Organic water-soluble fertilizer preparation parameter optimization method based on peat suspension

Abstract

The invention relates to the technical field of organic fertilizer preparation, and discloses an organic water-soluble fertilizer preparation parameter optimization method based on peat suspension, which comprises the following steps: determining a retrieval parameter database including an initial stage database, a middle stage database and a classification stage database, acquiring enzymolysis regulation and control parameters by utilizing an initial peat suspension liquid and an initial stage database, acquiring an enzymolysis stage suspension liquid based on the enzymolysis regulation and control parameters and the initial peat suspension liquid, and acquiring middle-stage regulation and control parameters by utilizing the enzymolysis stage suspension liquid and a middle-stage stage database, and obtaining a fermentation stage suspension based on the medium-term regulation and control parameters and the enzymolysis stage suspension, obtaining classification supplementation data by using the fermentation stage suspension and the classification stage database, and preparing the target water-soluble organic fertilizer by using the classification supplementation data and the fermentation stage suspension. The main purpose of the invention is to improve the accuracy and the intelligent degree of setting the preparation parameters of the organic water-soluble fertilizer.

Description

Technical Field

[0001] This invention relates to a method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension, belonging to the field of organic fertilizer preparation technology. Background Technology

[0002] With the rapid development of agriculture and the promotion of the concept of sustainable development, organic water-soluble fertilizers have received widespread attention due to their advantages such as high efficiency and environmental friendliness. Peat, as a natural resource rich in organic matter, has good fertilizing effects and soil-improving characteristics. Moreover, peat is widely used as a raw material in the production of organic water-soluble fertilizers. Correspondingly, how to improve the accuracy of parameter settings during the production of organic water-soluble fertilizers has become an urgent problem to be solved.

[0003] Currently, the parameters required for peat production of organic water-soluble fertilizers are mostly based on personal experience.

[0004] Although the above methods can set the parameters required for the production of organic water-soluble fertilizers, the characteristics of peat and the energy consumption of the set parameters are not considered before setting the parameters. Therefore, how to improve the accuracy and intelligence of parameter setting during the production of organic water-soluble fertilizers has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method, apparatus, and computer-readable storage medium for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension. Its main purpose is to improve the accuracy and intelligence of setting the preparation parameters of organic water-soluble fertilizer.

[0006] To achieve the above objectives, this invention provides a method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension, comprising:

[0007] Receive parameter optimization instructions, and confirm the parameter optimization environment based on the parameter optimization instructions. The parameter optimization environment includes the parameter optimization system and the initial peat suspension to be treated. The parameter optimization system includes a parameter detection unit, a parameter control unit and a parameter optimization unit.

[0008] The retrieval parameter database was identified using a parameter optimization system. This database includes an initial stage database, an intermediate stage database, and a classification stage database.

[0009] Initial parameter data is obtained using the initial peat suspension. Based on the initial parameter data, enzymatic hydrolysis control parameters are retrieved from the initial stage database. Enzymatic hydrolysis stage suspension is obtained based on the enzymatic hydrolysis control parameters and the initial peat suspension.

[0010] Enzymatic hydrolysis parameter data is obtained using the enzymatic hydrolysis stage suspension. Based on the enzymatic hydrolysis parameter data, mid-term control parameters are retrieved from the mid-term stage database. Based on the mid-term control parameters and the enzymatic hydrolysis stage suspension, the fermentation stage suspension is obtained.

[0011] Classification parameter data is obtained using the suspension during the fermentation stage. Supplementary classification data is retrieved from the classification stage database using the classification parameter data. The target water-soluble organic fertilizer is then prepared using the supplementary classification data and the suspension during the fermentation stage.

[0012] Optionally, the step of using the parameter optimization system to identify the retrieval parameter database includes:

[0013] Obtain a reference peat suspension set, which includes multiple reference peat suspensions. Extract reference peat suspensions sequentially from the reference peat suspension set and perform the following operations on the extracted reference peat suspensions:

[0014] The extracted reference peat suspension is stirred using a preset first stirring time and a pre-constructed stirring unit to obtain a stirred peat suspension. An analytical peat suspension group is obtained based on the stirred peat suspension and a preset initial volume. The analytical peat suspension group includes multiple analytical peat suspensions, and the volume of the analytical peat suspension is the initial volume.

[0015] The parameter detection instruction is received from the parameter detection unit, the parameter detection instruction is parsed, and a parameter detection model node set is obtained. The parameter detection model node set includes multiple parameter detection model nodes, and each parameter detection model node includes a parameter detection model and a reference detection error rate.

[0016] The following procedures were performed on each peat suspension in the peat suspension analysis group:

[0017] A filtration operation is performed on the peat suspension to obtain a filtered peat suspension. Based on the parameter detection model node set and the filtered peat suspension, a set of detected humic acid contents is obtained. The set of detected humic acid contents includes multiple detected humic acid contents, and the detected humic acid contents correspond one-to-one with the parameter detection model nodes.

[0018] The comprehensive humic acid content is calculated based on the detected humic acid content set and a pre-constructed comprehensive humic acid content relationship formula. The comprehensive humic acid content is then summarized to obtain a comprehensive humic acid content set. The comprehensive humic acid variance is obtained based on this set, and compared with a preset comprehensive humic acid threshold. If the comprehensive humic acid variance is less than or equal to the threshold, the comprehensive humic acid mean is obtained based on the comprehensive humic acid content set. This mean is compared with a preset humic acid mean threshold. If the comprehensive humic acid mean is greater than or equal to the threshold, the peat suspension group is labeled using the comprehensive humic acid mean, resulting in labeled peat. If the suspension group is otherwise, obtain the growth ratio of the first stirring time, calculate the product of the growth ratio and the first stirring time to obtain the second stirring time, take the second stirring time as the first stirring time, return to the step of stirring the extracted reference peat suspension using the preset first stirring time and the pre-constructed stirring unit to obtain the stirred peat suspension, count the number of times the second stirring time is obtained to obtain the number of cycles, when the number of cycles reaches the preset cycle threshold and the average value of comprehensive humic acid is less than the average value threshold of humic acid, then return to the step of sequentially extracting reference peat suspension from the reference peat suspension set and performing the following operations on the extracted reference peat suspension;

[0019] The identified peat suspension groups are aggregated to obtain the identified peat suspension group set, and the retrieval parameter database is constructed using the identified peat suspension group set.

[0020] Optionally, the relationship between the comprehensive humic acid content is as follows:

[0021]

[0022] Where Z represents the total humic acid content, α is a preset coefficient, and ω i ω j h represents the reference detection error rate corresponding to the i-th and j-th parameter detection model nodes in the parameter detection model node set, respectively. j The value represents the j-th humic acid content detected in the set of monitored humic acid content, and n represents the number of parameter detection model nodes included in the set of parameter detection model nodes.

[0023] Optionally, the step of constructing a retrieval parameter database using a set of identified peat suspensions includes:

[0024] For each labeled peat suspension group in the labeled peat suspension group set, the following operations shall be performed:

[0025] The parameter control instruction from the parameter control unit is confirmed to be received. The parameter control instruction is parsed to obtain the enzymatic hydrolysis parameter range set, wherein the enzymatic hydrolysis parameter range set includes four enzymatic hydrolysis parameter ranges, and the four enzymatic hydrolysis parameter ranges are the enzymatic hydrolysis concentration range, the enzymatic hydrolysis temperature range, the enzymatic hydrolysis pH range, and the enzymatic hydrolysis time range, respectively.

[0026] Perform the following operation for each enzymatic hydrolysis parameter range:

[0027] Using a preset division value and a pre-constructed uniform extraction algorithm, multiple first enzymatic hydrolysis parameters are extracted from the range of enzymatic hydrolysis parameters. The multiple first enzymatic hydrolysis parameters are summarized to obtain a first enzymatic hydrolysis parameter group. The first enzymatic hydrolysis parameter groups are summarized to obtain a first enzymatic hydrolysis parameter set. The number of first enzymatic hydrolysis parameters is the division value.

[0028] In a combined manner, multiple enzymatic hydrolysis parameter nodes are obtained by using multiple sets of first enzymatic hydrolysis parameters. Each enzymatic hydrolysis parameter node includes four first enzymatic hydrolysis parameters, and the first enzymatic hydrolysis parameters correspond one-to-one with the enzymatic hydrolysis parameter range.

[0029] Multiple enzymatic hydrolysis experimental groups were obtained based on the multiple enzymatic hydrolysis parameter nodes and the labeled peat suspension group, wherein each enzymatic hydrolysis experimental group includes an enzymatic hydrolysis parameter node and a labeled peat suspension.

