Crushing method and system for kitchen garbage treatment equipment

Abstract

The invention relates to the technical field of intelligent household appliances, and discloses a crushing method and system for kitchen garbage treatment equipment, and the method comprises the steps: carrying out the image collection of kitchen garbage to be treated, obtaining initial to-be-treated data, carrying out the analysis of the initial to-be-treated data, and determining the integrity of the initial to-be-treated data; extracting feature information of the to-be-processed data, analyzing all the feature information, and determining effective feature points; collecting physical feature information of the effective feature points, and calculating processing parameters of the corresponding feature points according to the physical feature information; and judging a crushing strategy of the kitchen garbage to be treated according to the treatment parameters, and generating a corresponding crushing scheme according to a judgment result. By monitoring and analyzing the to-be-treated garbage in real time, the type of the to-be-treated garbage can be accurately identified, so that a corresponding crushing strategy is formulated, the intelligent treatment mode can improve the treatment efficiency and effect of the kitchen garbage, and more refined garbage management is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a crushing method and system for kitchen waste processing equipment. Background Art

[0002] Kitchen waste is not only huge in quantity, but also has complex composition, including perishable organic matter such as fruits, vegetables, and leftovers. Traditional methods of kitchen waste treatment, such as landfill and incineration, have problems such as waste of land resources, environmental pollution and high treatment costs. Therefore, it is particularly important to develop efficient and environmentally friendly kitchen waste treatment technology.

[0003] Food waste disposal equipment is usually installed under the kitchen sink to grind food scraps and small garbage into fine particles and then discharge them through the sewer. This equipment can reduce the amount of household waste, avoid odor in the trash can, and help protect the environment.

[0004] At present, many food waste treatment equipment mainly relies on a combination of mechanical crushing and biodegradation, but in actual operation, there is a lack of effective analysis of the characteristics of the waste to be treated, resulting in unsatisfactory crushing effects. Summary of the invention

[0005] In view of this, the present invention proposes a crushing method and system for a kitchen waste treatment device, aiming to solve the problem of lack of analysis of kitchen waste in the current technology.

[0006] The present invention provides a comminution method for kitchen waste treatment equipment, comprising:

[0007] Capturing images of the food waste to be processed to obtain initial data to be processed, and analyzing the initial data to be processed to determine the integrity of the initial data to be processed;

[0008] Extracting feature information of the data to be processed, and analyzing all feature information to determine effective feature points;

[0009] Collecting physical feature information of the effective feature points, and calculating processing parameters of corresponding feature points according to the physical feature information;

[0010] The crushing strategy of the kitchen waste to be processed is determined according to the processing parameters, and a corresponding crushing plan is generated according to the determination result.

[0011] Further, analyzing the initial data to be processed to determine the integrity of the initial data to be processed includes:

[0012] Performing data preprocessing on the initial data to be processed, wherein the data preprocessing includes noise removal and standardization processing;

[0013] Inputting the processed initial data to be processed into a decision model, wherein the decision model includes a feature extraction module and a classification module;

[0014] An integrity value of the initial data to be processed after being processed based on the output of the decision model;

[0015] When the integrity value is greater than a preset integrity value, the initial data to be processed is determined as data to be processed;

[0016] When the integrity value is less than or equal to the preset integrity value, the data of the food waste to be processed is reacquired.

[0017] Furthermore, when extracting the feature information of the data to be processed and analyzing all the feature information to determine the effective feature points, it includes:

[0018] Convert the feature information into feature coordinates, and establish a feature point coordinate system according to the feature coordinates;

[0019] Fit all feature coordinates based on a preset algorithm to determine the feature point fitting curve;

[0020] The unfitted feature coordinates are deleted, and the coordinate points on the feature point fitting curve are taken as valid feature points.

[0021] Furthermore, when all feature information is converted into feature coordinates and a feature point coordinate system is established according to all feature coordinates, it includes:

[0022] The preset acquisition time is used as the X-axis coordinate value of the feature point;

[0023] Acquire physical parameters of the feature points, and construct a feature parameter sequence based on the physical parameters;

[0024] Determining a standard feature parameter sequence corresponding to the feature parameter sequence, and comparing the feature parameter sequence with the standard feature parameter sequence;

[0025] When the characteristic parameter in the characteristic parameter sequence is less than the standard characteristic parameter in the standard characteristic parameter sequence, a first characteristic parameter set is generated according to the corresponding characteristic parameter;

[0026] When the characteristic parameters in the characteristic parameter sequence are equal to the standard characteristic parameters in the standard characteristic parameter sequence, a second characteristic parameter set is generated according to the corresponding characteristic parameters;

[0027] When the characteristic parameter in the characteristic parameter sequence is greater than the standard characteristic parameter in the standard characteristic parameter sequence, a third characteristic parameter set is generated according to the corresponding characteristic parameter;

[0028] Calculate the Y-axis coordinate value of the feature point according to the first feature parameter set, the second feature parameter set and the third feature parameter set;

[0029] A feature point coordinate system is established according to the X-axis coordinate value and the Y-axis coordinate value.

