Dynamic load adjusting method and system for dish-washing machine energy efficiency and water efficiency test

Abstract

The invention discloses a dynamic load adjusting method and system for dish-washing machine energy efficiency and water efficiency test, and the method comprises the steps: setting a test set of different dirt degrees and types of tableware proportions, carrying out the transient performance analysis of each washing mode, predicting the fluid flow and temperature distribution in a cavity of a dish-washing machine, and recognizing an abnormal region; a first objective function based on the washing effect is constructed to optimize water pump parameters to improve a water flow path, a second objective function is constructed to adjust heating element parameters, energy efficiency and water efficiency testing is conducted to determine the optimal washing period, the water pump parameters and the heating element parameters as optimal operation parameters, and a decision tree algorithm is introduced; and learning a mapping relation between the test condition and the optimal operation parameters, and adjusting the operation parameters of the dish-washing machine in real time by means of a control algorithm according to the tableware dirt degree and proportion. According to the method, the energy efficiency and water efficiency balance of the dish washing machine under different load conditions can be effectively improved, the washing effect is enhanced, energy consumption and water consumption are reduced, and meanwhile good interpretability is achieved.

Description

Technical Field

[0001] The present invention relates to the field of equipment efficiency optimization, and in particular to a dynamic load adjustment method and system for dishwasher energy efficiency and water efficiency testing. Background Art

[0002] As people's awareness of energy conservation and environmental protection continues to increase, the energy efficiency and water efficiency of dishwashers have attracted more and more attention. In scenarios where dishwashers are used extensively, such as homes and commercial kitchens, reducing energy and water consumption while ensuring good washing results has become a key requirement.

[0003] Traditional dishwashers often use fixed washing modes and parameter settings, which are difficult to adapt to the changes in the proportion and degree of dirt of different types of dishes. For example, when there are fewer dishes or the dirt is light, running in standard mode will cause a waste of water and electricity resources; and when there are heavily soiled or a large number of dishes, fixed parameters may lead to incomplete washing.

[0004] A dynamic load adjustment method and system for testing the energy and water efficiency of a dishwasher. By accurately analyzing the fluid and temperature distribution inside the dishwasher and dynamically adjusting the parameters of the water pump and heating element, the washing process can be optimized according to the actual load conditions, the energy and water efficiency can be maximized, and the intelligence level and practicality of the dishwasher can be improved. Summary of the invention

[0005] The object of the present invention is to provide a dynamic load adjustment method and system for testing the energy efficiency and water efficiency of a dishwasher.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] A first aspect of the present invention provides a dynamic load adjustment method for energy efficiency and water efficiency testing of a dishwasher, comprising:

[0008] Set up a test set with different degrees of dirt and proportions of different types of tableware;

[0009] Using the test set to perform transient performance analysis on different washing modes of the dishwasher, and predicting the flow distribution and temperature distribution of the fluid inside the dishwasher cavity under each washing mode according to the analysis results;

[0010] identifying an abnormal area based on the fluid distribution and the temperature distribution;

[0011] Constructing a first objective function, adjusting the water pump parameters based on the abnormal area to optimize the water flow path; constructing a second objective function, adjusting the heating element parameters based on the adjusted water pump parameters; the first objective function and the second objective function are both constructed based on the washing effect;

[0012] Use the adjusted water pump parameters and heating element parameters to conduct energy and water efficiency tests, obtain the washing cycle with the best balance between energy and water efficiency of the dishwasher, and use the combination as the optimal operating parameters;

[0013] A decision tree algorithm is introduced to learn the mapping relationship between each test situation and the optimal operating parameters, and a control algorithm is set to adjust the dishwasher operating parameters in real time based on the decision tree algorithm.

[0014] As a further method, the method of using the test set to perform transient performance analysis on different washing modes of the dishwasher comprises:

[0015] Carry out 3D modeling based on the actual size and internal structure of the dishwasher and determine boundary conditions, including fluid inlet boundary conditions, fluid outlet conditions and wall boundary conditions;

[0016] Construct the flow equation of the fluid in the dishwasher, the expression is:

[0017]

[0018] Where ρ is the fluid density, is the fluid velocity vector, t is the time, is the gradient operator, p is the fluid pressure, μ is the dynamic viscosity, is the external body force, C s is the turbulence pulsation intensity coefficient, k is the turbulence pulsation kinetic energy, and are the turbulent stress term and the thermal buoyancy term respectively;

[0019] Construct the heat transfer equation for the fluid in the dishwasher, which is expressed as:

[0020]

[0021] Among them, c p is the constant pressure heat capacity of the fluid, T is the fluid temperature, u, v and w are the components of the fluid velocity in the directions x, y and z respectively, ξ is the thermal conductivity of the fluid, Φ is the viscous dissipation term, q v is the heat source term within the unit volume;

[0022] A transient analysis was performed on a dishwasher using a test set with different washing modes to obtain transient changes in fluid mechanics and fluid temperature under each test condition.

