Organic fertilizer low-carbon adaptability production management method
By using a dynamic time warping algorithm and an energy consumption gating feedback mechanism, the problem of reaction kinetic differences caused by non-standardized raw materials in organic fertilizer production was solved, achieving low-carbon production control and reducing carbon emissions and energy consumption.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HENAN LUOXIAOWANG BIOTECH CO LTD
- Filing Date
- 2026-03-04
- Publication Date
- 2026-06-09
AI Technical Summary
In existing organic fertilizer production, the non-standardization of raw materials makes it impossible for fixed process parameters to adapt to the real-time reaction state, leading to uncontrolled carbon emissions and increased ineffective energy consumption.
A dynamic time warping algorithm is used to map real-time data to the standard reaction process of historical high-quality batches. Combined with morphological difference analysis and energy consumption gating feedback mechanism, the operating parameters of production equipment are dynamically adjusted to achieve low-carbon control.
It achieves precise benchmarking based on reaction state, identifies hidden high energy consumption trends, and reduces carbon emissions and ineffective energy consumption in the production process.
Smart Images
Figure CN122172742A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of organic fertilizer production data processing technology, and in particular to a method for low-carbon adaptability production management of organic fertilizer. Background Technology
[0002] The production of organic fertilizers and functional liquid fertilizers typically involves complex biological fermentation or chemical chelation processes. In traditional production management models, process control often relies on manual experience or fixed standard operating procedures, strictly adhering to pre-set schedules to control parameters such as stirring speed, aeration rate, heating temperature, and reaction time. While this method of production following fixed procedures is feasible in chemical production where raw materials are highly standardized, it faces significant challenges in the field of organic fertilizer production.
[0003] The main reason for this predicament lies in the highly non-standardized nature of organic fertilizer raw materials. Raw materials such as humic acid and livestock manure often vary significantly in organic matter content and moisture content between different batches, resulting in inconsistent reaction kinetics characteristics for each batch. Forcing the use of fixed parameters in production leads to significant technical problems: when the raw materials are highly reactive, the fixed oxygen supply may be insufficient, triggering anaerobic fermentation and producing large amounts of greenhouse gases such as methane; conversely, when the raw materials are less reactive, fixed heating or stirring times become redundant, directly increasing ineffective energy consumption.
[0004] Furthermore, existing management systems typically focus only on whether instantaneous energy consumption exceeds limits, neglecting the inherent compatibility between processes and raw materials. This one-sided management approach often leads to pseudo-low-carbon phenomena. For example, simply slowing down the stirring speed to reduce instantaneous power may actually significantly extend the reaction time, causing the total energy consumption and total carbon emissions of the production cycle to increase rather than decrease. Therefore, the industry urgently needs a data processing method that can dynamically calculate and match the optimal process path based on the real-time reaction characteristics of raw materials. This would solve the problem of fixed process parameters being unable to adapt to real-time reaction states due to the non-standardization of raw materials, thereby achieving truly low-carbon production. Summary of the Invention
[0005] The purpose of this invention is to propose a low-carbon adaptability production management method for organic fertilizers, in order to solve the technical problem that the non-standardization of raw materials in the existing organic fertilizer production process leads to the inability of fixed process parameters to adapt to the real-time reaction state, thereby causing uncontrolled carbon emissions and increased ineffective energy consumption.
[0006] The technical solution of the low-carbon adaptability production management method for organic fertilizer proposed in this invention is as follows: The low-carbon adaptability production management method for organic fertilizer includes the following steps: Multidimensional time-series data of the organic fertilizer production process are collected, and the dynamic time warping algorithm is used to map the real-time data of the current moment to the standard reaction process of historical high-quality batches to obtain the corresponding reference data. The morphological difference between the real-time data and the reference data in each dimension is calculated separately. The morphological difference consists of a numerical deviation term and a trend deviation term, which are used to characterize the degree of deviation between the real-time reaction state and the standard low-carbon state in terms of numerical magnitude and rate of change. Based on the morphological differences of each dimension and their corresponding preset weights, a global dynamic adaptation deviation index reflecting the degree of deviation from the current production state is obtained through weighted calculation. An energy consumption gating feedback mechanism is constructed based on the global dynamic adaptation deviation index. The energy consumption difference between the real-time power and the reference power is calculated. If and only if there is an energy consumption difference and the global dynamic adaptation deviation index indicates an abnormality, an adjustment instruction for the operating parameters of the production equipment is generated to achieve low-carbon control of the production process.
