Kitchen garbage treatment system and method based on image recognition

Abstract

The invention relates to the technical field of image recognition, and discloses a kitchen garbage treatment system and method based on image recognition, and the system comprises a data collection module which is configured to collect video image data in a kitchen garbage treatment machine and quality data of garbage to be treated; the parameter determination module is configured to determine an initial crushing parameter of the to-be-treated garbage according to the comprehensive characteristic value and the quality data; the parameter optimization module is configured to determine an optimization coefficient of the initial crushing parameter according to the equipment deviation value and obtain an optimized crushing parameter; the parameter compensation module is configured to determine a compensation coefficient for optimizing the crushing parameter according to the response time difference value and obtain a compensation crushing parameter; and the execution module is configured to control the kitchen garbage processor to execute garbage processing operation according to the compensation crushing parameters. According to the invention, the accuracy and efficiency of garbage treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and more particularly, to a kitchen waste treatment system and method based on image recognition. Background Art

[0002] When dealing with the current kitchen waste problem, we usually adopt two main treatment methods: manual classification and mechanical crushing. However, this treatment method faces a series of problems and challenges. First of all, the process of manual classification is relatively inefficient, which not only consumes a large amount of human resources but also is particularly slow when dealing with a large amount of garbage. Secondly, due to the subjectivity and instability of manual classification, the accuracy of classification is often difficult to guarantee, which may lead to some materials that should be recycled being wrongly disposed of.

[0003] Therefore, it is necessary to design a kitchen waste treatment system and method based on image recognition to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes a kitchen waste treatment system and method based on image recognition, aiming to solve the problem of relatively low treatment efficiency when dealing with kitchen waste in the current technology.

[0005] On the one hand, the present invention proposes a kitchen waste treatment system based on image recognition, including:

[0006] A data acquisition module configured to acquire video image data inside the kitchen waste processor and the mass data of the waste to be processed;

[0007] A parameter determination module configured to analyze the video image data, and based on the analysis result, determine whether the waste to be processed appears in the video image data; if so, calculate the comprehensive feature value of the waste to be processed in the video image data with qualified clarity, and determine the initial crushing parameters of the waste to be processed according to the comprehensive feature value and the mass data;

[0008] A parameter optimization module configured to acquire the equipment status information of the kitchen waste processor, analyze the equipment status information to obtain the equipment status feature value, calculate the equipment deviation value based on the equipment status feature value, and determine whether to optimize the initial crushing parameters according to the equipment deviation value; if so, determine the optimization coefficient of the initial crushing parameters according to the equipment deviation value and obtain the optimized crushing parameters;

[0009] A parameter compensation module, configured to collect the actual response time of the food waste processor, and determine whether to compensate the optimized crushing parameters according to the actual response time; if so, calculate the difference between the actual response time and the standard response time, and record it as the response time difference, determine the compensation coefficient of the optimized crushing parameters according to the response time difference, and obtain the compensated crushing parameters;

[0010] An execution module, configured to receive the compensated crushing parameters and control the food waste processor to perform garbage disposal operations according to the compensated crushing parameters.

[0011] Further, when the parameter determination module determines whether the to-be-processed garbage appears in the video image data based on the parsing result, it includes:

[0012] Process the video image data using an object detection algorithm, identify the garbage objects in the video image data, and determine whether the garbage objects are the to-be-processed garbage;

[0013] When the garbage object is consistent with the preset object, determine that the garbage object is the to-be-processed garbage;

[0014] When the garbage object is inconsistent with the preset object, determine that the garbage object is not the to-be-processed garbage.

[0015] Further, when the parameter determination module calculates the comprehensive feature value of the to-be-processed garbage in the video image data with qualified clarity, it includes:

[0016] Preprocess the video image data with qualified clarity, and extract the shape features, color features, and texture features of the to-be-processed garbage;

[0017] Use a feature fusion algorithm to fuse the shape features, color features, and texture features to obtain the comprehensive feature value of the to-be-processed garbage.

[0018] Further, when the parameter determination module determines the initial crushing parameters of the to-be-processed garbage according to the comprehensive feature value and the quality data, it includes:

[0019] Extract features from the quality data to obtain a quality feature value;

[0020] Compare the quality feature value with the quality standard value according to the comprehensive feature value and the comprehensive feature threshold, and determine the initial crushing parameters of the to-be-processed garbage according to the comparison result;

[0021] When the comprehensive feature value is greater than or equal to the comprehensive feature threshold and the quality feature value is greater than or equal to the quality standard value, determine that the initial crushing parameters are the first crushing parameters;

[0022] When the comprehensive characteristic value is greater than or equal to the comprehensive characteristic threshold and the quality characteristic value is less than the quality standard value, determine the initial comminution parameter as the second comminution parameter;

[0023] When the comprehensive characteristic value is less than the comprehensive characteristic threshold and the quality characteristic value is greater than or equal to the quality standard value, determine the initial comminution parameter as the third comminution parameter;

[0024] When the comprehensive characteristic value is less than the comprehensive characteristic threshold and the quality characteristic value is less than the quality standard value, determine the initial comminution parameter as the fourth comminution parameter.

