Method and system for evaluating stirring performance of stirrer

Abstract

The invention relates to a method and a system for evaluating the stirring performance of a stirrer. The method comprises the following steps: S10, setting stirrer evaluation indexes, wherein the stirrer evaluation indexes comprise food stirring uniformity, food stirring time and stirring noise level; s20, preparing to-be-tested food materials; s30, initializing a mixer evaluation system, wherein the mixer evaluation system comprises an image acquisition module, an image processing module, a sound acquisition module and a sound processing module; s40, the uniformity of the color histogram of the food material state image, the standard deviation of food material particle size distribution and the distribution probability of the food materials in the stirring container are obtained through calculation, a stirring uniformity quantized value is obtained through calculation in combination with a comprehensive scoring formula, and the stirring noise average value is calculated through a sound processing module; and S50, mapping the stirring uniformity quantized value and the stirring noise average value to the stirrer evaluation index range of a scoring processing module to obtain a stirring performance score of the stirrer.

Description

Technical Field

[0001] The invention relates to a method and a system for evaluating the stirring performance of a mixer, and belongs to the field of intelligent quality inspection and evaluation of household appliances. Background Art

[0002] As one of the commonly used appliances in the kitchen, the performance of the blender directly affects the food processing effect and cooking experience. Currently, the performance evaluation of blenders on the market mostly relies on the user's subjective feelings and experience judgment, and lacks objective and scientific evaluation standards and methods. Therefore, it is of great practical significance to develop a method and system that can accurately evaluate the mixing performance of a blender. Summary of the invention

[0003] The present invention provides a method and a system for evaluating the stirring performance of a mixer, aiming to solve at least one of the technical problems existing in the prior art.

[0004] The technical solution of the present invention is a method for evaluating the stirring performance of a mixer, the method comprising the following steps:

[0005] S10, setting evaluation indicators for the blender, wherein the evaluation indicators for the blender include blending uniformity of food, blending time of food and blending noise level;

[0006] S20, preparing test ingredients, wherein the test ingredients include at least two ingredients of different hardness and variety mixed with water for testing, and using a single ice ingredient for testing;

[0007] S30, initializing a mixer evaluation system, wherein the mixer evaluation system includes an image acquisition module, an image processing module, a sound acquisition module and a sound processing module, and acquires a plurality of state images of the test mixer according to a set time interval, and sets a stirring speed range value of the mixer;

[0008] S40, putting the test food into the test blender, processing the initial state image of the food by the image acquisition module, and transmitting it to the image processing module, starting the test blender, starting the test blender, collecting food state images at each interval time point during the blending process, and transmitting them to the image processing module, analyzing a plurality of food state images, obtaining the uniformity of the color histogram of the food state image, the standard deviation of the food particle size distribution, and the food distribution probability in the blending container, and calculating the quantified value of the blending uniformity by combining with the comprehensive scoring formula, and at the same time collecting the blending noise value at each interval time point by the sound acquisition module, and calculating the average value of the blending noise by the sound processing module;

[0009] S50, mapping the quantized value of the mixing uniformity and the average value of the mixing noise to the mixer evaluation index range of the scoring processing module to obtain a mixing performance score of the mixer.

[0010] Further, in step S10: according to the mixer evaluation index, a corresponding mixer evaluation index table is formulated, and the header content of the mixer evaluation index table includes evaluation index category, specific index, description, scoring range and unit.

[0011] Further, in step S20: setting test standard values ​​for the test ingredients, the test standard values ​​including the ratio, moisture content and initial temperature of the test ingredients; the preparation process of the test ingredients includes mixing, weighing and dispensing into a test mixing container.