[0007] This invention solves the problem of reaction kinetic differences caused by non-standard raw materials through a dynamic time warping algorithm, achieving accurate benchmarking based on reaction state rather than physical time. In addition, this invention combines morphological difference analysis with energy consumption gating mechanism to identify hidden high energy consumption trends and ensure that intervention is only carried out when real-time energy consumption exceeds the standard and there is indeed room for energy saving. This effectively reduces carbon emissions and ineffective energy consumption in the production process while ensuring product quality.
[0008] Furthermore, the collection of multidimensional time-series data during the organic fertilizer production process includes: A sensor array is deployed inside the reactor vessel to collect real-time data on the internal temperature of the reactor body, the concentration of exhaust gas, the acidity and alkalinity of materials, and the real-time total power of related equipment at a preset frequency. The batch data with the lowest unit energy consumption and fertilizer efficiency were selected from the historical database as the benchmark database.
[0009] Furthermore, obtaining the corresponding reference data includes: The collected multidimensional time-series data sequence is used as the current real-time data sequence, and the time-series data sequence of the corresponding dimension in the benchmark library is used as the historical data sequence. The shortest path distance between the current real-time data sequence and the historical data sequence is calculated using a dynamic time warping algorithm to determine the standard reaction process time corresponding to the current moment. The historical average of the standard reaction process time is extracted as reference data for the current time.
[0010] By using the dynamic time warping algorithm to calculate the shortest path distance, the actual position of the current reaction state in the standard process can be accurately located, eliminating the time axis misalignment error caused by different reaction rates, and ensuring the consistency and comparability of real-time data and reference data at the level of chemical reaction mechanism.
[0011] Furthermore, the formula for calculating the morphological difference is:
[0012] In the formula, Indicates the first Each dimension The degree of morphological difference at any given moment; Indicates the first Each dimension Sensor measurements at any given time; This represents the standard low-carbon reference value obtained through mapping; Indicates the first The maximum permissible fluctuation range for each dimension; The weighting coefficients for the trend deviation term; Indicates the first Real-time rate of change in each dimension; This indicates the rate of change of the reference data at the corresponding standard reaction time. Indicates the first Standard rate of change benchmark values for each dimension; This represents the natural logarithm function.
[0013] By adopting a calculation formula that includes numerical deviation terms and trend deviation terms, and introducing the maximum allowable fluctuation range and the standard rate of change benchmark value as normalization factors, physical quantities of different dimensions are successfully unified under the same mathematical framework. In particular, the introduction of logarithmic terms improves the system's sensitivity to the rate of change, and can detect potential problems in advance where the values are still within the range but the trend of change is abnormal.
[0014] Furthermore, the first The real-time rate of change for each dimension is calculated using the sliding window difference method, and the calculation formula is as follows:
[0015] The calculation formula uses a 5-second sliding window. Indicates the current time The sensor measurements from 5 seconds ago.
[0016] The real-time rate of change is calculated by using a sliding window differential method, which effectively filters out high-frequency random noise generated during sensor acquisition and avoids misjudgments by the control system due to small data fluctuations.
[0017] Furthermore, the global dynamic adaptation deviation index, which reflects the degree of deviation from the current production state, obtained through weighted calculation, includes: The weighted difference is obtained by multiplying the morphological difference of each dimension by its corresponding importance weight. The weighted differences across all dimensions are summed, and the summation is squared to obtain the global dynamic adaptation deviation index.
