Kitchen waste treatment method, platform and medium for power generation

Abstract

The application discloses a kitchen garbage treatment method, platform and medium for power generation, and relates to the technical field of anaerobic fermentation. The method comprises the following steps: pretreating collected kitchen garbage, obtaining organic slurry parameters, performing solid-liquid separation, and obtaining multiple component data; extracting fermentation substrate parameters, introducing the anaerobic fermentation reactor, performing segmented temperature control fermentation, obtaining biogas generation parameters, performing combustion power generation, generating first electric energy generation parameters, performing supply and demand prediction, and obtaining an electric energy supply and demand prediction map; dehydrating and drying the solid component data, and then putting the solid component data into a pyrolysis furnace to obtain combustible synthetic gas parameters, perform combustion power generation, generate second electric energy generation parameters, use the second electric energy generation parameters as supplementary electric energy to compensate the first electric energy generation parameters, and obtain total power generation data. The technical problems of low kitchen garbage treatment efficiency and insufficient resource utilization in the prior art are solved, and the technical effects of improving garbage treatment efficiency and resource comprehensive utilization level are achieved.

Description

Technical Field

[0001] This invention relates to the field of anaerobic fermentation technology, specifically to a method, platform, and medium for treating kitchen waste for power generation. Background Technology

[0002] With the acceleration of urbanization and the improvement of residents' living standards, the amount of food waste continues to increase. Food waste is complex in composition, has a high water content, and is highly perishable; improper handling can have serious impacts on the environment and public health. Existing food waste treatment methods mainly include composting, anaerobic fermentation, and incineration, but in practice, these methods generally suffer from low treatment efficiency, insufficient energy recovery and utilization, and fragmented treatment pathways. For example, in the anaerobic fermentation pathway, only the liquid phase components are relied upon to generate biogas for power generation, while the solid phase is often neglected or has high treatment costs, resulting in a low overall resource conversion rate. At the same time, due to the large fluctuations in the composition of food waste, power generation efficiency is difficult to stabilize, making it unable to effectively meet the dynamic demands of the power grid load and limiting the large-scale promotion of food waste-to-energy treatment. Summary of the Invention

[0003] This application provides a method, platform, and medium for treating kitchen waste for power generation, which solves the technical problems of low efficiency and insufficient resource utilization in the prior art for treating kitchen waste.

[0004] The first aspect of this application provides a method for treating kitchen waste for power generation, the method comprising:

[0005] The collected kitchen waste is pretreated to obtain organic slurry parameters, which are then separated into solid and liquid components to obtain multiple component data, including liquid phase component data and solid phase component data. Based on the liquid phase component data, physicochemical adjustments are made to extract fermentation substrate parameters, which are then introduced into an anaerobic fermentation reactor for segmented temperature-controlled fermentation to obtain biogas generation parameters. Based on the biogas generation parameters, combustion power generation is performed to generate a first electricity generation parameter, which is then used for supply and demand forecasting to obtain an electricity supply and demand forecast map. The solid phase component data is dehydrated and dried before being fed into a pyrolysis furnace to obtain combustible syngas parameters, which are then used for combustion power generation to generate a second electricity generation parameter. According to the electricity supply and demand forecast map, the second electricity generation parameter is used as supplementary electricity to compensate for the first electricity generation parameter, resulting in total power generation data.

[0006] A second aspect of this application provides a food waste treatment platform for power generation, said platform comprising:

[0007] Pre-treatment module: pre-treats the collected kitchen waste, obtains organic slurry parameters, performs solid-liquid separation, and obtains multiple component data, including liquid phase component data and solid phase component data; Fermentation treatment module: performs physicochemical adjustment based on the liquid phase component data, extracts fermentation substrate parameters, introduces them into an anaerobic fermentation reactor for segmented temperature-controlled fermentation, and obtains biogas generation parameters; Prediction module: generates electricity through combustion based on the biogas generation parameters, generates a first electricity generation parameter, performs supply and demand prediction, and obtains an electricity supply and demand prediction map; Power generation module: dehydrates and dries the solid phase component data and feeds it into a pyrolysis furnace, obtains combustible syngas parameters, and generates electricity through combustion, producing a second electricity generation parameter; Compensation module: according to the electricity supply and demand prediction map, uses the second electricity generation parameter as supplementary electricity to compensate for the first electricity generation parameter, and obtains total power generation data.

[0008] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the food waste treatment method for power generation provided in this application.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] First, the collected kitchen waste is pretreated to obtain organic slurry parameters, which are then separated into solid and liquid components, yielding multiple component data, including both liquid and solid phase data. Next, based on the liquid phase data, physicochemical adjustments are made to extract fermentation substrate parameters, which are then introduced into an anaerobic fermentation reactor for segmented temperature-controlled fermentation to obtain biogas generation parameters. Then, based on the biogas generation parameters, combustion power generation is performed to generate a first electricity generation parameter, which is used for supply and demand forecasting to obtain an electricity supply and demand forecast map. Next, the solid phase component data is dehydrated and dried before being fed into a pyrolysis furnace to obtain combustible syngas parameters for combustion power generation, generating a second electricity generation parameter. Finally, according to the electricity supply and demand forecast map, the second electricity generation parameter is used as supplementary power to compensate for the first electricity generation parameter, yielding total power generation data. This method solves the technical problems of low kitchen waste treatment efficiency and insufficient resource utilization in existing technologies, achieving the technical effect of improving waste treatment efficiency and comprehensive resource utilization. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1A schematic diagram of a method for treating kitchen waste for power generation provided in an embodiment of this application;

[0013] Figure 2 This is a schematic diagram of a kitchen waste treatment platform for power generation provided in an embodiment of this application.

[0014] Explanation of reference numerals in the attached diagram: Pretreatment module 11, Fermentation module 12, Prediction module 13, Power generation module 14, Compensation module 15. Detailed Implementation

[0015] This application solves the technical problems of low efficiency and insufficient resource utilization in the prior art by providing a method, platform and medium for treating kitchen waste for power generation.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0018] Example 1, as Figure 1 As shown, this application provides a method for treating kitchen waste for power generation, wherein the method includes:

[0019] The collected kitchen waste is pretreated to obtain organic slurry parameters, which are then separated into solid and liquid components to obtain multiple component data, including liquid phase component data and solid phase component data.

