A control method and system for kitchen waste treatment based on electromagnetic heating

Abstract

The application provides a control method and system for kitchen waste treatment based on electromagnetic heating, and relates to the technical field of waste treatment. The kitchen waste is put into an iron heating container, the electromagnetic heating module is controlled by a main control unit, and temperature control information of the iron heater is generated. The temperature of the iron heater is monitored by a temperature detection unit, temperature monitoring information is obtained, heating abnormality analysis and adjustment are performed according to the temperature monitoring information, heating temperature adjustment information is obtained, and the preset target temperature standard is triggered. After the preset target temperature standard is triggered, the crushing and grinding control analysis is performed, the electromagnetic heating analysis and adjustment are performed according to the crushing and grinding control information, and the drying operation is completed. The application improves the processing efficiency, drying effect and system stability, reduces the energy consumption, manual intervention intensity and equipment failure risk, and realizes efficient, environmentally-friendly and resourceful treatment of kitchen waste.

Description

Technical Field

[0001] This invention proposes a control method and system for kitchen waste treatment based on electromagnetic heating, which relates to the field of waste treatment technology, specifically to the field of kitchen waste treatment based on electromagnetic heating. Background Technology

[0002] Food waste, a major component of urban organic waste, is characterized by high moisture content, high organic matter content, and easy biodegradability. Its efficient and environmentally friendly treatment has become a crucial issue in urban management and environmental protection. Currently, existing food waste treatment technologies suffer from inefficient heating methods, insufficient temperature control precision, and fixed heating parameters that cannot adapt to the heating needs of different types, quantities, and moisture contents of waste, leading to problems such as localized overheating and insufficient heating. Furthermore, temperature monitoring is often based on single-node data collection, making it difficult to comprehensively capture the temperature distribution within the container, resulting in inaccurate anomaly identification and haphazard adjustments. In addition, the crushing, grinding, heating, and drying processes are disconnected and lack coordinated control, leading to incomplete drying, significant energy waste, and high levels of manual intervention, resulting in overall low processing efficiency and failing to meet the practical needs of efficient resource recovery from food waste. Summary of the Invention

[0003] This invention provides a control method and system for kitchen waste treatment based on electromagnetic heating, in order to solve the above-mentioned problems: This invention proposes a control method and system for kitchen waste treatment based on electromagnetic heating, the method comprising: S1. Put kitchen waste into the iron heating container, and control the electromagnetic heating module through the main control unit to generate temperature control information for the iron heater; S2. The temperature of the iron heater is monitored by the temperature detection unit to obtain temperature monitoring information. The heating anomaly is analyzed and adjusted according to the temperature monitoring information to obtain heating temperature adjustment information until the preset target temperature standard is triggered. S3. When the preset target temperature standard is triggered, the crushing and grinding control analysis is performed, and the electromagnetic heating analysis and adjustment are performed according to the crushing and grinding control information until the drying operation is completed.

[0004] Further, S1 includes: Kitchen waste is pre-treated to obtain pre-treated waste; The pre-treated waste is put into an iron heating container to obtain waste input information; Based on the waste input information, a heating difficulty analysis was conducted to obtain heating difficulty analysis data; Electromagnetic heating parameters are generated based on the heating difficulty analysis data. The electromagnetic heating module is then controlled based on these parameters to obtain temperature control information.

[0005] Furthermore, the step of analyzing the heating difficulty based on waste input information to obtain heating difficulty analysis data includes: Feature extraction is performed on waste disposal information to obtain waste disposal feature information; Waste types are classified based on waste input characteristics to obtain waste type classification information; Waste impact classification is performed based on waste input characteristics to obtain waste impact classification information; The first heating difficulty coefficient is obtained by weighting and summing the waste type classification information and the preset weights of the waste types. The second heating difficulty coefficient is obtained by weighting and summing the waste impact information and the preset weights of the waste impact. Calculate the product of the first heating difficulty coefficient and the second heating difficulty coefficient to obtain the comprehensive heating difficulty coefficient; The overall heating difficulty coefficient is compared with the preset heating difficulty threshold to obtain the heating difficulty comparison result; The results of the heating difficulty comparison are the heating difficulty analysis data.

[0006] Furthermore, the step of generating electromagnetic heating parameters based on heating difficulty analysis data, controlling the electromagnetic heating module based on these parameters, and obtaining temperature control information includes: Obtain preset heating parameters, correct the preset heating parameters based on heating difficulty analysis data, and generate corresponding electromagnetic heating parameters; The electromagnetic heating module is controlled based on the electromagnetic heating parameters to obtain heating control data. Based on the heating control data, obtain the change data of heating difficulty analysis data to obtain the heating difficulty change data; The heating control data is compensated and corrected based on the data on changes in heating difficulty to obtain temperature control data.

[0007] Further, S2 includes: Temperature monitoring information is obtained by acquiring and analyzing the temperature of the iron heater at multiple nodes through a temperature monitoring unit. Heating anomaly analysis and identification are performed based on temperature monitoring information and temperature control information to obtain heating anomaly analysis and identification data. Heating adjustment parameters are generated based on the data identified by heating anomaly analysis. Heating anomaly adjustment is performed based on the heating adjustment parameters to obtain heating temperature adjustment information until the preset target temperature standard is triggered.

[0008] Furthermore, the step of acquiring and analyzing the temperature of the iron heater through a temperature monitoring unit to obtain temperature monitoring information includes: At least three contact temperature measurement nodes are set on the bottom and side wall of the iron heating container by using the contact temperature sensor and infrared temperature probe of the temperature monitoring unit to collect temperature data at each node on the inner wall of the container and obtain multi-node temperature data. Node temperature labeling management is performed based on multi-node temperature acquisition data to obtain node temperature labeling management information; Based on the node temperature change management information, identify nodes with abnormal temperatures, calculate the difference between the abnormal temperature nodes and the average temperature of each node, and obtain abnormal node temperature difference data. The node temperature labeling management information is updated based on the abnormal node temperature difference data to obtain temperature monitoring information.

