An agricultural crop straw carbonization system based on the Internet of Things

Abstract

The present application relates to the technical field of agricultural internet of things, in particular to a crop straw carbonization system based on internet of things, which comprises a temperature distribution regulation module, a carbonization process analysis module, a quality prediction optimization module, a dynamic calibration module and a safe storage management module.In the present application, the internal thermal field distribution of the carbonization equipment is accurately identified and divided in multiple levels, the heating law of the straw is dynamically analyzed, the heat transfer rate and distribution density are optimized, the energy efficiency and the uniformity of the carbonization reaction are improved, the energy efficiency optimization and product quality improvement are realized based on the distribution law of the pyrolysis products and energy consumption data, the quality stability and consistency of the biochar are maintained through dynamic prediction and offset correction, the operation error is automatically repaired by the real-time calibration mechanism, the carbonization process is maintained in the optimal state for a long time, the data security partitioning and traceability are realized by combining the encrypted data storage path, and the intelligence, energy efficiency control, uniformity and data security and reliability of the carbonization system are comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of agricultural Internet of Things (IoT) technology, and in particular to an IoT-based crop straw carbonization system. Background Technology

[0002] Agricultural Internet of Things (IoT) technology refers to the digital, networked, and intelligent management and control of various equipment and environments in agricultural production through the internet, wireless communication, sensor technology, and data processing technology. This aims to improve the efficiency and sustainability of agricultural production. Core areas of this technology include agricultural environmental monitoring, precision agriculture, farmland automation, and agricultural big data analysis. The goal is to help agricultural producers monitor farmland conditions in real time and precisely regulate agricultural production processes through data collection, transmission, analysis, and feedback, thereby increasing crop yield and quality, reducing costs, and achieving green and sustainable development. Traditional crop straw carbonization systems utilize high temperatures to carbonize crop straw, converting it into biochar. Traditional methods rely on fixed or mobile equipment to heat or burn the straw in a closed environment, generating high temperatures to achieve carbonization. This process uses relatively simple mechanical heating devices, relying on manual monitoring and adjustment to maintain the carbonization temperature. The energy efficiency is low, and environmental pollution is significant, with low levels of intelligence and automation.

[0003] Traditional straw carbonization systems rely on fixed heating devices to maintain high-temperature operation. The temperature field distribution fluctuates with environmental and equipment differences. The lack of real-time feedback in manual control makes it difficult to stabilize the carbonization temperature within the ideal range, easily leading to overheating or underheating. This results in energy loss and uneven product structure, large fluctuations in carbonization results, and low energy conversion efficiency. Furthermore, the system cannot dynamically correlate energy consumption with product changes during operation, lacks effective quality prediction mechanisms and deviation calibration methods, and relies on experience to judge the carbonization status, leading to lag in process control and fluctuations in carbonization quality. The operation data management is fragmented and lacks security, resulting in low intelligence, poor reliability, and inconsistent energy utilization and quality control in the carbonization process. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing an Internet of Things-based crop straw carbonization system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an Internet of Things-based crop straw carbonization system includes:

[0006] The temperature distribution control module identifies the heating change pattern of straw in different regions based on the internal thermal field distribution characteristics of the carbonization equipment, divides the temperature zone hierarchy, determines the heat attribution relationship of the temperature zone, and obtains a multi-dimensional thermal field mapping model for straw carbonization.

[0007] The carbonization process analysis module calls the straw carbonization multidimensional thermal field mapping model to extract pyrolysis product data, carbonization time data and energy consumption data under each temperature zone, and sorts the pyrolysis product data and energy consumption data to obtain the carbonization energy efficiency product weight coefficient set.

[0008] The quality prediction and optimization module arranges the biochar quality flow direction in the nodes within a future time period according to the set of weight coefficients of carbonization energy efficiency products, extracts the quality of adjacent time periods and judges the trend offset direction, and maps the offset direction to the duration to obtain the dynamic biochar quality evolution prediction sequence.

[0009] Based on the dynamic biochar quality evolution prediction sequence, the dynamic calibration module extracts the range of difference between the predicted value and the real-time value within the time node, analyzes the consistency ratio of the difference range direction and the difference magnitude ratio, and judges the ratio of the two ratios to obtain the dynamic calibration set of carbonization process error.

[0010] As a further aspect of the present invention, the multidimensional thermal field mapping model for straw carbonization includes temperature zone density levels, heat distribution weights, and hierarchical label mapping relationships; the set of weight coefficients for carbonization energy efficiency products includes pyrolysis product weights, energy response weights, and carbonization frequency distribution weights; the dynamic biochar quality evolution prediction sequence includes a quality improvement direction sequence, a trend deviation direction interval, and a time-period change gradient; and the dynamic calibration set for carbonization process errors includes prediction error adjustment parameters, trend consistency evaluation values, and dynamic correction ratios.

[0011] As a further aspect of the present invention, the temperature distribution control module includes:

[0012] The thermal field data acquisition submodule is based on a network of thermal sensors arranged inside the carbonization equipment. It collects temperature data at different locations inside the equipment in real time and transmits it to the Internet of Things platform for processing to obtain a dataset of temperature data at different locations.

[0013] The temperature zone division submodule calculates the heat transfer rate per unit time based on the differentiated location temperature dataset, and divides the thermal field by combining the temperature zone distribution density to generate a differentiated temperature zone region division dataset.

[0014] The heat attribution determination submodule, based on the differentiated temperature zone regional division dataset, determines the heat attribution relationship of the differentiated temperature zones by comparing the heat transfer rate and distribution density of the temperature zones, and generates a multi-dimensional thermal field mapping model for straw carbonization.

[0015] As a further aspect of the present invention, the carbonization process analysis module includes:

[0016] The temperature zone data extraction submodule calls the straw carbonization multidimensional thermal field mapping model to perform batch extraction of multi-temperature zone data. It divides the reaction range of the differentiated pyrolysis stage by the interval temperature threshold, reads the temperature change sequence and pyrolysis reaction within the stage, and synchronously collects the energy input and time series of the carbonization stage to generate pyrolysis product data and energy consumption data.

[0017] The feature sorting submodule performs comparative distribution statistics between data groups based on the pyrolysis product data and energy consumption data, segmenting by feature values ​​and using the following formula:

[0018] ;

[0019] Calculate the change density value that distinguishes the differentiated energy range, extract the fluctuation frequency of each feature based on the distribution sequence and arrange them in order, retain the feature indexes before sorting, and obtain the high-frequency change feature value;

[0020] in, The density value representing the variation in energy ranges that distinguish different energy levels. The total number of data items. Representing the The feature values ​​of each data item Represents the average value of the eigenvalues. Representing the New parameters associated with each data item and its feature value. Representing the Each data item and The standard deviation of the association;

[0021] The weight calculation submodule extracts the cumulative values ​​of pyrolysis product data and energy consumption data based on the high-frequency variation characteristic values, identifies the total proportion of each stage through a partitioned cumulative method, matches it with the carbonization time proportion, extracts and summarizes the characteristic proportion items, and obtains the weight coefficient set of carbonization energy efficiency products.

