Production line-oriented protocol analysis method and intelligent acquisition device

Abstract

The invention relates to a production line-oriented protocol analysis method and an intelligent acquisition device, and the method comprises the steps: building a word bag model of a known protocol message, calculating the word weight of each word in the message through employing a word frequency-reverse file frequency algorithm, mining keywords in the message based on all word weights, and carrying out the mining of the keywords in the message. Calculating the word frequency of each keyword appearing in the known protocol message and forming a word frequency matrix, inputting the word frequency matrix as a feature vector of the known protocol message into an online width learning recognition model, and preprocessing a protocol data packet captured from a network environment in which a target protocol message is located, and performing similarity evaluation on unknown protocol content and known protocol content of the preprocessed protocol data, obtaining a field sequence of the unknown protocol content according to an evaluation result, and adding the field sequence into an online width learning template library. Therefore, data interaction between different production devices and the numerical control machine tool on the industrial production line is realized, and cross-device and cross-system data acquisition work on the production line is achieved.

Description

Technical Field

[0001] The present invention relates to the industrial field, and in particular to a production line-oriented protocol parsing method and an intelligent collection device. Background Art

[0002] In industrial production, the efficient operation of production lines (or production lines) depends on the monitoring and coordination of various production links. It is necessary to monitor the operating efficiency of production equipment and to promptly detect and handle abnormalities and failures of production line equipment. Therefore, production line equipment data collection technology in industrial production workshops plays a key role.

[0003] However, existing industrial production line equipment and CNC lathe data acquisition often do not provide data acquisition interfaces, or use their own unique data transmission methods, making it impossible to uniformly collect and monitor data, making it difficult to achieve cross-device and cross-system data collection needs between different production equipment on the production line, reducing the efficiency of collecting operating data for industrial production line equipment. Summary of the invention

[0004] The first technical problem to be solved by the present invention is to provide a production line-oriented protocol parsing method suitable for cross-device and cross-system data collection in a production line in view of the above-mentioned prior art.

[0005] The second technical problem to be solved by the present invention is to provide an intelligent collection device for realizing the above-mentioned production line-oriented protocol parsing method.

[0006] The technical solution adopted by the present invention to solve the first technical problem is: a production line-oriented protocol parsing method, which is suitable for collecting production data of production line equipment, and is characterized in that it includes the following steps:

[0007] Step 1: Establish a bag-of-words model, process the known protocol message into a word set of a series of words, and convert the known protocol message into a word vector through N-gram;

[0008] Step 2, using the word frequency-inverse file frequency algorithm, calculate the word weight of each word in the known protocol message, and mine the keywords in the known protocol message based on all the obtained word weights;

[0009] Step 3, calculate the word frequency of each keyword in the corresponding known protocol message, form a word frequency matrix with all the calculated word frequencies, and use the word frequency matrix as the feature vector of the known protocol message;

[0010] Step 4, input the feature vector into an online width learning recognition model; wherein the protocol with the highest consistency with the current message is identified as a representative sample;

[0011] Step 5, using a network packet capture tool to capture a protocol data packet from the network environment where the target protocol message is located, and preprocessing the captured protocol data packet to obtain preprocessed protocol data;

[0012] Step 6: Perform similarity evaluation on the unknown protocol content and the known protocol content in the preprocessed protocol data:

[0013] When the unknown protocol content is a protocol with high similarity to the known protocol content, go to step 7; otherwise, go to step 8;

[0014] Step 7: Based on the specifications and information of the known protocol content, a bidirectional LSTM-CRF model is established to identify the fields in the unknown ICP, obtain the field sequence corresponding to the unknown protocol content, and proceed to step 9;

[0015] Step 8, calculate the information entropy of each byte in the unknown protocol content and the mutual information between the two adjacent bytes before and after, and use the information entropy peak point and the mutual information minimum point as the cutting point, so as to obtain the best field cutting point in the absence of prior knowledge, and obtain the field sequence corresponding to the unknown protocol content, and then go to step 9;

[0016] Step 9: Add the obtained field sequence of the unknown protocol content to the template library of online width learning.

