Biochemical material storage system based on Internet of Things

Abstract

The invention discloses a biochemical engineering material storage system based on the Internet of Things, and the system comprises a real-time monitoring module which collects the storage environment data of biochemical engineering materials and the mass change data of the materials in real time through a sensor, and relates to the technical field of biochemical engineering material storage. The method comprises the following steps: screening an initial group by taking a quality change standard function as a benchmark and combining historical data with a quality change prediction and energy consumption prediction model, and performing iterative optimization through a genetic algorithm, in an iteration process, on one hand, screening high-quality individuals for crossover variation by taking lowest energy consumption as guidance, and continuously approaching an optimal solution of energy consumption; and on the other hand, according to dynamic setting conditions, individuals with relatively low energy consumption and better quality change are brought into an optimization process in time, so that the scheme is ensured to be energy-saving and beneficial to the storage of the biochemical materials, and storage conditions which are beneficial to storage and low in energy consumption are customized for the storage of the biochemical materials.

Description

Technical Field

[0001] The present invention relates to the technical field of biochemical material storage, and in particular to a biochemical material storage system based on the Internet of Things. Background Art

[0002] Biochemical materials refer to various raw materials, intermediates and products involved in the field of biochemicals. In the field of biochemicals, material storage has always been a key link related to product quality, production costs and sustainable development of enterprises. With the continuous advancement of science and technology and the continuous expansion of the scale of the industry, traditional material storage methods have increasingly exposed many limitations and are unable to meet the current needs of refined, intelligent and green industrial development.

[0003] At present, the storage of biochemical materials, on the one hand, biochemical materials usually have complex components and are highly sensitive to the storage environment. Slight fluctuations in temperature, slight changes in humidity, changes in gas composition, and even different storage pressures may cause chemical reactions in the materials, leading to a decline in their quality, such as degradation of active ingredients, increase in impurities, loss of activity, and other problems. These quality deteriorations will not only directly affect the performance and quality stability of subsequent products, resulting in a decrease in the qualified rate of finished products and an increase in the rate of defective and scrapped products, but may also bring high material loss costs to enterprises and market reputation risks caused by quality problems. In the past, due to the lack of accurate and real-time monitoring methods, enterprises often found it difficult to detect the gradual change in the quality of materials during storage in a timely manner, and could only rely on periodic sampling. This lagging detection method is tantamount to "closing the fold after the sheep have been lost" and cannot fundamentally guarantee the quality of materials. On the other hand, in order to maintain a suitable storage environment for materials, various environmental control equipment, such as air conditioners, dehumidifiers, ventilation systems, etc., are widely used, but this also brings significant energy consumption problems. Traditional environmental control mostly sets fixed parameters based on experience, and fails to fully consider the real-time status of materials, dynamic changes in the environment, and the optimal balance of energy consumption.

[0004] This leads to the situation that in actual operation, either the intensity of environmental control is increased blindly in order to excessively pursue the stability of material quality, resulting in a large amount of energy waste; or excessive attention is paid to energy consumption control and the potential harm of environmental fluctuations to material quality is ignored, which ultimately does not make up for the gains. In order to obtain a storage environment condition scheme that is conducive to material preservation and can achieve low energy consumption, this application proposes a biochemical material storage system based on the Internet of Things. Summary of the invention

[0005] In order to solve the technical problems in actual operation, either the intensity of environmental control is blindly increased in order to excessively pursue the stability of material quality, resulting in a large amount of energy waste; or excessive attention is paid to energy consumption control while ignoring the potential harm of environmental fluctuations to material quality, which ultimately does not make up for the gains, the present invention proposes a biochemical material storage system based on the Internet of Things, with the aim of obtaining a storage environment condition solution that is beneficial to material preservation and can achieve low energy consumption.

