Early warning method and system for excessive catalysis of trace elements in biochar preparation

Abstract

The present application belongs to the field of time series analysis and dynamic early warning technology, and provides a method and system for early warning of excessive catalysis of trace elements in the preparation of biochar, specifically: first, preset a data acquisition unit in the fixation channel, and record the collection position corresponding to the data acquisition unit as the fixation monitoring site; monitor the surface conductance value and the medium-temperature thermal history value in real time from each fixation monitoring site; then, evaluate the crystallization residence matching according to the surface conductance value and the medium-temperature thermal history value, and obtain the residual risk degree of each fixation monitoring site; finally, perform early warning of excessive catalysis according to the residual risk degree. The evaluation of crystallization residence matching through time series analysis can effectively quantify the residual risk of high-energy state surface free metal centers caused by the mismatch between the crystallization enrichment degree in the fixation step of the biochar preparation process and the residence in the medium-temperature zone in the pyrolysis process, thereby improving the consistency of the carbon sequestration capacity of the biochar product and the product qualification rate of the production process.

Description

Technical Field

[0001] This invention belongs to the field of time series analysis and dynamic early warning technology, specifically relating to an early warning method and system for excessive catalysis of trace elements in biochar preparation. Background Technology

[0002] In soil improvement and carbon sequestration applications, micronutrient-modified biochar is often used to enhance the retention capacity of DOM-soluble organic matter. The mechanism lies in the fact that the modified biochar can construct a more stable organic carbon pool in the rhizosphere through cation bridges formed by transition metals. However, in field applications, a reverse carbon sequestration problem often occurs, manifesting as anomalies between batches of the same biochar product. Biochar with the same nominal value and meeting micronutrient loading standards not only exhibits unstable carbon dioxide release in the early stages of soil application but also induces a sudden increase, usually accompanied by a rapid decline in rhizosphere DOC and a significant short-term net loss of soil organic matter. This anomalous result leads to a lack of stability in the carbon sequestration effect of biochar products, becoming a key engineering challenge in the promotion of micronutrient-modified biochar. The reason for the carbon fixation backflow problem lies in the fact that during the drying and fixation step after the trace element impregnation and loading in the preparation process, the transition metal precursor that has been pyrolyzed and solidified is in an ionic state and has the characteristic of easy surface enrichment. Therefore, when it enters the pores of biochar, if the subsequent pyrolysis process fails to fully embed it into the carbon skeleton, it is very easy to retain it as a surface free metal center in a high-energy state. In turn, such metal centers can form cyclic Fe³⁺ / Fe²⁺ redox pairs. After being applied in the field, under the conditions of soil water film and oxidant, Fenton-like catalysis occurs, which accelerates the oxidative decomposition of the adsorbed and enriched hydrophilic DOM, so that the easily available carbon source is directly mineralized into CO2 instead of being converted into stable microbial residual carbon, thereby inducing the aforementioned carbon fixation backflow problem.

[0003] Existing technologies typically control product quality by testing the total trace element content or leaching release of modified biochar to improve the stability of carbon fixation. However, these tests tend to focus on static compliance, failing to distinguish between embedded / passivated and free catalytic states, and do not adequately address early mineralization risks in applications. Furthermore, to cover batch fluctuations, increased sampling frequency or field verification is often necessary, leading to higher costs for scrapped batches and reprocessing. Moreover, because the testing indicators are decoupled from catalytic activity thresholds, misjudgments and missed screenings of substandard batches remain common. Therefore, a method and system for early warning of excessive trace element catalysis in biochar preparation is urgently needed. This system should be able to promptly guide process parameter adjustments when abnormal catalytic signs due to insufficient fixation are detected, suppressing backflow during carbon fixation at the source and improving batch consistency and controllability of modified biochar carbon fixation. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for early warning of excessive catalysis of trace elements in biochar preparation, so as to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for early warning of excessive catalysis of trace elements in biochar preparation is provided, the method comprising the following steps: S100, a data acquisition unit is preset in the fixation channel, and the acquisition position corresponding to the data acquisition unit is recorded as the fixation monitoring point; S200 monitors surface conductivity and intermediate temperature thermal history in real time from each fixed monitoring point; S300, based on the surface electrical conductivity and the amount of thermal history at medium temperature, performs a crystallization residence matching assessment to obtain the residual risk level of each fixation monitoring site; S400 provides over-catalysis warnings based on residual risk levels.

[0006] Further, in step S100, the method for pre-setting data acquisition units in the fixation channel and recording the acquisition positions corresponding to the data acquisition units as fixation monitoring points is as follows: divide the fixation channel into several fixation monitoring sections according to the material flow direction, and arrange several data acquisition units horizontally in each section; configure a unique identifier for each data acquisition unit, and correspond its position number in the fixation monitoring section with the unique identifier, and define the spatial position of the data acquisition unit in the fixation channel as the fixation monitoring point.

[0007] The fixation monitoring site refers to a specific spatial location within the fixation channel, corresponding to the monitoring position of each data acquisition unit during the biochar preparation process. Each data acquisition unit is arranged within the fixation channel to monitor and record relevant physical information of the biochar during the impregnation and drying stages and the pyrolysis-intermediate solidification stages.

[0008] The fixation channel is the material flow area in the biochar preparation process, including the post-impregnation drying section and the pyrolysis medium-temperature solidification section. Based on the material flow direction within the fixation channel, it is divided into several fixation monitoring sections, each of which can be divided laterally or longitudinally as needed. Multiple data acquisition units are arranged within each fixation monitoring section in a parallel manner, enabling each unit to independently collect relevant material data and simultaneously perform acquisition tasks with other data acquisition units.

[0009] The data acquisition unit is a device used in the biochar preparation process to collect information in real time. Each data acquisition unit contains at least one conductivity sensor and one temperature sensor. Each data acquisition unit needs to be configured with a unique identifier to distinguish different data acquisition units. The position of each data acquisition unit in the fixation channel is determined by two parameters: 1) Fixation monitoring section number: Each monitoring section has a unique number to indicate the section location of the data acquisition unit. 2) Spatial position number: Within each fixation monitoring section, the position of the data acquisition unit is determined by a pre-planned horizontal or vertical numbering system.