[0030] For each of the multiple enzymatic digestion experimental groups, the following operations were performed:

[0031] Based on the enzymatic hydrolysis experimental group, the enzymatic hydrolysis organic content is obtained. The enzymatic hydrolysis organic content, the average comprehensive humic acid value corresponding to the peat suspension group, and the enzymatic hydrolysis parameter node corresponding to the enzymatic hydrolysis experimental group are associated to obtain associated enzymatic hydrolysis coordinates. The associated enzymatic hydrolysis coordinates are summarized to obtain associated enzymatic hydrolysis coordinate groups. The associated enzymatic hydrolysis coordinate groups are summarized to obtain associated enzymatic hydrolysis coordinate set. The retrieval parameter database is obtained based on the associated enzymatic hydrolysis coordinate set.

[0032] Optionally, obtaining the retrieval parameter database based on the associated enzymatic hydrolysis coordinate set includes:

[0033] Using the enzymatically hydrolyzed organic content in the associated enzymatic hydrolysis coordinate set as the dependent variable, an enzymatic hydrolysis fitting surface is obtained using the associated enzymatic hydrolysis coordinate set and a pre-constructed surface fitting model. An optimized enzymatic hydrolysis parameter set is obtained using the enzymatic hydrolysis fitting surface. The optimized enzymatic hydrolysis parameter set is the associated enzymatic hydrolysis coordinate set corresponding to the largest enzymatically hydrolyzed organic content in the enzymatic hydrolysis fitting surface. The optimized organic content is obtained based on the optimized enzymatic hydrolysis parameter set.

[0034] The ratio of the difference in enzymatic hydrolysis content is calculated using the optimized organic content and the corresponding organic content of the optimized enzymatic hydrolysis parameter set. The calculation formula is as follows:

[0035]

[0036] Where L represents the ratio of the difference in enzymatic hydrolysis content, l1 represents the optimized organic content, and l0 represents the enzymatic hydrolysis organic content corresponding to the optimized enzymatic hydrolysis parameter group;

[0037] Compare the ratio of the enzymatic hydrolysis content difference with a preset difference ratio threshold. After confirming that the ratio of the enzymatic hydrolysis content difference is less than or equal to the difference ratio threshold, calculate the product of the preset evaluation enzymatic hydrolysis ratio and the optimized organic content to obtain the evaluation organic content.

[0038] A database of search parameters is obtained based on the assessed organic content.

[0039] Optionally, the step of obtaining a retrieval parameter database based on the assessed organic content includes:

[0040] Using the aforementioned assessment of organic content, one or more initial enzymatic hydrolysis curves are extracted from the enzymatic hydrolysis fitting surface to obtain a set of unit energy consumption parameters during enzymatic hydrolysis. The set of unit energy consumption parameters includes four unit energy consumption parameters, namely, unit stirring energy consumption, unit enzymatic hydrolysis energy consumption, unit temperature energy consumption, and unit acid-base energy consumption.

[0041] The weighted reorganization of unit energy consumption is calculated based on the unit energy consumption parameter set, and the calculation formula is as follows:

[0042]

[0043] Where, q w g represents the weight of the w-th unit energy consumption in the unit energy consumption weight reorganization. w g represents the w-th unit energy consumption in the unit energy consumption parameter group. e This represents the e-th unit energy consumption in the unit energy consumption parameter group;

[0044] For each of the one or more initial enzymatic hydrolysis curves, the following operation shall be performed:

[0045] An initial enzymatic hydrolysis function is obtained based on the initial enzymatic hydrolysis curve. An analytical function relationship is constructed based on the initial enzymatic hydrolysis function and the unit energy consumption weighted recombination. A local optimal parameter set is obtained using the analytical function relationship. A local optimal energy consumption value is obtained based on the local optimal parameter set and the unit energy consumption parameter set. The local optimal energy consumption values ​​are summarized to obtain a local optimal energy consumption value set. The global optimal parameter set is determined using the local optimal energy consumption value set. The global optimal parameter set is the local optimal parameter set corresponding to the smallest local optimal energy consumption value in the local optimal energy consumption value set.

[0046] By associating the global optimal parameter group with the evaluation of organic content and the average value of comprehensive humic acid, initial parameter nodes are obtained. The initial parameter nodes are then summarized to obtain an initial stage database. Based on the initial stage database, a retrieval parameter database is obtained.

[0047] Optionally, the analytical function relation is as follows:

[0048]

[0049] Where E represents energy consumption. The values ​​represent the weights corresponding to the unit stirring energy consumption, unit enzymatic hydrolysis energy consumption, unit temperature energy consumption, and unit acid-base energy consumption in the unit energy consumption weighting reorganization, respectively. b1, b2, b3, and b4 represent the unit stirring energy consumption, unit enzymatic hydrolysis energy consumption, unit temperature energy consumption, and unit acid-base energy consumption during the enzymatic hydrolysis operation, respectively. f(b1,b2,b3,b4) represents the initial enzymatic hydrolysis function, p represents the evaluated organic content, J represents the analytical function relationship, and σ is a preset variable.

[0050] Optionally, obtaining the retrieval parameter database based on the initial stage database includes:

[0051] Based on each initial parameter node in the initial stage database, the initial enzymatic hydrolysis suspension is obtained, and the set of fermentation parameter ranges for fermentation is obtained. The set of fermentation parameter ranges includes multiple fermentation parameter ranges, including the range of fermentation strain concentration, the range of fermentation time, and the range of fermentation temperature.

[0052] Using the initial enzymatic hydrolysis suspension and fermentation parameter range set, a global fermentation parameter set is obtained. The global fermentation parameter set is associated with the initial parameter node to obtain intermediate stage data. The intermediate stage data is then summarized to obtain an intermediate stage database.

[0053] The retrieval parameter database is obtained based on the intermediate stage database.

[0054] Optionally, obtaining the retrieval parameter database based on the intermediate stage database includes:

[0055] Perform the following operations on each piece of data in the interim phase database:

[0056] The initial fermentation suspension is obtained based on the global fermentation parameter set corresponding to the mid-term stage data. The fermentation component concentration set is then obtained using the initial fermentation suspension. The fermentation component set includes the concentrations of multiple fermentation components identified by their names.

[0057] The fermentation component concentration groups are summarized to obtain a fermentation component concentration group set. The parameter optimization instruction from the parameter optimization unit is confirmed to be received. The parameter optimization instruction is parsed to obtain an analytical clustering method. The analytical clustering method is used to cluster the fermentation component groups in the fermentation component concentration group set to obtain one or more fermentation cluster sets.

[0058] For each of one or more fermentation clusters, perform the following operation:

[0059] Based on the fermentation component names, the concentrations of fermentation components in the fermentation clusters are summarized to obtain a set of fermentation component concentrations. Based on the set of fermentation component concentrations, a clustered fermentation concentration range is obtained, wherein the clustered fermentation concentration range is the intersection of the fermentation component concentrations in the set of fermentation component concentrations. The clustered fermentation concentration ranges are summarized to obtain a group of clustered fermentation concentration ranges. Based on the group of clustered fermentation concentration ranges, a group of configured fermentation concentrations is obtained, wherein the group of configured fermentation concentrations includes multiple configured fermentation concentrations, and the configured fermentation concentrations correspond one-to-one with the clustered fermentation concentration ranges, and the configured fermentation concentrations are the minimum values ​​of the clustered fermentation concentration ranges.

[0060] Obtain a set of configured component concentrations; based on the set of configured component concentrations and the set of configured fermentation concentrations, obtain a set of configured trace elements; associate the set of configured trace elements, the set of clustered fermentation concentration ranges, and the intermediate stage data to obtain classification parameter data; summarize the classification parameter data to obtain a classification stage database; summarize the initial stage database, the intermediate stage database, and the classification stage database to obtain a retrieval parameter database.

[0061] Optionally, retrieving supplementary classification data from the classification stage database using the classification parameter data includes:

[0062] Using the classification parameter data, the target cluster fermentation concentration range group is retrieved from the classification stage database. The classification fermentation concentrations in the classification parameter data are all located within the cluster fermentation concentration range corresponding to the target cluster fermentation concentration range group, and the classification supplementary data is the configuration trace element set corresponding to the target cluster fermentation concentration range group.

[0063] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0064] At least one processor; and,

[0065] A memory communicatively connected to the at least one processor; wherein,

[0066] The memory stores instructions that can be executed by the at least one processor to implement the above-described method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension.

[0067] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the above-described method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension.