[0017] Improved, in the production line-oriented protocol parsing method, the frequency of any keyword in the known protocol message is calculated as follows:

[0018]

[0019] Among them, TF ij For known protocol message d j Keywords in w j The word frequency, c ij For keyword w j In the known protocol message d j The total number of occurrences in ;

[0020] The inverse file frequency calculation method of any keyword in the known protocol message is as follows:

[0021]

[0022] Among them, IDF j Indicates keyword w j The inverse document frequency of |D| represents the total number of documents in the corpus, and |{i:w j ∈d i}| indicates the total number of files containing keywords;

[0023] TF-IDFij =TF ij *IDF j ;

[0024] TF-IDF ij Indicates keyword w j For known protocol packets d j importance.

[0025] As a further improvement, in the production line-oriented protocol parsing method, the preprocessing includes removing the Ethernet header, IP header and TCP header of the data packet to retain only the protocol content to be identified; the protocol data after preprocessing is a mixed flow containing the target protocol.

[0026] Furthermore, in the production line-oriented protocol parsing method, the preprocessed protocol data includes known protocol content and unknown protocol content. After the known protocol content is input into a pre-trained online width learning model, the online width learning model uses a representative sample of the known protocol content for parsing. After the unknown protocol content is input into the pre-trained online width learning model, format extraction processing is performed.

[0027] Further improved, in the production line-oriented protocol parsing method, in step 8, the information entropy of any byte in the unknown protocol content is calculated as follows:

[0028]

[0029] Among them, H(D) represents the information entropy of any byte D in the unknown protocol content, and the value set of byte D is {x1,x2,…,x n},p(x i ) is byte D with value x i The probability corresponding to .

[0030] Furthermore, in the production line-oriented protocol parsing method, in step 8, the mutual information between two adjacent bytes in the unknown protocol content is calculated as follows:

[0031]

[0032] Among them, D x and D y Indicates the two adjacent bytes before and after the unknown protocol content, byte D x The value set is {x1,x2,…,x n}, byte D y The value set is {y1,y2,…,y n},p(x i ) is byte D x The value is xi The corresponding probability, p(y j ) is byte D y The value is y j The probability corresponding to .

[0033] The technical solution adopted by the present invention to solve the second technical problem is: an intelligent collection device, characterized in that it implements any of the production line-oriented protocol parsing methods described.

[0034] Furthermore, in the invention, the intelligent collection device comprises:

[0035] The MCU module is configured to process data and control other modules in the intelligent acquisition device;

[0036] Ethernet network module, configured for communication between production equipment and a local area network or the Internet, and supporting remote data transmission;

[0037] The DTU module is configured to send the collected production equipment data to the cloud platform;

[0038] A wireless network module is configured to realize wireless network connection of production equipment;

[0039] The power module is configured to provide power supply for the intelligent collection device.

[0040] Improved, in the invention, the intelligent collection device further comprises:

[0041] SWD debugging module, used for programming and debugging of MCU, supports program uploading and troubleshooting;

[0042] LED lights to display the operating status of the device and perform buzzer alarms;

[0043] RS-485 module, used for communication between devices in industrial environments, and for controlling screens and LED lights to display the operating status of devices in real time;

[0044] The screen module displays the data interaction between the production equipment on the production line and the intelligent collection device, displays the operating status of the production equipment in real time, and realizes human-computer interaction.

[0045] Furthermore, in the intelligent acquisition device, the production equipment data collected by the DTU module includes: equipment operation mode, operating status, spindle speed, spindle load, spindle temperature, spindle current, spindle ratio, feed speed, feed ratio, current tool number, number of workpieces, running time, number of alarms, current coordinates, machine coordinates, workpiece coordinates and at least one of the remaining distance.

[0046] Compared with the prior art, the advantages of the present invention are:

[0047] First, the production line-oriented protocol parsing method of the invention establishes a bag-of-words model for known protocol messages in turn, calculates the word weight of each word in the known protocol message using the word frequency-reverse file frequency algorithm, and mines out the keywords in the known protocol message based on all the obtained word weights, and then calculates the word frequency of each keyword in the corresponding known protocol message and forms a word frequency matrix and inputs the word frequency matrix as the feature vector of the known protocol message into the online width learning recognition model, and then preprocesses the protocol data packet captured from the network environment where the target protocol message is located to obtain the preprocessed protocol data, and then performs a similarity evaluation and judgment on the unknown protocol content and the known protocol content in the preprocessed protocol data, and obtains the field sequence of the unknown protocol content based on the similarity evaluation results of the two, and then adds the obtained field sequence of the unknown protocol content to the template library of online width learning. In this way, data interaction between different production equipment and CNC machine tools on the industrial production line can be realized, and data collection work across equipment and systems on the production line can be realized;