[0023] As a further method, the method for predicting the flow distribution and temperature distribution of the fluid inside the dishwasher cavity in each washing mode according to the analysis results includes:

[0024] Grid the interior of the dishwasher cavity;

[0025] Determine the initial conditions and boundary conditions of the velocity field and pressure field according to the transient changes of fluid mechanics, and determine the initial temperature and temperature boundary conditions according to the transient changes of fluid temperature, including the temperature conditions of the heating element and the cavity wall;

[0026] The fluid equation of the fluid is discretized in each unit grid by the finite volume method, and the heat transfer equation of the fluid is discretized in the cabinet grid by the finite difference method. The iterative solution is performed based on the time step to obtain the fluid flow distribution and fluid temperature distribution in each washing mode.

[0027] As a further method, the method for identifying abnormal areas based on the fluid distribution and the temperature distribution includes: from the perspective of flow velocity distribution, if the regional flow velocity is lower than the appropriate cleaning flow velocity threshold or there is a turbulent flow direction that causes the area to not be covered by water flow, it is judged as an abnormal flow velocity area; from the perspective of temperature distribution, if the regional temperature is lower than the appropriate cleaning temperature threshold or the temperature standard deviation is greater than the set value, it is judged as an abnormal temperature area.

[0028] As a further method, the method of constructing the first objective function, adjusting the water pump parameters based on the abnormal area, and optimizing the water flow path includes:

[0029] Determine the position weight of each area inside the dishwasher cavity based on the placement and quantity of tableware;

[0030] The first objective function is expressed as:

[0031]

[0032] Where Ω is the control volume inside the dishwasher cavity, n is the number of divided control volumes, ω i is the position weight of the ith control volume, u i 、v i and w i are the x, y and z components of the flow velocity in the ith control volume, u i,des 、v i,des and w i,des are the x, y, and z components of the desired flow velocity in the ith control volume, u i,max 、v i,max and w i,max are the x, y and z components of the maximum flow velocity of the ith control volume, respectively, λ is the weight coefficient for balancing the flow velocity deviation and avoiding the shear stress deviation, is the boundary of the dishwasher cavity, τ is the wall shear stress, τ des is the desired wall shear stress;

[0033] With the goal of maximizing the first objective function value, the water pump parameters are iteratively optimized and calculated, and the termination condition is that the change rate of the first objective function value is less than 0.01% for 10 consecutive iterations.

[0034] As a further method, the method of constructing the second objective function and adjusting the parameters of the heating element based on the adjusted water pump parameters includes:

[0035] The second objective function is expressed as:

[0036]

[0037] Among them, T i and q i are the actual temperature and heat flux of the ith control volume, T i,des is the desired temperature of the ith control volume, T i,max and q i,max are the maximum temperature and maximum heat flux of the ith control volume, respectively; λ2 is the weight coefficient of the equilibrium temperature deviation and the heat flux deviation; λ3 is the weight coefficient of the equilibrium temperature deviation and the heating element power deviation; m is the number of heating elements; P j , P j,des and P j,max are the power, expected power and maximum power of the jth heating element respectively;

[0038] Set the upper and lower temperature limits according to the washing temperature requirements and normal working range of the dishwasher;

[0039] Considering the physical limitations of the heating element, within the range of upper and lower temperature limits, the heating element parameters are iteratively optimized with the goal of minimizing the second objective function value. The termination condition is that the change rate of the second objective function value is less than 0.01% for 10 consecutive iterations.

[0040] As a further method, the method of using the adjusted water pump parameters and heating element parameters to perform energy efficiency and water efficiency testing to obtain a washing cycle with an optimal balance between energy efficiency and water efficiency of the dishwasher includes:

[0041] A multi-objective genetic algorithm is used to encode the washing cycle duration, adjusted water pump parameters and heating element parameters into chromosomes to initialize the population;

[0042] The comprehensive index of energy efficiency and water efficiency balance of each individual in the population is calculated as the fitness value, and the expression is:

[0043]

[0044] Among them, C is the washing effect, E is the total energy consumption, ψ is the washing cycle duration, W is the total water consumption, F is the water pump flow, P is the water pump power, H is the heating element power, and t is the heating time;

[0045] Select individuals based on non-dominated sorting and crowding, and perform crossover and mutation operations to generate a new population;

[0046] Repeat fitness evaluation and population update until the preset termination condition is reached. At this time, the washing cycle duration corresponding to the optimal individual is the washing cycle with the best balance between energy efficiency and water efficiency.

[0047] As a further method, the method of introducing a decision tree algorithm to learn the mapping relationship between each test situation and the optimal operating parameters includes: using the CART algorithm to construct a decision tree, taking different levels of dirt and proportions of different types of tableware as input features of the decision tree, taking the optimal operating parameters as output targets, and training the model to learn the mapping relationship between each test situation and the optimal operating parameters.

