CN-121660393-B - Selenium-rich bio-organic fertilizer supply chain collaborative management system based on big data

CN121660393BCN 121660393 BCN121660393 BCN 121660393BCN-121660393-B

Abstract

The invention relates to the field of agricultural big data and fertilizer supply chain management, in particular to a selenium-rich bio-organic fertilizer supply chain collaborative management system based on big data, which comprises a source evaluation step, a source evaluation unit and a management center, wherein the management center invokes material attributes, and the source evaluation unit analyzes source quality fluctuation to distinguish batches; the method comprises a step of efficiency drift analysis, a step of supply and demand collaborative matching, a step of regulation and control execution, a step of regulation and control evaluation index comparison and generation of a regulation and control signal, a step of conversion analysis unit for efficiency drift evaluation feedback analysis, a step of supply and demand collaborative matching, a step of collaborative demand matching, a step of matching and dividing unit for regulation and control decision quantitative matching analysis, and a step of regulation and control execution.

Inventors

CHEN LONG
LI MEILIAN
WANG TAO
REN MINGSHUANG

Assignees

陕西永春生态科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260204

Claims (7)

1. The selenium-rich bio-organic fertilizer supply chain collaborative management system based on big data is characterized by comprising a supply chain management center, a raw material evaluation unit, a conversion analysis unit, a collaborative demand unit, a matching dividing unit and a regulation management unit; The supply chain management center is used for retrieving material attribute information of each node of the supply chain, sending the material attribute information to the raw material evaluation unit for source quality fluctuation evaluation analysis to obtain a conventional batch and a batch to be regulated, and judging the fluctuation evaluation value of the batch to be regulated to obtain a conventional regulation signal or an abnormal regulation signal; when an abnormal regulation signal is generated, the conversion analysis unit is used for performing efficiency drift evaluation feedback analysis on the acquired bioconversion process information of the batch to be regulated, so as to obtain a normal signal or a pseudo-abnormal signal; When a conventional regulation and control signal is generated, the cooperative demand unit is used for acquiring and analyzing the acquired supply and demand cooperative information of the batch to be regulated and controlled, processing the acquired raw material activity value and the soil bearing value to acquire the cooperative demand coefficient, and the matching dividing unit is used for carrying out regulation and control decision quantitative matching analysis on the acquired conversion risk information of the batch to be regulated and controlled, and comparing and analyzing the acquired regulation and control evaluation index to acquire a general regulation and control signal or an advanced regulation and control signal; the collaborative demand coefficient acquisition and analysis process comprises the following steps: obtaining supply and demand cooperative information of batches to be regulated and controlled within a time threshold, wherein the supply and demand cooperative information comprises a raw material activity value and a soil bearing value, and comparing the raw material activity value and the soil bearing value with a preset raw material activity value threshold and a preset soil bearing value threshold for analysis; The method comprises the steps of obtaining the number of the raw material activity value and the soil bearing value which are greater than or equal to the corresponding preset raw material activity value threshold and the corresponding preset soil bearing value threshold, and setting the number of the raw material activity value and the soil bearing value which are greater than or equal to the corresponding preset raw material activity value threshold and the corresponding preset soil bearing value threshold as a cooperative demand coefficient; the quantitative matching analysis process of the regulation decision is as follows: obtaining conversion risk information of a batch to be regulated and controlled within a time threshold, wherein the conversion risk information represents a full chain loss grade, a value obtained by multiplying a cooperative demand coefficient by a value corresponding to the full chain loss grade is set as a regulation and control evaluation index, the regulation and control evaluation index is compared with a preset regulation and control evaluation index threshold for analysis, and a high-level regulation and control signal is generated if the regulation and control evaluation index is larger than the preset regulation and control evaluation index threshold; The analysis process of the full-chain loss level comprises the steps of obtaining the total number of times of unabsorbed crops of a batch to be regulated within a time threshold, obtaining the total number of times of loss of active ingredients of the batch to be regulated, obtaining the missing number of reverse feedback patches of the batch to be regulated, multiplying the total number of times of unabsorbed crops of the batch to be regulated, the total number of times of loss of the active ingredients and the corresponding number of missing number of the reverse feedback patches, and setting the product value obtained by multiplying the total number of times of unabsorbed crops, the total number of times of loss of the active ingredients and the corresponding number of missing number of the reverse feedback patches as the full-chain loss level.
