Intelligent inulin receiving and storing system and management method

Abstract

The invention discloses an intelligent inulin receiving and storing system and a management method, and aims to improve the storage quality and management efficiency of inulin. The system comprises a storage bin, a detection unit, a preprocessing unit, a control unit, a prediction assembly and a decision support assembly. The detection unit collects environmental parameters in a warehouse in real time through a sensor network, and transmits the data to the preprocessing unit for processing and feature extraction. The prediction component predicts the quality change trend of the inulin within a certain time according to the environmental characteristics and generates prediction indexes. And the decision support component judges whether environment regulation and control are needed or not according to the real-time environment characteristics and the prediction indexes and a preset rule. The control unit receives the decision instruction and adjusts the temperature and humidity in the storage bin and the working state of the ventilation device so as to ensure the optimization of the inulin storage environment. Intelligent and automatic inulin storage environment regulation and control can be achieved, the quality loss is reduced, and the storage efficiency is improved.

Description

Technical Field

[0001] The invention relates to the field of inulin management equipment, and in particular to an intelligent inulin collection and storage system and a management method. Background Art

[0002] Inulin is a natural polysaccharide extracted from the root of chrysanthemum, which is widely used in the fields of medicine, food and health care products. Inulin has the advantages of good solubility, low calories and high fiber, and has a great market demand. The control of the storage environment of inulin directly affects the quality of inulin. In order to ensure the quality of inulin and extend its shelf life, reasonable storage management is required after the production of inulin. At present, the storage of inulin mainly relies on manual management, and the environment in the warehouse, such as temperature, humidity, and air quality, lacks intelligent monitoring and adjustment methods. As a result, long-term stacking of inulin can easily lead to moisture and mildew, especially in the case of high humidity or unstable temperature, inulin is prone to deterioration, which affects the quality and subsequent processing of inulin.

[0003] Therefore, in order to realize intelligent management of the inulin storage process and ensure that it is preserved under optimal storage conditions, the warehouse environment is adjusted in real time through real-time monitoring and feedback mechanisms. The present invention provides an intelligent inulin collection and storage system and management method. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes an intelligent inulin collection and storage system and management method, wherein the specific technical solution of the intelligent inulin collection and storage system is as follows:

[0005] An intelligent inulin collection and storage system, characterized by:

[0006] It includes storage bin, detection unit, pre-processing unit, control unit, prediction component and decision support component;

[0007] The storage warehouse is used to store inulin, and the detection unit collects environmental parameters in the warehouse in real time through a sensor network;

[0008] The preprocessing unit is used to process the environmental parameters and extract effective environmental features, and the preprocessing unit transmits the environmental features to the prediction component;

[0009] The prediction component is used to predict the quality change trend of inulin within a period of time according to environmental characteristics to output a prediction index;

[0010] The decision support component determines whether environmental regulation is required based on real-time environmental characteristics and prediction indicators and preset rules;

[0011] The control unit is used to receive instructions from the decision support component, and the control unit adjusts the working states of the temperature regulating device, the humidity regulating device and the ventilation device in the storage bin according to the instructions.

[0012] In order to better realize the present invention, it is possible to further:

[0013] A display unit is provided, which is used to display the current warehouse environmental parameters, predicted quality changes and control status in real time.

[0014] It may be further provided that: the sensor network includes a temperature sensor, a humidity sensor, an air quality sensor and a smoke sensor.

[0015] It may be further preferred that: a prediction model is preset in the prediction component, a rule model is preset in the decision support component, the prediction model adopts a regression model, and the rule model adopts a decision tree model.