[0009] Furthermore, the step of analyzing and identifying heating anomalies based on temperature monitoring information and temperature control information to obtain heating anomaly analysis and identification data includes: Obtain temperature difference data for abnormal nodes based on temperature monitoring information; The abnormal node temperature difference data is compared with the temperature control data to obtain abnormal temperature comparison information. Temperature control status is determined based on abnormal temperature comparison information to obtain temperature control status determination information. Based on the temperature control status assessment information and the heating difficulty analysis data, abnormal factors are identified. Heating anomaly analysis and identification data are generated based on the abnormal factor information and the temperature control status determination information.

[0010] Further, S3 includes: Once the preset target temperature standard is triggered, the electromagnetic heating module will switch to constant temperature mode. Waste crushing and grinding control is performed in constant temperature mode to obtain crushing and grinding control information; Temperature fluctuation information is obtained based on the grinding and crushing control information; Based on temperature fluctuation information, the electromagnetic heating module is used to perform constant temperature compensation and adjustment until the drying operation is completed.

[0011] Furthermore, the step of adjusting the temperature based on temperature fluctuation information in conjunction with the electromagnetic heating module to achieve constant temperature compensation until the drying operation is completed includes: Extract the fluctuation amplitude, frequency, and node location from the temperature fluctuation information, and establish a constant temperature compensation and regulation model by combining the target constant temperature threshold and the allowable fluctuation range. Temperature fluctuations are classified into three levels: slight, moderate, and severe. For slight temperature fluctuations, adjust the electromagnetic heating duty cycle and simultaneously adjust the crushing and grinding speed. For moderate temperature fluctuations, adjust the electromagnetic heating power, start the stirring auxiliary unit to reduce the local temperature difference, and update the constant temperature compensation adjustment model parameters simultaneously. For severe temperature fluctuations, reduce power, activate heat dissipation, restart crushing and grinding, and recalibrate the isothermal compensation adjustment model. Real-time collection of moisture content data of kitchen waste, generation of drying progress assessment data, and updating of constant temperature compensation adjustment strategy based on the rate of change of moisture content; The system verifies three indicators in real time: temperature fluctuation, moisture content, and particle size of waste. When all three indicators meet the drying completion conditions, a drying completion signal is generated, and the drying operation is completed.

[0012] Furthermore, the system includes: The temperature control module is used to put kitchen waste into the iron heating container, and the main control unit controls the electromagnetic heating module to generate temperature control information for the iron heater. The abnormal adjustment module is used to monitor the temperature of the iron heater through the temperature detection unit, obtain temperature monitoring information, analyze and adjust the heating abnormality based on the temperature monitoring information, and obtain heating temperature adjustment information until the preset target temperature standard is triggered. The grinding and drying module is used to perform grinding and pulverizing control analysis when the preset target temperature standard is triggered, and to perform electromagnetic heating analysis and adjustment based on the grinding and pulverizing control information until the drying operation is completed.

[0013] Beneficial effects of this invention: Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a control method for treating kitchen waste based on electromagnetic heating. Detailed Implementation

[0015] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0016] In one embodiment of the present invention, a control method and system for kitchen waste treatment based on electromagnetic heating is proposed, the method comprising: S1. Put kitchen waste into the iron heating container, and control the electromagnetic heating module through the main control unit to generate temperature control information for the iron heater; S2. The temperature of the iron heater is monitored by the temperature detection unit to obtain temperature monitoring information. The heating anomaly is analyzed and adjusted according to the temperature monitoring information to obtain heating temperature adjustment information until the preset target temperature standard is triggered. S3. When the preset target temperature standard is triggered, the grinding and crushing control analysis is performed. Based on the grinding and crushing control information, the electromagnetic heating is analyzed and adjusted until the drying operation is completed. Figure 1 As shown.

[0017] The working principle and technical effects of the above-mentioned technical solution are as follows: This method involves placing kitchen waste into an iron heating container adapted for electromagnetic heating. Utilizing the characteristic of the iron container to cut alternating magnetic lines of force and generate eddy currents for self-heating, the main control unit precisely controls the electromagnetic heating module, generating temperature control information adapted to the current waste treatment requirements. A temperature detection unit monitors the temperature status of the iron heating container in real time, capturing temperature changes and performing anomaly analysis, adjusting heating parameters promptly to ensure a stable heating process until the preset target temperature (the optimal temperature for waste decomposition and drying) is reached. Once the temperature is reached, the crushing and grinding process is initiated. Simultaneously, based on temperature fluctuations during crushing and grinding, the electromagnetic heating parameters are dynamically adjusted to achieve coordinated crushing and grinding with constant-temperature drying, until the entire kitchen waste treatment process is completed. The main control unit, as the core control hub, coordinates the operation of the electromagnetic heating module, temperature detection unit, and crushing and grinding unit, ensuring smooth connection and parameter matching between each process, forming a complete closed-loop control system.

[0018] This method addresses the technical problems of low efficiency and poor temperature control in existing kitchen waste treatment methods, which lead to slow decomposition, incomplete drying, and disjointed processes between heating, crushing, and drying, resulting in overall low processing efficiency. It achieves automated and intelligent control of the entire kitchen waste treatment process, requiring minimal manual intervention and improving the convenience and stability of waste treatment. Through precise control of electromagnetic heating and coordinated operation of each process, it enhances the decomposition efficiency and drying effect of kitchen waste, avoiding problems such as localized overheating and insufficient heating. Simultaneously, it reduces manual labor intensity and energy waste, achieving efficient and environmentally friendly kitchen waste treatment. Furthermore, compared to traditional heating methods, the application of the direct heating characteristics of electromagnetic heating further improves heat utilization and reduces heat loss, balancing processing efficiency with energy-saving requirements.

[0019] In one embodiment of the present invention, S1 includes: Kitchen waste is pre-treated to obtain pre-treated waste; The pre-treated waste is put into an iron heating container to obtain waste input information; Based on the waste input information, a heating difficulty analysis was conducted to obtain heating difficulty analysis data; Electromagnetic heating parameters are generated based on the heating difficulty analysis data. The electromagnetic heating module is then controlled based on these parameters to obtain temperature control information.