[0048] As a further method, the method of setting the control algorithm to adjust the operating parameters of the dishwasher in real time based on the decision tree algorithm includes:

[0049] After the dishwasher is started, the current tableware status data is collected at preset fixed time intervals, the dirt degree and tableware ratio information are updated, and the updated data is input into the decision tree model to obtain new operating parameters;

[0050] The control algorithm is set based on the PID controller. During the washing cycle, the operating state of the dishwasher is adjusted according to the difference between the new operating parameters and the current operating parameters. The expression is:

[0051]

[0052] Among them, P adj (t) and H adj (t) are the operating parameters of the water pump and the heating element at the current operating time t, respectively. last and H last are the pump parameters and heating element parameters output by the last decision tree, Δt is the preset fixed time interval, ξ is the adjustment coefficient of the control algorithm, P new and H new They are the water pump parameters and heating element parameters output by this decision tree respectively.

[0053] A second aspect of the present invention provides a dynamic load adjustment system for testing energy efficiency and water efficiency of a dishwasher, comprising:

[0054] A test set building module, used to set up test sets with different degrees of dirt and proportions of different types of tableware;

[0055] a fluid simulation module, configured to use the test set to perform transient performance analysis on different washing modes of the dishwasher, and predict the flow distribution and temperature distribution of the fluid inside the dishwasher cavity under each washing mode according to the analysis results;

[0056] an abnormal region identification module, used for identifying an abnormal region according to the fluid distribution and the temperature distribution;

[0057] A parameter optimization module, used to construct a first objective function, adjust the water pump parameters based on the abnormal area, and optimize the water flow path; construct a second objective function, and adjust the heating element parameters based on the adjusted water pump parameters; the first objective function and the second objective function are both constructed based on the washing effect;

[0058] A washing cycle module is used to perform energy efficiency and water efficiency tests using adjusted water pump parameters and heating element parameters, obtain the washing cycle with the best balance between energy efficiency and water efficiency of the dishwasher, and combine them as the optimal operating parameters;

[0059] The intelligent adjustment module is used to introduce a decision tree algorithm to learn the mapping relationship between each test situation and the optimal operating parameters, and set a control algorithm to adjust the dishwasher operating parameters in real time based on the degree of dirt on the tableware and the proportion of the tableware.

[0060] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0061] (1) The present invention studies the fluid flow and temperature distribution under different washing modes, and adjusts the parameters of the water pump and the heating element based on the washing effect. It can accurately determine the optimal operating parameter combination under different load conditions, and ensure stable and excellent washing effects while saving energy and water.

[0062] (2) The present invention uses a decision tree algorithm to learn the mapping relationship between test conditions and optimal parameters, and adjusts the operating parameters in real time based on the degree and proportion of tableware dirt detected in real time, so that the dishwasher can adapt to different washing task requirements and improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The present invention is a flowchart of the steps of a dynamic load adjustment method for testing the energy efficiency and water efficiency of a dishwasher in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0065] Reference Figure 1 As shown, the present invention provides a dynamic load adjustment method for energy efficiency and water efficiency testing of a dishwasher, comprising:

[0066] S1 sets a test set with different degrees of dirt and proportions of different types of tableware;

[0067] Specifically, a household built-in dishwasher needs to be tested, the internal cavity size of which is 50cm long, 45cm wide and 37cm high. The dishwasher has three basic washing modes: standard washing, strong washing and energy-saving washing;

[0068] In the actual evaluation, the test set is specifically set as:

[0069] Case 1: Light dirt: mainly a small amount of food residue and slight oil stains. Use 0.1 g / cm2 of simulated dirt (mixed with starch and a small amount of grease) to evenly spread on the surface of tableware; dinner plates account for 50%, bowls account for 30%, and cups account for 20%;

[0070] Case 2: Moderate dirt: There are more food residues and obvious oil stains, with a dirt volume of 0.3 grams per square centimeter (including more protein food residues and moderate grease); plates account for 30%, bowls account for 40%, and cups account for 30%;

[0071] Case 3: Severe dirt: simulates tableware that has not been washed for a long time, with the amount of dirt reaching 0.5 grams per square centimeter (a lot of burnt food residues, thick grease, etc.); plates account for 60%, bowls account for 20%, and cups account for 20%.

[0072] S2 uses the test set to perform transient performance analysis on different washing modes of the dishwasher, and predicts the flow distribution and temperature distribution of the fluid inside the dishwasher cavity under each washing mode according to the analysis results;

[0073] It should be explained that by predicting the fluid flow distribution and temperature distribution through transient performance analysis, the weak water flow coverage and abnormal temperature areas can be accurately located, which helps to adjust the parameters of the water pump and heating element in a targeted manner, optimize the water flow path and temperature field, thereby improving the washing effect, maximizing the use of energy efficiency and water efficiency, making the operation of the dishwasher more intelligent and efficient, and adapting to a variety of tableware cleaning needs;

[0074] In the actual evaluation, a three-dimensional model was established according to the actual size of the dishwasher. The fluid inlet boundary condition was set to the flow velocity distribution when the maximum flow rate of the water pump was 10 liters / minute, the fluid outlet condition was set to the pressure setting during normal drainage, and the wall boundary condition was set to the no-slip boundary condition;