[0030] Further, when calculating the Y-axis coordinate value of the feature point according to the first feature parameter set, the second feature parameter set and the third feature parameter set, it includes:

[0031] Counting the number of feature parameters in the first feature parameter set, the number of feature parameters in the second feature parameter set, and the number of feature parameters in the third feature parameter set;

[0032] The Y-axis coordinate value of the feature point is calculated according to the following formula:

[0033]

[0034] Where Y is the Y-axis coordinate value of the feature point, n is the number of feature parameters in the first feature parameter set, m is the number of feature parameters in the second feature parameter set, p is the number of feature parameters in the third feature parameter set, and d i is the i-th characteristic parameter in the first characteristic parameter set, r i is the standard characteristic parameter corresponding to the i-th characteristic parameter, g i is the weight of the i-th feature parameter, f j is the jth characteristic parameter in the third characteristic parameter set, s j is the standard characteristic parameter corresponding to the jth characteristic parameter, k j is the weight of the jth feature parameter, and h is the integration coefficient of the Y-axis coordinate value.

[0035] Furthermore, the integration coefficient h of the Y-axis coordinate value is determined according to the following method:

[0036] The integrated influence value of the feature point is calculated according to the following formula:

[0037]

[0038] Among them, C is the integrated impact value, q is the second quantity;

[0039] Preset a first preset integration influence value and a second preset integration influence value; preset a first preset integration coefficient h1, a second preset integration coefficient h2, and a third preset integration coefficient h3;

[0040] When the integration influence value is less than the first preset integration influence value, the first preset integration coefficient h1 is used as the integration coefficient h of the Y-axis coordinate value; when the integration influence value is greater than or equal to the first preset integration influence value and less than the second preset integration influence value, the second preset integration coefficient h2 is used as the integration coefficient h of the Y-axis coordinate value; when the integration influence value is greater than or equal to the second preset integration influence value, the third preset integration coefficient h3 is used as the integration coefficient h of the Y-axis coordinate value.

[0041] Furthermore, when collecting the physical feature information of all valid feature points and calculating the processing parameters of the corresponding feature points according to the physical feature information, it includes:

[0042] Collecting physical feature information of all valid feature points, wherein the physical feature information includes particle size, humidity and density;

[0043] Calculate the processing parameters of the corresponding feature points according to the particle size, humidity and density:

[0044]

[0045] Wherein, P is the processing parameter of the feature point, d is the particle size, h is the humidity, ρ is the density, k1 is the first weight, and 0.8≤k1≤1.2, k2 is the second weight, and 0.4≤k1≤0.6.

[0046] Furthermore, when determining the crushing strategy of the kitchen waste to be processed according to the processing parameters, it includes:

[0047] A processing parameter curve is obtained by fitting all processing parameters;

[0048] Determining a pre-fitted standard processing parameter curve, and judging a crushing strategy for the kitchen waste to be processed according to the processing parameter curve and the standard processing parameter curve;

[0049] If the processing parameter curve completely overlaps with the standard processing parameter curve, it is determined that the food waste to be processed can be crushed as planned;

[0050] If the processing parameter curve does not completely overlap with the standard processing parameter curve, it is determined that the comminution strategy of the food waste to be processed needs to be adjusted.

[0051] Furthermore, when a kitchen waste treatment plan is generated according to the judgment result, it includes:

[0052] When it is determined that the to-be-processed kitchen waste can be crushed as planned, a processable mark is generated;

[0053] When it is determined that the pulverization strategy of the to-be-processed food waste needs to be adjusted, determining a non-overlapping curve segment between the processing parameter curve and the standard processing parameter curve;

[0054] Determine the maximum non-overlap distance between the processing parameter curve and the standard processing parameter curve according to the non-overlapping curve segment; pre-set a first preset distance and a second preset distance; the first preset distance is smaller than the second preset distance;

[0055] When the maximum non-overlapping distance is less than the first preset distance, a first-level unprocessable mark is generated;

[0056] When the maximum non-overlapping distance is greater than or equal to the first preset distance and less than the second preset distance, a secondary unprocessable mark is generated;

[0057] When the maximum non-overlapping distance is greater than or equal to the second preset distance, a third-level unprocessable mark is generated;

[0058] Adjust the crushing strategy according to the level of unprocessable marks.