[0022] As a further aspect of the present invention, the quality prediction and optimization module includes:

[0023] The mass flow direction identification submodule extracts biochar mass flow path data in nodes based on the set of weight coefficients of carbonized energy efficiency products, marks the mass transfer channels and flow velocity sections between nodes, calculates the flow difference between nodes, identifies the flow increase and decrease intervals, and extracts node category features to obtain the mass data sequence of adjacent time periods.

[0024] The trend offset judgment submodule extracts the quality improvement trend vector based on the quality data sequence of the adjacent time periods, establishes a quality change vector group by dividing the time window, calculates the ratio of the vector angle offset to the improvement rate, and compares it with the time period length parameter to obtain the trend offset direction matching degree.

[0025] The prediction trend analysis submodule extracts the quality trend curve and direction angle sequence based on the trend offset direction matching degree, integrates the direction change data through interval filtering, reorganizes and extends the quality trend vector in time order, and obtains the dynamic biochar quality evolution prediction sequence.

[0026] As a further aspect of the present invention, the dynamic calibration module includes:

[0027] The error interval extraction submodule, based on the dynamic biochar quality evolution prediction sequence, statistically analyzes the duration and switching density of error directions, identifies the percentage of absolute error values, analyzes the proportion of direction changes, determines error trend fluctuations, and obtains the error identification value of nodes within a time period.

[0028] The consistency ratio calculation submodule performs statistical analysis of the duration of the error direction interval and determination of the direction switching density based on the error identification value of the node in the time period. It records the duration and switching frequency of the error direction by dividing the time period, extracts the total amount of the absolute value of the error in the duration interval of the error direction, judges the error trend fluctuation, and obtains the error trend offset index.

[0029] The offset data prediction submodule calls the error trend offset index to find the range of error offset index changes. It identifies the distribution location of error value abrupt changes and direction reversals by continuous interval scanning, extracts the corrected segment and merges it with the original trend data point by point, reconstructs the updated carbonization trend curve, and obtains the dynamic calibration set of carbonization process error.

[0030] As a further aspect of the present invention, the duration of the error direction refers to the duration during which the statistical error direction remains consistent within at least five consecutive sampling points.

[0031] The percentage of absolute error in the identification error refers to the proportion of the number of sampling points whose absolute error exceeds the preset threshold of the average absolute error value of the dynamic biochar quality evolution prediction sequence to the total number of sampling points in the dynamic biochar quality evolution prediction sequence.

[0032] The aforementioned error value mutation refers to the situation where the rate of change of error values ​​at three consecutive sampling points exceeds a preset threshold for the rate of change of error values ​​in the dynamic biochar quality evolution prediction sequence, which is then determined as an error value mutation.

[0033] The term "direction reversal" refers to the determination of direction reversal when the sign of the error direction changes between two consecutive sampling points.

[0034] The point-by-point fusion refers to merging each error value of the corrected segment with the corresponding error value of the original trend data using an equal-weighted average.

[0035] As a further aspect of the present invention, the system also includes a secure storage management module:

[0036] Based on the dynamic calibration set of carbonization process error, the secure storage management module extracts the interval equipment operation field, carbonization trajectory field, and time stamp field, separates the fields according to the field type, encrypts the equipment operation field with strength and distributes it to multiple nodes, records the encrypted index path of the encrypted data corresponding to the node, and obtains the encrypted carbonization operation data partition index path.

[0037] The encrypted carbonization operation data partition index path includes the encrypted device operation field index path, the carbonization trajectory field storage location, and the time stamp field partition mapping.

[0038] As a further aspect of the present invention, the secure storage management module includes:

[0039] The field separation submodule identifies the equipment operation field, carbonization trajectory field, and time stamp field based on the dynamic calibration set of carbonization process error, and performs classification processing according to the field identifier and annotation features to generate field classification and splitting results.

[0040] The distributed encryption submodule extracts the sequence structure and field bitmap from the device operation field in the field classification and splitting results, performs strength encryption according to the mapping relationship, and distributes the encrypted data to multiple nodes, records the key path and node number of each encrypted field, and obtains the encryption node distribution data;

[0041] The path index module classifies and maps the node paths based on the key paths and node indices in the encrypted node distribution data, organizes the index structure of the carbonization trajectory field and the time stamp field, and obtains the encrypted carbonization operation data partition index path.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] In this invention, by accurately identifying and dividing the internal thermal field distribution of the carbonization equipment into multiple levels, dynamic analysis of the heating patterns of straw in different regions is achieved. This allows for a coordinated balance between the heat transfer rate and distribution density, significantly improving energy utilization and the uniformity of the carbonization reaction. An energy efficiency weighting system is established based on the distribution patterns of pyrolysis products and energy consumption data, enabling the carbonization process to balance energy efficiency optimization and product quality improvement. Through dynamic prediction and offset correction of quality change trends, the stability and consistency of biochar quality can be maintained sustainably. A real-time difference calibration mechanism allows for automatic correction of operational errors, ensuring that the carbonization process maintains optimal operating conditions over a long period. Combined with encrypted data storage paths, secure partitioning and traceability of operational data are achieved, comprehensively improving the intelligence level, energy efficiency control accuracy, carbonization uniformity, and data security and reliability of the carbonization system. Attached Figure Description

[0044] Figure 1 This is a system flowchart of the present invention;

[0045] Figure 2 This is a flowchart of the temperature distribution control module in this invention;

[0046] Figure 3 This is a flowchart of the carbonization process analysis module in this invention;

[0047] Figure 4 This is a flowchart of the quality prediction and optimization module in this invention;

[0048] Figure 5 This is a flowchart of the dynamic calibration module in this invention;

[0049] Figure 6 This is a flowchart of the secure storage management module in this invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0051] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0052] Please see Figure 1 An Internet of Things-based crop straw carbonization system includes:

[0053] The temperature distribution control module identifies the heating change pattern of straw in different areas based on the internal thermal field distribution characteristics of the carbonization equipment, divides the temperature zone layers, extracts and compares the heat transfer rate and temperature zone distribution density per unit time, determines the heat attribution relationship of the temperature zone, and obtains a multi-dimensional thermal field mapping model for straw carbonization.

[0054] The carbonization process analysis module calls the straw carbonization multidimensional thermal field mapping model to extract pyrolysis product data, carbonization time data and energy consumption data under each temperature zone. The pyrolysis product data and energy consumption data are distributed and sorted to obtain the carbonization energy efficiency product weight coefficient set.

[0055] The quality prediction and optimization module arranges the biochar quality flow direction in the nodes within a future time period according to the weight coefficient set of carbonization energy efficiency products, extracts the quality of adjacent time periods and judges the trend offset direction, and maps the offset direction to the duration to obtain the dynamic biochar quality evolution prediction sequence.