[0048] Secondly, the intelligent collection device of this invention can enhance its data perception, acquisition and transmission capabilities for industrial production line equipment and CNC lathes by executing the protocol parsing method for the production line in this invention, and realize real-time collection capabilities for the operating status of equipment on the production line, effectively improving the monitoring and response capabilities of the production process of the production line, and improving production safety and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flowchart of a production line-oriented protocol parsing method according to an embodiment of the present invention;

[0050] Figure 2 The figure is a schematic diagram of the connection of the main components in the intelligent data collection device in the embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below with reference to the accompanying drawings.

[0052] This embodiment provides a production line-oriented protocol analysis method. Figure 1 As shown, the production line-oriented protocol parsing method of this embodiment includes the following steps:

[0053] Step 1, establish a bag-of-words model, process the known protocol message into a word set of a series of words, and convert the known protocol message into a word vector through N-gram (or N-gram model); wherein, in this technical field, the N-gram model here is a conventional technical means, which will not be described here;

[0054] Step 2, using the term frequency-inverse document frequency algorithm (i.e., TF-IDF), calculate the word weight of each word in the known protocol message, and mine out the keywords in the known protocol message based on all the obtained word weights; where, those skilled in the art are familiar with that term frequency (TF) refers to the frequency of a given word appearing in a document; inverse document frequency (IDF) is a measure of the general importance of a word; TF-IDF reflects the importance of a word to a document in a document collection, and is often used as a weight factor in text data mining and information extraction; the IDF of a specific word can be obtained by dividing the total number of documents by the number of documents containing the word, and then taking the logarithm of the quotient;

[0055] Step 3, calculate the word frequency of each keyword in the corresponding known protocol message, form a word frequency matrix together with all the calculated word frequencies, and use the word frequency matrix as the feature vector of the known protocol message; wherein, in this embodiment, the word frequency of any keyword in the known protocol message is calculated as follows:

[0056]

[0057] Among them, TF ij For known protocol message d j Keywords in w j The word frequency, c ij For keyword w j In the known protocol message d j The total number of occurrences in ;

[0058] The inverse file frequency of any keyword in a known protocol message is calculated as follows:

[0059]

[0060] Among them, IDF j Indicates keyword w j The inverse document frequency of |D| represents the total number of documents in the corpus, and |{i:w j ∈d i} represents the total number of documents containing keywords;

[0061] TF-IDF ij =TF ij *IDF j ;

[0062] TF-IDF ij Indicates keyword w j For known protocol packets d j the importance of

[0063] Step 4, input the feature vector into an online width learning recognition model; wherein the protocol with the highest consistency with the current message is identified as a representative sample;

[0064] Step 5, using a network packet capture tool to capture a protocol data packet from the network environment where the target protocol message is located, and preprocessing the captured protocol data packet to obtain preprocessed protocol data; wherein, in this embodiment, the preprocessing here includes removing the Ethernet header, IP header and TCP header of the data packet to retain only the protocol content to be identified; the preprocessed protocol data is a mixed flow containing the target protocol; the preprocessed protocol data includes known protocol content and unknown protocol content, after the known protocol content is input into the pretrained online width learning model, the online width learning model uses the representative sample of the known protocol content for parsing, and after the unknown protocol content is input into the pretrained online width learning model, the format extraction process is performed;

[0065] Step 6: Perform similarity evaluation on the unknown protocol content and the known protocol content in the preprocessed protocol data:

[0066] When the unknown protocol content is a protocol with high similarity to the known protocol content, go to step 7; otherwise, go to step 8;

[0067] Step 7: Based on the specifications and information of the known protocol content, a bidirectional LSTM-CRF model is established to identify the fields in the unknown ICP, obtain the field sequence corresponding to the unknown protocol content, and proceed to step 9; wherein the LSTM-CRF model here belongs to the prior art and will not be described in detail here;