[0059] On the other hand, the present invention further provides a pulverizing system for a kitchen waste treatment device, which is applied to the pulverizing method for the kitchen waste treatment device, comprising:

[0060] The integrity analysis module is used to collect images of the food waste to be processed, obtain initial data to be processed, and analyze the initial data to be processed to determine the integrity of the initial data to be processed;

[0061] The validity determination module is used to extract the feature information of the data to be processed, analyze all the feature information, and determine the valid feature points;

[0062] A processing parameter calculation module, used to collect physical feature information of all valid feature points and calculate processing parameters of corresponding feature points according to the physical feature information;

[0063] The strategy judgment module is used to judge the crushing strategy of the kitchen waste to be processed according to the processing parameters, and generate a corresponding crushing plan according to the judgment result.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] By performing image acquisition and data analysis on the kitchen waste to be processed, the characteristic information of the garbage can be obtained in real time. This precise information feedback enables the crushing equipment to adjust the processing parameters according to the actual situation, thereby improving the efficiency of garbage crushing. This solution can formulate corresponding crushing strategies according to different types and states of kitchen waste by extracting effective feature points and calculating the corresponding processing parameters. This personalized processing method can significantly improve the crushing effect and reduce energy consumption and time during the processing. The present invention can adjust the crushing strategy in real time according to changes in processing parameters, so that the processing system has higher flexibility. This feature can adapt to changes in different types of kitchen waste and improve the adaptability and operability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0067] Figure 1 This is a flow chart of a crushing method for kitchen waste treatment equipment according to an embodiment of the present invention;

[0068] Figure 2 The present invention is a functional block diagram of a comminution system for kitchen waste treatment equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0069] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0070] See also Figure 1 As shown, an embodiment of the present invention provides a pulverizing method for a kitchen waste treatment device, comprising:

[0071] Capturing images of the food waste to be processed to obtain initial data to be processed, and analyzing the initial data to be processed to determine the integrity of the initial data to be processed;

[0072] Extracting feature information of the data to be processed, and analyzing all feature information to determine effective feature points;

[0073] Collecting physical feature information of the effective feature points, and calculating processing parameters of corresponding feature points according to the physical feature information;

[0074] The crushing strategy of the kitchen waste to be processed is determined according to the processing parameters, and a corresponding crushing plan is generated according to the determination result.

[0075] It should be noted that the pulverizing method for kitchen waste treatment equipment provided by the embodiment of the present invention obtains the initial data to be processed through image acquisition, and analyzes it to determine the integrity of the data. This process ensures the accuracy and integrity of the information and provides a reliable basis for subsequent processing. Extracting the characteristic information of the data to be processed and analyzing it can effectively identify the different types and states of garbage and enhance the pertinence and scientificity of the treatment strategy. By collecting the physical characteristic information of the effective feature points, such as particle size, humidity and density, it provides the necessary basis for the calculation of the processing parameters, ensuring that the processing method of each feature point is consistent with its actual physical characteristics, thereby improving the accuracy of the processing. On this basis, the corresponding processing parameters are calculated according to the physical characteristic information to provide data support for determining the appropriate pulverizing strategy, thereby optimizing the pulverizing process, reducing energy consumption, and improving the processing efficiency. Finally, a specific pulverizing scheme is generated according to the processing parameters, making the treatment of kitchen waste more scientific and reasonable, reducing resource waste, and optimizing the processing process. Overall, this method significantly improves the efficiency and intelligence of kitchen waste treatment, and has important social and economic value for environmental protection and resource recovery.

[0076] In some embodiments of the present application, analyzing the initial data to be processed to determine the integrity of the initial data to be processed includes:

[0077] Performing data preprocessing on the initial data to be processed, wherein the data preprocessing includes noise removal and standardization processing;

[0078] Inputting the processed initial data to be processed into a decision model, wherein the decision model includes a feature extraction module and a classification module;

[0079] An integrity value of the initial data to be processed after being processed based on the output of the decision model;

[0080] When the integrity value is greater than a preset integrity value, the initial data to be processed is determined as data to be processed;

[0081] When the integrity value is less than or equal to the preset integrity value, the data of the food waste to be processed is reacquired.