[0006] The present invention proposes a biochemical material storage system based on the Internet of Things, comprising: Real-time monitoring module: through sensors, real-time collection of storage environment data of biochemical materials and material quality change data, and energy consumption data, and real-time transmission of storage environment data, material quality change data, and energy consumption data to the data storage module; Data storage module: stores data from the real-time monitoring module; Quality change prediction and energy consumption prediction model generation module: analyzes the storage environment data of the data storage module, the quality change data of the material, and the energy consumption data, and constructs the quality change prediction and energy consumption prediction models; Storage environment condition program generation module: For a certain biochemical material, a quality change standard function is set. According to the historical environmental parameters, through the quality change prediction and energy consumption prediction model, multiple groups of environmental parameters lower than the quality change standard function are obtained. Then, through data analysis, with the minimum energy consumption as the goal, a set of target environmental parameters is obtained.

[0007] Preferably, the sensor includes a temperature sensor, a humidity sensor, a pressure sensor, a gas sensor, a material weight sensor, and a liquid level sensor.

[0008] Preferably, the quality change prediction and energy consumption prediction models are constructed as follows: The storage environment data of the data storage module and the quality change data of the material are analyzed, and the quality change curve of a certain biochemical material under different environmental conditions per unit time is calculated by the support vector regression algorithm, and a quality change curve prediction model is constructed. In the quality change curve prediction model, the environmental parameters are used as input, and the quality change curve prediction result per unit time is used as output; Analyze the storage environment data and energy consumption data of the data storage module, calculate the energy consumption of a biochemical material under different environmental conditions per unit time through the support vector regression algorithm, and build an energy consumption prediction model. In the energy consumption prediction model, environmental parameters are used as input, and the energy consumption prediction results per unit time are used as output; The quality change curve prediction model and the energy consumption prediction model are integrated to construct quality change prediction and energy consumption prediction models. When new environmental parameters are input, they are input into the quality change curve prediction model and the energy consumption prediction model respectively. The quality change curve prediction model outputs the prediction result of the quality change curve per unit time, and the energy consumption prediction model outputs the energy consumption prediction result per unit time.

[0009] Preferably, in the storage environment condition solution generation module, the target environment parameters are obtained by the following method: S1. For a certain biochemical material, a quality change standard function is set. According to the historical environmental parameters, multiple groups of environmental parameters lower than the quality change standard function are obtained through the quality change prediction and energy consumption prediction model, which are set as the initial group. ; S2. For each environmental parameter that is lower than the quality change standard function, calculate the corresponding energy consumption through the quality change prediction and energy consumption prediction model, and take the X environmental parameters with the lowest energy consumption; S3, X environmental parameters are subjected to crossover and mutation operations through a genetic algorithm to obtain multiple new environmental parameters, and the quality change curve of each environmental parameter is calculated to obtain multiple sets of environmental parameters that are lower than the quality change standard function, and the obtained multiple sets of environmental parameters that are lower than the quality change standard function are compared with Merge to form new groups ; S4, repeat steps S2 and S3. After multiple rounds of iterations, when the absolute values ​​of the differences in the minimum energy consumption obtained in Y consecutive iterations are all less than the set threshold, the environmental parameter with the lowest energy consumption is taken as the target environmental parameter.

[0010] Preferably, X is a non-negative even number, and 4≤X; Y is a positive integer, and 3≤X.

[0011] Preferably, suppose there are n iterations, for the i-th iteration, i=1,2,...,n, X environmental parameters obtain X energy consumption values, X quality change curves, and the energy consumption value with the largest energy consumption value among the X energy consumption values ​​is set as E; For the population of the i-th iteration ,group If there is an environmental parameter whose energy consumption is not greater than 1.02E and whose quality change curve is lower than any of the X quality change curves, it will be brought into the crossover and mutation operations of the X environmental parameters.

[0012] Preferably, during the iteration process, the energy consumption values ​​and quality change curve values ​​corresponding to the environmental parameters in the current group are visualized in real time to form a two-dimensional scatter plot or a Pareto frontier plot; And the quality change standard function and genetic algorithm parameters can be manually adjusted according to the visualization results.