[0068] Step 8, calculate the information entropy of each byte in the unknown protocol content and the mutual information between the two adjacent bytes before and after, and use the information entropy peak point and the mutual information minimum point as the cutting point, so as to obtain the best field cutting point in the absence of prior knowledge, and obtain the field sequence corresponding to the unknown protocol content, and then go to step 9; wherein, in this embodiment:

[0069] The information entropy of any byte in the unknown protocol content is calculated as follows:

[0070]

[0071] Among them, H(D) represents the information entropy of any byte D in the unknown protocol content, and the value set of byte D is {x1,x2,…,x n},p(x i ) is byte D with value x i The corresponding probability when

[0072] The mutual information between two adjacent bytes in the unknown protocol content is calculated as follows:

[0073]

[0074] Among them, D x and D y Indicates the two adjacent bytes before and after the unknown protocol content, byte D x The value set is {x1,x2,…,x n}, byte D y The value set is {y1,y2,…,y n},p(x i ) is byte D x The value is x i The corresponding probability, p(y j ) is byte D y The value is y j The corresponding probability when

[0075] Step 9: Add the obtained field sequence of the unknown protocol content to the template library of online width learning.

[0076] This embodiment also provides an intelligent collection device for implementing the above-mentioned production line-oriented protocol parsing method. Figure 2 As shown, the intelligent acquisition device of this embodiment includes an MCU module 1 and an Ethernet module 2, a DTU module 3, a wireless network module 4, a power module 5, a SWD debugging module 6, an LED light 7, an RS-485 module 8 and a screen module 9 respectively connected to the MCU module 1. The Ethernet module 2, the DTU module 3, the wireless network module 4 and the power module 5 are respectively connected to the MCU module 1. Among them:

[0077] The MCU module 1 is configured to process data and control other modules in the intelligent acquisition device;

[0078] The Ethernet network module 2 is configured to be used for communication between the production equipment and the local area network or the Internet, and to support remote data transmission;

[0079] The DTU module 3 (a wireless data transmission module of the Internet of Things) is configured to send the collected production equipment data to the cloud platform; the production equipment data collected by the DTU module here include: equipment operation mode, operation status, spindle speed, spindle load, spindle temperature, spindle current, spindle ratio, feed speed, feed ratio, current tool number, number of workpieces, operation time, number of alarms, current coordinates, machine coordinates, workpiece coordinates and remaining distance;

[0080] The wireless network module 4 is configured to realize wireless network connection of the production equipment;

[0081] The power module 5 is configured to provide power supply for the intelligent collection device.

[0082] The SWD debugging module 6 is configured to be used for programming and debugging of the single-chip microcomputer, and supports program uploading and troubleshooting; the LED light 7 is configured to display the operating status of the device and to perform a buzzer alarm;

[0083] RS-485 module 8 is configured for communication between devices in industrial environments, as well as controlling screens and LED lights to display the operating status of devices in real time;

[0084] The screen module 9 is configured to display the data interaction between the production equipment on the production line and the intelligent acquisition device, to display the operating status of the production equipment in real time and to realize human-computer interaction.

[0085] Although the preferred embodiments of the present invention are described in detail above, it should be clearly understood that various modifications and variations are possible for those skilled in the art. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. The production line-oriented protocol parsing method is suitable for collecting production data of production line equipment, and is characterized by: The following steps are involved: Step 1: Establish a bag-of-words model, process the known protocol message into a word set of a series of words, and convert the known protocol message into a word vector through N-gram; Step 2, using the word frequency-inverse file frequency algorithm, calculate the word weight of each word in the known protocol message, and mine the keywords in the known protocol message based on all the obtained word weights; Step 3, calculate the word frequency of each keyword in the corresponding known protocol message, form a word frequency matrix with all the calculated word frequencies, and use the word frequency matrix as the feature vector of the known protocol message; Step 4, input the feature vector into an online width learning recognition model; wherein the protocol with the highest consistency with the current message is identified as a representative sample; Step 5, using a network packet capture tool to capture a protocol data packet from the network environment where the target protocol message is located, and preprocessing the captured protocol data packet to obtain preprocessed protocol data; Step 6: Perform similarity evaluation on the unknown protocol content and the known protocol content in the preprocessed protocol data: When the unknown protocol content is a protocol with high similarity to the known protocol content, go to step 7; otherwise, go to step 8; Step 7: Based on the specifications and information of the known protocol content, a bidirectional LSTM-CRF model is established to identify the fields in the unknown ICP, obtain the field sequence corresponding to the unknown protocol content, and proceed to step 9; Step 8, calculate the information entropy of each byte in the unknown protocol content and the mutual information between the two adjacent bytes before and after, and use the information entropy peak point and the mutual information minimum point as the cutting point, so as to obtain the best field cutting point in the absence of prior knowledge, and obtain the field sequence corresponding to the unknown protocol content, and then go to step 9; Step 9: Add the obtained field sequence of the unknown protocol content to the template library of online width learning.