[0020] The working principle and technical effects of the above technical solution are as follows: Pre-treatment of kitchen waste mainly involves removing large hard objects, metal debris, and other impurities to prevent damage to equipment (such as the crushing and grinding unit). Simultaneously, the waste is initially sorted to facilitate uniform heating and crushing, resulting in pre-treated waste that meets processing requirements. The pre-treated waste is then placed into an iron heating container. Sensors on the container (such as weight sensors, moisture content sensors, and thickness sensors) collect information on the amount of waste added, initial moisture content, and thickness, integrating this information to form waste input information. Based on this information, the impact of different waste characteristics on the heating process is analyzed to determine the current heating difficulty, generating heating difficulty analysis data to provide a basis for setting electromagnetic heating parameters. The main control unit adjusts and optimizes the preset basic heating parameters based on the heating difficulty analysis data, generating electromagnetic heating parameters (such as heating power, heating frequency, and preheating time) suitable for the current heating difficulty of the waste. Based on these parameters, the electromagnetic heating module is started and operated, while initial temperature feedback during the heating process is collected and integrated to form temperature control information.

[0021] This method addresses the technical problems in existing technologies, such as the susceptibility of untreated kitchen waste to equipment damage and uneven heating, as well as the inability to adapt fixed heating parameters to the heating needs of different types and amounts of waste, resulting in low heating efficiency and energy waste. It achieves standardized pretreatment of kitchen waste, reducing the risk of equipment damage and extending equipment lifespan. By analyzing waste input information and assessing heating difficulty, it enables personalized adaptation of electromagnetic heating parameters, avoiding underheating or overheating caused by fixed parameters and improving the accuracy of heating control. Furthermore, by analyzing heating difficulty in advance and matching corresponding parameters, it improves the stability and efficiency of the heating process, reduces energy consumption, and lowers the failure rate in waste treatment.

[0022] In one embodiment of the present invention, the step of performing heating difficulty analysis based on waste input information to obtain heating difficulty analysis data includes: Feature extraction is performed on waste disposal information to obtain waste disposal feature information; Waste is categorized based on its input characteristics to obtain waste classification information; the waste categories include vegetable waste, meat waste, and mixed waste, etc. Waste impact categories are classified based on waste input characteristics to obtain waste impact classification information; the waste impact categories include input amount, initial moisture content, and laying thickness, etc. The first heating difficulty coefficient is obtained by weighting and summing the waste type classification information and the preset weights of the waste types. The second heating difficulty coefficient is obtained by weighting and summing the waste impact information and the preset weights of the waste impact. The preset weights for waste types and the preset weights for waste impact are calibrated based on actual heating test data to ensure that the weighted calculation results closely match the actual heating difficulty.

[0023] Calculate the product of the first heating difficulty coefficient and the second heating difficulty coefficient to obtain the comprehensive heating difficulty coefficient; The overall heating difficulty coefficient is compared with the preset heating difficulty threshold to obtain the heating difficulty comparison result; The results of the heating difficulty comparison are the heating difficulty analysis data.

[0024] The working principle and technical effects of the above technical solution are as follows: The core of this method is to achieve accurate quantitative analysis of heating difficulty, avoiding the ambiguity of traditional qualitative analysis (such as "difficult / easy"). Feature extraction is performed on the collected waste input information to screen out core features related to heating difficulty (such as waste composition, input amount, initial moisture content, and laying thickness), forming waste input feature information. Irrelevant information is eliminated to ensure the accuracy of the analysis. Based on the waste input feature information, the waste is divided into different categories (vegetables, meat, mixed, etc.). Different types of waste have different heating difficulties due to their different compositions (e.g., meat has a high fat content, vegetables have a high moisture content), thus obtaining waste category information. Simultaneously, the factors affecting heating difficulty in the waste input feature information (input amount, initial moisture content, laying thickness) are classified into waste influence categories, clarifying the specific direction of each factor's influence on heating difficulty. Preset weights are introduced (these weights are determined through extensive...). Actual heating test data is calibrated to ensure that the weighting allocation fits the actual heating scenario and avoids deviations caused by subjective settings. The waste type classification information and waste impact classification information are weighted and summed to obtain the first heating difficulty coefficient (reflecting the impact of waste type on heating difficulty) and the second heating difficulty coefficient (reflecting the impact of factors such as the amount of waste and moisture content on heating difficulty). By multiplying the two coefficients, a comprehensive heating difficulty coefficient is obtained. This coefficient can comprehensively reflect the synergistic effect of waste type and influencing factors, with higher quantitative accuracy. The comprehensive heating difficulty coefficient is compared with the preset heating difficulty threshold (set according to equipment performance and processing requirements) to determine the current heating difficulty level of the waste (such as low difficulty, medium difficulty, high difficulty). The comparison result is the heating difficulty analysis data.

[0025] This method addresses the technical problem in existing technologies where the analysis of heating difficulty for kitchen waste is only a qualitative judgment, lacking precise quantification. This leads to unreasonable heating parameter settings, low heating efficiency, and uneven heating. It achieves precise quantitative analysis of heating difficulty by comprehensively considering the synergistic effects of waste type and various influencing factors on heating difficulty through hierarchical division, weighted summation, and product synthesis, thus improving the accuracy and scientific rigor of the analysis. Preset weights are calibrated using experimental data, avoiding subjective biases and ensuring the analysis results closely match actual processing scenarios. The heating difficulty data obtained through quantitative analysis effectively avoids energy waste and poor heating effects caused by unreasonable parameters, improving the accuracy and flexibility of heating control. It also reduces the risk of equipment failure due to misjudgment of heating difficulty, further ensuring the efficient and stable operation of the entire processing flow.

