Multi-type data transmission method and device for industrial digital supervision and medium

Abstract

The invention discloses a multi-type data transmission method and device for industrial digital supervision and a medium, and relates to the technical field of data transmission. The method comprises the steps that multiple types of data are accessed, the multiple types of data are classified according to data types, and the multiple types of data comprise character strings, pictures, documents and video data; based on the data type, selecting a transmission protocol for the classified data, and performing differential encryption; receiving data through a stream batch integrated calculation engine layer, selecting a calculation engine according to a data type, and adaptively storing the data to a corresponding storage medium; and monitoring a transmission full-link state in real time, recording blood relationship information in a transmission process, and linking a supervision service system to trigger a response operation. By means of the method, unified transmission of character strings, pictures, documents and videos in one system is achieved, and the complexity of operation and maintenance of multiple systems is eliminated.

Description

Technical Field

[0001] This application relates to the field of data transmission technology, and in particular to a multi-type data transmission method, device and medium for industrial digital supervision. Background Technology

[0002] With the deepening of digital industrial supervision, the types of data that need to be transmitted in factory production environments are becoming increasingly diverse, encompassing small strings generated by sensors, images captured by industrial cameras, documents generated by quality inspection systems, and continuous video streams recorded by security systems. Existing data transmission solutions are typically designed for single data types, such as using message queues to transmit strings, file transfer protocols to process documents, and streaming media protocols to carry video. This fragmented approach, with multiple protocols coexisting, necessitates the deployment and maintenance of multiple independent transmission systems in industrial settings. This not only increases operational complexity but also frequently leads to data transmission interruptions or the blocking of critical alarm information due to protocol incompatibility and severe resource contention. Furthermore, when dealing with large volumes of data in the GB or even TB range, existing solutions generally employ a holistic transmission mode. If the transmission is interrupted due to network fluctuations, it must be retransmitted from scratch, resulting in extremely low efficiency and failing to meet the timeliness requirements of industrial supervision.

[0003] While industry-specific optimization technologies exist, such as data sharding, breakpoint resumption, and general encryption, these technologies fail to address the fundamental contradictions in the transmission of large volumes of diverse data types in industrial systems. First, while general encryption algorithms ensure security, they fail to consider the varying efficiency requirements of different data types, leading to excessively long encryption times for large files. Second, traditional transmission scheduling strategies lack awareness of business priorities, failing to guarantee low-latency transmission of critical alarm data. Most importantly, existing technologies lack an integrated architecture that organically combines protocol adaptation, efficient transmission, secure encryption, and intelligent scheduling. This results in a disconnect between the transmission layer and subsequent computing, storage, and business application layers, hindering the formation of an efficient closed loop from data access to regulatory decision-making. Therefore, the industrial sector urgently needs an integrated data transmission solution that can uniformly adapt to multiple data types, intelligently schedule transmission resources, and ensure both security and efficiency.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows: Existing industrial data transmission solutions suffer from poor adaptability, low efficiency, weak security, and easy blockage of critical data due to protocol fragmentation, lack of differentiated security strategies and scheduling mechanisms, resulting in large-scale data transmission of various types. Summary of the Invention

[0005] This application provides a method, device, and medium for transmitting multiple types of data in industrial digital supervision, which can solve the problems of poor adaptability, low efficiency, weak security, and easy blockage of critical data in existing industrial data transmission schemes due to protocol fragmentation, lack of differentiated security strategies and scheduling mechanisms.

[0006] In a first aspect, embodiments of this application provide a method for transmitting multiple types of data in industrial digital supervision. The method includes: accessing multiple types of data and classifying them according to their data types, including strings, images, documents, and video data; selecting a transmission protocol for the classified data based on its data type and performing differentiated encryption; receiving data through a stream-batch integrated computing engine layer, selecting a computing engine based on the data type, and adapting it for storage on the corresponding storage medium; real-time monitoring of the entire transmission link status, recording transmission lineage information, and triggering response operations in conjunction with the supervisory business system.