[0068] Compared to the problems described in the background art, this invention utilizes a parameter optimization system to identify a retrieval parameter database. This database includes an initial stage database, a mid-stage database, and a classification stage database. Therefore, this invention considers dividing the preparation stages of organic water-soluble fertilizer before even starting the process of preparing water-soluble organic fertilizer from the initial peat suspension, and constructs different databases based on the characteristics of each stage. By retrieving the parameters required for processing the initial peat suspension from different databases, the accuracy and intelligence of setting the organic water-soluble fertilizer preparation parameters can be improved. This invention uses the initial peat suspension to obtain initial parameter data. Based on this initial parameter data, enzymatic hydrolysis control parameters are retrieved from the initial stage database. Based on the enzymatic hydrolysis control parameters and the initial peat suspension, an enzymatic hydrolysis stage suspension is obtained. Enzymatic hydrolysis parameter data is obtained from the enzymatic hydrolysis stage suspension. Based on the enzymatic hydrolysis parameter data, mid-stage control parameters are retrieved from the mid-stage database. Based on the mid-stage control parameters and the enzymatic hydrolysis stage suspension, a fermentation stage suspension is obtained. Classification parameter data is obtained from the fermentation stage suspension. Classification supplementary data is retrieved from the classification stage database using the classification parameter data. The target water-soluble organic fertilizer is then formulated using the classification supplementary data and the fermentation stage suspension. It is evident that before defining the database corresponding to each stage, this invention not only meets the requirements but also considers the parameters needed for that stage. This improves the intelligence and accuracy of setting parameters for organic water-soluble fertilizer preparation. Furthermore, when acquiring the suspension during the fermentation stage, only the classification parameter data is needed to retrieve supplementary classification data, improving the efficiency and intelligence of acquiring supplementary classification data. Therefore, the main objective of the proposed method, apparatus, electronic equipment, and computer-readable storage medium for optimizing organic water-soluble fertilizer preparation parameters based on peat suspension is to improve the accuracy and intelligence of setting organic water-soluble fertilizer preparation parameters. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating a method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension, provided in an embodiment of the present invention.

[0070] Figure 2 This is a schematic diagram of an electronic device for implementing the method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension, according to an embodiment of the present invention.

[0071] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0072] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0073] This application provides a method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension. The execution entity of this method includes, but is not limited to, at least one electronic device that can be configured to execute the method provided in this application, such as a server or a terminal. In other words, the method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0074] Example 1:

[0075] Reference Figure 1 The diagram shown is a flowchart illustrating a method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension according to an embodiment of the present invention. In this embodiment, the method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension includes:

[0076] S1. Receive parameter optimization instructions, and confirm the parameter optimization environment based on the parameter optimization instructions. The parameter optimization environment includes the parameter optimization system and the initial peat suspension to be treated. The parameter optimization system includes a parameter detection unit, a parameter control unit, and a parameter optimization unit.

[0077] It should be explained that the parameter optimization instruction is used to prepare peat suspension into water-soluble fertilizer. The parameter optimization environment refers to the necessary environment for preparing peat suspension into water-soluble fertilizer. This environment includes the initial peat suspension and the parameter optimization system. The parameter optimization system includes a parameter detection unit, a parameter control unit, and a parameter optimization unit. For specific applications of these units, please refer to subsequent embodiments. The initial peat suspension refers to the peat suspension to be treated.

[0078] For example, a producer of organic water-soluble fertilizer wants to use peat suspension to produce organic water-soluble fertilizer. Therefore, the producer issues the parameter optimization command and confirms the parameter optimization environment. By combining the parameters detected in the initial peat suspension, the parameter optimization system retrieves the parameters required to treat the initial peat suspension, thereby reducing the energy consumption required to treat the initial peat suspension. Therefore, the main purpose of this embodiment of the invention is to improve the accuracy of setting parameters for the preparation of organic water-soluble fertilizer.

[0079] S2. The retrieval parameter database is identified using a parameter optimization system. The retrieval parameter database includes an initial stage database, an intermediate stage database, and a classification stage database.

[0080] Understandably, the process of using the parameter optimization system to identify the retrieval parameter database includes:

[0081] Obtain a reference peat suspension set, which includes multiple reference peat suspensions. Extract reference peat suspensions sequentially from the reference peat suspension set and perform the following operations on the extracted reference peat suspensions:

[0082] The extracted reference peat suspension is stirred using a preset first stirring time and a pre-constructed stirring unit to obtain a stirred peat suspension. An analytical peat suspension group is obtained based on the stirred peat suspension and a preset initial volume. The analytical peat suspension group includes multiple analytical peat suspensions, and the volume of the analytical peat suspension is the initial volume.

[0083] The parameter detection instruction is received from the parameter detection unit, the parameter detection instruction is parsed, and a parameter detection model node set is obtained. The parameter detection model node set includes multiple parameter detection model nodes, and each parameter detection model node includes a parameter detection model and a reference detection error rate.

[0084] The following procedures were performed on each peat suspension in the peat suspension analysis group:

[0085] A filtration operation is performed on the peat suspension to obtain a filtered peat suspension. Based on the parameter detection model node set and the filtered peat suspension, a set of detected humic acid contents is obtained. The set of detected humic acid contents includes multiple detected humic acid contents, and the detected humic acid contents correspond one-to-one with the parameter detection model nodes.

[0086] The comprehensive humic acid content is calculated based on the detected humic acid content set and a pre-constructed comprehensive humic acid content relationship formula. The comprehensive humic acid content is then summarized to obtain a comprehensive humic acid content set. The comprehensive humic acid variance is obtained based on this set, and compared with a preset comprehensive humic acid threshold. If the comprehensive humic acid variance is less than or equal to the threshold, the comprehensive humic acid mean is obtained based on the comprehensive humic acid content set. This mean is compared with a preset humic acid mean threshold. If the comprehensive humic acid mean is greater than or equal to the threshold, the peat suspension group is labeled using the comprehensive humic acid mean, resulting in labeled peat. If the suspension group is otherwise, obtain the growth ratio of the first stirring time, calculate the product of the growth ratio and the first stirring time to obtain the second stirring time, take the second stirring time as the first stirring time, return to the step of stirring the extracted reference peat suspension using the preset first stirring time and the pre-constructed stirring unit to obtain the stirred peat suspension, count the number of times the second stirring time is obtained to obtain the number of cycles, when the number of cycles reaches the preset cycle threshold and the average value of comprehensive humic acid is less than the average value threshold of humic acid, then return to the step of sequentially extracting reference peat suspension from the reference peat suspension set and performing the following operations on the extracted reference peat suspension;

[0087] The identified peat suspension groups are aggregated to obtain the identified peat suspension group set, and the retrieval parameter database is constructed using the identified peat suspension group set.

[0088] It should be explained that the reference peat suspension refers to the collected peat suspension. Optionally, after collecting peat samples from a peat deposit, the collected peat samples are prepared into a reference peat suspension. Optionally, a mixer is used as the mixing unit. For example, if the total volume of the stirred peat suspension is 100 ml, with 10 ml as the preset initial volume, and three analytical peat suspensions are used as one analytical peat suspension group, then three analytical peat suspension groups can be obtained using the stirred peat suspension. The parameter detection model refers to the method that can determine the humic acid content in the analytical peat suspension. For example, the potassium dichromate oxidation method is used as the parameter detection model. The reference detection error rate refers to the error rate of the parameter detection model corresponding to the parameter detection model when performing detection. Optionally, the reference detection error rate is obtained through empirical methods. For example, if the error rate of the potassium dichromate oxidation method is ±5%, then the reference detection error rate is 5%.

[0089] It should be understood that filtering the analytical peat suspension refers to filtering out impurities from the analytical peat suspension. Optionally, atmospheric pressure filtration can be used to filter the analytical peat suspension. The variance of the comprehensive humic acid content refers to the variance of the total comprehensive humic acid content, and the mean of the comprehensive humic acid content refers to the mean of the total comprehensive humic acid content. The purpose of setting a threshold for the mean humic acid content is to verify that the humic acid in the reference peat suspension has recovery value. The purpose of setting a threshold for the comprehensive humic acid content is to confirm that the analytical peat suspension is uniformly stirred, thereby improving the accuracy of obtaining the comprehensive humic acid content. For example, if the mean comprehensive humic acid content of the analytical peat suspension group is 40%, then the analytical peat suspension group is identified as the 40% - analytical peat suspension group using the mean comprehensive humic acid content. Optionally, the growth rate is 20%, and if the first stirring time is 10 minutes, then the second stirring time is 2 minutes. Generally, when the number of cycles reaches a preset cycle threshold and the average humic acid value is less than the average humic acid value threshold, it indicates that the humic acid content in the reference peat suspension is too low and has no value for recovery. Therefore, it is necessary to obtain a new reference peat suspension from the reference peat suspension. Optionally, the average humic acid value threshold is set to 30% to ensure sufficient organic active ingredients in the organic water-soluble fertilizer. Before preparing the organic water-soluble fertilizer, it is also necessary to prepare nitrogen sources (such as urea, ammonium sulfate), phosphorus sources (such as monoammonium phosphate, potassium dihydrogen phosphate), potassium sources (such as potassium sulfate, potassium nitrate), and trace element compounds (such as magnesium sulfate, zinc sulfate, borax, etc.) according to the target fertilizer formula.