[0016] Among them, the specific technical solution of a management method of an intelligent inulin collection and storage system is as follows:

[0017] An intelligent management method for an inulin collection and storage system, characterized in that:

[0018] S1: The detection unit collects environmental parameters in the warehouse in real time through a sensor network;

[0019] S2: The preprocessing unit preprocesses the collected environmental parameters to extract effective environmental features;

[0020] S3: The preprocessing unit transmits the extracted features to the prediction component and stores them in the database;

[0021] S4: After receiving the preprocessed environmental features, the prediction component uses a regression model to predict the quality change trend of inulin over a period of time and generates a prediction indicator;

[0022] S5: The prediction component saves the prediction index in a database;

[0023] S6: the decision support component extracts real-time environmental features and prediction indicators from the database;

[0024] S7: The decision support component determines whether environmental regulation is required according to the real-time environmental characteristics and prediction indicators and the preset rules:

[0025] If the decision support component determines that regulation is required, it proceeds to S8, if the environmental parameters in the warehouse remain within the predetermined range and the inulin quality prediction is within the normal range, otherwise it proceeds to S10;

[0026] S8: the decision support component generates an adjustment instruction according to the control rule and sends it to the control unit;

[0027] S9: The control unit adjusts the working states of the temperature adjustment device, the humidity adjustment device and the ventilation device in the storage bin according to the adjustment instruction until the environmental conditions meet the set standards and stabilize within the target range;

[0028] S10: The control unit maintains the system in the current state.

[0029] In order to better realize the present invention, it is possible to further:

[0030] The S4 comprises the following steps:

[0031] S4-1: The prediction component uses a prediction model to build a model between environmental characteristics and inulin quality:

[0032] y p =β0+β1x1+β2x2+…+β n x n

[0033] Among them, y p is the prediction index, β0 is the intercept term of the regression model, β1, β2…, β n is the regression coefficient;

[0034] S4-2: The prediction component optimizes the regression coefficient β by minimizing the loss function to obtain a trained prediction model:

[0035]

[0036] Among them, (X n ,y n ) is the historical data set used to train the regression model;

[0037] S4-3: The prediction component receives the environmental feature data X from the pre-processing unit:

[0038] X=[x1,x2,...,x n ]

[0039] where x1,x2,...,x n For different environmental characteristics;

[0040] S4-4: After obtaining the new environmental feature data X, the prediction component obtains the prediction index y according to the prediction model n .

[0041] It may further be that: the decision support component constructs a decision tree model based on the real-time environmental characteristics and the predicted inulin quality index;

[0042] The decision support component constructs the root node of the decision tree model through the input real-time environmental characteristics and the predicted inulin quality index as the starting judgment basis of the decision tree, and each branch node represents a decision condition;

[0043] At the root node, the decision tree model analyzes the input environmental characteristics and prediction indicators to preliminarily determine whether the environment needs to be regulated. Based on the preliminary judgment results, the decision tree model will decide whether to continue splitting to subsequent nodes. If regulation is required, the decision tree model will further refine the judgment along the specified branch path. By judging the node threshold layer by layer, the decision tree model realizes automated decision-making on environmental regulation.

[0044] The beneficial effects of the present invention are as follows: the present invention collects environmental parameters in the warehouse in real time by setting a sensor network and combines environmental characteristics with inulin quality prediction indicators, makes intelligent judgments based on preset rules, and automatically adjusts environmental parameters such as temperature and humidity by analyzing changes in the inulin storage environment, thereby maintaining the optimal storage conditions of inulin and preventing inulin from becoming moldy, dry or deteriorating in quality due to environmental discomfort. By introducing a regression model for quality prediction, the system can predict the quality change trend of inulin over a period of time based on the environmental characteristics collected in real time, and generate accurate prediction indicators. The set prediction model helps to give early warning of fluctuations in inulin quality, so that the system can take effective control measures in advance. By integrating multiple environmental data sources and comprehensively analyzing environmental characteristics and inulin quality prediction indicators, a multi-dimensional intelligent management system is constructed. The display unit can display the environmental parameters, predicted quality changes and control status of the current warehouse in real time. Managers can make quick decisions based on the displayed information to ensure intelligent management of inulin storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is the overall structure diagram of the present invention;

[0046] Figure 2 It is the work flow chart of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] like Figure 1 As shown:

[0049] An intelligent inulin collection and storage system, comprising a storage bin, a detection unit, a pre-processing unit, a control unit, a prediction component, a decision support component and a display unit;

[0050] The storage bin is used to store inulin, and the detection unit collects environmental parameters in the bin in real time through a sensor network. Specifically, the sensor network includes a temperature sensor, a humidity sensor, an air quality sensor, and a smoke sensor.

