by normalizing each column of the judgment matrix Q.
[0065]
[0066] The sum vector WAA1=(0.53, 1.16, 1.16, 1.16, 0.98) is obtained by adding the matrix Q1 by row, and the weighted arithmetic average operator WAA=(0.11, 0.23, 0.23, 0.23, 0.20) is obtained by normalizing the WAA1. The operator WAA reflects the preference degree of the user for different factors, and the greater the preference degree, the greater the value, such as the washing time or the washing ratio. The sum of the values in the operator is 1, which represents the total weight of the user.
[0067] Now that we have the weighted arithmetic average operator (WAA) and the dimensionless evaluation factor data set, we can calculate the attribute value V through vector multiplication. This method allows us to calculate the attribute value V for different washing mode categories. The larger the attribute value V, the more favorable the decision for our users. By comparing the attribute values V, we can determine the washing mode we need most.
[0068] According to the weighted arithmetic average operator WAA and the initial evaluation factor data group a ij The product between them is the attribute value V, and the washing mode is matched according to the attribute value V currently calculated.
[0069] Weighted arithmetic average operator WAA and initial evaluation factor data group a ij The product of gets the attribute value V;
[0070] At this time a ij is the initial evaluation factor data set.
[0071] After constructing the judgment matrix Q based on the scoring results, the judgment matrix Q can be checked for consistency. If the check is incorrect, the user will be prompted to re-score.
[0072] If the verification is correct, the attribute weight w is calculated for the judgment matrix i .
[0073] After re-scoring, the judgment matrix Q is re-constructed, and based on the modified score value, the attribute value V is recalculated and the washing mode is matched;
[0074] Alternatively, the attribute value V is calculated according to the modified score value, a new washing mode is added, and the new mode is stored in the historical washing mode list.
[0075] Furthermore, the weighted arithmetic average operator WAA calculated based on the current score is compared with the evaluation factor data set [a1, a2, a3…an] of each mode. T Multiply to obtain the attribute value V of each mode; compare the attribute values V and select the washing mode corresponding to the maximum attribute value V.
[0076] When the attribute value V is the smallest, it means that the attribute value V is least compatible with the user's needs.
[0077] For example, after calculating the weighted arithmetic average operator WAA based on the user ratings, it is multiplied by the evaluation factor data set a1, a2, a3…an]T of each washing mode to obtain the attribute value V under each washing mode;
[0078] For example, it is found that the attribute value V corresponding to the gentle washing mode is 0.3; the attribute value V in the standard washing mode is 0.7; the attribute value V in the strong washing mode is 0.4; by comparing the relationship between the washing modes corresponding to the attribute values V, the standard washing mode with an attribute value V of 0.7 is selected according to user needs.
[0079] Based on the user's preference rating, there may be a strong washing mode, a standard washing mode, or a gentle washing mode that corresponds to a large attribute value V, or a small attribute value V; however, the current rating is only for the current user's rating, and the weighted arithmetic average operator WAA will be recalculated during the next rating to obtain a different attribute value V.
[0080] The washing mode of the present invention can be set to more washing modes, and the washing mode can also be set according to the needs of the washing machine product, such as a care washing mode and a quick washing mode.
[0081] When the difference between the attribute values V of at least three washing modes is less than or equal to the set value, the attribute value V is calculated based on the product of the weighted arithmetic average operator WAA obtained from the current score and the initial evaluation factor data group aij and a new washing mode is added.
[0082] The set value can be any number between 0.1 and 0.3, where the difference between the two attribute values V is no greater than;
[0083] For example, when the calculated attribute values V corresponding to each mode are V1 is 0.2, V2 is 0.3, and V3 is 0.5, the difference between V1 and V2 is 0.1; the difference between V2 and V3 is 0.2; then V1, V2, and V3 may not meet the user's needs. At this time, according to the current weighted arithmetic average operator WAA, the new attribute value V is multiplied by the initial evaluation factor data group, and the washing mode is customized for the attribute value V and added to the historical washing mode list.
[0084] The user can select the appropriate attribute value V calculated by the washing machine controller based on his or her own scoring results, create a new washing mode under the corresponding attribute value V and customize the name.
[0085] Washing attributes can be scored through mobile devices, cloud platforms, or directly through the buttons on the washing machine operation panel to enter the scoring page and score on the washing machine display. Click each scoring item to pop up the scoring level.
[0086] Control panel buttons: Design buttons and a display interface on the washing machine's control panel for users to rate washing attributes. Each button represents a specific attribute, and users can rate it by pressing the corresponding button. Pressing a button displays the rating page for that attribute.