[0026] In one embodiment of the present invention, the step of generating electromagnetic heating parameters based on heating difficulty analysis data, controlling the electromagnetic heating module based on the electromagnetic heating parameters, and obtaining temperature control information includes: Obtain preset heating parameters, correct the preset heating parameters based on heating difficulty analysis data, and generate corresponding electromagnetic heating parameters; The electromagnetic heating module is controlled based on the electromagnetic heating parameters to obtain heating control data. Based on the heating control data, obtain the change data of heating difficulty analysis data to obtain the heating difficulty change data; The heating control data is compensated and corrected based on the data on changes in heating difficulty to obtain temperature control data.

[0027] The working principle and technical effect of the above technical solution are as follows: The core of this method is to realize the dynamic adaptive correction of electromagnetic heating parameters, avoid the heating process mismatch problem caused by the traditional "one-time parameter setting", and ensure that the heating parameters always adapt to the changes in the difficulty of garbage heating. The main control unit acquires preset basic heating parameters (set according to the equipment's default settings and typical waste disposal requirements). Then, combining this with the heating difficulty analysis data obtained earlier, it makes targeted adjustments to the preset heating parameters. For example, when the heating difficulty is high, it appropriately increases the heating power and extends the preheating time; when the heating difficulty is low, it appropriately reduces the heating power to avoid energy waste, thus generating electromagnetic heating parameters adapted to the current waste heating difficulty. Based on the generated electromagnetic heating parameters, the main control unit sends control commands to the electromagnetic heating module, controlling the module to operate according to these parameters. Simultaneously, it collects real-time data during the heating process (such as heating power, heating frequency, container temperature, etc.) to form heating control data. Based on the heating control data, it analyzes changes in the waste heating difficulty during the heating process (such as a decrease in waste moisture content leading to a decrease in heating difficulty, or localized waste clumping leading to an increase in heating difficulty), obtaining heating difficulty change data. Based on the heating difficulty change data, it compensates and corrects the current heating control data. For example, when the heating difficulty decreases, it appropriately reduces the heating power; when the heating difficulty increases, it appropriately adjusts the heating frequency or power to ensure that the heating parameters always match the waste heating difficulty. The corrected heating control data is the temperature control information.

[0028] This method addresses the technical problems in existing technologies where electromagnetic heating parameters are fixed and cannot be dynamically adjusted according to changes in the difficulty of waste heating, resulting in low heating efficiency, energy waste, and a tendency for underheating or overheating. It achieves dynamic adaptive correction of electromagnetic heating parameters, ensuring that the heating parameters always adapt to real-time changes in the difficulty of waste heating, thus improving the accuracy and flexibility of heating control. It effectively avoids energy waste caused by fixed parameters, reduces energy consumption in waste treatment, and improves heating effect, preventing problems such as slow waste decomposition and incomplete drying due to underheating, and waste carbonization and equipment damage due to overheating. Furthermore, through dynamic compensation correction, it further improves the stability of the heating process and reduces the occurrence of heating anomalies.

[0029] In one embodiment of the present invention, S2 includes: Temperature monitoring information is obtained by acquiring and analyzing the temperature of the iron heater at multiple nodes through a temperature monitoring unit. Heating anomaly analysis and identification are performed based on temperature monitoring information and temperature control information to obtain heating anomaly analysis and identification data. Heating adjustment parameters are generated based on the data identified by heating anomaly analysis. Heating anomaly adjustment is performed based on the heating adjustment parameters to obtain heating temperature adjustment information until the preset target temperature standard is triggered.

[0030] The working principle and technical effect of the above technical solution are as follows: A temperature monitoring unit (composed of a contact temperature sensor and an infrared temperature probe) collects temperature data from multiple nodes in the iron heating container, avoiding temperature deviations caused by single-node acquisition. This comprehensively captures the temperature distribution within the container's inner wall and the waste interior. The collected temperature data is analyzed and filtered, eliminating abnormal fluctuations and integrating them to form temperature monitoring information that accurately reflects the current heating temperature status. The temperature monitoring information is compared and analyzed with the previously generated temperature control information (ideal temperature parameters adapted to the current difficulty of waste heating) to identify any heating anomalies (such as localized overheating, insufficient heating, or excessive temperature fluctuations). The type, degree, and possible causes of the anomalies are analyzed to form heating anomaly analysis and identification data. Based on this data, corresponding heating adjustment parameters are generated (such as adjusting heating power, heating frequency, and heating duty cycle), and differentiated adjustment strategies are adopted for different types of anomalies. The electromagnetic heating module is adjusted according to the heating adjustment parameters to obtain heating temperature adjustment information, correcting temperature deviations in real time during the heating process. This process is continuously repeated until the temperature of the iron heating container reaches the preset target temperature standard, triggering a temperature attainment signal and initiating the subsequent crushing, grinding, and drying stages.

[0031] This method addresses the technical problems of existing technologies, such as the limited monitoring of heating temperature, which fails to comprehensively capture temperature distribution and anomalies, and the inaccurate identification and untimely adjustment of anomalies, leading to unstable heating processes, large temperature deviations, and impacting waste decomposition and drying effects. It achieves multi-node, all-round monitoring of heating temperature, improving the accuracy and comprehensiveness of temperature monitoring and avoiding the limitations of single-node monitoring. By combining temperature monitoring information with temperature control information, it enables precise identification and cause analysis of heating anomalies, ensuring more targeted anomaly adjustments. Timely anomaly adjustments effectively prevent problems such as localized overheating and insufficient heating, improving the stability of the heating process and the precision of temperature control, ensuring rapid decomposition of waste under ideal temperature conditions. Simultaneously, continuous temperature adjustment reduces energy waste, lowers the risk of equipment damage due to temperature anomalies, and further improves the reliability and efficiency of the entire processing flow.

[0032] In one embodiment of the present invention, the step of acquiring and analyzing the temperature of the iron heater at multiple nodes through a temperature monitoring unit to obtain temperature monitoring information includes: At least three contact temperature measurement nodes are set on the bottom and side wall of the iron heating container by using the contact temperature sensor and infrared temperature probe of the temperature monitoring unit to collect temperature data at each node on the inner wall of the container and obtain multi-node temperature data. Node temperature labeling management is performed based on multi-node temperature acquisition data to obtain node temperature labeling management information; Based on the node temperature change management information, identify nodes with abnormal temperatures, calculate the difference between the abnormal temperature nodes and the average temperature of each node, and obtain abnormal node temperature difference data. The node temperature labeling management information is updated based on the abnormal node temperature difference data to obtain temperature monitoring information.

