Data transmission processing system for industrial internet

Abstract

The invention discloses a data transmission processing system for an industrial internet, and the system carries out the progressive sequence segmentation and local time sequence extraction of a time queue of a pushed data size through employing an AI-based data analysis and coding mode. And intelligently judging whether to start a data compression module to perform data compression on the push data according to the multi-gating time sequence propagation representation of the local time sequence mode characteristics of each push data volume. Therefore, when to perform data compression can be intelligently determined, the problem of resource waste or excessive compression possibly caused by a fixed compression strategy is avoided, the data compression quality is improved, the data transmission efficiency is improved, and meanwhile, the data quality and real-time performance are ensured.

Description

Technical Field

[0001] The present application relates to the field of data processing, and more specifically, to a data transmission and processing system for the industrial Internet. Background Art

[0002] With the rapid development of Industry 4.0 and smart manufacturing, the Industrial Internet has become one of the key technologies for realizing factory automation and intelligence. In this context, the data transmission and processing system plays a vital role. It not only needs to support efficient real-time data exchange, but also needs to ensure the security and reliability of data.

[0003] In industrial Internet applications, traditional data transmission systems face significant latency issues in long-distance communication scenarios. This high latency not only reduces the transmission efficiency of real-time data, but also prolongs the response time of key operations, thereby affecting the effectiveness of remote monitoring and control, and ultimately leading to reduced production efficiency and safety. Especially in industrial environments where quick decision-making is required, latency may have an adverse effect on the entire production process and even cause serious consequences. In addition, traditional data transmission solutions often lack sufficient security measures and are vulnerable to attacks or data leaks. For data containing sensitive information, simple encryption methods may not be sufficient to provide adequate protection, increasing the risk of data leakage and affecting the security and stability of the system.

[0004] Therefore, an optimized data transmission and processing system for the industrial Internet is desired, which can achieve efficient data transmission and enhance system security through advanced communication technology and data processing methods. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a data transmission processing system for the industrial Internet, which uses AI-based data analysis and encoding to perform progressive sequence segmentation and local timing extraction on the time queue of the pushed data volume, so as to intelligently judge whether to turn on the data compression module to compress the pushed data according to the multi-gated timing propagation representation of the local timing pattern characteristics of each pushed data volume. In this way, it is possible to intelligently decide when to perform data compression, avoid the waste of resources or over-compression problems that may be caused by fixed compression strategies, improve the quality of data compression, and improve data transmission efficiency, while ensuring the quality and real-time performance of the data.

[0006] According to one aspect of the present application, a data transmission processing system for industrial Internet is provided, which includes: a server, a 4G wireless network communication box and an integrated drive and control machine, wherein the integrated drive and control machine can transmit data to the server through the 4G wireless network communication box, and the server can remotely control the integrated drive and control machine through the 4G wireless network communication box; wherein the 4G wireless network communication box includes an STM32 microcontroller module, an antenna interface, an Ethernet interface, a power interface, an operating status indicator light, a power indicator light, a card removal hole and a SIM card slot, the antenna interface is used to connect an external antenna, the Ethernet interface is used to connect a network cable, the power interface is used to connect a power supply, the card removal hole is used to remove a SIM card, and the SIM card slot is used to install the SIM card.

[0007] In the above-mentioned data transmission processing system for industrial Internet, the STM32 microcontroller module includes: a push data receiving unit, which is used to receive the push data transmitted by the drive control integrated machine; a push data storage unit, which is used to store the push data according to the time queue to obtain the time queue of the push data; a push data amount calculation unit, which is used to calculate the data amount of each push data in the time queue of the push data to obtain the time queue of the push data amount; a data compression judgment unit, which is used to determine whether to start the data compression module to compress the push data based on the time queue of the push data amount, wherein the data compression judgment unit includes: a push data amount sequence segmentation subunit, which is used to calculate the push data amount The time queue is sequentially segmented to obtain a set of push data volume timing subsequences; a push data volume timing encoding subunit is used to perform sequence encoding on each push data volume timing subsequence in the set of push data volume timing subsequences to obtain a set of push data volume local timing pattern feature vectors; a push data timing significant dynamic propagation subunit is used to perform push data volume multi-gated timing dynamic propagation on the set of push data volume local timing pattern feature vectors to obtain a push data volume timing significant dynamic propagation representation vector; a decision result generating subunit is used to obtain a decision result based on the push data volume timing significant dynamic propagation representation vector, and the decision result is used to indicate whether to enable a data compression module to perform data compression on the push data.

[0008] In the above-mentioned data transmission and processing system for the industrial Internet, the push data volume timing encoding subunit is used to: input each push data volume timing subsequence in the set of push data volume timing subsequences into a sequence encoder based on a forward LSTM model to obtain a set of push data volume local timing pattern feature vectors.

[0009] In the above-mentioned data transmission processing system for industrial Internet, the push data timing significant dynamic propagation subunit includes: a push data volume mapping secondary subunit, which is used to map the set of the push data volume local timing pattern feature vectors to the Poincare space to obtain a set of the push data volume local timing heterogeneous modulation coding feature vectors; a push data volume significant propagation aggregation secondary subunit, which is used to calculate the propagation representation of the set of the push data volume local timing heterogeneous modulation coding feature vectors, and based on the propagation representation, significantly propagate and aggregate the set of the push data volume local timing heterogeneous modulation coding feature vectors to obtain the push data volume timing significant dynamic propagation representation vector.