[0013] A biochemical material storage method based on the Internet of Things comprises the following steps: Through sensors, the storage environment data of biochemical materials and the quality change data of materials are collected in real time, and energy consumption data is obtained, and the storage environment data, material quality change data, and energy consumption data are transmitted to the data storage module in real time; Extract storage environment data, material quality change data and energy consumption data from the data storage module, use support vector regression algorithm to build a quality change curve prediction model and an energy consumption prediction model, integrate the quality change curve prediction model and the energy consumption prediction model to form a quality change prediction and energy consumption prediction model, and after receiving the environmental parameter input, the quality change prediction and energy consumption prediction model respectively outputs the quality change curve prediction result and energy consumption prediction result per unit time; For a certain biochemical material, a quality change standard function is set. According to the historical environmental parameters, multiple groups of environmental parameters lower than the quality change standard function are obtained through the quality change prediction and energy consumption prediction model as the initial group. The energy consumption of each environmental parameter in the initial group is calculated through the quality change prediction and energy consumption prediction model, and the X environmental parameters with the lowest energy consumption are selected. The genetic algorithm is used to cross and mutate these X parameters to generate new parameters, and then the quality change curve of the new parameters is calculated using the quality change prediction and energy consumption prediction model. The remaining environmental parameters lower than the quality change standard function are merged with the initial group into a new group, and multiple rounds of iterations are performed: In the i-th iteration, i = 1, 2, ..., n, for X environmental parameters, obtain the corresponding X energy consumption values ​​and X quality change curves, find the maximum energy consumption value and record it as E, for the environmental parameters in the iteration group, if its energy consumption is not greater than 1.02E and its quality change curve is lower than any of the X quality change curves, then bring it into the crossover and mutation operation of the X environmental parameters; Continue to iterate until the absolute value of the difference in the minimum energy consumption obtained in Y consecutive iterations is less than the set threshold, with the minimum energy consumption as the goal, and finally select the environmental parameter with the lowest energy consumption as the target environmental parameter; During the iteration process, the energy consumption value and quality change curve value corresponding to the environmental parameters in the current group are visualized in real time, and the relationship between them is displayed through a two-dimensional scatter plot or Pareto frontier diagram; According to the visualization results, the parameters of the quality change standard function and the genetic algorithm can be manually adjusted to optimize the generation process of the storage environment condition scheme.

[0014] According to the final target environmental parameters, the storage environment of biochemical materials is regulated and controlled, and the operating parameters of relevant environmental control equipment (such as ventilation systems, dehumidifiers, air conditioners, heating devices, etc.) are adjusted to ensure that the materials are stored in an environment that meets quality requirements and has the lowest energy consumption.

[0015] In the present invention, the proposed biochemical material storage system based on the Internet of Things has the following beneficial technical effects: 1. The setting of the storage environment condition scheme generation module takes the quality change standard function as the benchmark, combines historical data with quality change prediction and energy consumption prediction model to screen the initial group, and iterates and optimizes through genetic algorithm. In the iterative process, on the one hand, high-quality individuals are selected for cross-mutation with the lowest energy consumption as the guide, and the optimal energy consumption solution is continuously approached; on the other hand, according to the dynamic setting conditions, individuals with relatively low energy consumption and better quality change are timely included in the optimization process to ensure that the scheme is both energy-saving and conducive to the preservation of biochemical materials. This is repeated for multiple rounds until the target environmental parameters that meet the requirements of continuous, stable and low energy consumption are found, and storage conditions that are conducive to preservation and low energy consumption are customized for the storage of biochemical materials.

