Method and system for reading industrial device data

Abstract

This invention provides a system and method for reading data from industrial equipment, particularly a technical solution for rapidly reading data from low-performance industrial equipment. The solution includes: a data acquisition and configuration reading stage, a data block feature analysis stage, a data acquisition drive load balancing stage, a long data segmentation stage, a short data merging stage, and a data batch forwarding stage. By analyzing the characteristics of the data blocks to be acquired from the equipment, and designing a reorganization optimization and multi-task load balancing scheduling based on these characteristics, this invention can solve the problem of rapid data acquisition from low-performance industrial equipment. By combining data blocks into an optimal balanced form, it more effectively improves channel utilization compared to existing technologies.

Description

Technical Field

[0001] This invention relates to the field of data acquisition, specifically to a method and system for reading data from industrial equipment. In particular, it relates to a technical solution for rapidly reading data from low-performance industrial equipment. Background Technology

[0002] With the rapid development of technologies such as the Industrial Internet and intelligent manufacturing, a large number of manufacturing enterprises have begun to digitally upgrade their existing manufacturing equipment. However, there are a large number of outdated and low-performance equipment in the current stock of manufacturing equipment. Due to production processes, product positioning and other reasons, these devices still have value in the product manufacturing process and need to be retained during the upgrade process. Therefore, they also need to be digitally upgraded.

[0003] However, during the digital upgrade process, more data needs to be collected from these devices to support more efficient use of the devices. This places higher demands on the data acquisition rate of these low-performance devices. The original data acquisition methods of the devices collect fewer data points and have low acquisition performance, which cannot meet the new requirements.

[0004] Currently, no technology has been found that addresses the issue of rapid data acquisition from aging, low-speed equipment during industrial equipment upgrades. A relatively close prior art is patent document CN108063809A, application number 201711315280.5. This patent document provides a data acquisition method and system for machine equipment that balances acquisition and transmission rates, ensuring short transmission times and maintaining data integrity even in the event of network instability, preventing data loss. This method uses NodeMCUrrent as the main controller to acquire data while simultaneously transmitting it via TCP. This patent document improves the acquisition rate by using the main controller to acquire data while simultaneously transmitting it via TCP, essentially acting as a network buffer for batch transmission. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the purpose of this invention is to provide a method and system for reading data from industrial equipment.

[0006] A system for reading industrial equipment data according to the present invention includes:

[0007] Data Acquisition Configuration Reading Module M100: Reads the data block to be acquired and related configuration information;

[0008] The data block feature analysis module M101 analyzes the data block to be collected to obtain its model features; based on the optimal balanced data block length determined by the relevant configuration information, and according to the model features of the data block to be collected, it triggers the long data segmentation module M103 to segment the data block, triggers the short data merging module M104 to merge the data block, or uses the currently configured data block based on the relevant configuration information to generate the optimal data block sequence.

[0009] The data acquisition driver load balancing module M102 manages the data acquisition driver and allocates data blocks from the reassembled optimal data acquisition block sequence to different processes, thereby achieving concurrent balanced management of multiple data acquisition processes.

[0010] Long data segmentation module M103: Segments long data blocks in the data to be collected;

[0011] Short data merging module M104: Merges short data blocks in the data to be collected;

[0012] Data batch forwarding module M105: It summarizes the collected data and forwards it in batches.

[0013] Preferably, in the acquisition configuration reading module M100:

[0014] Relevant configuration information is read from the data collection configuration file, wherein the relevant configuration information includes:

[0015] Device configuration information, which includes: device name, device type, and maximum number of devices that can be connected;

[0016] Connection protocol information, wherein the connection protocol includes: data acquisition protocol, longest data block of the adaptation protocol, and port number;

[0017] Data block feature model measurement information, wherein the data block feature model measurement information includes: data block proportion threshold TH, long data block standard Len_Long, and short data block standard Len_Short;

[0018] Process scheduling load balancing parameters, wherein the process scheduling load balancing parameters include: maximum number of processes N, time interval between different processes Del, and maximum number of data collected per process DataMax.

[0019] Preferably, in the feature analysis module M101 of the data block to be collected:

[0020] If the proportion of the number of long data blocks in the data blocks to be collected by the device to the total number of data blocks to be collected is greater than the threshold TH, then the data blocks to be collected are determined to have a large number of long data block model features and are recorded as model A.