[0010] In the above-mentioned data transmission processing system for industrial Internet, the push data volume significant propagation aggregation secondary subunit includes: a push data volume feature heterogeneous propagation representation tertiary subunit, which is used to forward LSTM sequence encode the set of the push data volume local timing heterogeneous modulation coding feature vectors to obtain the push data volume feature heterogeneous propagation representation vector as the propagation representation; a push data volume weight value calculation tertiary subunit, which is used to calculate the weight of each push data volume local timing heterogeneous modulation coding feature vector in the set of the push data volume local timing heterogeneous modulation coding feature vector based on the propagation representation. values ​​to obtain a set of push data volume feature transfer modulation weights; a push data volume heterogeneous feature significant propagation representation three-level sub-unit, used to calculate the position-weighted sum of the set of push data volume local temporal heterogeneous modulation coding feature vectors based on the set of push data volume feature transfer modulation weights to obtain the push data volume heterogeneous feature significant propagation representation vector; a push data volume feature fusion three-level sub-unit, used to perform residual connection and spatial mapping on the push data volume heterogeneous feature significant propagation representation vector and the push data volume feature heterogeneous propagation representation vector to obtain the push data volume temporal significant dynamic propagation representation vector.

[0011] In the above-mentioned data transmission processing system for industrial Internet, the three-level sub-unit for calculating the push data volume weight value is used to: calculate the semantic association between each push data volume local timing heterodomain modulation coding feature vector in the set of the push data volume local timing heterodomain modulation coding feature vector and the push data volume characteristic heterodomain propagation representation vector to obtain a set of push data volume semantic associations; calculate the semantic space jump value of each push data volume local timing heterodomain modulation coding feature vector in the set of the push data volume local timing heterodomain modulation coding feature vector relative to the push data volume characteristic heterodomain propagation representation vector to obtain a set of push data volume semantic space jump values; perform multi-gated feature modulation on the set of push data volume semantic associations and the set of push data volume semantic space jump values ​​to obtain a set of push data volume feature transmission modulation weights.

[0012] In the above-mentioned data transmission processing system for the industrial Internet, the push data volume feature fusion three-level sub-unit is used to: perform residual connection on the push data volume heterogeneous feature significant propagation representation vector and the push data volume feature heterogeneous propagation representation vector to obtain the push data volume heterogeneous feature significant propagation compensation representation vector; perform Euclidean space mapping on the push data volume heterogeneous feature significant propagation compensation representation vector to obtain the push data volume time series significant dynamic propagation representation vector.

[0013] In the above-mentioned data transmission processing system for the industrial Internet, the decision result generating subunit is used to: input the push data volume time series significant dynamic propagation representation vector into the classifier-based intelligent compression decision module to obtain the decision result, and the decision result is used to indicate whether to turn on the data compression module to compress the push data.

[0014] In the above-mentioned data transmission processing system for the industrial Internet, the decision result generating subunit is used to: use multiple fully connected layers of the classifier to fully connect the push data volume time series significant dynamic propagation representation vector to obtain an encoded classification feature vector; pass the encoded classification feature vector through the Softmax classification function of the classifier to obtain the decision result.

[0015] Compared with the prior art, the data transmission processing system for the industrial Internet provided by this application uses AI-based data analysis and encoding to perform progressive sequence segmentation and local time series extraction on the time queue of the pushed data volume, so as to intelligently determine whether to enable the data compression module to compress the pushed data according to the multi-gated time series propagation representation of the local time series pattern characteristics of each pushed data volume. In this way, it is possible to intelligently decide when to perform data compression, avoid the waste of resources or over-compression problems that may be caused by fixed compression strategies, improve the quality of data compression, improve data transmission efficiency, and ensure the quality and real-time performance of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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.

[0017] Figure 1 It is a block diagram of a data transmission and processing system for the industrial Internet according to an embodiment of the present application.

[0018] Figure 2 This is a data flow diagram of a data transmission and processing system for the industrial Internet according to an embodiment of the present application.

[0019] Figure 3 This is a block diagram of a 4G wireless network communication box in a data transmission processing system for the industrial Internet according to an embodiment of the present application.

[0020] Figure 4 This is a block diagram of an STM32 microcontroller module in a data transmission processing system for the industrial Internet according to an embodiment of the present application.

[0021] Figure 5 It is a block diagram of a data compression judgment unit in a data transmission processing system for the industrial Internet according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0023] As shown in this application 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.

[0024] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0025] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0026] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0027] In the technical solution of the present application, a data transmission and processing system for the industrial Internet is proposed. Figure 1 It is a block diagram of a data transmission and processing system for the industrial Internet according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of a data transmission processing system for industrial Internet according to an embodiment of the present application. Figure 1 and Figure 2 As shown, according to an embodiment of the present application, a data transmission processing system 300 for industrial Internet includes: a server 310; a 4G wireless network communication box 320; and an all-in-one drive and control machine 330. The all-in-one drive and control machine can transmit data to the server through the 4G wireless network communication box, and the server can remotely control the all-in-one drive and control machine through the 4G wireless network communication box.

[0028] In particular, the server 310. In the data transmission and processing system for the industrial Internet, the server serves as the control center of the entire system, and is responsible for receiving data transmitted from the integrated drive and control machine through the 4G wireless network communication box, and processing, analyzing and storing the data. At the same time, the server can also remotely monitor and control the integrated drive and control machine through the same communication path to achieve automated management. Such a design not only simplifies the system architecture, but also improves the flexibility and efficiency of operation.