[0020] Specifically, the collected kitchen waste undergoes pretreatment by being fed into a crushing device for crushing to obtain crushed material within a target particle size range. This target particle size range is set based on the processing capacity of subsequent separation equipment, for example, controlled within the range of 5–20 mm. The crushed material then enters a primary screening unit to remove large metals, plastics, bones, and other inorganic impurities. Further impurity removal is achieved through magnetic separation, air separation, or manual sorting. The treated mixed organic material is then homogenized with an appropriate amount of water in a high-shear mixing system to form an organic slurry with specific physical rheological properties. During this process, parameters such as viscosity, moisture content, and suspended solids ratio of the organic slurry are collected in real time, forming an organic slurry parameter set. Based on the collected organic slurry parameters, the appropriate solid-liquid separation method is determined: when the viscosity is higher than a preset viscosity threshold (e.g., 1500 mPa·s), a screw extrusion solid-liquid separator is used to separate the high-solids-content solid phase and the free-flowing liquid phase by pressing; when the viscosity is lower than or equal to the threshold, a centrifugal separator is used to separate the slurry by high-speed rotation, obtaining the supernatant (liquid phase) and sediment (solid phase). Finally, the data of the separated components are structured and labeled as liquid phase data and solid phase data, which are used for subsequent anaerobic fermentation and pyrolysis treatment processes, respectively.

[0021] Furthermore, the collected kitchen waste is pretreated to obtain organic slurry parameters, followed by solid-liquid separation to obtain data on multiple components. The methods include:

[0022] Kitchen waste is broken down to obtain a crushed particle size. Impurities are removed according to this particle size to obtain organic slurry parameters. Based on these parameters, viscosity is determined. If the viscosity is greater than a preset viscosity threshold, the organic slurry is separated by spiral extrusion to obtain solid phase component data. If the viscosity is less than or equal to the preset viscosity threshold, the organic slurry is separated by centrifugation to obtain liquid phase component data.

[0023] By pre-treating collected kitchen waste, parameters for organic slurry that can be used for subsequent energy conversion are obtained, and solid-liquid separation is performed accordingly to obtain multiple component data. Specifically, this includes: feeding the collected kitchen waste into a crushing device for breakage treatment, crushing the waste to a target particle size range (e.g., within 10mm) to form primary crushed material. Based on the crushed particle size, by setting parameters such as screen mesh size and vibration intensity, solid foreign matter (such as plastics, glass, metals, and fibrous materials) is separated in a vibrating screen or airflow separator to obtain pre-purified organic slurry material. Subsequently, the organic slurry material is fed into a slurrying system for moisture adjustment and homogenization to obtain a structurally homogeneous organic slurry. During this process, the physical parameters of the organic slurry, including viscosity, moisture content, and suspended solids content, are monitored in real time, forming an organic slurry parameter set.

[0024] Based on the above organic slurry parameters, the system performs a viscosity determination operation: if the viscosity value of the organic slurry is detected to be greater than the preset viscosity threshold (e.g., 1500 mPa·s), it is determined that the slurry is not suitable for direct gravity or centrifugal separation and needs to be processed by a screw extrusion solid-liquid separation device. The slurry is squeezed using the mechanical pressing principle to squeeze out the free water and some bound water, obtaining solid phase component data with low water content for subsequent pyrolysis path processing; if the viscosity of the organic slurry parameters is less than or equal to the preset viscosity threshold, it enters the centrifugal separation module, using high-speed rotation to separate the low-density liquid phase component and the high-density precipitated solid, thereby obtaining liquid phase component data mainly used for the fermentation path.

[0025] Based on the liquid phase component data, physicochemical adjustments are made, fermentation substrate parameters are extracted and introduced into an anaerobic fermentation reactor for segmented temperature-controlled fermentation to obtain biogas generation parameters.

[0026] After solid-liquid separation and obtaining liquid phase component data, the liquid phase components are physicochemically adjusted to ensure they meet the raw material requirements for the anaerobic fermentation system. Specifically, the liquid phase components are input into a physicochemical detection unit for online monitoring and analysis of key parameters, including but not limited to chemical oxygen demand (COD), biological oxygen demand (BOD), total organic carbon (TOC), pH, ammonia nitrogen concentration, alkalinity, C / N ratio, and dissolved oxygen content, generating a set of liquid phase physicochemical characteristic parameters. Based on this set of parameters and the substrate environmental conditions required for the target anaerobic fermentation process, the liquid phase components are optimized by adding buffers, nutrient supplements, carbon source regulators, or dilution water. This allows for dynamic adjustment of key indicators such as organic load, pH, and C / N ratio, extracting standardized fermentation substrate parameters. These fermentation substrate parameters are then introduced into the anaerobic fermentation reactor to execute a multi-stage, segmented temperature-controlled fermentation process. Specifically, through a pre-set fermentation sequence and temperature control strategy, the anaerobic fermentation process is divided into a hydrolysis-acidification stage, a gas production and proliferation stage, and a stable gas production stage, each corresponding to a different reaction temperature control range (e.g., 35℃~40℃ for the mesophilic stage and 50℃~55℃ for the hyperthermic stage). Temperature adjustment parameters, reaction time windows, and stirring rates are set for each stage, and the progress of the fermentation reaction is monitored in real time, including gas production rate, gas composition (methane, carbon dioxide, hydrogen, etc.), liquid phase pH, and volatile fatty acid (VFA) concentration, forming multi-stage fermentation feedback data. Based on the above multi-stage feedback information, gas phase product components are extracted, methane concentration and total gas production are analyzed, and finally, biogas generation parameters are obtained, including gas production rate per unit time, gas production efficiency per unit volume, and methane volume fraction.

[0027] Furthermore, based on the liquid phase component data, physicochemical adjustments are performed to extract fermentation substrate parameters, which are then introduced into an anaerobic fermentation reactor for segmented temperature-controlled fermentation to obtain biogas generation parameters. The method includes:

[0028] Physicochemical analysis is performed on the liquid phase component data to generate physicochemical characteristic parameters; the liquid phase component data is dynamically adjusted according to the physicochemical characteristic parameters to extract standardized fermentation substrate parameters; the standardized fermentation substrate parameters are correlated and arranged according to the fermentation sequence to construct fermentation substrate parameters; fermentation analysis is performed based on the fermentation substrate parameters to formulate a segmented temperature-controlled fermentation strategy; the fermentation substrate parameters are introduced into an anaerobic fermentation reactor to implement the segmented temperature-controlled fermentation strategy for monitoring, generating multi-stage fermentation feedback information; gas phase components are extracted based on the multi-stage fermentation feedback information to obtain the biogas generation parameters.

