Environment-friendly antioxidant production management system and method

Abstract

The invention relates to the field of intelligent management, and particularly discloses an environment-friendly antioxidant production management system and method.According to the environment-friendly antioxidant production management system and method, time sequences consumed by various energy types in the environment-friendly antioxidant production process are obtained, and the energy types comprise electric power, water and steam; and time sequence feature analysis and feature extraction are performed on the time sequence consumed by the multiple energy types based on an artificial intelligence technology, so that the links of energy waste can be intelligently determined. Through the mode, the system can identify modes and abnormities in energy use, so that the energy efficiency in the production process is optimized to accord with the green production concept of an environment-friendly antioxidant, and meanwhile, unnecessary energy consumption can be reduced, so that the production cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of intelligent management, and more specifically, to an environmentally friendly antioxidant production management system and method. Background Art

[0002] Environmentally friendly antioxidants are additives used to prevent oxidation of materials, especially during plastic manufacturing and processing. They can effectively delay the aging process of materials under the action of oxygen, light and heat, thereby protecting the materials from damage and extending their service life.

[0003] In the production process of environmentally friendly antioxidants, the use of energy is crucial because it directly affects production efficiency, cost and the environmental characteristics of the product. Energy waste in the production process of environmentally friendly antioxidants may cause many problems. First, it will increase production costs because energy is an important expense in the production process. Secondly, excessive energy consumption will also aggravate environmental pollution, especially when using fossil fuels, which will increase greenhouse gas emissions and have a negative impact on global climate change.

[0004] However, due to the lack of networking in traditional energy management systems, monitoring is decentralized, centralized management cannot be achieved, and defects cannot be discovered and alarms cannot be issued in a timely manner. In addition, most energy management systems can only provide feedback on the energy consumption of enterprises, but cannot analyze and identify energy waste points, requiring in-depth analysis by professionals.

[0005] Therefore, an optimized environmentally friendly antioxidant production management system is expected. Summary of the invention

[0006] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides an environmentally friendly antioxidant production management system and method, which obtains the time series of the consumption of various energy types in the production process of environmentally friendly antioxidants, wherein the energy types include electricity, water, and steam, and uses artificial intelligence technology to perform time series feature analysis and feature extraction on the time series of the consumption of the various energy types, so as to intelligently determine the links of energy waste. In this way, the system can identify patterns and anomalies in energy use, thereby optimizing the energy efficiency in the production process to comply with the green production concept of environmentally friendly antioxidants, while reducing unnecessary energy consumption and thereby reducing production costs.

[0007] According to one aspect of the present application, there is provided an environmentally friendly antioxidant production management system, which comprises:

[0008] The module for acquiring the time series of energy consumption in the production of environmentally friendly antioxidants is used to acquire the time series of consumption of various types of energy in the production process of environmentally friendly antioxidants, wherein the energy types include electricity, water, and steam;

[0009] An energy consumption time series feature extraction module is used to extract energy consumption time series features after arranging the time series of the consumption of the multiple energy types into multiple energy consumption time series input vectors to obtain multiple energy consumption multi-scale time series feature vectors;

[0010] A cross-scale energy consumption feature analysis module, used for arranging the plurality of energy consumption multi-scale time series feature vectors into an energy consumption type time series feature matrix and then performing cross-scale energy consumption feature analysis to obtain an energy consumption type multi-scale feature matrix;

[0011] An energy global feature capture module, used for capturing energy global features of the energy consumption type multi-scale feature matrix to obtain an energy consumption type global feature matrix;

[0012] The energy waste link judgment module is used to determine the energy waste link based on the energy consumption type global characteristic matrix.

[0013] According to another aspect of the present application, a method for managing production of an environmentally friendly antioxidant is provided, comprising:

[0014] Obtaining a time series of consumption of various types of energy during the production process of an environmentally friendly antioxidant, wherein the energy types include electricity, water, and steam;

[0015] Arranging the time series of the consumption of the multiple energy types into a plurality of energy consumption time series input vectors and then extracting energy consumption time series features to obtain a plurality of energy consumption multi-scale time series feature vectors;

[0016] Arranging the plurality of energy consumption multi-scale time series feature vectors into an energy consumption type time series feature matrix and then performing cross-scale energy consumption feature analysis to obtain an energy consumption type multi-scale feature matrix;

[0017] Capturing global energy features on the multi-scale feature matrix of energy consumption types to obtain a global feature matrix of energy consumption types;

[0018] Based on the global characteristic matrix of energy consumption types, the links of energy waste are determined.