[0029] In particular, the 4G wireless network communication box 320. The 4G wireless network communication box plays a vital role in the entire system, connecting the data flow between the drive control integrated machine and the server. The core of this communication box is the STM32 microcontroller module, which is the brain of the communication box, responsible for managing and coordinating the work of other components, performing functions such as data reception, storage, calculation, and intelligent judgment of whether to turn on data compression. In order to ensure stable and reliable wireless communication, the communication box is equipped with an antenna interface, which can be connected to an external antenna to enhance the signal reception and transmission capabilities; in addition, it also provides an Ethernet interface, allowing the device to access the Internet or local area network through a wired method when necessary, providing users with more network options. The power interface ensures that the communication box obtains a continuous and stable power supply to ensure its normal operation. The communication box is provided with an operating status indicator and a power indicator, which are used to display the current operating status of the device and the power connection status, respectively, so that the user can clearly understand the working status of the device. In the case of needing to replace or maintain the SIM card, the communication box is designed with a card removal hole to facilitate the user to remove the SIM card, and the Sim card is installed in a special card slot, so that the device can access the 4G network service provided by the mobile operator, ensuring the immediacy and stability of data transmission. In a specific example of this application, Figure 3As shown, the 4G wireless network communication box 320 includes: an STM32 microcontroller module 321, an antenna interface 322, an Ethernet interface 323, a power interface 324, an operation status indicator light 325, a power indicator light 326, a card removal hole 327 and a SIM card slot 328. The antenna interface is used to connect an external antenna, the Ethernet interface is used to connect a network cable, the power interface is used to connect a power supply, the card removal hole is used to remove a SIM card, and the SIM card slot is used to install the SIM card.

[0030] In particular, by using the 4G wireless network communication box as a communication bridge between the drive control integrated machine and the server, the data transmission distance is greatly shortened, and relatively high bandwidth and low latency are provided. Compared with traditional wired connections or low-speed wireless networks (such as 2G / 3G), 4G networks can support faster data transmission speeds and reduce delays caused by network bottlenecks.

[0031] In one example of this application, Figure 4 As shown, the STM32 microcontroller module 321 includes: a push data receiving unit 3211, used to receive the push data transmitted by the drive control integrated machine; a push data storage unit 3212, used to store the push data according to the time queue to obtain the time queue of the push data; a push data amount calculation unit 3213, used to calculate the data amount of each push data in the time queue of the push data to obtain the time queue of the push data amount.

[0032] Specifically, the push data receiving unit 3211 is used to receive the push data transmitted by the integrated drive and control machine. First, as a data acquisition terminal at the industrial site, the integrated drive and control machine is responsible for collecting data from various sensors, actuators and other equipment. These data can cover a variety of physical quantities such as temperature, humidity, pressure, position, speed, as well as the working status of the machine, fault information, etc. After the integrated drive and control machine performs preliminary processing on these raw data, it packages them into data packets suitable for network transmission. In order to ensure the smooth transmission of data in the network, the integrated drive and control machine will add necessary header information to each data packet, such as source address, destination address, timestamp, etc., and select an appropriate protocol (such as TCP / IP) to encapsulate the data. Subsequently, the integrated drive and control machine sends these data packets to a remote server through a 4G wireless network communication box.

[0033] When the data packet sent by the integrated drive and control machine arrives at the 4G wireless network communication box, the push data receiving unit in the STM32 microcontroller module starts working. The main task of this unit is to monitor the data flow on the communication channel. Once a data packet from the integrated drive and control machine is detected, it will be captured and initially parsed. The key to this stage is to verify the integrity and correctness of the data packet, such as checking whether there are missing or damaged data segments, and confirming the authenticity of the data source (that is, verifying that the data is indeed sent by the specified integrated drive and control machine). Only strictly verified data will be further processed, and this step is crucial to maintaining data security and reliability.

[0034] Specifically, the push data storage unit 3212 is used to store the push data according to a time queue to obtain a time queue of the push data. In one example, after preliminary verification, the push data receiving unit stores the received data in the push data storage unit. In this process, each data item will be timestamped and saved in the form of a time queue for subsequent processing. This chronological storage method is critical for maintaining the time series characteristics of data, because many industrial applications rely on the trend of data changes over time for analysis and decision-making. The push data storage unit is not only responsible for storing data, but also regularly cleans up expired or no longer needed data according to preset strategies to maintain effective use of storage space.

[0035] Specifically, the push data volume calculation unit 3213 is used to calculate the data volume of each push data in the time queue of the push data to obtain the time queue of the push data volume. That is, as the data continues to flow in, the push data volume calculation unit will further process the stored data and calculate the data volume in the time queue of each push data, thereby obtaining a time queue representing the trend of data volume changing over time. This step is very important for understanding the data flow pattern because it provides a basic basis for whether the data needs to be compressed later.

[0036] The data compression judgment unit 3214 is used to determine whether to start the data compression module to compress the pushed data based on the time queue of the pushed data volume. It should be understood that data compression plays a vital role in the data transmission system. By compressing the data, the size of the data can be significantly reduced, thereby reducing the demand for network bandwidth, which is particularly important for application scenarios with limited bandwidth or cost sensitivity. And a smaller amount of data means less transmission time, especially in real-time applications, where fast data transmission is key. However, traditional data compression algorithms usually set a fixed size or frequency threshold, and start compression when the data volume exceeds this threshold. However, this method lacks flexibility and cannot adapt to real-time changes in data. That is, in the case of large fluctuations in the amount of data, it may cause unnecessary compression (i.e., the amount of data has not reached the level that actually needs to be compressed) or fail to compress in time (i.e., the amount of data has exceeded but has not reached the threshold), thereby affecting the quality of the data. In one example of the present application, Figure 5 As shown, the data compression judgment unit 3214 includes: a push data volume sequence segmentation subunit 32141, which is used to sequence the time queue of the push data volume to obtain a set of push data volume timing subsequences; a push data volume timing encoding subunit 32142, which is used to sequence encode each push data volume timing subsequence in the set of push data volume timing subsequences to obtain a set of push data volume local timing pattern feature vectors; a push data timing significant dynamic propagation subunit 32143, which is used to push data volume multi-gated timing dynamic propagation on the set of push data volume local timing pattern feature vectors to obtain a push data volume timing significant dynamic propagation representation vector; a decision result generation subunit 32144, which is used to obtain a decision result based on the push data volume timing significant dynamic propagation representation vector, and the decision result is used to indicate whether to turn on the data compression module to compress the push data.