[0010] Each data acquisition unit's acquisition location is numbered and defined as a corresponding fixation monitoring site. The fixation monitoring site is the core unit for monitoring and evaluating the biochar fixation process. In actual production, the generation of fixation monitoring sites relies on the equipment reading the unique identifier of each data acquisition unit, combined with predefined cross-sectional and spatial location numbers, to determine the data acquisition unit at each location in real time on the production line. This method allows for real-time tracking of every critical location in the biochar preparation process, ensuring the integrity and accuracy of the monitoring data.

[0011] The layout of multiple fixation monitoring sites is crucial in this method. By arranging data acquisition units at multiple key locations in the fixation channels, the surface conductivity and temperature changes of each region can be monitored in real time, accurately capturing the spatial differences in crystallization enrichment and meso-temperature residence during the impregnation, drying, and pyrolysis processes of biochar. Because metal ions on the biochar surface migrate and crystallize along the pores during drying, if these metal ions fail to transform into stable coordination states through sufficient meso-temperature residence during pyrolysis, they will form high-energy free metal centers. These metal centers act as Fenton-like catalytic sites in the soil, which are key to identifying over-catalysis and carbon fixation backflow phenomena.

[0012] Further, in step S200, the method for real-time monitoring of surface conductivity and intermediate temperature thermal history from each fixation monitoring point is as follows: The fixation monitoring point includes a conductivity sensor and a temperature sensor. The surface conductivity value is the conductivity value collected by the conductivity sensor, and the thermal history is the temperature integral value of the intermediate temperature range collected by the temperature sensor. A measuring point is constructed every 30-120 seconds, and the surface conductivity value and intermediate temperature thermal history are obtained once for each measuring point.

[0013] The conductivity sensor is used to measure the surface conductivity of biochar in real time during the fixation process, reflecting changes in its electrical conductivity. Changes in conductivity can indirectly reflect the migration and crystallization enrichment of surface metal ions, especially during the drying stage when metal ions migrate and crystallize on the surface. This indicates whether there is excessive metal enrichment on the biochar surface, which could affect subsequent carbon fixation. The mesothermal thermal history is calculated from temperature data collected by a temperature sensor. Specifically, it is defined as the integral or weighted average value of the temperature within a preset mesothermal range of 350°C to 550°C. The temperature integral value reflects the residence time and heat distribution in this region, effectively characterizing whether the biochar obtains sufficient residence time during pyrolysis to complete the intercalation and passivation of metal ions. This is an important reference for identifying the occurrence of over-catalysis.

[0014] To more accurately reflect the impact of temperature on the biochar fixation process over different time periods, higher temperatures are assigned greater weight. This is because higher temperatures have a greater influence on the stabilization and intercalation of metal ions during pyrolysis. This weighting method amplifies the influence of higher temperature stages, thus more accurately assessing the effectiveness of temperature and its contribution to the fixation process during that period. Therefore, the intermediate-temperature thermal history is calculated by short-time integration of temperature data and processed using an incremental temperature weighting method. Specifically, within each sampling period (i.e., between two consecutive measuring points), the temperature values ​​collected by the temperature sensor are calculated as a weighted average. The weighting method is as follows: α is used as the intermediate variable for weighting, with 350-400°C weighted as α, 400-450°C as 2α, 450-500°C as 3α, and 500-550°C as 4α, ensuring that the sum of the weights in the weighting calculation is 1.

[0015] Further, in step S300, the method for obtaining the residual risk level of each fixation monitoring site by evaluating the crystallization residence matching based on the surface conductivity value and the intermediate temperature thermal history is as follows: Let a time period be defined as the monitoring period Tyd, where Tyd∈[0.5,2] hours; During the monitoring period, for any fixed monitoring site, the average surface conductivity value Suoe and the average thermal history value Thoe are obtained. The absolute value of the difference between the surface conductivity value and Suoe for each measuring point is calculated and recorded as the surface conductivity disturbance, and the absolute value of the difference between the thermal history value and Thoe for each measuring point is recorded as the thermal disturbance. The binary pair formed by the surface conductivity value and the thermal history value is recorded as the risk critical variable group. The surface conductivity disturbance and the thermal disturbance during the monitoring period are normalized and summed on a per-measuring-point basis. The measuring point with the maximum summation value is used as the residual baseline point. The cosine similarity between each risk critical variable group and the risk critical variable group corresponding to the residual baseline point is calculated and recorded as the critical variable anomaly. If any critical variable anomaly is less than the lower quartile of all critical variable anomalies in its corresponding fixed monitoring site, the measuring point is marked as a non-residual assessment point; otherwise, it is marked as a residual assessment point. For any residual assessment point, the sub-pre-risk value RLe is calculated. The risk threshold variable arrays within the monitoring period are normalized. If there is a residual benchmark point under the measurement point corresponding to the residual assessment point, the Manhattan distance between the current residual assessment point and the risk threshold variable arrays corresponding to the residual benchmark point is recorded as the sub-pre-risk value of the residual assessment point. If there are multiple residual benchmark points under the measurement point corresponding to the residual assessment point, the maximum Manhattan distance between the current residual assessment point and the risk threshold variable arrays corresponding to the multiple residual benchmark points is selected as the sub-pre-risk value of the residual assessment point. If there is no residual benchmark point under the measurement point corresponding to the residual assessment point, the sub-pre-risk value of the residual assessment point is 0.

[0016] The sub-risk value measures the difference between the current measurement point and the residual baseline point by calculating the Manhattan distance of the risk threshold array between the current residual assessment point and the residual baseline point, thus providing an important basis for early warning of excessive trace element catalysis. The risk threshold array reflects the deviation changes in surface conductivity and thermal history during biochar fixation, capturing anomalies in the fixation process caused by metal ion migration or temperature unevenness. Changes in surface conductivity and temperature directly reflect whether metal ions are excessively enriched or whether the temperature is insufficient. From a microscopic perspective, these changes fit the state of whether trace elements have formed unstable free metal centers, thus affecting the stability of carbon fixation. By normalizing these quantified data, biases caused by different dimensions or ranges are eliminated, ensuring that the calculation process is not affected by the scale of the original data.