[0029] First, the liquid phase component data obtained through solid-liquid separation is subjected to physicochemical analysis. A physicochemical analysis unit is configured to systematically collect and analyze the physicochemical indicators of the liquid phase raw materials. These indicators include, but are not limited to, pH value, total solids (TS), volatile solids (VS), chemical oxygen demand (COD), ammonia nitrogen (NH4⁺-N), total nitrogen, C / N ratio, and organic acid concentration, thus forming a set of physicochemical characteristic parameters for the liquid phase. Based on these physicochemical characteristic parameters, the liquid phase components are dynamically adjusted: when the test results deviate from the preset suitable range for biochemical fermentation, adaptive adjustments are made by adding carbon sources (such as glucose and sodium acetate), nitrogen sources (such as urea and ammonia), and buffer solutions (such as sodium bicarbonate) to achieve the required C / N ratio, pH stability, and microbial activity, thereby extracting standardized fermentation substrate parameters suitable for fermentation. Based on this, standardized fermentation substrate parameters are correlated and sorted in multiple stages according to the fermentation process sequence to construct a fermentation substrate parameter set. This set establishes sequential input rules according to dimensions such as time, batch size, and substrate concentration gradient, facilitating segmented control implementation. Based on the fermentation substrate parameter set, fermentation feasibility and energy potential analysis are conducted, and a matching segmented temperature-controlled fermentation strategy is formulated. This strategy divides the entire fermentation process into several stages, including hydrolysis, acid production, hydrogen and acetic acid production, and methanogenesis. Temperature control parameters (such as target temperature within the range of 35℃~55℃), reaction time, stirring rate, and pH adjustment methods are matched for each stage to achieve optimal reaction conditions in the multi-stage fermentation process. The standardized fermentation substrate parameters are input into the anaerobic fermentation reactor, and the reactor is run according to the formulated segmented temperature-controlled fermentation strategy. During fermentation, the fermentation environment and gas production characteristics at each stage are dynamically monitored, recording indicators such as changes in VFA concentration, methane production rate, fermentation broth pH changes, ammonia nitrogen concentration, and temperature fluctuations, generating multi-stage fermentation feedback information covering all stages. Finally, gas phase components were extracted based on multi-stage fermentation feedback information. During the extraction process, an online gas analyzer was used to obtain the concentration ratios of gases such as methane, carbon dioxide, hydrogen, and hydrogen sulfide. Combined with the gas yield per unit volume, the biogas generation parameters were finally obtained.

[0030] Furthermore, the method involves introducing the fermentation substrate parameters into an anaerobic fermentation reactor to implement the segmented temperature-controlled fermentation strategy for monitoring, generating multi-stage fermentation feedback information, and including:

[0031] Based on the segmented temperature-controlled fermentation strategy, a phased decomposition is performed to determine a first stage, a second stage, and a third stage. Each of the first, second, and third stages includes a first temperature control parameter, a second temperature control parameter, and a third temperature control parameter, respectively. The fermentation substrate parameters are introduced into the anaerobic fermentation reactor according to the first temperature control parameters to enter the first stage. The fermentation substrate parameters are then hydrolyzed and acidified to generate first-stage product data. The first stage is monitored in real-time for temperature control, and a fermentation concentration value is set. When the fermentation concentration value is less than a preset concentration threshold, a first-stage switching command is activated. The first-stage product data is then processed according to the first-stage switching command. The process involves switching from the first stage to the second stage, using the second temperature control parameters to generate gas and obtain second-stage product data. Real-time temperature-controlled fermentation monitoring is performed on the second stage, and a cumulative gas production rate is set for each stage. When the cumulative gas production rate exceeds a preset gas production threshold, a second-stage switching command is activated. Following the second-stage switching command, the second-stage product data is switched from the second stage to the third stage, using the third temperature control parameters for fermentation stabilization, generating third-stage product data. The first-stage product data, the second-stage product data, and the third-stage product data are then integrated to construct the multi-stage fermentation feedback information.

[0032] Based on a segmented temperature-controlled fermentation strategy, the fermentation process is broken down into three core stages: Stage 1, Stage 2, and Stage 3. Each stage has an independent temperature control target, referred to as the first temperature control parameter, the second temperature control parameter, and the third temperature control parameter, respectively. For example, the suitable temperature for Stage 1 can be set at 35±2℃ for hydrolysis and acidification; the temperature for Stage 2 is set at 38±1℃ to promote gas generation; and the temperature for Stage 3 is controlled at 55±2℃ to achieve stable gas production and system sterilization at the fermentation terminal.

[0033] In the first stage, the fermentation substrate parameters are introduced into the anaerobic fermentation reactor, and the reactor temperature is maintained according to the first temperature control parameter to perform hydrolysis and acidification treatment on the substrate. During the hydrolysis and acidification process, the macromolecular organic matter in the substrate is degraded into small molecule organic acids and intermediate metabolites by the microbial enzyme system, generating the first stage product data. The product data includes key fermentation indicators such as VFA (volatile fatty acid) concentration, pH value change, and soluble COD.

[0034] A fermentation concentration monitoring mechanism is set up in the first stage to acquire and analyze the changes in the concentration of soluble organic matter or VFA during fermentation in real time. When the detected fermentation concentration value is lower than a preset concentration threshold (e.g., below 2000 mg / L), a first-stage switching command is triggered. Based on this switching command, the first-stage product data is transferred from the first-stage reaction zone to the second stage, and the temperature is adjusted in conjunction with the second temperature control parameters to bring fermentation into the gas-producing reaction stage. The second stage is mainly characterized by hydrogen production, acetic acid production, and methanogenesis. By controlling the mesophilic or hyperthermic environment, the activity of gas-producing bacteria is maximized, and the second-stage product data is acquired. The second-stage product data includes parameters such as cumulative gas production, methane concentration, and gas production rate.