[0037] More specifically, the push data volume sequence segmentation subunit 32141 is used to perform sequence segmentation on the time queue of the push data volume to obtain a set of push data volume time series subsequences. Considering that each local time in the time queue of the push data volume has different local time series patterns, such as periodic fluctuations, burst peaks, etc. Therefore, in order to be able to more accurately understand and analyze the time series characteristics of each local time period in the future, in the technical solution of the present application, the time queue of the push data volume is sequence segmented to obtain a set of push data volume time series subsequences, that is, each subsequence represents the performance of the data in a specific time period. Through in-depth research on these local characteristics, the system can more accurately predict the future data change trend and provide a basis for starting the data compression module.

[0038] More specifically, the push data volume timing encoding subunit 32142 is used to perform sequence encoding on each push data volume timing subsequence in the set of push data volume timing subsequences to obtain a set of push data volume local timing pattern feature vectors. In the technical solution of the present application, in order to further capture and mine the local timing pattern information in each push data volume timing subsequence, in the technical solution of the present application, each push data volume timing subsequence in the set of push data volume timing subsequences is respectively input into a sequence encoder based on a forward LSTM model to obtain a set of push data volume local timing pattern feature vectors. That is, the forward LSTM model is a recursive neural network that is particularly suitable for processing and learning long-term dependencies in time series data. Through the forward LSTM model, the trend, periodicity and other complex time-dependent patterns of the push data volume changing over time can be captured.

[0039] More specifically, the push data time series significant dynamic propagation subunit 32143 is used to push the data volume multi-gated time series dynamic propagation of the set of the push data volume local time series pattern feature vectors to obtain the push data volume time series significant dynamic propagation representation vector. Considering that recursive neural networks such as forward LSTM can handle long-term dependencies in time series data, they have limitations in processing information transmission and integration across multiple subsequences. Specifically, the local time series pattern features of the push data volume at different time points may have different degrees of influence and significant semantic relevance in the entire time range. Therefore, in order to more effectively capture and utilize the association of the local time series pattern features of the push data volume at different time points, thereby constructing a more comprehensive data representation, in the technical solution of the present application, the push data volume multi-gated time series dynamic propagation is performed on the set of the push data volume local time series pattern feature vectors to obtain the push data volume time series significant dynamic propagation representation vector. In this way, a representation vector that can better reflect the intrinsic structure and dynamic changes of the data can be obtained through a more complex feature fusion and propagation mechanism, thereby supporting a higher level of data analysis and decision-making process.

[0040] Specifically, the specific process of performing multi-gated timing dynamic propagation of the push data volume for the set of local timing pattern feature vectors of the push data volume includes: first, mapping the set of local timing pattern feature vectors of the push data volume to the Poincare space to obtain a set of local timing heterogeneous modulation coding feature vectors of the push data volume. By mapping the set of local timing pattern feature vectors of the push data volume to the Poincare space, the nonlinear relationship and hierarchical structure between the local timing pattern characteristics of the push data volume are captured to obtain a set of local timing heterogeneous modulation coding feature vectors of the push data volume. A person of ordinary skill in the art should know that the Poincare space is a hyperbolic space that can effectively reduce the dimensionality curse while maintaining the distance relationship between nodes. More specifically, the set of local timing pattern feature vectors of the push data volume is mapped to the Poincare space according to the following formula to obtain a set of local timing heterogeneous modulation coding feature vectors of the push data volume; wherein the formula is: ;in, is the set of local time series pattern feature vectors of the pushed data volume, and are the first, second, and third in the set of local time series pattern feature vectors of the pushed data volume. and The local time series pattern feature vector of the pushed data volume, and are the first weight matrix and the second weight matrix respectively, and are the first, second, and third in the set of local temporal heterogeneous modulation coding feature vectors of the pushed data volume. and The local time series heterogeneous modulation coding feature vector of the pushed data volume, It is a set of local timing heterogeneous modulation coding feature vectors of the pushed data volume.

[0041] Next, the propagation representation of the set of local temporal heterogeneous modulation coding feature vectors of the pushed data volume is calculated, and based on the propagation representation, the set of local temporal heterogeneous modulation coding feature vectors of the pushed data volume is significantly propagated and aggregated to obtain the push data volume time-series significant dynamic propagation representation vector. That is, in an embodiment of the present application, first, the set of local temporal heterogeneous modulation coding feature vectors of the pushed data volume is forward LSTM sequence encoded, so as to use the feature sequence encoder based on the forward LSTM model to perform a simulated transmission based on the Poincare semantic space on the set of local temporal heterogeneous modulation coding feature vectors of the pushed data volume to obtain the push data volume feature heterogeneous propagation representation vector. The forward long short-term memory (LSTM) network can not only capture the long-term dependencies in the time series, but also realize the selective memory and update of information through the synergy of the forget gate, input gate and output gate, so as to avoid overfitting when processing long time series while maintaining the memory of key information. More specifically, the set of the local temporal heterogeneous modulation coding feature vectors of the pushed data volume is forward LSTM sequence encoded using the following forward LSTM sequence encoding formula to obtain a pushed data volume feature heterogeneous propagation representation vector as the propagation representation; wherein the formula is: ;in, Encode the forward LSTM. It is the vector representing the heterogeneous propagation of the pushed data volume characteristics.