[0090] Furthermore, the relationship between the comprehensive humic acid content is as follows:

[0091]

[0092] Where Z represents the total humic acid content, α is a preset coefficient, and ω i ω j h represents the reference detection error rate corresponding to the i-th and j-th parameter detection model nodes in the parameter detection model node set, respectively. j The value represents the j-th humic acid content detected in the set of monitored humic acid content, and n represents the number of parameter detection model nodes included in the set of parameter detection model nodes.

[0093] Understandably, the formula for calculating the comprehensive humic acid content improves the accuracy of the calculation by assigning a larger weight to the humic acid content corresponding to the parameter detection model node with a smaller reference detection error rate. Furthermore, the accuracy of the obtained comprehensive humic acid content can be improved by fusing multiple reference detection models.

[0094] Furthermore, the construction of a retrieval parameter database using a set of identified peat suspensions includes:

[0095] For each labeled peat suspension group in the labeled peat suspension group set, the following operations shall be performed:

[0096] The parameter control instruction from the parameter control unit is confirmed to be received. The parameter control instruction is parsed to obtain the enzymatic hydrolysis parameter range set, wherein the enzymatic hydrolysis parameter range set includes four enzymatic hydrolysis parameter ranges, and the four enzymatic hydrolysis parameter ranges are the enzymatic hydrolysis concentration range, the enzymatic hydrolysis temperature range, the enzymatic hydrolysis pH range, and the enzymatic hydrolysis time range, respectively.

[0097] Perform the following operation for each enzymatic hydrolysis parameter range:

[0098] Using a preset division value and a pre-constructed uniform extraction algorithm, multiple first enzymatic hydrolysis parameters are extracted from the range of enzymatic hydrolysis parameters. The multiple first enzymatic hydrolysis parameters are summarized to obtain a first enzymatic hydrolysis parameter group. The first enzymatic hydrolysis parameter groups are summarized to obtain a first enzymatic hydrolysis parameter set. The number of first enzymatic hydrolysis parameters is the division value.

[0099] In a combined manner, multiple enzymatic hydrolysis parameter nodes are obtained by using multiple sets of first enzymatic hydrolysis parameters. Each enzymatic hydrolysis parameter node includes four first enzymatic hydrolysis parameters, and the first enzymatic hydrolysis parameters correspond one-to-one with the enzymatic hydrolysis parameter range.

[0100] Multiple enzymatic hydrolysis experimental groups were obtained based on the multiple enzymatic hydrolysis parameter nodes and the labeled peat suspension group, wherein each enzymatic hydrolysis experimental group includes an enzymatic hydrolysis parameter node and a labeled peat suspension.

[0101] For each of the multiple enzymatic digestion experimental groups, the following operations were performed:

[0102] Based on the enzymatic hydrolysis experimental group, the enzymatic hydrolysis organic content is obtained. The enzymatic hydrolysis organic content, the average comprehensive humic acid value corresponding to the peat suspension group, and the enzymatic hydrolysis parameter node corresponding to the enzymatic hydrolysis experimental group are associated to obtain associated enzymatic hydrolysis coordinates. The associated enzymatic hydrolysis coordinates are summarized to obtain associated enzymatic hydrolysis coordinate groups. The associated enzymatic hydrolysis coordinate groups are summarized to obtain associated enzymatic hydrolysis coordinate set. The retrieval parameter database is obtained based on the associated enzymatic hydrolysis coordinate set.

[0103] It should be explained that the enzymatic hydrolysis concentration range refers to the concentration range of the enzyme used when enzymatically hydrolyzing the labeled peat suspension. The enzymatic hydrolysis temperature range refers to the temperature range during enzymatic hydrolysis of the labeled peat suspension. The enzymatic hydrolysis pH range refers to the pH range of the labeled peat suspension after pH adjustment before enzymatic hydrolysis. The enzymatic hydrolysis time range refers to the time range set during enzymatic hydrolysis of the labeled peat suspension. Optionally, the enzymatic hydrolysis concentration range is 0.1% to 0.5% of the mass of the labeled peat suspension, the enzymatic hydrolysis temperature range is 30°C to 40°C, the enzymatic hydrolysis pH range is 5.5 to 6.5, and the enzymatic hydrolysis time range is 2 to 4 hours. Adjusting the pH of the labeled peat suspension is beneficial for the dissolution and absorption of organic water-soluble fertilizers in the soil.

[0104] It is understood that the uniform extraction algorithm refers to a method that can uniformly extract multiple values ​​within a range. Optionally, a linear interpolation algorithm is used as the uniform extraction algorithm. Obtaining the enzymatically hydrolyzed organic content based on the enzymatic hydrolysis experimental group includes: setting parameters for the enzymatic hydrolysis reaction using the enzymatic hydrolysis parameter nodes in the enzymatic hydrolysis experimental group. These parameters include a first enzymatic hydrolysis parameter corresponding to the enzymatic concentration range, a first enzymatic hydrolysis parameter corresponding to the enzymatic temperature range, and a first enzymatic hydrolysis parameter corresponding to the enzymatic time range. The pH of the labeled peat suspension is adjusted using the first enzymatic hydrolysis parameter corresponding to the enzymatic pH range to obtain an adjusted peat suspension. Under stirring conditions, the adjusted peat suspension is subjected to enzymatic hydrolysis to obtain an enzymatically hydrolyzed peat suspension. The enzymatically hydrolyzed organic content is then obtained based on the enzymatically hydrolyzed peat suspension.

[0105] It should be explained that pH adjustment refers to adjusting the pH of the peat suspension to the first enzymatic hydrolysis parameter corresponding to the pH range of enzymatic hydrolysis. Enzymatic hydrolysis organic content refers to the content of dissolved organic carbon contained in the enzymatically hydrolyzed peat suspension. Optionally, the organic content can be obtained using ultraviolet spectrophotometry; other techniques can achieve the same effect and will not be elaborated further. Enzymatic hydrolysis refers to the operation of using enzymes to hydrolyze the substances in the peat suspension, which is existing technology and will not be elaborated further. Enzymatic hydrolysis can decompose large molecular cellulose, proteins, and other organic matter in the peat suspension into small molecular sugars, amino acids, etc., improving the nutrient availability of organic water-soluble fertilizers.

[0106] It is understood that obtaining the retrieval parameter database based on the associated enzymatic hydrolysis coordinate set includes:

[0107] Using the enzymatically hydrolyzed organic content in the associated enzymatic hydrolysis coordinate set as the dependent variable, an enzymatic hydrolysis fitting surface is obtained using the associated enzymatic hydrolysis coordinate set and a pre-constructed surface fitting model. An optimized enzymatic hydrolysis parameter set is obtained using the enzymatic hydrolysis fitting surface. The optimized enzymatic hydrolysis parameter set is the associated enzymatic hydrolysis coordinate set corresponding to the largest enzymatically hydrolyzed organic content in the enzymatic hydrolysis fitting surface. The optimized organic content is obtained based on the optimized enzymatic hydrolysis parameter set.

[0108] The ratio of the difference in enzymatic hydrolysis content is calculated using the optimized organic content and the corresponding organic content of the optimized enzymatic hydrolysis parameter set. The calculation formula is as follows:

[0109]

[0110] Where L represents the ratio of the difference in enzymatic hydrolysis content, l1 represents the optimized organic content, and l0 represents the enzymatic hydrolysis organic content corresponding to the optimized enzymatic hydrolysis parameter group;

[0111] Compare the ratio of the enzymatic hydrolysis content difference with a preset difference ratio threshold. After confirming that the ratio of the enzymatic hydrolysis content difference is less than or equal to the difference ratio threshold, calculate the product of the preset evaluation enzymatic hydrolysis ratio and the optimized organic content to obtain the evaluation organic content.

[0112] A database of search parameters is obtained based on the assessed organic content.

[0113] Furthermore, a surface fitting model refers to a model or algorithm capable of fitting discrete points of multiple independent variables or a single dependent variable to a surface. Optionally, the least squares method is used as the surface fitting model. The method for obtaining the optimized organic content using the optimized enzymatic hydrolysis parameter set is the same as the method for obtaining the enzymatic hydrolysis organic content using the enzymatic hydrolysis experimental set, and will not be repeated here. The evaluation enzymatic hydrolysis ratio can be a manually set value, used to evaluate the proportion that achieves the optimal degree of enzymatic hydrolysis. For example, if the evaluation enzymatic hydrolysis ratio is 80%, then when the peat suspension is enzymatically hydrolyzed, the enzymatic hydrolysis product can reach 80% of the ideal enzymatic hydrolysis product.