[0018] Furthermore, the importance weights are set as follows: When the production process is fermentation, the importance weights of the two dimensions, tail gas concentration and reactor internal temperature, are set higher than the importance weights of other dimensions. When the production process is a chelation process, the importance weight of the material's acidity / alkalinity dimension is set higher than that of other dimensions.
[0019] The weight allocation is flexibly adjusted according to the different characteristics of fermentation or chelation processes, reflecting the refined management for different biochemical reaction mechanisms. By highlighting the influence of key process parameters, it ensures that the generated deviation index can truly reflect the core state of the current process type, thereby improving the pertinence and adaptability of the control strategy.
[0020] Furthermore, the generation of operating parameter adjustment instructions for the production equipment includes calculating the negative adjustment amount of the control parameters, the calculation formula of which is:
[0021] In the formula, This indicates a suggested reduction in the equipment operating parameter values; Indicates the basic adjustment step size; Indicates the global dynamic adaptation deviation index; Indicates a nonlinear response exponent; Indicates the time decay factor; Indicates the remaining process time; This indicates the real-time power of the currently associated device group; This represents the standard low-carbon power at the corresponding standard reaction process moment; Indicates an energy consumption sensitivity scale; This represents the function that takes the maximum value. This represents an exponential function with the natural constant e as its base.
[0022] Furthermore, the control logic of the energy consumption gating feedback mechanism is as follows: The direction of energy consumption difference is determined by taking the maximum value function. When the real-time power is greater than the standard low-carbon power, the calculated recommended reduction value of the equipment operating parameters is greater than zero, and the system performs frequency reduction intervention. When the real-time power is less than or equal to the standard low-carbon power, the calculated recommended reduction in equipment operating parameters is zero, and the system maintains its current operating state without adjustment.
[0023] Furthermore, the operating parameter adjustment instructions include adjusting the mixer speed, adjusting the oxygen supply fan frequency, or adjusting the heating device power.
[0024] The beneficial effects of this invention are as follows: By introducing a dynamic time warping algorithm, this invention effectively solves the problem of reaction kinetic differences caused by the non-standardization of raw materials in organic fertilizer production. This method no longer relies on a single physical time axis, but instead maps real-time data to a standard reaction process, thereby achieving precise benchmarking based on the actual state of the chemical reaction and eliminating comparison errors caused by varying reaction rates.
[0025] Building upon this foundation, this invention proposes a morphological difference calculation model comprised of numerical deviation and trend deviation. This model unifies physical quantities with different dimensions within the same mathematical framework and enhances sensitivity to the rate of change. This enables the system to keenly detect hidden high-energy-consumption precursors where the numerical values are still within acceptable limits but the changing trends are abnormal, allowing for intervention before energy consumption surges due to runaway reactions.
[0026] Furthermore, this invention constructs an energy consumption gating feedback mechanism, using the difference between real-time energy consumption and standard energy consumption as a necessary condition for generating control commands. This logic-enforced system only intervenes to adjust when the current energy consumption is higher than the standard and there is indeed room for energy saving. This breaks the limitation of traditional control that only focuses on process parameter errors, ensuring that every control action brings substantial energy consumption reduction, and effectively solving the problems of runaway carbon emissions and increased ineffective energy consumption caused by non-standardized raw materials. Attached Figure Description
[0027] Figure 1 This is a flowchart of the steps in the low-carbon adaptability production management method for organic fertilizer of the present invention; Figure 2 This is a schematic diagram illustrating the analysis of temperature changes and morphological differences during the reaction process in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the comparative analysis of cumulative carbon emissions throughout the entire production cycle in an embodiment of the present invention. Figure 4 This is a schematic diagram of the device operating power regulation curve according to an embodiment of the present invention. Detailed Implementation
[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
[0029] like Figure 1As shown, this embodiment provides a low-carbon adaptive production management method for organic fertilizer. This method does not rely on visual image processing, but rather on multi-dimensional time-series data from the production process. By constructing a morphological difference model and an energy consumption gating feedback mechanism, it achieves adaptive adjustment of production control parameters. Specifically, it includes the following steps: S1. Collect multi-dimensional time-series data during the organic fertilizer production process, and use the dynamic time warping algorithm to map the real-time data at the current moment to the standard reaction process of historical high-quality batches to obtain the corresponding reference data.