[0019] Compared with the prior art, the present application provides an environmentally friendly antioxidant production management system and method, which obtains the time series of various energy types consumed in the production process of environmentally friendly antioxidants, wherein the energy types include electricity, water, and steam, and uses artificial intelligence technology to perform time series feature analysis and feature extraction on the time series of the various energy types consumed, thereby intelligently determining the energy waste link. In this way, the system can identify patterns and anomalies in energy use, thereby optimizing energy efficiency in the production process to meet the green production concept of environmentally friendly antioxidants, while reducing unnecessary energy consumption and thus reducing production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0021] Figure 1 4 is a block diagram of an environmentally friendly antioxidant production management system according to an embodiment of the present application.

[0022] Figure 2 Schematic diagram of the structure of the environmentally friendly antioxidant production management system according to an embodiment of the present application.

[0023] Figure 3 4 is a block diagram of an energy consumption time series feature extraction module in an environmentally friendly antioxidant production management system according to an embodiment of the present application.

[0024] Figure 4 The present invention is a block diagram of a module for determining energy waste in an environmentally friendly antioxidant production management system according to an embodiment of the present application.

[0025] Figure 5 The figure is a flow chart of an environmentally friendly antioxidant production management method according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, but rather these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0027] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0028] It should be noted that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that the specific order or sequence of "first\second\third" can be interchanged where permitted. It should be understood that the objects distinguished by "first\second\third" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0029] As shown in the present disclosure and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0030] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0031] Environmentally friendly antioxidants generally refer to those antioxidants that are environmentally friendly and do not contain harmful substances. They can be extracted from natural resources or synthesized, but they all need to meet strict environmental standards. For example, some environmentally friendly antioxidants are extracted from plants, such as certain flavonoids and polyphenols, which have strong antioxidant capacity and are safe for the human body and the environment. In addition, environmentally friendly antioxidants also include some specific synthetic compounds that are designed with their environmental impact in mind to ensure that they do not release harmful substances during use. Therefore, the development and application of environmentally friendly antioxidants is an important part of the sustainable development strategy of the plastics industry. They can effectively delay the aging process of materials under the action of oxygen, light and heat, thereby protecting the materials from damage and extending their service life.

[0032] In the production process of environmentally friendly antioxidants, the use of energy is crucial because it directly affects production efficiency, cost, and the environmental characteristics of the product. Energy waste in the production process of environmentally friendly antioxidants may cause many problems. First, it will increase production costs because energy is an important expense in the production process. Secondly, excessive energy consumption will also aggravate environmental pollution, especially when using fossil fuels, which will increase greenhouse gas emissions and have a negative impact on global climate change. In addition, energy waste may also lead to unsustainable use of resources because most energy resources are limited. In the chemical industry, especially in the production of antioxidants, the application of energy-saving and consumption-reducing technologies can significantly reduce energy consumption, improve energy utilization efficiency, and reduce environmental pollution and production costs. Therefore, it is crucial to develop and implement more efficient production technologies and energy management systems. This will not only help protect the environment, but also improve the competitiveness and market sustainability of enterprises.

[0033] However, due to the lack of networking in traditional energy management systems, monitoring is decentralized, centralized management cannot be achieved, and defects cannot be discovered and alarms cannot be issued in a timely manner. In addition, most energy management systems can only provide feedback on the energy consumption of enterprises, but cannot analyze and identify energy waste points, requiring in-depth analysis by professionals.

[0034] Therefore, an optimized environmentally friendly antioxidant production management system is desired, which obtains the time series of various energy types consumed in the production process of environmentally friendly antioxidants, wherein the energy types include electricity, water, and steam, and uses artificial intelligence technology to perform time series feature analysis and feature extraction on the time series of the various energy types consumed, so as to intelligently determine the links of energy waste. In this way, the system can identify patterns and anomalies in energy use, thereby optimizing the energy efficiency in the production process to meet the green production concept of environmentally friendly antioxidants, and at the same time can reduce unnecessary energy consumption, thereby reducing production costs.