[0075] In the actual evaluation, the dishwasher cavity was gridded, with a total of 500 unit grids. The initial conditions of the velocity field and pressure field were determined according to the transient changes in fluid mechanics under different washing modes. The initial flow velocity in standard washing mode was 1m / s, the initial flow velocity in strong washing mode was 1.5m / s, and the initial flow velocity in energy-saving washing mode was 0.8m / s. The initial condition of the temperature field was room temperature of 25°C. In terms of temperature boundary conditions, the temperature of the heating element can reach 70°C in strong washing, 62°C in standard washing, and 53°C in energy-saving washing. The cavity wall temperature was calculated based on heat conduction. The equations were discretized using the finite volume method and the finite difference method, and the iterative solution was performed based on a time step of 0.1 seconds.

[0076] S3 identifying an abnormal area according to the fluid distribution and the temperature distribution;

[0077] In the actual evaluation, for the abnormal flow rate areas, the suitable cleaning flow rate threshold was set to 0.3m / s. For the abnormal temperature areas, in the drain area, the suitable cleaning temperature threshold was set to 40℃. The temperature standard deviation was 5℃. A total of 26 abnormal areas were identified, including 10 abnormal flow rate areas and 16 abnormal temperature areas.

[0078] S4 constructs a first objective function, adjusts the water pump parameters based on the abnormal area, and optimizes the water flow path; constructs a second objective function, and adjusts the heating element parameters based on the adjusted water pump parameters; the first objective function and the second objective function are both constructed based on the washing effect;

[0079] It needs to be explained that the water pump parameters are adjusted to optimize the water flow path to eliminate the influence of abnormal areas, and the heating element parameters are adjusted to improve the washing effect during washing heating;

[0080] In the actual evaluation, the position weights of each area inside the dishwasher cavity were determined, where the weight of the central area was 0.8, the weight of the edge area was 0.5, and the weight coefficient for balancing the flow velocity deviation and avoiding the shear stress deviation was 0.6. After iterative optimization calculations, in the standard washing mode, the initial blade angle was adjusted from 30° to 32°, the water pump flow rate was adjusted from the initial 6.0 liters / minute to 7.2 liters / minute, the water pump power was adjusted from 104W to 120W, and the outlet pipe diameter pressure was increased from the initial 18kPa to 23kPa. The number of iterations was 50 to reach the termination condition; the weight coefficients of the equilibrium temperature deviation and the heat flux density deviation were set to 0.4, and the equilibrium temperature deviation The weight coefficient of the heating element power deviation is set to 0.3, where the upper and lower limits of the temperature in the standard washing mode are specifically: 55℃-65℃. The dishwasher has three heating elements, which are located in the center of the bottom and the bottom corners on both sides. The initial heating element power is 1000W in the center and 600W on both sides. After iterative calculation, the power of the central heating element is adjusted to 900W, the power of the heating element in the left bottom corner is adjusted to 700W, and the power of the heating element in the right bottom corner is adjusted to 650W, so that the temperature field distribution is more uniform and meets the upper and lower limit requirements of the temperature. After 65 iterations, the rate of change of the second objective function value for 10 consecutive iterations is less than 0.01%, and the termination condition is met.

[0081] S5 uses the adjusted water pump parameters and heating element parameters to perform energy and water efficiency tests, obtains the washing cycle with the best balance between energy and water efficiency of the dishwasher, and uses the combination as the optimal operating parameters;

[0082] It should be explained that by using the adjusted water pump and heating element parameters for testing, operating the dishwasher under different working conditions, collecting energy consumption, water consumption and washing effect data, and comprehensively evaluating various parameter combinations through a multi-objective optimization algorithm, the washing cycle that achieves a balance between energy efficiency and water efficiency and the best washing effect is determined, and it is integrated with the corresponding parameters into the optimal operating parameters to achieve efficient and energy-saving dishwashing operations;

[0083] In the actual evaluation, the population size was set to 150, and the washing cycle duration (range 50-160 minutes), adjusted water pump parameters (blade angle, flow, power, outlet pipe pressure) and heating element parameters (power distribution of three heating elements, total power) were encoded as chromosomes, and the comprehensive index of energy efficiency and water efficiency balance was calculated as the fitness value. The washing effect score of an individual was 88 points, the total energy consumption was 1.1 kWh, the total water consumption was 16.6 liters, the washing cycle duration was 20 minutes, the water pump blade angle was 35°, the flow was 8.5 liters / minute, the power was 125W, and the outlet pipe pressure was 23 .7kPa, the total power of the heating element is 2250W (871W in the center, 729W on the left, and 650W on the right), and the heating time is 8 minutes. After 300 generations of evolution, the washing cycle with the best balance between energy efficiency and water efficiency in each test case is obtained. Specifically: for case 1, the optimal washing cycle is 67 minutes for standard washing, 42 minutes for strong washing, and 90 minutes for energy-saving washing; for case 2, the optimal washing cycle is 71 minutes for standard washing, 58 minutes for strong washing, and 102 minutes for energy-saving washing; for case 3, the optimal washing cycle is 73 minutes for standard washing, 60 minutes for strong washing, and 117 minutes for energy-saving washing.