[0087] The display interface on the operation panel can be used in combination with the button. When the button is pressed, the display interface shows the rating page.
[0088] Different scoring indicators correspond to different interval levels. For example, 1 represents that both are equally important, and the better parameter is selected.
[0089] The corresponding interval levels of the cleaning ratio are: 0-30% medium, 31%-60% good, 61%-100% good.
[0090] The interval ranges of the sterilization rates correspond to interval levels: 0-30% low, 31%-60% medium, and 61%-100% high.
[0091] The dehydration rate ranges correspond to the following levels: 0-30% weak, 31%-60% general, and 61%-100% strong.
[0092] The energy consumption range in the cost type corresponds to the interval level: 0-3kWh high, 4-6kWh medium, and 7-10kWh low.
[0093] The water volume range corresponds to the interval level: 0-3L: less, 4-6L: moderate, 7-10L: more.
[0094] The interval ranges of washing time correspond to interval levels: 0-20 minutes: short, 25-40 minutes: moderate, 45-60 minutes: long.
[0095] The interval levels corresponding to the dosage range are: 0-30 ml: less, 31-60 ml: moderate, 65-100 ml: more.
[0096] User rating: Users can rate different washing attributes according to their needs by pressing the corresponding buttons during the washing process. The rating refers to the level of parameters corresponding to the selected attribute, such as high, medium, and low; or users can enter the unit parameters corresponding to the attribute themselves, and the washing machine will match the level corresponding to the parameter range based on the user's input parameters.
[0097] Rating Records: The system records user ratings on the control panel and stores them for future processing. Users can rate wash attributes based on their preferences and needs, thereby determining the most appropriate wash mode for their washing machine. This personalized rating method improves the user experience and ensures that the laundry process is more aligned with user expectations.
[0098] Combined with the scoring results of each attribute, a decision model is constructed, and the weighted arithmetic average operator WAA is obtained according to the decision model. The weighted arithmetic average operator WAA and the normalized evaluation factor data group a are combined. ijThe product between them is used to obtain the attribute value V, and the washing mode is matched according to the attribute value V;
[0099] Normalization is the process of evaluating the data set a ij Perform normalization processing and evaluate the factor data group a ij The types of evaluation factors in are divided into cost type and benefit type; when the evaluation factor is cost type, let r ij =min ij / a ij ;
[0100] When the evaluation factor is benefit type, let r ij =a ij / max ij ; Use the dimensionless method to process the attributes of different units into decimals between 0 and 1 to obtain data of the same magnitude.
[0101] In this embodiment, before matching the final washing mode, a consistency check can be performed to construct a consistency matrix so that each row and column of the matrix is in a multiple relationship; that is, if a ij ×a jk =a ik , then the matrix is called a consistent matrix. A consistency check is required before calculating attribute weights.
[0102] Compare each attribute of the washing mode in the decision model pairwise to obtain the attribute weight w i ; Each attribute corresponds to an attribute weight w i .
[0103] The attribute weight w for each attribute i Perform a sum operation to obtain the weighted arithmetic average operator WAA.
[0104] When a new washing mode needs to be added according to the attribute value V, confirm the evaluation factor data group a ij The washing attributes in the software, including required washing time, water volume, energy consumption, amount of water dispensed, cleaning ratio, sterilization rate, and dehydration rate, are calculated to calculate the attribute value V. The user can then customize the washing mode based on the calculated attribute value V. This provides users with more choices and optimizes the washing experience.
[0105] Provide a more personalized and intelligent laundry experience. Attribute weights are calculated, depending on the application scenario and method used in the multi-attribute decision-making model. In some multi-attribute decision-making models, the calculated attribute weights can be calculated using mathematical or statistical methods. For example, in the Analytic Hierarchy Process (AHP), the relative weights of various attributes can be calculated through expert questionnaires or pairwise comparison matrices. Alternatively, in data-driven methods such as principal component analysis (PCA), attribute weights are derived through mathematical operations.
[0106] In practical applications, calculated attribute weights are generally more objective and scientific, yielding relatively reasonable weights based on data and analysis. Pre-set attribute weights, on the other hand, rely more on the decision maker's subjective awareness and experience. Therefore, choosing the appropriate attribute weighting method depends on the specific decision context, available information, and the decision maker's preferences and risk tolerance.
[0107] The attribute weight reflects that the attribute is a more important washing attribute relative to other attributes.