[0056] The dynamic calibration module is based on the dynamic biochar quality evolution prediction sequence. It extracts the range of difference between the predicted value and the real-time value within the time node, analyzes the consistency ratio of the difference range direction and the difference magnitude ratio, and judges the ratio of the two ratios to obtain the dynamic calibration set of carbonization process error.

[0057] The secure storage management module extracts the interval equipment operation field, carbonization trajectory field, and time stamp field based on the dynamic calibration set of carbonization process error. It separates the fields according to their types, encrypts the equipment operation field with strength, and distributes it to multiple nodes. It records the encrypted index path of the encrypted data corresponding to each node, thus obtaining the encrypted carbonization operation data partition index path.

[0058] The multidimensional thermal field mapping model for straw carbonization includes temperature zone density levels, heat distribution weights, and hierarchical label mapping relationships. The set of weight coefficients for carbonization energy efficiency products includes pyrolysis product weights, energy response weights, and carbonization frequency distribution weights. The dynamic biochar quality evolution prediction sequence includes quality improvement direction sequences, trend deviation direction intervals, and time-period change gradients. The dynamic calibration set for carbonization process errors includes prediction error adjustment parameters, trend consistency evaluation values, and dynamic correction ratios. The encrypted carbonization operation data partition index path includes encrypted equipment operation field index paths, carbonization trajectory field storage locations, and time stamp field partition mappings.

[0059] Please see Figure 2 The temperature distribution control module includes:

[0060] The thermal field data acquisition submodule is based on a network of thermal sensors arranged inside the carbonization equipment. It collects temperature data at different locations inside the equipment in real time and transmits it to the Internet of Things platform for processing to obtain a dataset of temperature data at different locations.

[0061] Based on the internal thermal sensor network of the carbonization equipment, three K-type thermocouple sensors are installed at radial distances of 0.1 meters, 0.3 meters, and 0.5 meters inside the straw carbonization reactor. A group of sensors is arranged every 0.5 meters along the length of the reactor, for a total of 18 sensors. The measurement range is 0℃ to 1200℃, with an accuracy of ±0.75%. The sensors collect the temperature of the monitoring points every second. For example, 10 minutes after the start of carbonization, the temperature at monitoring point A is 350.2℃, the temperature at monitoring point B is 345.8℃, and the temperature at monitoring point C is 362.1℃. At the same time, timestamp information is collected, such as 08:00:10 on October 23. The temperature data is transmitted to the IoT platform via the Modbus TCP protocol. The platform performs data format verification and preliminary noise reduction processing, such as marking and removing data points that are outside the physical measurement range, or using a moving average filtering process based on five consecutive sampling points. Finally, the processed temperature data is stored as a differentiated location temperature dataset, which includes monitoring point ID, timestamp, and temperature value fields, and is stored in JSON format.

[0062] The temperature zone division submodule calculates the heat transfer rate per unit time based on the differential location temperature dataset, and divides the thermal field by combining the temperature zone distribution density, generating a differential temperature zone region division dataset.

[0063] Based on a differential location temperature dataset, for example, at monitoring point A, the temperature rose from 350.2℃ to 355.7℃ 10 to 11 minutes after carbonization began. The thermal conductivity of the refractory bricks in the carbonization furnace wall is 0.8 W / (m·K), the average specific heat capacity of straw biomass is 1.5 kJ / (kg·K), and the bulk density is 200 kg / m³. Using the temperature gradient, the heat transfer rate in this area was calculated to be 500 J / s. The temperature zone distribution density was obtained by spatial interpolation and DBSCAN cluster analysis on the temperature dataset. The clustering algorithm was set with a minimum number of points of 5 and a maximum radius of 0.1 meters. Areas with similar and spatially continuous temperatures in the temperature field were divided into temperature zones, for example, 300℃ to 400℃ is temperature zone 1, and 400℃ to 500℃ is temperature zone 2. The temperature zone distribution density was quantified as a percentage of the reactor volume occupied. Temperature zone 1 occupies 35%, and temperature zone 2 occupies 25%. A density value below 15% is considered a low-density temperature zone, 15% to 40% is a medium-density temperature zone, and above 40% is a high-density temperature zone. Combining the heat transfer rate and temperature zone distribution density, for example, temperature zone 1 has a heat transfer rate of 500 J / s and a density of 35% (medium density), while temperature zone 2 has a heat transfer rate of 650 J / s and a density of 25% (medium density), rules are set to divide the thermal field. If the heat transfer rate of a temperature zone exceeds 600 J / s and the density is medium or high, it is judged as a high heat flux high-density temperature zone. If the heat transfer rate is below 300 J / s and the density is low, it is judged as a low heat flux low-density temperature zone. A differentiated temperature zone region division dataset is generated, which includes temperature zone ID, spatial coordinate range, average temperature, heat transfer rate, and distribution density information.

[0064] The heat attribution determination submodule is based on a differentiated temperature zone regional division dataset. By comparing the heat transfer rate and distribution density of temperature zones, it determines the heat attribution relationship of differentiated temperature zones and generates a multi-dimensional thermal field mapping model for straw carbonization.

[0065] Based on a dataset of differentiated temperature zones, the heat transfer rate and distribution density of each zone are compared to determine the heat transfer relationship between them. For example, zone A has a heat transfer rate of 500 J / s and a density of 35%, while zone B has a heat transfer rate of 650 J / s and a density of 25%. The data is input into a decision tree model. The model first determines whether the heat transfer rate of a zone exceeds a threshold of 600 J / s (20% of the maximum stable heat flux of the carbonization furnace). This threshold is calibrated using historical carbonization experimental data. When the heat transfer rate exceeds 600 J / s, the biochar yield decreases by more than 5%. If the heat transfer rate of zone A does not exceed 600 J / s... Zone B is classified as a "stable heat supply zone". If the heat transfer rate of zone B exceeds 600 J / s, but the density is 25% which does not reach high density (>40%), it is classified as a "local overheating risk zone". If the heat transfer rate of zone C is 480 J / s and the density is 45%, and it is spatially adjacent to zone A and has a similar heat transfer rate, it is determined to have a heat cooperative affiliation relationship, indicating that they jointly maintain the heat field. If the heat transfer rate of zone D is 100 J / s and the density is 10%, it is determined to be a "heat loss or insufficient zone". A multi-dimensional heat field mapping model of straw carbonization is generated. The model is stored in the form of a semantic network, which shows the spatial location, heat transfer rate, distribution density and heat affiliation relationship of each temperature zone.

[0066] Please see Figure 3 The carbonization process analysis module includes:

[0067] The temperature zone data extraction submodule calls the straw carbonization multidimensional thermal field mapping model to perform batch extraction of multi-temperature zone data. It divides the reaction range of the differentiated pyrolysis stage by the interval temperature threshold, reads the temperature change sequence and pyrolysis reaction within the stage, and synchronously collects the energy input and time series of the carbonization stage to generate pyrolysis product data and energy consumption data.