[0025] Further, when the parameter optimization module calculates the equipment deviation value based on the equipment state characteristic value and determines whether to optimize the initial comminution parameter according to the equipment deviation value, it includes:

[0026] Obtain the equipment state standard value corresponding to each equipment state characteristic value;

[0027] Calculate the number of equipment state characteristic values greater than the equipment state standard value, and denote it as the first quantity;

[0028] Calculate the number of equipment state characteristic values less than or equal to the equipment state standard value, and denote it as the second quantity;

[0029] Calculate the equipment deviation value according to the equipment state characteristic value, equipment state standard value, first quantity and second quantity;

[0030] Compare the equipment deviation value with the equipment deviation threshold, and determine whether to optimize the initial comminution parameter according to the comparison result;

[0031] When the equipment deviation value is less than the equipment deviation threshold, it is determined not to optimize the initial comminution parameter;

[0032] When the equipment deviation value is greater than or equal to the equipment deviation threshold, it is determined to optimize the initial comminution parameter.

[0033] Further, when the parameter optimization module determines the optimization coefficient of the initial comminution parameter according to the equipment deviation value and obtains the optimized comminution parameter, it includes:

[0034] Compare the equipment deviation value with the first equipment deviation value and the second equipment deviation value, and determine the optimization coefficient of the initial comminution parameter according to the comparison result; wherein the first equipment deviation value is less than the second equipment deviation value;

[0035] Set an optimization coefficient range, where the optimization coefficient range includes a first optimization coefficient, a second optimization coefficient, and a third optimization coefficient;

[0036] When the device deviation value is less than or equal to the first device deviation value, determine the optimization coefficient as the first optimization coefficient, and use the product value of the first optimization coefficient and the initial comminution parameter as the optimized comminution parameter;

[0037] When the device deviation value is greater than the first device deviation value and less than or equal to the second device deviation value, determine the optimization coefficient as the second optimization coefficient, and use the product value of the second optimization coefficient and the initial comminution parameter as the optimized comminution parameter;

[0038] When the device deviation value is greater than the second device deviation value, determine the optimization coefficient as the third optimization coefficient, and use the product value of the third optimization coefficient and the initial comminution parameter as the optimized comminution parameter.

[0039] Further, when the parameter compensation module determines whether to compensate the optimized comminution parameter according to the actual response time, it includes:

[0040] Compare the actual response time with the standard response time, and determine whether to compensate the optimized comminution parameter according to the comparison result;

[0041] When the actual response time is greater than the standard response time, it is determined to compensate the optimized comminution parameter;

[0042] When the actual response time is less than or equal to the standard response time, it is determined not to compensate the optimized comminution parameter.

[0043] Further, when the parameter compensation module determines the compensation coefficient of the optimized comminution parameter according to the response time difference and obtains the compensated comminution parameter, it includes:

[0044] Compare the response time difference with historical data, and determine the compensation coefficient of the optimized comminution parameter according to the comparison result;

[0045] When there is a historical response time difference in the historical data that is the same as the response time difference, use the historical compensation coefficient corresponding to the historical response time difference as the compensation coefficient of the optimized comminution parameter, and use the product value of the historical compensation coefficient and the optimized comminution parameter as the compensated comminution parameter;

[0046] When there is no historical response time difference in the historical data that is the same as the response time difference, calculate the difference between the response time difference and the historical data one by one, record it as the difference response time, extract the minimum value of the difference response time, record it as the minimum difference response time, determine the compensation coefficient of the optimized crushing parameters according to the minimum difference response time, and obtain the compensated crushing parameters.

[0047] Further, when the parameter compensation module determines the compensation coefficient of the optimized crushing parameters and obtains the compensated crushing parameters, it includes:

[0048] Compare the minimum difference response time with the first minimum difference response time and the second minimum difference response time, and determine the compensation coefficient of the optimized crushing parameters according to the comparison result; wherein, the first minimum difference response time is less than the second minimum difference response time;

[0049] When the minimum difference response time is less than or equal to the first minimum difference response time, determine the compensation coefficient as the first compensation coefficient, and take the product value of the first compensation coefficient and the optimized crushing parameters as the compensated crushing parameters;

[0050] When the minimum difference response time is greater than the first minimum difference response time and less than or equal to the second minimum difference response time, determine the compensation coefficient as the second compensation coefficient, and take the product value of the second compensation coefficient and the optimized crushing parameters as the compensated crushing parameters;

[0051] When the minimum difference response time is greater than the second minimum difference response time, determine the compensation coefficient as the third compensation coefficient, and take the product value of the third compensation coefficient and the optimized crushing parameters as the compensated crushing parameters.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: The kitchen waste treatment system based on image recognition provided by the present invention realizes the intelligent control of the kitchen waste treatment process through the mutual cooperation of the data acquisition module, the parameter determination module, the parameter optimization module, the parameter compensation module and the execution module, improves the accuracy and efficiency of waste treatment, and provides strong support for urban waste classification and resource utilization.

