A method and device for predicting remaining life of washing machine

Abstract

The embodiments of the present invention provide a method and device for predicting the remaining life of a washing machine, which comprises the following steps: obtaining operating parameters of the washing machine; determining the influence coefficients of the operating parameters on a plurality of materials, and determining separate aging indexes for the materials based on the influence coefficients; determining associated materials with an associated relationship from the plurality of materials; determining an interaction effect path between the plurality of associated materials for representing the influence of a material on the aging of other materials; determining a comprehensive aging index for characterizing the aging relationship of the materials through the interaction effect path and the separate aging index; and generating a life prediction result for characterizing the remaining life of the washing machine based on the comprehensive aging index, thereby realizing the prediction of the life of the washing machine.

Description

Technical Field

[0001] The present invention relates to the technical field of remaining life prediction for washing machines, and in particular to a remaining life prediction method for washing machines, a remaining life prediction device for washing machines, an electronic device and a computer-readable storage medium. Background Art

[0002] With the continuous development of washing machine technology, users have higher and higher requirements for the service life and performance maintenance of equipment. During the long-term use of washing machines, various components (such as rubber seals, metal drums, plastic components, etc.) gradually age under the repeated effects of humidity, temperature and mechanical force, which will affect the performance and safety of the equipment. Traditional maintenance methods lack accurate prediction and active protection of material aging, and cannot meet users' requirements for long-term stability of equipment. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, electronic device and computer-readable storage medium for predicting the remaining life of a washing machine to overcome the above problems or at least partially solve the above problems.

[0004] The embodiment of the present invention discloses a method for predicting the remaining life of a washing machine, comprising:

[0005] Get the operating parameters of the washing machine;

[0006] Determining the influence degree coefficients of the operating parameters on a plurality of materials, and determining individual aging indices for the materials based on the influence degree coefficients;

[0007] Determining related materials having a related relationship from the plurality of said materials;

[0008] Determine the interaction effect paths between multiple related materials to represent the impact of materials on the aging of other materials;

[0009] Determine a comprehensive aging index for characterizing the aging relationship of the material through the influencing interaction effect path and the individual aging index;

[0010] Based on the comprehensive aging index, a life prediction result for characterizing the remaining life of the washing machine is generated.

[0011] Optionally, the operating parameter includes a temperature parameter, and the step of determining the influence coefficient of the operating parameter on the multiple materials includes:

[0012] determining a material aging rate constant of the material as a function of temperature, and an activation energy of the material;

[0013] Based on the material aging rate constant related to the temperature of the material and the activation energy, a coefficient of the degree of temperature influence of the temperature parameter on the material is determined.

[0014] Optionally, the operating parameter includes a humidity parameter, and the step of determining the influence coefficient of the operating parameter on the multiple materials includes:

[0015] determining a material aging rate constant of the material related to humidity, and material characteristic parameters of the material;

[0016] Based on the material aging rate constant related to the material and the material characteristic parameter, a coefficient of the humidity influence of the humidity parameter on the material is determined.

[0017] Optionally, the operating parameter includes a vibration coefficient, and the step of determining the influence coefficient of the operating parameter on the multiple materials includes:

[0018] determining a material aging rate constant of the material associated with vibration, and a material constant of the material;

[0019] The vibration influence coefficient of the vibration coefficient on the material is determined based on the material aging rate constant related to the material and the material constant.

[0020] Optionally, the step of determining a separate aging index for the material based on the influence degree coefficient comprises:

[0021] The temperature influence degree coefficient, the humidity influence degree coefficient and the vibration influence degree coefficient are used to determine a separate aging index for the material.

[0022] Optionally, the step of determining the interaction effect paths between a plurality of associated materials for representing the influence of a material on the aging of other materials comprises:

[0023] Determine target materials that have a correlation relationship with the material;

[0024] Determining an aging influence coefficient of the target material on the material;

[0025] The aging influence coefficient and the separate aging index are used to calculate the influencing interaction effect path.

[0026] Optionally, it also includes:

[0027] When the washing machine completes the washing operation, the step of determining the aging influence coefficient of the target material on the material is re-executed.

[0028] Optionally, it also includes:

[0029] When the comprehensive aging index is greater than a preset threshold, a maintenance reminder message is generated.

[0030] Optionally, it also includes:

[0031] When it is determined that the user ignores the maintenance reminder information, determining the target washing parameter;

[0032] The next washing operation is performed based on the washing parameters.

[0033] The embodiment of the present invention also discloses a device for predicting the remaining life of a washing machine, comprising:

[0034] An operating parameter acquisition module, used to acquire the operating parameters of the washing machine;

[0035] A separate aging index determination module, used to determine the influence degree coefficient of the operating parameters on multiple materials, and determine the separate aging index for the material based on the influence degree coefficient;

[0036] An associated material determination module, used for determining associated materials having an associated relationship from a plurality of the materials;

[0037] An influence interaction effect path determination module is used to determine the interaction effect paths between multiple associated materials for representing the influence of a material on the aging of other materials;

[0038] A comprehensive aging index determination module, used to determine a comprehensive aging index for characterizing the aging relationship of the material through the influencing interaction effect path and the individual aging index;

[0039] The life prediction result generating module is used to generate a life prediction result for characterizing the remaining life of the washing machine based on the comprehensive aging index.

[0040] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0041] The memory is used to store computer programs;

[0042] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0043] The embodiment of the present invention further discloses a computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processors to execute the method described in the embodiment of the present invention.

[0044] The embodiments of the present invention include the following advantages:

[0045] According to an embodiment of the present invention, operating parameters of a washing machine are obtained; influence coefficients of the operating parameters on a plurality of materials are determined, and individual aging indexes for the materials are determined based on the influence coefficients; associated materials having an associated relationship are determined from the plurality of materials; interaction effect paths between the plurality of associated materials are determined for representing the influence of a material on the aging of other materials; a comprehensive aging index for characterizing the aging relationship of the materials is determined through the interaction effect paths and the individual aging indexes; and a life prediction result for characterizing the remaining life of the washing machine is generated based on the comprehensive aging index, thereby achieving a prediction of the life of the washing machine.

[0046] Furthermore, the following beneficial effects can be achieved:

[0047] Comprehensiveness: The impact of multiple factors on material aging is taken into account.