[0012] Further, in step S40: the following steps are performed by the image processing module: the initial state image of the food and multiple food state images are preprocessed, the preprocessing includes graying, denoising and contrast enhancement of the image; the color histogram of the preprocessed image is calculated, and the uniformity of the color histogram of the food state image is evaluated by the entropy value of the color histogram; the food particles in the image are identified and segmented by an image segmentation algorithm, the area or volume of each of the food particles is calculated, and the number distribution of particles of different sizes is counted, and then the standard deviation of the food particle size distribution is calculated using the covariance or standard deviation statistics, which is used to evaluate the uniformity of the food particle size distribution; the image is spatially partitioned, the number of food particles in each partition area is counted, and the probability of food distribution in the mixing container is evaluated by using the spatial distribution uniformity index; weights are assigned according to the entropy value of the color histogram, the standard deviation of the food particle size distribution and the probability of food distribution in the mixing container, and a comprehensive scoring formula is input to calculate a quantified value of mixing uniformity.

[0013] Further, in step S40, the comprehensive scoring formula is: stirring uniformity quantification value = w1*entropy value of color histogram + w2*standard deviation of food particle size distribution + w3*probability of food distribution in the stirring container, wherein w1, w2 and w3 are pre-set weights.

[0014] Further, in step S40, before the sound collection module collects the stirring noise value, the test mixer is set to measure the stirring noise value in an operating environment, the background noise value of the operating environment ranges from 5dB to 16Db, and the sound collection module is set at least 1m away from the test mixer.

[0015] Further, in step S50, the standardization process of the stirring uniformity quantization value includes converting the stirring uniformity quantization value into an evaluation on a scoring scale of 0 to 100.

[0016] A system for evaluating the mixing performance of a blender, characterized in that the system executes the method as described above, including: an image acquisition module, used to acquire an initial state image of the blender to be evaluated processing food and to acquire a state image of the food at each interval time point during the mixing process; an image processing module, which receives and processes the image from the image acquisition module and calculates a quantified value of the mixing uniformity; a sound acquisition module, which is used to acquire the mixing noise value of the blender to be evaluated; a sound processing module, which receives and processes the sound from the sound acquisition module and calculates an average value of the mixing noise; and a score processing module, which receives the quantified value of the mixing uniformity and the average value of the mixing noise and processes and obtains a mixing performance score of the blender.

[0017] The technical solution of the present invention also relates to a computer device, comprising a memory and a processor, wherein the processor implements the above method when executing a computer program stored in the memory.

[0018] The technical solution of the present invention also relates to a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor.

[0019] According to some embodiments of the present invention,

[0020] The beneficial effects of the present invention are as follows.

[0021] The above-mentioned blender mixing performance evaluation method and system can objectively evaluate the performance of the blender from multiple aspects, including the uniformity of mixing food and the noise level. It also combines the image processing module to pre-process and analyze the food status image, and can objectively and accurately evaluate the color histogram uniformity of the food, the standard deviation of the food particle size distribution and the probability of food distribution in the mixing container. These analysis results provide reliable data support for evaluating the mixing uniformity of the blender. Through the coordinated use of the sound collection module and the sound processing module, the method can accurately measure the mixing noise value of the blender in the set working environment, ensuring the accuracy of the noise level evaluation.

[0022] In addition, additional aspects and advantages of the present invention will be given in part in the following description, and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 4 is an overall flow chart of a method for evaluating stirring performance of a mixer according to an embodiment of the present invention.

[0024] Figure 2 Schematic diagram of a system for evaluating stirring performance of a mixer according to an embodiment of the present invention.

[0025] Figure 3a3 is a first angle schematic diagram of a test mixer used in a method for evaluating the mixing performance of a mixer according to an embodiment of the present invention.

[0026] Figure 3b 3 is a second angle schematic diagram of a test mixer used in a method for evaluating the mixing performance of a mixer according to an embodiment of the present invention.

[0027] Figure 4a 1 is an image of the initial state of the food tested by the test blender used in the blender blending performance evaluation method according to Example 1 of the present invention.

[0028] Figure 4b It is an image of food status collected at one time point during the stirring operation of the test blender used in the blender stirring performance evaluation method according to Example 1 of the present invention.