[0082] It is understandable that the analysis and integrity determination process of the initial data to be processed is carried out through a series of steps to ensure the quality and reliability of the data. First, the initial data to be processed is preprocessed to remove noise and perform standardization. This process can eliminate interference information, improve the clarity of the data, and make it more suitable for subsequent analysis. Then, the processed data is input into the decision model, which contains a feature extraction module and a classification module, which can systematically analyze the data features, extract key indicators, and ensure the validity and accuracy of the information. Based on this decision model, the processed data integrity value is output to evaluate the quality of the data. When the integrity value exceeds the preset threshold, the initial data is confirmed to be valid and can be used for subsequent processing; otherwise, the data of the food waste to be processed needs to be reacquired. This process effectively improves the accuracy of data processing, ensures the effectiveness of subsequent analysis and processing, and lays the foundation for the efficient treatment of food waste.

[0083] In some embodiments of the present application, when extracting the feature information of the data to be processed and analyzing all the feature information to determine the effective feature points, it includes:

[0084] Convert the feature information into feature coordinates, and establish a feature point coordinate system according to the feature coordinates;

[0085] Fit all feature coordinates based on a preset algorithm to determine the feature point fitting curve;

[0086] The unfitted feature coordinates are deleted, and the coordinate points on the feature point fitting curve are taken as valid feature points.

[0087] It should be noted that the extraction of feature information and the determination of effective feature points are carried out through systematic steps to improve the accuracy of data analysis. First, the feature information of the data to be processed is converted into feature coordinates, and the feature point coordinate system is established based on this, which provides a clear spatial framework for subsequent data analysis. Next, all feature coordinates are fitted based on the preset algorithm to generate a feature point fitting curve. This process can effectively capture the main change trends and characteristics of the data. Subsequently, the unfitted feature coordinates will be deleted to ensure that only valid data points related to the fitting curve are retained. Finally, the coordinate points on the feature point fitting curve will be used as effective feature points for further analysis and decision-making. This method not only improves the data processing efficiency, but also enhances the reliability of feature extraction, providing a more accurate basis for subsequent kitchen waste treatment.

[0088] In some embodiments of the present application, when all feature information is converted into feature coordinates and a feature point coordinate system is established according to all feature coordinates, it includes:

[0089] The preset acquisition time is used as the X-axis coordinate value of the feature point;

[0090] Acquire physical parameters of the feature points, and construct a feature parameter sequence based on the physical parameters;

[0091] Determining a standard feature parameter sequence corresponding to the feature parameter sequence, and comparing the feature parameter sequence with the standard feature parameter sequence;

[0092] When the characteristic parameter in the characteristic parameter sequence is less than the standard characteristic parameter in the standard characteristic parameter sequence, a first characteristic parameter set is generated according to the corresponding characteristic parameter;

[0093] When the characteristic parameters in the characteristic parameter sequence are equal to the standard characteristic parameters in the standard characteristic parameter sequence, a second characteristic parameter set is generated according to the corresponding characteristic parameters;

[0094] When the characteristic parameter in the characteristic parameter sequence is greater than the standard characteristic parameter in the standard characteristic parameter sequence, a third characteristic parameter set is generated according to the corresponding characteristic parameter;

[0095] Calculate the Y-axis coordinate value of the feature point according to the first feature parameter set, the second feature parameter set and the third feature parameter set;

[0096] A feature point coordinate system is established according to the X-axis coordinate value and the Y-axis coordinate value.

[0097] It should be noted that the process of converting all feature information into feature coordinates and establishing a feature point coordinate system is achieved through a series of precise steps. First, the preset acquisition time is used as the X-axis coordinate value of the feature point, laying the foundation for the time series analysis of subsequent data. Then, the physical parameters of the feature point are obtained, and a feature parameter sequence is constructed based on these parameters to facilitate a systematic analysis of the features. Subsequently, a standard feature parameter sequence corresponding to the feature parameter sequence is determined and compared to evaluate the quality of the data to be processed. According to the comparison results, when the feature parameter is less than the standard parameter, a first feature parameter set is generated; when the two are equal, a second feature parameter set is generated; when the feature parameter is greater than the standard parameter, a third feature parameter set is generated. Finally, these feature parameter sets will be used to calculate the Y-axis coordinate value of the feature point, thereby completely establishing a feature point coordinate system. Through this method, the characteristics of kitchen waste can be more accurately described and analyzed, providing solid data support for subsequent processing and decision-making.