[0030] In this step, an industrial-grade high-precision sensor array is deployed inside the organic fertilizer fermentation tank or liquid fertilizer chelation reactor, and connected to the programmable logic controller (PLC) control center via signal transmission lines. The system is set to a sampling frequency of 1 Hz to ensure that subtle changes in the reaction process can be captured. Key physical quantities collected include the reactor's internal temperature, exhaust gas concentration, material pH, and the real-time total power of currently associated equipment. The exhaust gas concentration here primarily refers to the concentration of carbon dioxide or ammonia. After analog-to-digital conversion, the above data is stored in real-time in a temporary buffer of the control system in time-series format.
[0031] During the data processing and benchmark database construction phase, the system performs a screening procedure from the historical production database, selecting 50 high-quality historical batches based on the criteria of lowest unit energy consumption and compliance with final product fertilizer efficiency indicators to construct a standard low-carbon benchmark database. For the current production batch, the system retrieves the collected multidimensional data sequence in real time as the current real-time data sequence and uses the corresponding dimension's time-series data from the benchmark database as the historical data sequence. A distance matrix is constructed using a dynamic time warping algorithm. By calculating the shortest path distance between two sequences, the standard reaction process time corresponding to the current physical moment is determined, and the historical average of the standard reaction process time is extracted as reference data.
[0032] To illustrate this mapping mechanism more concretely, let's assume that the high bioactivity of the raw materials leads to a faster reaction rate. Although only 100 seconds have passed in the current physical time, based on the morphological characteristics of the multidimensional data, the reaction has actually reached the state at the 105th second of the standard curve. In this case, the dynamic time warping algorithm maps the current physical time of 100 seconds to the standard reaction process time of 105 seconds. Subsequently, the system reads the historical average value from the benchmark library at the 105th second and uses it as the reference data for the current time.
[0033] This invention effectively solves the problem of time axis misalignment caused by different reaction rates due to differences in raw material characteristics through the above-mentioned data acquisition and dynamic time warping processing. It ensures that subsequent deviation calculations are based on the same chemical reaction process, laying a data foundation for precise low-carbon control.
[0034] S2. Calculate the morphological difference between the real-time data and the reference data for each dimension. The morphological difference consists of a numerical deviation term and a trend deviation term, which are used to characterize the degree of deviation between the real-time reaction state and the standard low-carbon state in terms of numerical magnitude and rate of change.
[0035] To accurately assess whether the current reaction state has deviated from the preset low-carbon trajectory, this step calculates the morphological difference degree for each independent dimension. This morphological difference degree considers not only the magnitude of the value but also the trend of the rate of change. By constructing a mathematical model that includes numerical deviation terms and trend deviation terms, a comprehensive assessment of the reaction state is achieved. The specific calculation formula is as follows:
[0036] The first part of the formula is the numerical deviation term, and the second part is the trend deviation term. Among them, For the first Each dimension The degree of morphological difference at any given time; the larger the value of this indicator, the more severe the deviation.
[0037] Indicates the first Each dimension Sensor measurements at any given time This refers to the standard low-carbon reference value obtained through dynamic time warping algorithm mapping. This represents the standard reaction process time obtained through dynamic time warping mapping.
[0038] For the first The maximum permissible fluctuation range in each dimension, this parameter acts as a normalization factor, converting absolute numerical deviations into dimensionless relative deviations.
[0039] This is the weighting coefficient for the trend deviation term. In practice, this coefficient can be set to 1.5 to appropriately increase the system's sensitivity to abnormal reaction rates.
[0040] Indicates the first The real-time rate of change in each dimension is calculated using the sliding window difference method, and the calculation formula is as follows:
[0041] The formula uses a 5-second sliding window. Indicates the current time The sensor measurements from 5 seconds ago.