2. The selenium-enriched bio-organic fertilizer supply chain collaborative management system based on big data according to claim 1, wherein the source quality fluctuation assessment and analysis process is as follows: Acquiring the operation period of a supply chain production line, setting the operation period of the supply chain production line as a time threshold, setting each raw material pile in the supply chain production line as a node pile, acquiring material attribute information of each node pile in the time threshold, wherein the material attribute information represents a component fluctuation request instruction code, and judging the material attribute information of the node pile; if the material attribute information of the node pile body contains the component fluctuation request instruction codes, a fluctuation request instruction is generated, meanwhile, the node pile body corresponding to the fluctuation request instruction is set as a batch to be regulated, if the material attribute information of the node pile body does not contain the component fluctuation request instruction codes, a conventional instruction is generated, and the node pile body corresponding to the conventional instruction is set as a conventional batch.
3. The selenium-rich bio-organic fertilizer supply chain collaborative management system based on big data according to claim 2, wherein the set fermentation interval duration of the batch to be regulated in the time threshold is obtained, and meanwhile, the duration from the current last physicochemical property detection end time to the current time of the batch to be regulated in the time threshold is obtained, and the duration from the current last physicochemical property detection end time to the current time is set as the quality maintenance duration; And setting a value obtained by subtracting the quality maintenance time from the set fermentation interval time as a fluctuation evaluation value, comparing the fluctuation evaluation value with a preset fluctuation threshold value, and generating an abnormal regulation signal if the fluctuation evaluation value is larger than the preset fluctuation threshold value, and generating a conventional regulation signal if the fluctuation evaluation value is smaller than or equal to the preset fluctuation threshold value.
4. The selenium-enriched bio-organic fertilizer supply chain collaborative management system based on big data according to claim 1, wherein the efficacy drift assessment feedback analysis process is as follows: Acquiring parameter formula information of a fermentation process in a batch to be regulated within a time threshold, wherein the parameter formula information of the fermentation process comprises formula codes and formula risk values, extracting characters of the formula codes, setting character strings formed by character extraction of the formula codes as formula information strings, acquiring latest standard formula codes of the fermentation process, and setting character strings formed by character extraction of the latest standard formula codes as formula latest strings; Comparing and analyzing the formula information string and the formula latest string, if the formula information string is inconsistent with the formula latest string, generating a feedback instruction, and if the formula information string is consistent with the formula latest string, generating a latest formula signal.
5. The selenium-enriched bio-organic fertilizer supply chain collaborative management system based on big data according to claim 4, wherein when generating a feedback instruction, comparing a formula risk value with a preset formula risk value threshold value, and generating a pseudo-abnormal signal if the formula risk value is greater than the preset formula risk value threshold value; The formula risk value represents the product value obtained by multiplying the total conversion rate substandard value of the current fermentation process of the batch to be regulated and the conversion lag response time length obtained by carrying out data normalization processing on the conversion rate substandard value, and the conversion lag response time length represents the time length from the time point of generating substandard data to the time point of correcting the process parameters.
6. The selenium-rich bio-organic fertilizer supply chain collaborative management system based on big data according to claim 1, wherein raw material activity value represents a product value obtained by multiplying an active ingredient release value of a batch to be regulated by a self degradation evaluation value after data normalization processing, the active ingredient release value represents total times of up-to-standard organic selenium conversion of the batch to be regulated, and the self degradation evaluation value represents a duration that a cellulose degradation rate exceeds a preset cellulose degradation rate threshold; The soil bearing value represents a product value obtained by multiplying the number of the field environment data corresponding value of the batch to be regulated and the value obtained by multiplying the performance test value of the batch to be regulated by the value obtained by carrying out data normalization processing on the number of the field environment data corresponding value of the batch to be regulated and the value obtained by subtracting the preset blocking rate threshold value from the crop absorption blocking rate of the batch to be regulated.
7. The selenium-rich bio-organic fertilizer supply chain collaborative management system based on big data according to claim 1, wherein when generating an advanced regulation signal, the regulation management unit is configured to respond to the advanced regulation signal to generate a process parameter reverse correction instruction, the process parameter reverse correction instruction includes an adjustment amplitude value for fermentation pile turning frequency, microbial inoculation amount and carbon nitrogen ratio parameter, and send the process parameter reverse correction instruction to a production node upstream of a supply chain for execution.