[0035] During the second stage of continuous operation, a cumulative gas production threshold is set, such as 0.5 m³ / kgVS. When the cumulative gas production in the second stage reaches or exceeds the preset threshold, a second-stage switching command is automatically activated. Based on this switching command, the second-stage product data is guided into the third stage, and further fermentation is carried out in conjunction with the third temperature control parameters. The third stage is mainly used for stabilization treatment at the end of fermentation, enhancing the degradation of residual substrate by increasing the temperature, while simultaneously inactivating harmful bacteria and preparing the system for discharge. The product data in the third stage mainly includes indicators such as residual organic matter concentration, system load balance value, and gas composition stability.

[0036] Finally, the product data from the first stage, the second stage, and the third stage were structurally integrated to construct a complete multi-stage fermentation feedback information system.

[0037] Based on the biogas generation parameters, combustion power generation is performed to generate first electrical energy generation parameters for supply and demand prediction, thereby obtaining an electrical energy supply and demand prediction map.

[0038] Furthermore, based on the biogas generation parameters, combustion power generation is performed to generate first electricity generation parameters for supply and demand forecasting, resulting in an electricity supply and demand forecast map. The method includes:

[0039] Based on the biogas generation parameters, biogas purification parameters are obtained; the biogas purification parameters are fed into an internal combustion generator to drive the generator to generate electricity through combustion, generating the first power generation parameters; historical power supply and demand data are retrieved for power fluctuation analysis, nonlinear fluctuation parameters are captured to perform rolling prediction, and a power supply and demand prediction data chain is generated, which is arranged according to the supply and demand time sequence; based on the power supply and demand prediction data chain, a multi-level data layer is constructed for supply and demand identification, and the power supply and demand prediction data chain is mapped to the multi-level data layer according to the supply and demand tags to construct the power supply and demand prediction map.

[0040] First, the acquired biogas generation parameters are pre-processed and purified to obtain biogas purification parameters suitable for stable combustion. Specifically, a desulfurization tower is used to remove hydrogen sulfide from the biogas generation parameters until the hydrogen sulfide concentration is below 50 ppm, thus obtaining biogas purification parameters with balanced composition and stable calorific value. Subsequently, the biogas purification parameters are fed into a configured internal combustion generator set, driving the generator to continuously and stably generate electricity under set air-fuel ratio and ignition conditions. During the operation of the internal combustion generator, by real-time monitoring of output active power, reactive power, voltage, current, and other parameters, combined with information such as gas usage flow rate and power generation duration, a first set of electrical energy generation parameters is generated. This parameter characterizes the energy efficiency, load capacity, and power generation fluctuation characteristics based on biogas combustion.

[0041] Based on the acquired initial power generation parameters, historical power supply and demand data matching the current operating area or load scenario are retrieved. Power fluctuation analysis is performed by comparing the power load curves and power generation output curves for different time periods. During this process, machine learning models are used to identify key fluctuation factors, including seasonal changes, sudden event interference, and energy consumption behavior patterns. Nonlinear fluctuation parameters are extracted as input variables to perform rolling predictions of future power supply and demand, thereby generating a power supply and demand forecast data chain. This data chain is arranged logically according to the power supply sequence, with time as the main axis, and qualitatively and quantitatively labeled based on the power demand and adjustable output capacity for each time period. A multi-level data layer is constructed based on the power supply and demand forecast data chain, with each layer corresponding to different power states such as power supply and demand balance, short-term shortage, and excess energy storage. By constructing multi-level data layers and mapping the data chain between each layer, refined labeling and graphical display of supply and demand states within different time segments are achieved. Finally, a power supply and demand forecast map is constructed based on the mapping results.

[0042] After the solid component data is dehydrated and dried, it is fed into a pyrolysis furnace to obtain combustible syngas parameters for combustion power generation, generating a second electrical energy generation parameter.

[0043] The separated solid component data is preprocessed to reduce its moisture content and increase its calorific value density. Specifically, a multi-stage dehydration and drying device is used to process the solid component data, including mechanical pressure filtration dehydration and hot air circulation drying processes, to ensure that the moisture content of the final solid material is controlled at the target threshold (e.g., below 15%), thereby improving the efficiency of subsequent pyrolysis.

[0044] After dehydration and drying, the resulting dried solid components are fed into a pyrolysis furnace equipped with a multi-stage heating structure. The pyrolysis process is carried out in an oxygen-deficient or micro-oxygen environment. The furnace body achieves segmented temperature control and heating based on temperature control feedback signals, allowing the solid components to undergo stages of dry distillation, cracking, and gasification within a temperature range of 250℃ to 800℃, ultimately transforming into combustible syngas mainly composed of CO, H2, and CH4. By condensing and filtering the tar and impurity components released during the pyrolysis process, the target components are extracted to form combustible syngas parameters.

[0045] Combustible syngas parameters are fed into a dedicated gas generator set, where stable combustion occurs under set gas supply pressure, flow rate, and ignition parameters, converting the gas into electrical energy. The combustion power generation system outputs corresponding power generation parameter information in real time based on the calorific value, composition ratio, and supply continuity of the syngas, including power generation capacity, thermoelectric conversion efficiency, and voltage stability, thus generating secondary electrical energy generation parameters.

[0046] Furthermore, the solid component data is dehydrated and dried before being fed into a pyrolysis furnace to obtain combustible syngas parameters. The method includes:

[0047] The solid phase component data is subjected to dehydration treatment. When the water content of the solid phase component data is less than a preset water content threshold, solid phase dehydration data is obtained. Based on the solid phase dehydration data, temperature analysis is performed to obtain the solid phase calorific value. When the solid phase calorific value is greater than a preset calorific value, the solid phase dehydration data is subjected to rotary kiln drying analysis to obtain first drying data. When the solid phase calorific value is less than the preset calorific value, the solid phase dehydration data is subjected to solar drying analysis to obtain second drying data. Pyrolysis treatment is performed according to the first drying data or the second drying data to generate the combustible syngas parameters.

[0048] Prioritize dehydration of the solid phase data by using multi-stage physical dehydration equipment (such as screw presses or plate and frame filter presses) to mechanically press the initially separated high-moisture-content solid phase components, gradually reducing their moisture content. When the moisture content of the dehydrated solid phase data is less than a preset moisture threshold (e.g., 30%), i.e., when the acceptable heat treatment critical value is reached, the data is recorded as solid phase dehydration data.

