Intelligent storage environment adjusting system using graphene material

Abstract

The application discloses an intelligent storage environment adjusting system using graphene materials, and relates to the technical field of environment adjusting.The system comprises: a constraint condition obtaining module, which is used for performing storage environment constraint analysis based on basic information of graphene materials in a storage space to obtain first storage environment constraint conditions; a monitoring array set obtaining module, which is used for obtaining an air ion concentration monitoring array sequence, a temperature monitoring set and a humidity monitoring set; and an environment adjusting module, which is used for identifying the air ion concentration monitoring array sequence, the temperature monitoring set and the humidity monitoring set in combination with the first storage environment constraint conditions, obtaining a first environment adjusting scheme, and adjusting the environment of a target storage space based on the first environment adjusting scheme.The application solves the technical problem that the prior art of adjusting the storage environment of graphene materials is divorced from the actual situation, resulting in poor material preservation quality, and achieves the technical effect of improving the reliability of environment adjusting.

Description

Technical Field

[0001] This invention relates to the field of environmental regulation technology, specifically to an intelligent storage and environmental regulation system using graphene materials. Background Technology

[0002] Currently, graphene materials are widely used in electronic devices, energy storage, and composite materials due to their excellent electrical conductivity, thermal conductivity, and mechanical strength. However, graphene materials are highly susceptible to environmental factors such as humidity, temperature, and air ion concentration, which can lead to performance degradation. Therefore, researching suitable storage environments is of significant practical importance in maintaining the stability of graphene materials.

[0003] Existing graphene material storage solutions typically rely on a single method of temperature and humidity control, neglecting the potential impact of air ion concentration on graphene performance. Furthermore, current methods often lack intelligent control over the storage environment, failing to precisely adapt to actual conditions. This limitation in environmental regulation can lead to material aging or performance degradation.

[0004] Existing technologies suffer from the problem that the storage environment for graphene materials is not properly adjusted to suit the actual conditions, resulting in poor material preservation quality. Summary of the Invention

[0005] This application provides an intelligent storage environment regulation system for graphene materials, which addresses the technical problem that existing technologies for regulating the storage environment of graphene materials are out of touch with reality, resulting in poor material preservation quality.

[0006] In view of the above problems, this application provides an intelligent storage environment regulation system using graphene materials, the system comprising:

[0007] The constraint condition acquisition module is used to perform storage environment constraint analysis based on the basic information of graphene materials in the storage space, and obtain the first storage environment constraint condition.

[0008] The monitoring array set acquisition module is used to deploy an air ion meter array in the storage space, continuously monitor the ion concentration based on the air ion meter array, obtain an air ion concentration monitoring array sequence, and call the temperature and humidity sensor group of the storage space to continuously monitor and obtain a temperature monitoring set and a humidity monitoring set.

[0009] The environmental control module is used to identify the air ion concentration monitoring array sequence, temperature monitoring set and humidity monitoring set in combination with the first storage environment constraints, obtain the first environmental control scheme, and control the environment of the target storage space based on the first environmental control scheme.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] This application analyzes the storage environment constraints based on the fundamental information of the graphene material within the storage space to obtain the first storage environment constraint condition. Then, an air ion meter array is deployed within the storage space for continuous monitoring of ion concentration, yielding an air ion concentration monitoring array sequence. Furthermore, the temperature and humidity monitoring sets obtained from continuous monitoring by the temperature and humidity sensor group within the storage space are used. These, combined with the first storage environment constraint condition, are identified to obtain a first environmental adjustment scheme. Based on this scheme, the target storage space is then environmentally adjusted. This achieves the technical effect of dynamically adapting to the storage environment and improving the accuracy of environmental adjustment. Attached Figure Description

[0012] Figure 1 A schematic diagram of the structure of an intelligent storage and environmental regulation system using graphene materials provided in this application embodiment;

[0013] Figure 2 A schematic diagram of the centralized humidity monitoring and acquisition unit in the intelligent storage environment regulation system using graphene materials provided in the embodiments of this application;

[0014] Explanation of reference numerals in the attached figures: Constraint condition acquisition module 11, monitoring array set acquisition module 12, environmental control module 13. Detailed Implementation

[0015] This application provides an intelligent storage environment regulation system for graphene materials, which addresses the technical problem that existing technologies for regulating the storage environment of graphene materials are out of touch with reality, resulting in poor material preservation quality.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, systems, products, or devices.