[0048] Accuracy: By building an interaction effect model, the accuracy of the prediction is improved.

[0049] Interpretability: The physical meaning of the model is clear, making it easy to understand and improve. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flowchart of the steps of a method for predicting the remaining life of a washing machine provided in an embodiment of the present invention;

[0051] Figure 2 is a flow chart of a method for predicting the remaining life of a washing machine provided in an embodiment of the present invention;

[0052] Figure 3 is a structural block diagram of a device for predicting remaining life of a washing machine provided in an embodiment of the present invention;

[0053] Figure 4 is a hardware structure block diagram of an electronic device provided in an embodiment of the present invention;

[0054] Figure 5 is a schematic diagram of a computer-readable medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] Reference Figure 1 , shows a flowchart of a method for predicting the remaining life of a washing machine provided in an embodiment of the present invention, which may specifically include the following steps:

[0057] Step 101, obtaining the operating parameters of the washing machine;

[0058] Step 102, determining the influence coefficients of the operating parameters on multiple materials, and determining individual aging indexes for the materials based on the influence coefficients;

[0059] Step 103, determining associated materials having an associated relationship from the multiple materials;

[0060] Step 104, determining an interaction effect path between a plurality of associated materials for representing the influence of a material on aging of other materials;

[0061] Step 105, determining a comprehensive aging index for characterizing the aging relationship of the material through the influencing interaction effect path and the individual aging index;

[0062] Step 106: Generate a life prediction result for characterizing the remaining life of the washing machine based on the comprehensive aging index.

[0063] In a specific implementation, the washing machine life prediction model of the embodiment of the present invention monitors and analyzes the operating parameters of the washing machine, combines the material aging model, evaluates the aging degree of each component of the washing machine, and predicts the remaining life of the washing machine.

[0064] Exemplarily, the operating parameters may include, but are not limited to: Temperature T: the temperature in the drum, in degrees Celsius. Humidity H: the relative humidity in the drum, in percentage. Vibration intensity V: the vibration amplitude of the drum, in millimeters. Time t: the accumulated usage time, in hours. Number of washes n: the total number of accumulated washes.

[0065] Specifically, the temperature, humidity, vibration amplitude, cumulative operating time and number of washes inside the drum can be monitored and recorded in real time through built-in sensors or external devices, and key data of the washing machine during operation can be collected to provide a basis for the establishment of subsequent aging models.

[0066] In the embodiment of the present invention, the influence coefficient of the operating parameter on various materials can be determined, and the individual aging index for the material can be determined based on the influence coefficient;

[0067] In a specific implementation, the influence degree coefficient can be used to characterize the influence degree of operating parameters on different materials; the embodiment of the present invention can determine the influence degree coefficient of parameters such as temperature, humidity, vibration, etc. on the aging rate of different materials through experiments or historical data analysis, and calculate the individual aging index of each material by combining the influence degree coefficient and operating parameters. The index reflects the aging degree of the material under the current working conditions, thereby quantifying the influence degree of different operating parameters on the aging of each material, and calculating the individual aging index of each material.

[0068] In the embodiment of the present invention, associated materials with an associated relationship can be determined from a plurality of the materials; in a specific implementation, the interaction between different materials can be analyzed through material science knowledge and experimental data, thereby identifying those aging processes that affect each other, laying the foundation for subsequent interaction effect analysis. For example, the corrosion of metal parts may accelerate the aging of rubber seals, so metal and rubber are associated materials.

[0069] In a specific implementation, the embodiment of the present invention can determine the interaction effect path between multiple related materials to represent the influence of materials on the aging of other materials; by representing the related materials with nodes and representing the influence relationship between them with edges. Each edge represents an interaction effect and is assigned a weight to represent the strength of the influence, thereby establishing a network to represent the mutual influence relationship between different materials.

[0070] In a specific implementation, an embodiment of the present invention can determine a comprehensive aging index used to characterize the aging relationship of the materials through the influencing interaction effect path and the individual aging index; the comprehensive aging index reflects the overall aging state of all materials and the results of their mutual influence, and comprehensively considers the individual aging and mutual influence of the materials to obtain a more accurate overall aging index.

[0071] In a specific implementation, an embodiment of the present invention can generate a life prediction result for characterizing the remaining life of the washing machine based on the comprehensive aging index, establish a relationship model between the comprehensive aging index and the equipment life through historical data, and predict the remaining life of the equipment based on the current comprehensive aging index.

[0072] Steps 101-106 are a comprehensive washing machine life prediction model, which aims to achieve the goal of early maintenance and extending the service life of the equipment by monitoring the operating parameters of the washing machine, analyzing the aging of materials, and predicting the remaining life of the equipment.

[0073] By introducing the influence degree coefficient and the separate aging index, the effect of different operating parameters on material aging is quantified, and a more sophisticated material aging model is established.

[0074] By introducing associated materials and interaction effect paths, the mutual influence between different materials is taken into account, which improves the accuracy of the model.

[0075] Based on the individual aging index and the comprehensive aging index, an equipment life prediction model was established, which provided a basis for equipment maintenance.

[0076] In view of the complex mechanism of material aging, the influencing factors are quantified, and the qualitative aging effects are converted into quantitative values ​​through the influence degree coefficient, which is convenient for calculation and analysis; considering multiple materials at the same time, by establishing separate aging indexes, the aging conditions of different materials are evaluated separately; in view of the lack of accurate prediction models, a comprehensive aging index is established, which comprehensively considers the individual aging and interaction of materials, improves the accuracy of prediction, and constructs a life prediction model. Based on historical data and current status, a life prediction model is established to provide a basis for equipment maintenance. In view of the problem of weak user maintenance awareness, life prediction results are generated, and users are reminded to perform maintenance by predicting the remaining life of the equipment, and personalized reminders are given, and personalized maintenance suggestions are provided according to the specific situation of the equipment. In view of the problem of interactive aging effects of materials, an interactive effect path is proposed. By establishing the interactive effect path, the influence relationship between different materials is clarified, the comprehensive aging index is determined, and the interaction of materials is comprehensively considered to improve the accuracy of prediction.

[0077] Through the above steps, the model effectively solves the problem of material aging in washing machines, which is specifically reflected in:

[0078] Improved awareness of equipment status: Accurately assess equipment health status through real-time monitoring of operating parameters.