[0035] Figure 1 4 is a block diagram of an environmentally friendly antioxidant production management system according to an embodiment of the present application. Figure 2 FIG. 1 is a schematic diagram of the structure of the environmentally friendly antioxidant production management system according to an embodiment of the present application. Figure 1 and Figure 2As shown, according to the embodiment of the present application, the environmentally friendly antioxidant production management system 100 includes: an environmentally friendly antioxidant production energy consumption time series acquisition module 110, which is used to obtain the time series of various energy types consumed in the environmentally friendly antioxidant production process, wherein the energy types include electricity, water, and steam; an energy consumption time series feature extraction module 120, which is used to arrange the time series of the various energy types consumed into multiple energy consumption time series input vectors and then perform energy consumption time series feature extraction to obtain multiple energy consumption multi-scale time series feature vectors; a cross-scale energy consumption feature analysis module 130, which is used to arrange the multiple energy consumption multi-scale time series feature vectors into an energy consumption type time series feature matrix and then perform cross-scale energy consumption feature analysis to obtain an energy consumption type multi-scale feature matrix; an energy global feature capture module 140, which is used to perform energy global feature capture on the energy consumption type multi-scale feature matrix to obtain an energy consumption type global feature matrix; and an energy waste link judgment module 150, which is used to determine the energy waste link based on the energy consumption type global feature matrix.

[0036] In the embodiment of the present application, the energy consumption time series acquisition module 110 for producing environmentally friendly antioxidants is used to obtain the time series of various energy types consumed in the production process of environmentally friendly antioxidants, wherein the energy types include electricity, water, and steam. It should be understood that considering that the electricity consumption is the energy consumption generated by the operation of various motors, machinery, lighting, computer systems, automation equipment, and electronic equipment in the factory. The water consumption is the energy consumption of environmentally friendly antioxidants for cooling, heating, cleaning, process, boiler feed water, etc. in the production process. The steam consumption is usually generated by boilers for heating, disinfection, driving steam turbines, or as a direct energy source for certain processes. These electricity, water, and steam are three basic energy types commonly seen in industrial production, and their consumption has a significant impact on the cost and production efficiency in the production process of environmentally friendly antioxidants. Based on this, in the technical solution of the present application, the time series of various energy types consumed in the production process of environmentally friendly antioxidants is obtained, wherein the energy types include electricity, water, and steam, and the time series analysis and correlation thereof are performed, which can provide a good basis and support for the determination of subsequent energy waste links.

[0037] In an embodiment of the present application, the energy consumption time series feature extraction module 120 is used to arrange the time series of the consumption of the multiple energy types into multiple energy consumption time series input vectors and then perform energy consumption time series feature extraction to obtain multiple energy consumption multi-scale time series feature vectors. Figure 3 is a block diagram of an energy consumption time series feature extraction module in an environmentally friendly antioxidant production management system according to an embodiment of the present application. Specifically, in the embodiment of the present application, Figure 3As shown, the energy consumption time series feature extraction module 120 includes: an energy consumption time sample arrangement unit 121, used to arrange the time series of consumption of the multiple energy types into the multiple energy consumption time series input vectors according to the time dimension and the sample dimension; and a time series feature extraction unit 122, used to input the multiple energy consumption time series input vectors into the energy consumption time series feature extraction network based on 1D-CNN to obtain the multiple energy consumption multi-scale time series feature vectors.

[0038] Specifically, the energy consumption time sample arrangement unit 121 is used to arrange the time series of the consumption of the multiple energy types according to the time dimension and the sample dimension into the multiple energy consumption time series input vectors. Accordingly, considering that each energy consumption in the multiple energy types has time series characteristic information on the time scale, that is, the consumption of each energy type changes with the continuous increase of time. And the multiple energy sources are also interrelated in time series. Based on this, in order to retain the time series characteristic information of each energy consumption in the multiple energy types and to analyze and process each energy separately, in the technical solution of the present application, the time series of the consumption of the multiple energy types are arranged according to the time dimension and the sample dimension into the multiple energy consumption time series input vectors. Specifically, arranging the time series of the consumption of the multiple energy types according to the time dimension can ensure that the time series input vector is arranged in the order of time series acquisition, which provides more accurate input for subsequent analysis of time series characteristics such as periodic changes of energy, short-term fluctuations of energy, etc. Arranging the time series of the consumption of the multiple energy types according to the sample dimension enables independent analysis and understanding of each energy source, thereby enabling more accurate analysis of the time series characteristic information of each energy source.