[0021] If the proportion of the number of short data blocks in the data blocks to be collected by the device to the total number of data blocks to be collected is greater than the threshold TH, then the data blocks to be collected are determined to have a large number of short data block model features and are recorded as model B.

[0022] If the proportion of long data blocks and short data blocks to the total number of data blocks to be collected is not greater than the threshold TH, then the data blocks to be collected are determined to have the characteristics of a data balance model and are recorded as model C.

[0023] Based on the model characteristics, data blocks of varying lengths are reorganized into an optimal data block sequence: the optimal balanced data block length is determined according to the connection protocol information in the relevant configuration information; Model A calls the long data segmentation module M103 to segment long data blocks into the optimal balanced data block length; Model B calls the short data merging module M104 to merge short data blocks into the optimal balanced data block length; Model C then uses the currently configured data blocks as the optimal balanced data block length; the optimal balanced data blocks are sorted to obtain the optimal acquisition data block sequence.

[0024] Preferably, in the data acquisition and load balancing module M102:

[0025] After the data acquisition driver module M200 generates data acquisition driver process groups 1, 2, ..., N according to the configured maximum number of processes N, the acquisition driver load balancing module M102 sorts the processes in groups 1, 2, ..., N according to the process misalignment time set to Del milliseconds in the relevant configuration information, so that the acquisition interval between processes is Del milliseconds; according to the maximum number of data to be acquired by a single process DataMax, the data blocks in the reorganized optimal acquisition data block sequence are allocated to different processes.

[0026] Preferably, for model A and model B, the optimal balanced data block length is determined as follows:

[0027] Optimal balanced data block length = Min(maximum single packet communication data length in the selected protocol * 80%, maximum number of data collected per process DataMax).

[0028] A method for reading industrial equipment data according to the present invention includes:

[0029] Step 1, Configuration Reading Steps: Read the data block to be collected and related configuration information;

[0030] Step 2, Feature Analysis of the Data Block to be Collected: Analyze the data block to be collected to obtain the model features of the data block to be collected;

[0031] Step 3, Optimal Data Block Sequence Determination Step: Based on the optimal balanced data block length determined by the relevant configuration information, and according to the model characteristics of the data block to be collected, the optimal data block sequence is generated by triggering a long data splitting step to split the data block, triggering a short data merging step to merge the data block, or by using the data block currently configured by the relevant configuration information as the standard.

[0032] Step 4, Load balancing of acquisition driver: Manage the acquisition driver and allocate data blocks from the reorganized optimal acquisition data block sequence to different processes to achieve concurrent load balancing management of multiple acquisition processes;

[0033] Step 5, Batch Data Forwarding: Summarize the collected data and forward it in batches;

[0034] Long data segmentation steps: Segment the long data blocks in the data to be collected;

[0035] Short data merging step: Merge short data blocks in the data blocks to be collected.

[0036] Preferably, in the step of acquiring configuration reading:

[0037] Relevant configuration information is read from the data collection configuration file, wherein the relevant configuration information includes:

[0038] Device configuration information, which includes: device name, device type, and maximum number of devices that can be connected;

[0039] Connection protocol information, wherein the connection protocol includes: data acquisition protocol, longest data block of the adaptation protocol, and port number;

[0040] Data block feature model measurement information, wherein the data block feature model measurement information includes: data block proportion threshold TH, long data block standard Len_Long, and short data block standard Len_Short;

[0041] Process scheduling load balancing parameters, wherein the process scheduling load balancing parameters include: maximum number of processes N, time interval between different processes Del, and maximum number of data collected per process DataMax.

[0042] Preferably, in the feature analysis step of the data block to be collected:

[0043] If the proportion of the number of long data blocks in the data blocks to be collected by the device to the total number of data blocks to be collected is greater than the threshold TH, then the data blocks to be collected are determined to have a large number of long data block model features and are recorded as model A.

[0044] If the proportion of the number of short data blocks in the data blocks to be collected by the device to the total number of data blocks to be collected is greater than the threshold TH, then the data blocks to be collected are determined to have a large number of short data block model features and are recorded as model B.

[0045] If the proportion of long data blocks and short data blocks to the total number of data blocks to be collected is not greater than the threshold TH, then the data blocks to be collected are determined to have the characteristics of a data balance model and are recorded as model C.