[0079] Enhanced prediction capabilities: By establishing a life prediction model, early warning of equipment failures can be given to avoid sudden failures.

[0080] Provides a scientific basis for maintenance: provides data support for equipment maintenance, improving the pertinence and effectiveness of maintenance.

[0081] Extended equipment service life: Through timely maintenance, the service life of the equipment is extended and the maintenance cost is reduced.

[0082] A variety of factors are taken into account: not only the aging of the material itself, but also the interaction between different materials.

[0083] Quantified the aging process: By introducing the influence coefficient and aging index, the qualitative aging process is converted into a quantitative indicator.

[0084] A feasible solution is provided: Based on the prediction results of the model, corresponding maintenance strategies can be formulated.

[0085] According to an embodiment of the present invention, operating parameters of a washing machine are obtained; influence coefficients of the operating parameters on a plurality of materials are determined, and individual aging indexes for the materials are determined based on the influence coefficients; associated materials having an associated relationship are determined from the plurality of materials; interaction effect paths between the plurality of associated materials are determined for representing the influence of a material on the aging of other materials; a comprehensive aging index for characterizing the aging relationship of the materials is determined through the interaction effect paths and the individual aging indexes; and a life prediction result for characterizing the remaining life of the washing machine is generated based on the comprehensive aging index, thereby achieving a prediction of the life of the washing machine.

[0086] Furthermore, the following beneficial effects can be achieved:

[0087] Comprehensiveness: The impact of multiple factors on material aging is taken into account.

[0088] Accuracy: By building an interaction effect model, the accuracy of the prediction is improved.

[0089] Interpretability: The physical meaning of the model is clear, making it easy to understand and improve.

[0090] Based on the above embodiment, a variant embodiment of the above embodiment is proposed. It should be noted that in order to make the description concise, only the differences from the above embodiment are described in the variant embodiment.

[0091] In an optional embodiment of the present invention, the operating parameter includes a temperature parameter, and the step of determining the influence coefficient of the operating parameter on the plurality of materials includes:

[0092] determining a material aging rate constant of the material as a function of temperature, and an activation energy of the material;

[0093] Based on the material aging rate constant related to the temperature of the material and the activation energy, a coefficient of the degree of temperature influence of the temperature parameter on the material is determined.

[0094] The Arrhenius model is a very important model in chemical reaction rate theory, which can be used to describe the effect of temperature on chemical reaction rate. In material aging research, the Arrhenius model is widely used to describe the effect of temperature on material aging rate.

[0095] Exemplarily, the embodiment of the present invention may be implemented by the following formula (Arrhenius model).

[0096]

[0097] in, is the material node, Indicates temperature The aging effects on the material, is the material aging rate constant related to temperature, is the activation energy of the material, is the gas constant, is the absolute temperature, It is the accumulated usage time in hours.

[0098] Physical meaning:

[0099] activation energy( ): Indicates the minimum energy required for a molecule to undergo a chemical reaction. The greater the activation energy, the more difficult the reaction and the more stable the material.

[0100] temperature( ): As the temperature rises, molecular motion intensifies, the collision frequency increases, and the reaction rate accelerates.

[0101] Aging rate constant ( ): reflects the aging rate of a material at a specific temperature.

[0102] Applications in prediction of material aging in washing machines include:

[0103] Determine the aging rate constant and activation energy of the material:

[0104] Experimental method: Place samples of different materials in environments with different temperatures, measure their performance changes regularly, and obtain the aging rate constant and activation energy of the material by fitting the experimental data.

[0105] Literature data: For some common materials, you can consult the literature to obtain their aging rate constants and activation energy data.

[0106] Calculate the temperature influence coefficient:

[0107] Using the Arrhenius formula: Substitute the obtained material aging rate constant and activation energy into the formula to calculate the aging impact of the material at different temperatures.

[0108] Physical meaning: The larger the value, the greater the effect of temperature on the aging of the material.

[0109] Building material aging models:

[0110] Combine the temperature influence coefficient with other factors: In addition to temperature, humidity, vibration and other factors also affect the aging of materials. The influence coefficients of these factors can be combined with the temperature influence coefficient to establish a comprehensive material aging model.

[0111] Advantages of the model:

[0112] Solid physical foundation: The Arrhenius model has a solid physical foundation and can well describe the effect of temperature on material aging.

[0113] Clear parameters: The parameters in the model have clear physical meanings, which are easy to understand and interpret.

[0114] High prediction accuracy: By reasonably determining the model parameters, higher prediction accuracy can be obtained.

[0115] The Arrhenius model provides an effective tool for predicting the aging of washing machine materials. Through this model, the effect of temperature on the aging of different materials can be quantitatively evaluated, providing a scientific basis for equipment maintenance. However, in practical applications, other factors and models need to be combined to more accurately predict the life of the equipment.

[0116] In an optional embodiment of the present invention, the operating parameter includes a humidity parameter, and the step of determining the influence coefficient of the operating parameter on the plurality of materials includes:

[0117] determining a material aging rate constant of the material related to humidity, and material characteristic parameters of the material;

[0118] Based on the material aging rate constant related to the material and the material characteristic parameter, a coefficient of the humidity influence of the humidity parameter on the material is determined.

[0119] Exemplarily, the embodiment of the present invention may be implemented by the following formula (humidity sensitivity model).

[0120]

[0121] in, Indicates humidity The aging effects on the material, is the material aging rate constant related to humidity, For humidity, is the material characteristic parameter, It is the accumulated usage time in hours.

[0122] The formula (humidity sensitivity model) describes the aging process of a material in a humidity environment.

[0123] Indicates the degree of influence of humidity H on the aging of material j. The larger the value, the greater the influence of humidity on the aging process of the material such as corrosion and oxidation.

[0124] : is the humidity aging rate constant of material j. This constant reflects the inherent characteristics of the material, and different materials have different k values.

[0125] t: is the cumulative use time, that is, the time the material is exposed to a wet environment. The longer the time, the more serious the material aging.

[0126] H: is humidity, which indicates the humidity level in the environment.

[0127] : It is the material characteristic parameter of material j, which reflects the material's sensitivity to humidity. The larger the value of m_j, the more sensitive the material is to humidity.