[0017] When comparing risk thresholds between different measurement points, the Manhattan distance measures the sum of absolute differences across all dimensions between the two points. In this method, the Manhattan distance reflects the change in reaction conditions at the current measurement point compared to the baseline. A smaller sub-pre-risk value at the current measurement point indicates a smaller difference between the current point and the baseline, suggesting that the reaction conditions at that point have not changed significantly and are in a stable state, indicating a low residual risk. Conversely, a larger sub-pre-risk value means that the reaction conditions at that measurement point may have changed abnormally during biochar preparation, potentially leading to over-catalysis, where trace elements fail to stably embed in the carbon framework and remain in a high-energy free metal form, thus causing carbon fixation backflow problems.

[0018] Calculate the residual risk level Resk based on the sub-pre-risk value and the residual baseline: ; Where i1 is the cumulative variable, ln is the logarithmic function with the constant e as the base, Retoal and Fetoal are the number of residual assessment points and non-residual assessment points in the current measurement point, respectively, and RLe(i1) is the sub-pre-risk value of the i1th residual assessment point in the current measurement point.

[0019] Since the calculation of residual risk requires processing the sub-pre-risk values ​​corresponding to the measurement points, it can effectively quantify the residual risk of high-energy surface free metal centers caused by the mismatch between the degree of crystal enrichment and the temperature residence during pyrolysis in the fixation step. However, the acquisition of sub-pre-risk values ​​is too dependent on the residual benchmark point, which leads to excessive sensitivity to input noise and causes the results to deviate from the true residual risk. This can easily lead to misjudgment and missed screening of inferior batches, causing decision-making bias, especially during periods when residual assessment points are densely or unevenly distributed. However, the existing technology cannot effectively compensate for this bias. To eliminate this influence, the present invention proposes a better solution as follows: Preferably, in step S300, the method for obtaining the residual risk level of each fixation monitoring site by evaluating the crystallization residence matching based on the surface conductivity value and the intermediate temperature thermal history is as follows: Let a time period be defined as the monitoring period Tyd, where Tyd∈[0.5,2] hours; The binary combination consisting of surface electrical conductivity and thermal history is denoted as the risk-critical variable array. During the monitoring period, for any fixed monitoring site, an empty sequence is constructed and denoted as the main wave sequence Atf.Ls; If the surface conductivity and thermal history of any measuring point are both greater than or equal to the median of all surface conductivity and thermal history values ​​at the corresponding fixation monitoring sites, then the measuring point is marked as the first residual point; otherwise, it is marked as the second residual point, and the risk criticality array corresponding to the first residual point is added to the sequence Atf.Ls. In any sequence Atf.Ls, obtain the risk threshold arrays corresponding to the maximum values ​​of surface conductivity and thermal history, and denot them as the surface conductivity offset array and the thermal history offset array, respectively. If the risk threshold arrays corresponding to the maximum values ​​of surface conductivity and thermal history are the same, then the surface conductivity offset array and the thermal history offset array are the same.

[0020] Calculate the mean square error between the risk critical variable array of any first residual point and the electrical offset array and thermal offset array in the sequence Atf.Ls, and record the maximum value of the mean square error as the risk principal driver ARiq of the risk critical variable array; The primary risk driver quantifies the potential risk of a measurement point by calculating the mean square error (MSE) of the risk threshold array corresponding to the maximum values ​​of surface conductivity and thermal history at the first residual point. The MSE is a commonly used indicator to measure the degree of difference between two arrays. By selecting the maximum value to focus on the most significant difference, this helps identify key measurement points deviating from ideal conditions or extreme situations. The risk threshold array at the first residual point reflects the specific changes in surface electrical properties and thermal history characteristics at that point, which are related to the distribution of trace elements, temperature fluctuations during pyrolysis, and changes in electrical properties. Therefore, fitting the surface conductivity and temperature during fixation reflects whether metal ions can effectively and stably embed into the carbon framework, or whether unstable free metal centers have formed. By calculating the MSE, the primary risk driver quantifies the degree of deviation of the surface electrical properties and thermal history characteristics of the measurement point from extreme conditions. A smaller value indicates a smaller difference between the measurement point and the extreme state, suggesting a potentially higher residual risk, i.e., a potential risk of over-catalysis. This microscopic analysis can effectively identify regions of metal ions that may not be fully passivated, thereby identifying whether they will trigger the risk of carbon sequestration backflow in subsequent stages.

[0021] At any given measurement point, acquire each second residual point and construct the sub-wave sequence Btf.Ls; Calculate the mean square error between the risk criticality array of any second residual point and the average of all risk criticality arrays in the sequence Btf.Ls, and denote it as the risk secondary driver BRiq; Risk secondary drivers quantify the risk of a measurement point by calculating the mean square error of the risk threshold array of the second residual point and the average of the risk threshold arrays in its sequence. The second residual point represents a point where the surface conductivity and thermal history have not reached the ideal state, but are still within the preset normal variation range. Changes at these points indicate low risk or temporary deviations, but it is still necessary to monitor whether they potentially deviate from the expected stable state. By calculating the mean square error of these changes, risk secondary drivers help identify regions in the system that may not be fully stable, allowing for timely adjustments to possible process fluctuations. This helps fit and quantify regions in the scenario where temperature and electrical properties change relatively smoothly but still pose potential risks, preventing premature mineralization caused by over-catalysis.

[0022] Calculate the residual risk Resk based on the primary and secondary fluctuation sequences: ; Where j1 and j2 are cumulative variables, AFF and BFF are the number of the first and second residual points at the current measurement point, respectively, and ARiq j1 and Briq j2 Let be the primary risk driver at the j1-th first residual point and the secondary risk driver at the j2-th second residual point, respectively. Snft is the cosine similarity between the risk critical variable array corresponding to the maximum value of the surface conductivity and the risk critical variable array corresponding to the maximum value of the thermal history at the current measuring point. exp() is an exponential function with the natural constant e as the base, and lg is a logarithmic function with the constant 10 as the base.

[0023] Beneficial Effects: Since the residual risk level is obtained based on the time-series analysis of surface conductivity and intermediate-temperature thermal history, the time-series analysis for crystallization residence matching assessment can effectively quantify the risk of high-energy surface free metal center residues caused by the mismatch between the degree of crystal enrichment and the temperature residence during pyrolysis in the fixation step of the biochar preparation process. It allows for early warning of over-catalysis risk and process intervention during the intermediate-temperature solidification stage of pyrolysis in the fixation step, transforming trace elements from easily cyclic redox active states to embedded passivated states. This reduces the degree of redox reaction enrichment in biochar and prevents carbon sequestration backflow problems induced after biochar enters the soil, significantly improving the consistency of carbon sequestration capacity and product qualification rate of biochar products.