[0053] On the other hand, the present invention also proposes a method for treating kitchen waste based on image recognition, including the following steps:

[0054] S100: Collect video image data inside the kitchen waste processor and the mass data of the waste to be treated;

[0055] S200: Parse the video image data, and determine whether the garbage to be processed appears in the video image data based on the parsing result; if so, calculate the comprehensive feature value of the garbage to be processed in the video image data with qualified clarity, and determine the initial crushing parameters of the garbage to be processed according to the comprehensive feature value and the quality data;

[0056] S300: Collect the device status information of the food waste processor, parse the device status information to obtain the device status feature value, calculate the device deviation value based on the device status feature value, and determine whether to optimize the initial crushing parameters according to the device deviation value; if so, determine the optimization coefficient of the initial crushing parameters according to the device deviation value, and obtain the optimized crushing parameters;

[0057] S400: Collect the actual response time of the food waste processor, and determine whether to compensate the optimized crushing parameters according to the actual response time; if so, calculate the difference between the actual response time and the standard response time, and record it as the response time difference, determine the compensation coefficient of the optimized crushing parameters according to the response time difference, and obtain the compensated crushing parameters;

[0058] S500: Receive the compensated crushing parameters, and control the food waste processor to perform the garbage disposal operation according to the compensated crushing parameters.

[0059] It can be understood that the above food waste disposal system and method based on image recognition have the same beneficial effects, which will not be elaborated here. Description of the Drawings

[0060] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments, and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0061] Figure 1 is the structural block diagram of the food waste disposal system based on image recognition provided by the embodiment of the present invention;

[0062] Figure 2 is the flowchart of the food waste disposal method based on image recognition provided by the embodiment of the present invention. Detailed Embodiments

[0063] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0064] Referring to Figure 1 As shown, in some embodiments of the present application, this embodiment provides a kitchen waste treatment system based on image recognition, including:

[0065] A data acquisition module configured to acquire video image data inside the kitchen waste processor and quality data of the waste to be processed;

[0066] A parameter determination module configured to analyze the video image data, and based on the analysis result, determine whether the waste to be processed appears in the video image data; if so, calculate a comprehensive feature value of the waste to be processed in the video image data with qualified clarity, and determine an initial crushing parameter of the waste to be processed according to the comprehensive feature value and the quality data;

[0067] A parameter optimization module configured to acquire device status information of the kitchen waste processor, analyze the device status information to obtain a device status feature value, calculate a device deviation value based on the device status feature value, and determine whether to optimize the initial crushing parameter according to the device deviation value; if so, determine an optimization coefficient of the initial crushing parameter according to the device deviation value, and obtain an optimized crushing parameter;

[0068] A parameter compensation module configured to acquire the actual response time of the kitchen waste processor, and determine whether to compensate the optimized crushing parameter according to the actual response time; if so, calculate a difference between the actual response time and the standard response time, and record it as a response time difference, determine a compensation coefficient of the optimized crushing parameter according to the response time difference, and obtain a compensated crushing parameter;

[0069] An execution module configured to receive the compensated crushing parameter and control the kitchen waste processor to perform a waste treatment operation according to the compensated crushing parameter.

[0070] It can be understood that the kitchen waste treatment system based on image recognition provided in this embodiment realizes the intelligent control of the kitchen waste treatment process through the mutual cooperation of the data acquisition module, parameter determination module, parameter optimization module, parameter compensation module, and execution module, improving the accuracy and efficiency of waste treatment, and providing strong support for urban waste classification and resource utilization. Specifically: First, the data acquisition module is responsible for collecting the video image data inside the kitchen waste processor and the quality data of the waste to be treated. These data are the basis for subsequent processing and analysis, and are crucial for ensuring the accuracy and pertinence of waste treatment. Next, the parameter determination module analyzes the video image data to determine whether the waste to be treated appears in the video image data. Once the waste to be treated appears, the module calculates the comprehensive characteristic value of the waste to be treated in the video image data with qualified clarity, and determines the initial crushing parameters of the waste to be treated based on the comprehensive characteristic value and the quality data. This process makes full use of image recognition technology and improves the intelligent level of waste treatment. Then, the parameter optimization module collects the equipment status information of the kitchen waste processor, analyzes the equipment status information, and obtains the equipment status characteristic value. Based on the equipment status characteristic value, the module calculates the equipment deviation value, and determines whether to optimize the initial crushing parameters according to the equipment deviation value. If optimization is required, the module determines the optimization coefficient of the initial crushing parameters based on the equipment deviation value and obtains the optimized crushing parameters. This process ensures that the waste processor operates in the best state and improves the efficiency and effect of waste treatment. In the parameter compensation stage, the parameter compensation module collects the actual response time of the kitchen waste processor and determines whether to compensate the optimized crushing parameters according to the actual response time. If compensation is required, the module calculates the difference between the actual response time and the standard response time and records it as the response time difference. Based on the response time difference, the module determines the compensation coefficient of the optimized crushing parameters and obtains the compensated crushing parameters. This process further improves the accuracy and stability of waste treatment. Finally, the execution module receives the compensated crushing parameters and controls the kitchen waste processor to perform waste treatment operations according to the compensated crushing parameters. This process realizes the precise control of the waste treatment process and ensures the smooth progress of waste treatment.