[0039] Specifically, the time series feature extraction unit 122 is used to input the multiple energy consumption time series input vectors into the energy consumption time series feature extraction network based on 1D-CNN to obtain the multiple energy consumption multi-scale time series feature vectors. It should be understood that, considering that in energy consumption data, specific patterns and trends in time series are crucial to determining the links of energy waste. That is, the multiple energy consumption time series input vectors have time series characteristics about each energy on a local time scale, and considering that the 1D-CNN is suitable for processing time series data, it can effectively capture the local time series pattern and feature information in the time series. Therefore, in the technical solution of the present application, the multiple energy consumption time series input vectors are input into the energy consumption time series feature extraction network based on 1D-CNN to obtain the multiple energy consumption multi-scale time series feature vectors, so that the local time series feature information about each energy type in each energy consumption time series input vector can be captured from the multiple energy consumption time series input vectors, thereby obtaining multiple energy consumption multi-scale time series feature vectors containing more abundant time series feature information.

[0040] In particular, in an achievable embodiment of the present application, the time series of the consumption of the multiple energy types are arranged into multiple energy consumption time series input vectors, and then the energy consumption time series feature extraction is performed to obtain multiple energy consumption multi-scale time series feature vectors, which can be achieved in the following way. First, the time series data of the consumption of multiple energy types are sorted into multiple energy consumption time series input vectors, each vector representing the time series data of an energy type. Then, feature extraction is performed on each energy consumption time series input vector, using traditional time series feature extraction, such as autocorrelation, lag correlation, etc. In detail, autocorrelation refers to the correlation between the time series data and itself at different time points. If a time series has a correlation between itself at a certain time point and at another time point, the relationship between the two time points can be described by an autocorrelation function. Specifically, the autocorrelation function can be measured by calculating the correlation coefficient of the time series at different time lags. Commonly used methods include Pearson correlation coefficient, Spearman correlation coefficient, etc., to help us understand the periodicity, trend and other important patterns in the time series data. Lag correlation refers to the correlation between time series data at different time points. By comparing the values ​​of the time series at different time points, the lag relationship between the data can be revealed. Specifically, the lag correlation can be measured by calculating the correlation coefficient of the time series at different time lags. By calculating the correlation coefficients at different lag orders, the lag relationship between the data can be understood, which can be used to predict future data trends, identify causal relationships between data, etc. Then, the characteristics of each energy type are merged into a multi-scale time series feature vector, and simple splicing or weighted averaging methods can be used to obtain the multiple energy consumption multi-scale time series feature vectors. Through the above steps, the feature extraction and processing of time series data of multiple energy types can be achieved to obtain multiple energy consumption multi-scale time series feature vectors, and used for intelligent identification and optimization of energy waste links in environmentally friendly antioxidant production management systems.