[0018] Examples, such as Figure 1 As shown, this application provides an intelligent storage environment regulation system using graphene materials, wherein the system includes:

[0019] The constraint condition acquisition module 11 is used to perform storage environment constraint analysis based on the basic information of graphene materials in the storage space and obtain the first storage environment constraint condition.

[0020] In one possible embodiment, the first storage environment constraint is the range of environmental parameters required to ensure the stability of the graphene material's performance during storage, including temperature, humidity, and air ion concentration. The basic information of the graphene material refers to relevant information about the graphene material stored in the storage space, including material type (e.g., single-layer graphene, multilayer graphene, graphene oxide, etc.) and storage time (recent or long-term storage). Different types of graphene materials have different environmental adaptability, therefore analysis based on basic information is necessary. The storage space is any space where graphene materials are stored and environmental regulation is required.

[0021] Preferably, basic information about the graphene material is obtained from the storage space, and constraint analysis of the storage environment is performed based on this information. Multiple sample material types and storage times are acquired, and those skilled in the art set corresponding storage environment constraints for multiple samples according to actual storage conditions, forming a sample training dataset. Then, the sample training dataset is used to supervise the training of a framework built on a convolutional neural network, and the network parameters of the framework are updated and adjusted during the training process until the training converges, obtaining a trained constraint analyzer. Specifically, the material type and storage time extracted from the basic information are input into the constraint analyzer for analysis, thereby intelligently outputting the first storage environment constraints.

[0022] By analyzing the specific conditions of the materials, the constraints of the storage environment can be determined to adapt to the needs of different material types and storage times, thereby avoiding material performance degradation or aging problems caused by environmental mismatch.

[0023] The monitoring array set acquisition module 12 is used to deploy an air ion meter array in the storage space, continuously monitor the ion concentration based on the air ion meter array, obtain an air ion concentration monitoring array sequence, and call the temperature and humidity sensor group of the storage space to continuously monitor and obtain a temperature monitoring set and a humidity monitoring set.

[0024] In one possible embodiment, the air ion meter array is a monitoring array composed of multiple air ion concentration sensors distributed at different locations within the storage space. It can sense the concentration of positive and negative ions in the air in real time and record their changing trends. Since the performance of graphene materials may be affected by charged particles in the air, monitoring air ion concentration is a crucial step in regulating the storage environment. The temperature and humidity sensor group is used to monitor the temperature and humidity within the storage space. These sensors are distributed at different locations within the storage environment, continuously collecting temperature and humidity data for subsequent analysis and processing by the environmental control module.

[0025] The air ion concentration monitoring array sequence is a data array obtained by the air ion meter array after monitoring the air ion concentration at different locations within the storage space, showing its changes over time. The temperature monitoring set reflects the temperature conditions collected by the temperature sensor within the storage space. The humidity monitoring set reflects the humidity conditions collected by the humidity sensor within the storage space. After integrating these data in terms of time and space, they can be used to analyze the changing trends of air ion concentration, temperature, and humidity, and whether the set storage environment constraints are exceeded.

[0026] Preferably, the placement of the air ion meter array is rationally determined based on factors such as the size of the storage space, airflow distribution, and material placement, ensuring that the monitoring range covers the entire storage space. For example, assuming a storage room is 10 meters long, 5 meters wide, and 3 meters high, containing multiple sealed storage cabinets for graphene, with relatively weak air circulation, a "grid layout method" can be adopted. This involves evenly distributing 10 air ion sensors throughout the space, with two additional sensors added in key airflow areas (such as exhaust vents and air inlets) to improve monitoring accuracy. Air ion meters are installed at designated locations, ensuring stable operation and avoiding external interference affecting the monitoring data.

[0027] The deployed air ion meters are connected wirelessly (e.g., LoRa, Zigbee) or wired (e.g., Ethernet) to remotely acquire data, forming an air ion meter array that continuously collects air ion concentration data. Once activated, the air ion concentration monitoring array monitors the concentration of positive and negative ions in the air in real time for subsequent analysis and adjustment.

[0028] The temperature and humidity sensor array is deployed in the same manner as the air ion meter array, but the data from the temperature and humidity sensor array can be collected synchronously or asynchronously. By providing comprehensive environmental monitoring capabilities, it ensures that the environmental parameters of the storage space can be accurately sensed and recorded, achieving the technical effect of providing data support for analyzing the actual environmental conditions of the storage space.