[0046] In the step of determining the optimal data block sequence:

[0047] Based on the model characteristics, data blocks of varying lengths are reorganized into an optimal data block sequence: the optimal balanced data block length is determined according to the connection protocol information in the relevant configuration information; Model A calls the long data segmentation module M103 to segment long data blocks into the optimal balanced data block length; Model B calls the short data merging module M104 to merge short data blocks into the optimal balanced data block length; Model C then uses the currently configured data blocks as the optimal balanced data block length; the optimal balanced data blocks are sorted to obtain the optimal acquisition data block sequence.

[0048] Preferably, in the data acquisition and load balancing step:

[0049] Data acquisition driver process groups 1, 2, ..., N are generated based on the configured maximum number of processes N. The process misalignment time is set to Del milliseconds according to the relevant configuration information. The processes in groups 1, 2, ..., N are sorted so that the acquisition interval between processes is Del milliseconds. Based on the maximum number of data to be acquired per process DataMax, the data blocks in the reorganized optimal acquisition data block sequence are allocated to different processes.

[0050] Preferably, for model A and model B, the optimal balanced data block length is determined as follows:

[0051] Optimal balanced data block length = Min(maximum single packet communication data length in the selected protocol * 80%, maximum number of data collected per process DataMax).

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. This invention analyzes the characteristics of the data blocks to be collected from the equipment, and designs a reorganization optimization and multi-task load balancing scheduling based on the characteristics of the data blocks to be collected, which can solve the problem of rapid data collection from low-performance industrial equipment.

[0054] 2. This invention improves channel utilization more effectively than existing technologies by combining data into an optimal balanced block format.

[0055] 3. Based on the beneficial effects in point 2 above, this invention also uses a load balancing module to collect data simultaneously through multiple processes and staggers the collection times of each process. This effectively reduces the equipment load while achieving multi-process data collection. Attached Figure Description

[0056] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0057] Figure 1 This is a modular framework diagram of the present invention.

[0058] Figure 2 This is a flowchart for analyzing the data blocks to be collected.

[0059] Figure 3 Flowchart for balancing the data collection process.

[0060] Figure 4 This is the overall flowchart of the present invention. Detailed Implementation

[0061] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0062] This invention provides a method and system for reading data from industrial equipment, and more particularly, a technical solution for rapidly reading data from industrial equipment. By analyzing the characteristics of the data blocks to be collected from the equipment, and designing a reorganization optimization and multi-task load balancing scheduling based on these characteristics, this invention effectively solves the problem of rapid data acquisition from low-performance industrial equipment.

[0063] A system for reading industrial equipment data according to the present invention includes:

[0064] Data Acquisition Configuration Reading Module M100: Reads the data block to be acquired and related configuration information;

[0065] The data block feature analysis module M101 analyzes the data block to be collected to obtain its model features; based on the optimal balanced data block length determined by the relevant configuration information, and according to the model features of the data block to be collected, it triggers the long data segmentation module M103 to segment the data block, triggers the short data merging module M104 to merge the data block, or uses the currently configured data block based on the relevant configuration information to generate the optimal data block sequence.

[0066] The data acquisition driver load balancing module M102 manages the data acquisition driver and allocates data blocks from the reassembled optimal data acquisition block sequence to different processes, thereby achieving concurrent balanced management of multiple data acquisition processes.

[0067] Long data segmentation module M103: Segments long data blocks in the data to be collected;

[0068] Short data merging module M104: Merges short data blocks in the data to be collected;

[0069] Data batch forwarding module M105: It summarizes the collected data and forwards it in batches.

[0070] In the acquisition configuration reading module M100:

[0071] Relevant configuration information is read from the data collection configuration file, wherein the relevant configuration information includes:

[0072] Device configuration information, which includes: device name, device type, and maximum number of devices that can be connected;

[0073] Connection protocol information, wherein the connection protocol includes: data acquisition protocol, longest data block of the adaptation protocol, and port number;

[0074] Data block feature model measurement information, wherein the data block feature model measurement information includes: data block proportion threshold TH, long data block standard Len_Long, and short data block standard Len_Short;

[0075] Process scheduling load balancing parameters, wherein the process scheduling load balancing parameters include: maximum number of processes N, time interval between different processes Del, and maximum number of data collected per process DataMax.