[0051] The preprocessing unit is used to process the environmental parameters and extract effective environmental features, and the preprocessing unit transmits the environmental features to the prediction component;

[0052] The prediction component is used to predict the quality change trend of inulin within a period of time according to environmental characteristics to output a prediction index;

[0053] The decision support component determines whether environmental regulation is required based on preset rules according to real-time environmental characteristics and prediction indicators. The prediction component has a preset prediction model, the decision support component has a preset rule model, the prediction model uses a regression model, and the rule model uses a decision tree model.

[0054] The control unit is used to receive instructions from the decision support component, and the control unit adjusts the working states of the temperature regulating device, the humidity regulating device and the ventilation device in the storage bin according to the instructions.

[0055] The display unit is used to display the current warehouse environmental parameters, predicted quality changes and control status in real time.

[0056] like Figure 2 As shown, the specific technical solution of a management method of an intelligent inulin collection and storage system is as follows:

[0057] The specific technical solution of a management method of an intelligent inulin collection and storage system is as follows:

[0058] A management method for an intelligent inulin collection and storage system comprises the following steps:

[0059] S1: The detection unit collects environmental parameters in the warehouse in real time through a sensor network;

[0060] S2: The preprocessing unit preprocesses the collected environmental parameters to extract effective environmental features;

[0061] S3: The preprocessing unit transmits the extracted features to the prediction component and stores them in the database;

[0062] S4: After receiving the preprocessed environmental features, the prediction component uses a regression model to predict the quality change trend of inulin over a period of time and generates a prediction index;

[0063] Specifically:

[0064] S4 includes the following steps:

[0065] S4-1: The prediction component uses a prediction model to build a model between environmental characteristics and inulin quality:

[0066] y p =β0+β1x1+β2x2+…+β n x n

[0067] Among them, y p is the prediction index, β0 is the intercept term of the regression model, β1, β2…, β n is the regression coefficient;

[0068] S4-2: The prediction component optimizes the regression coefficient β by minimizing the loss function to obtain a trained prediction model:

[0069]

[0070] Among them, (X n ,y n ) is the historical data set used to train the regression model;

[0071] S4-3: The prediction component receives the environmental feature data X from the pre-processing unit:

[0072] X=[x1,x2,...,x n ]

[0073] Among them, x1,x2,…,x n For different environmental characteristics;

[0074] S4-4: After obtaining the new environmental feature data X, the prediction component obtains the prediction index y according to the prediction model n .

[0075] S5: The prediction component saves the prediction index in a database;

[0076] S6: the decision support component extracts real-time environmental features and prediction indicators from the database;

[0077] Specifically: the decision support component constructs a decision tree model based on real-time environmental characteristics and predicted inulin quality indicators;

[0078] The decision support component constructs the root node of the decision tree model through the input real-time environmental characteristics and the predicted inulin quality index as the starting judgment basis of the decision tree, and each branch node represents a decision condition;

[0079] At the root node, the decision tree model analyzes the input environmental characteristics and prediction indicators to preliminarily determine whether the environment needs to be regulated. Based on the preliminary judgment results, the decision tree model will decide whether to continue splitting to subsequent nodes. If regulation is required, the decision tree model will further refine the judgment along the specified branch path. By judging the node threshold layer by layer, the decision tree model realizes automated decision-making on environmental regulation.

[0080] S7: The decision support component determines whether environmental regulation is required according to the real-time environmental characteristics and prediction indicators and the preset rules:

[0081] If the decision support component determines that regulation is required, it proceeds to S8, if the environmental parameters in the warehouse remain within the predetermined range and the inulin quality prediction is within the normal range, otherwise it proceeds to S10;

[0082] S8: the decision support component generates an adjustment instruction according to the control rule and sends it to the control unit;

[0083] S9: The control unit adjusts the working states of the temperature adjustment device, the humidity adjustment device and the ventilation device in the storage bin according to the adjustment instruction until the environmental conditions meet the set standards and stabilize within the target range;

[0084] S10: The control unit maintains the system in the current state.