[0029] The environmental control module 13 is used to identify the air ion concentration monitoring array sequence, temperature monitoring set and humidity monitoring set in combination with the first storage environment constraints, obtain the first environmental control scheme, and control the environment of the target storage space based on the first environmental control scheme.

[0030] In one possible embodiment, after integrating and analyzing the data from the air ion concentration monitoring array sequence, temperature monitoring set, and humidity monitoring set respectively, the data is distinguished from the parameter ranges in the first storage environment constraints to obtain the degree of deviation from the actual situation. Then, an intelligent adjustment scheme is generated based on the degree of deviation to obtain the first environmental adjustment scheme. The first environmental adjustment scheme is an implementation method that adjusts the environment within the storage space to an environment most suitable for graphene materials. For example, if the temperature is too high, the scheme may suggest starting an air conditioner to cool it down; if the humidity is too high, the scheme may suggest turning on a dehumidifier; if the air ion concentration is too high, the scheme may suggest starting an air purifier. By adjusting the temperature, humidity, and air ion concentration based on real-time monitoring data, the technical effect of avoiding performance degradation of the material due to an unstable storage environment is achieved.

[0031] Furthermore, the environmental control module 13 also includes:

[0032] A centralized humidity monitoring value acquisition unit is used to filter the data of the temperature monitoring set and the humidity monitoring set respectively, and determine the centralized temperature monitoring value and the centralized humidity monitoring value.

[0033] The ion concentration uniformity acquisition unit is used to analyze the air ion concentration monitoring array sequence in two dimensions: concentration and uniformity, to obtain the concentrated air ion concentration and air ion concentration uniformity.

[0034] The first environmental regulation scheme acquisition unit is used to identify deviations from the centralized temperature monitoring value, centralized humidity monitoring value, centralized air ion concentration and air ion concentration uniformity based on the first storage environment constraints, and to perform adjustment analysis based on the deviation identification results to obtain the first environmental regulation scheme.

[0035] Furthermore, the first environmental regulation scheme obtaining unit also includes:

[0036] The deviation value acquisition micro-unit is used to identify deviations in the concentrated temperature monitoring value, concentrated humidity monitoring value, concentrated air ion concentration and air ion concentration uniformity based on the first storage environment constraints, and to obtain temperature monitoring deviation value, humidity monitoring deviation value, concentrated air ion concentration deviation value and air ion concentration uniformity deviation value.

[0037] The adjustment scheme obtains a micro-unit, which is used to identify the temperature monitoring deviation value, humidity monitoring deviation value, concentrated air ion concentration deviation value, and air ion concentration uniformity deviation value using an adjustment scheme identifier, and obtains a first environmental adjustment scheme.

[0038] In one possible embodiment, the concentrated temperature monitoring value reflects the general temperature situation within the storage space. The concentrated humidity monitoring value reflects the general humidity situation within the storage space. The concentrated air ion concentration reflects the general air ion concentration situation within the storage space. The air ion concentration uniformity reflects the degree of uniformity of air ion concentration at different locations within the storage space. The lower the uniformity, the greater the difference in air ion concentration at different locations. This will lead to different types and quantities of ions adsorbed on different surface areas of the graphene material, thereby affecting the electronic structure of graphene and thus its performance.

[0039] Preferably, after obtaining the centralized temperature monitoring value, centralized humidity monitoring value, centralized air ion concentration, and air ion concentration uniformity, the difference between these values ​​and the data ranges in the first storage environment constraints is calculated to obtain the temperature monitoring deviation value, humidity monitoring deviation value, centralized air ion concentration deviation value, and air ion concentration uniformity deviation value. These deviation values ​​reflect the degree of environmental deviation of the storage space from the dimensions of temperature, humidity, air ion concentration, and air ion concentration uniformity, respectively. Using these deviation values ​​as input, an adjustment scheme identifier is used to identify the adjustment scheme and output the first environmental adjustment scheme.

[0040] Preferably, training samples are obtained through big data, and the sample example table is shown in Table 1.

[0041] Table 1

[0042] Sample number ΔT (°C) ΔH(%) <![CDATA[ΔI(ion / cm 3 )]]> ΔU(%) 1 2.5 -3 120 -5 2 -1 5 -80 3 3 3 2 200 7 ... ... ... ... ...