[0024] Further, in step S400, the method for over-catalysis early warning based on residual risk is as follows: the residual risk of each fixation monitoring site within the monitoring period is constructed into a set denoted as TResk, and the KS test method is used to identify whether there is an abnormal distribution in the set TResk; when the KS test result is abnormal, the current residual risk of each fixation monitoring site within the same fixation monitoring section is collected to construct a section risk sequence; each section risk sequence is clustered using a clustering algorithm to obtain a main cluster and edge clusters. If the distance between the cluster center of the edge cluster and the cluster center of the main cluster is an abnormal threshold, when the abnormal threshold is greater than the cluster diameter of the main cluster, it is considered that over-catalysis risk has occurred, and the fixation monitoring section corresponding to each element in the edge cluster is sent to the administrator client.

[0025] The value of Tyd during the monitoring period is directly obtained from step S300 and does not need to be set separately; Tyd ∈ [0.5, 2] hours. The KS test method, i.e., the Kolmogorov-Smirnov method, assumes a normal distribution. The Kolmogorov-Smirnov method is called through the scipy.stats.kstest() function. If a significant deviation is found, it indicates an abnormal risk in certain areas or time periods, which may indicate over-catalysis. The reason for assuming a normal distribution here is that if process parameters such as heating rate and temperature distribution remain stable during the fixation process, the residual risk should fluctuate around a central value, exhibiting typical normal distribution characteristics. Therefore, the normal distribution assumes that the risk changes at most measurement points tend to be concentrated. The specific criterion for determining significant deviation is that the p-value of the KS test is less than the preset significance level (default 0.05), indicating that the maximum difference between the empirical distribution function and the normal distribution function of the sample data has occurred, thus fitting the high risk of over-catalysis caused by process runaway during the actual fixation process.

[0026] The cross-sectional risk sequence is constructed based on the residual risk of each fixation monitoring site within the fixation monitoring section in S100. Therefore, based on the pre-planned horizontal or vertical numbering system, each fixation monitoring section can obtain a fixation monitoring site sequence of the same length and the same order, which can then be mapped to the constructed cross-sectional risk sequence of the same length and the same order.

[0027] The clustering algorithm is either K-means clustering or DBSCAN. The number of clusters is 2. The cluster with the most elements is designated as the master cluster, and the other is designated as the edge cluster.

[0028] The risk of over-catalysis corresponds to the possibility that some regions in the biochar preparation process contain metal ions that have not been sufficiently passivated and remain in a high-energy state as free metal centers. If the distance between the center of the edge cluster and the center of the main cluster exceeds a threshold and is greater than the diameter of the main cluster, it indicates that the fixation process in this region leads to an abnormality in which trace elements fail to stably embed into the carbon framework during pyrolysis, instead forming overly active free metal centers. This, in turn, accelerates the oxidative decomposition of organic carbon and induces the carbon fixation backflow problem.

[0029] Based on the identification of the risk of over-catalysis, the relevant sections need to be temperature-controlled or have their heating rate adjusted to ensure that trace elements can complete effective intercalation and transformation within the mesophilic range, avoiding excessive activity. For sections with higher risk, the residence time in the mesophilic range should be extended first, or the heating rate should be appropriately reduced to suppress the free state of metal ions and ensure that they can be stably intercalated into the biochar structure. This reduces the over-catalysis of organic carbon, decreases the risk of subsequent carbon sequestration backflow during field carbon dioxide release, and improves the stability of biochar's carbon sequestration effect in the soil.

[0030] Preferably, all undefined variables in this invention, if not explicitly defined, can be manually set thresholds.

[0031] This invention also provides an early warning system for excessive catalysis of trace elements in biochar preparation. The early warning system for excessive catalysis of trace elements in biochar preparation includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the early warning method for excessive catalysis of trace elements in biochar preparation. The early warning system for excessive catalysis of trace elements in biochar preparation can run on computing devices such as desktop computers, laptops, handheld computers, and cloud data centers. The runnable system may include, but is not limited to, processors, memory, and server clusters. The processor executes the computer program within the following system units: The site identification unit is used to preset data acquisition units in the fixation channel and record the acquisition position corresponding to the data acquisition unit as the fixation monitoring site; The data monitoring unit is used to monitor the surface electrical conductivity and intermediate temperature thermal history from each fixed monitoring site in real time. The matching assessment unit is used to assess the matching of crystallization residence based on the surface electrical conductivity and the intermediate temperature thermal history, and to obtain the residual risk level of each fixation monitoring site. The catalytic anomaly early warning unit is used to provide early warning of over-catalysis based on the residual risk level.

[0032] The beneficial effects of this invention are as follows: This invention provides a method and system for early warning of excessive catalysis of trace elements in biochar preparation. By evaluating the matching of crystallization residence through time-series analysis, it can effectively quantify the risk of high-energy surface free metal center residue caused by the mismatch between the degree of crystal enrichment and the temperature residence during pyrolysis in the fixation step of the biochar preparation process. It can provide early warning of the risk of excessive catalysis and implement process intervention in advance during the medium-temperature solidification stage of pyrolysis in the fixation step, so that trace elements are transformed from easily cyclic redox active state to embedded passivated state, thereby reducing the degree of redox enrichment in biochar and preventing the problem of carbon fixation backflow induced after biochar enters the soil. It greatly improves the consistency of carbon fixation capacity of biochar products and the product qualification rate of the production process, and improves the stability of carbon fixation capacity between batches in the production process. Attached Figure Description

[0033] The above and other features of the present invention will become more apparent from the detailed description of the embodiments shown in conjunction with the accompanying drawings. In the accompanying drawings, the same reference numerals denote the same or similar elements. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort. In the drawings: Figure 1 The diagram shows a flowchart of an early warning method for excessive catalysis of trace elements in biochar preparation. Figure 2 The diagram shows the structure of an early warning system for excessive catalysis of trace elements in biochar preparation. Detailed Implementation