[0114] It should be understood that the database for obtaining search parameters based on the assessed organic content includes:

[0115] Using the aforementioned assessment of organic content, one or more initial enzymatic hydrolysis curves are extracted from the enzymatic hydrolysis fitting surface to obtain a set of unit energy consumption parameters during enzymatic hydrolysis. The set of unit energy consumption parameters includes four unit energy consumption parameters, namely, unit stirring energy consumption, unit enzymatic hydrolysis energy consumption, unit temperature energy consumption, and unit acid-base energy consumption.

[0116] The weighted reorganization of unit energy consumption is calculated based on the unit energy consumption parameter set, and the calculation formula is as follows:

[0117]

[0118] Where, q w g represents the weight of the w-th unit energy consumption in the unit energy consumption weight reorganization. w g represents the w-th unit energy consumption in the unit energy consumption parameter group. e This represents the e-th unit energy consumption in the unit energy consumption parameter group;

[0119] For each of the one or more initial enzymatic hydrolysis curves, the following operation shall be performed:

[0120] An initial enzymatic hydrolysis function is obtained based on the initial enzymatic hydrolysis curve. An analytical function relationship is constructed based on the initial enzymatic hydrolysis function and the unit energy consumption weighted recombination. A local optimal parameter set is obtained using the analytical function relationship. A local optimal energy consumption value is obtained based on the local optimal parameter set and the unit energy consumption parameter set. The local optimal energy consumption values ​​are summarized to obtain a local optimal energy consumption value set. The global optimal parameter set is determined using the local optimal energy consumption value set. The global optimal parameter set is the local optimal parameter set corresponding to the smallest local optimal energy consumption value in the local optimal energy consumption value set.

[0121] By associating the global optimal parameter group with the evaluation of organic content and the average value of comprehensive humic acid, initial parameter nodes are obtained. The initial parameter nodes are then summarized to obtain an initial stage database. Based on the initial stage database, a retrieval parameter database is obtained.

[0122] Understandably, by using the aforementioned assessment of organic content, one or more initial enzymatic hydrolysis curves are extracted from the enzymatic hydrolysis fitting surface. This is equivalent to constructing a cutoff plane using the assessed organic content. The value corresponding to the cutoff plane is the assessed organic content, and the initial enzymatic hydrolysis curve is the curve corresponding to the intersection of the enzymatic hydrolysis fitting surface and the cutoff plane. Unit stirring energy consumption refers to the unit energy consumption required to maintain stirring conditions. Unit temperature energy consumption refers to the unit energy consumption required to maintain the temperature required for enzymatic hydrolysis. Unit enzymatic hydrolysis energy consumption is the ratio of the maximum energy consumption of the enzyme to time; here, time can be calculated using the minimum value within the enzymatic hydrolysis time range. Unit acid-base energy consumption refers to the ratio of the maximum energy consumption corresponding to adjusting pH to time; here, time can be calculated using the minimum value within the enzymatic hydrolysis time range. Generally, unit enzymatic hydrolysis energy consumption and unit acid-base energy consumption are smaller than unit temperature energy consumption and unit stirring energy consumption. Therefore, in this embodiment of the invention, estimated values ​​are used as unit enzymatic hydrolysis energy consumption and unit acid-base energy consumption to simplify calculations.

[0123] Furthermore, the initial enzymatic hydrolysis curve can be fitted to an initial enzymatic hydrolysis function using the least squares method. The locally optimal parameter set consists of the enzymatic concentration, temperature, pH, and time at which energy consumption is minimized, calculated using analytical functional relationships. Obtaining the locally optimal energy consumption value based on the locally optimal parameter set and the unit energy consumption parameter set means summing the energy consumption values ​​obtained by multiplying the parameters in the locally optimal parameter set by their corresponding weight values.

[0124] It should be explained that the analytical function relationship is as follows:

[0125]

[0126] Where E represents energy consumption. The values ​​represent the weights corresponding to the unit stirring energy consumption, unit enzymatic hydrolysis energy consumption, unit temperature energy consumption, and unit acid-base energy consumption in the unit energy consumption weighting reorganization, respectively. b1, b2, b3, and b4 represent the unit stirring energy consumption, unit enzymatic hydrolysis energy consumption, unit temperature energy consumption, and unit acid-base energy consumption during the enzymatic hydrolysis operation, respectively. f(b1,b2,b3,b4) represents the initial enzymatic hydrolysis function, p represents the evaluated organic content, J represents the analytical function relationship, and σ is a preset variable.

[0127] It should be understood that obtaining the local optimal parameter set using analytical function relations refers to obtaining the enzymatic hydrolysis concentration, enzymatic hydrolysis temperature, enzymatic hydrolysis pH and enzymatic hydrolysis time under the condition of minimum energy consumption after solving the constructed analytical function relations. The solution process is to solve the Lagrange equation under the condition of extreme value, which is the existing technology and will not be described in detail here.

[0128] Further, the step of obtaining the retrieval parameter database based on the initial stage database includes:

[0129] Based on each initial parameter node in the initial stage database, the initial enzymatic hydrolysis suspension is obtained, and the set of fermentation parameter ranges for fermentation is obtained. The set of fermentation parameter ranges includes multiple fermentation parameter ranges, including the range of fermentation strain concentration, the range of fermentation time, and the range of fermentation temperature.

[0130] Using the initial enzymatic hydrolysis suspension and fermentation parameter range set, a global fermentation parameter set is obtained. The global fermentation parameter set is associated with the initial parameter node to obtain intermediate stage data. The intermediate stage data is then summarized to obtain an intermediate stage database.

[0131] The retrieval parameter database is obtained based on the intermediate stage database.

[0132] Understandably, the initial enzymatic hydrolysis suspension is a peat suspension obtained under the conditions of the initial parameter nodes, using a labeled peat suspension. The fermentation strain concentration range refers to the range of fermentation strain concentrations required for fermenting the initial enzymatic hydrolysis suspension. The fermentation time range refers to the range of time required for fermenting the initial enzymatic hydrolysis suspension. The fermentation temperature range refers to the range of temperatures required for fermenting the initial enzymatic hydrolysis suspension. The method for obtaining the global fermentation parameter set using the aforementioned initial enzymatic hydrolysis suspension and fermentation parameter range set is the same as the method for obtaining the globally optimal parameter set, and will not be elaborated here.

[0133] It should be explained that the step of obtaining the retrieval parameter database based on the intermediate stage database includes:

[0134] Perform the following operations on each piece of data in the interim phase database:

[0135] The initial fermentation suspension is obtained based on the global fermentation parameter set corresponding to the mid-term stage data. The fermentation component concentration set is then obtained using the initial fermentation suspension. The fermentation component set includes the concentrations of multiple fermentation components identified by their names.

[0136] The fermentation component concentration groups are summarized to obtain a fermentation component concentration group set. The parameter optimization instruction from the parameter optimization unit is confirmed to be received. The parameter optimization instruction is parsed to obtain an analytical clustering method. The analytical clustering method is used to cluster the fermentation component groups in the fermentation component concentration group set to obtain one or more fermentation cluster sets.

[0137] For each of one or more fermentation clusters, perform the following operation:

[0138] Based on the fermentation component names, the concentrations of fermentation components in the fermentation clusters are summarized to obtain a set of fermentation component concentrations. Based on the set of fermentation component concentrations, a clustered fermentation concentration range is obtained, wherein the clustered fermentation concentration range is the intersection of the fermentation component concentrations in the set of fermentation component concentrations. The clustered fermentation concentration ranges are summarized to obtain a group of clustered fermentation concentration ranges. Based on the group of clustered fermentation concentration ranges, a group of configured fermentation concentrations is obtained, wherein the group of configured fermentation concentrations includes multiple configured fermentation concentrations, and the configured fermentation concentrations correspond one-to-one with the clustered fermentation concentration ranges, and the configured fermentation concentrations are the minimum values ​​of the clustered fermentation concentration ranges.

[0139] Obtain a set of configured component concentrations; based on the set of configured component concentrations and the set of configured fermentation concentrations, obtain a set of configured trace elements; associate the set of configured trace elements, the set of clustered fermentation concentration ranges, and the intermediate stage data to obtain classification parameter data; summarize the classification parameter data to obtain a classification stage database; summarize the initial stage database, the intermediate stage database, and the classification stage database to obtain a retrieval parameter database.