[0033] The working principle and technical effect of the above technical solution are as follows: The core of this method is to achieve accurate and comprehensive temperature monitoring. Through multi-node acquisition, labeling management and anomaly identification, it ensures that the temperature monitoring information can truly and accurately reflect the heating status. Optimize the placement of temperature measurement nodes: At least three contact-type temperature measurement nodes should be installed at the bottom (the core heating area of ​​electromagnetic heating) and side walls (areas prone to uneven temperature distribution) of the iron heating container. These contact sensors directly contact the inner wall of the container, ensuring the accuracy and reliability of the collected temperature data. Simultaneously, infrared temperature probes should be used to assist in collecting internal temperature data of the waste, overcoming the limitation of contact sensors which can only collect temperature data from the container's inner wall. This comprehensive collection of temperature data from all nodes forms a multi-node temperature data set. The collected multi-node temperature data should be labeled and managed, including the location, collection time, and temperature value of each measurement node, forming node temperature labeling and management information. This information should be analyzed to determine the temperature change trend of each node, identify abnormal temperature nodes (such as nodes whose temperature is significantly higher or lower than other nodes), calculate the difference between the abnormal node and the average temperature of all nodes, clarify the degree of abnormality, and form abnormal node temperature difference data. Based on the abnormal node temperature difference data, the node temperature labeling and management information should be updated, supplementing information such as the degree of abnormality and the magnitude of the difference, ultimately forming a complete temperature monitoring information set.

[0034] This method addresses the technical problems of existing technologies, such as the inability to comprehensively capture the temperature distribution within the container due to single temperature acquisition nodes and methods, and insufficient accuracy in identifying temperature anomalies, leading to inaccurate temperature monitoring information and affecting the effectiveness of heating anomaly regulation. It achieves multi-node, multi-method temperature acquisition, comprehensively covering key areas such as the container bottom and sidewalls, improving the comprehensiveness and accuracy of temperature acquisition and avoiding temperature deviations caused by single-node acquisition. Through node temperature labeling management and anomaly node identification, the location and severity of temperature anomalies can be quickly located, providing precise evidence for subsequent anomaly analysis and regulation, improving the targeting and efficiency of anomaly regulation. Simultaneously, the updated and labeled temperature monitoring information is more complete and accurate, effectively avoiding missed or false detections of heating anomalies due to inaccurate temperature monitoring, further improving the stability of the heating process and the precision of temperature control. This provides reliable temperature assurance for efficient waste decomposition and reduces energy waste and equipment failure caused by temperature monitoring errors.

[0035] In one embodiment of the present invention, the step of analyzing and identifying heating anomalies based on temperature monitoring information and temperature control information to obtain heating anomaly analysis and identification data includes: Obtain temperature difference data for abnormal nodes based on temperature monitoring information; The abnormal node temperature difference data is compared with the temperature control data to obtain abnormal temperature comparison information. Temperature control status is determined based on abnormal temperature comparison information to obtain temperature control status determination information. Based on the temperature control status assessment information and the heating difficulty analysis data, abnormal factors are identified. Heating anomaly analysis and identification data are generated based on the abnormal factor information and the temperature control status determination information.

[0036] The working principle and technical effect of the above technical solution are as follows: The core of this method is to achieve accurate identification and cause tracing of heating anomalies, avoiding the blind adjustment caused by traditional methods that only identify anomalies without analyzing the causes, and ensuring that anomaly adjustment is more targeted. The system extracts temperature difference data from abnormal nodes from temperature monitoring information to identify key information such as the location and magnitude of the temperature difference, providing foundational data for anomaly analysis. It then compares this data with the previously generated temperature control data (ideal temperature parameters and fluctuation range) to determine whether the temperature deviation exceeds the allowable range and its specific magnitude, forming anomaly temperature comparison information to clarify the severity of the anomaly. Based on this comparison information, it determines the current temperature control status (e.g., localized overheating, insufficient heating, excessive temperature fluctuations), identifying the type of anomaly and forming temperature control status determination information. Crucially, it combines heating difficulty analysis data to trace the cause of the anomaly. For example, if heating difficulty is high (e.g., large waste volume, high moisture content) and insufficient heating occurs, the anomaly is determined to be insufficient heating power. If heating difficulty is low but localized overheating occurs, the anomaly is determined to be uneven waste distribution or a faulty temperature measurement node, thus identifying the anomaly factor. Finally, it integrates the anomaly factor information with the temperature control status determination information to clarify the anomaly type, severity, location, and cause, generating heating anomaly analysis and identification data.

[0037] This method addresses the technical problem in existing technologies that can only identify heating temperature anomalies but cannot accurately analyze their causes, leading to blind and ineffective adjustments, or even recurring anomalies. It achieves precise identification and root cause tracing of heating anomalies by combining temperature anomalies with heating difficulty analysis, avoiding the limitations of isolated anomaly assessments and improving the accuracy of anomaly cause analysis. Clear information about the anomaly's cause allows for rapid implementation of corresponding measures to resolve the anomaly, improving the efficiency and effectiveness of anomaly adjustments and avoiding energy waste and equipment damage caused by blind adjustments. Simultaneously, precise anomaly analysis can promptly detect potential equipment faults (such as temperature measurement node failures), facilitating timely troubleshooting, reducing equipment failure rates, further ensuring the stability of the heating process and the efficient operation of the entire process, and improving the system's reliability and practicality.

[0038] In one embodiment of the present invention, S3 includes: Once the preset target temperature standard is triggered, the electromagnetic heating module will switch to constant temperature mode. Waste crushing and grinding control is performed in constant temperature mode to obtain crushing and grinding control information; Temperature fluctuation information is obtained based on the grinding and crushing control information; Based on temperature fluctuation information, the electromagnetic heating module is used to perform constant temperature compensation and adjustment until the drying operation is completed.