[0034] The following will provide a clear and complete description of the concept, specific structure, and technical effects of the present invention in conjunction with the embodiments and accompanying drawings, so as to fully understand the purpose, solution, and effects of the present invention. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0035] Example 1 This embodiment verifies the method of the present invention on a continuous modified biochar production line. The production line includes a fixation channel consisting of an impregnation and drying section and a pyrolysis medium-temperature curing section. The material is straw-like biomass impregnated with iron and manganese salts. The impregnation solution has a total metal molar concentration of 0.05–0.10 mol / L (Fe and Mn). After impregnation, the material enters the drying section after dehydration, where the pore water film gradually recedes, inducing metal ions to migrate along the pores and form crystals and enrichment on the surface. Subsequently, it enters the medium-temperature curing section (350°C–550°C) to complete the rearrangement of metal coordination states and partial embedding passivation. Finally, it enters the high-temperature section to complete carbonization and shaping. Because this mechanism of enrichment followed by embedding is highly sensitive to the spatial temperature field and residence uniformity, if the drying rate is too fast or the material layer thickness is uneven in a certain section, it will lead to a mismatch where enrichment has occurred but residence is insufficient. This retains high-energy surface free metal centers, providing active sites for subsequent Fenton-like catalysis in the soil, and inducing the risk of carbon fixation backflow.

[0036] like Figure 1 The diagram shows a flowchart of an early warning method for trace element over-catalysis in biochar preparation. The following section will combine... Figure 1 This invention describes a method for early warning of excessive catalysis of trace elements in biochar preparation, according to an embodiment of the present invention. The method includes the following steps: S100, a data acquisition unit is preset in the fixation channel, and the acquisition position corresponding to the data acquisition unit is recorded as the fixation monitoring point; S200 monitors surface conductivity and intermediate temperature thermal history in real time from each fixed monitoring point; S300, based on the surface electrical conductivity and the amount of thermal history at medium temperature, performs a crystallization residence matching assessment to obtain the residual risk level of each fixation monitoring site; S400 provides over-catalysis warnings based on residual risk levels.

[0037] Further, in step S100, the method for pre-setting data acquisition units in the fixation channel and recording the acquisition positions corresponding to the data acquisition units as fixation monitoring points is as follows: divide the fixation channel into 20 fixation monitoring sections according to the material flow direction, and arrange 5 data acquisition units horizontally in each section; configure a unique identifier for each data acquisition unit, and correspond its position number in the fixation monitoring section with the unique identifier, and define the spatial position of the data acquisition unit in the fixation channel as the fixation monitoring point.

[0038] The fixation monitoring site refers to a specific spatial location within the fixation channel, corresponding to the monitoring position of each data acquisition unit during the biochar preparation process. Each data acquisition unit is arranged within the fixation channel to monitor and record relevant physical information of the biochar during the impregnation and drying stages and the pyrolysis-intermediate solidification stages.

[0039] The fixation channel is the material flow area in the biochar preparation process, including the post-impregnation drying section and the pyrolysis medium-temperature solidification section. Based on the material flow direction within the fixation channel, it is divided into 20 fixation monitoring sections. Each monitoring section can be divided laterally or longitudinally as needed. Multiple data acquisition units are arranged within each fixation monitoring section in a parallel manner, enabling each unit to independently collect relevant material data and simultaneously perform acquisition tasks with other data acquisition units.

[0040] The data acquisition unit is a device used in the biochar preparation process to collect information in real time. Each data acquisition unit contains at least one conductivity sensor and one temperature sensor. Each data acquisition unit needs to be configured with a unique identifier to distinguish different data acquisition units. The position of each data acquisition unit in the fixation channel is determined by two parameters: 1) Fixation monitoring section number: Each monitoring section has a unique number to indicate the section location of the data acquisition unit. 2) Spatial position number: Within each fixation monitoring section, the position of the data acquisition unit is determined by a pre-planned horizontal or vertical numbering system.

[0041] Further, in step S200, the method for real-time monitoring of surface conductivity and intermediate temperature thermal history from each fixation monitoring point is as follows: The fixation monitoring point includes a conductivity sensor and a temperature sensor. The surface conductivity value is the conductivity value collected by the conductivity sensor, and the thermal history is the temperature integral value of the intermediate temperature range collected by the temperature sensor. A measuring point is constructed every 30-120 seconds, and the surface conductivity value and intermediate temperature thermal history are obtained once for each measuring point.

[0042] The conductivity sensor is used to measure the surface conductivity of biochar in real time during the fixation process, reflecting changes in its electrical conductivity. Changes in conductivity can indirectly reflect the migration and crystallization enrichment of surface metal ions, especially during the drying stage when metal ions migrate and crystallize on the surface. This indicates whether there is excessive metal enrichment on the biochar surface, which could affect subsequent carbon fixation. The mesothermal thermal history is calculated from temperature data collected by a temperature sensor. Specifically, it is defined as the integral or weighted average value of the temperature within a preset mesothermal range of 350°C to 550°C. The temperature integral value reflects the residence time and heat distribution in this region, effectively characterizing whether the biochar obtains sufficient residence time during pyrolysis to complete the intercalation and passivation of metal ions. This is an important reference for identifying the occurrence of over-catalysis.

[0043] The intermediate-temperature thermal history is calculated by short-time integration of temperature data and processed using an incrementally weighted approach. Specifically, within each sampling period (i.e., between two consecutive measuring points), the temperature values ​​collected by the temperature sensor are calculated as a weighted average. The weighting method is as follows: α is used as the intermediate variable for weighting. The weight for 350-400°C is set to α, for 400-450°C to 2α, for 450-500°C to 3α, and for 500-550°C to 4α, ensuring that the sum of the weights in the weighted calculation is 1.