[0042] Then, based on the propagation representation, the weight values ​​of each push data volume local time series foreign domain modulation coding feature vector in the set of the push data volume local time series foreign domain modulation coding feature vectors are calculated to obtain a set of push data volume feature transmission modulation weights. In an embodiment of the present application, the specific steps of calculating the weight values ​​of each push data volume local time series foreign domain modulation coding feature vector in the set of the push data volume local time series foreign domain modulation coding feature vectors include: first, calculating the semantic association between each initial feature foreign domain modulation coding feature vector in the time queue of the initial feature foreign domain modulation coding feature vector and the initial feature foreign domain propagation representation vector to obtain a time queue of semantic association. Here, the semantic association is used to represent the semantic intrinsic association between the message propagation characteristics in the Poincare space and each push data volume local time series foreign domain modulation coding feature vector, so as to facilitate the subsequent introduction of an attention mechanism to enhance the model's attention to specific features. Next, the semantic space jump value of each push data volume local time series heterogeneous modulation coding feature vector in the set of the push data volume local time series heterogeneous modulation coding feature vector relative to the push data volume feature heterogeneous propagation representation vector is calculated to obtain a set of push data volume semantic space jump values. This indicator measures the changing trend and rate of feature expression over time by the degree of difference in the spatial span between the message propagation feature in the Poincare space and each push data volume local time series heterogeneous modulation coding feature vector. The calculation of the semantic space jump value can not only reveal the dynamic characteristics of the feature, but also discover periodic patterns or abnormal events in the data. Subsequently, the set of the push data volume semantic association and the set of the push data volume semantic space jump value are subjected to multi-gated feature modulation to obtain a set of push data volume feature transmission modulation weights. In this process, the direction and intensity of the information flow are dynamically adjusted by combining the semantic association and the semantic space jump value through a multi-gating mechanism. This mechanism enables the network to adaptively and selectively strengthen or weaken the transmission of specific information according to the relationship between features, thereby improving the accuracy and robustness of feature representation.

[0043] Wherein, based on the propagation representation, calculating the weight value of each push data amount local timing alien modulation coding feature vector in the set of the push data amount local timing alien modulation coding feature vector to obtain a set of push data amount feature transfer modulation weights, including: based on the propagation representation, calculating the weight value of each push data amount local timing alien modulation coding feature vector in the set of the push data amount local timing alien modulation coding feature vector using the following weight calculation formula to obtain a set of push data amount feature transfer modulation weights; wherein, the formula is: ;in, To calculate the length of a vector, is the inverse hyperbolic cosine function, and are the first, second, and third in the set of semantic relevance of the pushed data volume. and The semantic relevance of the pushed data volume, is a set of semantic relevance of the pushed data volume, and are the first, second, and third values ​​in the set of semantic space jump values ​​of the pushed data volume. and The semantic space jump value of the pushed data volume, is a set of hop values ​​of the semantic space of the pushed data volume, and are the semantic association weight matrix and the semantic space weight matrix respectively. is matrix multiplication, is the bias vector, yes function, It is a set of push data volume feature transfer modulation weights.

[0044] Furthermore, based on the set of modulation weights for the push data volume feature transfer, the position-weighted sum of the set of the push data volume local temporal heterogeneous modulation coding feature vectors is calculated to obtain the push data volume heterogeneous feature significant propagation representation vector. That is, by taking into account the importance and temporal changes of the push data volume local temporal heterogeneous modulation coding features in a weighted summation manner, a push data volume heterogeneous feature significant propagation representation vector is obtained that comprehensively reflects the characteristics of the feature evolution over time.

[0045] Finally, the push data volume heterogeneous feature significant propagation representation vector and the push data volume feature heterogeneous propagation representation vector are residually connected and spatially mapped to obtain the push data volume temporal significant dynamic propagation representation vector. That is, first, the push data volume heterogeneous feature significant propagation representation vector and the push data volume feature heterogeneous propagation representation vector are residually connected to obtain the push data volume heterogeneous feature significant propagation compensation representation vector; the residual connection technology is used to solve the gradient vanishing problem that may occur during the training of deep networks and promote the direct transmission of features. By introducing the residual learning framework, good performance can be maintained even when the network is very deep, while enhancing the richness and diversity of feature representation. The residual unit solves the gradient vanishing problem in deep networks by introducing jump connections, and also provides an additional learning path for the model. Furthermore, the push data volume heterogeneous feature significant propagation compensation representation vector is Euclidean space mapped to obtain the push data volume temporal significant dynamic propagation representation vector. Here, the push data volume heterogeneous feature significant propagation compensation representation vector is input into the Euclidean space mapper to convert the complex processed feature vector back to the traditional Euclidean space for the subsequent standard machine learning algorithm to use. Through appropriate mapping functions and parameter adjustments, effective projection from high-dimensional feature space to low-dimensional space can be achieved, which facilitates feature dimensionality reduction and visualization.

[0046] Among them, residual connection and spatial mapping are performed on the pushed data volume heterogeneous feature significant propagation representation vector and the pushed data volume feature heterogeneous propagation representation vector to obtain the pushed data volume temporal significant dynamic propagation representation vector, including: residual connection and spatial mapping are performed on the pushed data volume heterogeneous feature significant propagation representation vector and the pushed data volume feature heterogeneous propagation representation vector using the following residual connection and spatial mapping formula to obtain the pushed data volume temporal significant dynamic propagation representation vector; wherein, the formula is: ;in, yes The corresponding push data volume feature transfer modulation weight, is the number of vectors in the set of local timing heterogeneous modulation coding feature vectors of the pushed data volume, is the compensation representation vector for the significant propagation of heterogeneous features in the pushed data volume, and are the third weight matrix and the fourth weight matrix respectively, is the Euclidean space mapping operation, is a vector representing the significant dynamic propagation of the pushed data volume in timing.