[0041] In an embodiment of the present application, the cross-scale energy consumption feature analysis module 130 is used to arrange the multiple energy consumption multi-scale time series feature vectors into an energy consumption type time series feature matrix and then perform cross-scale energy consumption feature analysis to obtain the energy consumption type multi-scale feature matrix. Specifically, in an embodiment of the present application, the cross-scale energy consumption feature analysis module is used to: arrange the multiple energy consumption multi-scale time series feature vectors into the energy consumption type time series feature matrix and then input it into a cross-scale energy consumption feature analysis network to obtain the energy consumption type multi-scale feature matrix. In particular, the cross-scale energy consumption feature analysis network includes: a first convolution layer, a second convolution layer, and a cascade layer, wherein the first convolution layer is parallel to the second convolution layer, the cascade layer is connected to the first convolution layer and the second convolution layer, and the first convolution layer and the second convolution layer use different convolution kernel scales. It should be understood that, considering that there is a direct temporal correlation between the multiple energy consumption multi-scale time series feature vectors, therefore, in the technical solution of the present application, the multiple energy consumption multi-scale time series feature vectors are arranged into the energy consumption type time series feature matrix. Then, considering that the energy consumption type time series feature matrix contains the time series patterns and characteristic laws of different time scales in the energy data, therefore, in order to capture and mine the characteristics of energy consumption at different time scales of the energy consumption type time series feature matrix, in the technical solution of the present application, the energy consumption type time series feature matrix is ​​input into the cross-scale energy consumption feature analysis network to obtain the energy consumption type multi-scale feature matrix. In other words, the energy consumption type time series features of different scales in the energy consumption type time series feature matrix can provide information at different levels and angles, so as to capture the characteristic information at different scales, which is helpful to fully understand the complex patterns in the energy consumption data, so as to better describe the time series patterns and change laws of the energy data, thereby improving the discrimination and characterization ability of the energy consumption type multi-scale feature matrix.

[0042] In an embodiment of the present application, the energy global feature capture module 140 is used to capture the energy global features of the multi-scale feature matrix of the energy consumption type to obtain the global feature matrix of the energy consumption type. Specifically, in an embodiment of the present application, the energy global feature capture module is used to: input the multi-scale feature matrix of the energy consumption type into the energy global feature capture module based on the non-local neural network to obtain the global feature matrix of the energy consumption type. Accordingly, considering that the multi-scale feature matrix of the energy consumption type is obtained by arranging a plurality of multi-scale time series feature vectors of energy consumption and performing multi-scale extraction, and the multi-scale extraction captures the local time series information of different scales in the multi-scale feature matrix of the energy consumption type, therefore, in order to better represent the overall information of the multi-scale feature matrix of the energy consumption type in the global scope, thereby improving the model's understanding and representation ability of global information, in the technical solution of the present application, the multi-scale feature matrix of the energy consumption type is input into the energy global feature capture module based on the non-local neural network to obtain the global feature matrix of the energy consumption type. It is worth mentioning that the non-local neural network can help the model better capture the correlation and dependency between global features by introducing non-local operations, thereby improving the effect of the model in processing global information. That is to say, the non-local neural network can establish dependencies between features in the global scope of the multi-scale feature matrix of the energy consumption type, thereby helping the model to better understand the structure and pattern of the overall energy consumption data, which helps to improve the model's ability to capture global information, thereby improving the model's performance and accuracy in energy consumption data analysis and energy waste judgment tasks.

[0043] In the embodiment of the present application, the energy waste link judgment module 150 is used to determine the energy waste link based on the energy consumption type global characteristic matrix. Figure 4 : is a block diagram of a link judgment module for energy waste in an environmentally friendly antioxidant production management system according to an embodiment of the present application. Specifically, in the embodiment of the present application, Figure 4 As shown, the energy waste link judgment module 150 includes: a feature optimization unit 151, used to optimize the global feature matrix of the energy consumption type to obtain an optimized global feature matrix of the energy consumption type; and a link classification unit 152, used to input the optimized global feature matrix of the energy consumption type into a classifier-based energy link discriminator to obtain a classification result of the energy waste link.

[0044] Specifically, the feature optimization unit 151 is used to optimize the global feature matrix of the energy consumption type to obtain the optimized global feature matrix of the energy consumption type. Specifically, in an embodiment of the present application, the global feature matrix of the energy consumption type is stacked in a structure depth based on the feature distribution space to obtain the optimized global feature matrix of the energy consumption type. In particular, in the technical solution of the present application, considering that energy consumption data often comes from multiple energy types, each energy type has its own unique characteristics and consumption patterns. By improving the information aggregation degree of the global feature matrix of the logarithmic transformation energy consumption type, the information of various energy types can be better integrated, so that the model can fully understand the relationship and influence between different energy types. Energy consumption data usually contains features of different time scales, such as short-term fluctuations and long-term trends. By improving the information aggregation degree, feature information of different scales can be better integrated, so that the model can simultaneously capture the energy consumption characteristics at different time scales and improve the comprehensive understanding of energy consumption changes. In the process of energy consumption data processing, information loss is a common problem, especially in the data processing of multiple energy types and multiple time scales. By improving the information aggregation degree of the global feature matrix of logarithmically transformed energy consumption types, the possibility of information loss can be reduced, ensuring that the model can make full use of all available information for accurate energy consumption analysis. In the task of classifying energy waste links, accurately identifying energy waste links is crucial to saving energy and reducing waste. By improving the information aggregation degree of the global feature matrix of logarithmically transformed energy consumption types, the model's ability to understand and characterize energy consumption data can be improved, thereby improving the accuracy and reliability of energy waste link classification. Based on this, in the technical solution of the present application, the global feature matrix of energy consumption types is stacked in a structural depth based on the feature distribution space to obtain the optimized global feature matrix of energy consumption types.