2. The production line-oriented protocol analysis method according to claim 1, characterized in that: The frequency calculation method of any keyword in the known protocol message is as follows: Among them, TF ij For known protocol message d j Keywords in w j The word frequency, c ij For keyword w j In the known protocol message d j The total number of occurrences in ; The inverse file frequency calculation method of any keyword in the known protocol message is as follows: Among them, IDF j Indicates keyword w j The inverse document frequency of |D| represents the total number of documents in the corpus, and |{i:w j ∈d i } represents the total number of documents containing keywords; TF-IDF ij =TF ij *IDF j ; TF-IDF ij Indicates keyword w j For known protocol packets d j importance.

3. The production line-oriented protocol analysis method according to claim 1, characterized in that: The preprocessing includes removing the Ethernet header, IP header and TCP header of the data packet to retain only the protocol content to be identified; The pre-processed protocol data is a mixed flow containing the target protocol.

4. The production line-oriented protocol analysis method according to claim 3, characterized in that: The preprocessed protocol data includes known protocol content and unknown protocol content. After the known protocol content is input into a pre-trained online width learning model, the online width learning model uses a representative sample of the known protocol content for parsing. After the unknown protocol content is input into the pre-trained online width learning model, format extraction processing is performed.

5. The production line-oriented protocol analysis method according to claim 3, characterized in that: In step 8, the information entropy of any byte in the unknown protocol content is calculated as follows: Among them, H(D) represents the information entropy of any byte D in the unknown protocol content, and the value set of byte D is {x1,x2,…,x n },p(x i ) is byte D with value x i The corresponding probability.

6. The production line-oriented protocol analysis method according to claim 5, characterized in that: In step 8, the mutual information between two adjacent bytes in the unknown protocol content is calculated as follows: Among them, D x and D y Indicates the two adjacent bytes before and after the unknown protocol content, byte D x The value set is {x1,x2,…,x n }, byte D y The value set is {y1,y2,…,y n },p(x i ) is byte D x The value is x i The corresponding probability, p(y j ) is byte D y The value is y j The corresponding probability.

7. Intelligent collection device, characterized in that: Implement the production line-oriented protocol parsing method described in any one of claims 1 to 6.

8. The intelligent data collection device according to claim 7, characterized in that: include: The MCU module (1) is configured to process data and control other modules in the intelligent data collection device; An Ethernet network module (2) configured to communicate between the production equipment and a local area network or the Internet, and to support remote data transmission; The DTU module (3) is configured to send the collected production equipment data to the cloud platform; A wireless network module (4) is configured to realize wireless network connection of production equipment; The power module (5) is configured to provide power supply for the intelligent collection device.

9. The intelligent data collection device according to claim 8, characterized in that: Also includes: SWD debugging module (6), used for programming and debugging of single-chip microcomputers, supporting program uploading and troubleshooting; LED light (7), indicating the operating status of the device and executing buzzer alarm; RS-485 module (8), used for communication between devices in industrial environments, and for controlling screens and LED lights to display the operating status of devices in real time; The screen module (9) displays the data interaction between the production equipment on the production line and the intelligent collection device, displays the operating status of the production equipment in real time, and realizes human-computer interaction.

10. The intelligent data collection device according to claim 8, characterized in that: The production equipment data collected by the DTU module includes: equipment operation mode, operating status, spindle speed, spindle load, spindle temperature, spindle current, spindle ratio, feed speed, feed ratio, current tool number, number of workpieces, running time, number of alarms, current coordinates, machine coordinates, workpiece coordinates and at least one of the remaining distance.

Images