[0042] The rate of change of the reference data at the corresponding standard reaction time point; This is the benchmark value for the standard rate of change.
[0043] The formula uses a natural logarithm function with the natural constant e as the base, combined with absolute value calculation. This design can quickly amplify the difference signal when the numerical deviation is small but the trend of change is drastic, thereby effectively identifying hidden abnormal working conditions.
[0044] The following will take the internal temperature of the reactor body as a key dimension as an example for detailed explanation.
[0045] Assuming at the current moment in the production process Real-time temperature collected by the sensor The temperature is 60℃, while the standard low-carbon reference temperature is obtained after mapping using a dynamic time warping algorithm. The temperature is 55℃. Setting the maximum permissible fluctuation range M for this dimension to 10, the numerical deviation term is: .
[0046] At the same time, the system synchronously evaluates the temperature change trend. Assuming the monitored real-time heating rate... The rate is 1℃ / s, while the reference heating rate for the standard reaction process at the corresponding time is... The rate is 0.5℃ / s. The standard rate of change is set to a baseline value. The trend deviation term is set to 0.5, and the weighting coefficient λ for the trend term is set to 1.5. .
[0047] Combining the calculation results from the two parts above, the numerical deviation term of 0.25 and the trend deviation term of 1.04 are added together to finally obtain the morphological difference degree of the temperature dimension at that moment. The value is 1.29. This calculation example intuitively demonstrates the core advantage of this invention: by introducing the normalization factors M and K, it successfully unifies static numerical deviations and dynamic trend rate deviations within the same mathematical evaluation framework. This mechanism enables the system to possess high sensitivity, accurately identifying the hidden high-energy-consumption precursor of excessively rapid heating rates even before the temperature deviation itself exceeds a threshold, thus providing a solid data foundation for subsequent precise low-carbon control.
[0048] S3. Based on the morphological differences of each dimension and their corresponding preset weights, a global dynamic adaptation deviation index reflecting the degree of deviation from the current production state is obtained through weighted calculation.
[0049] After calculating the morphological differences of each independent dimension, in order to accurately assess the deviation between the current production state and the preset standard state from an overall perspective, this step uses a weighted summation and square root aggregation operation to synthesize the multi-dimensional difference indicators into a global indicator. The specific calculation formula is as follows:
[0050] in, The Global Dynamic Adaptation Deviation Index is a dimensionless comprehensive value used to quantitatively characterize the overall degree of anomaly in the production process. The total number of dimensions involved in the calculation; For the first The importance weights of each dimension are such that the sum of the importance weights of all dimensions is 1.
[0051] This step employs differentiated allocation strategies based on different production process types to ensure that the evaluation results accurately reflect the core characteristics of the process. Specifically, when organic fertilizer production is in the bio-fermentation stage, because the metabolic activity of microorganisms is extremely sensitive to temperature and their respiration is directly reflected in changes in the exhaust gas composition, the system will assign significantly higher importance weights to the dimensions of exhaust gas concentration and reactor internal temperature than to other dimensions. This allows the control system to prioritize responses to fluctuations at the bioreaction level. Conversely, when the production process is in the chemical chelation stage of liquid fertilizer production, given that the acidity or alkalinity of the solution is a decisive factor in determining the efficiency of the chelation reaction and the stability of the product, the system will assign a higher importance weight to the dimension of material pH than to other dimensions.
[0052] By using this flexible weight configuration based on process mechanism, the algorithm successfully integrates multi-dimensional discrete deviations into an intuitive global index, thereby accurately reflecting the overall deviation of the current production state from the ideal low-carbon state.
[0053] S4. Based on the global dynamic adaptation deviation index, construct an energy consumption gating feedback mechanism, calculate the energy consumption difference between real-time power and reference power, and generate an operation parameter adjustment command for the production equipment if and only if there is an energy consumption difference and the global dynamic adaptation deviation index indicates an abnormality, so as to achieve low-carbon control of the production process.