[0007] In one implementation of this application, multiple types of data are accessed and classified according to their data types. Specifically, this includes: extracting file header features and data packet sizes of the multiple types of data, and statistically analyzing the sending frequency to obtain the data types; deduplicating strings based on data content and timestamps, and uniformly converting the deduplicated strings into a standard format; performing format conversion and resolution adaptation adjustments on images; optimizing the encoding and extracting keyframes from video data; and compressing the multiple types of data separately using a lightweight compression algorithm.

[0008] In one implementation of this application, a transmission protocol is selected for the preprocessed data based on the data type, and differential encryption is performed. Specifically, this includes: adaptively selecting the corresponding transmission protocol from a pre-configured protocol library according to the data type; and adjusting the transmission protocol parameters according to the real-time network bandwidth utilization.

[0009] In one implementation of this application, the method further includes: dynamically calculating the fragment size based on the data packet size and real-time network bandwidth, fragmenting the data according to the calculated fragment size, and transmitting the fragments in parallel through multiple transmission channels; using distributed storage to record the transmission status of each fragment; performing hash verification on each fragment, and performing overall integrity verification at the receiving end.

[0010] In one implementation of this application, a transmission protocol is selected for the preprocessed data based on the data type, and differential encryption is performed. Specifically, this includes: deriving a root key and generating encryption keys for different data types based on the root key; selecting a symmetric encryption mode according to the data type and performing encryption; and performing layered integrity verification on the encrypted data to locate and retransmit tampered data.

[0011] In one implementation of this application, the method further includes: calculating a data transmission priority score based on the service type, data urgency, and data size; allocating bandwidth ratios based on the priority scores; and using a token bucket algorithm to limit the transmission rate of data with priority scores below a threshold.

[0012] In one implementation of this application, the calculation engine is selected according to the data type and the storage is adapted to the corresponding storage medium. Specifically, this includes: receiving data type and transmission status information; if the data is streaming data, a streaming calculation engine is used for real-time parsing and alarm triggering; if the data is batch-transmitted data, a batch calculation engine is used for offline analysis and report generation.

[0013] In one implementation of this application, the real-time monitoring of the entire transmission link status, recording of transmission lineage information, and linkage with the regulatory business system to trigger response operations specifically include: collecting transmission rate, fragmentation success rate, and bandwidth utilization indicators through the monitoring system and displaying them visually; recording data transmission protocol, encryption mode, fragment size, and reception status through the workflow engine to form the transmission link lineage; and pushing priority data with a priority score higher than the threshold to the regulatory dashboard in real time and triggering alarms.

[0014] Secondly, embodiments of this application also provide a multi-type data transmission device for industrial digital supervision. The device includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to: access multi-type data and classify the multi-type data according to the data type, including strings, images, documents, and video data; select a transmission protocol for the classified data based on the data type and perform differentiated encryption; receive data through a stream-batch integrated computing engine layer, select a computing engine according to the data type, and adapt and store the data to the corresponding storage medium; monitor the entire transmission link status in real time, record the transmission lineage information, and trigger response operations in conjunction with the supervision business system.

[0015] Thirdly, this application embodiment also provides a non-volatile computer storage medium for multi-type data transmission in industrial digital supervision, storing computer-executable instructions. The computer-executable instructions are configured to: access multi-type data and classify the multi-type data according to the data type, including string, image, document, and video data; select a transmission protocol for the classified data based on the data type and perform differentiated encryption; receive data through a stream-batch integrated computing engine layer, select a computing engine according to the data type, and adapt and store it to the corresponding storage medium; monitor the status of the entire transmission link in real time, record the transmission lineage information, and trigger response operations in conjunction with the supervision business system.