[0029] Figure 5a 1 is an image of the initial state of ingredients of a test blender used in the blender blending performance evaluation method according to Example 2 of the present invention.

[0030] Figure 5b It is an image of food status collected at the first time point during the stirring operation of the test blender used in the blender stirring performance evaluation method according to Example 2 of the present invention. Figure 5c It is an image of food status collected at the second time point during the stirring operation of the test blender used in the blender stirring performance evaluation method according to Example 2 of the present invention. DETAILED DESCRIPTION

[0031] The concept, specific structure and technical effects of the present invention will be clearly and completely described below in combination with the embodiments and drawings to fully understand the purpose, scheme and effect of the present invention.

[0032] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to another feature, or it may be indirectly fixed or connected to another feature. The singular forms "a", "said" and "the" used herein are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art. The terms used in this specification are intended only to describe specific embodiments and are not intended to limit the invention. The term "and / or" used herein includes any combination of one or more of the related listed items.

[0033] It should be understood that, although the term first, second, third etc. may be adopted to describe various elements in the present disclosure, these elements should not be limited to these terms. These terms are only used to distinguish the same type of elements from each other. For example, without departing from the scope of the present disclosure, the first element may also be referred to as the second element, and similarly, the second element may also be referred to as the first element. The use of any and all examples or exemplary language ("for example", "such as" etc.) provided herein is only intended to better illustrate embodiments of the present invention, and unless otherwise required, the scope of the present invention will not be limited.

[0034] Reference Figure 1 To FIG5, in some embodiments, the present invention discloses a method for evaluating stirring performance of a mixer, the method comprising the following steps:

[0035] S10, setting blender evaluation indicators, wherein the blender evaluation indicators include blending food uniformity, blending food time, and blending noise level.

[0036] S20, preparing test ingredients, wherein the test ingredients include at least two ingredients of different hardness and variety mixed with water for testing, and using a single ice ingredient for testing.

[0037] S30, initializing a mixer evaluation system, wherein the mixer evaluation system includes an image acquisition module, an image processing module, a sound acquisition module and a sound processing module, and acquires a plurality of state images of the test mixer according to a set time interval, and sets a mixing speed range value of the mixer.

[0038] S40, placing the test food into the test blender, processing the initial state image of the food through the image acquisition module, and transmitting it to the image processing module, starting the test blender, starting the test blender, collecting food state images at each interval time point during the blending process, and transmitting them to the image processing module, analyzing multiple food state images to obtain the uniformity of the food state image color histogram, the standard deviation of the food particle size distribution and the food distribution probability in the blending container, and calculating the quantified value of the blending uniformity in combination with the comprehensive scoring formula, and at the same time collecting the blending noise value at each interval time point through the sound acquisition module, and calculating the average value of the blending noise through the sound processing module.

[0039] S50, mapping the quantized value of the mixing uniformity and the average value of the mixing noise to the mixer evaluation index range of the scoring processing module to obtain a mixing performance score of the mixer.

[0040] The above-mentioned blender mixing performance evaluation method can objectively evaluate the performance of the blender from multiple aspects, including the uniformity of mixing food and the noise level. It also combines the image processing module to preprocess and analyze the food status image, and can objectively and accurately evaluate the color histogram uniformity of the food, the standard deviation of the food particle size distribution, and the probability of food distribution in the mixing container. These analysis results provide reliable data support for evaluating the mixing uniformity of the blender. Through the coordinated use of the sound collection module and the sound processing module, this method can accurately measure the mixing noise value of the blender in the set working environment, ensuring the accuracy of the noise level evaluation.