[0068] A multi-dimensional thermal field mapping model for straw carbonization is invoked. For example, the preheating zone of 200℃ to 300℃ in the initial stage of pyrolysis and the reaction zone of 350℃ to 550℃ in the rapid pyrolysis stage are extracted. The pyrolysis stages are divided by temperature thresholds: below 250℃ is defined as the drying and dehydration stage (200℃ to 250℃), 250℃ to 400℃ is the slow pyrolysis stage, 400℃ to 550℃ is the rapid pyrolysis stage, and above 550℃ is the carbonization maturation stage (550℃ to 650℃). The temperature change sequence within each stage is read according to the thresholds. For example, the temperature sequence of monitoring point A in the rapid pyrolysis stage is 405℃, 412℃, 420℃, 428℃, and 435℃. Simultaneously, the types of biomass pyrolysis reactions, such as cellulose, hemicellulose, and lignin decomposition reactions, the amount of gaseous products released per second, and the energy input during the carbonization stage are recorded, such as the real-time power of the electric heater (15kW), as well as the corresponding time series, such as 08:00:00 to 08:30:00, recorded once per second. By matching the timestamps, the pyrolysis product data, including the composition and yield of gaseous products, and energy consumption data, such as the electrical energy consumed per minute (0.25kWh), are integrated to generate pyrolysis product data and energy consumption data. The dataset includes timestamps, temperature sequences, pyrolysis reaction types, pyrolysis product quantities and compositions, and energy input information.

[0069] The feature ranking submodule performs comparative distribution statistics between data groups based on pyrolysis product data and energy consumption data. It segments the data by feature values ​​and uses the following formula:

[0070] ;

[0071] Calculate the change density value that distinguishes the differentiated energy range, extract the fluctuation frequency of each feature based on the distribution sequence and arrange them in order, retain the feature indexes before sorting, and obtain the high-frequency change feature value;

[0072] in, The density value representing the variation in energy ranges that distinguish different energy levels. The total number of data items. Representing the The feature values ​​of each data item Represents the average value of the eigenvalues. Representing the New parameters associated with each data item and its feature value. Representing the Each data item and The standard deviation of the association;

[0073] Based on pyrolysis product data and energy consumption data, for example, collecting biochar yield (e.g., 30%, 32%, 29% respectively) and specific energy consumption (e.g., 1.5 MJ / kg, 1.45 MJ / kg, 1.55 MJ / kg respectively) from different carbonization batches (e.g., batches 101, 102, 103), segmenting by characteristic values, for example, dividing biochar yield into three intervals: <30%, 30%-35%, and >35%, and specific energy consumption into three intervals: <1.4 MJ / kg, 1.4-1.6 MJ / kg, and >1.6 MJ / kg, for the formula... This represents the density value of variation that distinguishes different energy ranges, used to quantify the dispersion of different carbonization batches in key performance indicators, while also incorporating the significance of the features. This represents the total number of data items. For example, this statistic includes data from 5 carbonization batches, so... , Representing the The characteristic values ​​of each data item, such as biochar yield, The average value represents the characteristic value; for example, the average biochar yield of 5 batches is 31%. Representing the A new parameter associated with each data item and its feature value, for example, if If it's biochar yield, then... This could be the fixed carbon content of the biochar. The higher the fixed carbon content, the greater the weight should be given to the variability in biochar yield during the evaluation. Representing the Each data item and The standard deviation of the association, this parameter reflects volatility, when A lower value indicates a stable fixed carbon content. The value of the term will be large, making The fluctuations were amplified;

[0074] This formula calculates the absolute deviation of each data point from the mean and multiplies it by a weighting factor. The biases are weighted, and then the average of all weighted biases is calculated. This weighting method makes the correlation parameters more stable. When the eigenvalues ​​are stable and have high values, Even tiny fluctuations can produce large results. This allows for more sensitive capture of differentiated energy ranges that significantly impact key performance indicators, such as those for biochar yield data from five batches. (Unit: %), Average Assuming the corresponding fixed carbon content (Unit: %), and the standard deviation of fixed carbon content. (unit:%);

[0075] The calculation process is as follows:

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] The results indicate that, considering the fixed carbon content and its stability, the overall change density of biochar yield is 55.18. When the value exceeds a preset threshold of 40, it is determined that there is high-frequency fluctuation and further analysis is required. This threshold is determined through historical data analysis. When the frequency exceeds 40, it indicates significant instability in the carbonization process. The fluctuation frequency of each feature is extracted based on the distribution sequence. For example, Fourier transform is performed on the biochar yield data to analyze its amplitude at different frequencies, obtaining the period and intensity of yield fluctuations. The fluctuation frequency is quantified as the number of times the feature value crosses its average value or a specific threshold per unit time. For example, if the biochar yield fluctuates more than 3 times above or below the average value in 1 hour, its fluctuation frequency is 3 times / hour. By setting a fluctuation frequency threshold, such as 5 times / hour, when the fluctuation frequency exceeds this threshold, it is identified as a high-frequency fluctuation feature. Then, the identified high-frequency fluctuation features, such as biochar yield, fixed carbon content, and specific energy consumption, are arranged in order from high to low according to their fluctuation frequency, retaining the feature indicators before sorting. For example, if the fluctuation frequency of biochar yield is 8 times / hour and specific energy consumption is 6 times / hour, then biochar yield is ranked before specific energy consumption, obtaining the high-frequency fluctuation feature value. This feature value is a list of key performance indicators after sorting and screening.

[0083] Table 1: Key performance indicators for carbonized batches;

[0084]

[0085] Table 1 shows the biochar yield, fixed carbon content, and standard deviation data for five carbonization batches. The data were used to calculate the change density values ​​that distinguish the energy ranges.

[0086] The advantage of the formula lies in the introduction of correlation parameters. and its standard deviation For eigenvalues The fluctuations are weighted, which amplifies the characteristics with higher stability and numerical values ​​in biochar quality (such as fixed carbon content), and small changes in yield can be amplified. This allows for more accurate identification of energy range differences that significantly affect the quality of the final biochar product, improving the sensitivity and analytical depth of the carbonization process stability.

[0087] The weight calculation submodule extracts the cumulative values ​​of pyrolysis product data and energy consumption data based on high-frequency variation characteristic values. It identifies the total proportion of each stage through a partitioned cumulative method and matches it with the carbonization time proportion. It then extracts and summarizes the characteristic proportion items to obtain the set of weight coefficients for carbonization energy efficiency products.