[0128] The physical significance of this model is that the aging rate of materials is not only related to time, but also to the ambient humidity and the characteristics of the material itself. The higher the humidity, the faster the aging rate of the material; the higher the humidity sensitivity of the material, the faster the aging rate.

[0129] Through this model, the aging of different materials in different humidity environments can be predicted, thus providing a reference for material selection and equipment design.

[0130] Combining this model with other factors (such as temperature, stress, etc.) can create a more complex equipment life prediction model.

[0131] By comparing different materials and Values ​​can be used to evaluate the moisture resistance of different materials.

[0132] This humidity sensitivity model provides a method to quantitatively describe the aging process of materials in a humidity environment. Through in-depth research and improvement of the model, it can provide strong support for fields such as materials science and engineering design.

[0133] In an optional embodiment of the present invention, the operating parameter includes a vibration coefficient, and the step of determining the influence coefficient of the operating parameter on the plurality of materials includes:

[0134] determining a material aging rate constant of the material associated with vibration, and a material constant of the material;

[0135] The vibration influence coefficient of the vibration coefficient on the material is determined based on the material aging rate constant related to the material and the material constant.

[0136] Exemplarily, the embodiment of the present invention may be implemented by the following formula (stress-life model).

[0137]

[0138] Among them, I V，j Indicates vibration The aging effects on the material, is the material aging rate constant related to vibration, is the reference vibration amplitude, is the material constant of the material, It is the accumulated usage time in hours.

[0139] The formula (stress-life model) describes the aging process of a material under vibration conditions.

[0140] I V，j : Indicates the degree of influence of vibration V on the aging of material j. The larger the value, the greater the influence of vibration on the aging process such as fatigue damage of the material.

[0141] : is the vibration aging rate constant of material j. This constant reflects the inherent characteristics of the material, and different materials have different k values.

[0142] t: is the cumulative use time, that is, the time the material is subjected to vibration. The longer the time, the more serious the material aging.

[0143] V: is the vibration amplitude, indicating the intensity of the vibration.

[0144] V0: is the reference vibration amplitude, used to normalize the vibration amplitude.

[0145] : is the material constant of material j, which reflects the sensitivity of the material to vibration. The larger the value, the more sensitive the material is to vibration.

[0146] The physical significance of this model is that the aging rate of materials is not only related to time, but also to the vibration intensity and the characteristics of the material itself. The greater the vibration intensity, the faster the aging rate of the material; the higher the vibration sensitivity of the material, the faster the aging rate.

[0147] Through this model, the fatigue life of materials under different vibration conditions can be predicted, thus providing a basis for the design of mechanical equipment.

[0148] Combining this model with other factors (such as temperature, humidity, etc.) can assess the reliability of the equipment.

[0149] By comparing the K V，j and n j The vibration resistance of different materials can be evaluated by using the value.

[0150] This material aging model under the influence of vibration provides us with a method to quantitatively describe the aging process of materials under vibration environment. Through in-depth research and improvement of the model, it can provide strong support for mechanical engineering, material science and other fields.

[0151] In an optional embodiment of the present invention, the step of determining a separate aging index for the material based on the influence degree coefficient comprises:

[0152] The temperature influence degree coefficient, the humidity influence degree coefficient and the vibration influence degree coefficient are used to determine a separate aging index for the material.

[0153] For example, for a material node, its individual aging index can be calculated by the following formula:

[0154]

[0155] It shows that the individual aging index of material node j is composed of three parts: the aging index affected by temperature, the aging index affected by humidity and the aging index affected by vibration.

[0156] The meaning of each part:

[0157] I self，j : Individual aging index of material node j. This index reflects the aging degree of the material under a specific environment due to the combined effects of factors such as temperature, humidity and vibration.

[0158] I T，j : The influence of temperature on the aging of material node j. This index reflects the degree of damage to the material caused by temperature changes.

[0159] I H，j : The influence of humidity on the aging of material node j. This index reflects the degree of damage to the material caused by humidity changes.

[0160] I V，j : The influence of vibration on the aging of material node j. This index reflects the degree of damage to the material caused by vibration.

[0161] The physical meaning of the formula:

[0162] This formula shows that the aging of materials is a comprehensive process that is affected by multiple factors. By calculating the effects of these factors separately and then adding them together, the total aging degree of the material can be obtained. This formula provides us with a way to quantify the aging degree of materials.

[0163] By calculating the individual aging index of a material, the life of the material can be predicted. When the aging index reaches a certain threshold, it means that the material is close to failure. By comparing the individual aging indexes of different materials, the aging resistance of different materials can be evaluated. According to the individual aging index of the material, a reasonable equipment maintenance plan can be formulated.

[0164] The above formula provides a simple and effective method to evaluate the individual aging degree of materials. By comprehensively considering the influence of factors such as temperature, humidity and vibration, the life of materials can be predicted more accurately, providing a reference for equipment maintenance and design.

[0165] In an optional embodiment of the present invention, the step of determining the interaction effect paths between a plurality of associated materials for representing the influence of a material on the aging of other materials comprises:

[0166] Determine target materials that have a correlation relationship with the material;

[0167] Determining an aging influence coefficient of the target material on the material;

[0168] The aging influence coefficient and the separate aging index are used to calculate the influencing interaction effect path.

[0169] Exemplarily, the embodiment of the present invention can be implemented by the following formula.

[0170]

[0171] in, Can be material The comprehensive aging index, The target material About Materials The aging influence coefficient can be obtained through experiments or historical data;

[0172] Display material Individual aging indices.

[0173] The above formula shows that the comprehensive aging index of material j is determined by its own aging index and the influence of other materials on it.

[0174] The meaning of each part:

[0175]

[0176] Represents the comprehensive aging index of material j. This index reflects the overall aging degree of material j under the interaction with other materials.

[0177] Represents the individual aging index of material j. This index reflects the aging degree of material j without the influence of other materials.

[0178] Indicates the aging influence coefficient of target material i on material j. This coefficient indicates the influence of material i on the aging degree of material j.

[0179] Represents the individual aging index of material i.

[0180] The physical meaning of the formula:

[0181] This formula shows that the aging of a material is not only affected by its own material properties, but also by other materials around it. When multiple materials interact with each other, their interactions can accelerate or slow down their respective aging processes.