[0043] Preferably, a regulation scheme recognizer is constructed based on a convolutional neural network. The input layer includes features constructed from temperature monitoring deviations, humidity monitoring deviations, concentrated air ion concentration deviations, and air ion concentration uniformity deviations. Local pattern features are extracted through a one-dimensional convolutional layer (Conv1D), and dimensionality is reduced through a max-pooling layer (MaxPooling1D) to improve computational efficiency. Then, features are fused through a fully connected layer (Dense), and the first environmental regulation scheme is finally output. To improve the training effect of the model, cross-entropy loss is used. After training, the model can automatically predict the optimal environmental regulation scheme by taking new environmental parameters as input, thereby realizing intelligent regulation and control of the storage space.

[0044] Preferably, the concentrated air ion concentration is obtained based on the same principle as that used in the concentrated humidity monitoring unit for obtaining concentrated temperature and humidity monitoring values. A variance sequence is obtained by calculating the fluctuation variance of each air ion concentration monitoring array in the air ion concentration monitoring array sequence. This variance sequence reflects the uniformity of air ion concentration within the storage space at different times. Furthermore, the mean of the variance sequence is calculated to obtain the air ion concentration uniformity. This achieves the technical effect of analyzing both the magnitude and uniformity of air ion concentration within the storage space.

[0045] Furthermore, such as Figure 2 As shown, the centralized humidity monitoring value acquisition unit further includes:

[0046] The coordinate vector set obtains a sub-unit, which is used to construct a two-dimensional coordinate system with time as the horizontal axis and temperature as the total coordinate axis. The temperature monitoring set is input into the two-dimensional coordinate system to obtain the temperature monitoring scatter point set and the temperature scatter point coordinate vector set.

[0047] The first data filtering line obtaining sub-unit is used to obtain the first data filtering line, wherein the first data filtering line is a line that passes through the mean of all vertical coordinate elements in the scatter point coordinate vector set and is parallel to the horizontal coordinate axis.

[0048] The centralized temperature monitoring value determination subunit is used to filter straight lines based on the first data and combine the temperature monitoring scatter point set to filter data and determine the centralized temperature monitoring value.

[0049] The centralized humidity monitoring value determination subunit is used to construct a two-dimensional coordinate system with time as the horizontal axis and humidity as the total coordinate axis. The humidity monitoring set is input into the two-dimensional coordinate system, and a humidity monitoring scatter set and a humidity scatter coordinate vector set are constructed based on the humidity monitoring set. Data filtering is then performed to determine the centralized humidity monitoring value.

[0050] In one embodiment of this application, the temperature monitoring scatter set is a discrete set of points formed by projecting the data points of the temperature monitoring set onto a time-temperature coordinate system. The humidity monitoring scatter set is a discrete set of points formed by projecting the data points of the humidity monitoring set onto a time-humidity two-dimensional coordinate system. The temperature scatter coordinate vector set refers to a set of coordinate vectors mapped onto the coordinate axes after constructing the two-dimensional coordinate system. These coordinate vectors contain time information (horizontal axis elements) and monitoring value information (vertical axis elements), used for subsequent calculations and analysis. A straight line passing through the mean of all vertical axis elements in the scatter coordinate vector set and parallel to the horizontal axis is used as the first data filtering line. This first data filtering line is an auxiliary line used to find densely distributed locations in the temperature monitoring set. The humidity scatter coordinate vector set refers to a set of coordinate vectors mapped onto the coordinate axes after constructing the two-dimensional coordinate system.

[0051] Preferably, all temperature monitoring points are plotted in a two-dimensional coordinate system with time as the x-axis and temperature as the y-axis, forming a temperature monitoring scatter plot. Simultaneously, the coordinate information of each point is recorded, forming a set of temperature scatter plot coordinate vectors. Similarly, a humidity monitoring scatter plot is constructed with time as the x-axis and humidity as the y-axis, and the set of humidity scatter plot coordinate vectors is recorded. Using the first data filtering line, the temperature monitoring scatter plot is filtered to obtain the concentrated temperature monitoring value that best represents the general temperature situation of the temperature monitoring scatter plot. Based on the same principle as obtaining the concentrated temperature monitoring value, the concentrated temperature monitoring value is obtained.