[0049] Temperature analysis based on solid-phase dehydration data includes: calculating the corresponding solid-phase calorific value by combining its compositional characteristics, carbon content, and residual organic matter content, using specific heat capacity measurement and lower heating value estimation models. If the detected solid-phase calorific value is greater than a preset calorific value threshold (e.g., 12 MJ / kg), a rotary kiln is selected as the drying device, and rotary kiln drying analysis is performed on the solid-phase dehydration data. This process is carried out in a sealed and controllable environment, using drum rotation and counter-current hot air exchange to uniformly dry the material, and collecting the obtained drying efficiency, carbon content, and final moisture content indicators to generate the first drying data.

[0050] If the solid phase calorific value is lower than the preset calorific value threshold, it indicates that its organic matter content or pyrolysis efficiency is low. At this time, the process is switched to solar drying. Using solar-assisted drying devices such as heat collection plates and solar-thermal air ducts, solar drying analysis is performed on the solid phase dehydration data. The drying efficiency is improved by continuous exposure, hot air convection, and infrared heat exchange, reducing energy consumption and maintaining the activity of the material. The obtained drying efficiency, carbon content, and final moisture content are collected to generate secondary drying data.

[0051] After the drying path is selected, the corresponding dried material is fed into the pyrolysis unit based on the obtained first or second drying data. The pyrolysis process takes place in a pyrolysis furnace, with the pyrolysis temperature controlled between 300℃ and 800℃. The heating rate and residence time are controlled according to the pyrolysis reaction kinetic model, allowing the dried material to decompose in an oxygen-deficient environment to generate combustible syngas mainly composed of CO, CH4, and H2, while simultaneously releasing tar and carbon black byproducts. By collecting, filtering, and evaluating the calorific value of the gas, the final parameters for the combustible syngas are obtained.

[0052] According to the power supply and demand forecast map, the second power generation parameter is used as supplementary power to compensate the first power generation parameter for power generation, thereby obtaining the total power generation data.

[0053] Based on the power supply and demand forecast map, the fluctuation trend of power demand within the target power supply cycle is analyzed, and time-series supply and demand difference segments in the forecast map are extracted and marked as power supply and demand gap areas. The power supply and demand gap areas are quantitatively described by timestamps and predicted power gap values, forming a power gap matching matrix. Based on this, a first power generation parameter, i.e., power data generated by combustion power generation after anaerobic fermentation biogas purification, is called and mapped to the supply and demand gap matrix along the time dimension to obtain a preliminary first power supply coverage area. By comparing this coverage area with the gap matrix, the remaining gap amount not covered by the first power generation parameter is identified. For the above remaining gap amount, a second power generation parameter, i.e., power data from the combustion power generation of combustible syngas generated in the pyrolysis furnace, is called as supplementary power. The second power generation parameter is scheduled according to power level and time interval using a supply and demand matching algorithm to fill the remaining gap. Finally, the first power generation parameter and the second power generation parameter used for compensation are merged and summarized on the corresponding time axis to calculate the total power output and output duration in each time period, and generate total power generation data in a unified format.

[0054] Furthermore, according to the aforementioned power supply and demand forecast map, the second power generation parameter is used as supplementary power to compensate the first power generation parameter for power generation, thereby obtaining total power generation data. The method includes:

[0055] Based on the power supply and demand forecast map, map analysis is performed to obtain analytical parameters, which are either supply and demand gap intensity parameters or supply and demand surplus characteristic parameters. When the analytical parameter is the supply and demand gap intensity parameter, gap analysis is performed according to the supply and demand gap intensity parameter to determine the type of supply and demand gap. According to the type of supply and demand gap, the second power generation parameter is retrieved to synchronize the first power generation parameter with the grid. Power generation compensation is performed according to the synchronization result to obtain the total power generation data. When the analytical parameter is the supply and demand surplus characteristic parameter, the second power generation parameter is stored as backup power, and the storage result is summed with the first power generation parameter to obtain the total power generation data.

[0056] Based on the electricity supply and demand forecast map, map analysis is performed to extract the supply and demand distribution structure information from the map. An analytical model is then used to identify features of time-series nodes, electricity demand curves, and electricity supply response curves in the map, generating corresponding map analysis parameters. These parameters include a supply-demand gap strength parameter and a supply-demand surplus characteristic parameter. The supply-demand gap strength parameter indicates a situation where there is insufficient electricity supply within a certain forecast time window, while the supply-demand surplus characteristic parameter indicates sections where electricity supply exceeds forecast demand.

[0057] When the graph analysis parameter is the supply-demand gap intensity parameter, gap analysis is performed based on the magnitude and distribution range of this parameter value to identify and classify the current power gap type. Power gap types include intermittent gaps, persistent gaps, and peak-response gaps. For different gap types, a second power generation parameter, namely the supplementary power obtained from the combustion of pyrolysis synthesis gas, is invoked. This parameter is then connected to the first power generation parameter according to the scheduling strategy, and power generation data is synchronized in the grid-connected dispatch system to ensure that the second power generation parameter maintains grid-connected consistency with the first power generation parameter in terms of time period, voltage level, and frequency indicators. After successful grid-connected synchronization, the output ratio or time window of the second power generation parameter is dynamically adjusted according to the gap matching requirements to perform power generation compensation processing, ultimately generating the total power generation data.

[0058] When the graph analysis parameters are supply and demand surplus characteristics, it indicates that the current power supply exceeds demand. In this case, instead of immediately using the second power generation parameters for grid-connected generation, the second power generation parameters are transferred to the energy storage system for orderly storage as backup power. The storage process configures parameters based on factors such as the current energy storage status, battery capacity, and calorific value margin to ensure the stability of subsequent adjustable power resources. After the backup power is stored, the first power generation parameters and the storage results are accumulated to generate total power generation data that includes both immediate power supply and backup adjustable power supply, which is used for subsequent energy management or dispatch strategy formulation.

[0059] Furthermore, based on the aforementioned electricity supply and demand forecast map, map analysis is performed to obtain parameters of the supply and demand gap period and characteristics of the supply and demand surplus. The method includes:

[0060] Load analysis is performed based on the power supply and demand forecast map to extract load power parameters; time periods are identified in the power supply and demand forecast map according to the load power parameters to determine gap time periods; power supply and demand are identified according to the gap time periods, and gap intensity parameters are calculated based on the identification results; the power supply and demand forecast map is segmented according to the time axis to generate multiple time units; multi-scale surplus analysis is performed by traversing the multiple time units to determine multi-scale surplus indicators for feature analysis and construct multiple surplus feature vectors; the multiple surplus feature vectors are added to the supply and demand surplus feature parameters.