[0182] Calculation steps:

[0183] Determine associated materials: First, it is necessary to determine the material i that has an associated relationship with material j. There may be physical contact, chemical reaction or other forms of interaction between these materials.

[0184] Determine the aging influence coefficient: Determine the aging influence coefficient of target material i on material j through experiments or historical data This factor can be positive (accelerates aging) or negative (slows aging).

[0185] Calculate the comprehensive aging index: Substitute the individual aging index of each material and the influence coefficient between them into the formula to calculate the comprehensive aging index of material j.

[0186] Application of the formula:

[0187] Material life prediction: By calculating the comprehensive aging index of the material, the material life can be predicted more accurately.

[0188] Material selection: When designing a product, you can choose a suitable material combination based on the comprehensive aging index of the material to improve the durability of the product.

[0189] Failure analysis: When a product fails, the cause of the failure can be found out by analyzing the comprehensive aging index of the material.

[0190] The above formula provides a material aging model that takes into account the interaction between materials. Through this model, the life and reliability of materials can be evaluated more accurately.

[0191] In an optional embodiment of the present invention, it also includes:

[0192] When the washing machine completes the washing operation, the step of determining the aging influence coefficient of the target material on the material is re-executed.

[0193] ij Coefficient: The coefficient representing the aging effect of material i on material j. The larger the value, the greater the aging effect of material i on material j.

[0194] Aging Association Network: Through ij The coefficients relate the aging effects between different materials to form a network structure.

[0195] Dynamic update: real-time adjustment based on changes in actual operating parameters (such as humidity) ij The coefficient enables the aging correlation network to reflect the real-time changes of the material aging state.

[0196] Working principle:

[0197] Monitoring operating parameters: During each washing process, the system will monitor various operating parameters in real time, such as temperature, humidity, vibration, etc.

[0198] Identify influencing factors: The system will identify which operating parameters have a greater impact on material aging based on preset rules or algorithms. For example, increased humidity may accelerate the oxidation of rubber to metal.

[0199] Adjustment ij Coefficient: When a certain operating parameter is detected to have changed, the system will adjust the corresponding ij If the humidity increases, the oxidation effect of rubber on metal will ij It will increase.

[0200] Updating aging association networks: by adjusting ij The system updates the aging correlation network in real time so that the network can reflect the latest changes in the material aging status.

[0201] Requires dynamic update ij The reason for the coefficient is:

[0202] Material aging is a dynamic process: the aging process of materials is affected by many factors, and these factors will change with time and environment.

[0203] Improving forecast accuracy: Through dynamic updates ij The coefficient can more accurately predict the aging of materials and thus take preventive measures in advance.

[0204] Adapt to complex environments: The actual operating environment is often very complex. ij The coefficient can make the system better adapt to the complex and changing environment.

[0205] Specific implementation method:

[0206] Establish a knowledge base: Establish a knowledge base that includes various materials, environmental factors and the interactions between them.

[0207] Design adjustment algorithm: Design an algorithm to automatically adjust the ij coefficient.

[0208] Introducing machine learning: Machine learning technology can be used to build more complex models to predict by analyzing historical data. ij The change of coefficient.

[0209] Example:

[0210] Assume that there is an interaction between the rubber seal and the metal casing in a washing machine.

[0211] Initial state: Under normal working conditions, rubber has little effect on metal oxidation. ij The coefficient is relatively small.

[0212] Increased humidity: If the humidity in the environment in which the washing machine is operating increases, the system detects this change.

[0213] Adjustment ij Coefficient: The system calculates the coefficient of rubber oxidation on metal according to the preset rules or algorithms. ij Increase.

[0214] Update aging correlation network: The system updates the aging correlation network to reflect the enhanced effect of rubber on metal oxidation.

[0215] Via dynamic updates ij The coefficient can achieve the following beneficial effects:

[0216] Improved prediction accuracy: The degradation of materials can be predicted more accurately, allowing preventive measures to be taken in advance.

[0217] Prolong equipment life: The service life of equipment can be extended through timely maintenance and replacement.

[0218] Reduce maintenance costs: Avoid sudden failures and reduce repair costs.

[0219] In an optional embodiment of the present invention, it also includes:

[0220] When the comprehensive aging index is greater than a preset threshold, a maintenance reminder message is generated.

[0221] When the aging index of a material Reaching the preset threshold When the system triggers a maintenance reminder,

[0222]

[0223] The system notifies the user through the user interface to perform preventive maintenance (such as lubrication, cleaning or replacement of parts).

[0224] Comprehensive aging index: calculated by the above formula , which reflects the overall aging degree of material j under the current environment and usage conditions.

[0225] Preset Threshold : A pre-set value. When the comprehensive aging index of a material exceeds this value, it is considered that the material has reached or is close to the limit of its service life and needs maintenance or replacement.

[0226] Maintenance reminder message: A warning issued by the system to the user or maintenance personnel, indicating that a component or system needs to be inspected, repaired or replaced.

[0227] Workflow:

[0228] Real-time monitoring: The system continuously monitors the comprehensive aging index of each material.

[0229] Threshold comparison: Compare the comprehensive aging index monitored in real time with the preset threshold.

[0230] Trigger alarm: When the comprehensive aging index exceeds the threshold, the system triggers an alarm and generates a maintenance reminder message.

[0231] Information push: Push maintenance reminder information to relevant personnel, such as equipment maintenance personnel, system administrators, etc.

[0232] Maintenance reminder information:

[0233] Affected Materials: Indicate which specific material or component has exceeded the aging index.

[0234] Aging index value: Displays the current aging index value.

[0235] Preset Threshold: Displays the preset threshold for comparison.

[0236] Recommended maintenance measures: Based on the characteristics and aging of the materials, corresponding maintenance suggestions are made, such as replacing parts, adjusting parameters, etc.

[0237] Potential risks: Describe the harm or loss that may occur if maintenance is not carried out in time.

[0238] Specific implementation method:

[0239] Database: Establish a database to store comprehensive aging indices, preset thresholds and maintenance recommendations for all materials.

[0240] Algorithm: Design an algorithm to monitor the aging index in real time and compare it with the threshold.

[0241] Alarm system: Establish an alarm system that can notify relevant personnel in a timely manner when the alarm is triggered.