[0076] In the feature analysis module M101 of the data block to be collected:

[0077] Feature analysis is performed on the data blocks to be collected. If the proportion of long data blocks in the data blocks to be collected is greater than the threshold TH, the data blocks to be collected are determined to have the feature of a large number of long data blocks and are recorded as Model A; if the proportion of short data blocks in the data blocks to be collected is greater than the threshold TH, the data blocks to be collected are determined to have the feature of a large number of short data blocks and are recorded as Model B; if the proportions of both long and short data blocks are not greater than the threshold TH, the data blocks to be collected are determined to have the feature of a data balance model and are recorded as Model C.

[0078] Furthermore, based on the model characteristics, data blocks of varying lengths are reorganized into an optimal data block sequence. According to the connection protocol information in the relevant configuration information, the optimal balanced data block length is determined. Model A calls the long data segmentation module M103 to segment long data blocks, Model B calls the short data merging module M104 to merge short data blocks, and Model C uses the currently configured data blocks to generate the current optimal data block sequence.

[0079] In the acquisition-driven load balancing module M102:

[0080] The data acquisition drive load balancing module M102 generates data acquisition drive process groups 1, 2, ..., N according to the configured maximum number of processes N. Based on the process stagger time set in the configuration file as Del milliseconds, the module M102 sorts the processes in groups 1, 2, ..., N by time stagger, ensuring that the acquisition interval between processes is Del milliseconds, thus reducing the concurrent load on the device at the same time. Simultaneously, the module M102 distributes data blocks from the reorganized optimal acquisition data block sequence to different processes according to the maximum number of data blocks that can be acquired per process (DataMax). After completing the load balancing of the data acquisition processes, industrial equipment data acquisition is executed, and the finally acquired data is sent to the data batch forwarding module M105 for batch forwarding.

[0081] Step 1, Configuration Reading Steps: Read the data block to be collected and related configuration information;

[0082] Step 2, Feature Analysis of the Data Block to be Collected: Analyze the data block to be collected to obtain the model features of the data block to be collected;

[0083] Step 3, Optimal Data Block Sequence Determination Step: Based on the optimal balanced data block length determined by the relevant configuration information, and according to the model characteristics of the data block to be collected, the optimal data block sequence is generated by triggering a long data splitting step to split the data block, triggering a short data merging step to merge the data block, or by using the data block currently configured by the relevant configuration information as the standard.

[0084] Step 4, Load balancing of acquisition driver: Manage the acquisition driver and allocate data blocks from the reorganized optimal acquisition data block sequence to different processes to achieve concurrent load balancing management of multiple acquisition processes;

[0085] Step 5, Batch Data Forwarding: Summarize the collected data and forward it in batches;

[0086] Long data segmentation steps: Segment the long data blocks in the data to be collected;

[0087] Short data merging step: Merge short data blocks in the data blocks to be collected.

[0088] Step 1 includes the following steps:

[0089] Step 1.1: Read the collection configuration information from the relevant configuration files, such as... Figure 4 Process S100 includes device-related information, such as device name, device type, and maximum number of devices that can be connected, for example:

[0090] Device Name

[0091] Equipment type: Point device / Block device

[0092] Maximum number of devices that can be connected: 100

[0093] The device name is used to identify the operating device, and each device name is unique. The device type definition is used to define the device's usual data transmission mode, such as whether the data is transmitted by data point or by data block. The maximum number of connections a device can support describes how many acquisition processes the device can support at the same time.

[0094] Step 1.2: Read and connect to the relevant protocol information, including the data acquisition protocol, the longest data block size of the adapted protocol, and the port number. The data acquisition protocol specifies which acquisition protocol should be used to collect data from the device, such as the common Modbus protocol, OPC UA acquisition protocol, MQTT protocol, etc. The longest data block size of the adapted protocol describes the maximum data block size that can be transmitted in a single transmission under this protocol specification. The port number information is determined according to different protocol specifications and the device's need to communicate on a designated port.

[0095] Step 1.3: As Figure 4 As shown in process S200, the data block proportion threshold TH related to the data block feature model determination is read, along with the long data block standard Len_Long and the short data block standard Len_Short. The data block proportion threshold TH is defined as follows:

[0096] Formula 1: Long data block percentage (LRate) = (Number of long data blocks) / Total number of data blocks

[0097] Formula 2: Short data block percentage (SRate) = (Number of short data blocks) / Total number of data blocks

[0098] In the subsequent data block model feature analysis, the proportion of long data blocks (LRate) and the proportion of short data blocks (SRate) will be compared with the defined data block proportion threshold (TH) to further define the data block model features.