[0071] Specifically, when the parameter determination module determines whether the waste to be treated appears in the video image data based on the analysis result, it includes:

[0072] Processing the video image data by using an object detection algorithm, identifying the waste target in the video image data, and determining whether the waste target is the waste to be treated;

[0073] When the waste target is consistent with the preset target, it is determined that the waste target is the waste to be treated;

[0074] When the garbage target is inconsistent with the preset target, it is determined that the garbage target is not the garbage to be processed.

[0075] It can be understood that the preset target refers to the type of kitchen waste that is preset in the system and needs to be processed. For example, the preset target may include common kitchen waste such as vegetable residues, fruit peels, leftovers, etc. By processing the video image data through a target detection algorithm, the system can automatically identify the garbage target in the video and compare it with the preset target, thereby determining whether the garbage target is the garbage to be processed. This process greatly improves the accuracy and efficiency of garbage recognition.

[0076] Specifically, when the parameter determination module calculates the comprehensive feature value of the garbage to be processed in the video image data with qualified clarity, it includes:

[0077] Preprocess the video image data with qualified clarity, and extract the shape features, color features, and texture features of the garbage to be processed;

[0078] Use a feature fusion algorithm to fuse the shape features, color features, and texture features to obtain the comprehensive feature value of the garbage to be processed.

[0079] It can be understood that the video image data with qualified clarity refers to the video image data whose image quality meets the system-set standard, and these data can accurately reflect the characteristic information of the garbage to be processed.

[0080] It can be understood that the specific process of the fusion process is to perform weighted summation or weighted averaging on the shape features, color features, and texture features to obtain a more accurate and comprehensive comprehensive feature value of the garbage to be processed. This process fully considers the characteristic information of the garbage to be processed in different aspects and improves the accuracy and integrity of feature extraction.

[0081] Specifically, when the parameter determination module determines the initial crushing parameter of the garbage to be processed according to the comprehensive feature value and the quality data, it includes:

[0082] Extract the features of the quality data to obtain the quality feature value;

[0083] Compare the quality feature value with the quality standard value according to the comprehensive feature value and the comprehensive feature threshold, and determine the initial crushing parameter of the garbage to be processed according to the comparison result;

[0084] When the comprehensive feature value is greater than or equal to the comprehensive feature threshold and the quality feature value is greater than or equal to the quality standard value, it is determined that the initial crushing parameter is the first crushing parameter;

[0085] When the comprehensive characteristic value is greater than or equal to the comprehensive characteristic threshold and the quality characteristic value is less than the quality standard value, determine the initial crushing parameter as the second crushing parameter;

[0086] When the comprehensive characteristic value is less than the comprehensive characteristic threshold and the quality characteristic value is greater than or equal to the quality standard value, determine the initial crushing parameter as the third crushing parameter;

[0087] When the comprehensive characteristic value is less than the comprehensive characteristic threshold and the quality characteristic value is less than the quality standard value, determine the initial crushing parameter as the fourth crushing parameter.

[0088] It can be understood that the quality characteristic value refers to the specific value after quantifying the quality attributes of the garbage to be processed, and these values reflect the physical characteristics and processing requirements of the garbage. The comprehensive characteristic threshold is obtained based on the system's statistical analysis of historical processing data and garbage type characteristics, and is a critical value used to distinguish different garbage types and processing requirements. The quality standard value is set according to the environmental protection requirements and resource utilization objectives of garbage treatment, and is used to evaluate whether the garbage to be processed meets the treatment standards. By comparing the comprehensive characteristic value, the quality characteristic value with the corresponding thresholds and standard values, the system can intelligently select the most appropriate initial crushing parameter to ensure the pertinence and efficiency of garbage treatment.

[0089] In this embodiment, the first crushing parameter > the second crushing parameter > the third crushing parameter > the fourth crushing parameter. This means that when the comprehensive characteristic value and the quality characteristic value of the garbage to be processed are both high, the system will select a more intense crushing parameter to ensure that the garbage can be fully crushed and processed. On the contrary, when the comprehensive characteristic value and the quality characteristic value of the garbage to be processed are both low, the system will select a milder crushing parameter to avoid unnecessary energy consumption and equipment wear. This intelligent parameter selection method not only improves the efficiency of garbage treatment, but also extends the service life of the equipment and reduces the operating cost.