[0084] S6 introduces a decision tree algorithm to learn the mapping relationship between each test situation and the optimal operating parameters, and sets a control algorithm to adjust the dishwasher operating parameters in real time based on the decision tree algorithm.

[0085] It should be explained that the mapping relationship between each test situation and the optimal operating parameters is learned through the decision tree algorithm, and in the subsequent process, the operating parameters of the dishwasher can be dynamically adjusted based on the real-time monitoring situation;

[0086] In the actual evaluation, the test data is used as the training set, and the decision tree is trained by dividing it into training set, test set and validation set in a ratio of 7:2:1;

[0087] In the actual evaluation, after the dishwasher is started, the current tableware status data is collected at a preset fixed time interval of 10 minutes. After 30 minutes of operation, it is detected that the current dirt level is moderately dirty, and the tableware ratio is 40% for plates, 40% for bowls, and 20% for cups and other small tableware. It is input into the decision tree model to obtain new operating parameters. The operating state of the dishwasher is adjusted based on the PID controller. The new water pump blade angle is required to be 33°, and the current angle is 35°. The blade angle is adjusted at a certain rate; if the power of the heating element needs to be adjusted to 920W in the center, 680W on the left, and 650W on the right, and the current one is 900W in the center, 700W on the left, and 650W on the right, the power output of each heating element is gradually adjusted to achieve real-time and precise control of the dishwasher operating parameters, ensuring that a good energy and water efficiency balance is always maintained throughout the washing process to adapt to various tableware loads and dirt conditions.

[0088] In this embodiment, the method of using the test set to perform transient performance analysis on different washing modes of a dishwasher includes:

[0089] Carry out 3D modeling based on the actual size and internal structure of the dishwasher and determine boundary conditions, including fluid inlet boundary conditions, fluid outlet conditions and wall boundary conditions;

[0090] Construct the flow equation of the fluid in the dishwasher, the expression is:

[0091]

[0092] Where ρ is the fluid density, is the fluid velocity vector, t is the time, is the gradient operator, p is the fluid pressure, μ is the dynamic viscosity, is the external body force, C s is the turbulence pulsation intensity coefficient, k is the turbulence pulsation kinetic energy, and are the turbulent stress term and the thermal buoyancy term respectively;

[0093] Construct the heat transfer equation for the fluid in the dishwasher, which is expressed as:

[0094]

[0095] Among them, c p is the constant pressure heat capacity of the fluid, T is the fluid temperature, u, v and w are the components of the fluid velocity in the directions x, y and z respectively, ξ is the thermal conductivity of the fluid, Φ is the viscous dissipation term, q v is the heat source term within the unit volume;

[0096] A transient analysis was performed on a dishwasher using a test set with different washing modes to obtain transient changes in fluid mechanics and fluid temperature under each test condition.

[0097] In this embodiment, the method for predicting the flow distribution and temperature distribution of the fluid inside the dishwasher cavity in each washing mode according to the analysis results includes:

[0098] Grid the interior of the dishwasher cavity;

[0099] Determine the initial conditions and boundary conditions of the velocity field and pressure field according to the transient changes of fluid mechanics, and determine the initial temperature and temperature boundary conditions according to the transient changes of fluid temperature, including the temperature conditions of the heating element and the cavity wall;

[0100] The fluid equation of the fluid is discretized in each unit grid by the finite volume method, and the heat transfer equation of the fluid is discretized in the cabinet grid by the finite difference method. The iterative solution is performed based on the time step to obtain the fluid flow distribution and fluid temperature distribution in each washing mode.

[0101] In this embodiment, the method for identifying abnormal areas based on the fluid distribution and the temperature distribution includes: from the perspective of flow velocity distribution, if the regional flow velocity is lower than the suitable cleaning flow velocity threshold or there is a turbulent flow direction that causes the area to not be covered by water flow, it is judged as an abnormal flow velocity area; from the perspective of temperature distribution, if the regional temperature is lower than the suitable cleaning temperature threshold or the temperature standard deviation is greater than the set value, it is judged as an abnormal temperature area.

[0102] In this embodiment, the method of constructing the first objective function, adjusting the water pump parameters based on the abnormal area, and optimizing the water flow path includes:

[0103] Determine the position weight of each area inside the dishwasher cavity based on the placement and quantity of tableware;

[0104] The first objective function is expressed as:

[0105]

[0106] Where Ω is the control volume inside the dishwasher cavity, n is the number of divided control volumes, ω i is the position weight of the ith control volume, u i 、v i and w i are the x, y and z components of the flow velocity in the ith control volume, u i,des 、v i,des and w i,des are the x, y, and z components of the desired flow velocity in the ith control volume, u i,max 、v i,max and w i,max are the x, y and z components of the maximum flow velocity of the ith control volume, respectively, λ is the weight coefficient for balancing the flow velocity deviation and avoiding the shear stress deviation, is the boundary of the dishwasher cavity, τ is the wall shear stress, τ des is the desired wall shear stress;

[0107] With the goal of maximizing the first objective function value, the water pump parameters are iteratively optimized and calculated, and the termination condition is that the change rate of the first objective function value is less than 0.01% for 10 consecutive iterations.