[0242] User Interface: Design a user-friendly interface that displays maintenance reminders and provides detailed fault diagnosis and repair instructions.

[0243] For example, suppose the comprehensive aging index of the rubber seal of a washing machine exceeds the preset threshold. The system will generate the following maintenance reminder information:

[0244] Warning: The rubber seal of the washing machine is seriously aged.

[0245] Current aging index: 0.95

[0246] Default threshold: 0.90

[0247] Recommendation: Replace the rubber seal immediately to prevent water leaks.

[0248] Potential risks: Continued use may cause water leakage, damage to the internal components of the washing machine, or even cause safety accidents.

[0249] Threshold setting: The preset threshold needs to be reasonably set according to the characteristics of the material, the use environment and safety requirements.

[0250] Alarm frequency: Avoid frequent false alarms while ensuring that potential problems are discovered in a timely manner.

[0251] Information accuracy: Maintenance reminder information must be accurate and reliable to avoid misleading maintenance personnel.

[0252] Scalability: The system should be designed to be scalable and able to adapt to new materials and new maintenance requirements.

[0253] By monitoring the comprehensive aging index of materials in real time and comparing it with the preset threshold, potential failures can be discovered in time and corresponding maintenance measures can be taken, thereby improving the reliability and service life of the equipment.

[0254] In an optional embodiment of the present invention, it also includes:

[0255] When it is determined that the user ignores the maintenance reminder information, determining the target washing parameter;

[0256] The next washing operation is performed based on the washing parameters.

[0257] Exemplarily, if the user ignores the maintenance reminder, the system will automatically adjust the washing parameters at the next wash to reduce the load on the aged material, which can be achieved by the following formula.

[0258] Drum speed: Reduce speed , to reduce vibration intensity.

[0259]

[0260] in, is the adjusted speed, is the maximum allowable aging index.

[0261] This formula describes a strategy for adjusting the speed of a washing machine based on the aging of the material.

[0262] : Adjusted speed.

[0263] N: original speed.

[0264] : The maximum aging index that material j can withstand.

[0265] The meaning of the formula is: when the aging degree of the material The closer to its limit value (Imax), the faster the speed That is to say, by reducing the rotation speed, the vibration damage to the aged material can be reduced, thus extending its service life.

[0266] In a washing machine, this formula can be applied to the dynamic adjustment of the drum speed. Specifically:

[0267] Monitoring material aging: The system will continuously monitor the aging index of key components inside the washing machine (such as rubber seals, bearings, etc.).

[0268] Calculate and adjust the speed: When the aging index Ij of a component is close to Imax, the system will calculate a new speed N' according to the above formula.

[0269] Adjust the speed: The washing machine will automatically adjust the speed to N' during the next wash to reduce the load on aging parts.

[0270] The reasons why reducing the rotation speed can reduce the load on the aging material are:

[0271] Reduce vibration: Lowering the rotation speed directly leads to a reduction in vibration amplitude, thereby reducing the mechanical impact on the material and delaying fatigue damage.

[0272] Reduce friction: As the speed decreases, the friction will also decrease accordingly, reducing the wear on the material.

[0273] Reduce heat generation: As the rotation speed decreases, the heat generated will also decrease, slowing down the thermal aging of the material.

[0274] advantage

[0275] Extend equipment life: By dynamically adjusting the speed, the service life of key washing machine components can be extended and the number of repairs can be reduced.

[0276] Improve equipment reliability: Timely adjustment of the rotation speed can avoid failures caused by aging of components and improve equipment reliability.

[0277] Reduce energy consumption: Without affecting the washing effect, reducing the speed can save energy.

[0278]

[0279] Among them, tp' preheating time, To extend the time for (preheating).

[0280] The above formula expresses that when the aging index Ij of the material is larger, that is, the more serious the aging degree of the material is, the longer the preheating time tp' will be. By extending the preheating time, the influence of temperature on the material can be reduced and the aging process can be slowed down.

[0281] In order to enable those skilled in the art to better understand the embodiments of the present invention, a complete example is used below to illustrate the embodiments of the present invention.

[0282] Figure 2 is a flow chart of a method for predicting the remaining life of a washing machine provided in an embodiment of the present invention;

[0283] A drum maintenance system based on a multi-factor model is proposed. By monitoring and analyzing the key parameters of the drum and related components during the washing process, the degree of material aging can be predicted and corresponding preventive measures can be taken. The specific implementation method is as follows:

[0284] 1. Construction of multi-factor aging model:

[0285] The system presets aging models for different materials (such as rubber seals, metal rollers, plastic components, etc.) and makes predictions based on the physical properties, stress distribution, environmental conditions, and aging experimental data of the materials. The models include:

[0286] 1.1 Arrhenius model: used to predict the effect of temperature on the aging rate of materials.

[0287]

[0288] in, is the aging rate constant, is the frequency factor, is the activation energy, is the gas constant, is the absolute temperature.

[0289] 1.2 Stress-life model (SN curve): used to predict the fatigue life of materials under specific stress.

[0290]

[0291] in, is the fatigue life, is the stress amplitude, is the base stress, is the material constant.

[0292] 1.3 Humidity influence model: The humidity influence model on the material is based on the humidity sensitivity coefficient Build relationships.

[0293]

[0294] in:

[0295] • is the material moisture sensitivity constant;

[0296] • For humidity, is the material characteristic parameter.

[0297] 2. Parameter collection:

[0298] During each washing process, the system collects the following parameters in real time through sensors:

[0299] Temperature T: The temperature inside the drum, in degrees Celsius.

[0300] Humidity H: Relative humidity in the drum, in percentage.

[0301] Vibration intensity V: The vibration amplitude of the drum, in millimeters.

[0302] Time t: cumulative usage time, in hours.

[0303] Washing times n: The total number of accumulated washing times.

[0304] 3. Composition of the aging association network:

[0305] The aging correlation network is used to characterize the mutual correlation between different materials in the aging process through the network structure of material nodes and interaction effect paths.

[0306] 3.1 Material nodes (Nodes):

[0307] Each material (such as rubber seals, metal rollers, plastic components, etc.) is treated as an independent node in the network. Each node contains core indicators that represent the aging state of the material, including temperature sensitivity, humidity sensitivity, stress tolerance, etc. , its aging index It can be calculated by the following formula:

[0308]

[0309] in:

[0310] • Indicates temperature The aging effect on the material can be calculated based on the Arrhenius model:

[0311]

[0312] in, is the material aging rate constant related to temperature, is the activation energy of the material, is the gas constant, is the absolute temperature, It is the accumulated usage time in hours.