[0088] Based on high-frequency fluctuation characteristics, such as biochar yield and specific energy consumption, cumulative values ​​of pyrolysis product data and energy consumption data are extracted. For example, in the drying stage (0-30 minutes), the cumulative moisture content of biochar products is 15%, and the cumulative energy consumption is 1.2 kWh. In the rapid pyrolysis stage (30-90 minutes), the cumulative fixed carbon content of biochar products is 60%, and the cumulative energy consumption is 8.5 kWh. The total proportion of each stage is identified by a partitioned accumulation method. For example, the carbonization process is divided into four stages: drying and dehydration, slow pyrolysis, rapid pyrolysis, and carbonization maturation. The total amount of biochar products and total energy consumption in each stage are accumulated, and the cumulative fixed carbon in each stage is calculated as a percentage of the total fixed carbon. For example, the rapid pyrolysis stage contributes 70% of the fixed carbon. The energy consumption in each stage accounts for 65% of the total energy consumption, and this is matched with the carbonization time ratio. For example, the entire carbonization process takes 180 minutes. The rapid pyrolysis stage lasted 60 minutes, accounting for 33.3% of the total time. Its contribution of fixed carbon was 70%, and its energy consumption was 65%. Dividing the fixed carbon contribution ratio (70%) of the rapid pyrolysis stage by the time ratio (33.3%) yielded a matching coefficient of 2.1. Feature ratio items, such as fixed carbon contribution ratio, volatile matter release ratio, and energy consumption ratio, were extracted and summarized, and weighted. For example, the fixed carbon contribution ratio had a weight of 0.4, and the energy consumption ratio had a weight of 0.3. Through weighted summation, a set of weighted coefficients for carbonization energy efficiency products was obtained. For example, the coefficient for the rapid pyrolysis stage was 0.4 × 2.1 + 0.3 × 1.95 = 1.425. This coefficient reflects the comprehensive impact of this stage on the final product quality and energy efficiency. The rationality of the weighted coefficient set was verified through 100 experiments. When the rapid pyrolysis stage coefficient reached 1.4 or higher, the fixed carbon content of the final biochar product increased by an average of 3%, and energy consumption decreased by 2%.

[0089] Please see Figure 4 The quality prediction and optimization module includes:

[0090] The mass flow direction identification submodule extracts biochar mass flow path data from nodes based on the weight coefficient set of carbonization energy efficiency products, marks the mass transport channels and velocity sections between nodes, and uses the following formula:

[0091] ;

[0092] Calculate the traffic difference between nodes, identify the traffic increase / decrease intervals, and extract node category features to obtain quality data sequences for adjacent time periods;

[0093] in, This represents the difference in traffic between nodes. The flow of node k. The flow of node j The carbonization energy efficiency weighting coefficient representing node k. The carbonization energy efficiency weighting coefficient represents node j;

[0094] Based on the weighting coefficient set of carbonization energy efficiency products, for example, the weighting coefficient for the rapid pyrolysis stage is 1.425, and the weighting coefficient for the carbonization maturation stage is 1.15, biochar mass flow path data in the nodes is extracted. For example, each physical monitoring point inside the carbonization furnace is considered a node, and a mass transfer channel is formed between adjacent monitoring points. The path of biochar precursors from the preheating zone (node ​​A), through the rapid pyrolysis zone (node ​​B), and finally to the carbonization maturation zone (node ​​C) is recorded. The mass transfer channels and flow rate segments between nodes are marked. For example, in the channel from node A to node B, biomass decomposes into char and gas, and the mass flow rate reaches 0.5 kg / min in the rapid pyrolysis stage, while in the channel from node B to node C, carbonization and carbon fixation mainly occur, and the mass flow rate decreases to 0.3 kg / min. For the formula... This represents the flow difference between nodes, used to quantify the differences in biochar mass flow between adjacent nodes. Weighting coefficients are used to weight this difference to reflect the impact of energy efficiency differences at different stages on mass flow direction. Representative node The flow rate, for example, the biochar mass flow rate through node B is 0.5 kg / min. Representative node The flow rate, for example, the biochar mass flow rate through node A is 0.7 kg / min. Representative node The carbonization energy efficiency weighting coefficient, for example, the weighting coefficient for node B is 1.425. Representative node The carbonization energy efficiency weighting coefficient, for example, the weighting coefficient of node A is 1.25;

[0095] This formula calculates the absolute difference in mass flow rate between adjacent nodes and multiplies it by a correction factor that reflects the difference in energy efficiency weights. It quantifies the changes in mass flow direction, where the correction factor makes nodes with higher energy efficiency ( The flow differences between the regions are amplified, thus highlighting the impact of mass flow changes in high-efficiency areas on the entire process;

[0096] For example, calculate the traffic difference from node A to node B:

[0097] kg / min;

[0098] The result indicates that the flow difference between node A and node B is 0.2135 kg / min. Therefore, this flow difference represents a decrease in flow. If the flow rate exceeds a set threshold of 0.1 kg / min, it indicates a significant mass conversion or loss between nodes. This threshold is set based on historical production data and material balance results. When the flow rate difference exceeds 0.1 kg / min, it means that more than 10% of the material has been converted into gas or tar, requiring attention. It is important to identify the flow rate increase / decrease intervals. For example, from node A to node B, if the mass flow rate decreases from 0.7 kg / min to 0.5 kg / min, this is considered a flow rate decrease interval, indicating a significant material conversion or decomposition in this region. From node B to node C, if the mass flow rate decreases from 0.5 kg / min... When the flow rate decreases from 0.7 kg / min to 0.3 kg / min, it is also identified as a flow rate reduction zone, and node category features are extracted. For example, node A is marked as the preheating zone, node B as the rapid pyrolysis reaction zone, and node C as the carbon fixation maturity zone. This yields a mass data sequence for adjacent time periods. For example, the time series data of the mass flow rate and its changes between nodes within a specific time period, such as [Time 1: (A→B flow rate 0.7 kg / min, B→C flow rate 0.5 kg / min), Time 2: (A→B flow rate 0.65 kg / min, B→C flow rate 0.45 kg / min)];

[0099] The advantage of the formula is that by combining the mass flow difference between nodes with the carbonization energy efficiency weighting coefficient of each node, it not only quantifies the physical mass loss or conversion, but also highlights the mass changes that occur in stages with higher or more important energy conversion efficiency. This allows the analysis of mass flow to more accurately target those links that have a key impact on the final biochar yield and quality, and enhances the pertinence of mass flow identification.

[0100] The trend offset judgment submodule extracts the quality improvement trend vector based on the quality data sequence of adjacent time periods, establishes a quality change vector group by dividing the time window, calculates the ratio of the vector angle offset to the improvement rate, and compares it with the time period length parameter to obtain the trend offset direction matching degree.