[0052] By using the method of filtering straight lines through data, abnormal data with large deviations are effectively removed, so that the final calculated centralized temperature and humidity monitoring values ​​can better reflect the actual storage environment, thereby improving the accuracy and stability of the adjustment scheme.

[0053] Furthermore, the centralized temperature monitoring value determination subunit also includes:

[0054] The first lower-level temperature monitoring scatter set obtains micro-units, which are used to divide the temperature monitoring scatter set into a first upper-level temperature monitoring scatter set and a first lower-level temperature monitoring scatter set based on the first data filtering line.

[0055] The second data filtering line obtains a micro-unit, which is used to rotate the first data filtering line counterclockwise according to a preset rotation angle to obtain the second data filtering line when the scatter statistics of the first upper temperature monitoring scatter set are greater than the scatter statistics of the first lower temperature monitoring scatter set.

[0056] The second lower-level temperature monitoring scatter set obtains micro-units, which are used to divide the temperature monitoring scatter set into a second upper-level temperature monitoring scatter set and a second lower-level temperature monitoring scatter set based on the second data filtering line.

[0057] The displacement iteration direction is used to obtain micro-units. When the scatter statistics of the second upper temperature monitoring scatter set are greater than the scatter statistics of the second lower temperature monitoring scatter set, the second data filtering line is rotated counterclockwise according to a preset rotation angle until the number of rotations meets the maximum number of rotations, and then the rotation is stopped. The angle data filtering line and the displacement iteration direction are obtained. The displacement iteration direction is either the direction closer to the horizontal axis or the direction farther away from the horizontal axis.

[0058] The target data filtering line obtains micro-units, which are used to perform displacement iteration on the angle data filtering line in the temperature monitoring scatter point set according to a preset iteration bandwidth and displacement iteration direction to obtain the target data filtering line;

[0059] Temperature monitoring values ​​are obtained by micro-units, which are used to construct the target filtering neighborhood of the target data filtering line. The mean value of the ordinate elements corresponding to all temperature monitoring scatter points in the target filtering neighborhood is calculated to obtain the concentrated temperature monitoring values.

[0060] Furthermore, the centralized temperature monitoring value determination subunit also includes:

[0061] The angle data is filtered into a straight line to obtain micro-units. When the scatter statistics of the second upper temperature monitoring scatter set are less than or equal to the scatter statistics of the second lower temperature monitoring scatter set, the rotation stops and the second data filtering line is used as the angle data filtering line.

[0062] In one possible embodiment, a first data filtering line, determined by the mean of all temperature monitoring scatter points and parallel to the horizontal axis (time axis), serves as the initial filtering line. This line initially distinguishes between upper and lower layers of data points in the temperature monitoring scatter point set, thereby obtaining a first upper-layer temperature monitoring scatter point set and a first lower-layer temperature monitoring scatter point set. The first upper-layer temperature monitoring scatter point set is the set of temperature monitoring points located above the first data filtering line. The first lower-layer temperature monitoring scatter point set is the set of temperature monitoring points located below the first data filtering line.

[0063] Preferably, the number of scatter points in the first upper-layer temperature monitoring scatter point set and the first lower-layer temperature monitoring scatter point set are counted respectively. When the scatter point count of the first upper-layer temperature monitoring scatter point set is greater than that of the first lower-layer temperature monitoring scatter point set, it indicates that there are more scatter points above the first data filtering line. The first data filtering line needs to be rotated counterclockwise by a preset rotation angle to obtain a line closer to the densely distributed data area in the temperature monitoring scatter point set, which is the second data filtering line. Similarly, if the scatter point count of the first upper-layer temperature monitoring scatter point set is less than that of the first lower-layer temperature monitoring scatter point set, the first data filtering line needs to be rotated clockwise by a preset rotation angle. The preset rotation angle is a single rotation angle pre-set by those skilled in the art, and can be selected as 18 degrees. The maximum number of rotations is 18 times; that is, when it continues to rotate, aligning the resulting line with the initial first data filtering line is used as a constraint to stop the rotation.