[0054] Based on the global dynamic adaptation deviation index, the system further generates specific control commands for the production equipment. To avoid ineffective adjustments and ensure energy-saving effects, this step introduces energy consumption gating logic to calculate the negative adjustment amount of the control parameters, i.e., the recommended reduction in equipment operating parameter values. The formula for calculating this negative adjustment amount is:
[0055] In the formula, This indicates a recommended reduction in the operating parameters of the equipment. For example, for a mixer, this value represents a reduction in the mixing speed. This indicates the basic adjustment step size, used to set the reference range for a single adjustment, such as 50 revolutions per minute in mixer control. This represents the global dynamic adaptation deviation index.
[0056] This represents the response nonlinearity exponent, typically with a value of 2. This exponent acts on the global dynamic adaptation bias exponent. This results in a non-linear positive correlation between the adjustment amount and the degree of deviation.
[0057] This represents the time decay factor, with a value of 0.01. Indicates the remaining process time; This indicates the real-time power of the currently associated device group; This represents the standard low-carbon power at the corresponding standard reaction process moment; This represents an energy consumption sensitivity scale, used to normalize energy consumption differences.
[0058] This represents the function that takes the maximum value. This represents an exponential function with the natural constant e as its base. A one-way filter is constructed using the maximum value function and the exponential function. The control logic of the energy consumption gating feedback mechanism is as follows: when the real-time power is less than or equal to the standard low-carbon power, the calculation result of the exponential function will cause the value within the parentheses to be less than or equal to zero. At this time, the maximum value function is forced to output zero, thus affecting the final adjustment amount. When the value is zero, the system does not intervene; only when the real-time power exceeds the standard low-carbon power does this item output a positive coefficient between 0 and 1, allowing the system to generate effective parameter reduction commands. This design ensures that the system will only perform frequency reduction operation under the dual conditions of having actual energy-saving potential and process deviation.
[0059] To further illustrate the regulation logic of the above control model, a detailed explanation will be provided below using specific examples.
[0060] Assume the remaining process time until the end of the process at the current moment. The global dynamic adaptation deviation index is calculated over a period of 100 seconds. The system's preset basic adjustment step size is 1.5. The response nonlinearity exponent P is set to 2, with a value of 50, and the energy sensitivity scale is set to 2. The value is 50. First, calculate the first part of the formula, which is the value of the deviation influence factor. Substitute the above parameters into the calculation to get: .
[0061] Based on this benchmark, two typical energy consumption scenarios are considered to verify the effectiveness of the energy consumption gating logic.
[0062] The first scenario assumes the device is currently operating in a high-power consumption state. Assume real-time power... It is 120kW, while the corresponding standard low-carbon reference power The initial power consumption is 100kW, and the energy difference between the two is 20kW. Substituting this value into the exponential term in the latter part of the formula, the input value of the exponential function is calculated to be -0.4, and its corresponding natural exponential function value is approximately 0.67. The maximum value function output value is then calculated to be 0.33. The final calculated adjustment amount is: Based on the calculation results, the system will generate specific control commands, suggesting a reduction in the mixer speed by approximately 19 revolutions per minute, thereby implementing a substantial frequency reduction intervention.
[0063] The second scenario assumes the device is currently operating in a relatively energy-efficient state. Assume real-time power... The power consumption is 80kW, which is lower than the standard low-carbon reference power of 100kW, resulting in an energy consumption difference of -20kW. Substituting this into the formula, the exponent term becomes 0.4, corresponding to a natural exponential function value of approximately 1.49. The calculated maximum value of the function outputs a value of 0. The final calculated recommended adjustment amount is... The result is 0. This indicates that even though there is a high global dynamic adaptation bias index, the system determines that the bias is within a benign range because the real-time energy consumption is lower than the reference standard, and therefore no parameter adjustments are made.