[0313] • Indicates humidity For the aging effect on this material, a humidity sensitivity model is used, e.g.

[0314]

[0315] is the material aging rate constant related to humidity, For humidity, is the material characteristic parameter, It is the accumulated usage time in hours.

[0316] • Indicates vibration The aging effect on the material is usually calculated by the stress-life model as

[0317] in, is the material aging rate constant related to vibration, is the reference vibration amplitude, is the material constant of the material, It is the accumulated usage time in hours.

[0318] 3.2 Interaction effect paths (Edges): The interaction effect paths between different materials represent the impact of materials on the aging of other materials. The interaction effect paths are expressed by coefficients To quantify the material About Materials The intensity of the impact. The effect of the path depends on the contact frequency and contact mode of the materials and their physical and chemical properties. The comprehensive aging index It is expressed as:

[0319]

[0320] in:

[0321] • ij The target material About Materials The aging influence coefficient can be obtained through experiments or historical data;

[0322] • Display material Individual aging indices.

[0323] 3.3 Associated network update mechanism:

[0324] The system will update according to the actual parameters after each washing process. ij For example, if the system detects that the oxidation effect of rubber on metal increases when humidity is high, the system will adjust the ij The value of enables the aging correlation network to dynamically reflect the comprehensive state of material aging.

[0325] 4. Aging index calculation:

[0326] The system calculates the aging index of each material based on the collected parameters through the aging model of each material. , and combined with the aging correlation network to calculate the comprehensive aging index of the material The overall aging index of the material It is expressed as:

[0327]

[0328] This value is used to evaluate the aging status of the entire system and provide a basis for maintenance decisions.

[0329] 5. Preventive maintenance reminder:

[0330] When the aging index of a material Reaching the preset threshold The system will trigger a maintenance reminder when:

[0331]

[0332] The system notifies the user through the user interface to perform preventive maintenance (such as lubrication, cleaning or replacement of parts).

[0333] 6. Automatic adjustment of washing parameters:

[0334] If the user ignores the maintenance reminder, the system will automatically adjust the washing parameters at the next wash to reduce the load on the aged material:

[0335] Drum speed: Reduce speed , to reduce vibration intensity.

[0336]

[0337] in, is the adjusted speed, is the maximum allowable aging index.

[0338] Washing intensity: Reduce washing intensity and extend preheating time To reduce the impact of temperature on materials.

[0339]

[0340] 7. Model modification:

[0341] The system updates the aging model in real time based on the user's actual maintenance behavior (such as lubrication, cleaning or replacement of parts), and corrects the aging rate constant by recording the changes in parameters after maintenance. , and , in order to improve the accuracy of the prediction.

[0342] The system stores critical data for each wash in local memory and optionally uploads it to a cloud server for remote diagnosis and maintenance recommendations by the manufacturer or service personnel.

[0343] The system interface also provides a historical data query function to help users understand the health status and maintenance history of the equipment.

[0344] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0345] Reference Figure 3 , shows a structural block diagram of a washing machine remaining life prediction device provided in an embodiment of the present invention, which may specifically include the following modules:

[0346] An operating parameter acquisition module 301 is used to acquire operating parameters of the washing machine;

[0347] A separate aging index determination module 302 is used to determine the influence degree coefficient of the operating parameters on multiple materials, and determine the separate aging index for the material based on the influence degree coefficient;

[0348] The associated material determination module 303 is used to determine associated materials having an associated relationship from the multiple materials;

[0349] An influence interaction effect path determination module 304 is used to determine an influence interaction effect path between a plurality of associated materials for representing the influence of a material on the aging of other materials;

[0350] A comprehensive aging index determination module 305, used to determine a comprehensive aging index for characterizing the material aging relationship through the influencing interaction effect path and the individual aging index;

[0351] The life prediction result generating module 306 is used to generate a life prediction result for representing the remaining life of the washing machine based on the comprehensive aging index.

[0352] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0353] In addition, an embodiment of the present invention further provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned washing machine remaining life prediction method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0354] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned washing machine remaining life prediction method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0355] Figure 4 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0356] The electronic device 400 includes but is not limited to: a radio frequency unit 401, a network module 402, an audio output unit 403, an input unit 404, a sensor 405, a display unit 406, a user input unit 407, an interface unit 408, a memory 409, a processor 410, and a power supply 411. Those skilled in the art will appreciate that Figure 4 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In the embodiments of the present invention, the electronic device includes but is not limited to a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted terminal, a wearable device, and a pedometer.

[0357] It should be understood that in the embodiment of the present invention, the radio frequency unit 401 can be used for receiving and sending signals during information transmission or communication. Specifically, after receiving downlink data from the base station, it is sent to the processor 410 for processing; in addition, uplink data is sent to the base station. Generally, the radio frequency unit 401 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc. In addition, the radio frequency unit 401 can also communicate with the network and other devices through a wireless communication system.

[0358] The electronic device provides users with wireless broadband Internet access through the network module 402, such as helping users to send and receive emails, browse web pages, and access streaming media.

[0359] The audio output unit 403 can convert the audio data received by the RF unit 401 or the network module 402 or stored in the memory 409 into an audio signal and output it as sound. Moreover, the audio output unit 403 can also provide audio output related to a specific function performed by the electronic device 400 (for example, a call signal reception sound, a message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, a receiver, etc.

[0360] The input unit 404 is used to receive audio or video signals. The input unit 404 may include a graphics processor (GPU) 4041 and a microphone 4042. The graphics processor 4041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The processed image frame can be displayed on the display unit 406. The image frame processed by the graphics processor 4041 can be stored in the memory 409 (or other storage medium) or sent via the radio frequency unit 401 or the network module 402. The microphone 4042 can receive sound and can process such sound into audio data. The processed audio data can be converted into a format output that can be sent to a mobile communication base station via the radio frequency unit 401 in the case of a telephone call mode.

[0361] The electronic device 400 also includes at least one sensor 405, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 4061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 4061 and / or the backlight when the electronic device 400 is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used to identify the posture of the electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; the sensor 405 can also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which will not be repeated here.