[0140] It should be understood that the initial fermentation suspension refers to the suspension obtained after fermenting the initial enzymatic hydrolysis suspension under the conditions of the global fermentation parameter set. This is existing technology and will not be elaborated further here. During fermentation, the organic matter in the initial enzymatic hydrolysis suspension will be further decomposed, producing beneficial metabolites such as humic acids and plant growth hormones. At the same time, some inorganic nutrients will be converted into organic forms, improving the fertilizer efficiency and stability of the organic water-soluble fertilizer. Fermentation component concentration refers to the concentration of the component used as an indicator in the initial fermentation suspension. For example, fermentation component concentration refers to the concentration of potassium ions in the initial fermentation suspension. Fermentation component name refers to the name of the fermentation component concentration, such as potassium ions. Optionally, the k-means clustering algorithm can be used as the analytical clustering algorithm. Other techniques can achieve the same effect and will not be elaborated further here. Configuring trace elements refers to the concentration that needs to be supplemented calculated using the configured component concentrations in the configured component concentration set and the corresponding configured fermentation concentrations in the configured fermentation concentration set. For example, if the fermentation concentration is set at 5% potassium ion concentration and the component concentration is set at 10% potassium ion concentration, then it means that an additional 5% potassium ion concentration needs to be added. Here, 5% is the configuration of trace elements.

[0141] It should be explained that the clustered fermentation concentration range refers to the concentration corresponding to a specific fermentation component name after fermentation of a class of initial peat suspensions. Using clustering to obtain configured fermentation concentration sets can clearly identify the components of a particular class of initial peat suspensions after fermentation, thereby improving the efficiency of peat suspension classification. Optionally, the configured component concentration set can be obtained through production standards.

[0142] Understandably, this invention identifies optimal parameter sets for different peat suspensions at different stages. Here, the optimal parameter set refers to the parameter set with the lowest energy consumption. The optimal parameter set is then used to determine the parameters corresponding to the peat suspension in the next stage. These parameters characterize the peat suspension, including organic matter content, etc. By associating the optimal parameter sets of different peat suspensions at different stages, a database for each stage is obtained. This allows for the retrieval of different parameter sets from the database, based on the actual parameters for preparing organic water-soluble fertilizers at different stages. This improves the accuracy and intelligence of the parameters required for preparing organic water-soluble fertilizers from peat suspensions.

[0143] S3. Obtain initial parameter data using the initial peat suspension, retrieve enzymatic hydrolysis control parameters from the initial stage database based on the initial parameter data, and obtain the enzymatic hydrolysis stage suspension based on the enzymatic hydrolysis control parameters and the initial peat suspension.

[0144] It should be explained that the initial parameter data refers to the monitored humic acid in the initial peat suspension. The method for obtaining the monitored humic acid is the same as the method for obtaining the average comprehensive humic acid value, and will not be repeated here. The enzymatic hydrolysis control parameters refer to the globally optimal parameter set corresponding to the monitored humic acid. The method for obtaining the enzymatic hydrolysis stage suspension based on the enzymatic hydrolysis control parameters and the initial peat suspension is the same as the method for obtaining the enzymatically hydrolyzed peat suspension, and will not be repeated here.

[0145] S4. Obtain enzymatic hydrolysis parameter data using the enzymatic hydrolysis stage suspension, retrieve mid-term control parameters from the mid-term stage database based on the enzymatic hydrolysis parameter data, and obtain the fermentation stage suspension based on the mid-term control parameters and the enzymatic hydrolysis stage suspension.

[0146] It should be explained that the enzymatic hydrolysis parameter data refers to the enzymatic hydrolysis organic content. The method for obtaining the enzymatic hydrolysis organic content is the same as the method for obtaining the assessed organic content, and will not be repeated here. The enzymatic hydrolysis organic content refers to the concentration of organic matter in the suspension during the enzymatic hydrolysis stage. The mid-term control parameters refer to the global fermentation parameter set corresponding to the assessed organic content. Generally speaking, obtaining the fermentation stage suspension based on the mid-term control parameters and the enzymatic hydrolysis stage suspension means fermenting the enzymatic hydrolysis stage suspension under the conditions of the mid-term control parameters, which is existing technology and will not be repeated here. Generally speaking, when a corresponding parameter cannot be found in the database, a similar parameter can be used as a substitute. Here, a similar parameter refers to a parameter whose Euclidean distance is less than a certain value.

[0147] S5. Obtain classification parameter data using the suspension during the fermentation stage, retrieve supplementary classification data from the classification stage database using the classification parameter data, and formulate the target water-soluble organic fertilizer using the supplementary classification data and the suspension during the fermentation stage.

[0148] It should be explained that the method for obtaining the classification parameter data is the same as the method for obtaining the fermentation component concentration group, which is existing technology and will not be elaborated here. The method of using classification supplementation data and fermentation stage suspension to prepare the target water-soluble organic fertilizer refers to: using classification supplementation data to obtain specific amounts of the micronutrients to be supplemented, and using the micronutrients to be supplemented and the fermentation stage suspension to prepare the target water-soluble organic fertilizer; this is existing technology and will not be elaborated here. For example, the micronutrients to be supplemented and the fermentation stage suspension are added to a stirring device, the stirring device is turned on, and the components are thoroughly mixed. The stirring speed is controlled at 100-200 rpm, and the stirring time is 30-60 minutes. The nutrient concentration in the mixture is monitored in real time using chemical analysis methods (such as the Kjeldahl method for nitrogen content determination, the molybdenum-antimony colorimetric method for phosphorus content determination, and the flame photometry method for potassium content determination, etc.) to ensure that the nutrient ratio requirements of the target water-soluble organic fertilizer are met.

[0149] Furthermore, retrieving supplementary classification data from the classification stage database using the classification parameter data includes:

[0150] Using the classification parameter data, the target cluster fermentation concentration range group is retrieved from the classification stage database. The classification fermentation concentrations in the classification parameter data are all located within the cluster fermentation concentration range corresponding to the target cluster fermentation concentration range group, and the classification supplementary data is the configuration trace element set corresponding to the target cluster fermentation concentration range group.

[0151] It should be explained that the target clustering fermentation concentration range group can quickly identify the names and concentrations of trace elements that need to be supplemented, thereby improving the intelligence of the initial peat suspension classification. The method for treating the initial peat suspension is the same as the method for obtaining the retrieval parameter database, and will not be repeated here. Generally, appropriate adjuvants, such as anti-crystallization agents, preservatives, and chelating agents, can be added when preparing target water-soluble organic fertilizers to improve the physicochemical properties of the fertilizer and extend the product's shelf life. For example, polycarboxylate substances can be used as anti-crystallization agents, with an addition amount of 0.1%-0.3% of the fertilizer mass; sodium benzoate can be used as a preservative, with an addition amount of 0.05%-0.1%; and ethylenediaminetetraacetic acid (EDTA) and its salts can be used as chelating agents to chelate trace elements, improving their stability and effectiveness. The addition amount can be determined according to the type and content of the trace elements.

[0152] Compared to the problems described in the background art, this invention utilizes a parameter optimization system to identify a retrieval parameter database. This database includes an initial stage database, a mid-stage database, and a classification stage database. Therefore, this invention considers dividing the preparation stages of organic water-soluble fertilizer before even starting the process of preparing water-soluble organic fertilizer from the initial peat suspension, and constructs different databases based on the characteristics of each stage. By retrieving the parameters required for processing the initial peat suspension from different databases, the accuracy and intelligence of setting the organic water-soluble fertilizer preparation parameters can be improved. This invention uses the initial peat suspension to obtain initial parameter data. Based on this initial parameter data, enzymatic hydrolysis control parameters are retrieved from the initial stage database. Based on the enzymatic hydrolysis control parameters and the initial peat suspension, an enzymatic hydrolysis stage suspension is obtained. Enzymatic hydrolysis parameter data is obtained from the enzymatic hydrolysis stage suspension. Based on the enzymatic hydrolysis parameter data, mid-stage control parameters are retrieved from the mid-stage database. Based on the mid-stage control parameters and the enzymatic hydrolysis stage suspension, a fermentation stage suspension is obtained. Classification parameter data is obtained from the fermentation stage suspension. Classification supplementary data is retrieved from the classification stage database using the classification parameter data. The target water-soluble organic fertilizer is then formulated using the classification supplementary data and the fermentation stage suspension. It is evident that before defining the database corresponding to each stage, this invention not only meets the requirements but also considers the parameters needed for that stage. This improves the intelligence and accuracy of setting parameters for organic water-soluble fertilizer preparation. Furthermore, when acquiring the suspension during the fermentation stage, only the classification parameter data is needed to retrieve supplementary classification data, improving the efficiency and intelligence of acquiring supplementary classification data. Therefore, the main objective of the proposed method, apparatus, electronic equipment, and computer-readable storage medium for optimizing organic water-soluble fertilizer preparation parameters based on peat suspension is to improve the accuracy and intelligence of setting organic water-soluble fertilizer preparation parameters.

[0153] Example 2:

[0154] like Figure 2 The diagram shown is a schematic representation of an electronic device for implementing a method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension, according to an embodiment of the present invention.