[0098] In some embodiments of the present application, when calculating the Y-axis coordinate value of the feature point according to the first feature parameter set, the second feature parameter set, and the third feature parameter set, it includes:

[0099] Counting the number of feature parameters in the first feature parameter set, the number of feature parameters in the second feature parameter set, and the number of feature parameters in the third feature parameter set;

[0100] The Y-axis coordinate value of the feature point is calculated according to the following formula:

[0101]

[0102] Where Y is the Y-axis coordinate value of the feature point, n is the number of feature parameters in the first feature parameter set, m is the number of feature parameters in the second feature parameter set, p is the number of feature parameters in the third feature parameter set, and d i is the i-th characteristic parameter in the first characteristic parameter set, r i is the standard characteristic parameter corresponding to the i-th characteristic parameter, g i is the weight of the i-th feature parameter, f j is the jth characteristic parameter in the third characteristic parameter set, s j is the standard characteristic parameter corresponding to the jth characteristic parameter, k j is the weight of the jth feature parameter, and h is the integration coefficient of the Y-axis coordinate value.

[0103] It should be noted that the process of calculating the Y-axis coordinate value of the feature point is achieved by performing quantitative statistics and weight analysis on each feature parameter set. First, the number of feature parameters in the first feature parameter set, the second feature parameter set, and the third feature parameter set is counted to understand the distribution of different types of features in the data. Then, using the preset calculation formula, these quantities are combined with the specific values ​​of the feature parameters to generate the Y-axis coordinate value. This formula takes into account the specific value of each feature parameter and its corresponding standard feature parameter, and introduces a weight coefficient to reflect the degree of influence of different features on the Y-axis coordinate value. In addition, the setting of the integration coefficient h allows the final calculation result to be adjusted so that the Y-axis coordinate value can more accurately reflect the characteristic information of kitchen waste. This calculation method effectively improves the accuracy of feature analysis and provides a reliable basis for the formulation of subsequent processing strategies.

[0104] In some embodiments of the present application, the integration coefficient h of the Y-axis coordinate value is determined according to the following method:

[0105] The integrated influence value of the feature point is calculated according to the following formula:

[0106]

[0107] Among them, C is the integrated impact value, q is the second quantity;

[0108] Preset a first preset integration influence value and a second preset integration influence value; preset a first preset integration coefficient h1, a second preset integration coefficient h2, and a third preset integration coefficient h3;

[0109] When the integration influence value is less than the first preset integration influence value, the first preset integration coefficient h1 is used as the integration coefficient h of the Y-axis coordinate value; when the integration influence value is greater than or equal to the first preset integration influence value and less than the second preset integration influence value, the second preset integration coefficient h2 is used as the integration coefficient h of the Y-axis coordinate value; when the integration influence value is greater than or equal to the second preset integration influence value, the third preset integration coefficient h3 is used as the integration coefficient h of the Y-axis coordinate value.

[0110] It should be noted that the integration coefficient h of the Y-axis coordinate value is determined by calculating the integrated influence value of the feature point. As a key indicator, the integrated influence value can reflect the overall quality and reliability of the feature parameter set. The preset first and second integrated influence values, and the corresponding integration coefficients h1, h2 and h3, constitute the reference standard for judging the integrated influence value. Specifically, when the integrated influence value is lower than the first preset integrated influence value, h1 is selected as the integration coefficient to ensure that the calculation result is still conservative and reliable when the feature parameters are low; if the integrated influence value is between the first and second preset integrated influence values, h2 is used to adapt to the processing of medium-quality features; and when the integrated influence value is higher than or equal to the second preset integrated influence value, h3 is used to make full use of its information advantage when the feature parameters are of high quality. This dynamic adjustment mechanism makes the calculation of the Y-axis coordinate value more flexible and adaptable, thereby improving the accuracy and effectiveness of data analysis in the process of kitchen waste treatment.

[0111] In some embodiments of the present application, when collecting physical feature information of all valid feature points and calculating processing parameters of corresponding feature points according to the physical feature information, it includes:

[0112] Collecting physical feature information of all valid feature points, wherein the physical feature information includes particle size, humidity and density;

[0113] Calculate the processing parameters of the corresponding feature points according to the particle size, humidity and density:

[0114]

[0115] Wherein, P is the processing parameter of the feature point, d is the particle size, h is the humidity, ρ is the density, k1 is the first weight, and 0.8≤k1≤1.2, k2 is the second weight, and 0.4≤k1≤0.6.

[0116] It should be noted that when collecting the physical feature information of all valid feature points, the three key parameters of particle size, humidity and density are focused on. These physical feature information provide basic data for the subsequent calculation of processing parameters. By combining particle size, humidity and density with corresponding weights, the processing parameter P of the feature point can be effectively obtained. This method sets the first weight and the second weight to assign different weight ranges to the influence of particle size and humidity, respectively, so as to ensure that under different physical feature conditions, the calculation results of the processing parameters can reflect the changes in the actual situation.