[0090] Specifically, when the parameter optimization module calculates the equipment deviation value based on the equipment state characteristic value and determines whether to optimize the initial crushing parameter according to the equipment deviation value, it includes:

[0091] Obtain the equipment state standard value corresponding to each equipment state characteristic value;

[0092] Calculate the number of equipment state characteristic values greater than the equipment state standard value, and record it as the first number;

[0093] Calculate the number of equipment state characteristic values less than or equal to the equipment state standard value, and record it as the second number;

[0094] Calculate the equipment deviation value according to the equipment status characteristic value, the equipment status standard value, the first quantity, and the second quantity;

[0095] Compare the equipment deviation value with the equipment deviation threshold, and determine whether to optimize the initial crushing parameters according to the comparison result;

[0096] When the equipment deviation value is less than the equipment deviation threshold, it is determined not to optimize the initial crushing parameters;

[0097] When the equipment deviation value is greater than or equal to the equipment deviation threshold, it is determined to optimize the initial crushing parameters.

[0098] In this embodiment, the equipment status characteristic value refers to various performance indicators and status parameters shown by the equipment during operation, and these characteristic values can reflect the current working status and performance of the equipment. For example, the equipment status characteristic value may include the rotation speed, temperature, vibration condition, wear degree, etc. of the equipment. The equipment status standard value refers to the standard numerical values that each performance indicator and status parameter should reach under the normal operation state of the equipment. These standard numerical values are comprehensively determined based on factors such as the design specifications, operation experience, and maintenance requirements of the equipment, and are used to evaluate whether the operation state of the equipment is normal.

[0099] In this embodiment, the calculation process of the equipment deviation value is to calculate the difference between each equipment status characteristic value and its corresponding equipment status standard value to obtain the deviation degree of each equipment status characteristic value; then, perform weighted average processing on the deviation degree according to the first quantity and the second quantity to obtain the overall deviation value of the equipment. The equipment deviation threshold is obtained based on the statistical analysis of historical equipment status data and garbage treatment effects by the system, and is used to judge whether the equipment status deviates from the normal range, so as to decide whether to optimize the initial crushing parameters. This process ensures that the garbage processor operates in the best state and avoids poor treatment effects and increased energy consumption caused by poor equipment status.

[0100] Specifically, when the parameter optimization module determines the optimization coefficient of the initial crushing parameters according to the equipment deviation value and obtains the optimized crushing parameters, it includes:

[0101] Compare the equipment deviation value with the first equipment deviation value and the second equipment deviation value, and determine the optimization coefficient of the initial crushing parameters according to the comparison result; wherein the first equipment deviation value is less than the second equipment deviation value;

[0102] Set an optimization coefficient interval, wherein the optimization coefficient interval includes a first optimization coefficient, a second optimization coefficient, and a third optimization coefficient;

[0103] When the device deviation value is less than or equal to the first device deviation value, determine the optimization coefficient as the first optimization coefficient, and take the product value of the first optimization coefficient and the initial crushing parameters as the optimized crushing parameters;

[0104] When the device deviation value is greater than the first device deviation value and less than or equal to the second device deviation value, determine the optimization coefficient as the second optimization coefficient, and take the product value of the second optimization coefficient and the initial crushing parameters as the optimized crushing parameters;

[0105] When the device deviation value is greater than the second device deviation value, determine the optimization coefficient as the third optimization coefficient, and take the product value of the third optimization coefficient and the initial crushing parameters as the optimized crushing parameters.

[0106] It can be understood that the first optimization coefficient < the second optimization coefficient < the third optimization coefficient. This means that when the device deviation value is small, the system will select a smaller optimization coefficient and make a smaller adjustment to the initial crushing parameters to maintain the stable operation of the device. When the device deviation value is large, the system will select a larger optimization coefficient and make a larger adjustment to the initial crushing parameters to quickly restore the device to its optimal state. This intelligent optimization method not only improves the stability and reliability of waste treatment but also further enhances the operation efficiency and treatment effect of the device.

[0107] Specifically, when the parameter compensation module determines whether to compensate the optimized crushing parameters according to the actual response time, it includes:

[0108] Compare the actual response time with the standard response time, and determine whether to compensate the optimized crushing parameters according to the comparison result;

[0109] When the actual response time is greater than the standard response time, it is determined to compensate the optimized crushing parameters;

[0110] When the actual response time is less than or equal to the standard response time, it is determined not to compensate the optimized crushing parameters.

[0111] It can be understood that the standard response time refers to the time required for the system to start executing the processing operation from receiving the processing instruction under ideal conditions. The actual response time may be affected by various factors such as device status and network latency, resulting in a difference from the standard response time. When the actual response time is greater than the standard response time, it means that the system response speed slows down, which may lead to a delay in the garbage disposal operation. To compensate for this delay, the parameter compensation module will compensate the optimized crushing parameters to ensure the timeliness and accuracy of the garbage disposal operation. The magnitude of the compensation coefficient is directly proportional to the difference in response time, that is, the greater the difference in response time, the greater the compensation coefficient, and the corresponding adjustment range of the optimized crushing parameters also increases, which further improves the stability and accuracy of garbage disposal and ensures the reliable operation of the garbage disposal system.