[0016] 2. Storage environment condition solution generation During the iteration process, the energy consumption values ​​and quality change curve values ​​corresponding to the environmental parameters in the current group are visualized in real time to form a two-dimensional scatter plot or Pareto frontier diagram; this helps users intuitively observe the algorithm's search process, understand the trade-off between energy consumption and quality, and promptly discover whether the algorithm has abnormalities, such as falling into a local optimum that causes points to gather in a certain area. Based on the visualization results, the quality change standard function and genetic algorithm parameters can be manually adjusted to achieve human-machine collaborative optimization, so that the generation of storage environment solutions is no longer a mechanical algorithm execution, but a flexible customization that incorporates human experience and wisdom, further improving the accuracy and practicality of the solution, and improving the satisfaction with the final target environmental parameters.

[0017] 3. By collecting biochemical material storage environment data and material quality change data in real time, and obtaining energy consumption data at the same time, this real-time data collection can effectively avoid material quality problems and energy waste caused by information lag.

[0018] 4. Setting of quality change prediction and energy consumption prediction model generation module. The quality change prediction model uses the support vector regression algorithm to deeply analyze the intrinsic relationship between environmental parameters and material quality change curves. By inputting environmental parameters, the material quality change trend per unit time can be accurately output. This enables enterprises to proactively grasp the stability of materials under different storage conditions, plan the turnover and use of materials in advance, minimize the risk of material deterioration, ensure the consistency of product quality, reduce the output of defective products, and save a lot of costs for enterprises. The energy consumption prediction model also uses the support vector regression algorithm to clearly reveal the quantitative relationship between environmental factors and energy consumption. In production operations, enterprises can use this model to estimate energy consumption under different environmental control strategies in advance, help formulate scientific and reasonable energy-saving plans, optimize energy distribution, reduce operating costs, and improve economic benefits, which is in line with the current green and low-carbon development concept. The integrated quality change and energy consumption prediction model realizes the input of environmental parameters once and the simultaneous output of two key information of quality change and energy consumption, providing one-stop support for comprehensive decision-making, avoiding the tediousness of switching and comparing between different models, and greatly improving decision-making efficiency.

[0019] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a principle block diagram of the system of the present invention; Figure 2 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0021] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar symbols throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0022] like Figure 1 A biochemical material storage system based on the Internet of Things is shown, comprising: Real-time monitoring module: through sensors, real-time collection of storage environment data of biochemical materials and material quality change data, and energy consumption data, and real-time transmission of storage environment data, material quality change data, and energy consumption data to the data storage module; Sensors include temperature sensors, humidity sensors, pressure sensors, gas sensors (which can detect harmful gas concentrations), material weight sensors, and liquid level sensors; By collecting biochemical material storage environment data and material quality change data in real time, as well as energy consumption data, this real-time data collection can effectively avoid material quality problems and energy waste caused by information lag.

[0023] Data storage module: stores data from the real-time monitoring module; Quality change prediction and energy consumption prediction model generation module: analyzes the storage environment data of the data storage module, the quality change data of the material, and the energy consumption data, and constructs the quality change prediction and energy consumption prediction models; Construct quality change prediction and energy consumption prediction models as follows: The storage environment data of the data storage module and the quality change data of the material are analyzed, and the quality change curve of a certain biochemical material under different environmental conditions per unit time is calculated by the support vector regression algorithm, and a quality change curve prediction model is constructed. In the quality change curve prediction model, the environmental parameters are used as input, and the quality change curve prediction result per unit time is used as output; Analyze the storage environment data and energy consumption data of the data storage module, calculate the energy consumption of a biochemical material under different environmental conditions per unit time through the support vector regression algorithm, and build an energy consumption prediction model. In the energy consumption prediction model, environmental parameters are used as input, and the energy consumption prediction results per unit time are used as output; The quality change curve prediction model and the energy consumption prediction model are integrated to construct the quality change prediction and energy consumption prediction models. When new environmental parameters are input, they are input into the quality change curve prediction model and the energy consumption prediction model respectively. The quality change curve prediction model outputs the prediction result of the quality change curve per unit time, and the energy consumption prediction model outputs the prediction result of the energy consumption per unit time. The quality change prediction and energy consumption prediction model generation modules are set up. The quality change prediction model uses the support vector regression algorithm to deeply analyze the intrinsic relationship between environmental parameters and material quality change curves. By inputting environmental parameters, the material quality change trend per unit time can be accurately output. This enables enterprises to proactively grasp the stability of materials under different storage conditions, plan the turnover and use of materials in advance, minimize the risk of material deterioration, ensure the consistency of product quality, reduce the output of defective products, and save a lot of costs for enterprises. The energy consumption prediction model also uses the support vector regression algorithm to clearly reveal the quantitative relationship between environmental factors and energy consumption. In production operations, enterprises can use this model to estimate energy consumption under different environmental control strategies in advance, help formulate scientific and reasonable energy-saving plans, optimize energy distribution, reduce operating costs, and improve economic benefits, which is in line with the current green and low-carbon development concept. The integrated quality change and energy consumption prediction model realizes the input of environmental parameters once and the simultaneous output of two key information of quality change and energy consumption, providing one-stop support for comprehensive decision-making, avoiding the tediousness of switching and comparing between different models, and greatly improving decision-making efficiency.