[0045] More specifically, in an embodiment of the present application, the feature optimization unit is used to: calculate the variance of a feature set composed of eigenvalues ​​at all positions of the global feature matrix of the energy consumption type to obtain the eigenvalue variance of the global feature matrix of the energy consumption type; calculate the square of the absolute value of the eigenvalues ​​at each position of the global feature matrix of the energy consumption type and then perform a logarithmic operation with base two to obtain a logarithmic-transformed global feature matrix of the energy consumption type; accumulate and sum all eigenvalues ​​of the global feature matrix of the logarithmic-transformed energy consumption type to obtain the sum of the global feature matrix of the logarithmic-transformed energy consumption type; and divide the sum of the global feature matrix of the logarithmic-transformed energy consumption type by twice the eigenvalue variance of the global feature matrix of the energy consumption type and then multiply it by the eigenvalues ​​at each position of the global feature matrix of the energy consumption type to obtain the optimized global feature matrix of the energy consumption type.

[0046] Preferably, in an embodiment of the present application, the feature optimization unit is used to: perform structural depth stacking of the global feature matrix of the energy consumption type based on the feature distribution space using the following feature optimization formula to obtain the optimized global feature matrix of the energy consumption type; wherein the feature optimization formula is:

[0047]

[0048] Where M represents the global characteristic matrix of the energy consumption type, m i,j is the eigenvalue of the (i, j)th position of the global characteristic matrix of the energy consumption type, σ 2 i,j (m i,j ) represents the eigenvalue set m i,j ∈M, and W and H are the width and height of the global feature matrix M of the energy consumption type, log 2 Represents the logarithm with base 2, m' i,j It is the eigenvalue of the (i, j)th position of the global characteristic matrix of the optimized energy consumption type.

[0049] In particular, here, in order to improve the information aggregation degree of the global feature matrix of the energy consumption type, in the technical solution of the present application, the global feature matrix of the energy consumption type is subjected to structural depth stacking based on the feature distribution space, and the logarithmic value of the absolute phase stacking of the eigenvalues ​​at each position in the global feature matrix of the energy consumption type is used to represent the structural information of the global feature matrix of the energy consumption type at that position, and then a wavelet-like function is used to aggregate the structural information of the eigenvalues ​​at all positions in the global feature matrix of the energy consumption type, and the global feature matrix of the energy consumption type is stacked based on the feature distribution space using the set variance of the structural information and the feature distribution of the global feature matrix of the energy consumption type. In this way, it helps to maintain the spatial relationship between the features in the global feature matrix of the energy consumption type, thereby restricting the spatial sparsity of the global feature matrix of the energy consumption type to improve the information aggregation degree of the global feature matrix of the energy consumption type.

[0050] Specifically, the link classification unit 152 is used to input the optimized energy consumption type global feature matrix into the energy link discriminator based on the classifier to obtain the classification result of the energy waste link. That is, the energy waste link is intelligently determined by classifying the optimized energy consumption type global feature matrix. In this way, the system can identify patterns and anomalies in energy use, thereby optimizing the energy efficiency in the production process to meet the green production concept of environmentally friendly antioxidants, while reducing unnecessary energy consumption and thus reducing production costs.