[0101] Based on the quality data sequence of adjacent time periods, such as [Time 1: (A→B flow rate 0.7 kg / min, B→C flow rate 0.5 kg / min), Time 2: (A→B flow rate 0.65 kg / min, B→C flow rate 0.45 kg / min), Time 3: (A→B flow rate 0.6 kg / min, B→C flow rate 0.4 kg / min)], a quality improvement trend vector is extracted. Regression analysis is then performed on the biochar yield curve to obtain a vector representing the slope and direction. ,in To increase the rate of productivity, To improve the directional angle, a mass change vector group is established by dividing the time window into 30-minute intervals. For example, within the first 30-minute window, the biochar yield increases from 30% to 31.5%, forming a vector. Within the second 30-minute window, the yield increased from 31.5% to 32.5%, forming a vector. Calculate the vector angle offset, for example, calculate... and Angle between ,like Approaching 0 degrees, the trend is consistent, if Approaching 180 degrees, the trend reverses; simultaneously, the rate of increase is calculated, for example, if... Rate 0.05% / min The rate is 0.03% / min, and the rate ratio is 0.6. When compared with the time period length parameter, if the angle deviation is less than 15 degrees and the rate ratio is greater than 0.8, and the time period length meets the standard, it is judged as a continuous and stable upward trend. If the angle deviation is greater than 60 degrees or the rate ratio is less than 0.5, it is judged as an unstable or downward trend. The trend deviation direction matching degree is obtained. For example, if the angle deviation is 10 degrees, the rate ratio is 0.9, and the matching degree score is 95%, it indicates that the trend is consistent.

[0102] The predictive trend analysis submodule extracts the quality trend curve and direction angle sequence based on the trend offset direction matching degree, integrates the direction change data through interval filtering, reorganizes and extends the quality trend vector in time order, and obtains the dynamic biochar quality evolution prediction sequence.

[0103] Based on the trend offset direction matching degree, for example, a matching degree score of 95%, the quality trend curve and direction angle sequence are extracted. The curve of biochar quality changing over time is extracted from historical data, and the direction of quality change at each time point is recorded. The direction change data is integrated by interval filtering, and time intervals with a matching degree higher than 80% are selected. The quality trend curve segments within the interval are integrated, fluctuations are removed, and the quality trend vector is reorganized and extended in chronological order. The integrated trend curve segments are spliced ​​together by time. After the current time point, based on the 95% matching degree, the average increase rate of the two most recent time windows, for example, 0.04% / min, is used as the prediction extension rate to extrapolate the quality trend for the next 30 minutes, forming a continuous quality change prediction sequence. This results in a dynamic biochar quality evolution prediction sequence, which includes the predicted value and confidence interval of biochar quality over a period of time. For example, the fixed carbon content of biochar will increase from 80% to 81.2% ± 0.5% in the next 30 minutes.

[0104] Please see Figure 5 The dynamic calibration module includes:

[0105] The error interval extraction submodule is based on the dynamic biochar quality evolution prediction sequence. It statistically analyzes the duration and switching density of error direction, identifies the percentage of absolute error value, analyzes the proportion of direction change, determines the error trend fluctuation, and obtains the error identification value of the node in the time period.

[0106] The duration of error direction refers to the duration during which the statistical error direction remains consistent across at least five consecutive sampling points.

[0107] The percentage of absolute error in the identification error refers to the proportion of the number of sampling points whose absolute error exceeds the preset threshold of the average absolute error value of the dynamic biochar quality evolution prediction sequence to the total number of sampling points in the dynamic biochar quality evolution prediction sequence.

[0108] Based on the dynamic biochar quality evolution prediction sequence, for example, if the prediction sequence shows that the fixed carbon content of biochar will increase from 80% to 81.2% in the next 30 minutes, and the actual monitoring data shows an increase from 80% to 80.8%, the duration of the error direction is statistically analyzed. If the actual value is lower than the predicted value for 6 consecutive sampling points, the error direction is considered negative, and the duration is 6 sampling points. At least 5 consecutive sampling points must maintain the same direction to be counted as the duration. Simultaneously, the direction switching density is also statistically analyzed; for example, if the error direction switches twice within 30 minutes, the percentage of absolute error is identified. The average absolute error of the dynamic biochar quality evolution prediction sequence is calculated. If the average absolute error is 0.2%, then sampling points with an absolute error exceeding 0.2% × 15% = 0.03% are considered to have an error. The quantity is analyzed, for example, out of 100 sampling points, 25 sampling points have an absolute error value exceeding 0.03%, accounting for 25%. The proportion of change in the direction of error is analyzed, for example, in the predicted sequence, sampling points with positive error direction account for 55%, and sampling points with negative error direction account for 45%. The error trend fluctuation is judged. If the duration of the error direction exceeds 10 sampling points and the percentage of the absolute error value is less than 10%, it is judged as "stable small fluctuation". If the duration of the error direction is less than 5 sampling points and the direction switching density is greater than 5 times / hour, and the percentage of the absolute error value is greater than 30%, it is judged as "unstable fluctuation". The error identification value of the node in the time period is obtained, for example, 0.75, indicating the error fluctuation level in the current time period.

[0109] The consistency ratio calculation submodule performs statistical analysis of the duration of error direction intervals and determination of direction switching density based on the error identification value of nodes within a time period. It records the duration and switching frequency of error direction by dividing the time period, extracts the total amount of absolute error within the duration interval of error direction, judges the error trend fluctuation, and obtains the error trend offset index.

[0110] Based on the error identification value of the node within a time period, for example, an error identification value of 0.75, the duration of the error direction interval is statistically analyzed. For example, in the past hour, the longest duration of a positive error direction was 15 minutes, and the longest duration of a negative error direction was 20 minutes. The direction switching density is also measured, for example, if the error direction switches 4 times in 1 hour. The duration of error direction maintenance is recorded by dividing the hour into four 15-minute intervals, recording the duration of error direction maintenance within each 15-minute interval, as well as the switching frequency, for example, switching once in the first 15 minutes. The absolute error within the duration interval of the error direction is then extracted. The total value is used to determine the error trend fluctuation. For example, if the cumulative absolute value of the error is 2.5% (in units of fixed carbon content) within a 10-minute interval with a continuous negative error, the error trend fluctuation is judged as "stable and controllable". If the duration of the error direction is more than 20 minutes and the direction switching density is less than 2 times / hour, and the total absolute value of the error is less than 5%, the error trend fluctuation is judged as "stable and controllable". If the duration of the error direction is less than 10 minutes and the direction switching density is more than 5 times / hour, and the total absolute value of the error is greater than 10%, it is judged as "fluctuating". The error trend deviation index is obtained, for example, 0.68, indicating that the error trend is in a medium fluctuation state.

[0111] The offset data prediction submodule calls the error trend offset index to find the range of error offset index changes. Through continuous interval scanning, it identifies the distribution location of error value abrupt change and direction reversal, extracts the corrected segment and merges it with the original trend data point by point, reconstructs the updated carbonization trend curve, and obtains the dynamic calibration set of carbonization process error.

[0112] The identification of error value mutations refers to the situation where the rate of change of error values ​​at three consecutive sampling points exceeds a preset threshold for the rate of change of error values ​​in the dynamic biochar quality evolution prediction sequence, which is then judged as an error value mutation.

[0113] Direction reversal refers to the situation where the sign of the error direction changes between two consecutive sampling points.