[0016] This application provides a method, device, and medium for transmitting multiple types of data in industrial digital supervision. Through automatic data type identification and adaptive protocol selection, it achieves unified transmission of strings, images, documents, and videos within a single system, eliminating the complexity of multi-system operation and maintenance. Dynamic fragmentation and parallel transmission mechanisms significantly improve the throughput of large-volume data transmission and reduce transmission latency. Type-based differentiated encryption and layered verification meet high-level security requirements while significantly optimizing encryption and verification efficiency. Dynamic bandwidth scheduling and flow control based on business priorities ensure the real-time performance of critical alarm data and avoid network congestion. Finally, through close collaboration between the transport layer, the integrated stream-batch computing engine, and the business system, it comprehensively improves the real-time performance, reliability, and intelligence level of industrial digital supervision. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a multi-type data transmission method for industrial digital supervision, as provided in this application embodiment; Figure 2 This is a schematic diagram of the internal structure of a multi-type data transmission device for industrial digital supervision, provided as an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] This application provides a method, device, and medium for transmitting multiple types of data in industrial digital supervision, which solves the problems of poor adaptability, low efficiency, weak security, and easy blockage of critical data in existing industrial data transmission schemes due to protocol fragmentation, lack of differentiated security strategies and scheduling mechanisms.

[0020] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0021] Figure 1 This document provides a flowchart of a multi-type data transmission method for industrial digital supervision, as illustrated in an embodiment of this application. Figure 1As shown in the embodiment of this application, a multi-type data transmission method for industrial digital supervision specifically includes the following steps: Step 10: Integrate multiple data types and classify them according to their data types. The multiple data types include strings, images, documents, and video data.

[0022] In this embodiment, multiple types of data are first accessed from various data sources in the industrial field. These data sources include, but are not limited to, sensors, industrial cameras, document servers, and webcams. The generated data differ significantly in format, volume, and frequency of generation.

[0023] As an optional embodiment, multiple types of data are accessed and classified according to the data type. Specifically, this may include: Step 101: Extracting the file header features and data packet size of the multiple types of data, and statistically analyzing the sending frequency to obtain the data type.

[0024] In this step, the initial fixed bytes of the data stream are read and matched against a predefined magic number library to identify potential file formats; the size of individual data packets is monitored in real time, and the data type is initially determined based on their range; within a sliding time window, the arrival rate of data packets is calculated to distinguish between real-time streaming data and offline batch data. By integrating the features of the above three dimensions, the system constructs a decision model to accurately identify the input data as a string, image, document, or video.

[0025] Step 102: Deduplicate the strings based on data content and timestamps, and convert the deduplicated strings into a standard format; In this step, the string content and the generated timestamp are combined to form a unique identifier, which is then compared within a set time window to filter out redundant data where both content and timestamp are duplicated. Subsequently, the deduplicated string data—that is, sensor readings from different protocols—is uniformly converted into a standardized structure format, such as JSON, to provide a consistent interface for subsequent transmission and processing.

[0026] Step 103: Convert the image format and adjust the resolution.

[0027] In this step, the received raw image is converted into a modern image format with a higher compression ratio. At the same time, the image resolution is adjusted according to the available bandwidth of the current network. For example, the image resolution is automatically reduced when bandwidth is tight, so as to reduce the amount of data required for transmission while ensuring that key information is not lost.

[0028] Step 104: Optimize the encoding of the video data and extract keyframes.

[0029] In this step, the video stream is converted from its original encoding format to a more efficient one to reduce the video bitrate. Furthermore, keyframes containing complete information are extracted from the video stream in real time. This reduces the total amount of data that needs to be transmitted and provides high-quality image material for subsequent computational analysis.

[0030] Step 105: Compress the various data types separately using a lightweight compression algorithm.

[0031] In this step, a lightweight compression layer is applied to all types of data. A lossless or lossy compression algorithm with low computational overhead and fast compression speed is selected to perform fast compression at the data source or edge side, aiming to further reduce the data volume and alleviate the bandwidth pressure on the core transmission network.

[0032] Step 20: Based on the data type, select a transmission protocol for the categorized data and perform differentiated encryption.

[0033] As an optional embodiment, based on the data type, a transmission protocol is selected for the preprocessed data and differential encryption is performed. Specifically, this may include: Step 201: Adaptively select the corresponding transmission protocol from the pre-configured protocol library according to the data type.

[0034] In this step, a pre-configured protocol library maps the relationship between data types and optimal transmission protocols. For high-frequency, small-volume string data, a low-latency message queue protocol is selected; for image and document data, a hypertext transfer protocol that supports efficient file transfer is selected; and for video data, a streaming media protocol that ensures the stability of streaming transmission is selected. After the data is classified, the system automatically queries the protocol library to assign a suitable transmission protocol to the data.