[0112] Specifically, when the parameter compensation module determines the compensation coefficient of the optimized crushing parameters based on the difference in response time and obtains the compensated crushing parameters, it includes:

[0113] Comparing the difference in response time with historical data, and determining the compensation coefficient of the optimized crushing parameters according to the comparison result;

[0114] When there is a historical response time difference in the historical data that is the same as the difference in response time, taking the historical compensation coefficient corresponding to the historical response time difference as the compensation coefficient of the optimized crushing parameters, and taking the product value of the historical compensation coefficient and the optimized crushing parameters as the compensated crushing parameters;

[0115] When there is no historical response time difference in the historical data that is the same as the difference in response time, calculating the difference between the difference in response time and the historical data one by one, denoted as the difference response time, extracting the minimum value of the difference response time, denoted as the minimum difference response time, determining the compensation coefficient of the optimized crushing parameters according to the minimum difference response time, and obtaining the compensated crushing parameters.

[0116] It can be understood that the historical data includes several historical response time differences and their corresponding historical compensation coefficients. By comparing the response time differences in the historical data with the current response time difference, the system can intelligently select the appropriate compensation coefficient. When there is a situation in the historical data where the response time difference is the same as the current one, the system can directly adopt the historical compensation coefficient, which greatly improves the accuracy and efficiency of the compensation. When there is no exactly the same situation in the historical data, the system will calculate the difference response time and select the minimum difference response time to determine the compensation coefficient, and this process ensures the refinement and intelligence of the compensation.

[0117] Specifically, when the parameter compensation module determines the compensation coefficient of the optimized crushing parameters based on the minimum difference response time and obtains the compensated crushing parameters, it includes:

[0118] Comparing the minimum difference response time with the first minimum difference response time and the second minimum difference response time, and determining the compensation coefficient of the optimized crushing parameters according to the comparison result; wherein, the first minimum difference response time is less than the second minimum difference response time;

[0119] When the minimum difference response time is less than or equal to the first minimum difference response time, determining the compensation coefficient as the first compensation coefficient, and taking the product value of the first compensation coefficient and the optimized crushing parameters as the compensated crushing parameters;

[0120] When the minimum difference response time is greater than the first minimum difference response time and less than or equal to the second minimum difference response time, determining the compensation coefficient as the second compensation coefficient, and taking the product value of the second compensation coefficient and the optimized crushing parameters as the compensated crushing parameters;

[0121] When the minimum difference response time is greater than the second minimum difference response time, determining the compensation coefficient as the third compensation coefficient, and taking the product value of the third compensation coefficient and the optimized crushing parameters as the compensated crushing parameters.

[0122] It can be understood that the first compensation coefficient < the second compensation coefficient < the third compensation coefficient. This means that when the minimum difference response time is small, the system will select a smaller compensation coefficient and make a smaller adjustment to the optimized crushing parameters to avoid unstable equipment operation caused by overcompensation. When the minimum difference response time is large, the system will select a larger compensation coefficient and make a larger adjustment to the optimized crushing parameters to quickly make up for the response time delay and ensure the timeliness and accuracy of garbage disposal. This intelligent compensation mechanism not only improves the stability and efficiency of garbage disposal, but also further enhances the adaptability and robustness of the system, enabling it to maintain excellent processing performance in various complex environments.

[0123] In this embodiment, the first minimum difference response time and the second minimum difference response time are obtained based on the statistical analysis of the historical response time difference and compensation effect by the system, and are used to distinguish the influence degree of different response time differences on the selection of the compensation coefficient.

[0124] Refer to Figure 2 As shown, in some embodiments of the present application, this embodiment provides a kitchen waste treatment method based on image recognition, including the following steps:

[0125] S100: Collect video image data inside the food waste processor and quality data of the waste to be processed;

[0126] S200: Analyze the video image data, and based on the analysis result, determine whether the waste to be processed appears in the video image data; if so, calculate the comprehensive eigenvalue of the waste to be processed in the video image data with qualified clarity, and determine the initial crushing parameters of the waste to be processed according to the comprehensive eigenvalue and the quality data;

[0127] S300: Collect the device status information of the food waste processor, analyze the device status information to obtain the device status eigenvalue, calculate the device deviation value based on the device status eigenvalue, and determine whether to optimize the initial crushing parameters according to the device deviation value; if so, determine the optimization coefficient of the initial crushing parameters according to the device deviation value, and obtain the optimized crushing parameters;

[0128] S400: Collect the actual response time of the food waste processor, and determine whether to compensate the optimized crushing parameters according to the actual response time; if so, calculate the difference between the actual response time and the standard response time, and record it as the response time difference, determine the compensation coefficient of the optimized crushing parameters according to the response time difference, and obtain the compensated crushing parameters;

[0129] S500: Receive the compensated crushing parameters, and control the food waste processor to perform the waste treatment operation according to the compensated crushing parameters.