[0114] Point-by-point fusion refers to merging each error value of the corrected segment with the corresponding error value of the original trend data using an equal-weighted average.

[0115] The error trend offset index (e.g., 0.68) is invoked to search for its variation range. By analyzing historical error trend offset index sequences, the range where the index value increases from 0.5 to 0.8 is identified. A continuous interval scanning method, such as a sliding window scanning method with 5 sampling points, is used to scan the error value sequence point by point to identify abrupt error changes. For example, if the rate of change of error value for three consecutive sampling points (e.g., from 0.05% to 0.2% and then to 0.5%) exceeds 20% (i.e., 0.2%) of the maximum rate of change of error value (e.g., 1%) in the dynamic biochar quality evolution prediction sequence, it is determined to be an abrupt error change. Simultaneously, the distribution location of direction reversal is identified; for example, when the error direction changes from +0.1 to -0.2, it is determined to be a direction reversal. The corrected segment is extracted, and the error value abrupt change is identified. After the change and direction reversal intervals, the information is used to correct the prediction curve of this segment through local regression and Kalman filtering. For example, the predicted value is lowered from 81.2% to 80.9%, and it is fused point by point with the original trend data. For example, each error value of the corrected segment (e.g., -0.3%) is fused with the corresponding error value of the original trend data (e.g., -0.1%) using equal weights. After fusion, the error value is -0.2%. The updated carbonization trend curve is reconstructed, and the fused error value is reapplied to the original prediction sequence to obtain a carbonization trend curve that is closer to reality. For example, the updated fixed carbon content prediction curve shows that it will increase from 80% to 80.9% ± 0.2% in the next 30 minutes. A dynamic calibration set of carbonization process error is obtained, which contains the dynamically calibrated biochar quality evolution prediction sequence.

[0116] Please see Figure 6 The secure storage management module includes:

[0117] The field separation submodule identifies equipment operation fields, carbonization trajectory fields, and time stamp fields based on the dynamic calibration set of carbonization process errors. It then performs classification processing based on field identifiers and annotation features to generate field classification and splitting results.

[0118] Based on the dynamic calibration set of carbonization process errors, such as the predicted sequence of fixed carbon content in biochar after calibration, the system identifies equipment operation fields, carbonization trajectory fields, and time stamp fields. For example, heating power, feed rate, and furnace pressure are equipment operation fields; temperature curve, gas product release rate, and solid product residence time are carbonization trajectory fields; and data acquisition timestamp, carbonization stage start time, and calibration occurrence time are time stamp fields. Classification is performed based on field identifiers and annotation characteristics. Field names are matched using predefined regular expressions; for example, fields starting with "power_" are identified as power data. The system analyzes the distribution characteristics of field values ​​using a machine learning model; for example, the furnace pressure field ranges from 0.1 MPa to 0.5 MPa to confirm its field type. For timestamp fields, their date and time formats are identified and marked as time stamp fields. The system generates field classification and splitting results, resulting in a structured data table listing the category of each original data field.

[0119] The distributed encryption submodule extracts the sequence structure and field bitmap from the device operation field in the field classification and splitting results, performs strength encryption according to the mapping relationship, and distributes the encrypted data to multiple nodes, records the key path and node number of each encrypted field, and obtains the encryption node distribution data;

[0120] Based on the equipment operation fields in the field classification and segmentation results, such as heating power and feeding rate, the sequence structure is extracted. For example, heating power is continuous time series data, while feeding rate is discrete event-triggered data. Bitmaps are generated for each equipment operation field, indicating its start position and length in the original data packet. Encryption strength is applied based on the mapping relationship, with different encryption algorithms set. AES-256 symmetric encryption (256-bit key length) is used for heating power data, and RSA asymmetric encryption (2048-bit key length) is used for feeding rate data. The key lengths pass security assessments and meet FIPS 140-2 standards. To accurately determine the distribution of encrypted data across multiple nodes, for example, the encrypted heating power data can be divided into 10 segments and stored on 5 different storage server nodes. Each node stores 2 segments and records the key path for each encrypted field. For example, the key for the first segment of the heating power data is stored in the key management system under the path / key_vault / power / segment1, along with the node number, such as the segment stored on the server with node ID "node_001". The encrypted node distribution data is obtained, and the data records in detail the storage location, key path, and corresponding node ID of each encrypted data segment.

[0121] The path index module classifies and maps the node paths based on the key paths and node indices in the encrypted node distribution data, organizes the index structure of the carbonization trajectory field and the time stamp field, and obtains the encrypted carbonization operation data partition index path.

[0122] Based on the key path and node index in the encrypted node distribution data, for example, if the key path is / key_vault / power / segment1 and the node index is node_001, the node paths are categorized. For instance, all encrypted data segment node paths related to heating power are categorized under the "Equipment Operating Parameters - Power" category. A mapping table is then established to map the logical data item "Heating Power_Segment1" to the physical storage path "node_001: / data / power / segment1.enc". Carbonization trajectory fields, such as biochar yield sequence and gas product component sequence, are then organized, along with time stamp fields, such as precision... A B+ tree index is constructed for the biochar yield sequence using timestamps accurate to milliseconds, enabling fast lookups by time range. An inverted index is constructed for the gaseous product components, facilitating quick retrieval of data containing specific components. This yields the encrypted carbonization operation data partition index path, which is an indexing system that allows for rapid location and access to specific carbonization operation data scattered across multiple encrypted storage nodes. For example, by querying "Time range: 10-23 08:00:00 to 09:00:00, Equipment operation field: Heating power, Carbonization trajectory field: Biochar yield," all encrypted data fragments of heating power and biochar yield within the corresponding time period, along with their decryption key paths and storage node information, can be obtained.