[0108] Attribute weighting in a washing machine specifies the degree to which each wash attribute influences the final wash mode selection. By assigning weights to each attribute, the importance of different attributes in wash mode selection can be adjusted based on user preferences and needs, achieving personalized washing results.
[0109] Users can adjust the evaluation results of each washing attribute according to their needs and preferences. The user's evaluation results can influence the attribute weight to achieve a washing effect that meets their needs. For example, if the user attaches more importance to the sterilization rate and cleaning ratio, they can focus on scoring these two attributes to ensure that the clothes are fully cleaned and disinfected.
[0110] Setting attribute weights can help users optimize washing results and ensure that the washing process meets their needs and expectations. By adjusting the weights of different attributes, you can implement targeted washing plans and improve washing efficiency and results.
[0111] Personalized attribute weight settings can enhance the user experience, allowing users to more conveniently select a washing mode that suits their needs, thereby improving the usability and user satisfaction of the washing machine.
[0112] Normalizing data using dimensionless processing can usually be achieved through the following methods:
[0113] Min-max normalization: Also known as deviation normalization, it is the process of linearly mapping the original data to the [0,1] interval.
[0114] Z-score normalization: also known as standard deviation normalization, transforms the original data so that its mean is 0 and the standard deviation is 1. The specific formula is as follows:
[0115] Decimal scaling normalization: Normalization is performed by moving the decimal point position of the data.
[0116] It can help convert data of different ranges and units into a unified scale, making it easier to compare and analyze. The choice of method depends on the characteristics of the data and the specific application scenario.
[0117] For example, if a user is more concerned about energy and water conservation, he may adjust the scores of cost-type attributes such as energy consumption, water volume and washing time higher; while another user may be more concerned about the washing ratio, sterilization rate and dehydration rate, and he will adjust the scores of these benefit-type attributes higher, thereby choosing the washing mode that best meets his needs.
[0118] Calculate the attribute weights between each two sets of attribute values V using the analytic hierarchy process;
[0119] Each attribute corresponds to an attribute weight, and a weighted arithmetic average operator is calculated using the product of the attribute value V and the attribute weight, and a corresponding washing mode is formed according to the weighted arithmetic average operator.
[0120] The Analytic Hierarchy Process (AHP) is a quantitative analysis method used to solve complex decision-making problems. It helps determine the relative importance and weight of various factors. In the laundry attribute selection process, we can use the AHP method to calculate the attribute weights between each pair of attribute values V and form a corresponding washing pattern based on the calculated results.
[0121] The general steps for calculating attribute weights include:
[0122] Establish a hierarchical structure: First, determine the hierarchy of laundry attributes, dividing cost-related and benefit-related attributes into different levels. For example, cost-related and benefit-related attributes form the first level, and specific attributes (energy consumption, water volume, wash time, dosage, wash ratio, sterilization rate, dehydration rate) form the second level.
[0123] Construct a judgment matrix: For each pair of attributes, create a pairwise comparison judgment matrix. Based on expert opinion or personal subjective experience, fill in the importance comparison value between each pair of attributes. This is typically scored on a scale of 1-9, where 1 indicates equal importance and 9 indicates extreme difference in importance.
[0124] Calculate consistency indicators: Perform consistency checks on each judgment matrix to ensure the consistency of expert judgments or subjective scores.
[0125] Calculate the eigenvector: The weight of each attribute is obtained by calculating the eigenvector and the maximum eigenvalue.
[0126] Consistency check: Finally, a consistency check is performed to ensure the rationality and stability of the calculation results.
[0127] Based on the calculated attribute weights, we can multiply each attribute value V by its corresponding attribute weight and sum them to obtain a weighted average value according to the principle of the weighted arithmetic average operator. Finally, the corresponding washing mode is formed based on these weighted average values to achieve a personalized washing effect.
[0128] When the user is not satisfied with the final matched washing mode, the user can modify the score value of the washing attribute, and recalculate and match a new washing mode based on the modified score value.
[0129] A new washing pattern can be formed based on the existing washing pattern by using a method of calculating a weighted arithmetic mean operator;
[0130] Alternatively, a new washing mode is formed based on the attribute value V and the attribute weight corresponding to the current scoring result, and is added to the existing washing mode.
[0131] If the user is not satisfied with the final matched washing mode, they can recalculate and match a new washing mode by modifying the washing attribute score. This dynamic adjustment method allows users to personalize the washing mode selection based on their actual needs and feedback.
[0132] Modify attribute ratings: Based on user feedback, you can modify the ratings of certain attributes, i.e., adjust the weights of washing attributes. For example, users can increase or decrease the importance they place on a certain attribute to reflect their actual needs.