[0039] The working principle and technical effect of the above technical solution are as follows: When the temperature detection unit reports that the temperature of the iron heating container has reached the preset target temperature standard, the main control unit controls the electromagnetic heating module to switch from the heating mode to the constant temperature mode, maintaining the temperature inside the container within the target temperature range. This provides a stable temperature environment for the crushing, grinding, and drying of waste. The constant temperature environment ensures the continuous decomposition of waste while avoiding excessively high temperatures that could lead to carbonization and excessively low temperatures that could result in low drying efficiency. In constant temperature mode, the main control unit starts the crushing and grinding unit to crush and grind the softened and decomposed kitchen waste into fine particles (facilitating rapid evaporation of moisture). At the same time, real-time data during the crushing and grinding process (such as grinding speed and grinding load) is collected. The system generates grinding and crushing control information, including running time, etc. During the grinding process, friction between the waste and the grinding blades generates additional heat, and changes in particle size affect heat transfer, causing temperature fluctuations within the container. Therefore, based on the grinding and crushing control information, the system captures these temperature fluctuations (e.g., amplitude and frequency) to obtain temperature fluctuation information. Based on this information, the electromagnetic heating module is adjusted for constant temperature compensation. For example, the heating power is appropriately reduced when the temperature rises and appropriately increased when the temperature falls, ensuring the container temperature remains stable within the target range. Simultaneously, this constant temperature environment facilitates rapid drying of the waste particles. This process is continuously repeated until the waste is completely dried, ending the entire food waste treatment process.

[0040] This method addresses the technical problems in existing technologies where the crushing and grinding processes and the heating and drying processes operate independently without coordinated control, resulting in large temperature fluctuations, low drying efficiency, and incomplete drying after crushing, which affects subsequent resource utilization. It achieves coordinated linkage between crushing and grinding and constant-temperature drying. The constant-temperature mode provides a stable temperature environment for both crushing and drying, improving crushing efficiency (softened waste is easier to crush) and drying effect. By capturing temperature fluctuations during the crushing process and making timely constant-temperature compensation adjustments, the method avoids the impact of temperature fluctuations on the drying effect, ensuring uniform and rapid drying of waste, and improving the consistency and thoroughness of drying. Simultaneously, coordinated control reduces energy waste, lowers equipment operating load, and improves the efficiency and stability of the entire processing flow. Furthermore, the finer crushed waste particles facilitate subsequent resource utilization (such as making organic fertilizer), increasing the recycling rate of kitchen waste and achieving the dual benefits of environmental protection and resource recovery.

[0041] In one embodiment of the present invention, the step of adjusting the temperature based on temperature fluctuation information in conjunction with the electromagnetic heating module to achieve constant temperature compensation until the drying operation is completed includes: Extract the fluctuation amplitude, frequency, and node location from the temperature fluctuation information, and establish a constant temperature compensation and regulation model by combining the target constant temperature threshold and the allowable fluctuation range. Temperature fluctuations are classified into three levels: slight, moderate, and severe. For slight temperature fluctuations, adjust the electromagnetic heating duty cycle and simultaneously adjust the crushing and grinding speed. For moderate temperature fluctuations, adjust the electromagnetic heating power, start the stirring auxiliary unit to reduce the local temperature difference, and update the constant temperature compensation adjustment model parameters simultaneously. For severe temperature fluctuations, reduce power, activate heat dissipation, restart crushing and grinding, and recalibrate the isothermal compensation adjustment model. Real-time collection of moisture content data of kitchen waste, generation of drying progress assessment data, and updating of constant temperature compensation adjustment strategy based on the rate of change of moisture content; The system verifies three indicators in real time: temperature fluctuation, moisture content, and particle size of waste. When all three indicators meet the drying completion conditions, a drying completion signal is generated, and the drying operation is completed.

[0042] The working principle and technical effect of the above technical solution are as follows: The core of this method is to achieve precise, differentiated and adaptive constant temperature compensation adjustment, to ensure a stable and efficient drying process, and at the same time to accurately judge the drying completion status to avoid incomplete drying or over-drying. Core parameters (fluctuation amplitude, fluctuation frequency, and fluctuation node location) are extracted from temperature fluctuation information. Combined with a preset target constant temperature threshold (the optimal temperature for waste drying) and a fluctuation allowable range (the temperature fluctuation range to ensure drying effect), a constant temperature compensation adjustment model is established. This model can automatically match the corresponding compensation adjustment strategy based on temperature fluctuation parameters, providing a basis for differentiated adjustment. Based on parameters such as fluctuation amplitude and frequency, temperature fluctuations are divided into three levels: slight, moderate, and severe. Different levels correspond to different adjustment priorities and strategies, ensuring more targeted adjustment. For slight temperature fluctuations (small fluctuation amplitude and low frequency), there is no need to significantly adjust the heating power; only fine-tuning the electromagnetic heating duty cycle is needed to maintain a constant temperature. Simultaneously, the grinding speed is adjusted to reduce the impact of grinding load fluctuations on temperature and prevent further fluctuations. For moderate temperature fluctuations (moderate fluctuation amplitude and frequency), the electromagnetic heating power is appropriately adjusted, and the stirring auxiliary unit is activated to accelerate heat transfer from waste particles, eliminate local temperature differences, and update the constant temperature compensation adjustment model parameters to improve the accuracy of subsequent compensation adjustments. For severe temperature fluctuations (large amplitude and high frequency), the electromagnetic heating power is first reduced and the heat dissipation unit is activated to prevent the temperature from continuously deviating from the target range. Then, the crushing and grinding operation is paused to investigate the cause of the fluctuation (such as waste clumping, equipment failure, etc.). After the investigation is completed, the crushing and grinding is restarted, and the constant temperature compensation adjustment model is recalibrated to ensure the accuracy of subsequent adjustments. Throughout the compensation adjustment process, the moisture content data of kitchen waste is collected in real time, the rate of change of moisture content is analyzed, and drying progress assessment data is generated. The constant temperature compensation adjustment strategy is updated according to the progress data. For example, when the moisture content decreases rapidly, the heating power is appropriately reduced to avoid over-drying; when the moisture content decreases slowly, the heating power is appropriately increased to accelerate the drying efficiency. At the same time, three core indicators (temperature fluctuation, moisture content, and waste particle size) are verified in real time. Only when all three indicators meet the preset drying completion conditions (temperature fluctuation within the allowable range, moisture content meeting resource utilization requirements, and particle size meeting subsequent processing standards) will the main control unit generate a drying completion signal, shut down the relevant units, and complete the drying operation to ensure that the drying effect meets the standards.