[0061] The electricity supply and demand forecast map is a multi-time-series electricity supply and demand mapping structure generated based on historical electricity data and biogas power generation data. It includes forecasted electricity supply curves and forecasted electricity demand curves arranged in the time dimension. By analyzing the map data, the load power parameter of each time-series node is extracted. The load power parameter is the forecasted electricity supply and demand difference data per unit time, which is used to reflect the degree of supply and demand balance at that node.

[0062] The power supply and demand forecast map is marked with time periods based on the extracted load power parameters. By detecting segments in the load power parameters that have negative values ​​or are below the power supply stability threshold, potential supply-demand gap periods are identified, thus determining the gap period information. The gap period information includes key parameters such as start and end times, corresponding load offsets, and load density change trends.

[0063] Based on the information of the gap period, the power supply and demand status is identified and analyzed, and the gap status is calculated to obtain the gap intensity parameter. The gap intensity parameter is used to quantitatively represent the degree of power gap within a certain gap period. The calculation method includes, but is not limited to, weighted indicators such as load fluctuation amplitude, duration of continuous gap, and peak difference.

[0064] The power supply and demand forecast map is segmented along a unified time axis to generate multiple time units. Each time unit represents a local time window within the map, with time granularities such as 15 minutes, 30 minutes, and 1 hour, supporting the analysis of power surplus at different scales in the short and medium to long term. Multiple time units are traversed, and power supply and demand change data are extracted for each unit to perform multi-scale surplus analysis. This multi-scale surplus analysis includes cumulative statistics, trend tracking, and fluctuation amplitude identification for power supply exceeding demand segments at different granularities, forming multiple surplus discrimination indicators. Based on this, feature analysis is further performed, organizing the surplus analysis results of each time unit into a surplus feature vector. This vector includes multiple dimensions such as time location, total surplus, peak time, continuous time period length, and average surplus power. Finally, multiple surplus feature vectors are aggregated and added to the supply and demand surplus feature parameters to construct a complete power supply and demand feature set, providing a basis for subsequent energy replenishment regulation, energy storage strategy planning, and power generation dispatch control.

[0065] In summary, the embodiments of this application have at least the following technical effects:

[0066] First, the collected kitchen waste is pretreated to obtain organic slurry parameters, which are then separated into solid and liquid components, yielding multiple component data, including both liquid and solid phase data. Next, based on the liquid phase data, physicochemical adjustments are made to extract fermentation substrate parameters, which are then introduced into an anaerobic fermentation reactor for segmented temperature-controlled fermentation to obtain biogas generation parameters. Then, based on the biogas generation parameters, combustion power generation is performed to generate a first electricity generation parameter, which is used for supply and demand forecasting to obtain an electricity supply and demand forecast map. Next, the solid phase component data is dehydrated and dried before being fed into a pyrolysis furnace to obtain combustible syngas parameters for combustion power generation, generating a second electricity generation parameter. Finally, according to the electricity supply and demand forecast map, the second electricity generation parameter is used as supplementary power to compensate for the first electricity generation parameter, yielding total power generation data. This method solves the technical problems of low kitchen waste treatment efficiency and insufficient resource utilization in existing technologies, achieving the technical effect of improving waste treatment efficiency and comprehensive resource utilization.

[0067] Example 2, based on the same inventive concept as the kitchen waste treatment method for power generation in the foregoing examples, such as... Figure 2 As shown, this application provides a food waste treatment platform for power generation, wherein the platform includes:

[0068] Pretreatment module 11: Pre-treats the collected kitchen waste, obtains organic slurry parameters, performs solid-liquid separation, and obtains multiple component data, including liquid phase component data and solid phase component data; Fermentation treatment module 12: Based on the liquid phase component data, performs physicochemical adjustment, extracts fermentation substrate parameters, introduces them into an anaerobic fermentation reactor for segmented temperature-controlled fermentation, and obtains biogas generation parameters; Prediction module 13: Based on the biogas generation parameters, performs combustion power generation, generates a first power generation parameter, performs supply and demand prediction, and obtains a power supply and demand prediction map; Power generation module 14: After dehydrating and drying the solid phase component data, it is fed into a pyrolysis furnace to obtain combustible syngas parameters for combustion power generation, generating a second power generation parameter; Compensation module 15: According to the power supply and demand prediction map, the second power generation parameter is used as supplementary power to compensate the first power generation parameter for power generation, and obtains total power generation data.

[0069] Furthermore, the preprocessing module 11 is used to perform the following method:

[0070] Kitchen waste is broken down to obtain a crushed particle size. Impurities are removed according to this particle size to obtain organic slurry parameters. Based on these parameters, viscosity is determined. If the viscosity is greater than a preset viscosity threshold, the organic slurry is separated by spiral extrusion to obtain solid phase component data. If the viscosity is less than or equal to the preset viscosity threshold, the organic slurry is separated by centrifugation to obtain liquid phase component data.

[0071] Furthermore, the fermentation processing module 12 is used to perform the following methods:

[0072] Physicochemical analysis is performed on the liquid phase component data to generate physicochemical characteristic parameters; the liquid phase component data is dynamically adjusted according to the physicochemical characteristic parameters to extract standardized fermentation substrate parameters; the standardized fermentation substrate parameters are correlated and arranged according to the fermentation sequence to construct fermentation substrate parameters; fermentation analysis is performed based on the fermentation substrate parameters to formulate a segmented temperature-controlled fermentation strategy; the fermentation substrate parameters are introduced into an anaerobic fermentation reactor to implement the segmented temperature-controlled fermentation strategy for monitoring, generating multi-stage fermentation feedback information; gas phase components are extracted based on the multi-stage fermentation feedback information to obtain the biogas generation parameters.