[0155] The electronic device 1 may include a processor 10, a memory 11, a bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a parameter optimization program for the preparation of organic water-soluble fertilizer based on peat suspension.

[0156] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard. Furthermore, the memory 11 can include both internal and external storage units of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code for an optimization program of organic water-soluble fertilizer preparation parameters based on peat suspension, but also to temporarily store data that has been output or will be output.

[0157] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a parameter optimization program for organic water-soluble fertilizer preparation based on peat suspension), and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0158] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0159] Figure 2 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 2The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0160] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0161] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0162] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0163] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0164] The optimization program for preparing organic water-soluble fertilizer based on peat suspension, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0165] Receive parameter optimization instructions, and confirm the parameter optimization environment based on the parameter optimization instructions. The parameter optimization environment includes the parameter optimization system and the initial peat suspension to be treated. The parameter optimization system includes a parameter detection unit, a parameter control unit and a parameter optimization unit.

[0166] The retrieval parameter database was identified using a parameter optimization system. This database includes an initial stage database, an intermediate stage database, and a classification stage database.

[0167] Initial parameter data is obtained using the initial peat suspension. Based on the initial parameter data, enzymatic hydrolysis control parameters are retrieved from the initial stage database. Enzymatic hydrolysis stage suspension is obtained based on the enzymatic hydrolysis control parameters and the initial peat suspension.

[0168] Enzymatic hydrolysis parameter data is obtained using the enzymatic hydrolysis stage suspension. Based on the enzymatic hydrolysis parameter data, mid-term control parameters are retrieved from the mid-term stage database. Based on the mid-term control parameters and the enzymatic hydrolysis stage suspension, the fermentation stage suspension is obtained.

[0169] Classification parameter data is obtained using the suspension during the fermentation stage. Supplementary classification data is retrieved from the classification stage database using the classification parameter data. The target water-soluble organic fertilizer is then prepared using the supplementary classification data and the suspension during the fermentation stage.

[0170] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 2 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0171] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0172] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0173] Receive parameter optimization instructions, and confirm the parameter optimization environment based on the parameter optimization instructions. The parameter optimization environment includes the parameter optimization system and the initial peat suspension to be treated. The parameter optimization system includes a parameter detection unit, a parameter control unit and a parameter optimization unit.

[0174] The retrieval parameter database was identified using a parameter optimization system. This database includes an initial stage database, an intermediate stage database, and a classification stage database.

[0175] Initial parameter data is obtained using the initial peat suspension. Based on the initial parameter data, enzymatic hydrolysis control parameters are retrieved from the initial stage database. Enzymatic hydrolysis stage suspension is obtained based on the enzymatic hydrolysis control parameters and the initial peat suspension.

[0176] Enzymatic hydrolysis parameter data is obtained using the enzymatic hydrolysis stage suspension. Based on the enzymatic hydrolysis parameter data, mid-term control parameters are retrieved from the mid-term stage database. Based on the mid-term control parameters and the enzymatic hydrolysis stage suspension, the fermentation stage suspension is obtained.

[0177] Classification parameter data is obtained using the suspension during the fermentation stage. Supplementary classification data is retrieved from the classification stage database using the classification parameter data. The target water-soluble organic fertilizer is then prepared using the supplementary classification data and the suspension during the fermentation stage.

[0178] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0179] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0180] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension, characterized in that, The method includes: Receive parameter optimization instructions, and confirm the parameter optimization environment based on the parameter optimization instructions. The parameter optimization environment includes the parameter optimization system and the initial peat suspension to be treated. The parameter optimization system includes a parameter detection unit, a parameter control unit and a parameter optimization unit. The retrieval parameter database was identified using a parameter optimization system. This database includes an initial stage database, an intermediate stage database, and a classification stage database. Initial parameter data is obtained using the initial peat suspension. Based on the initial parameter data, enzymatic hydrolysis control parameters are retrieved from the initial stage database. Enzymatic hydrolysis stage suspension is obtained based on the enzymatic hydrolysis control parameters and the initial peat suspension. Enzymatic hydrolysis parameter data is obtained using the enzymatic hydrolysis stage suspension. Based on the enzymatic hydrolysis parameter data, mid-term control parameters are retrieved from the mid-term stage database. Based on the mid-term control parameters and the enzymatic hydrolysis stage suspension, the fermentation stage suspension is obtained. Classification parameter data is obtained using the suspension during the fermentation stage. Supplementary classification data is retrieved from the classification stage database using the classification parameter data. The target water-soluble organic fertilizer is then prepared using the supplementary classification data and the suspension during the fermentation stage.

2. The method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension as described in claim 1, characterized in that, The process of using a parameter optimization system to identify the retrieval parameter database includes: Obtain a reference peat suspension set, which includes multiple reference peat suspensions. Extract reference peat suspensions sequentially from the reference peat suspension set and perform the following operations on the extracted reference peat suspensions: The extracted reference peat suspension is stirred using a preset first stirring time and a pre-constructed stirring unit to obtain a stirred peat suspension. An analytical peat suspension group is obtained based on the stirred peat suspension and a preset initial volume. The analytical peat suspension group includes multiple analytical peat suspensions, and the volume of the analytical peat suspension is the initial volume. The parameter detection instruction is received from the parameter detection unit, the parameter detection instruction is parsed, and a parameter detection model node set is obtained. The parameter detection model node set includes multiple parameter detection model nodes, and each parameter detection model node includes a parameter detection model and a reference detection error rate. The following procedures were performed on each peat suspension in the peat suspension analysis group: A filtration operation is performed on the peat suspension to obtain a filtered peat suspension. Based on the parameter detection model node set and the filtered peat suspension, a set of detected humic acid contents is obtained. The set of detected humic acid contents includes multiple detected humic acid contents, and the detected humic acid contents correspond one-to-one with the parameter detection model nodes. The comprehensive humic acid content is calculated based on the detected humic acid content set and a pre-constructed comprehensive humic acid content relationship formula. The comprehensive humic acid content is then summarized to obtain a comprehensive humic acid content set. The comprehensive humic acid variance is obtained based on this set, and compared with a preset comprehensive humic acid threshold. If the comprehensive humic acid variance is less than or equal to the threshold, the comprehensive humic acid mean is obtained based on the comprehensive humic acid content set. This mean is compared with a preset humic acid mean threshold. If the comprehensive humic acid mean is greater than or equal to the threshold, the peat suspension group is labeled using the comprehensive humic acid mean, resulting in labeled peat. If the suspension group is otherwise, obtain the growth ratio of the first stirring time, calculate the product of the growth ratio and the first stirring time to obtain the second stirring time, take the second stirring time as the first stirring time, return to the step of stirring the extracted reference peat suspension using the preset first stirring time and the pre-constructed stirring unit to obtain the stirred peat suspension, count the number of times the second stirring time is obtained to obtain the number of cycles, when the number of cycles reaches the preset cycle threshold and the average value of comprehensive humic acid is less than the average value threshold of humic acid, then return to the step of sequentially extracting reference peat suspension from the reference peat suspension set and performing the following operations on the extracted reference peat suspension; The identified peat suspension groups are aggregated to obtain the identified peat suspension group set, and the retrieval parameter database is constructed using the identified peat suspension group set.

3. The method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension as described in claim 2, characterized in that, The formula for the comprehensive humic acid content is as follows: Where Z represents the total humic acid content, α is a preset coefficient, and ω i ω j h represents the reference detection error rate corresponding to the i-th and j-th parameter detection model nodes in the parameter detection model node set, respectively. j The value represents the j-th humic acid content detected in the monitoring set, and n represents the number of parameter detection model nodes included in the parameter detection model node set.