[0043] This method addresses the shortcomings of existing technologies, such as the reliance on a single isothermal compensation adjustment method that cannot adapt to varying degrees of temperature fluctuations, low adjustment precision, and a single standard for determining drying completion, leading to incomplete drying, over-drying, or energy waste. Furthermore, it addresses the poor synergy between grinding and isothermal regulation. This method achieves graded and differentiated adjustment of temperature fluctuations, employing corresponding strategies for different levels of fluctuation, thus improving the accuracy and efficiency of isothermal compensation adjustment and avoiding energy waste and poor drying results caused by blind adjustment. The establishment, dynamic updating, and calibration of the isothermal compensation adjustment model enable adaptive optimization of the adjustment strategy, enhancing the system's adaptability to temperature fluctuations and ensuring a consistently stable drying process. Within the target temperature range, the synergy between grinding and constant-temperature drying is further enhanced by adjusting the grinding speed and activating the stirring auxiliary unit, reducing temperature fluctuations and improving drying uniformity. Multiple indicators (temperature, moisture content, particle size) are used to collaboratively determine the drying completion status, avoiding incomplete or over-drying caused by a single indicator, ensuring that the dried waste meets resource utilization requirements and improving waste recycling rates. Simultaneously, the adaptive adjustment of the entire drying process reduces the need for manual intervention, improves the system's intelligence and operational stability, further reduces energy consumption and equipment failure risks, and achieves efficient, environmentally friendly, and resource-based treatment of kitchen waste.

[0044] According to one embodiment of the present invention, the system includes: The temperature control module is used to put kitchen waste into the iron heating container, and the main control unit controls the electromagnetic heating module to generate temperature control information for the iron heater. The abnormal adjustment module is used to monitor the temperature of the iron heater through the temperature detection unit, obtain temperature monitoring information, analyze and adjust the heating abnormality based on the temperature monitoring information, and obtain heating temperature adjustment information until the preset target temperature standard is triggered. The grinding and drying module is used to perform grinding and pulverizing control analysis when the preset target temperature standard is triggered, and to perform electromagnetic heating analysis and adjustment based on the grinding and pulverizing control information until the drying operation is completed.

[0045] The working principle and technical effects of the above-mentioned technical solution are as follows: This method involves placing kitchen waste into an iron heating container adapted for electromagnetic heating. Utilizing the characteristic of the iron container to cut alternating magnetic lines of force and generate eddy currents for self-heating, the main control unit precisely controls the electromagnetic heating module, generating temperature control information adapted to the current waste treatment requirements. A temperature detection unit monitors the temperature status of the iron heating container in real time, capturing temperature changes and performing anomaly analysis, adjusting heating parameters promptly to ensure a stable heating process until the preset target temperature (the optimal temperature for waste decomposition and drying) is reached. Once the temperature is reached, the crushing and grinding process is initiated. Simultaneously, based on temperature fluctuations during crushing and grinding, the electromagnetic heating parameters are dynamically adjusted to achieve coordinated crushing and grinding with constant-temperature drying, until the entire kitchen waste treatment process is completed. The main control unit, as the core control hub, coordinates the operation of the electromagnetic heating module, temperature detection unit, and crushing and grinding unit, ensuring smooth connection and parameter matching between each process, forming a complete closed-loop control system.

[0046] This method addresses the technical problems of low efficiency and poor temperature control in existing kitchen waste treatment methods, which lead to slow decomposition, incomplete drying, and disjointed processes between heating, crushing, and drying, resulting in overall low processing efficiency. It achieves automated and intelligent control of the entire kitchen waste treatment process, requiring minimal manual intervention and improving the convenience and stability of waste treatment. Through precise control of electromagnetic heating and coordinated operation of each process, it enhances the decomposition efficiency and drying effect of kitchen waste, avoiding problems such as localized overheating and insufficient heating. Simultaneously, it reduces manual labor intensity and energy waste, achieving efficient and environmentally friendly kitchen waste treatment. Furthermore, compared to traditional heating methods, the application of the direct heating characteristics of electromagnetic heating further improves heat utilization and reduces heat loss, balancing processing efficiency with energy-saving requirements.

[0047] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A control method for kitchen waste treatment based on electromagnetic heating, characterized in that, The method includes: S1. Put kitchen waste into the iron heating container, and control the electromagnetic heating module through the main control unit to generate temperature control information for the iron heater; S2. The temperature of the iron heater is monitored by the temperature detection unit to obtain temperature monitoring information. The heating anomaly is analyzed and adjusted according to the temperature monitoring information to obtain heating temperature adjustment information until the preset target temperature standard is triggered. S3. When the preset target temperature standard is triggered, the crushing and grinding control analysis is performed, and the electromagnetic heating analysis and adjustment are performed according to the crushing and grinding control information until the drying operation is completed.

2. The control method for kitchen waste treatment based on electromagnetic heating according to claim 1, characterized in that, S1 includes: Kitchen waste is pre-treated to obtain pre-treated waste; The pre-treated waste is put into an iron heating container to obtain waste input information; Based on the waste input information, a heating difficulty analysis was conducted to obtain heating difficulty analysis data; Electromagnetic heating parameters are generated based on the heating difficulty analysis data. The electromagnetic heating module is then controlled based on these parameters to obtain temperature control information.