[0064] The above comparative calculations intuitively demonstrate the core advantages of the energy consumption gating mechanism of this invention. That is, the system abandons the traditional control approach of wasting energy by blindly pursuing mathematical perfection of process parameters, and establishes the control principle of intervening and adjusting only when both of the necessary conditions of abnormal process deviation and actual energy-saving space are met, thereby effectively reducing redundant energy consumption in the production process.
[0065] The following is Figures 2-4 The solution and effects of the present invention will be further explained.
[0066] Figure 2 This figure showcases the real-time monitoring and compatibility analysis results of key indicators during organic fertilizer fermentation. Using reactor temperature as the vertical axis and fermentation time as the horizontal axis, the figure visually compares the standard low-carbon reference curve with the real-time monitoring curve. The specially marked areas of mismatched trend deviation vividly reflect the algorithm's ability to capture these deviations in actual operation. Specifically, when the rate of change in real-time data fluctuates abnormally, the trend term in the morphological difference calculation formula increases significantly, thus accurately pinpointing moments where the values have not yet exceeded the standard but the reaction trend has deviated from the preset trajectory. This visual comparison verifies that the invention can promptly identify hidden high-energy-consumption precursors during the production process.
[0067] Figure 3This paper presents a comparative analysis of cumulative carbon emissions throughout the entire production cycle. The graph, with the percentage of production progress on the horizontal axis and the cumulative carbon emission equivalent on the vertical axis, clearly depicts the emission trajectory under the fixed process control mode of existing technologies and the emission trajectory under the low-carbon adaptive control mode of this invention. The comparison reveals that the filled area between the two curves represents the additional carbon emissions caused by ineffective energy consumption. As the production process progresses, this area gradually expands, strongly demonstrating the existence of significant redundant energy consumption in existing technologies. In contrast, this invention, through a dynamic intervention strategy, effectively suppresses the rapid growth of carbon emissions, conclusively verifying the significant effect of this scheme in reducing ineffective energy consumption.
[0068] Figure 4 This paper reveals the power regulation strategy for key equipment such as oxygen supply fans or heating devices. The figure compares the traditional fixed-frequency or timed control mode with the adaptive regulation mode based on the adaptation index of this invention. Traditional control curves exhibit rigid step-like or linear shapes, indicating that the equipment operates at high power for extended periods; while the regulation curve of this invention displays smooth dynamic fluctuations, flexibly adjusting power output according to the actual needs of the reaction stage. The energy-saving range shown in the figure intuitively demonstrates the effectiveness of the energy-gating logic, indicating that the system successfully avoids maintaining unnecessary high-power operation in pursuit of process parameters, achieving optimal matching between equipment operating status and real-time reaction requirements.
[0069] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.
Claims
1. A method for low-carbon adaptability production management of organic fertilizer, characterized in that, include: Multidimensional time-series data of the organic fertilizer production process are collected, and the dynamic time warping algorithm is used to map the real-time data of the current moment to the standard reaction process of historical high-quality batches to obtain the corresponding reference data. The morphological difference between the real-time data and the reference data in each dimension is calculated separately. The morphological difference consists of a numerical deviation term and a trend deviation term, which are used to characterize the degree of deviation between the real-time reaction state and the standard low-carbon state in terms of numerical magnitude and rate of change. Based on the morphological differences of each dimension and their corresponding preset weights, a global dynamic adaptation deviation index reflecting the degree of deviation from the current production state is obtained through weighted calculation. An energy consumption gating feedback mechanism is constructed based on the global dynamic adaptation deviation index. The energy consumption difference between the real-time power and the reference power is calculated. If and only if there is an energy consumption difference and the global dynamic adaptation deviation index indicates an abnormality, an adjustment instruction for the operating parameters of the production equipment is generated to achieve low-carbon control of the production process.
2. The method for low-carbon adaptability production management of organic fertilizer according to claim 1, characterized in that, The collection of multidimensional time-series data during the organic fertilizer production process includes: A sensor array is deployed inside the reactor vessel to collect real-time data on the internal temperature of the reactor body, the concentration of exhaust gas, the acidity and alkalinity of materials, and the real-time total power of related equipment at a preset frequency. The batch data with the lowest unit energy consumption and fertilizer efficiency were selected from the historical database as the benchmark database.