[0044] Further, in step S300, the method for obtaining the residual risk level of each immobilization monitoring site by evaluating the crystallization residence matching based on the surface conductivity value and the intermediate temperature thermal history is as follows: A time period is designated as the monitoring period Tyd. The monitoring period is 0.5 hours. During the monitoring period, for any fixed monitoring site, the average surface conductivity value Suoe and the average thermal history value Thoe are obtained. The absolute value of the difference between the surface conductivity value and Suoe for each measuring point is recorded as the surface conductivity disturbance, and the absolute value of the difference between the thermal history value and Thoe for each measuring point is recorded as the thermal disturbance. The binary pair formed by the surface conductivity value and the thermal history value is recorded as the risk critical variable array. The surface conductivity disturbance and the thermal disturbance during the monitoring period are normalized and summed on a per-measuring-point basis. The measuring point with the maximum summation value is used as the residual baseline point. The cosine similarity between each risk critical variable array and the risk critical variable array corresponding to the residual baseline point is calculated and recorded as the critical variable anomaly. If any critical variable anomaly is less than the lower quartile of all critical variable anomalies in its corresponding fixed monitoring site, the measuring point is marked as a non-residual assessment point; otherwise, it is marked as a residual assessment point. For any residual assessment point, the sub-pre-risk value RLe is calculated. The risk threshold variable arrays within the monitoring period are normalized. If there is a residual benchmark point under the measurement point corresponding to the residual assessment point, the Manhattan distance between the current residual assessment point and the risk threshold variable arrays corresponding to the residual benchmark point is recorded as the sub-pre-risk value of the residual assessment point. If there are multiple residual benchmark points under the measurement point corresponding to the residual assessment point, the maximum Manhattan distance between the current residual assessment point and the risk threshold variable arrays corresponding to the multiple residual benchmark points is selected as the sub-pre-risk value of the residual assessment point. If there is no residual benchmark point under the measurement point corresponding to the residual assessment point, the sub-pre-risk value of the residual assessment point is 0.

[0045] Calculate the residual risk level Resk based on the sub-pre-risk value and the residual baseline: ; Where i1 is the cumulative variable, ln is the logarithmic function with the constant e as the base, Retoal and Fetoal are the number of residual assessment points and non-residual assessment points in the current measurement point, respectively, and RLe(i1) is the sub-pre-risk value of the i1th residual assessment point in the current measurement point.

[0046] Further, in step S400, the method for over-catalysis early warning based on residual risk is as follows: the residual risk of each fixation monitoring site within the monitoring period is constructed into a set denoted as TResk, and the KS test method is used to identify whether there is an abnormal distribution in the set TResk; when the KS test result is abnormal, the current residual risk of each fixation monitoring site within the same fixation monitoring section is collected to construct a section risk sequence; each section risk sequence is clustered using a clustering algorithm to obtain a main cluster and edge clusters. If the distance between the cluster center of the edge cluster and the cluster center of the main cluster is an abnormal threshold, when the abnormal threshold is greater than the cluster diameter of the main cluster, it is considered that over-catalysis risk has occurred, and the fixation monitoring section corresponding to each element in the edge cluster is sent to the administrator client.

[0047] The value of Tyd during the monitoring period is directly obtained from step S300 and does not need to be set separately; Tyd ∈ [0.5, 2] hours. The KS test method, i.e., the Kolmogorov-Smirnov method, assumes a normal distribution. The Kolmogorov-Smirnov method is called through the scipy.stats.kstest() function. If a significant deviation is found, it indicates an abnormal risk in certain areas or time periods, which may indicate over-catalysis. The reason for assuming a normal distribution here is that if process parameters such as heating rate and temperature distribution remain stable during the fixation process, the residual risk should fluctuate around a central value, exhibiting typical normal distribution characteristics. Therefore, the normal distribution assumes that the risk changes at most measurement points tend to be concentrated. The specific criterion for determining significant deviation is that the p-value of the KS test is less than the preset significance level (default 0.05), indicating that the maximum difference between the empirical distribution function and the normal distribution function of the sample data has occurred, thus fitting the high risk of over-catalysis caused by process runaway during the actual fixation process.

[0048] The cross-sectional risk sequence is constructed based on the residual risk of each fixation monitoring site within the fixation monitoring section in S100. Therefore, based on the pre-planned horizontal or vertical numbering system, each fixation monitoring section can obtain a fixation monitoring site sequence of the same length and the same order, which can then be mapped to the constructed cross-sectional risk sequence of the same length and the same order.

[0049] The clustering algorithm is K-means clustering, with two clusters. The cluster with the most elements is designated as the master cluster, and the other cluster is designated as the edge cluster.

[0050] When the risk of over-catalysis occurs, the local heating slope of the corresponding region of the fixation monitoring section for each element in the edge cluster is adjusted, and the equivalent residence of this section in the 450–500°C sub-interval is increased, with a control time of 10 minutes.

[0051] Example 2 Example 2 uses the same early warning method as Example 1. The difference is that in step S300, the method for obtaining the residual risk of each fixation monitoring site by evaluating the crystallization residence matching based on the surface conductivity value and the intermediate temperature thermal history is as follows: a time period is set as the monitoring period Tyd, with a value of 0.5 hours; the binary combination of surface conductivity value and thermal history is recorded as the risk threshold array; within the monitoring period, for any fixation monitoring site, an empty sequence is constructed and recorded as the main fluctuation sequence Atf.Ls; if the surface conductivity value and thermal history of any measuring point are both greater than or equal to the median value of all surface conductivity values ​​and thermal history of the corresponding fixation monitoring sites, then the measuring point is marked as the first residual point; otherwise, it is marked as the second residual point, and the risk threshold array corresponding to the first residual point is added to the sequence Atf.Ls. In any sequence Atf.Ls, obtain the risk threshold arrays corresponding to the maximum values ​​of surface conductivity and thermal history, and denot them as the surface conductivity offset array and the thermal history offset array, respectively. If the risk threshold arrays corresponding to the maximum values ​​of surface conductivity and thermal history are the same, then the surface conductivity offset array and the thermal history offset array are the same.

[0052] Calculate the mean square error between the risk critical variable array of any first residual point and the electrical offset array and thermal offset array in the sequence Atf.Ls, and record the maximum value of the mean square error as the risk principal driver ARiq of the risk critical variable array; in any measurement point, obtain each second residual point and construct the sub-fluctuation sequence Btf.Ls; Calculate the mean square error between the risk criticality array of any second residual point and the average of all risk criticality arrays in its corresponding sequence Btf.Ls, and denote it as the secondary risk driver BRiq; calculate the residual risk level Resk based on the primary and secondary volatility sequences. ; Where j1 and j2 are cumulative variables, AFF and BFF are the number of the first and second residual points at the current measurement point, respectively, and ARiq j1 and Briq j2Let be the primary risk driver at the j1-th first residual point and the secondary risk driver at the j2-th second residual point, respectively. Snft is the cosine similarity between the risk critical variable array corresponding to the maximum value of the surface conductivity and the risk critical variable array corresponding to the maximum value of the thermal history at the current measuring point. exp() is an exponential function with the natural constant e as the base, and lg is a logarithmic function with the constant 10 as the base.