[0041] With reference to the three embodiments for different test ingredients shown in FIG. 3 to FIG. 5 , a specific embodiment of the method for evaluating the stirring performance of a blender is as follows:

[0042] In step S10, S10, set the mixer evaluation index, the mixer evaluation index includes the mixing food uniformity, mixing food time and mixing noise level, specifically, according to the mixer evaluation index, formulate a corresponding mixer evaluation index table, the header content of the mixer evaluation index table includes the evaluation index category, specific index, description, scoring range and unit. The table of specific indicators is as follows:

[0043] Evaluation indicator category Specific indicators describe Rating Range unit Mixing uniformity Color histogram uniformity Uniformity of food color distribution 0-100 ％ Standard deviation of food particle size distribution Consistency of particle size 0-100 ％ Probability of food distribution in a mixing container Evenness of distribution of ingredients in the mixing container 0-100 ％ Mixing time Total mixing time Total time required to complete mixing 0-Unlimited minute Mixing noise level Noise average Noise level during mixing 0-100 Decibel (dB)

[0044] In step S20, S20, prepare test ingredients, the test ingredients include at least two ingredients with different hardness and types mixed with water for testing operation, and use single ice cube ingredients for testing operation; the test operation is as shown in Figures 3 to 5, select apple mixed water as shown in Figure 4; ice cubes in Figure 5. At least two ingredients, ice cubes are also tested as a kind of ingredient here, and the selected ingredients can also be coffee beans, carrots and other ingredients of different hardness and types. Set test standard values ​​for the test ingredients, and the test standard values ​​include the ratio, moisture content and initial temperature of the test ingredients; the preparation process of the test ingredients includes mixing, weighing and dispensing into a test stirring container. The test ingredients are weighed in combination with water to obtain the test ingredients in the initial proportion.

[0045] S30. Initialize the blender evaluation system, which includes an image acquisition module, an image processing module, a sound acquisition module and a sound processing module. The system acquires multiple status images of the test blender according to the set time interval, and sets the blender stirring speed range value at the same time. There is no requirement for the running speed of the tested blender. It is only necessary to use the best stirring speed range corresponding to the rotation speed when testing different ingredients.

[0046] In step S40, S40, the test food is placed in the test blender, the image acquisition module processes the initial state image of the food to be evaluated, and transmits it to the image processing module, the test blender is started, the test blender is started, and the food state image is collected at each interval time point during the mixing process, and the image is transmitted to the image processing module, and multiple food state images are analyzed to obtain the uniformity of the food state image color histogram, the standard deviation of the food particle size distribution and the food distribution probability in the mixing container, and the mixing uniformity quantification value is calculated in combination with the comprehensive scoring formula, and the mixing noise value at each interval time point is collected by the sound acquisition module, and the mixing noise average value is calculated by the sound processing module. The image processing module performs the following steps: preprocessing the initial state image of the food material and the multiple state images of the food material, wherein the preprocessing includes graying, denoising and contrast enhancement of the image; calculating the color histogram of the preprocessed image, and evaluating the uniformity of the color histogram of the food material state image by the entropy value of the color histogram, that is, counting the frequency of each color value (or color interval) in the image, and evaluating the uniformity of the color distribution by the entropy value of the color histogram. The higher the entropy value, the more uniform the color distribution.

[0047] An image segmentation algorithm (such as threshold segmentation, edge detection or clustering algorithm) is used to identify and segment the food particles in the image, calculate the area or volume of each food particle, and count the number distribution of particles of different sizes. The covariance or standard deviation statistics are then used to calculate the standard deviation of the food particle size distribution, which is used to evaluate the uniformity of the food particle size distribution.

[0048] The image is spatially partitioned, for example, the image is divided into multiple small areas, the number of food particles in each partition is counted, and the probability of food distribution in the mixing container is evaluated by using spatial distribution uniformity indicators such as variance or Gini coefficient.