Description

Selenium-rich bio-organic fertilizer supply chain collaborative management system based on big data Technical Field The invention relates to the field of agricultural big data and fertilizer supply chain management, in particular to a selenium-rich bio-organic fertilizer supply chain collaborative management system based on big data. Background In the application scene of the selenium-rich bio-organic fertilizer supply chain collaborative management, a supply chain system guarantees the quality stability and the environment adaptability of fertilizer products by means of accurately controlling the upstream raw material attribute and the downstream agricultural demand, and a management center generally needs to combine the material attribute information of each node and the feedback data of a field monitoring station to sense the running state of the whole link of the supply chain in real time; Aiming at regulation and control management of a supply chain, the existing scheme generally adopts a unidirectional linear scheduling framework, namely, physicochemical data of a raw material storage yard or a fermentation workshop are collected through a universal interface, and production equipment is directly driven to operate by utilizing a fixed process standard; although the scheme has certain feasibility in an ideal environment with single raw material source and constant quality, due to the fact that the scheme excessively depends on static preset parameters and lacks of logic constraint on the coupling relation between source fluctuation and supply and demand, when the physical and chemical properties of nonstandard biomass waste severely fluctuate or the downstream soil environment is complex and changeable, the conventional management algorithm is extremely easy to misjudge the conventional quality fluctuation of the raw material as abnormal system or neglect the specificity of the soil bearing capacity, so that an erroneous regulation and control decision is caused; In addition, the linear monitoring mode driven by pure data has weaker comprehensive processing capability on multi-source heterogeneous data, and is difficult to remove pseudo-abnormal signals which do not accord with biochemical reaction rules, so that response delay or excessive regulation and control are easy to occur to a system when the system faces efficiency drift or supply and demand mismatch, and high-frequency and high-precision reverse process correction is difficult to support a supply chain, therefore, how to establish a closed-loop management mechanism with source fluctuation perception and supply and demand collaborative matching capability, and the robustness of the overall efficiency and the risk resistance capability of the supply chain is improved while the risk of real process runaway is effectively screened, so that the technical problem to be solved is urgent. Disclosure of Invention In order to solve the technical problems, the invention provides a selenium-rich bio-organic fertilizer supply chain collaborative management system based on big data, and specifically, the technical scheme of the invention comprises the following steps: the system comprises a supply chain management center, a raw material evaluation unit, a conversion analysis unit, a collaborative demand unit, a matching dividing unit and a regulation management unit; the supply chain management center is used for retrieving material attribute information of each node of the supply chain, sending the material attribute information to the raw material evaluation unit for source quality fluctuation evaluation analysis to obtain a conventional batch and a batch to be regulated, and judging and processing a fluctuation evaluation value of the batch to be regulated to obtain a conventional regulation signal or an abnormal regulation signal; when an abnormal regulation signal is generated, the conversion analysis unit is used for performing efficiency drift evaluation feedback analysis on the acquired bioconversion process information of the batch to be regulated, so as to obtain a normal signal or a pseudo-abnormal signal; When a conventional regulation and control signal is generated, the cooperative demand unit is used for acquiring and analyzing the acquired supply and demand cooperative information of the batch to be regulated and controlled, processing the acquired raw material activity value and the soil bearing value to acquire the cooperative demand coefficient, and the matching dividing unit is used for carrying out regulation and control decision quantitative matching analysis on the acquired conversion risk information of the batch to be regulated and controlled, and comparing and analyzing the acquired regulation and control evaluation index to acquire the general regulation and control signal or the high-grade regulation and control signal. Preferably, the source quality fluctuation assessment analysis process comprises the steps of collecting the