[0130] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1A device for the functions specified in one or more boxes.

[0132] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the functions specified in one Figure 1 process or more processes and / or boxes Figure 1 A function specified in one box or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 process or more processes and / or boxes Figure 1 A function specified in one box or more boxes.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A food waste treatment system based on image recognition, characterized in that, Including: A data acquisition module configured to acquire video image data inside the food waste processor and quality data of the waste to be processed; A parameter determination module configured to analyze the video image data and determine whether the waste to be processed appears in the video image data based on the analysis result; If so, calculate the comprehensive feature value of the waste to be processed in the video image data with qualified clarity, and determine the initial crushing parameters of the waste to be processed according to the comprehensive feature value and the quality data; A parameter optimization module configured to acquire the device status information of the food waste processor, analyze the device status information to obtain the device status feature value, calculate the device deviation value based on the device status feature value, and determine whether to optimize the initial crushing parameters according to the device deviation value; If so, determine the optimization coefficient of the initial crushing parameters according to the device deviation value and obtain the optimized crushing parameters; A parameter compensation module configured to acquire the actual response time of the food waste processor and determine whether to compensate the optimized crushing parameters according to the actual response time; if so, calculate the difference between the actual response time and the standard response time, denoted as the response time difference, determine the compensation coefficient of the optimized crushing parameters according to the response time difference, and obtain the compensated crushing parameters; An execution module configured to receive the compensated crushing parameters and control the food waste processor to perform waste processing operations according to the compensated crushing parameters.

2. The kitchen waste treatment system based on image recognition according to claim 1, wherein When the parameter determination module determines whether the waste to be processed appears in the video image data based on the analysis result, it includes: Processing the video image data using an object detection algorithm to identify the waste objects in the video image data and determining whether the waste objects are the waste to be processed; When the waste object is consistent with the preset object, it is determined that the waste object is the waste to be processed; When the waste object is inconsistent with the preset object, it is determined that the waste object is not the waste to be processed.

3. The food waste treatment system based on image recognition according to claim 2, characterized in that When the parameter determination module calculates the comprehensive feature value of the waste to be processed in the video image data with qualified clarity, it includes: Preprocessing the video image data with qualified clarity to extract the shape feature, color feature and texture feature of the waste to be processed; Using a feature fusion algorithm to fuse the shape feature, color feature and texture feature to obtain the comprehensive feature value of the waste to be processed.

4. The food waste treatment system based on image recognition according to claim 3, characterized in that, When the parameter determination module determines the initial crushing parameters of the waste to be processed according to the comprehensive feature value and the quality data, it includes: Performing feature extraction on the quality data to obtain the quality feature value; Comparing the quality feature value with the quality standard value according to the comprehensive feature value and the comprehensive feature threshold, and determining the initial crushing parameters of the waste to be processed according to the comparison result; When the comprehensive feature value is greater than or equal to the comprehensive feature threshold and the quality feature value is greater than or equal to the quality standard value, it is determined that the initial crushing parameter is the first crushing parameter; When the comprehensive characteristic value is greater than or equal to the comprehensive characteristic threshold and the quality characteristic value is less than the quality standard value, determine the initial comminution parameter as the second comminution parameter; When the comprehensive characteristic value is less than the comprehensive characteristic threshold and the quality characteristic value is greater than or equal to the quality standard value, determine the initial comminution parameter as the third comminution parameter; When the comprehensive characteristic value is less than the comprehensive characteristic threshold and the quality characteristic value is less than the quality standard value, determine the initial comminution parameter as the fourth comminution parameter.

5. The food waste treatment system based on image recognition according to claim 4, wherein, When the parameter optimization module calculates the equipment deviation value based on the equipment state characteristic value and determines whether to optimize the initial comminution parameter according to the equipment deviation value, it includes: Obtain the equipment state standard value corresponding to each equipment state characteristic value; Calculate the number of equipment state characteristic values greater than the equipment state standard value, and denote it as the first quantity; Calculate the number of equipment state characteristic values less than or equal to the equipment state standard value, and denote it as the second quantity; Calculate the equipment deviation value according to the equipment state characteristic value, equipment state standard value, first quantity and second quantity; Compare the equipment deviation value with the equipment deviation threshold, and determine whether to optimize the initial comminution parameter according to the comparison result; When the equipment deviation value is less than the equipment deviation threshold, determine that the initial comminution parameter is not optimized; When the equipment deviation value is greater than or equal to the equipment deviation threshold, determine that the initial comminution parameter is optimized.