[0024] Storage environment condition program generation module: For a certain biochemical material, a quality change standard function is set, and according to historical environmental parameters, multiple sets of environmental parameters lower than the quality change standard function are obtained through quality change prediction and energy consumption prediction models. Then, through data analysis, a set of target environmental parameters is obtained with the minimum energy consumption as the goal; In the storage environment condition solution generation module, the target environment parameters are obtained by the following method: S1. For a certain biochemical material, a quality change standard function is set. According to the historical environmental parameters, multiple groups of environmental parameters lower than the quality change standard function are obtained through the quality change prediction and energy consumption prediction model, which are set as the initial group. ; An environmental parameter that is lower than the standard function of quality change means that the quality change curve of the environmental parameter is lower than the standard function of quality change; S2. For each environmental parameter that is lower than the quality change standard function, calculate the corresponding energy consumption through the quality change prediction and energy consumption prediction model, and take the X environmental parameters with the lowest energy consumption; In an optional embodiment, X is a non-negative even number, and 4≤X; S3, X environmental parameters are subjected to crossover and mutation operations through a genetic algorithm to obtain multiple new environmental parameters, and the quality change curve of each environmental parameter is calculated to obtain multiple sets of environmental parameters that are lower than the quality change standard function, and the obtained multiple sets of environmental parameters that are lower than the quality change standard function are compared with Merge to form new groups ; S4, repeat steps S2 and S3. After multiple rounds of iterations, when the absolute values ​​of the differences in the minimum energy consumption obtained in Y consecutive iterations are all less than the set threshold, the environmental parameter with the lowest energy consumption is taken as the target environmental parameter.

[0025] In an optional embodiment, Y is a positive integer, and 3≤X; Repeat steps S2 and S3 for multiple rounds of iterations, for example Repeat steps S2 and S3 to form a new group ,Then Repeat steps S2 and S3 to form a new group , thus forming a cycle, allowing the group to continuously evolve towards lower energy consumption while satisfying the standard function of quality change.

[0026] Assume that there are n iterations. For the i-th iteration, i=1,2,...,n, X environmental parameters obtain X energy consumption values ​​and X quality change curves. Assume that the energy consumption value with the largest X energy consumption values ​​is E. For the population of the i-th iteration , group = In the process, if there is an environmental parameter whose energy consumption is not greater than 1.02E and whose quality change curve is lower than any one of the X quality change curves, it will be brought into the crossover and mutation operation of the X environmental parameters, that is, the environmental parameters for the crossover and mutation operation through the genetic algorithm are no longer X, but the environmental parameters with energy consumption not greater than 1.02E and whose quality change curve is lower than any one of the X quality change curves will be added.