3. The control method for kitchen waste treatment based on electromagnetic heating according to claim 2, characterized in that, The step of analyzing the heating difficulty based on waste input information to obtain heating difficulty analysis data includes: Feature extraction is performed on waste disposal information to obtain waste disposal feature information; Waste types are classified based on waste input characteristics to obtain waste type classification information; Waste impact classification is performed based on waste input characteristics to obtain waste impact classification information; weighted summation is performed based on waste type classification information and preset weights for waste type to obtain the first heating difficulty coefficient; The second heating difficulty coefficient is obtained by weighting and summing the waste impact information and the preset weights of the waste impact. Calculate the product of the first heating difficulty coefficient and the second heating difficulty coefficient to obtain the comprehensive heating difficulty coefficient; The overall heating difficulty coefficient is compared with the preset heating difficulty threshold to obtain the heating difficulty comparison result; The results of the heating difficulty comparison are the heating difficulty analysis data.

4. The control method for kitchen waste treatment based on electromagnetic heating according to claim 2, characterized in that, The process of generating electromagnetic heating parameters based on heating difficulty analysis data, controlling the electromagnetic heating module based on these parameters, and obtaining temperature control information includes: Obtain preset heating parameters, correct the preset heating parameters based on heating difficulty analysis data, and generate corresponding electromagnetic heating parameters; The electromagnetic heating module is controlled based on the electromagnetic heating parameters to obtain heating control data. Based on the heating control data, obtain the change data of heating difficulty analysis data to obtain the heating difficulty change data; The heating control data is compensated and corrected based on the data on changes in heating difficulty to obtain temperature control data.

5. The control method for kitchen waste treatment based on electromagnetic heating according to claim 1, characterized in that, S2 includes: Temperature monitoring information is obtained by acquiring and analyzing the temperature of the iron heater at multiple nodes through a temperature monitoring unit. Heating anomaly analysis and identification are performed based on temperature monitoring information and temperature control information to obtain heating anomaly analysis and identification data. Heating adjustment parameters are generated based on the data identified by heating anomaly analysis. Heating anomaly adjustment is performed based on the heating adjustment parameters to obtain heating temperature adjustment information until the preset target temperature standard is triggered.

6. The control method for kitchen waste treatment based on electromagnetic heating according to claim 5, characterized in that, The process of acquiring and analyzing multi-node temperatures of the iron heater through a temperature monitoring unit to obtain temperature monitoring information includes: At least three contact temperature measurement nodes are set on the bottom and side wall of the iron heating container by using the contact temperature sensor and infrared temperature probe of the temperature monitoring unit to collect temperature data at each node on the inner wall of the container and obtain multi-node temperature data. Node temperature labeling management is performed based on multi-node temperature acquisition data to obtain node temperature labeling management information; Based on the node temperature change management information, identify nodes with abnormal temperatures, calculate the difference between the abnormal temperature nodes and the average temperature of each node, and obtain abnormal node temperature difference data. The node temperature labeling management information is updated based on the abnormal node temperature difference data to obtain temperature monitoring information.

7. The control method for kitchen waste treatment based on electromagnetic heating according to claim 5, characterized in that, The step of analyzing and identifying heating anomalies based on temperature monitoring information and temperature control information to obtain heating anomaly analysis and identification data includes: Obtain temperature difference data for abnormal nodes based on temperature monitoring information; The abnormal node temperature difference data is compared with the temperature control data to obtain abnormal temperature comparison information. Temperature control status is determined based on abnormal temperature comparison information to obtain temperature control status determination information. Based on the temperature control status assessment information and the heating difficulty analysis data, abnormal factors are identified. Heating anomaly analysis and identification data are generated based on the abnormal factor information and the temperature control status determination information.

8. The control method for kitchen waste treatment based on electromagnetic heating according to claim 1, characterized in that, S3 includes: Once the preset target temperature standard is triggered, the electromagnetic heating module will switch to constant temperature mode. Waste crushing and grinding control is performed in constant temperature mode to obtain crushing and grinding control information; Temperature fluctuation information is obtained based on the grinding and crushing control information; Based on temperature fluctuation information, the electromagnetic heating module is used to perform constant temperature compensation and adjustment until the drying operation is completed.

9. The control method for kitchen waste treatment based on electromagnetic heating according to claim 8, characterized in that, The process of adjusting the temperature based on temperature fluctuation information and the electromagnetic heating module to achieve constant temperature compensation until the drying operation is completed includes: Extract the fluctuation amplitude, frequency, and node location from the temperature fluctuation information, and establish a constant temperature compensation and regulation model by combining the target constant temperature threshold and the allowable fluctuation range. Temperature fluctuations are classified into three levels: slight, moderate, and severe. For slight temperature fluctuations, adjust the electromagnetic heating duty cycle and simultaneously adjust the crushing and grinding speed. For moderate temperature fluctuations, adjust the electromagnetic heating power, start the stirring auxiliary unit to reduce the local temperature difference, and update the constant temperature compensation adjustment model parameters simultaneously. For severe temperature fluctuations, reduce power, activate heat dissipation, restart crushing and grinding, and recalibrate the isothermal compensation adjustment model. Real-time collection of moisture content data of kitchen waste, generation of drying progress assessment data, and updating of constant temperature compensation adjustment strategy based on the rate of change of moisture content; The system verifies three indicators in real time: temperature fluctuation, moisture content, and particle size of waste. When all three indicators meet the drying completion conditions, a drying completion signal is generated, and the drying operation is completed.

10. A control system for kitchen waste treatment based on electromagnetic heating, characterized in that, The system includes: The temperature control module is used to put kitchen waste into the iron heating container, and the main control unit controls the electromagnetic heating module to generate temperature control information for the iron heater. The abnormal adjustment module is used to monitor the temperature of the iron heater through the temperature detection unit, obtain temperature monitoring information, analyze and adjust the heating abnormality based on the temperature monitoring information, and obtain heating temperature adjustment information until the preset target temperature standard is triggered. The grinding and drying module is used to perform grinding and pulverizing control analysis when the preset target temperature standard is triggered, and to perform electromagnetic heating analysis and adjustment based on the grinding and pulverizing control information until the drying operation is completed.

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