[0123] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A crop straw carbonization system based on the Internet of Things, characterized in that, The system includes: The temperature distribution control module identifies the heating change pattern of straw in different regions based on the internal thermal field distribution characteristics of the carbonization equipment, divides the temperature zone hierarchy, determines the heat attribution relationship of the temperature zone, and obtains a multi-dimensional thermal field mapping model for straw carbonization. The temperature distribution control module includes: The thermal field data acquisition submodule is based on a network of thermal sensors arranged inside the carbonization equipment. It collects temperature data at different locations inside the equipment in real time and transmits it to the Internet of Things platform for processing to obtain a dataset of temperature data at different locations. The temperature zone division submodule calculates the heat transfer rate per unit time based on the differentiated location temperature dataset, and divides the thermal field by combining the temperature zone distribution density to generate a differentiated temperature zone region division dataset. The heat attribution determination submodule, based on the differentiated temperature zone region division dataset, determines the heat attribution relationship of the differentiated temperature zones by comparing the heat transfer rate and distribution density of the temperature zones, and generates a multi-dimensional thermal field mapping model for straw carbonization. The carbonization process analysis module calls the straw carbonization multidimensional thermal field mapping model to extract pyrolysis product data, carbonization time data and energy consumption data under each temperature zone, and sorts the pyrolysis product data and energy consumption data to obtain the carbonization energy efficiency product weight coefficient set. The carbonization process analysis module includes: The temperature zone data extraction submodule calls the straw carbonization multidimensional thermal field mapping model to perform batch extraction of multi-temperature zone data. It divides the reaction range of the differentiated pyrolysis stage by the interval temperature threshold, reads the temperature change sequence and pyrolysis reaction within the stage, and synchronously collects the energy input and time series of the carbonization stage to generate pyrolysis product data and energy consumption data. The feature sorting submodule performs comparative distribution statistics between data groups based on the pyrolysis product data and energy consumption data, segmenting by feature values ​​and using the following formula: ； Calculate the change density value that distinguishes the differentiated energy range, extract the fluctuation frequency of each feature based on the distribution sequence and arrange them in order, retain the feature indexes before sorting, and obtain the high-frequency change feature value; in, The density value representing the variation in energy ranges that distinguish different energy levels. The total number of data items. Representing the The feature values ​​of each data item Represents the average value of the eigenvalues. Representing the New parameters associated with each data item and its feature value. Representing the Each data item and The standard deviation of the association; The weight calculation submodule extracts the cumulative value of pyrolysis product data and energy consumption data based on the high-frequency variation characteristic value, identifies the total proportion of each stage through partitioned accumulation and matches it with the carbonization time proportion, extracts and summarizes the characteristic proportion items, and obtains the set of weight coefficients for carbonization energy efficiency products. The quality prediction and optimization module arranges the biochar quality flow direction in the nodes within a future time period according to the set of weight coefficients of carbonization energy efficiency products, extracts the quality of adjacent time periods and judges the trend offset direction, and maps the offset direction to the duration to obtain the dynamic biochar quality evolution prediction sequence. The quality prediction and optimization module includes: The mass flow direction identification submodule extracts biochar mass flow path data in nodes based on the set of weight coefficients of carbonized energy efficiency products, marks the mass transfer channels and flow velocity sections between nodes, calculates the flow difference between nodes, identifies the flow increase and decrease intervals, and extracts node category features to obtain the mass data sequence of adjacent time periods. The trend offset judgment submodule extracts the quality improvement trend vector based on the quality data sequence of the adjacent time periods, establishes a quality change vector group by dividing the time window, calculates the ratio of the vector angle offset to the improvement rate, and compares it with the time period length parameter to obtain the trend offset direction matching degree. The prediction trend analysis submodule extracts the quality trend curve and direction angle sequence based on the trend offset direction matching degree, integrates the direction change data through interval filtering, reorganizes and extends the quality trend vector in time order, and obtains the dynamic biochar quality evolution prediction sequence. Based on the dynamic biochar quality evolution prediction sequence, the dynamic calibration module extracts the range of difference between the predicted value and the real-time value within the time node, analyzes the consistency ratio of the difference range direction and the difference magnitude ratio, and judges the ratio of the two ratios to obtain the dynamic calibration set of carbonization process error.

2. The IoT-based crop straw carbonization system according to claim 1, characterized in that, The multidimensional thermal field mapping model for straw carbonization includes temperature zone density levels, heat distribution weights, and hierarchical label mapping relationships. The set of weight coefficients for carbonization energy efficiency products includes pyrolysis product weights, energy response weights, and carbonization frequency distribution weights. The dynamic biochar quality evolution prediction sequence includes a quality improvement direction sequence, a trend deviation direction interval, and a time-period change gradient. The dynamic calibration set for carbonization process errors includes prediction error adjustment parameters, trend consistency evaluation values, and dynamic correction ratios.

3. The crop straw carbonization system based on the Internet of Things according to claim 1, characterized in that, The dynamic calibration module includes: The error interval extraction submodule, based on the dynamic biochar quality evolution prediction sequence, statistically analyzes the duration and switching density of error directions, identifies the percentage of absolute error values, analyzes the proportion of direction changes, determines error trend fluctuations, and obtains the error identification value of nodes within a time period. The consistency ratio calculation submodule performs statistical analysis of the duration of the error direction interval and determination of the direction switching density based on the error identification value of the node in the time period. It records the duration and switching frequency of the error direction by dividing the time period, extracts the total amount of the absolute value of the error in the duration interval of the error direction, judges the error trend fluctuation, and obtains the error trend offset index. The offset data prediction submodule calls the error trend offset index to find the range of error offset index changes. It identifies the distribution location of error value abrupt change and direction reversal by continuous interval scanning, extracts the corrected segment and merges it with the original trend data point by point, reconstructs the updated carbonization trend curve, and obtains the dynamic calibration set of carbonization process error. The duration of the error direction refers to the duration during which the statistical error direction remains consistent within at least five consecutive sampling points. The percentage of absolute error in the identification error refers to the proportion of the number of sampling points whose absolute error exceeds the preset threshold of the average absolute error value of the dynamic biochar quality evolution prediction sequence to the total number of sampling points in the dynamic biochar quality evolution prediction sequence. The aforementioned error value mutation refers to the situation where the rate of change of error values ​​at three consecutive sampling points exceeds a preset threshold for the rate of change of error values ​​in the dynamic biochar quality evolution prediction sequence, which is then determined as an error value mutation. The term "direction reversal" refers to the determination of direction reversal when the sign of the error direction changes between two consecutive sampling points. The point-by-point fusion refers to merging each error value of the corrected segment with the corresponding error value of the original trend data using an equal-weighted average.

4. The IoT-based crop straw carbonization system according to claim 1, characterized in that, The system also includes a secure storage management module: Based on the dynamic calibration set of carbonization process error, the secure storage management module extracts the interval equipment operation field, carbonization trajectory field, and time stamp field, separates the fields according to the field type, encrypts the equipment operation field with strength and distributes it to multiple nodes, records the encrypted index path of the encrypted data corresponding to the node, and obtains the encrypted carbonization operation data partition index path. The encrypted carbonization operation data partition index path includes the encrypted device operation field index path, the carbonization trajectory field storage location, and the time stamp field partition mapping.

5. The IoT-based crop straw carbonization system according to claim 4, characterized in that, The secure storage management module includes: The field separation submodule identifies the equipment operation field, carbonization trajectory field, and time stamp field based on the dynamic calibration set of carbonization process error, and performs classification processing according to the field identifier and annotation features to generate field classification and splitting results. The distributed encryption submodule extracts the sequence structure and field bitmap from the device operation field in the field classification and splitting results, performs strength encryption according to the mapping relationship, and distributes the encrypted data to multiple nodes, records the key path and node number of each encrypted field, and obtains the encryption node distribution data; The path index module classifies and maps the node paths based on the key paths and node indices in the encrypted node distribution data, organizes the index structure of the carbonization trajectory field and the time stamp field, and obtains the encrypted carbonization operation data partition index path.

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