[0027] The setting of the storage environment condition scheme generation module takes the quality change standard function as the benchmark, combines historical data with quality change prediction and energy consumption prediction model to screen the initial group, and iterates and optimizes through genetic algorithm. In the iterative process, on the one hand, high-quality individuals are selected for cross-mutation with the lowest energy consumption as the guide, and the optimal energy consumption solution is continuously approached; on the other hand, according to the dynamic setting conditions, individuals with relatively low energy consumption and better quality change are promptly included in the optimization process to ensure that the scheme is both energy-saving and conducive to the preservation of biochemical materials. This is repeated for multiple rounds until the target environmental parameters that meet the requirements of continuous, stable and low energy consumption are found, and storage conditions that are conducive to preservation and low energy consumption are customized for the storage of biochemical materials.

[0028] During the iteration process, the energy consumption values ​​and quality change curve values ​​corresponding to the environmental parameters in the current group are visualized in real time to form a two-dimensional scatter plot or Pareto frontier diagram; this helps users intuitively observe the algorithm's search process, understand the trade-off between energy consumption and quality, and promptly discover whether the algorithm has abnormalities (such as falling into a local optimum that causes points to gather in a certain area). Based on the visualization results, the quality change standard function and genetic algorithm parameters can be manually adjusted to achieve human-machine collaborative optimization and improve the satisfaction with the final target environmental parameters.

[0029] Biochemical materials include: Biomass raw materials: sugars, starch, cellulose, oils and fats; Microorganisms: bacteria, yeast, mold; Bioactive substances and intermediates: enzymes, amino acids, organic acids; Culture medium components: carbon source, nitrogen source, inorganic salts and trace elements.

[0030] This system aims to use the Internet of Things technology to monitor the storage environment and material quality changes of biochemical materials in real time, ensure the quality and safety of materials, minimize energy consumption while ensuring material quality, reduce material loss and defective rate from the perspective of material quality assurance, ensure stable product quality, and enhance market competitiveness; from the perspective of energy management, energy-saving regulation and control can reduce energy consumption expenses; and improve the efficiency and economy of storage management.

[0031] like Figure 2 A biochemical material storage method based on the Internet of Things is shown, comprising the following steps: Through sensors, the storage environment data of biochemical materials and the quality change data of materials are collected in real time, and energy consumption data is obtained, and the storage environment data, material quality change data, and energy consumption data are transmitted to the data storage module in real time; Extract storage environment data, material quality change data and energy consumption data from the data storage module, use support vector regression algorithm to build a quality change curve prediction model and an energy consumption prediction model, integrate the quality change curve prediction model and the energy consumption prediction model to form a quality change prediction and energy consumption prediction model, and after receiving the environmental parameter input, the quality change prediction and energy consumption prediction model respectively outputs the quality change curve prediction result and energy consumption prediction result per unit time; For a certain biochemical material, a quality change standard function is set. According to the historical environmental parameters, multiple groups of environmental parameters lower than the quality change standard function are obtained through the quality change prediction and energy consumption prediction model as the initial group. The energy consumption of each environmental parameter in the initial group is calculated through the quality change prediction and energy consumption prediction model, and the X environmental parameters with the lowest energy consumption are selected. The genetic algorithm is used to cross and mutate these X parameters to generate new parameters, and then the quality change curve of the new parameters is calculated using the quality change prediction and energy consumption prediction model. The remaining environmental parameters lower than the quality change standard function are merged with the initial group into a new group, and multiple rounds of iterations are performed: In the i-th iteration, i = 1, 2, ..., n, for X environmental parameters, obtain the corresponding X energy consumption values ​​and X quality change curves, find the maximum energy consumption value and record it as E, for the environmental parameters in the iteration group, if its energy consumption is not greater than 1.02E and its quality change curve is lower than any of the X quality change curves, then bring it into the crossover and mutation operation of the X environmental parameters; Continue to iterate until the absolute value of the difference in the minimum energy consumption obtained in Y consecutive iterations is less than the set threshold, with the minimum energy consumption as the goal, and finally select the environmental parameter with the lowest energy consumption as the target environmental parameter; During the iteration process, the energy consumption value and quality change curve value corresponding to the environmental parameters in the current group are visualized in real time, and the relationship between them is displayed through a two-dimensional scatter plot or Pareto frontier diagram; According to the visualization results, the parameters of the quality change standard function and the genetic algorithm can be manually adjusted to optimize the generation process of the storage environment condition scheme.