[0362] The display unit 406 is used to display information input by the user or information provided to the user. The display unit 406 may include a display panel 4061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0363] The user input unit 407 can be used to receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device. Specifically, the user input unit 407 includes a touch panel 4071 and other input devices 4072. The touch panel 4071, also known as a touch screen, can collect the user's touch operations on or near it (such as the user's operation on the touch panel 4071 or near the touch panel 4071 using any suitable object or accessory such as a finger, stylus, etc.). The touch panel 4071 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 410, receives the command sent by the processor 410 and executes it. In addition, the touch panel 4071 can be implemented in various types such as resistive, capacitive, infrared and surface acoustic waves. In addition to the touch panel 4071, the user input unit 407 may also include other input devices 4072. Specifically, other input devices 4072 may include but are not limited to a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail here.

[0364] Furthermore, the touch panel 4071 may be overlaid on the display panel 4061. When the touch panel 4071 detects a touch operation on or near it, it is transmitted to the processor 410 to determine the type of the touch event. Then, the processor 410 provides a corresponding visual output on the display panel 4061 according to the type of the touch event. Figure 4 In the figure, the touch panel 4071 and the display panel 4061 are two independent components to realize the input and output functions of the electronic device. However, in some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to realize the input and output functions of the electronic device, which is not limited here.

[0365] The interface unit 408 is an interface for connecting an external device to the electronic device 400. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, etc. The interface unit 408 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the electronic device 400 or may be used to transmit data between the electronic device 400 and an external device.

[0366] The memory 409 can be used to store software programs and various data. The memory 409 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 409 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0367] The processor 410 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 409, and calling data stored in the memory 409, so as to monitor the electronic device as a whole. The processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 410.

[0368] The electronic device 400 may also include a power source 411 (such as a battery) for supplying power to various components. Preferably, the power source 411 may be logically connected to the processor 410 via a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system.

[0369] In addition, the electronic device 400 includes some functional modules not shown, which will not be described in detail here.

[0370] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0371] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, computer, server, air conditioner, or network equipment, etc.) to execute the methods described in each embodiment of the present invention.

[0372] like Figure 5 As shown, in another embodiment provided by the present invention, a computer-readable storage medium 501 is also provided, in which instructions are stored. When the computer-readable storage medium 501 is run on a computer, the computer executes the remaining life prediction method of the washing machine described in the above embodiment.

[0373] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

[0374] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0375] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0376] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0377] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0378] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0379] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0380] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for predicting the remaining life of a washing machine, characterized in that: include: Get the operating parameters of the washing machine; Determining the influence degree coefficients of the operating parameters on a plurality of materials, and determining individual aging indices for the materials based on the influence degree coefficients; Determining related materials having a related relationship from the plurality of said materials; Determine an interactive effect path between multiple associated materials for representing the influence of a material on the aging of other materials; the interactive effect path is used to represent the relationship of mutual influence between different materials; Determine a comprehensive aging index for characterizing the aging relationship of the material through the influencing interaction effect path and the individual aging index; Based on the comprehensive aging index, a life prediction result for characterizing the remaining life of the washing machine is generated.

2. The method according to claim 1, characterized in that The operating parameters include temperature parameters, and the step of determining the influence coefficients of the operating parameters on the various materials includes: determining a material aging rate constant of the material as a function of temperature, and an activation energy of the material; Based on the material aging rate constant related to the temperature of the material and the activation energy, a coefficient of the degree of temperature influence of the temperature parameter on the material is determined.

3. The method according to claim 2, characterized in that The operating parameters include humidity parameters, and the step of determining the influence coefficients of the operating parameters on the various materials includes: determining a material aging rate constant of the material related to humidity, and material characteristic parameters of the material; Based on the material aging rate constant related to the material and the material characteristic parameter, a coefficient of the humidity influence of the humidity parameter on the material is determined.

4. The method according to claim 3, characterized in that The operating parameters include a vibration coefficient, and the step of determining the influence coefficient of the operating parameters on the various materials includes: determining a material aging rate constant of the material associated with vibration, and a material constant of the material; The vibration influence coefficient of the vibration coefficient on the material is determined based on the material aging rate constant related to the material and the material constant.

5. The method according to claim 4, characterized in that The step of determining a separate aging index for the material based on the influence degree coefficient comprises: The temperature influence degree coefficient, the humidity influence degree coefficient and the vibration influence degree coefficient are used to determine a separate aging index for the material.

6. The method according to claim 1, characterized in that The step of determining the interaction effect paths between multiple associated materials for representing the influence of a material on the aging of other materials comprises: Determine target materials that have a correlation relationship with the material; Determining an aging influence coefficient of the target material on the material; The aging influence coefficient and the separate aging index are used to calculate the influencing interaction effect path.

7. The method according to claim 6, characterized in that Also includes: When the washing machine completes the washing operation, the step of determining the aging influence coefficient of the target material on the material is re-executed.

8. The method according to claim 1, characterized in that Also includes: When the comprehensive aging index is greater than a preset threshold, a maintenance reminder message is generated.

9. The method according to claim 8, characterized in that Also includes: When it is determined that the user ignores the maintenance reminder information, determining the target washing parameter; The next washing operation is performed based on the washing parameters.

10. A device for predicting remaining life of a washing machine, characterized in that: include: An operating parameter acquisition module, used to acquire the operating parameters of the washing machine; A separate aging index determination module, used to determine the influence degree coefficient of the operating parameters on multiple materials, and determine the separate aging index for the material based on the influence degree coefficient; An associated material determination module, used for determining associated materials having an associated relationship from a plurality of the materials; An influence interaction effect path determination module is used to determine an influence interaction effect path between multiple associated materials for representing the influence of a material on the aging of other materials; the influence interaction effect path is used to represent the relationship of mutual influence between different materials; A comprehensive aging index determination module, used to determine a comprehensive aging index for characterizing the aging relationship of the material through the influencing interaction effect path and the individual aging index; The life prediction result generating module is used to generate a life prediction result for characterizing the remaining life of the washing machine based on the comprehensive aging index.

11. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the method according to any one of claims 1 to 9 when executing the program stored in the memory.

12. A computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform the method according to any one of claims 1 to 9.

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