[0035] Step 202: Adjust the transmission protocol parameters based on the real-time network bandwidth utilization.

[0036] In this step, network bandwidth utilization is continuously monitored during data transmission. When the utilization exceeds or falls below a certain threshold, the system will dynamically adjust the core parameters of the selected protocol, such as adjusting the frame rate of the streaming media protocol or the window size of the file transfer protocol, in order to achieve the best balance between transmission efficiency and network load and avoid network congestion or bandwidth waste.

[0037] As an optional embodiment, the method may further include: Step 203: dynamically calculating the fragment size based on the data packet size and real-time network bandwidth, and fragmenting according to the calculated fragment size, and transmitting the fragments in parallel through multiple transmission channels; Step 204: using distributed storage to record the transmission status of each fragment; Step 205: performing hash verification on each fragment, and performing overall integrity verification at the receiving end.

[0038] In this step, at the transport layer, each data fragment is hashed before being sent. Upon receiving a fragment, the receiving end immediately calculates its hash and compares it to ensure that each fragment has not been tampered with or corrupted during transmission. After all fragments have been received, the receiving end constructs a complete verification tree, such as a Merkle tree, based on the hash values ​​of all fragments. It then performs a final comparison between the root node of the verification tree and the root hash provided by the sender, thus efficiently verifying the integrity of the entire file without needing to transmit the entire file.

[0039] As an optional embodiment, based on the data type, a transmission protocol is selected for the preprocessed data and differential encryption is performed. Specifically, this may include: Step 206: derive the root key and generate encryption keys for different data types based on the root key.

[0040] In this step, a protected root key is generated. Based on this root key, and combined with different data type identifiers, independent encryption keys are generated for strings, images, documents, and videos through key derivation functions. This mechanism achieves key isolation, so even if the key for one type of data is leaked, it will not endanger the security of other types of data.

[0041] Step 207: Select the symmetric encryption mode according to the data type and perform encryption.

[0042] In this step, a counter mode is used for string data requiring low latency; a password block chaining mode is used for image and document data requiring high security; and an output feedback mode is used for video stream data. The selected type key and encryption mode are used to encrypt the corresponding data.

[0043] Step 208: Perform layered integrity verification on the encrypted data to locate and retransmit the tampered data.

[0044] In this step, for large batches of encrypted data, when the receiving end verifies that the overall file is incomplete, it can quickly locate which one or more fragments have a problem by traversing this tree structure. Thus, it is only necessary to retransmit these abnormal fragments instead of the entire file, which greatly improves the efficiency of verification and repair.

[0045] Step 30: Receive data through the stream-batch integrated computing engine layer, and select the computing engine and adapt the storage to the corresponding storage medium according to the data type; As an optional embodiment, data is received through the stream-batch integrated computing engine layer, and the computing engine is selected and adapted to the corresponding storage medium according to the data type. Specifically, it may include: Step 301: Receiving data type and transmission status information.

[0046] In this step, when the data arrives, the engine layer simultaneously receives two types of key metadata: one is the data type determined by the classification and identification module, and the other is the transmission status information from the transmission layer, such as identifying the data as "real-time streaming arrival" or "offline batch ready".

[0047] Step 302: If the data is streaming data, use the streaming computing engine for real-time parsing and alarm triggering.

[0048] In this step, for data whose metadata indicates streaming, the system routes it to the streaming computing engine, which is designed for unbounded data streams and can process data in real time with millisecond-level latency. Specifically, it parses sensor strings in real time, matches them against preset alarm rules, and triggers an alert immediately upon detecting an anomaly; at the same time, it performs real-time analysis of the video stream, extracting keyframes to detect equipment status or process defects.

[0049] Step 303: If the data is transmitted in batches, use the batch computing engine to perform offline analysis and report generation.

[0050] In this step, for data whose metadata indicates batch transmission, such as daily summarized quality inspection documents or historically archived offline videos, the system schedules them to the batch computing engine. This engine is suitable for bounded datasets and is responsible for performing computationally intensive offline analysis tasks. For example, it can batch process a large number of quality inspection documents to extract key indicators to generate compliance reports; or perform retrospective analysis on historical video data to identify optimization points in the production process.