[0064] Preferably, based on the same partitioning principle, the temperature monitoring scatter plot is divided into upper and lower layers using the second data filtering line, resulting in a second upper-layer temperature monitoring scatter plot and a second lower-layer temperature monitoring scatter plot. Then, when the scatter plot statistics of the second upper-layer temperature monitoring scatter plot are greater than those of the second lower-layer temperature monitoring scatter plot, the second data filtering line is rotated counterclockwise by a preset rotation angle until the maximum number of rotations is reached. The rotation is then stopped, and the last obtained line is used as the angle data filtering line. In other words, at this point, the angle data filtering line conforms to the dense data distribution area in terms of tilt angle. When rotation stops, if the scatter plot statistics of the upper-layer set are greater than those of the lower-layer set, the displacement iteration direction is towards the horizontal axis. When rotation stops, if the scatter plot statistics of the upper-layer set are less than or equal to those of the lower-layer set, the displacement iteration direction is away from the horizontal axis.

[0065] Then, according to the displacement iteration direction, the angle data filtering line is subjected to displacement iteration according to the preset iteration bandwidth to obtain the target data filtering line. The preset iteration bandwidth is the distance moved in a single displacement iteration, pre-set by those skilled in the art. The target data filtering line is the line located in the densest distribution area of ​​the temperature monitoring scatter point set. Scatter points in the temperature monitoring scatter point set whose distance to the target data filtering line is less than or equal to the preset iteration bandwidth are added to the target filtering neighborhood. The scatter points in the target filtering neighborhood are those that best reflect the overall general situation of the temperature monitoring scatter point set. The mean of the ordinate elements corresponding to all temperature monitoring scatter points in the target filtering neighborhood is calculated to obtain the concentrated temperature monitoring value that best represents the temperature situation within the storage space.

[0066] Furthermore, the target data filtering line to obtain the micro-unit also includes:

[0067] The first iteration angle data filtering line obtains a sub-unit, which is used to iterate the angle data filtering line according to a preset iteration bandwidth and displacement iteration direction to obtain the first iteration angle data filtering line.

[0068] The first iteration scatter point quantity sub-unit is used to count the number of scatter points in the temperature monitoring scatter point set whose distance to the first iteration angle data filtering line is less than or equal to the preset iteration bandwidth, and obtain the first iteration scatter point quantity.

[0069] The second iteration angle data filtering line obtains a sub-unit, which is used to iterate the first iteration angle data filtering line again according to the preset iteration bandwidth and displacement iteration direction to obtain the second iteration angle data filtering line.

[0070] The second iteration scatter point quantity sub-unit is used to count the number of scatter points in the temperature monitoring scatter point set whose distance to the second iteration angle data filtering line is less than or equal to the preset iteration bandwidth, and obtain the second iteration scatter point quantity.

[0071] The scatter point quantity judgment subunit is used to determine whether the second iteration scatter point quantity is greater than or equal to the first iteration scatter point quantity. If so, the second iteration angle data filtering line is iterated according to the preset iteration bandwidth and displacement iteration direction until the preset number of iterations is met, and the target data filtering line is obtained.

[0072] Furthermore, the method of obtaining micro-units by filtering target data along a straight line also includes:

[0073] A straight line sub-unit is set to stop iteration when the amount of scatter points in the second iteration is less than the amount of scatter points in the first iteration, and the straight line used for filtering the angle data in the first iteration is taken as the target data filtering line.

[0074] In one possible embodiment, the angle data filtering line is moved along the preset iteration bandwidth according to the displacement iteration direction to obtain the first iterative angle data filtering line. Then, the number of scatter points in the temperature monitoring scatter point set whose distance to the first iterative angle data filtering line is less than or equal to the preset iteration bandwidth is counted to obtain the first iterative scatter point quantity. The first iterative scatter point quantity reflects the density of scatter points gathered around the first iterative angle data filtering line; the larger the scatter point quantity, the higher the scatter point density.

[0075] The first iterative angle data filtering line is moved along the preset iteration bandwidth according to the displacement iteration direction to obtain the second iterative angle data filtering line. Similarly, the number of scatter points in the temperature monitoring scatter point set whose distance to the second iterative angle data filtering line is less than or equal to the preset iteration bandwidth is counted to obtain the second iteration scatter point quantity. The second iteration scatter point quantity reflects the density of scatter points gathered around the second iterative angle data filtering line; the larger the scatter point quantity, the higher the scatter point density. At this point, the second iterative angle data filtering line is iterated according to the preset iteration bandwidth and displacement iteration direction. When the number of iterations meets the preset number of iterations, iteration stops, and the line obtained in the last iteration is taken as the target data filtering line. The preset number of iterations is a maximum number of iterations pre-set by those skilled in the art.