[0051] It should be understood that the classifier can map the input optimized energy consumption type global feature matrix to a specific category or label, thereby realizing the classification and judgment of the energy waste link, which helps to identify and distinguish different types of energy consumption, and then find out the links where energy waste may exist. This can provide support and reference for decision makers, helping them to better identify and understand which links have energy waste problems, and then formulate corresponding improvement and optimization measures. In this way, the classifier-based energy link discriminator can effectively classify and judge the optimized energy consumption type global feature matrix, help identify energy waste links, provide decision support, and realize automatic identification, thereby effectively optimizing energy utilization, reducing energy waste, and improving energy utilization efficiency.

[0052] In summary, the environmentally friendly antioxidant production management system 100 based on the embodiment of the present application is explained, which obtains the time series of various energy types consumed in the production process of environmentally friendly antioxidants, wherein the energy types include electricity, water, and steam, and uses artificial intelligence technology to perform time series feature analysis and feature extraction on the time series of the various energy types consumed, thereby intelligently determining the energy waste link. In this way, the system can identify patterns and anomalies in energy use, thereby optimizing energy efficiency in the production process to meet the green production concept of environmentally friendly antioxidants, while reducing unnecessary energy consumption and thereby reducing production costs.

[0053] Figure 5 Flow chart of the environmentally friendly antioxidant production management method according to an embodiment of the present application. Figure 5 As shown, according to the environmentally friendly antioxidant production management method of the embodiment of the present application, the method includes: S110, obtaining the time series of various energy types consumed in the production process of the environmentally friendly antioxidant, wherein the energy types include electricity, water, and steam; S120, arranging the time series of the various energy types consumed into multiple energy consumption time series input vectors and then performing energy consumption time series feature extraction to obtain multiple energy consumption multi-scale time series feature vectors; S130, arranging the multiple energy consumption multi-scale time series feature vectors into an energy consumption type time series feature matrix and then performing cross-scale energy consumption feature analysis to obtain an energy consumption type multi-scale feature matrix; S140, capturing energy global features of the energy consumption type multi-scale feature matrix to obtain an energy consumption type global feature matrix; and, S150, determining the energy waste link based on the energy consumption type global feature matrix.

[0054] Here, those skilled in the art will appreciate that the specific operations of each step in the above-mentioned environmentally friendly antioxidant production management method have been referred to above. Figures 1 to 4 The environmentally friendly antioxidant production management system has been described in detail, and therefore, its repeated description will be omitted.

[0055] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, the computer program product is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is provided, on which a computer program is stored, and when the program is executed by a processor, the method described above is implemented.

[0056] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, devices, equipment, and computer program products implemented according to the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0057] In addition, although the operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in a sequential order, or requiring that all illustrated operations be performed to achieve the desired results. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments may also be implemented in a single implementation in combination. Conversely, various features described in the context of a single implementation may also be implemented in multiple implementations individually or in any suitable sub-combination.

[0058] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Instead, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. An environmentally friendly antioxidant production management system, characterized in that: include: The module for acquiring the time series of energy consumption in the production of environmentally friendly antioxidants is used to acquire the time series of consumption of various types of energy in the production process of environmentally friendly antioxidants, wherein the energy types include electricity, water, and steam; An energy consumption time series feature extraction module is used to extract energy consumption time series features after arranging the time series of the consumption of the multiple energy types into multiple energy consumption time series input vectors to obtain multiple energy consumption multi-scale time series feature vectors; A cross-scale energy consumption feature analysis module, used for arranging the plurality of energy consumption multi-scale time series feature vectors into an energy consumption type time series feature matrix and then performing cross-scale energy consumption feature analysis to obtain an energy consumption type multi-scale feature matrix; An energy global feature capture module, used for capturing energy global features of the energy consumption type multi-scale feature matrix to obtain an energy consumption type global feature matrix; The energy waste link judgment module is used to determine the energy waste link based on the energy consumption type global characteristic matrix.

2. The environmentally friendly antioxidant production management system according to claim 1, characterized in that: The energy consumption time series feature extraction module includes: An energy consumption time sample arrangement unit, configured to arrange the time series of the consumption of the multiple energy types into the multiple energy consumption time series input vectors according to the time dimension and the sample dimension; The time series feature extraction unit is used to input the multiple energy consumption time series input vectors into the energy consumption time series feature extraction network based on 1D-CNN to obtain the multiple energy consumption multi-scale time series feature vectors.