[0117] Specifically, the reasonable weighting of particle size, humidity and density enables the processing parameter P to comprehensively consider the impact of these characteristics on the kitchen waste shredding process, and improve the accuracy and adaptability of the shredding strategy. The implementation of this method can better meet the differentiated needs of different types of kitchen waste in the processing process, thereby improving the efficiency and effect of overall waste treatment.

[0118] In some embodiments of the present application, when determining the crushing strategy of the to-be-processed kitchen waste according to the processing parameters, it includes:

[0119] A processing parameter curve is obtained by fitting all processing parameters;

[0120] Determining a pre-fitted standard processing parameter curve, and judging a crushing strategy for the kitchen waste to be processed according to the processing parameter curve and the standard processing parameter curve;

[0121] If the processing parameter curve completely overlaps with the standard processing parameter curve, it is determined that the food waste to be processed can be crushed as planned;

[0122] If the processing parameter curve does not completely overlap with the standard processing parameter curve, it is determined that the comminution strategy of the food waste to be processed needs to be adjusted.

[0123] In the above embodiment, the crushing strategy refers to a crushing time of 300 s and a crushing rotation speed of 1500 rpm.

[0124] It should be noted that when judging the crushing strategy of the food waste to be processed based on the processing parameters, all processing parameters are first fitted to generate a processing parameter curve. This curve provides visual data support for subsequent analysis. Next, it is compared with the pre-fitted standard processing parameter curve, and the crushing strategy of the food waste to be processed is judged by comparing the overlap of the two curves.

[0125] If the processing parameter curve completely overlaps with the standard processing parameter curve, it will be judged that the food waste to be processed can be crushed as planned, which indicates that the current processing parameters meet the expected crushing standards and can ensure the effectiveness and safety of the crushing process. However, if the processing parameter curve does not completely overlap with the standard processing parameter curve, it will be judged that the crushing strategy of the food waste to be processed needs to be adjusted, which means that the current processing parameters may not be sufficient to ensure an effective crushing effect, and then the processing parameters need to be re-evaluated and adjusted to adapt to the actual characteristics of the garbage, so as to achieve better processing efficiency. This method can significantly improve the flexibility and intelligence level of food waste treatment and ensure the optimization and safety of the crushing process.

[0126] In some embodiments of the present application, when a kitchen waste treatment plan is generated according to the judgment result, it includes:

[0127] When it is determined that the to-be-processed kitchen waste can be crushed as planned, a processable mark is generated;

[0128] When it is determined that the pulverization strategy of the to-be-processed food waste needs to be adjusted, determining a non-overlapping curve segment between the processing parameter curve and the standard processing parameter curve;

[0129] Determine the maximum non-overlap distance between the processing parameter curve and the standard processing parameter curve according to the non-overlapping curve segment; pre-set a first preset distance and a second preset distance; the first preset distance is smaller than the second preset distance;

[0130] When the maximum non-overlapping distance is less than the first preset distance, a first-level unprocessable mark is generated;

[0131] When the maximum non-overlapping distance is greater than or equal to the first preset distance and less than the second preset distance, a secondary unprocessable mark is generated;

[0132] When the maximum non-overlapping distance is greater than or equal to the second preset distance, a third-level unprocessable mark is generated;

[0133] The crushing strategy is adjusted according to the level of the unprocessable mark. When the first level unprocessable mark is generated, the crushing time is 400s and the crushing speed is 1600rpm; when the second level unprocessable mark is generated, the crushing time is 450s and the crushing speed is 1700rpm; when the third level unprocessable mark is generated, the crushing time is 500s and the crushing speed is 1800rpm;

[0134] It should be noted that when generating a kitchen waste treatment plan, it is first marked based on the previous judgment results. If the judgment result shows that the kitchen waste to be treated can be crushed as planned, a treatable mark will be generated, which indicates that the current processing parameters and crushing strategies are valid and suitable for the next step. If the judgment result shows that the crushing strategy needs to be adjusted for the kitchen waste to be treated, the non-overlapping part between the processing parameter curve and the standard processing parameter curve will be further analyzed. Specifically, the non-overlapping curve segments will be determined, and the maximum non-overlapping distance between the two curves will be calculated. By comparing with the preset first and second distances, the degree of unqualifiedness will be graded.