[0047] More specifically, the decision result generating subunit 32144 is used to obtain a decision result based on the push data volume time series significant dynamic propagation representation vector, and the decision result is used to indicate whether to enable the data compression module to perform data compression on the push data. In a specific example of the present application, the push data volume time series significant dynamic propagation representation vector is input into the intelligent compression decision module based on the classifier to obtain the decision result, and the decision result is used to indicate whether to enable the data compression module to perform data compression on the push data. That is, the push data volume time series significant dynamic propagation representation vector obtained by multi-gated timing propagation using the set of local time series pattern feature vectors of the push data volume is classified and processed, so as to intelligently determine whether to enable the data compression module to perform data compression on the push data. In this way, it is possible to intelligently decide when to perform data compression, avoid the waste of resources or over-compression problems that may be caused by fixed compression strategies, improve the quality of data compression, improve data transmission efficiency, and ensure the quality and real-time performance of data. Specifically, the specific classification process of the classifier includes: using multiple fully connected layers of the classifier to fully connect the push data volume time series significant dynamic propagation representation vector to obtain an encoded classification feature vector; passing the encoded classification feature vector through the Softmax classification function of the classifier to obtain the decision result.

[0048] Here, when each local time series pattern feature vector of the pushed data volume in the set of the local time series pattern feature vectors represents the time series correlation characteristics of the pushed data volume in the local time domain, after the feature dynamic propagation of the cross-domain multiple gate structure is performed on it, the push data volume timing significant dynamic propagation representation vector will also have significant cross-domain propagation distribution richness due to the difference in structural dynamics between local time domains, resulting in repetition of unstructured probability mapping, affecting the accuracy of the classification results.

[0049] Therefore, preferably, when the push data volume time series significant dynamic propagation representation vector is input into the classifier-based intelligent compression decision module to obtain a decision result, the push data volume time series significant dynamic propagation representation vector is optimized. Specifically, the optimization includes the following steps: Subtract the zero norm of the push data volume time series significant dynamic propagation representation vector to determine the number of zero eigenvalues ​​of the push data volume time series significant dynamic propagation representation vector , and the number of zero eigenvalues Subtract one to get the push data volume timing significant dynamic propagation zero differentiable value .

[0050] The push data volume time series significantly dynamically propagates zero differentiable values Multiply and divide by the number of zero eigenvalues ​​respectively Get the first push data volume time series significant dynamic propagation field fitting value and the second push data volume time series significant dynamic propagation field fitting value .

[0051] Calculate the square root of the sum of squares of all eigenvalues ​​of the push data volume time series significant dynamic propagation representation vector to obtain the push data volume time series significant dynamic propagation mode representation value ;in, The first vector representing the significant dynamic spread of the push data amount time series is eigenvalues, The total number of eigenvalues ​​of the vector representing the significant dynamic spread of the push data volume time series, Indicates the characteristic value of the dynamic propagation pattern of the push data volume timing.

[0052] Significant dynamic propagation of zero differentiable values ​​with the push data volume timing The amount of data pushed is calculated as an exponential power function of each eigenvalue of the representation vector with significant dynamic propagation , and multiply the first push data volume time series significant dynamic propagation field fitting value To obtain the first push data volume time series significantly dynamically propagates the intermediate representation vector .

[0053] The push data volume time series significant dynamic propagation representation vector and the second push data volume time series significant dynamic propagation field fitting value The push data volume and timing characteristics of the dynamic propagation mode are significant Perform point multiplication to obtain the intermediate representation vector of the second push data volume timing significant dynamic propagation .

[0054] Calculate the first push data volume time series significant dynamic propagation intermediate representation vector The intermediate representation vector is propagated dynamically with the second push data volume The weighted points minus To obtain the optimized push data volume timing significant dynamic propagation representation vector, where, and represents the weighted hyperparameter, It means point multiplication by position. Indicates adding by position point.

[0055] That is, based on the vector field fitting law of high-dimensional manifold, the isolated zeros in the push data volume time series significant dynamic propagation representation vector are used as the differentiable dimension of the high-dimensional manifold, so as to fix the normal direction pointing to the distribution center near the local coordinates represented by the eigenvalues ​​of the push data volume time series significant dynamic propagation representation vector, thereby achieving effective alignment of the characteristic distribution pattern of the push data volume time series significant dynamic propagation representation vector to the probability density differentiable field to avoid invalid repetition in the probability mapping process caused by the unstructured characteristics of the rich distribution of the push data volume time series significant dynamic propagation representation vector, and improve the accuracy of the decision result obtained by its input based on the classifier The intelligent compression decision module is improved.

[0056] In particular, the drive and control integrated machine 330 can transmit data to the server through the 4G wireless network communication box, and the server can remotely control the drive and control integrated machine through the 4G wireless network communication box. In the industrial Internet environment, the drive and control integrated machine (DCIM) plays a vital role. It is not only a terminal device that executes commands, but also an important source of data collection. According to the technical solution described in this application, the drive and control integrated machine 330 can transmit data to the server through the 4G wireless network communication box, and the server can also remotely control the drive and control integrated machine through the same 4G wireless network communication box. This design significantly improves the flexibility, reliability and security of the system, and solves the problems of high latency and security risks in traditional data processing systems.