[0073] Furthermore, the fermentation processing module 12 is used to perform the following methods:

[0074] Based on the segmented temperature-controlled fermentation strategy, a phased decomposition is performed to determine a first stage, a second stage, and a third stage. Each of the first, second, and third stages includes a first temperature control parameter, a second temperature control parameter, and a third temperature control parameter, respectively. The fermentation substrate parameters are introduced into the anaerobic fermentation reactor according to the first temperature control parameters to enter the first stage. The fermentation substrate parameters are then hydrolyzed and acidified to generate first-stage product data. The first stage is monitored in real-time for temperature control, and a fermentation concentration value is set. When the fermentation concentration value is less than a preset concentration threshold, a first-stage switching command is activated. The first-stage product data is then processed according to the first-stage switching command. The process involves switching from the first stage to the second stage, using the second temperature control parameters to generate gas and obtain second-stage product data. Real-time temperature-controlled fermentation monitoring is performed on the second stage, and a cumulative gas production rate is set for each stage. When the cumulative gas production rate exceeds a preset gas production threshold, a second-stage switching command is activated. Following the second-stage switching command, the second-stage product data is switched from the second stage to the third stage, using the third temperature control parameters for fermentation stabilization, generating third-stage product data. The first-stage product data, the second-stage product data, and the third-stage product data are then integrated to construct the multi-stage fermentation feedback information.

[0075] Furthermore, the prediction module 13 is used to perform the following method:

[0076] Based on the biogas generation parameters, biogas purification parameters are obtained; the biogas purification parameters are fed into an internal combustion generator to drive the generator to generate electricity through combustion, generating the first power generation parameters; historical power supply and demand data are retrieved for power fluctuation analysis, nonlinear fluctuation parameters are captured to perform rolling prediction, and a power supply and demand prediction data chain is generated, which is arranged according to the supply and demand time sequence; based on the power supply and demand prediction data chain, a multi-level data layer is constructed for supply and demand identification, and the power supply and demand prediction data chain is mapped to the multi-level data layer according to the supply and demand tags to construct the power supply and demand prediction map.

[0077] Furthermore, the power generation module 14 is used to perform the following methods:

[0078] The solid phase component data is subjected to dehydration treatment. When the water content of the solid phase component data is less than a preset water content threshold, solid phase dehydration data is obtained. Based on the solid phase dehydration data, temperature analysis is performed to obtain the solid phase calorific value. When the solid phase calorific value is greater than a preset calorific value, the solid phase dehydration data is subjected to rotary kiln drying analysis to obtain first drying data. When the solid phase calorific value is less than the preset calorific value, the solid phase dehydration data is subjected to solar drying analysis to obtain second drying data. Pyrolysis treatment is performed according to the first drying data or the second drying data to generate the combustible syngas parameters.

[0079] Furthermore, the compensation module 15 is used to perform the following method:

[0080] Based on the power supply and demand forecast map, map analysis is performed to obtain analytical parameters, which are either supply and demand gap intensity parameters or supply and demand surplus characteristic parameters. When the analytical parameter is the supply and demand gap intensity parameter, gap analysis is performed according to the supply and demand gap intensity parameter to determine the type of supply and demand gap. According to the type of supply and demand gap, the second power generation parameter is retrieved to synchronize the first power generation parameter with the grid. Power generation compensation is performed according to the synchronization result to obtain the total power generation data. When the analytical parameter is the supply and demand surplus characteristic parameter, the second power generation parameter is stored as backup power, and the storage result is summed with the first power generation parameter to obtain the total power generation data.

[0081] Furthermore, the compensation module 15 is used to perform the following method:

[0082] Load analysis is performed based on the power supply and demand forecast map to extract load power parameters; time periods are identified in the power supply and demand forecast map according to the load power parameters to determine gap time periods; power supply and demand are identified according to the gap time periods, and gap intensity parameters are calculated based on the identification results; the power supply and demand forecast map is segmented according to the time axis to generate multiple time units; multi-scale surplus analysis is performed by traversing the multiple time units to determine multi-scale surplus indicators for feature analysis and construct multiple surplus feature vectors; the multiple surplus feature vectors are added to the supply and demand surplus feature parameters.

[0083] Example 3: Based on the same inventive concept as the food waste treatment method for power generation in the foregoing examples, this example provides a computer-readable storage medium that can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the food waste treatment method for power generation in this application. The processor executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory, thereby realizing the aforementioned food waste treatment method for power generation.

[0084] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0085] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0086] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for treating kitchen waste for power generation, characterized in that, The method includes: The collected kitchen waste is pretreated to obtain organic slurry parameters, which are then separated into solid and liquid components to obtain multiple component data, including liquid phase component data and solid phase component data. Based on the liquid phase component data, physicochemical adjustments are made, fermentation substrate parameters are extracted and introduced into an anaerobic fermentation reactor for segmented temperature-controlled fermentation, and biogas generation parameters are obtained. Based on the biogas generation parameters, combustion power generation is performed to generate first electricity generation parameters for supply and demand prediction, and an electricity supply and demand prediction map is obtained. After the solid component data is dehydrated and dried, it is fed into a pyrolysis furnace to obtain combustible syngas parameters for combustion power generation, thereby generating a second electrical energy generation parameter. According to the power supply and demand forecast map, the second power generation parameter is used as supplementary power to compensate the first power generation parameter for power generation, thereby obtaining the total power generation data.

2. The method for treating kitchen waste for power generation as described in claim 1, characterized in that, The collected kitchen waste is pretreated to obtain organic slurry parameters, followed by solid-liquid separation to obtain data on multiple components. The methods include: The kitchen waste is broken down to obtain the crushed particle size, and impurities are removed according to the crushed particle size to obtain the organic slurry parameters. Based on the organic slurry parameters, viscosity is determined. When the viscosity of the organic slurry parameters is greater than a preset viscosity threshold, the organic slurry parameters are separated by spiral extrusion to obtain the solid phase component data. When the viscosity of the organic slurry parameter is less than or equal to a preset viscosity threshold, the organic slurry parameter is centrifuged to obtain the liquid phase component data.

3. The method for treating kitchen waste for power generation as described in claim 1, characterized in that, Based on the liquid phase component data, physicochemical adjustments are performed, fermentation substrate parameters are extracted, and the substrate is introduced into an anaerobic fermentation reactor for segmented temperature-controlled fermentation to obtain biogas production parameters. The method includes: Physicochemical tests are performed based on the liquid phase component data to generate physicochemical property parameters; The liquid phase component data are dynamically adjusted according to the physicochemical properties parameters to extract standardized fermentation substrate parameters. The standardized fermentation substrate parameters are correlated and arranged according to the fermentation time sequence to construct the fermentation substrate parameters; Fermentation analysis was conducted based on the fermentation substrate parameters to formulate a segmented temperature-controlled fermentation strategy. The fermentation substrate parameters are introduced into the anaerobic fermentation reactor to implement the segmented temperature-controlled fermentation strategy for monitoring, and multi-stage fermentation feedback information is generated. Based on the multi-stage fermentation feedback information, gas phase components are extracted to obtain the biogas generation parameters.