[0049] According to the entropy value of the color histogram, the standard deviation of the food particle size distribution and the probability of food distribution in the mixing container, weights are assigned respectively, and the comprehensive scoring formula is input to calculate the quantified value of mixing uniformity. The image collection of the food in Figure 4 or Figure 5 is initialized by the camera, and the food state image is collected at each interval time point during the mixing process. For example, multiple food state images are collected 30 seconds, 1 minute or 1.5 minutes after the test blender is working, and the uniformity of the color histogram, the standard deviation of the food particle size distribution, and the distribution probability of the food in the mixing cup container are calculated, or the food is poured out into a container such as Figure 4b In the sieve, the uniformity of the color histogram, the standard deviation of the particle size distribution of the food material, and the distribution probability in the sieve container are analyzed and evaluated. Specifically, the comprehensive scoring formula used is:

[0050] Quantified value of mixing uniformity = w1*entropy of color histogram + w2*standard deviation of food particle size distribution + w3*probability of food distribution in mixing container

[0051] Among them, w1, w2 and w3 are pre-set weights, and the standardization processing of the stirring uniformity quantization value includes converting the stirring uniformity quantization value into an evaluation on a scoring scale of 0 to 100.

[0052] In step S40, when evaluating the noise level of the mixer, acoustic measurement and analysis methods are usually used to quantify the noise level. These methods are based on acoustic principles and international standards, such as the IEC 60704 series of standards, to ensure the accuracy and consistency of the measurement. Before the sound collection module collects the stirring noise value, the test mixer is set to measure the stirring noise value in an operating environment, the background noise value of the operating environment ranges from 5dB to 16Db, and the sound collection module is set at least 1m away from the test mixer.

[0053] Reference Figure 2 As shown, the present invention also discloses a blender mixing performance evaluation system, wherein the system executes the above method, including: an image acquisition module, which is used to collect the initial state image of the blender to be evaluated processing the food and collect the food state image at each interval time point during the mixing process. An image processing module, which receives and processes the image from the image acquisition module, and calculates the mixing uniformity quantification value. A sound acquisition module, which is used to collect the mixing noise value of the blender to be evaluated. A sound processing module, which receives and processes the sound from the sound acquisition module, and calculates the mixing noise average value. A scoring processing module, which receives the mixing uniformity quantification value and the mixing noise average value, and processes and obtains the blender mixing performance score.

[0054] The present invention also discloses a computer device, comprising a memory and a processor, wherein the processor implements the above method when executing a computer program stored in the memory. A computer-readable storage medium stores program instructions thereon, wherein the program instructions implement the above method when executed by the processor.

[0055] It should be appreciated that the method steps in the embodiments of the present invention can be implemented or implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The method can use standard programming techniques. Each program can be implemented in a high-level process or object-oriented programming language to communicate with a computer system. However, if necessary, the program can be implemented in an assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, the program can be run on a programmed ASIC for this purpose.

[0056] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that may be executed by one or more processors.

[0057] Further, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, a RAM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or portions thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention can also include the computer itself.

[0058] The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents physical and tangible objects, including specific visual depictions of physical and tangible objects produced on the display.

[0059] The above is only a preferred embodiment of the present invention. The present invention is not limited to the above implementation. As long as the technical effect of the present invention is achieved by the same means, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of protection of the present invention. Within the scope of protection of the present invention, its technical scheme and / or implementation method may have various modifications and changes.

Claims

1. A method for evaluating stirring performance of a stirrer, characterized in that: The method comprises the following steps: S10, setting evaluation indicators for the blender, wherein the evaluation indicators for the blender include uniformity of blended food, blending time of food, and blending noise level; S20, preparing test ingredients, wherein the test ingredients include at least two ingredients of different hardness and variety mixed with water for testing, and using a single ice ingredient for testing; S30, initializing a mixer evaluation system, wherein the mixer evaluation system includes an image acquisition module, an image processing module, a sound acquisition module and a sound processing module, and acquires a plurality of state images of the test mixer according to a set time interval, and sets a stirring speed range value of the mixer; S40, putting the test food into the test blender, processing the initial state image of the food by the image acquisition module, and transmitting it to the image processing module, starting the test blender, starting the test blender, collecting food state images at each interval time point during the blending process, and transmitting them to the image processing module, analyzing a plurality of food state images, obtaining the uniformity of the color histogram of the food state image, the standard deviation of the food particle size distribution, and the food distribution probability in the blending container, and calculating the quantified value of the blending uniformity by combining with the comprehensive scoring formula, and at the same time collecting the blending noise value at each interval time point by the sound acquisition module, and calculating the average value of the blending noise by the sound processing module; S50, mapping the quantized value of the mixing uniformity and the average value of the mixing noise to the mixer evaluation index range of the scoring processing module to obtain a mixing performance score of the mixer.