4. The method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension as described in claim 3, characterized in that, The method of constructing a retrieval parameter database using a set of identified peat suspensions includes: For each labeled peat suspension group in the labeled peat suspension group set, the following operations shall be performed: The parameter control instruction from the parameter control unit is confirmed to be received. The parameter control instruction is parsed to obtain the enzymatic hydrolysis parameter range set, wherein the enzymatic hydrolysis parameter range set includes four enzymatic hydrolysis parameter ranges, and the four enzymatic hydrolysis parameter ranges are the enzymatic hydrolysis concentration range, the enzymatic hydrolysis temperature range, the enzymatic hydrolysis pH range, and the enzymatic hydrolysis time range, respectively. Perform the following operation for each enzymatic hydrolysis parameter range: Using a preset division value and a pre-constructed uniform extraction algorithm, multiple first enzymatic hydrolysis parameters are extracted from the range of enzymatic hydrolysis parameters. The multiple first enzymatic hydrolysis parameters are summarized to obtain a first enzymatic hydrolysis parameter group. The first enzymatic hydrolysis parameter groups are summarized to obtain a first enzymatic hydrolysis parameter set. The number of first enzymatic hydrolysis parameters is the division value. In a combined manner, multiple enzymatic hydrolysis parameter nodes are obtained by using multiple sets of first enzymatic hydrolysis parameters. Each enzymatic hydrolysis parameter node includes four first enzymatic hydrolysis parameters, and the first enzymatic hydrolysis parameters correspond one-to-one with the enzymatic hydrolysis parameter range. Multiple enzymatic hydrolysis experimental groups were obtained based on the multiple enzymatic hydrolysis parameter nodes and the labeled peat suspension group, wherein each enzymatic hydrolysis experimental group includes an enzymatic hydrolysis parameter node and a labeled peat suspension. For each of the multiple enzymatic digestion experimental groups, the following operations were performed: Based on the enzymatic hydrolysis experimental group, the enzymatic hydrolysis organic content is obtained. The enzymatic hydrolysis organic content, the average comprehensive humic acid value corresponding to the peat suspension group, and the enzymatic hydrolysis parameter node corresponding to the enzymatic hydrolysis experimental group are associated to obtain associated enzymatic hydrolysis coordinates. The associated enzymatic hydrolysis coordinates are summarized to obtain associated enzymatic hydrolysis coordinate groups. The associated enzymatic hydrolysis coordinate groups are summarized to obtain associated enzymatic hydrolysis coordinate set. The retrieval parameter database is obtained based on the associated enzymatic hydrolysis coordinate set.

5. The method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension as described in claim 4, characterized in that, The step of obtaining the retrieval parameter database based on the associated enzymatic hydrolysis coordinate set includes: Using the enzymatically hydrolyzed organic content in the associated enzymatic hydrolysis coordinate set as the dependent variable, an enzymatic hydrolysis fitting surface is obtained using the associated enzymatic hydrolysis coordinate set and a pre-constructed surface fitting model. An optimized enzymatic hydrolysis parameter set is obtained using the enzymatic hydrolysis fitting surface. The optimized enzymatic hydrolysis parameter set is the associated enzymatic hydrolysis coordinate set corresponding to the largest enzymatically hydrolyzed organic content in the enzymatic hydrolysis fitting surface. The optimized organic content is obtained based on the optimized enzymatic hydrolysis parameter set. The ratio of the difference in enzymatic hydrolysis content is calculated using the optimized organic content and the corresponding organic content of the optimized enzymatic hydrolysis parameter set. The calculation formula is as follows: Where L represents the ratio of the difference in enzymatic hydrolysis content, l1 represents the optimized organic content, and l0 represents the enzymatic hydrolysis organic content corresponding to the optimized enzymatic hydrolysis parameter group; Compare the ratio of the enzymatic hydrolysis content difference with a preset difference ratio threshold. After confirming that the ratio of the enzymatic hydrolysis content difference is less than or equal to the difference ratio threshold, calculate the product of the preset evaluation enzymatic hydrolysis ratio and the optimized organic content to obtain the evaluation organic content. A database of search parameters is obtained based on the assessed organic content.

6. The method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension as described in claim 5, characterized in that, The database for obtaining search parameters based on the assessed organic content includes: Using the aforementioned assessment of organic content, one or more initial enzymatic hydrolysis curves are extracted from the enzymatic hydrolysis fitting surface to obtain a set of unit energy consumption parameters during enzymatic hydrolysis. The set of unit energy consumption parameters includes four unit energy consumption parameters, namely, unit stirring energy consumption, unit enzymatic hydrolysis energy consumption, unit temperature energy consumption, and unit acid-base energy consumption. The weighted reorganization of unit energy consumption is calculated based on the unit energy consumption parameter set, and the calculation formula is as follows: Where, q w g represents the weight of the w-th unit energy consumption in the unit energy consumption weight reorganization. w g represents the w-th unit energy consumption in the unit energy consumption parameter group. e This represents the e-th unit energy consumption in the unit energy consumption parameter group; For each of the one or more initial enzymatic hydrolysis curves, the following operation shall be performed: An initial enzymatic hydrolysis function is obtained based on the initial enzymatic hydrolysis curve. An analytical function relationship is constructed based on the initial enzymatic hydrolysis function and the unit energy consumption weighted recombination. A local optimal parameter set is obtained using the analytical function relationship. A local optimal energy consumption value is obtained based on the local optimal parameter set and the unit energy consumption parameter set. The local optimal energy consumption values ​​are summarized to obtain a local optimal energy consumption value set. The global optimal parameter set is determined using the local optimal energy consumption value set. The global optimal parameter set is the local optimal parameter set corresponding to the smallest local optimal energy consumption value in the local optimal energy consumption value set. By associating the global optimal parameter group with the evaluation of organic content and the average value of comprehensive humic acid, initial parameter nodes are obtained. The initial parameter nodes are then summarized to obtain an initial stage database. Based on the initial stage database, a retrieval parameter database is obtained.

7. The method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension as described in claim 6, characterized in that, The analytical function relationship is shown below: Where E represents energy consumption. The values ​​represent the weights corresponding to the unit stirring energy consumption, unit enzymatic hydrolysis energy consumption, unit temperature energy consumption, and unit acid-base energy consumption in the unit energy consumption weighting reorganization, respectively. b1, b2, b3, and b4 represent the unit stirring energy consumption, unit enzymatic hydrolysis energy consumption, unit temperature energy consumption, and unit acid-base energy consumption during the enzymatic hydrolysis operation, respectively. f(b1,b2,b3,b4) represents the initial enzymatic hydrolysis function, p represents the evaluated organic content, J represents the analytical function relationship, and σ is a preset variable.

8. The method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension as described in claim 7, characterized in that, The step of obtaining the retrieval parameter database based on the initial stage database includes: Based on each initial parameter node in the initial stage database, the initial enzymatic hydrolysis suspension is obtained, and the set of fermentation parameter ranges for fermentation is obtained. The set of fermentation parameter ranges includes multiple fermentation parameter ranges, including the range of fermentation strain concentration, the range of fermentation time, and the range of fermentation temperature. Using the initial enzymatic hydrolysis suspension and fermentation parameter range set, a global fermentation parameter set is obtained. The global fermentation parameter set is associated with the initial parameter node to obtain intermediate stage data. The intermediate stage data is then summarized to obtain an intermediate stage database. The retrieval parameter database is obtained based on the intermediate stage database.

9. The method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension as described in claim 8, characterized in that, The step of obtaining the retrieval parameter database based on the intermediate stage database includes: Perform the following operations on each piece of data in the interim phase database: The initial fermentation suspension is obtained based on the global fermentation parameter set corresponding to the mid-term stage data. The fermentation component concentration set is then obtained using the initial fermentation suspension. The fermentation component set includes the concentrations of multiple fermentation components identified by their names. The fermentation component concentration groups are summarized to obtain a fermentation component concentration group set. The parameter optimization instruction from the parameter optimization unit is confirmed to be received. The parameter optimization instruction is parsed to obtain an analytical clustering method. The analytical clustering method is used to cluster the fermentation component groups in the fermentation component concentration group set to obtain one or more fermentation cluster sets. For each of one or more fermentation clusters, perform the following operation: Based on the fermentation component names, the concentrations of fermentation components in the fermentation clusters are summarized to obtain a set of fermentation component concentrations. Based on the set of fermentation component concentrations, a clustered fermentation concentration range is obtained, wherein the clustered fermentation concentration range is the intersection of the fermentation component concentrations in the set of fermentation component concentrations. The clustered fermentation concentration ranges are summarized to obtain a group of clustered fermentation concentration ranges. Based on the group of clustered fermentation concentration ranges, a group of configured fermentation concentrations is obtained, wherein the group of configured fermentation concentrations includes multiple configured fermentation concentrations, and the configured fermentation concentrations correspond one-to-one with the clustered fermentation concentration ranges, and the configured fermentation concentrations are the minimum values ​​of the clustered fermentation concentration ranges. Obtain a set of configured component concentrations; based on the set of configured component concentrations and the set of configured fermentation concentrations, obtain a set of configured trace elements; associate the set of configured trace elements, the set of clustered fermentation concentration ranges, and the intermediate stage data to obtain classification parameter data; summarize the classification parameter data to obtain a classification stage database; summarize the initial stage database, the intermediate stage database, and the classification stage database to obtain a retrieval parameter database.

10. The method for optimizing the preparation parameters of organic water-soluble fertilizer based on peat suspension as described in claim 9, characterized in that, The step of retrieving supplementary classification data from the classification stage database using the classification parameter data includes: Using the classification parameter data, the target cluster fermentation concentration range group is retrieved from the classification stage database. The classification fermentation concentrations in the classification parameter data are all located within the cluster fermentation concentration range corresponding to the target cluster fermentation concentration range group, and the classification supplementary data is the configuration trace element set corresponding to the target cluster fermentation concentration range group.

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