[0135] If the maximum non-overlapping distance is less than the first preset distance, a level one unprocessable mark will be generated, indicating that the processing parameters are slightly deviated and can be improved through small adjustments. If the distance is greater than or equal to the first preset distance and less than the second preset distance, a level two unprocessable mark will be generated, indicating that the processing parameters are greatly deviated and require major adjustments. Finally, if the maximum non-overlapping distance is greater than or equal to the second preset distance, a level three unprocessable mark will be generated, indicating that the processing parameters are seriously non-compliant and the entire treatment plan may need to be re-evaluated. This hierarchical management method provides clear guidance for subsequent adjustments to garbage disposal strategies to ensure processing efficiency and safety.

[0136] See also Figure 2 As shown, an embodiment of the present invention further provides a pulverizing system for a kitchen waste treatment device, comprising:

[0137] The integrity analysis module is used to collect images of the food waste to be processed, obtain initial data to be processed, and analyze the initial data to be processed to determine the integrity of the initial data to be processed;

[0138] The validity determination module is used to extract the feature information of the data to be processed, analyze all the feature information, and determine the valid feature points;

[0139] A processing parameter calculation module, used to collect physical feature information of all valid feature points and calculate processing parameters of corresponding feature points according to the physical feature information;

[0140] The strategy judgment module is used to judge the crushing strategy of the kitchen waste to be processed according to the processing parameters, and generate a corresponding crushing plan according to the judgment result.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A comminution method for kitchen waste treatment equipment, characterized in that: include: Capturing images of the food waste to be processed to obtain initial data to be processed, and analyzing the initial data to be processed to determine the integrity of the initial data to be processed; Extracting feature information of the data to be processed, and analyzing all feature information to determine effective feature points; Collecting physical feature information of the effective feature points, and calculating processing parameters of corresponding feature points according to the physical feature information; The comminution strategy of the kitchen waste is determined according to the processing parameters, and a corresponding comminution plan is generated according to the determination result.

2. The pulverizing method for kitchen waste treatment equipment according to claim 1, characterized in that: Analyzing the initial data to be processed to determine the integrity of the initial data to be processed includes: Performing data preprocessing on the initial data to be processed, wherein the data preprocessing includes noise removal and standardization processing; Inputting the processed initial data to be processed into a decision model, wherein the decision model includes a feature extraction module and a classification module; An integrity value of the initial data to be processed after being processed based on the output of the decision model; When the integrity value is greater than a preset integrity value, the initial data to be processed is determined as data to be processed; When the integrity value is less than or equal to the preset integrity value, the data of the food waste to be processed is reacquired.

3. The pulverizing method for kitchen waste treatment equipment according to claim 1, characterized in that: When extracting the feature information of the data to be processed and analyzing all the feature information to determine the effective feature points, it includes: Convert the feature information into feature coordinates, and establish a feature point coordinate system according to the feature coordinates; Fit all feature coordinates based on a preset algorithm to determine the feature point fitting curve; The unfitted feature coordinates are deleted, and the coordinate points on the feature point fitting curve are taken as valid feature points.

4. The pulverizing method for kitchen waste treatment equipment according to claim 3, characterized in that: When all feature information is converted into feature coordinates and a feature point coordinate system is established based on all feature coordinates, it includes: The preset acquisition time is used as the X-axis coordinate value of the feature point; Acquire physical parameters of the feature points, and construct a feature parameter sequence based on the physical parameters; Determining a standard feature parameter sequence corresponding to the feature parameter sequence, and comparing the feature parameter sequence with the standard feature parameter sequence; When the characteristic parameter in the characteristic parameter sequence is less than the standard characteristic parameter in the standard characteristic parameter sequence, a first characteristic parameter set is generated according to the corresponding characteristic parameter; When the characteristic parameters in the characteristic parameter sequence are equal to the standard characteristic parameters in the standard characteristic parameter sequence, a second characteristic parameter set is generated according to the corresponding characteristic parameters; When the characteristic parameter in the characteristic parameter sequence is greater than the standard characteristic parameter in the standard characteristic parameter sequence, a third characteristic parameter set is generated according to the corresponding characteristic parameter; Calculate the Y-axis coordinate value of the feature point according to the first feature parameter set, the second feature parameter set and the third feature parameter set; A feature point coordinate system is established according to the X-axis coordinate value and the Y-axis coordinate value.