[0057] The integrated drive and control machine combines the functions of the driver and controller in one, so that the motion control of mechanical equipment and the collection of relevant operation data can be completed in the same device. This integration reduces the complexity and cost of the traditional need to configure the driver and controller separately, and also improves the compactness and reliability of the system. The integrated drive and control machine integrates the motor drive circuit inside, which can directly control the working status of various motors, hydraulic pumps and other power components connected to it, such as starting, stopping, speed regulation, etc. At the same time, the built-in microprocessor or PLC (programmable logic controller) is used to process information from sensors and other input devices, execute preset operating procedures or respond to instructions from the server.

[0058] As a data acquisition node, the integrated drive and control machine is responsible for obtaining real-time operating parameters from various sensors, actuators and other equipment on site. These data include but are not limited to physical quantities such as temperature, humidity, pressure, position, speed, as well as the working status and fault information of the machine. The integrated drive and control machine is connected to external devices through communication interfaces such as RS485, CAN bus, Ethernet, etc. to ensure that data from various monitoring points can be received stably and reliably. The received data will be initially processed locally, such as formatting, verification, screening, etc., to remove invalid or redundant data and reduce the burden of subsequent transmission. The processed valid data will be packaged into data frames suitable for network transmission, and the necessary metadata (such as timestamp, device ID, etc.) will be attached, and then sent to the remote server through the 4G wireless network communication box.

[0059] The integrated drive and control machine supports two-way communication with the server, which makes it not only a data source but also an execution end. It can upload the collected data to the server regularly or on demand for further analysis and decision-making; at the same time, the server can also send control commands to the integrated drive and control machine through the 4G wireless network communication box, such as adjusting the working mode, changing the set value, performing specific actions, etc. After receiving these instructions, the integrated drive and control machine will immediately parse and execute them, achieving the purpose of remote monitoring and automated management. This two-way communication capability is crucial for industrial Internet applications because it allows managers to understand the on-site situation anytime and anywhere and respond in a timely manner, thereby improving production efficiency and response speed.

[0060] In order to better meet the needs of the Industrial Internet, modern integrated drive and control machines usually have certain intelligent features. Built-in diagnostic algorithms can help detect the status of their own hardware and software, detect potential problems in a timely manner and alert users; use machine learning algorithms to analyze historical data, predict possible future failures, arrange maintenance plans in advance, and reduce unplanned downtime; optimize energy consumption strategies, dynamically adjust power output according to actual load conditions, and reduce operating costs. These intelligent features enhance the functions of integrated drive and control machines, enabling them to not only efficiently complete basic tasks, but also provide more value-added services for factories.

[0061] Considering the special requirements in industrial environments, the design of the integrated drive and control machine focuses on safety and reliability. Made of industrial-grade materials, it has dustproof, waterproof, and shock-resistant functions to adapt to harsh working conditions; key components provide redundant backup, so even if some components fail, it will not affect the overall operation; built-in firewalls, encryption protocols and other security measures protect sensitive information from attacks or leaks. The sturdy and durable structure and multiple security mechanisms ensure that the integrated drive and control machine can maintain stable operation under any circumstances and will not be interrupted by external factors.

[0062] The integrated drive and control machine is an important part of the data transmission and processing system of the industrial Internet. It not only undertakes the task of executing commands, but also is a key node for data collection. Through efficient data processing and two-way communication capabilities, combined with intelligent analysis algorithms, effective monitoring and management of industrial processes are achieved. At the same time, the safety and reliability design of the integrated drive and control machine ensures the stable operation of the system and avoids data loss and service interruption caused by equipment failure or network security issues. In the entire industrial Internet architecture, the integrated drive and control machine plays the role of a bridge, connecting the on-site equipment with the background management system, promoting the rapid flow of information and the timeliness of decision-making, and ultimately promoting the entire industrial system to develop in a more intelligent direction.

[0063] Through the above-mentioned optimizations in many aspects, the integrated drive and control machine not only ensures its own high performance, but also brings significant advantages to the entire industrial Internet data transmission and processing system. First, it greatly simplifies on-site wiring, reduces installation costs and maintenance difficulties. Secondly, the data transmission achieved through the 4G wireless network communication box not only improves the transmission speed, but also reduces the delay problem caused by wiring. More importantly, the built-in intelligent algorithm and self-diagnosis function of the integrated drive and control machine enable it to actively identify and solve some common problems, reduce the need for manual intervention, and improve the degree of automation of the system. In addition, the two-way communication capability enables the integrated drive and control machine to respond quickly according to the server's instructions, achieving instant feedback and precise control, which is particularly important for application scenarios that require quick response.

[0064] As described above, the data transmission processing system 300 for the industrial Internet according to the embodiment of the present application can be implemented in various wireless terminals, such as a server with a data transmission processing algorithm for the industrial Internet. In one possible implementation, the data transmission processing system 300 for the industrial Internet according to the embodiment of the present application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the data transmission processing system 300 for the industrial Internet can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the data transmission processing system 300 for the industrial Internet can also be one of the many hardware modules of the wireless terminal.

[0065] Alternatively, in another example, the data transmission processing system 300 for the industrial Internet and the wireless terminal may also be separate devices, and the data transmission processing system 300 for the industrial Internet may be connected to the wireless terminal via a wired and / or wireless network, and transmit interactive information in accordance with an agreed data format.

[0066] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A data transmission and processing system for industrial Internet, characterized in that: include: A server, a 4G wireless network communication box and an integrated drive and control machine, wherein the integrated drive and control machine can transmit data to the server through the 4G wireless network communication box, and the server can remotely control the integrated drive and control machine through the 4G wireless network communication box; wherein the 4G wireless network communication box includes an STM32 microcontroller module, an antenna interface, an Ethernet interface, a power interface, an operating status indicator light, a power indicator light, a card removal hole and a Sim card slot, the antenna interface is used to connect an external antenna, the Ethernet interface is used to connect a network cable, the power interface is used to connect a power supply, the card removal hole is used to remove a SIM card, and the Sim card slot is used to install the SIM card.