3. The environmentally friendly antioxidant production management system according to claim 2, characterized in that: The cross-scale energy consumption characteristic analysis module is used to: The multiple energy consumption multi-scale time series feature vectors are arranged into the energy consumption type time series feature matrix and then input into a cross-scale energy consumption feature analysis network to obtain the energy consumption type multi-scale feature matrix.

4. The environmentally friendly antioxidant production management system according to claim 3, characterized in that: The cross-scale energy consumption characteristic analysis network includes: A first convolutional layer, a second convolutional layer, and a cascade layer, wherein the first convolutional layer is parallel to the second convolutional layer, the cascade layer is connected to the first convolutional layer and the second convolutional layer, and the first convolutional layer and the second convolutional layer use convolution kernels of different scales.

5. The environmentally friendly antioxidant production management system according to claim 4, characterized in that: The energy global feature capturing module is used to: The energy consumption type multi-scale feature matrix is ​​input into an energy global feature capture module based on a non-local neural network to obtain the energy consumption type global feature matrix.

6. The environmentally friendly antioxidant production management system according to claim 5, characterized in that: The energy waste link judgment module includes: A feature optimization unit, used for optimizing the energy consumption type global feature matrix to obtain an optimized energy consumption type global feature matrix; The link classification unit is used to input the optimized energy consumption type global feature matrix into the classifier-based energy link discriminator to obtain the classification result of the energy waste link.

7. The environmentally friendly antioxidant production management system according to claim 6, characterized in that: The feature optimization unit is used to: The global feature matrix of energy consumption type is stacked in structure depth based on feature distribution space to obtain the optimized global feature matrix of energy consumption type.

8. The environmentally friendly antioxidant production management system according to claim 7, characterized in that: The feature optimization unit is used to: Calculating the variance of a feature set consisting of eigenvalues ​​at all positions of the global feature matrix of the energy consumption type to obtain the eigenvalue variance of the global feature matrix of the energy consumption type; Calculating the square of the absolute value of the eigenvalues ​​at each position of the global characteristic matrix of energy consumption type and then performing a logarithmic operation with base 2 to obtain a logarithmic transformation global characteristic matrix of energy consumption type; Accumulating and summing all eigenvalues ​​of the global characteristic matrix of the logarithmic transformation energy consumption type to obtain a sum value of the global characteristic matrix of the logarithmic transformation energy consumption type; The sum of the logarithmic transformation energy consumption type global feature matrix is ​​divided by twice the eigenvalue variance of the energy consumption type global feature matrix and then multiplied by the eigenvalue of each position of the energy consumption type global feature matrix to obtain the optimized energy consumption type global feature matrix.

9. A method for production management of environmentally friendly antioxidants, characterized in that: include: Obtaining a time series of consumption of various types of energy during the production process of an environmentally friendly antioxidant, wherein the energy types include electricity, water, and steam; Arranging the time series of the consumption of the multiple energy types into a plurality of energy consumption time series input vectors and then extracting energy consumption time series features to obtain a plurality of energy consumption multi-scale time series feature vectors; Arranging the plurality of energy consumption multi-scale time series feature vectors into an energy consumption type time series feature matrix and then performing cross-scale energy consumption feature analysis to obtain an energy consumption type multi-scale feature matrix; Capturing global energy features on the multi-scale feature matrix of energy consumption types to obtain a global feature matrix of energy consumption types; Based on the global characteristic matrix of energy consumption types, the links of energy waste are determined.

10. The environmentally friendly antioxidant production management method according to claim 9, characterized in that: Arranging the time series of the consumption of the multiple energy types into multiple energy consumption time series input vectors and then extracting energy consumption time series features to obtain multiple energy consumption multi-scale time series feature vectors, including: Arranging the time series of consumption of the multiple energy types into the multiple energy consumption time series input vectors according to the time dimension and the sample dimension; The multiple energy consumption time series input vectors are input into an energy consumption time series feature extraction network based on 1D-CNN to obtain the multiple energy consumption multi-scale time series feature vectors.

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