[0099] The long data block standard Len_Long and the short data block standard Len_Short are used to determine whether a data block is a long data block or a short data block. When the number of sampling points in a data block is less than the short data block standard Len_Short, the data block is a short data block. When the number of sampling points in a data block is greater than the long data block standard Len_Long, the data block is a long data block.

[0100] Step 1.4: Read the parameters related to process scheduling and load balancing: Maximum number of processes N, time interval between different processes Del, and maximum number of data collected per process DataMax. Maximum number of processes N is the maximum number of acquisition processes configured based on the performance of this device and the data acquisition computer. Time interval between different processes Del is used to stagger the intervals between different acquisition processes to reduce the device load at any given time. Maximum number of data collected per process DataMax specifies the maximum number of data points that a single acquisition process can collect at one time. These parameters are used by the acquisition-driven load balancing step.

[0101] Step 2 includes the following steps:

[0102] Step 2.1: After obtaining the relevant configuration information for data acquisition, perform rapid feature analysis on the data blocks to be acquired. Scan the data block configuration information of the device to be acquired, and record the total number of configured data blocks as Nsum, the number of long data blocks as Nl, and the number of short data blocks as Ns.

[0103] Step 2.2: Calculate the proportion of long data blocks (LRate) and the proportion of short data blocks (SRate) according to Formula 1 and Formula 2 respectively.

[0104] Long data block percentage LRate = (Nl) / Nsum

[0105] Short data block percentage SRate = (Ns) / Nsum

[0106] Step 2.3: As Figure 2 In process S301, determine whether the proportion of long data blocks (LRate) is greater than the threshold TH. If it is greater than the threshold TH, mark the data to be collected as having a large number of long data block model features (model A), as in process S304; according to Figure 2 In process S302, it is determined whether the proportion of short data blocks SRate is greater than the threshold TH. If it is greater than the threshold TH, the data to be collected is marked as having the feature of a large number of short data blocks (model B), as in process S305. If neither the number of long data blocks nor the number of short data blocks is greater than the threshold TH or other conditions exist, the data to be collected is marked as having the feature of a data balance model (model C).

[0107] Step 3 includes the following steps:

[0108] Step 3.1: The data blocks to be collected by the device were marked as Model A and Model B based on their characteristic distribution. Then, the optimal balanced data block length was determined according to the protocol type selected in the current configuration file.

[0109] The specific determination method is as follows:

[0110] Formula 3: Optimal balanced data block length = Min(maximum single packet communication data length in the selected protocol * 80%, maximum number of data collected per process DataMax)

[0111] If the data block is a feature of model C, then its optimal balanced data block length is not applicable, meaning that it is assumed by default that the data blocks are reasonably distributed under this model.

[0112] Step 3.2: Complete the selection of the optimal balanced data block length (e.g., Figure 4 After process S307), if the current data block model is model A, meaning the data to be collected has many long data block model characteristics, then the long data splitting step is called to split the long data block into optimally balanced data blocks, such as... Figure 4 In process S308, the segmented data blocks are sorted into the optimal data block sequence. If the current data block model is Model B, meaning the data to be collected has many short data block model features, then the short data block merging step is called to concatenate the short data blocks into the optimal balanced data block, such as... Figure 4 In process S309, the concatenated data blocks are sorted into the optimal data block sequence. If the current data block model is model C, that is, the data to be collected has a large number of short data block model features, then the configured data blocks are directly concatenated into a sequence, which is considered to be the current optimal data block sequence.

[0113] Step 4 includes the following steps:

[0114] Step 4.1: Generate data acquisition driver processes 1, 2, ..., N according to the configured maximum number of processes N, such as... Figure 4 The purpose of generating multiple data acquisition drivers in process S401 is to improve the concurrency efficiency of data acquisition by utilizing the concurrency capabilities of the acquisition master computer.

[0115] Step 4.2: Based on the process stagger time set to Del milliseconds in the configuration file, sort the processes in groups 1, 2, ..., N by time stagger, so that the sampling interval between processes is Del milliseconds. That is, assuming process 1 starts sampling from standard time T0, then sampling process 2 starts sampling from standard time T0 + Del, and sampling process N starts sampling from standard time T0 + (N-1) * Del. Figure 4 Process S403. By reducing the concurrent load of devices at the same time through timing errors in the acquisition process.