2. The data transmission and processing system for industrial Internet according to claim 1 is characterized in that: The STM32 microcontroller module includes: a push data receiving unit, which is used to receive the push data transmitted by the drive control integrated machine; a push data storage unit, which is used to store the push data according to the time queue to obtain the time queue of the push data; a push data amount calculation unit, which is used to calculate the data amount of each push data in the time queue of the push data to obtain the time queue of the push data amount; a data compression judgment unit, which is used to determine whether to start the data compression module to compress the push data based on the time queue of the push data amount, wherein the data compression judgment unit includes: a push data amount sequence segmentation subunit, which is used to sequence the time queue of the push data amount To obtain a set of push data volume timing subsequences; a push data volume timing encoding subunit, used to perform sequence encoding on each push data volume timing subsequence in the set of push data volume timing subsequences to obtain a set of push data volume local timing pattern feature vectors; a push data timing significant dynamic propagation subunit, used to perform push data volume multi-gated timing dynamic propagation on the set of push data volume local timing pattern feature vectors to obtain a push data volume timing significant dynamic propagation representation vector; a decision result generating subunit, used to obtain a decision result based on the push data volume timing significant dynamic propagation representation vector, and the decision result is used to indicate whether to enable a data compression module to perform data compression on the push data.

3. The data transmission and processing system for industrial Internet according to claim 2 is characterized in that: The push data volume timing encoding subunit is used to: input each push data volume timing subsequence in the set of push data volume timing subsequences into a sequence encoder based on a forward LSTM model to obtain a set of push data volume local timing pattern feature vectors.

4. The data transmission and processing system for industrial Internet according to claim 3 is characterized in that: The push data timing significant dynamic propagation subunit includes: a push data volume mapping secondary subunit, which is used to map the set of the push data volume local timing pattern feature vectors to the Poincare space to obtain a set of the push data volume local timing heterogeneous modulation coding feature vectors; a push data volume significant propagation aggregation secondary subunit, which is used to calculate the propagation representation of the set of the push data volume local timing heterogeneous modulation coding feature vectors, and based on the propagation representation, significantly propagate and aggregate the set of the push data volume local timing heterogeneous modulation coding feature vectors to obtain the push data volume timing significant dynamic propagation representation vector.

5. The data transmission and processing system for industrial Internet according to claim 4 is characterized in that: The push data volume significant propagation aggregation secondary sub-unit includes: a push data volume feature heterogeneous propagation representation tertiary sub-unit, which is used to perform forward LSTM sequence encoding on the set of the push data volume local temporal heterogeneous modulation coding feature vectors to obtain a push data volume feature heterogeneous propagation representation vector as the propagation representation; a push data volume weight value calculation tertiary sub-unit, which is used to calculate the weight value of each push data volume local temporal heterogeneous modulation coding feature vector in the set of the push data volume local temporal heterogeneous modulation coding feature vectors based on the propagation representation to obtain a set of push data volume feature transfer modulation weights; a push data volume heterogeneous feature significant propagation representation tertiary sub-unit, which is used to calculate the position-weighted sum of the set of the push data volume local temporal heterogeneous modulation coding feature vectors based on the set of push data volume feature transfer modulation weights to obtain a push data volume heterogeneous feature significant propagation representation vector; a push data volume feature fusion tertiary sub-unit, which is used to perform residual connection and spatial mapping on the push data volume heterogeneous feature significant propagation representation vector and the push data volume feature heterogeneous propagation representation vector to obtain the push data volume temporal significant dynamic propagation representation vector.

6. The data transmission and processing system for industrial Internet according to claim 5, characterized in that: The third-level sub-unit for calculating the pushed data amount weight value is used to: calculate the semantic association between each push data amount local timing heterodomain modulation coding feature vector in the set of the push data amount local timing heterodomain modulation coding feature vector and the push data amount characteristic heterodomain propagation representation vector to obtain a set of pushed data amount semantic associations; calculate the semantic space jump value of each push data amount local timing heterodomain modulation coding feature vector in the set of the push data amount local timing heterodomain modulation coding feature vector relative to the push data amount characteristic heterodomain propagation representation vector to obtain a set of push data amount semantic space jump values; perform multi-gated feature modulation on the set of push data amount semantic associations and the set of push data amount semantic space jump values ​​to obtain a set of push data amount characteristic transmission modulation weights.

7. The data transmission and processing system for industrial Internet according to claim 6, characterized in that: The pushed data volume feature fusion three-level sub-unit is used to: perform residual connection on the pushed data volume heterogeneous feature significant propagation representation vector and the pushed data volume feature heterogeneous propagation representation vector to obtain the pushed data volume heterogeneous feature significant propagation compensation representation vector; perform Euclidean space mapping on the pushed data volume heterogeneous feature significant propagation compensation representation vector to obtain the pushed data volume temporal significant dynamic propagation representation vector.

8. The data transmission and processing system for industrial Internet according to claim 7, characterized in that: The decision result generating subunit is used to: input the push data volume time series significant dynamic propagation representation vector into the classifier-based intelligent compression decision module to obtain the decision result, and the decision result is used to indicate whether to start the data compression module to compress the push data.

9. The data transmission and processing system for industrial Internet according to claim 8, characterized in that: The decision result generating subunit is used to: use the multiple fully connected layers of the classifier to fully connect the push data volume time series significant dynamic propagation representation vector to obtain the encoded classification feature vector; and pass the encoded classification feature vector through the Softmax classification function of the classifier to obtain the decision result.

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