[0053] Comparative Example The comparative example uses the same raw material impregnation formula, the same equipment, and the same nominal process as Example 1 to produce modified biochar, but does not set up fixation monitoring sites to obtain surface conductivity and mesothermal thermal history, does not calculate residual risk level Resk, and does not perform TResk KS test and cross-sectional cluster screening; only at the finished product end is the total trace element content test or the extraction release test performed in the existing manner and then released.

[0054] Table 1

[0055] Table 1 shows the differences before and after the implementation of this early warning method, with 6 batches implemented for each batch. The method for measuring field release in the early stage was as follows: each batch of biochar was mixed with soil from the same source at a fixed application rate (2% based on soil dry weight), the moisture content was adjusted to approximately 60% of field capacity, and the mixture was incubated indoors under constant temperature conditions (25°C). Each treatment was replicated more than 3 times. The mixed soil samples were placed in sealed culture bottles with a known effective volume. After sealing, headspace gas samples were collected at fixed time points, and the CO2 volume fraction was quantified using gas chromatography. After each sampling, the bottle cap was immediately opened for ventilation or fresh air replacement to restore approximate initial atmospheric CO2 conditions, and then the bottle was sealed again for the next sampling cycle. The CO2 concentration increments obtained at each sampling interval within 0–7 days were converted into CO2 release amounts for that interval using the ideal gas law, combined with the headspace volume, temperature, and pressure of the culture bottle. These were then accumulated over time intervals to obtain the cumulative CO2 release amount for 0–7 days. Finally, the CO2 was converted to mg CO2 / kg dry soil based on the soil sample dry weight. This comparison table clearly demonstrates that, under the same raw material system and production line conditions, the comparative example, lacking online early warning and closed-loop intervention for residual risk, fails to identify and correct the residual risk of high-energy surface free metal centers during the fixation stage. This ultimately manifests as higher CO2 release in the early stages, often indicating a faster decline in rhizosphere DOC and significant batch fluctuations. In contrast, Example 1 can further pinpoint a few abnormal sections after the distribution anomalies occur, and implement targeted mid-temperature residence compensation or heating slope correction on a section-by-section basis, thus more effectively suppressing the carbon fixation backflow problem caused by insufficient local fixation. Example 2 demonstrates adaptability to overall rhythm misalignment; when anomalies are not concentrated in specific sections, global parameter correction stabilizes the overall risk distribution, significantly improving the consistency of product carbon fixation efficiency and batch pass rate. Therefore, this method not only possesses the ability to pre-identify over-catalysis risks but also distinguishes between regional and overall mismatches and provides corresponding intervention paths, substantially reducing the probability of early-stage mineralization pulses in the field from the production end, thereby improving the stable output of modified biochar carbon fixation effects.

[0056] An embodiment of the present invention provides an early warning system for excessive catalysis of trace elements in biochar preparation, such as... Figure 2 The diagram shows a structural diagram of an early warning system for excessive catalysis of trace elements in biochar preparation according to the present invention. The early warning system for excessive catalysis of trace elements in biochar preparation according to this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-described early warning method embodiment for excessive catalysis of trace elements in biochar preparation.

[0057] The system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program in units of the following system: The site identification unit is used to preset data acquisition units in the fixation channel and record the acquisition position corresponding to the data acquisition unit as the fixation monitoring site; The data monitoring unit is used to monitor the surface electrical conductivity and intermediate temperature thermal history from each fixed monitoring site in real time. The matching assessment unit is used to assess the matching of crystallization residence based on the surface electrical conductivity and the intermediate temperature thermal history, and to obtain the residual risk level of each fixation monitoring site. The catalytic anomaly early warning unit is used to provide early warning of over-catalysis based on the residual risk level.

[0058] The aforementioned early warning system for trace element over-catalysis in biochar preparation can operate on computing devices such as desktop computers, laptops, handheld computers, and cloud servers. The system capable of operating this early warning system for trace element over-catalysis in biochar preparation may include, but is not limited to, processors and memory. Those skilled in the art will understand that the example described is merely an illustration of an early warning system for trace element over-catalysis in biochar preparation and does not constitute a limitation on such a system. It may include more or fewer components, combinations of certain components, or different components. For example, the aforementioned early warning system for trace element over-catalysis in biochar preparation may also include input / output devices, network access devices, buses, etc.

[0059] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the early warning system for trace element over-catalysis in biochar preparation, connecting various parts of the system via various interfaces and circuits.

[0060] The memory can be used to store the computer program and / or modules. The processor, by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory, realizes various functions of the early warning system for trace element over-catalysis in biochar preparation. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0061] Although the invention has been described in considerable detail and particularly with regard to several of the described embodiments, it is not intended to limit itself to any of these details or embodiments or any particular embodiment, thereby effectively covering the intended scope of the invention. Furthermore, the invention has been described above with respect to embodiments foreseeable by the inventors in order to provide a useful description, and non-substantial modifications to the invention that have not yet been foreseen may still represent equivalent modifications.