[0076] Preferably, when the number of scatter points in the second iteration is less than the number of scatter points in the first iteration, it indicates that the number of scatter points gathered around the first iteration angle data filtering line has reached its maximum. At this point, the iteration is stopped, and the first iteration angle data filtering line is taken as the target data filtering line. Through continuous iterative optimization, it is ensured that the selected target data filtering line can reflect the trend of the actual data to the greatest extent, thereby achieving a higher fitting effect.

[0077] In summary, the embodiments of this application have at least the following technical effects:

[0078] 1. This application uses a constraint condition acquisition module to automatically analyze suitable storage environment constraints based on the basic information of the graphene material within the storage space. This allows for precise environmental adaptation for graphene materials of different specifications or applications, avoiding performance degradation due to improper environmental adjustments.

[0079] 2. By comprehensively considering factors such as temperature, humidity, and air ion concentration, the technology has achieved the effect of improving the accuracy of environmental regulation and its fit with actual conditions.

[0080] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0081] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0082] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. An intelligent storage environment regulation system using graphene materials, characterized in that, The system includes: The constraint condition acquisition module is used to perform storage environment constraint analysis based on the basic information of graphene materials in the storage space, and obtain the first storage environment constraint condition. The monitoring array set acquisition module is used to deploy an air ion meter array in the storage space, continuously monitor the ion concentration based on the air ion meter array, obtain an air ion concentration monitoring array sequence, and call the temperature and humidity sensor group of the storage space to continuously monitor and obtain a temperature monitoring set and a humidity monitoring set. The environmental control module is used to identify the air ion concentration monitoring array sequence, temperature monitoring set and humidity monitoring set in combination with the first storage environment constraints, obtain the first environmental control scheme, and control the environment of the target storage space based on the first environmental control scheme. The environmental control module includes: A centralized humidity monitoring value acquisition unit is used to filter the data of the temperature monitoring set and the humidity monitoring set respectively, and determine the centralized temperature monitoring value and the centralized humidity monitoring value. The ion concentration uniformity acquisition unit is used to analyze the air ion concentration monitoring array sequence in two dimensions: concentration and uniformity, to obtain the concentrated air ion concentration and air ion concentration uniformity. The first environmental regulation scheme acquisition unit is used to identify deviations from the centralized temperature monitoring value, centralized humidity monitoring value, centralized air ion concentration and air ion concentration uniformity based on the first storage environment constraints, and to perform adjustment analysis based on the deviation identification results to obtain the first environmental regulation scheme. The centralized humidity monitoring value acquisition unit includes: The coordinate vector set obtains a sub-unit, which is used to construct a two-dimensional coordinate system with time as the horizontal axis and temperature as the total coordinate axis. The temperature monitoring set is input into the two-dimensional coordinate system to obtain the temperature monitoring scatter point set and the temperature scatter point coordinate vector set. The first data filtering line obtaining sub-unit is used to obtain the first data filtering line, wherein the first data filtering line is a line that passes through the mean of all vertical coordinate elements in the scatter point coordinate vector set and is parallel to the horizontal coordinate axis. The centralized temperature monitoring value determination subunit is used to filter straight lines based on the first data and combine the temperature monitoring scatter point set to filter data and determine the centralized temperature monitoring value. The centralized humidity monitoring value determination subunit is used to construct a two-dimensional coordinate system with time as the horizontal axis and humidity as the total coordinate axis. The humidity monitoring set is input into the two-dimensional coordinate system, and a humidity monitoring scatter set and a humidity scatter coordinate vector set are constructed based on the humidity monitoring set. Data filtering is then performed to determine the centralized humidity monitoring value. The centralized temperature monitoring value determination subunit includes: The first lower-level temperature monitoring scatter set obtains micro-units, which are used to divide the temperature monitoring scatter set into a first upper-level temperature monitoring scatter set and a first lower-level temperature monitoring scatter set based on the first data filtering line. The second data filtering line obtains a micro-unit, which is used to rotate the first data filtering line counterclockwise according to a preset rotation angle to obtain the second data filtering line when the scatter statistics of the first upper temperature monitoring scatter set are greater than the scatter statistics of the first lower temperature monitoring scatter set. The second lower-level temperature monitoring scatter set obtains micro-units, which are used to divide the temperature monitoring scatter set into a second upper-level temperature monitoring scatter set and a second lower-level temperature monitoring scatter set based on the second data filtering line. The displacement iteration direction is obtained as a micro-unit. When the scatter statistics of the second upper-level temperature monitoring scatter set are greater than the scatter statistics of the second lower-level temperature monitoring scatter set, the second data filtering line is rotated counterclockwise according to a preset rotation angle until the number of rotations meets the maximum number of rotations, and then the rotation is stopped. The angle data filtering line and the displacement iteration direction are obtained. The displacement iteration direction is either closer to the horizontal axis or farther from the horizontal axis. When the rotation stops, if the scatter statistics of the upper-level set are greater than the scatter statistics of the lower-level set, the displacement iteration direction is closer to the horizontal axis. When the rotation stops, if the scatter statistics of the upper-level set are less than or equal to the scatter statistics of the lower-level set, the displacement iteration direction is farther from the horizontal axis. The target data filtering line obtains micro-units, which are used to perform displacement iteration on the angle data filtering line in the temperature monitoring scatter point set according to a preset iteration bandwidth and displacement iteration direction to obtain the target data filtering line; Temperature monitoring value acquisition micro-units are used to construct the target screening neighborhood of the target data screening line. The mean value of the ordinate elements corresponding to all temperature monitoring scatter points in the target screening neighborhood is calculated to obtain the concentrated temperature monitoring value. Among them, scatter points in the set of temperature monitoring scatter points whose distance to the target data screening line is less than or equal to the preset iteration bandwidth are added to the target screening neighborhood. The centralized temperature monitoring value determination subunit also includes: The angle data is filtered into a straight line to obtain micro-units. When the scatter statistics of the second upper temperature monitoring scatter set are less than or equal to the scatter statistics of the second lower temperature monitoring scatter set, the rotation stops and the second data filtering line is used as the angle data filtering line.