[0133] Recalculate attribute weights: Recalculate attribute weights based on the modified attribute score values to obtain new attribute weight values.
[0134] Forming a new washing mode based on the new attribute weights: According to the new attribute weight values and the current washing mode, the score of each washing mode can be recalculated using a weighted arithmetic average operator method to form a new washing mode.
[0135] Update washing mode library: add new washing modes to existing washing modes for users to choose from.
[0136] Attribute weight w i Used to evaluate user preferences; evaluation factor data set a ij Used to evaluate the quality of washing machine parameters such as the wash ratio and make decisions to determine the optimal strategy.
[0137] This approach allows users to dynamically adjust the washing mode according to their needs, resulting in a more personalized washing experience. At the same time, continuously updating and optimizing the washing mode library can improve user experience and satisfaction.
[0138] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with the preferred embodiments as above, it is not intended to limit the present application, and any skilled person in the art can make some changes or modifications to the above-mentioned technical content with the prompt to make equivalent embodiments with equivalent changes, but as long as it does not deviate from the technical solution of the present application, and any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application still belongs to the scope of the present application.
Claims
1. A washing mode control method based on a multi-attribute decision model, characterized in that: Construct the initial evaluation factor data set a according to the washing properties of the washing machine ij , Scoring of washing attributes, calculating weighted arithmetic average operator WAA, According to the weighted arithmetic average operator WAA and the initial evaluation factor data group a ij The product between them is the attribute value V, and the washing mode is matched according to the attribute value V.
2. The washing mode control method based on the multi-attribute decision model according to claim 1, characterized in that: Construct a judgment matrix Q based on the scoring results, and calculate the weight w for the judgment matrix Q i The matrix Q1 is obtained, and the matrix Q1 is added row by row to obtain the sum vector WAA1. The sum vector WAA1 is then normalized to obtain the weighted arithmetic average operator WAA.
3. The washing mode control method based on the multi-attribute decision model according to claim 2, characterized in that: The types of evaluation factors are divided into cost type and benefit type. When the evaluation factor is cost type, let r ij =mina ij / a ij , calculate the initial mina ij Attribute a corresponding to the cost factor ij The ratio between them is used to get the normalized evaluation factor data set r ij ; When the evaluation factor is benefit type, let r ij =a ij / maxa ij ; Calculate the attribute a corresponding to the benefit factor ij With the initial maxa ij The ratio between them is used to get the normalized evaluation factor data set r ij ; At least three washing modes are included, and each washing mode corresponds to a set of initial evaluation factor data sets aij.
4. The washing mode control method based on the multi-attribute decision model according to claim 3 is characterized in that: Weighted arithmetic average operator WAA and initial evaluation factor data group a ij The product of gets the attribute value V; 5. The washing mode control method based on the multi-attribute decision model according to claim 4 is characterized in that: The weighted arithmetic average operator WAA calculated based on the current score and the evaluation factor data set [a1, a2, a3…an] of each mode T Multiply to obtain the attribute value V of each mode; compare the attribute values V and select the washing mode corresponding to the maximum attribute value V.
6. The washing mode control method based on the multi-attribute decision model according to claim 5, characterized in that: When the difference between the attribute values V of at least three washing modes is less than or equal to the set value, the attribute value V is calculated based on the product of the weighted arithmetic average operator WAA obtained from the current score and the initial evaluation factor data group aij and a new washing mode is added.
7. The washing mode control method based on a multi-attribute decision model according to claim 6, characterized in that: Perform consistency check on the judgment matrix Q. If the check is wrong, prompt the user to re-score; If the verification is correct, the attribute weight w is calculated for the judgment matrix Q i .
8. The washing mode control method based on the multi-attribute decision model according to claim 7, characterized in that: After re-scoring, the judgment matrix Q is re-constructed, and based on the modified score value, the attribute value V is recalculated and the washing mode is matched; Alternatively, the attribute value V is calculated according to the modified score value, a new washing mode is added, and the new mode is stored in the historical washing mode list.
9. A washing mode control method based on a multi-attribute decision model according to claims 1-8, characterized in that: Washing attributes were scored using a pairwise comparison method.
10. The washing mode control method based on the multi-attribute decision model according to claim 9, characterized in that: Cost-type attributes include energy consumption, water volume, washing time, and dosage; benefit-type attributes include cleaning ratio, sterilization rate, and dehydration rate; attributes of different units are processed into decimals between 0 and 1 using the dimensionless method to obtain data of the same order of magnitude.