[0051] Step 40: Monitor the status of the entire transmission link in real time, record the transmission lineage information, and trigger response operations in conjunction with the regulatory business system.

[0052] As an optional embodiment, the method may further include: calculating a data transmission priority score based on the service type, data urgency, and data size; allocating bandwidth proportions according to the priority score; and using a token bucket algorithm to limit the transmission rate of data with a priority score below a threshold.

[0053] As an optional implementation, real-time monitoring of the entire transmission link status, recording of transmission lineage information, and linkage with the regulatory business system to trigger response operations can specifically include: Step 401: Collect transmission rate, fragmentation success rate, and bandwidth utilization rate indicators through the monitoring system and display them visually.

[0054] In this step, key performance indicators are continuously collected from various nodes of the transport layer through an integrated monitoring agent, including the real-time transmission rate of various types of data, the success rate of fragmentation of large batches of data, and the network bandwidth utilization rate. These indicators are aggregated into a time-series database and dynamically displayed through a visual dashboard, enabling operations and maintenance personnel to have a clear global understanding of the health status and performance of the transmission link.

[0055] Step 402: Record the data transmission protocol, encryption mode, fragment size and reception status through the workflow engine to form the transmission link lineage.

[0056] In this step, the workflow engine records key metadata for each piece of data throughout its entire transmission lifecycle, including the transmission protocol used, the encryption mode applied, the fragment size, and the final reception status. All this information is linked together to form a complete, queryable transmission link lineage graph. When a transmission failure occurs, this lineage allows for rapid reverse tracing to accurately pinpoint the root cause of the problem—whether it was due to encryption failure leading to rejection, or fragment loss causing incompleteness.

[0057] Step 403: Push the priority data of the level with a priority score higher than the threshold to the regulatory dashboard in real time and trigger an alarm.

[0058] In this step, once data with a priority score higher than the predetermined threshold is identified, the transmission will be guaranteed through a scheduling mechanism. After successful reception, the content will be pushed to the core dashboard of the regulatory business in real time. At the same time, the system will automatically trigger corresponding alarm actions, such as sending SMS or email, thereby directly transforming the transmission results into regulatory decisions and emergency responses.

[0059] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a multi-type data transmission device for industrial digital supervision, the structure of which is as follows: Figure 2 As shown.

[0060] Figure 2 This is a schematic diagram of the internal structure of a multi-type data transmission device for industrial digital monitoring, provided as an embodiment of this application. Figure 2 As shown, the device includes: At least one processor 201; And a memory 202 that is communicatively connected to at least one processor; The memory 202 stores instructions that can be executed by at least one processor. These instructions are executed by at least one processor 201 to enable the at least one processor 201 to: access multiple types of data and classify the multiple types of data according to their data types, including strings, images, documents, and video data; select a transmission protocol for the classified data based on its data type and perform differentiated encryption; receive data through a stream-batch integrated computing engine layer and select a computing engine and adapt it to the corresponding storage medium according to its data type; monitor the status of the entire transmission link in real time, record the transmission lineage information, and trigger response operations in conjunction with the monitoring business system.

[0061] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for multi-type data transmission in industrial digital supervision is disclosed, storing computer-executable instructions. These instructions are configured to: receive multi-type data and classify the data according to its data type (including strings, images, documents, and video data); select a transmission protocol for the classified data based on its data type and perform differentiated encryption; receive data through a stream-batch integrated computing engine layer, select a computing engine based on the data type, and adapt and store the data to the corresponding storage medium; monitor the entire transmission link status in real time, record transmission lineage information, and trigger response operations in conjunction with the supervisory business system.

[0062] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0063] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0064] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0068] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0069] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0070] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0071] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0072] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for transmitting multiple types of data in industrial digital supervision, characterized in that, The method includes: The system accesses multiple types of data and categorizes them according to their data types, including strings, images, documents, and video data. Based on the data type, a transmission protocol is selected for the categorized data, and differentiated encryption is performed. The data is received through the stream-batch integrated computing engine layer, and the computing engine is selected and adapted to the corresponding storage medium according to the data type. Real-time monitoring of the entire transmission link status, recording of transmission lineage information, and linkage with the regulatory business system to trigger response operations.