2. The intelligent storage environment regulation system using graphene materials as described in claim 1, characterized in that, The target data filtering line to obtain micro-units includes: The first iteration angle data filtering line obtains a sub-unit, which is used to iterate the angle data filtering line according to a preset iteration bandwidth and displacement iteration direction to obtain the first iteration angle data filtering line. The first iteration scatter point quantity sub-unit is used to count the number of scatter points in the temperature monitoring scatter point set whose distance to the first iteration angle data filtering line is less than or equal to the preset iteration bandwidth, and obtain the first iteration scatter point quantity. The second iteration angle data filtering line obtains a sub-unit, which is used to iterate the first iteration angle data filtering line again according to the preset iteration bandwidth and displacement iteration direction to obtain the second iteration angle data filtering line. The preset iteration bandwidth is the distance moved in a single displacement iteration that is preset by those skilled in the art. The second iteration scatter point quantity sub-unit is used to count the number of scatter points in the temperature monitoring scatter point set whose distance to the second iteration angle data filtering line is less than or equal to the preset iteration bandwidth, and obtain the second iteration scatter point quantity. The scatter point quantity judgment subunit is used to determine whether the second iteration scatter point quantity is greater than or equal to the first iteration scatter point quantity. If so, the second iteration angle data filtering line is iterated according to the preset iteration bandwidth and displacement iteration direction until the preset number of iterations is met, and the target data filtering line is obtained.

3. The intelligent storage environment regulation system using graphene materials as described in claim 2, characterized in that, The process of obtaining micro-units by filtering target data into straight lines also includes: A straight line sub-unit is set to stop iteration when the amount of scatter points in the second iteration is less than the amount of scatter points in the first iteration, and the straight line used for filtering the angle data in the first iteration is taken as the target data filtering line.

4. The intelligent storage environment regulation system using graphene materials as described in claim 1, characterized in that, The first environmental regulation scheme acquisition unit includes: The deviation value acquisition micro-unit is used to identify deviations in the concentrated temperature monitoring value, concentrated humidity monitoring value, concentrated air ion concentration and air ion concentration uniformity based on the first storage environment constraints, and to obtain temperature monitoring deviation value, humidity monitoring deviation value, concentrated air ion concentration deviation value and air ion concentration uniformity deviation value. The adjustment scheme obtains a micro-unit, which is used to identify the temperature monitoring deviation value, humidity monitoring deviation value, concentrated air ion concentration deviation value, and air ion concentration uniformity deviation value using an adjustment scheme identifier, and obtains a first environmental adjustment scheme.

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