5. The pulverizing method for kitchen waste treatment equipment according to claim 4, characterized in that: When calculating the Y-axis coordinate value of the feature point according to the first feature parameter set, the second feature parameter set, and the third feature parameter set, the method includes: Counting the number of feature parameters in the first feature parameter set, the number of feature parameters in the second feature parameter set, and the number of feature parameters in the third feature parameter set; The Y-axis coordinate value of the feature point is calculated according to the following formula: Where Y is the Y-axis coordinate value of the feature point, n is the number of feature parameters in the first feature parameter set, m is the number of feature parameters in the second feature parameter set, p is the number of feature parameters in the third feature parameter set, and d i is the i-th characteristic parameter in the first characteristic parameter set, r i is the standard characteristic parameter corresponding to the i-th characteristic parameter, g i is the weight of the i-th feature parameter, f j is the jth characteristic parameter in the third characteristic parameter set, s j is the standard characteristic parameter corresponding to the jth characteristic parameter, k j is the weight of the jth feature parameter, and h is the integration coefficient of the Y-axis coordinate value.

6. The pulverizing method for kitchen waste treatment equipment according to claim 5, characterized in that: Determine the integration coefficient h of the Y-axis coordinate value according to the following method: The integrated influence value of the feature point is calculated according to the following formula: Among them, C is the integrated impact value, q is the second quantity; Preset a first preset integration influence value and a second preset integration influence value; preset a first preset integration coefficient h1, a second preset integration coefficient h2, and a third preset integration coefficient h3; When the integration influence value is less than the first preset integration influence value, the first preset integration coefficient h1 is used as the integration coefficient h of the Y-axis coordinate value; when the integration influence value is greater than or equal to the first preset integration influence value and less than the second preset integration influence value, the second preset integration coefficient h2 is used as the integration coefficient h of the Y-axis coordinate value; when the integration influence value is greater than or equal to the second preset integration influence value, the third preset integration coefficient h3 is used as the integration coefficient h of the Y-axis coordinate value.

7. The pulverizing method for kitchen waste treatment equipment according to claim 1, characterized in that: When collecting the physical feature information of all valid feature points and calculating the processing parameters of the corresponding feature points according to the physical feature information, it includes: Collecting physical feature information of all valid feature points, wherein the physical feature information includes particle size, humidity and density; Calculate the processing parameters of the corresponding feature points according to the particle size, humidity and density: Wherein, P is the processing parameter of the feature point, d is the particle size, h is the humidity, ρ is the density, k1 is the first weight, and 0.8≤k1≤1.2, k2 is the second weight, and 0.4≤k1≤0.

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8. The pulverizing method for kitchen waste treatment equipment according to claim 1, characterized in that: When determining the comminution strategy of the kitchen waste according to the processing parameters, it includes: A processing parameter curve is obtained by fitting all processing parameters; Determining a pre-fitted standard processing parameter curve, and judging a crushing strategy for the kitchen waste to be processed according to the processing parameter curve and the standard processing parameter curve; If the processing parameter curve completely overlaps with the standard processing parameter curve, it is determined that the food waste to be processed can be crushed as planned; If the processing parameter curve does not completely overlap with the standard processing parameter curve, it is determined that the comminution strategy of the food waste to be processed needs to be adjusted.

9. The pulverizing method for kitchen waste treatment equipment according to claim 8, characterized in that: When generating a kitchen waste treatment plan based on the judgment results, it includes: When it is determined that the to-be-processed kitchen waste can be crushed as planned, a processable mark is generated; When it is determined that the pulverization strategy of the to-be-processed food waste needs to be adjusted, determining a non-overlapping curve segment between the processing parameter curve and the standard processing parameter curve; Determine the maximum non-overlap distance between the processing parameter curve and the standard processing parameter curve according to the non-overlapping curve segment; pre-set a first preset distance and a second preset distance; the first preset distance is smaller than the second preset distance; When the maximum non-overlapping distance is less than the first preset distance, a first-level unprocessable mark is generated; When the maximum non-overlapping distance is greater than or equal to the first preset distance and less than the second preset distance, a secondary unprocessable mark is generated; When the maximum non-overlapping distance is greater than or equal to the second preset distance, a third-level unprocessable mark is generated; Adjust the crushing strategy according to the level of unprocessable marks.

10. A pulverizing system for kitchen waste treatment equipment, applied to the pulverizing method for kitchen waste treatment equipment as claimed in any one of claims 1 to 9, characterized in that: include: The integrity analysis module is used to collect images of the food waste to be processed, obtain initial data to be processed, and analyze the initial data to be processed to determine the integrity of the initial data to be processed; The validity determination module is used to extract the feature information of the data to be processed, analyze all the feature information, and determine the valid feature points; A processing parameter calculation module, used to collect physical feature information of all valid feature points and calculate processing parameters of corresponding feature points according to the physical feature information; The strategy judgment module is used to judge the crushing strategy of the kitchen waste to be processed according to the processing parameters, and generate a corresponding crushing plan according to the judgment result.

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