Claims

1. A method for early warning of excessive catalysis of trace elements in biochar preparation, characterized in that, The method includes the following steps: S100, a data acquisition unit is preset in the fixation channel, and the acquisition position corresponding to the data acquisition unit is recorded as the fixation monitoring point; S200 monitors surface conductivity and intermediate temperature thermal history in real time from each fixed monitoring point; S300, based on the surface electrical conductivity and the amount of thermal history at medium temperature, performs a crystallization residence matching assessment to obtain the residual risk level of each fixation monitoring site; S400 provides over-catalysis warnings based on residual risk levels; The medium-temperature thermal history is the integral value of the medium-temperature range collected by the temperature sensor. Specifically, step S300 involves calculating the surface electrical disturbance and temperature disturbance by using the average surface conductivity and the average temperature thermal history during the monitoring period; constructing a risk threshold variable array using the surface conductivity and temperature thermal history; obtaining the residual baseline point by summing the normalized surface electrical disturbance and temperature disturbance at the measurement points; calculating the threshold variable anomaly by the cosine similarity between the risk threshold variable array and the risk threshold variable array corresponding to the residual baseline point; obtaining the non-residual assessment point and the residual assessment point based on the lower quartile of the threshold variable anomaly; normalizing the risk threshold variable array and calculating the sub-pre-risk value using the Manhattan distance; and constructing the residual risk degree using the sub-pre-risk value and the residual baseline point. In step S400, the method for over-catalysis early warning based on residual risk is as follows: the residual risk of each fixation monitoring site within the monitoring period is constructed into a set denoted as TResk, and the KS test method is used to identify whether there is an abnormal distribution in the set TResk; when the KS test result is abnormal, the current residual risk of each fixation monitoring site within the same fixation monitoring section is collected to construct a section risk sequence; each section risk sequence is clustered using a clustering algorithm to obtain a main cluster and edge clusters. If the distance between the cluster center of the edge cluster and the cluster center of the main cluster is an abnormal threshold, when the abnormal threshold is greater than the cluster diameter of the main cluster, it is considered that over-catalysis risk has occurred, and the fixation monitoring section corresponding to each element in the edge cluster is sent to the administrator client.

2. The method for early warning of excessive catalysis of trace elements in biochar preparation according to claim 1, characterized in that, In step S100, the method for pre-setting data acquisition units in the fixation channel and recording the acquisition positions corresponding to the data acquisition units as fixation monitoring points is as follows: divide the fixation channel into several fixation monitoring sections according to the material flow direction, and arrange several data acquisition units horizontally in each section; configure a unique identifier for each data acquisition unit, and correspond its position number in the fixation monitoring section with the unique identifier, and define the spatial position of the data acquisition unit in the fixation channel as the fixation monitoring point.

3. The method for early warning of excessive catalysis of trace elements in biochar preparation according to claim 1, characterized in that, In step S200, the method for real-time monitoring of surface conductivity and intermediate temperature thermal history from each fixation monitoring site is as follows: The fixation monitoring site includes a conductivity sensor and a temperature sensor. The surface conductivity value is the conductivity value collected by the conductivity sensor, and the intermediate temperature thermal history is the temperature integral value of the intermediate temperature range collected by the temperature sensor. A measuring point is constructed every 30-120 seconds, and the surface conductivity value and intermediate temperature thermal history are obtained once for each measuring point.

4. The method for early warning of excessive catalysis of trace elements in biochar preparation according to claim 1, characterized in that, In step S300, the method for assessing the crystallization residence matching based on surface conductivity and intermediate temperature thermal history to obtain the residual risk level of each immobilization monitoring site is as follows: During the monitoring period, for any immobilization monitoring site, obtain the average surface conductivity value (Suoe) and the average intermediate temperature thermal history value (Thoe); calculate the absolute value of the difference between the surface conductivity value and Suoe corresponding to each measuring point as the surface conductivity disturbance, and the absolute value of the difference between the intermediate temperature thermal history value and Thoe corresponding to each measuring point as the thermal disturbance; record the binary pair formed by the surface conductivity value and the intermediate temperature thermal history value as the risk threshold variable group; after normalizing the surface conductivity disturbance and the thermal disturbance during the monitoring period, sum them up on a per-measuring-point basis, and the summation value with the maximum value is obtained. The measuring point serves as the residual baseline point. The cosine similarity between each risk threshold array and the risk threshold array corresponding to the residual baseline point is calculated and recorded as the threshold anomaly value. If any threshold anomaly value is less than the lower quartile of all threshold anomalies in its corresponding fixation monitoring site, the measuring point is marked as a non-residual assessment point; otherwise, it is marked as a residual assessment point. A sub-pre-risk value (RLe) is calculated for each residual assessment point. The sub-pre-risk value RLe is used to measure the deviation strength of the residual assessment point relative to the residual baseline point: after normalizing the risk threshold array, the difference between the assessment point and the baseline point is calculated using the Manhattan distance; if the baseline point is unique, this distance is used; if there are multiple baseline points, the maximum distance is used; if there is no baseline point, the value is 0. The residual risk degree is calculated based on the obtained sub-pre-risk value and the residual baseline point.

5. The method for early warning of excessive catalysis of trace elements in biochar preparation according to claim 1, characterized in that, In step S300, the method for obtaining the residual risk level of each immobilization monitoring site by evaluating the crystallization residence matching based on the surface conductivity value and the intermediate temperature thermal history is as follows: the binary pair composed of the surface conductivity value and the intermediate temperature thermal history is denoted as the risk threshold array; during the monitoring period, for any immobilization monitoring site, an empty sequence is constructed and denoted as the main fluctuation sequence Atf.Ls; if the surface conductivity value and the intermediate temperature thermal history of any measuring point are both greater than or equal to the median value of all surface conductivity values ​​and intermediate temperature thermal history of the immobilization monitoring sites corresponding to that measuring point, then the measuring point is marked as the first residual point; otherwise, it is marked as the second residual point, and the risk threshold array corresponding to the first residual point is added to the sequence Atf.Ls. In any sequence Atf.Ls, obtain the risk threshold arrays corresponding to the maximum values ​​of surface conductivity and mesothermal thermal history, and denote them as the surface conductivity offset array and the mesothermal thermal offset array; calculate the mean square error between the risk threshold array of any first residual point and the surface conductivity offset array and the mesothermal thermal offset array in the sequence Atf.Ls, and denote the maximum value of the mean square error as the risk principal driver ARiq of the risk threshold array; At any measurement point, obtain each second residual point and construct a secondary fluctuation sequence Btf.Ls; calculate the mean square error between the risk critical variable array of any second residual point and the average value of all risk critical variable arrays in the sequence Btf.Ls, and record it as the risk secondary driver; calculate the residual risk degree based on the primary fluctuation sequence and the secondary fluctuation sequence.

6. An early warning system for excessive catalysis of trace elements in biochar preparation, characterized in that, The aforementioned early warning system for trace element over-catalysis in biochar preparation includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the early warning method for trace element over-catalysis in biochar preparation according to any one of claims 1-5. The aforementioned early warning system for trace element over-catalysis in biochar preparation is operated on a desktop computer, a laptop computer, a handheld computer, or a computing device in a cloud data center.

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