6. The food waste treatment system based on image recognition according to claim 5, characterized in that, When the parameter optimization module determines the optimization coefficient of the initial comminution parameter according to the equipment deviation value and obtains the optimized comminution parameter, it includes: Compare the equipment deviation value with the first equipment deviation value and the second equipment deviation value, and determine the optimization coefficient of the initial comminution parameter according to the comparison result; where the first equipment deviation value is less than the second equipment deviation value; Set an optimization coefficient interval, where the optimization coefficient interval includes a first optimization coefficient, a second optimization coefficient and a third optimization coefficient; When the equipment deviation value is less than or equal to the first equipment deviation value, determine the optimization coefficient as the first optimization coefficient, and use the product value of the first optimization coefficient and the initial comminution parameter as the optimized comminution parameter; When the equipment deviation value is greater than the first equipment deviation value and less than or equal to the second equipment deviation value, determine the optimization coefficient as the second optimization coefficient, and use the product value of the second optimization coefficient and the initial comminution parameter as the optimized comminution parameter; When the equipment deviation value is greater than the second equipment deviation value, determine the optimization coefficient as the third optimization coefficient, and use the product value of the third optimization coefficient and the initial comminution parameter as the optimized comminution parameter.

7. The kitchen waste treatment system based on image recognition according to claim 6, wherein, When the parameter compensation module determines whether to compensate the optimized comminution parameter according to the actual response time, it includes: Compare the actual response time with the standard response time, and determine whether to compensate the optimized comminution parameter according to the comparison result; When the actual response time is greater than the standard response time, it is determined that compensation is made to the optimized crushing parameters; When the actual response time is less than or equal to the standard response time, it is determined that no compensation is made to the optimized crushing parameters.

8. The food waste treatment system based on image recognition according to claim 7, characterized in that When the parameter compensation module determines the compensation coefficient of the optimized crushing parameters according to the response time difference and obtains the compensated crushing parameters, it includes: Comparing the response time difference with historical data, and determining the compensation coefficient of the optimized crushing parameters according to the comparison result; When there is a historical response time difference in the historical data that is the same as the response time difference, taking the historical compensation coefficient corresponding to the historical response time difference as the compensation coefficient of the optimized crushing parameters, and taking the product value of the historical compensation coefficient and the optimized crushing parameters as the compensated crushing parameters; When there is no historical response time difference in the historical data that is the same as the response time difference, calculating the difference between the response time difference and the historical data one by one, denoting it as the difference response time, extracting the minimum value of the difference response time, denoting it as the minimum difference response time, determining the compensation coefficient of the optimized crushing parameters according to the minimum difference response time, and obtaining the compensated crushing parameters.

9. The food waste treatment system based on image recognition according to claim 8, wherein, When the parameter compensation module determines the compensation coefficient of the optimized crushing parameters according to the minimum difference response time and obtains the compensated crushing parameters, it includes: Comparing the minimum difference response time with the first minimum difference response time and the second minimum difference response time, and determining the compensation coefficient of the optimized crushing parameters according to the comparison result; wherein, the first minimum difference response time is less than the second minimum difference response time; When the minimum difference response time is less than or equal to the first minimum difference response time, determining the compensation coefficient as the first compensation coefficient, and taking the product value of the first compensation coefficient and the optimized crushing parameters as the compensated crushing parameters; When the minimum difference response time is greater than the first minimum difference response time and less than or equal to the second minimum difference response time, determining the compensation coefficient as the second compensation coefficient, and taking the product value of the second compensation coefficient and the optimized crushing parameters as the compensated crushing parameters; When the minimum difference response time is greater than the second minimum difference response time, determining the compensation coefficient as the third compensation coefficient, and taking the product value of the third compensation coefficient and the optimized crushing parameters as the compensated crushing parameters.

10. A kitchen waste treatment method based on image recognition, which is applied to the kitchen waste treatment system based on image recognition according to any one of claims 1-9, and is characterized in that, It includes: Collecting video image data inside the food waste processor and the mass data of the waste to be processed; Parsing the video image data, and determining whether the waste to be processed appears in the video image data based on the parsing result; If so, calculating the comprehensive feature value of the waste to be processed in the video image data with qualified clarity, and determining the initial crushing parameters of the waste to be processed according to the comprehensive feature value and the mass data; Collect the device status information of the food waste processor, parse the device status information to obtain the device status characteristic value, calculate the device deviation value based on the device status characteristic value, and determine whether to optimize the initial crushing parameters according to the device deviation value; If so, determine the optimization coefficient of the initial crushing parameters according to the device deviation value and obtain the optimized crushing parameters; Collect the actual response time of the food waste processor, and determine whether to compensate the optimized crushing parameters according to the actual response time; if so, calculate the difference between the actual response time and the standard response time, denoted as the response time difference, determine the compensation coefficient of the optimized crushing parameters according to the response time difference, and obtain the compensated crushing parameters; Receive the compensated crushing parameters and control the food waste processor to perform the garbage disposal operation according to the compensated crushing parameters.

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