3. The method for low-carbon adaptability production management of organic fertilizer according to claim 2, characterized in that, The acquisition of the corresponding reference data includes: The collected multidimensional time-series data sequence is used as the current real-time data sequence, and the time-series data sequence of the corresponding dimension in the benchmark library is used as the historical data sequence. The shortest path distance between the current real-time data sequence and the historical data sequence is calculated using a dynamic time warping algorithm to determine the standard reaction process time corresponding to the current moment. The historical average of the standard reaction process time is extracted as reference data for the current time.
4. The method for low-carbon adaptability production management of organic fertilizer according to claim 1, characterized in that, The formula for calculating the morphological difference is: In the formula, Indicates the first Each dimension The degree of morphological difference at any given moment; Indicates the first Each dimension Sensor measurements at any given time; This represents the standard low-carbon reference value obtained through mapping; Indicates the first The maximum permissible fluctuation range for each dimension; The weighting coefficient for the trend deviation term; Indicates the first Real-time rate of change in each dimension; This indicates the rate of change of the reference data at the corresponding standard reaction time. Indicates the first Standard rate of change benchmark values for each dimension; This represents the natural logarithm function.
5. The method for low-carbon adaptability production management of organic fertilizer according to claim 4, characterized in that, The first The real-time rate of change for each dimension is calculated using the sliding window difference method, and the calculation formula is as follows: The calculation formula uses a 5-second sliding window. Indicates the current time The sensor measurements from 5 seconds ago.
6. The method for low-carbon adaptability production management of organic fertilizer according to claim 1, characterized in that, The global dynamic adaptation deviation index, which reflects the degree of deviation from the current production state and is obtained through weighted calculation, includes: The weighted difference is obtained by multiplying the morphological difference of each dimension by its corresponding importance weight. The weighted differences across all dimensions are summed, and the summation is squared to obtain the global dynamic adaptation deviation index.
7. The method for low-carbon adaptability production management of organic fertilizer according to claim 6, characterized in that, The importance weights are set as follows: When the production process is fermentation, the importance weights of the two dimensions, tail gas concentration and reactor internal temperature, are set higher than the importance weights of other dimensions. When the production process is a chelation process, the importance weight of the material's acidity / alkalinity dimension is set higher than that of other dimensions.
8. The method for low-carbon adaptability production management of organic fertilizer according to claim 6, characterized in that, The generation of operating parameter adjustment instructions for production equipment includes calculating the negative adjustment amount of the control parameters, and the calculation formula is as follows: In the formula, This indicates a suggested reduction in the equipment operating parameter values; Indicates the basic adjustment step size; Indicates the global dynamic adaptation deviation index; Indicates a nonlinear response exponent; Indicates the time decay factor; Indicates the remaining process time; This indicates the real-time power of the currently associated device group; This represents the standard low-carbon power at the corresponding standard reaction process moment; Indicates an energy consumption sensitivity scale; This represents the function that takes the maximum value. This represents an exponential function with the natural constant e as its base.
9. The method for low-carbon adaptability production management of organic fertilizer according to claim 8, characterized in that, The control logic of the energy consumption gating feedback mechanism is as follows: The direction of energy consumption difference is determined by taking the maximum value function. When the real-time power is greater than the standard low-carbon power, the calculated recommended reduction value of the equipment operating parameters is greater than zero, and the system performs frequency reduction intervention. When the real-time power is less than or equal to the standard low-carbon power, the calculated recommended reduction in equipment operating parameters is zero, and the system maintains its current operating state without adjustment.
10. The method for low-carbon adaptability production management of organic fertilizer according to claim 1, characterized in that, The operating parameter adjustment commands include adjusting the mixer speed, adjusting the oxygen supply fan frequency, or adjusting the heating device power.