[0108] In this embodiment, the method of constructing the second objective function and adjusting the parameters of the heating element based on the adjusted water pump parameters includes:

[0109] The second objective function is expressed as:

[0110]

[0111] Among them, T i and q i are the actual temperature and heat flux of the ith control volume, T i,des is the desired temperature of the ith control volume, T i,max and q i,max are the maximum temperature and maximum heat flux of the ith control volume, respectively; λ2 is the weight coefficient of the equilibrium temperature deviation and the heat flux deviation; λ3 is the weight coefficient of the equilibrium temperature deviation and the heating element power deviation; m is the number of heating elements; P j , P j,des and P j,max are the power, expected power and maximum power of the jth heating element respectively;

[0112] Set the upper and lower temperature limits according to the washing temperature requirements and normal working range of the dishwasher;

[0113] Considering the physical limitations of the heating element, within the range of upper and lower temperature limits, the heating element parameters are iteratively optimized with the goal of minimizing the second objective function value. The termination condition is that the change rate of the second objective function value is less than 0.01% for 10 consecutive iterations.

[0114] In this embodiment, the method of using the adjusted water pump parameters and heating element parameters to perform energy efficiency and water efficiency testing to obtain a washing cycle with an optimal balance between energy efficiency and water efficiency of the dishwasher includes:

[0115] A multi-objective genetic algorithm is used to encode the washing cycle duration, adjusted water pump parameters and heating element parameters into chromosomes to initialize the population;

[0116] The comprehensive index of energy efficiency and water efficiency balance of each individual in the population is calculated as the fitness value, and the expression is:

[0117]

[0118] Among them, C is the washing effect, E is the total energy consumption, ψ is the washing cycle duration, W is the total water consumption, F is the water pump flow, P is the water pump power, H is the heating element power, and t is the heating time;

[0119] Select individuals based on non-dominated sorting and crowding, and perform crossover and mutation operations to generate a new population;

[0120] Repeat fitness evaluation and population update until the preset termination condition is reached. At this time, the washing cycle duration corresponding to the optimal individual is the washing cycle with the best balance between energy efficiency and water efficiency.

[0121] In this embodiment, the method of introducing a decision tree algorithm to learn the mapping relationship between each test condition and the optimal operating parameters includes: using the CART algorithm to construct a decision tree, taking different degrees of dirt and proportions of different types of tableware as input features of the decision tree, taking the optimal operating parameters as output targets, and training the model to learn the mapping relationship between each test condition and the optimal operating parameters.

[0122] In this embodiment, the method for setting the control algorithm to adjust the operating parameters of the dishwasher in real time based on the decision tree algorithm includes:

[0123] After the dishwasher is started, the current tableware status data is collected at preset fixed time intervals, the dirt degree and tableware ratio information are updated, and the updated data is input into the decision tree model to obtain new operating parameters;

[0124] The control algorithm is set based on the PID controller. During the washing cycle, the operating state of the dishwasher is adjusted according to the difference between the new operating parameters and the current operating parameters. The expression is:

[0125]

[0126] Among them, P adj (t) and H adj (t) are the operating parameters of the water pump and the heating element at the current operating time t, respectively. last and H last are the pump parameters and heating element parameters output by the last decision tree, Δt is the preset fixed time interval, ξ is the adjustment coefficient of the control algorithm, P new and H new They are the water pump parameters and heating element parameters output by this decision tree respectively.

[0127] The second aspect of the present invention also provides a dynamic load adjustment system for testing the energy efficiency and water efficiency of a dishwasher, comprising:

[0128] A test set building module, used to set up test sets with different degrees of dirt and proportions of different types of tableware;

[0129] a fluid simulation module, configured to use the test set to perform transient performance analysis on different washing modes of the dishwasher, and predict the flow distribution and temperature distribution of the fluid inside the dishwasher cavity under each washing mode according to the analysis results;

[0130] an abnormal region identification module, used for identifying an abnormal region according to the fluid distribution and the temperature distribution;

[0131] A parameter optimization module, used to construct a first objective function, adjust the water pump parameters based on the abnormal area, and optimize the water flow path; construct a second objective function, and adjust the heating element parameters based on the adjusted water pump parameters; the first objective function and the second objective function are both constructed based on the washing effect;

[0132] A washing cycle module is used to perform energy efficiency and water efficiency tests using adjusted water pump parameters and heating element parameters, obtain the washing cycle with the best balance between energy efficiency and water efficiency of the dishwasher, and combine them as the optimal operating parameters;

[0133] The intelligent adjustment module is used to introduce a decision tree algorithm to learn the mapping relationship between each test situation and the optimal operating parameters, and set a control algorithm to adjust the dishwasher operating parameters in real time based on the decision tree algorithm.