[0032] According to the final target environmental parameters, the storage environment of biochemical materials is regulated and controlled, and the operating parameters of relevant environmental control equipment (such as ventilation systems, dehumidifiers, air conditioners, heating devices, etc.) are adjusted to ensure that the materials are stored in an environment that meets quality requirements and has the lowest energy consumption.

[0033] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0034] In the embodiments provided by the present invention, it should be understood that the disclosed system or method can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0035] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0036] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0037] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic features of the present invention.

[0038] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A biochemical material storage system based on the Internet of Things, characterized in that: include: Real-time monitoring module: through sensors, real-time collection of storage environment data of biochemical materials and material quality change data, and energy consumption data, and real-time transmission of storage environment data, material quality change data, and energy consumption data to the data storage module; Data storage module: stores data from the real-time monitoring module; Quality change prediction and energy consumption prediction model generation module: analyzes the storage environment data of the data storage module, the quality change data of the material, and the energy consumption data, and constructs the quality change prediction and energy consumption prediction models; Storage environment condition program generation module: For a certain biochemical material, a quality change standard function is set. According to the historical environmental parameters, through the quality change prediction and energy consumption prediction model, multiple groups of environmental parameters lower than the quality change standard function are obtained. Then, through data analysis, with the minimum energy consumption as the goal, a set of target environmental parameters is obtained.

2. The biochemical material storage system based on the Internet of Things according to claim 1 is characterized in that: Sensors include temperature sensors, humidity sensors, pressure sensors, gas sensors, material weight sensors, and liquid level sensors.

3. The biochemical material storage system based on the Internet of Things according to claim 1 is characterized in that: Construct quality change prediction and energy consumption prediction models as follows: The storage environment data of the data storage module and the quality change data of the material are analyzed, and the quality change curve of a certain biochemical material under different environmental conditions per unit time is calculated by the support vector regression algorithm, and a quality change curve prediction model is constructed. In the quality change curve prediction model, the environmental parameters are used as input, and the quality change curve prediction result per unit time is used as output; Analyze the storage environment data and energy consumption data of the data storage module, calculate the energy consumption of a biochemical material under different environmental conditions per unit time through the support vector regression algorithm, and build an energy consumption prediction model. In the energy consumption prediction model, environmental parameters are used as input, and the energy consumption prediction results per unit time are used as output; The quality change curve prediction model and the energy consumption prediction model are integrated to construct quality change prediction and energy consumption prediction models. When new environmental parameters are input, they are input into the quality change curve prediction model and the energy consumption prediction model respectively. The quality change curve prediction model outputs the prediction result of the quality change curve per unit time, and the energy consumption prediction model outputs the energy consumption prediction result per unit time.

4. The biochemical material storage system based on the Internet of Things according to claim 3 is characterized in that: In the storage environment condition solution generation module, the target environment parameters are obtained by the following method: S1. For a certain biochemical material, a quality change standard function is set. According to the historical environmental parameters, multiple groups of environmental parameters lower than the quality change standard function are obtained through the quality change prediction and energy consumption prediction model, which are set as the initial group. ; S2. For each environmental parameter that is lower than the quality change standard function, calculate the corresponding energy consumption through the quality change prediction and energy consumption prediction model, and take the X environmental parameters with the lowest energy consumption; S3, X environmental parameters are subjected to crossover and mutation operations through a genetic algorithm to obtain multiple new environmental parameters, and the quality change curve of each environmental parameter is calculated to obtain multiple sets of environmental parameters that are lower than the quality change standard function, and the obtained multiple sets of environmental parameters that are lower than the quality change standard function are compared with Merge to form new groups ; S4, repeat steps S2 and S3. After multiple rounds of iterations, when the absolute values ​​of the differences in the minimum energy consumption obtained in Y consecutive iterations are all less than the set threshold, the environmental parameter with the lowest energy consumption is taken as the target environmental parameter.