[0116] Step 4.3: After completing the time-staggered distribution of the data acquisition process, the data acquisition drive load balancing module M102 allocates data blocks from the reassembled optimal data block sequence to different processes based on the maximum number of data blocks that can be acquired per process (DataMax). For example... Figure 4 In process S404, first check whether all data blocks to be collected have been allocated to the data acquisition process. If not, check whether the acquisition load data allocated to the currently available process i is less than DataMax. If it is less, it means that the current acquisition process i is not fully loaded and can continue to be allocated. Then, the block to be allocated in the optimal acquisition data block sequence is allocated to process i. If the current process i is fully loaded, the acquisition drive load balancing module M102 obtains a new process from the process queue, sets the process number i to increment by 1, and then repeats the aforementioned acquisition block balancing allocation process.

[0117] Step 4.4: After the load balancing of the data acquisition process is completed, the industrial equipment data acquisition is executed. Finally, step 5, the data batch forwarding step, is performed to forward the data in batches. Alternatively, the system's cache can be used to cache the data, and then the data can be sent to the data receiving end in a unified manner when needed.

[0118] Those skilled in the art will understand that, in addition to implementing the system, apparatus, and their modules provided by this invention in purely computer-readable program code, the same program can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system, apparatus, and their modules provided by this invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; alternatively, modules for implementing various functions can be considered both software programs implementing the method and structures within the hardware component.

[0119] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A system for reading industrial equipment data, characterized by, include: Data Acquisition Configuration Reading Module M100: Reads the data block to be acquired and related configuration information; Data block feature analysis module M1 01: Analyze the data block to be collected to obtain its model characteristics; based on the optimal balanced data block length determined by the relevant configuration information, and according to the model characteristics of the data block to be collected, trigger the long data segmentation module M103 to segment the data block, trigger the short data merging module M104 to merge the data block, or use the data block currently configured by the relevant configuration information as the standard to generate the optimal data block sequence. The data acquisition driver load balancing module M102 manages the data acquisition driver and allocates data blocks from the reassembled optimal data acquisition block sequence to different processes, thereby achieving concurrent balanced management of multiple data acquisition processes. Long data segmentation module M103: Segments long data blocks in the data to be collected; Short data merging module M104: Merges short data blocks in the data to be collected; Data batch forwarding module M105: summarizes the collected data and forwards it in batches; In the feature analysis module M101 of the data block to be collected: If the proportion of the number of long data blocks in the data blocks to be collected by the device to the total number of data blocks to be collected is greater than the threshold TH, then the data blocks to be collected are determined to have a large number of long data block model features and are recorded as model A. If the proportion of the number of short data blocks in the data blocks to be collected by the device to the total number of data blocks to be collected is greater than the threshold TH, then the data blocks to be collected are determined to have a large number of short data block model features and are recorded as model B. If the proportion of long data blocks and short data blocks to the total number of data blocks to be collected is not greater than the threshold TH, then the data blocks to be collected are determined to have the characteristics of a data balance model and are recorded as model C. Based on the model characteristics, data blocks of varying lengths are reorganized into an optimal data block sequence: the optimal balanced data block length is determined based on the connection protocol information in the relevant configuration information; Model A calls the long data segmentation module M103 to segment long data blocks into optimal balanced data block lengths. Model B calls the short data merging module M104 to merge short data blocks into the optimal balanced data block length; Model C uses the currently configured data blocks as the optimal balanced data block length; the optimal balanced data blocks are sorted to obtain the optimal data block sequence. The standard for long data blocks is Len_Long, and the standard for short data blocks is Len_Short. A data block is considered a short data block when the number of sampling points in it is less than the standard for short data blocks, Len_Short. A data block is considered a long data block when the number of sampling points in it is greater than the standard for long data blocks, Len_Long.

2. The system for reading industrial device data of claim 1, wherein, In the acquisition configuration reading module M100: Relevant configuration information is read from the data collection configuration file, wherein the relevant configuration information includes: Device configuration information, which includes: device name, device type, and maximum number of devices that can be connected; Connection protocol information, wherein the connection protocol includes: data acquisition protocol, longest data block of the adaptation protocol, and port number; Data block feature model measurement information, wherein the data block feature model measurement information includes: data block proportion threshold TH, long data block standard Len_Long, and short data block standard Len_Short; Process scheduling load balancing parameters, wherein the process scheduling load balancing parameters include: maximum number of processes N, time interval between different processes Del, and maximum number of data collected per process DataMax.