4. The method for treating kitchen waste for power generation as described in claim 3, characterized in that, The method involves introducing the fermentation substrate parameters into an anaerobic fermentation reactor to implement the segmented temperature-controlled fermentation strategy for monitoring, generating multi-stage fermentation feedback information, and including: Based on the segmented temperature-controlled fermentation strategy, the stages are decomposed to determine the first stage, the second stage, and the third stage. Each of the first stage, the second stage, and the third stage includes a first temperature control parameter, a second temperature control parameter, and a third temperature control parameter. According to the first temperature control parameter, the fermentation substrate parameter is introduced into the anaerobic fermentation reactor to enter the first stage, and the fermentation substrate parameter is hydrolyzed and acidified to generate the first stage product data. The first stage of temperature-controlled fermentation is monitored in real time, and a fermentation concentration value is set. When the fermentation concentration value is less than the preset concentration threshold, the first stage switching command is activated. According to the first stage switching instruction, the first stage product data is switched from the first stage to the second stage, and gas is generated in combination with the second temperature control parameters to obtain the second stage product data. The second stage of temperature-controlled fermentation is monitored in real time, and the cumulative gas production of the stage is set. When the cumulative gas production of the stage is greater than the preset gas production threshold, the second stage switching command is activated. According to the second stage switching instruction, the second stage product data is switched from the second stage to the third stage, and fermentation is stabilized in combination with the third temperature control parameters to generate the third stage product data. The product data of the first stage, the product data of the second stage, and the product data of the third stage are integrated to construct the multi-stage fermentation feedback information.

5. The method for treating kitchen waste for power generation as described in claim 1, characterized in that, Based on the biogas generation parameters, combustion power generation is performed to generate first electricity generation parameters for supply and demand forecasting, resulting in an electricity supply and demand forecast map. The method includes: Based on the biogas generation parameters, biogas purification parameters are obtained; The biogas purification parameters are fed into the internal combustion generator to drive the internal combustion generator to generate electricity through combustion, thereby generating the first electrical energy generation parameters. Historical power supply and demand data are retrieved for power fluctuation analysis, nonlinear fluctuation parameters are captured to perform rolling forecasts, and a power supply and demand forecast data chain is generated, which is arranged according to the supply and demand time sequence. Based on the power supply and demand forecasting data chain, a multi-level data layer is constructed to identify supply and demand. The power supply and demand forecasting data chain is mapped to the multi-level data layer according to the supply and demand tags to construct the power supply and demand forecasting map.

6. The method for treating kitchen waste for power generation as described in claim 1, characterized in that, The solid phase component data is dehydrated and dried before being fed into a pyrolysis furnace to obtain combustible synthesis gas parameters. The method includes: The solid phase component data is subjected to dehydration treatment. When the water content of the solid phase component data is less than a preset water content threshold, solid phase dehydration data is obtained. Temperature analysis is performed based on the solid phase dehydration data to obtain the solid phase calorific value. When the solid phase calorific value is greater than the preset calorific value, rotary kiln drying analysis is performed on the solid phase dehydration data to obtain the first drying data. When the solid phase calorific value is less than the preset calorific value, the solid phase dehydration data is analyzed by solar drying to obtain second drying data; The combustible syngas parameters are generated by performing pyrolysis treatment according to the first drying data or the second drying data.

7. The method for treating kitchen waste for power generation as described in claim 1, characterized in that, According to the aforementioned power supply and demand forecast map, the second power generation parameter is used as supplementary power to compensate the first power generation parameter for power generation, thereby obtaining total power generation data. The method includes: Based on the power supply and demand forecast map, map analysis is performed to obtain analytical parameters, which are either supply and demand gap intensity parameters or supply and demand surplus characteristic parameters. When the analytical parameter is the supply-demand gap strength parameter, gap analysis is performed according to the supply-demand gap strength parameter to determine the type of supply-demand gap. According to the type of supply and demand gap, the second power generation parameter is retrieved to synchronize the first power generation parameter with the grid. Based on the synchronization result, power generation compensation is performed to obtain the total power generation data. When the analytical parameter is the supply and demand surplus characteristic parameter, the second power generation parameter is stored as backup power, and the storage result is added to the first power generation parameter to obtain the total power generation data.

8. The method for treating kitchen waste for power generation as described in claim 7, characterized in that, Based on the aforementioned power supply and demand forecast map, map analysis is performed to obtain parameters of the supply and demand gap during specific periods and characteristic parameters of the supply and demand surplus. The method includes: Load analysis is performed based on the aforementioned power supply and demand forecast map to extract load power parameters; Based on the load power parameters, the power supply and demand forecast map is marked with time periods to determine the gap period information; Based on the gap time period information, power supply and demand are identified, and gap strength parameters are calculated based on the identification results. Based on the aforementioned power supply and demand forecast map, multiple time units are generated by dividing it according to the time axis; Multi-scale earnings analysis is performed by traversing the multiple time units, multi-scale earnings indicators are determined for feature analysis, and multiple earnings feature vectors are constructed. The plurality of surplus feature vectors are added to the supply and demand surplus feature parameters.

9. A kitchen waste treatment platform for power generation, characterized in that, The platform is used to implement the kitchen waste treatment method for power generation according to any one of claims 1-8, wherein the platform comprises: Pre-processing module: pre-processes the collected kitchen waste, obtains organic slurry parameters, performs solid-liquid separation, and obtains multiple component data, including liquid phase component data and solid phase component data; Fermentation processing module: Based on the liquid phase component data, physicochemical adjustments are made, fermentation substrate parameters are extracted and introduced into an anaerobic fermentation reactor for segmented temperature-controlled fermentation, and biogas generation parameters are obtained; Prediction module: Based on the biogas generation parameters, it generates electricity through combustion, generates first electricity generation parameters, performs supply and demand prediction, and obtains an electricity supply and demand prediction map; Power generation module: After dehydrating and drying the solid phase component data, it is fed into a pyrolysis furnace to obtain combustible syngas parameters for combustion power generation, generating a second electrical energy generation parameter; Compensation module: According to the power supply and demand forecast map, the second power generation parameter is used as supplementary power to compensate the first power generation parameter for power generation, so as to obtain the total power generation data.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for treating kitchen waste for power generation as described in any one of claims 1-8.

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