[0085] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0086] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. An intelligent inulin collection and storage system, characterized by: It includes storage bin, detection unit, pre-processing unit, control unit, prediction component and decision support component; The storage warehouse is used to store inulin, and the detection unit collects environmental parameters in the warehouse in real time through a sensor network; The preprocessing unit is used to process the environmental parameters and extract effective environmental features, and the preprocessing unit transmits the environmental features to the prediction component; The prediction component is used to predict the quality change trend of inulin within a period of time according to environmental characteristics to output a prediction index; The decision support component determines whether environmental regulation is required based on real-time environmental characteristics and prediction indicators and preset rules; The control unit is used to receive instructions from the decision support component, and the control unit adjusts the working states of the temperature regulating device, the humidity regulating device and the ventilation device in the storage bin according to the instructions.

2. The intelligent inulin collection and storage system according to claim 1, characterized in that: A display unit is provided, which is used to display the current warehouse environmental parameters, predicted quality changes and control status in real time.

3. The intelligent inulin collection and storage system according to claim 2, characterized in that: The sensor network includes a temperature sensor, a humidity sensor, an air quality sensor, and a smoke sensor.

4. The intelligent inulin collection and storage system according to claim 3, characterized in that: A prediction model is preset in the prediction component, and a rule model is preset in the decision support component. The prediction model adopts a regression model, and the rule model adopts a decision tree model.

5. The management method of the intelligent inulin collection and storage system according to claim 4 is characterized in that: S1: The detection unit collects environmental parameters in the warehouse in real time through a sensor network; S2: The preprocessing unit preprocesses the collected environmental parameters to extract effective environmental features; S3: The preprocessing unit transmits the extracted features to the prediction component and stores them in the database; S4: After receiving the preprocessed environmental features, the prediction component uses a regression model to predict the quality change trend of inulin over a period of time and generates a prediction index; S5: The prediction component saves the prediction index in a database; S6: the decision support component extracts real-time environmental features and prediction indicators from the database; S7: The decision support component determines whether environmental regulation is required according to the real-time environmental characteristics and prediction indicators and the preset rules: If the decision support component determines that regulation is required, it proceeds to S8, if the environmental parameters in the warehouse remain within the predetermined range and the inulin quality prediction is within the normal range, otherwise it proceeds to S10; S8: the decision support component generates an adjustment instruction according to the control rule and sends it to the control unit; S9: The control unit adjusts the working states of the temperature adjustment device, the humidity adjustment device and the ventilation device in the storage bin according to the adjustment instruction until the environmental conditions meet the set standards and stabilize within the target range; S10: The control unit maintains the system in the current state.

6. The management method of the intelligent inulin collection and storage system according to claim 5, characterized in that: The S4 comprises the following steps: S4-1: The prediction component uses a prediction model to build a model between environmental characteristics and inulin quality: y p =β0+β1x1+β2x2+…+β n x n Among them, y p is the prediction index, β0 is the intercept term of the regression model, β1, β2…, β n is the regression coefficient; S4-2: The prediction component optimizes the regression coefficient β by minimizing the loss function to obtain a trained prediction model: Among them, (X n ,y n ) is the historical data set used to train the regression model; S4-3: The prediction component receives the environmental feature data X from the pre-processing unit: X=[x1,x2,...,x n ] where x1,x2,...,x n For different environmental characteristics; S4-4: After obtaining the new environmental feature data X, the prediction component obtains the prediction index y according to the prediction model n .

7. The management method of an intelligent inulin collection and storage system according to claim 6, characterized in that: The decision support component constructs a decision tree model based on real-time environmental characteristics and predicted inulin quality indicators; The decision support component constructs the root node of the decision tree model through the input real-time environmental characteristics and the predicted inulin quality index as the starting judgment basis of the decision tree, and each branch node represents a decision condition; At the root node, the decision tree model analyzes the input environmental characteristics and prediction indicators to preliminarily determine whether the environment needs to be regulated. Based on the preliminary judgment results, the decision tree model will decide whether to continue splitting to subsequent nodes. If regulation is required, the decision tree model will further refine the judgment along the specified branch path. By judging the node threshold layer by layer, the decision tree model realizes automated decision-making on environmental regulation.

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