3. The system for reading industrial device data of claim 1, wherein, In the acquisition-driven load balancing module M102: Based on the relevant configuration information, the process misalignment time is set to Del milliseconds. The processes in groups 1, 2, ..., N are sorted so that the collection interval between processes is Del milliseconds. Based on the maximum number of data to be collected per process, DataMax, the data blocks in the reorganized optimal data block sequence are allocated to different processes.

4. The system for reading industrial device data of claim 1, wherein, For model A and model B, the optimal balanced data block length is determined as follows: Optimal balanced data block length = Min (maximum single packet communication data length in the selected protocol * 80%, maximum number of data collected per process DataMax).

5. A method of reading industrial device data, characterized by, include: Step 1, Configuration Reading Steps: Read the data block to be collected and related configuration information; Step 2, Feature Analysis of the Data Block to be Collected: Analyze the data block to be collected to obtain the model features of the data block to be collected; Step 3, Optimal Data Block Sequence Determination Step: Based on the optimal balanced data block length determined by the relevant configuration information, and according to the model characteristics of the data block to be collected, the optimal data block sequence is generated by triggering a long data splitting step to split the data block, triggering a short data merging step to merge the data block, or by using the data block currently configured by the relevant configuration information as the standard. Step 4, Load balancing of acquisition driver: Manage the acquisition driver and allocate data blocks from the reorganized optimal acquisition data block sequence to different processes to achieve concurrent load balancing management of multiple acquisition processes; Step 5, Batch Data Forwarding: Summarize the collected data and forward it in batches; Long data segmentation steps: Segment the long data blocks in the data to be collected; Short data merging step: Merge short data blocks in the data blocks to be collected; In the feature analysis step of the data block to be collected: If the proportion of the number of long data blocks in the data blocks to be collected by the device to the total number of data blocks to be collected is greater than the threshold TH, then the data blocks to be collected are determined to have a large number of long data block model features and are recorded as model A. If the proportion of the number of short data blocks in the data blocks to be collected by the device to the total number of data blocks to be collected is greater than the threshold TH, then the data blocks to be collected are determined to have a large number of short data block model features and are recorded as model B. If the proportion of long data blocks and short data blocks to the total number of data blocks to be collected is not greater than the threshold TH, then the data blocks to be collected are determined to have the characteristics of a data balance model and are recorded as model C. Based on the model characteristics, data blocks of varying lengths are reorganized into an optimal data block sequence: the optimal balanced data block length is determined based on the connection protocol information in the relevant configuration information; Model A calls the long data segmentation module M103 to segment long data blocks into optimal balanced data block lengths. Model B calls the short data merging module M104 to merge short data blocks into the optimal balanced data block length; Model C uses the currently configured data blocks as the optimal balanced data block length; the optimal balanced data blocks are sorted to obtain the optimal data block sequence. The standard for long data blocks is Len_Long, and the standard for short data blocks is Len_Short. A data block is considered a short data block when the number of sampling points in it is less than the standard for short data blocks, Len_Short. A data block is considered a long data block when the number of sampling points in it is greater than the standard for long data blocks, Len_Long.

6. The method of reading industrial device data of claim 5, wherein, In the step of collecting and reading configuration: Relevant configuration information is read from the data collection configuration file, wherein the relevant configuration information includes: Device configuration information, which includes: device name, device type, and maximum number of devices that can be connected; Connection protocol information, wherein the connection protocol includes: data acquisition protocol, longest data block of the adaptation protocol, and port number; Data block feature model measurement information, wherein the data block feature model measurement information includes: data block proportion threshold TH, long data block standard Len_Long, and short data block standard Len_Short; Process scheduling load balancing parameters, wherein the process scheduling load balancing parameters include: maximum number of processes N, time interval between different processes Del, and maximum number of data collected per process DataMax.

7. The method of reading industrial device data of claim 5, wherein, In the acquisition-driven load balancing step: Based on the relevant configuration information, the process misalignment time is set to Del milliseconds. The processes in groups 1, 2, ..., N are sorted so that the collection interval between processes is Del milliseconds. Based on the maximum number of data to be collected per process, DataMax, the data blocks in the reorganized optimal data block sequence are allocated to different processes.

8. The method of reading industrial device data of claim 5, wherein, For model A and model B, the optimal balanced data block length is determined as follows: Optimal balanced data block length = Min (maximum single packet communication data length in the selected protocol * 80%, maximum number of data collected per process DataMax).

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