[0134] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A dynamic load adjustment method for energy efficiency and water efficiency testing of a dishwasher, characterized in that: The following steps are involved: Set up a test set with different degrees of dirt and proportions of different types of tableware; Using the test set to perform transient performance analysis on different washing modes of the dishwasher, and predicting the flow distribution and temperature distribution of the fluid inside the dishwasher cavity under each washing mode according to the analysis results; identifying an abnormal area based on the fluid distribution and the temperature distribution; Constructing a first objective function, adjusting the water pump parameters based on the abnormal area to optimize the water flow path; constructing a second objective function, adjusting the heating element parameters based on the adjusted water pump parameters; the first objective function and the second objective function are both constructed based on the washing effect; Use the adjusted water pump parameters and heating element parameters to conduct energy and water efficiency tests, obtain the washing cycle with the best balance between energy and water efficiency of the dishwasher, and use the combination as the optimal operating parameters; A decision tree algorithm is introduced to learn the mapping relationship between each test situation and the optimal operating parameters, and a control algorithm is set to adjust the dishwasher operating parameters in real time based on the decision tree algorithm.

2. The dynamic load adjustment method for dishwasher energy efficiency and water efficiency test according to claim 1, characterized in that: The method of using the test set to perform transient performance analysis on different washing modes of a dishwasher comprises: Carry out 3D modeling based on the actual size and internal structure of the dishwasher and determine boundary conditions, including fluid inlet boundary conditions, fluid outlet conditions and wall boundary conditions; Construct the flow equation of the fluid in the dishwasher, the expression is: Where ρ is the fluid density, is the fluid velocity vector, t is the time, is the gradient operator, p is the fluid pressure, μ is the dynamic viscosity, is the external body force, C s is the turbulence pulsation intensity coefficient, k is the turbulence pulsation kinetic energy, and are the turbulent stress term and the thermal buoyancy term respectively; Construct the heat transfer equation for the fluid in the dishwasher, which is expressed as: Among them, c p is the constant pressure heat capacity of the fluid, T is the fluid temperature, u, v and w are the components of the fluid velocity in the directions x, y and z respectively, ξ is the thermal conductivity of the fluid, Φ is the viscous dissipation term, q v is the heat source term within the unit volume; A transient analysis was performed on a dishwasher using a test set with different washing modes to obtain transient changes in fluid mechanics and fluid temperature under each test condition.

3. The dynamic load adjustment method for dishwasher energy efficiency and water efficiency test according to claim 1, characterized in that: The method for predicting the flow distribution and temperature distribution of the fluid inside the dishwasher cavity in each washing mode according to the analysis results includes: Grid the interior of the dishwasher cavity; Determine the initial conditions and boundary conditions of the velocity field and pressure field according to the transient changes of fluid mechanics, and determine the initial temperature and temperature boundary conditions according to the transient changes of fluid temperature, including the temperature conditions of the heating element and the cavity wall; The fluid equation of the fluid is discretized in each unit grid by the finite volume method, and the heat transfer equation of the fluid is discretized in the cabinet grid by the finite difference method. The iterative solution is performed based on the time step to obtain the fluid flow distribution and fluid temperature distribution in each washing mode.

4. The dynamic load adjustment method for dishwasher energy efficiency and water efficiency test according to claim 1, characterized in that: The method for identifying abnormal areas based on the fluid distribution and the temperature distribution includes: from the perspective of flow velocity distribution, if the regional flow velocity is lower than the appropriate cleaning flow velocity threshold or there is a turbulent flow direction that causes the area to be unable to be covered by water flow, it is judged as an abnormal flow velocity area; from the perspective of temperature distribution, if the regional temperature is lower than the appropriate cleaning temperature threshold or the temperature standard deviation is greater than the set value, it is judged as an abnormal temperature area.

5. The dynamic load adjustment method for dishwasher energy efficiency and water efficiency test according to claim 1, characterized in that: The method of constructing the first objective function, adjusting the water pump parameters based on the abnormal area, and optimizing the water flow path includes: Determine the position weight of each area inside the dishwasher cavity based on the placement and quantity of tableware; The first objective function is expressed as: Where Ω is the control volume inside the dishwasher cavity, n is the number of divided control volumes, ω i is the position weight of the ith control volume, u i 、v i and w i are the x, y and z components of the flow velocity in the ith control volume, u i,des 、v i,des and w i,des are the x, y, and z components of the desired flow velocity in the ith control volume, u i,max 、v i,max and w i,max are the x, y and z components of the maximum flow rate of the ith control volume, λ is the weight coefficient for balancing the flow rate deviation and avoiding the shear stress deviation, θΩ is the boundary of the dishwasher cavity, τ is the wall shear stress, and τ des is the desired wall shear stress; With the goal of maximizing the first objective function value, the water pump parameters are iteratively optimized and calculated, and the termination condition is that the change rate of the first objective function value is less than 0.01% for 10 consecutive iterations.