2. The method according to claim 1, characterized in that In step S10: According to the mixer evaluation index, a corresponding mixer evaluation index table is formulated, and the header content of the mixer evaluation index table includes evaluation index category, specific index, description, scoring range and unit.

3. The method according to claim 1, characterized in that In step S20: Setting test standard values ​​for the test food, wherein the test standard values ​​include the ratio, moisture content and initial temperature of the test food; The test ingredients were prepared by mixing, weighing and dispensing into test mixing containers.

4. The method according to claim 1, characterized in that: In step S40: The image processing module performs the following steps: Preprocessing the food initial state image and the plurality of food state images, wherein the preprocessing includes graying, denoising and contrast enhancement of the images; Calculating a color histogram of the preprocessed image, and evaluating the uniformity of the color histogram of the food status image by using the entropy value of the color histogram; Identify and segment food particles in the image using an image segmentation algorithm, calculate the area or volume of each food particle, and count the number distribution of particles of different sizes, and then use covariance or standard deviation statistics to calculate the standard deviation of the food particle size distribution to evaluate the uniformity of the food particle size distribution; Perform spatial partitioning on the image, count the number of food particles in each partition area, and evaluate the distribution probability of food in the mixing container by using the spatial distribution uniformity index; Weights are assigned according to the entropy value of the color histogram, the standard deviation of the food particle size distribution, and the probability of food distribution in the mixing container, and a comprehensive scoring formula is input to calculate a quantified value of the mixing uniformity.

5. The method according to claim 4, characterized in that In step S40, The comprehensive scoring formula is: Quantified value of mixing uniformity = w1*entropy of color histogram + w2*standard deviation of food particle size distribution + w3*probability of food distribution in mixing container Among them, w1, w2 and w3 are pre-set weights.

6. The method according to claim 1, characterized in that In step S40, Before the sound collection module collects the stirring noise value, the test mixer is set up in an operating environment for measuring the stirring noise value. The background noise value of the operating environment ranges from 5dB to 16Db, and the sound collection module is set at least 1m away from the test mixer.

7. The method according to claim 1, characterized in that In step S50, The standardization process of the stirring uniformity quantification value includes converting the stirring uniformity quantification value into an evaluation on a scoring scale of 0 to 100.

8. A stirring performance evaluation system for a stirrer, characterized in that: The system executes the method according to any one of claims 1 to 7, comprising: The image acquisition module is used to acquire the initial state image of the food being processed by the blender to be evaluated and to acquire the state image of the food at each interval time point during the blending process. The image processing module receives and processes the image from the image acquisition module, and calculates and obtains a quantified value of the stirring uniformity. The sound collection module is used to collect the mixing noise value of the mixer to be evaluated. The sound processing module receives and processes the sound from the sound collection module, and calculates the average value of the stirring noise. The scoring processing module receives the quantified value of the mixing uniformity and the average value of the mixing noise, processes and obtains a mixing performance score of the mixer.

9. A computer device comprising a memory and a processor, characterized in that: The method according to any one of claims 1 to 7 is implemented when the processor executes the computer program stored in the memory. 10 . A computer-readable storage medium having program instructions stored thereon, wherein the program instructions are executed by a processor to implement the method according to claim 1 .

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