2. The multi-type data transmission method for industrial digital supervision according to claim 1, characterized in that, The process of accessing multiple types of data and classifying them according to data type specifically includes: Extract the file header features and data packet size of the various data types, and statistically analyze the sending frequency to obtain the data types; The string is deduplicated based on data content and timestamp, and the deduplicated string is then converted into a standard format. The image is then converted in format and its resolution is adjusted for adaptation. The video data is encoded and optimized, and keyframes are extracted. The various data types are compressed separately using a lightweight compression algorithm.

3. The multi-type data transmission method for industrial digital supervision according to claim 1, characterized in that, The step of selecting a transmission protocol for the preprocessed data based on the data type and performing differentiated encryption specifically includes: Based on the data type, the corresponding transport protocol is adaptively selected from the pre-configured protocol library; Adjust transmission protocol parameters based on real-time network bandwidth utilization.

4. The multi-type data transmission method for industrial digital supervision according to claim 3, characterized in that, The method further includes: The fragment size is dynamically calculated based on the data packet size and real-time network bandwidth, and the fragments are then divided according to the calculated fragment size and transmitted in parallel through multiple transmission channels. Use distributed storage to record the transmission status of each fragment; Each shard is hash-verified, and the overall integrity is verified at the receiving end.

5. The multi-type data transmission method for industrial digital supervision according to claim 1, characterized in that, The step of selecting a transmission protocol for the preprocessed data based on the data type and performing differentiated encryption specifically includes: Derive the root key and generate encryption keys for different data types based on the root key; Select the symmetric encryption mode according to the data type and perform encryption; The encrypted data is subjected to layered integrity verification in order to locate and retransmit the tampered data.

6. The multi-type data transmission method for industrial digital supervision according to claim 1, characterized in that, The method further includes: Calculate a data transmission priority score based on business type, data urgency, and data size; The bandwidth ratio is allocated according to the priority score, and the transmission rate of data with a priority score below a threshold is limited using the token bucket algorithm.

7. The multi-type data transmission method for industrial digital supervision according to claim 1, characterized in that, The step of selecting a computing engine and adapting storage to the corresponding storage medium based on the data type specifically includes: Receive the data type and transmission status information; If the data is streaming data, a streaming computing engine is used for real-time parsing and alarm triggering; If the data is transmitted in batches, use a batch computing engine for offline analysis and report generation.

8. The multi-type data transmission method for industrial digital supervision according to claim 1, characterized in that, The real-time monitoring of the entire transmission link status, recording of transmission lineage information, and linkage with the regulatory business system to trigger response operations specifically include: The system collects and visualizes metrics such as transmission rate, fragmentation success rate, and bandwidth utilization. The workflow engine records the data transmission protocol, encryption mode, fragment size, and reception status to form the transmission link lineage; Priority data with scores higher than the threshold will be pushed to the regulatory dashboard in real time and an alarm will be triggered.

9. A multi-type data transmission device for industrial digital monitoring, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: The system accesses multiple types of data and categorizes them according to their data types, including strings, images, documents, and video data. Based on the data type, a transmission protocol is selected for the categorized data, and differentiated encryption is performed. The data is received through the stream-batch integrated computing engine layer, and the computing engine is selected and adapted to the corresponding storage medium according to the data type. Real-time monitoring of the entire transmission link status, recording of transmission lineage information, and linkage with the regulatory business system to trigger response operations.

10. A non-volatile computer storage medium for multi-type data transmission in industrial digital supervision, storing computer-executable instructions, characterized in that, The computer-executable instructions are set as follows: The system accesses multiple types of data and categorizes them according to their data types, including strings, images, documents, and video data. Based on the data type, a transmission protocol is selected for the categorized data, and differentiated encryption is performed. The data is received through the stream-batch integrated computing engine layer, and the computing engine is selected and adapted to the corresponding storage medium according to the data type. Real-time monitoring of the entire transmission link status, recording of transmission lineage information, and linkage with the regulatory business system to trigger response operations.

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