6. The dynamic load adjustment method for dishwasher energy efficiency and water efficiency test according to claim 1, characterized in that: The method of constructing the second objective function and adjusting the parameters of the heating element based on the adjusted water pump parameters includes: The second objective function is expressed as: Among them, T i and q i are the actual temperature and heat flux of the ith control volume, T i,des is the desired temperature of the ith control volume, T i,max and q i,max are the maximum temperature and maximum heat flux of the ith control volume, respectively; λ2 is the weight coefficient of the equilibrium temperature deviation and the heat flux deviation; λ3 is the weight coefficient of the equilibrium temperature deviation and the heating element power deviation; m is the number of heating elements; P j , P j,des and P j,max are the power, expected power and maximum power of the jth heating element respectively; Set the upper and lower temperature limits according to the washing temperature requirements and normal working range of the dishwasher; Considering the physical limitations of the heating element, within the range of upper and lower temperature limits, the heating element parameters are iteratively optimized with the goal of minimizing the second objective function value. The termination condition is that the change rate of the second objective function value is less than 0.01% for 10 consecutive iterations.

7. The dynamic load adjustment method for dishwasher energy efficiency and water efficiency test according to claim 1, characterized in that: The method of using the adjusted water pump parameters and heating element parameters to perform energy efficiency and water efficiency testing to obtain a washing cycle with an optimal balance between energy efficiency and water efficiency of the dishwasher includes: A multi-objective genetic algorithm is used to encode the washing cycle duration, adjusted water pump parameters and heating element parameters into chromosomes to initialize the population; The comprehensive index of energy efficiency and water efficiency balance of each individual in the population is calculated as the fitness value, and the expression is: Among them, C is the washing effect, E is the total energy consumption, ψ is the washing cycle duration, W is the total water consumption, F is the water pump flow, P is the water pump power, H is the heating element power, and t is the heating time; Select individuals based on non-dominated sorting and crowding, and perform crossover and mutation operations to generate a new population; Repeat fitness evaluation and population update until the preset termination condition is reached. At this time, the washing cycle duration corresponding to the optimal individual is the washing cycle with the best balance between energy efficiency and water efficiency.

8. The dynamic load adjustment method for dishwasher energy efficiency and water efficiency test according to claim 1, characterized in that: The method of introducing a decision tree algorithm to learn the mapping relationship between each test situation and the optimal operating parameters includes: using a CART algorithm to construct a decision tree, using different degrees of dirt and proportions of different types of tableware as input features of the decision tree, using the optimal operating parameters as output targets, and training a model to learn the mapping relationship between each test situation and the optimal operating parameters.

9. The dynamic load adjustment method for dishwasher energy efficiency and water efficiency test according to claim 1, characterized in that: The method of setting a control algorithm to adjust the operating parameters of a dishwasher in real time based on a decision tree algorithm includes: After the dishwasher is started, the current tableware status data is collected at preset fixed time intervals, the dirt degree and tableware ratio information are updated, and the updated data is input into the decision tree model to obtain new operating parameters; The control algorithm is set based on the PID controller. During the washing cycle, the operating state of the dishwasher is adjusted according to the difference between the new operating parameters and the current operating parameters. The expression is: Among them, P adj (t) and H adj (t) are the operating parameters of the water pump and the heating element at the current operating time t, respectively. last and H last are the pump parameters and heating element parameters output by the last decision tree, Δt is the preset fixed time interval, ξ is the adjustment coefficient of the control algorithm, P new and H new They are the water pump parameters and heating element parameters output by this decision tree respectively.

10. A dynamic load adjustment system for a dishwasher energy efficiency and water efficiency test, used to execute a dynamic load adjustment method for a dishwasher energy efficiency and water efficiency test according to any one of claims 1 to 9, characterized in that: The system comprises: A test set building module, used to set up test sets with different degrees of dirt and proportions of different types of tableware; a fluid simulation module, configured to use the test set to perform transient performance analysis on different washing modes of the dishwasher, and predict the flow distribution and temperature distribution of the fluid inside the dishwasher cavity under each washing mode according to the analysis results; an abnormal region identification module, used for identifying an abnormal region according to the fluid distribution and the temperature distribution; A parameter optimization module, used to construct a first objective function, adjust the water pump parameters based on the abnormal area, and optimize the water flow path; construct a second objective function, and adjust the heating element parameters based on the adjusted water pump parameters; the first objective function and the second objective function are both constructed based on the washing effect; A washing cycle module is used to perform energy efficiency and water efficiency tests using adjusted water pump parameters and heating element parameters, obtain the washing cycle with the best balance between energy efficiency and water efficiency of the dishwasher, and combine them as the optimal operating parameters; The intelligent adjustment module is used to introduce a decision tree algorithm to learn the mapping relationship between each test situation and the optimal operating parameters, and set a control algorithm to adjust the dishwasher operating parameters in real time based on the decision tree algorithm.

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