5. The biochemical material storage system based on the Internet of Things according to claim 4 is characterized in that: X is a non-negative even number, and 4≤X; Y is a positive integer, and 3≤X.

6. The biochemical material storage system based on the Internet of Things according to claim 5 is characterized in that There are n iterations. For the i-th iteration, i=1,2,...,n, X environmental parameters obtain X energy consumption values ​​and X quality change curves. Let the energy consumption value with the largest energy consumption value be E. For the population of the i-th iteration ,group If there is an environmental parameter whose energy consumption is not greater than 1.02E and whose quality change curve is lower than any of the X quality change curves, it will be brought into the crossover and mutation operations of the X environmental parameters.

7. The biochemical material storage system based on the Internet of Things according to claim 6 is characterized in that: During the iteration process, the energy consumption value and quality change curve value corresponding to the environmental parameters in the current group are visualized in real time to form a two-dimensional scatter plot or Pareto frontier graph; And the quality change standard function and genetic algorithm parameters can be manually adjusted according to the visualization results.

8. The method for storing biochemical materials based on the Internet of Things according to any one of claims 1 to 7, characterized in that: The following steps are involved: Through sensors, the storage environment data of biochemical materials and the quality change data of materials are collected in real time, and energy consumption data is obtained, and the storage environment data, material quality change data, and energy consumption data are transmitted to the data storage module in real time; Extract storage environment data, material quality change data and energy consumption data from the data storage module, use support vector regression algorithm to build a quality change curve prediction model and an energy consumption prediction model, integrate the quality change curve prediction model and the energy consumption prediction model to form a quality change prediction and energy consumption prediction model, and after receiving the environmental parameter input, the quality change prediction and energy consumption prediction model respectively outputs the quality change curve prediction result and energy consumption prediction result per unit time; For a certain biochemical material, a quality change standard function is set. According to the historical environmental parameters, multiple groups of environmental parameters lower than the quality change standard function are obtained through the quality change prediction and energy consumption prediction model as the initial group. The energy consumption of each environmental parameter in the initial group is calculated through the quality change prediction and energy consumption prediction model, and X environmental parameters with the lowest energy consumption are selected. The genetic algorithm is used to cross and mutate these X parameters to generate new parameters, and then the quality change curve of the new parameters is calculated using the quality change prediction and energy consumption prediction model. The remaining environmental parameters lower than the quality change standard function are merged with the initial group into a new group, and multiple rounds of iterations are performed: In the i-th iteration, i = 1, 2, ..., n, for X environmental parameters, obtain the corresponding X energy consumption values ​​and X quality change curves, find the maximum energy consumption value and record it as E, for the environmental parameters in the iteration group, if its energy consumption is not greater than 1.02E and its quality change curve is lower than any of the X quality change curves, then bring it into the crossover and mutation operation of the X environmental parameters; Continue to iterate until the absolute value of the difference in the minimum energy consumption obtained in Y consecutive iterations is less than the set threshold, with the minimum energy consumption as the goal, and finally select the environmental parameter with the lowest energy consumption as the target environmental parameter; During the iteration process, the energy consumption value and quality change curve value corresponding to the environmental parameters in the current group are visualized in real time, and the relationship between them is displayed through a two-dimensional scatter plot or a Pareto frontier plot. According to the visualization results, the quality change standard function and the parameters of the genetic algorithm can be manually adjusted; The storage environment of biochemical materials is regulated based on the final determined target environmental parameters.

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