CN110858125A

CN110858125A - Industrial data acquisition and storage method and system for numerical control system

Info

Publication number: CN110858125A
Application number: CN201810972877.5A
Authority: CN
Inventors: 惠恩明; 冯冰艳; 杨建中; 陈吉红
Original assignee: Huazhong University of Science and Technology
Current assignee: Huazhong University of Science and Technology
Priority date: 2018-08-24
Filing date: 2018-08-24
Publication date: 2020-03-03
Anticipated expiration: 2038-08-24
Also published as: CN110858125B

Abstract

The application relates to the field of numerical control systems, and discloses a method and a system for acquiring and storing industrial data of a numerical control system. Therefore, various data generated by the numerical control equipment can be effectively correlated, and the efficiency of further analysis and processing is improved. In the invention, a plurality of data sources from a numerical control system synchronously acquire a plurality of data and store the data in a cache, and synchronously acquire real-time G code line number information from the numerical control system; combining multiple data corresponding to the same G code line number information in time in a cache to generate one or more combined data; the combined data is stored.

Description

Industrial data acquisition and storage method and system for numerical control system

Technical Field

The application relates to the technical field of numerical control systems, in particular to a numerical control system industrial data acquisition and storage technology.

Background

Industrial big data is a product of a combination of internet, big data and industrialization.

In other words, industrial big data is the core driving force for smart manufacturing. The industrial big data can promote the application of the big data in all links of the whole process of an industrial chain, such as process design, production and manufacturing, production line management, system maintenance and the like, and promote the manufacturing mode change and industrial transformation and upgrading in the aspects of analyzing and sensing user requirements, creating an intelligent factory and promoting the manufacturing mode change.

Therefore, the industrial big data plays an important role in the aspects of realizing intelligent production and manufacturing and the like.

The first step in industrial big data applications is data source and data collection.

Specifically, the numerical control equipment can generate a large amount of data information in the production process, such as numerical control system information (shaft position, current, voltage, and the like), external sensor information (force, heat, and the like), and data acquisition is responsible for performing necessary signal conversion and the like on the information and writing the information into a data pipeline.

However, in practical application, the problems of low completeness and reliability of data acquisition, low data utilization rate and the like still exist.

Disclosure of Invention

The application aims to provide a method and a system for acquiring and storing industrial data of a numerical control system, so that various data generated by numerical control equipment can be effectively correlated, and the efficiency of further analysis and processing is improved.

In order to solve the above problems, the present application discloses a method for collecting and storing industrial data of a numerical control system, comprising:

synchronously acquiring various data from a plurality of data sources from a numerical control system, storing the data in a cache, and synchronously acquiring real-time G code line number information from the numerical control system;

combining the multiple data corresponding to the same G code line number information in time in the cache to generate one or more combined data;

the combined data is stored.

In a preferred embodiment, the combined data includes a G code line number.

In a preferred example, the numerical control system is a common numerical control machine, or a machining center, or a robot.

In a preferred embodiment, the plurality of data includes one or any combination of the following:

program number, command position of each motion axis, actual position and load current, tool number, tool changing time and spindle current.

In a preferred embodiment, before the step of synchronously acquiring a plurality of data from a plurality of data sources from the numerical control system and storing the data in the cache, the method further comprises:

and configuring the collection attributes of the various data to be collected.

In a preferred example, the configuring the collection attributes of the plurality of data to be collected includes:

and carrying out uniform attribute setting on the multiple types of data in the combined data by utilizing the combined data structure.

In a preferred embodiment, the collection attribute includes one or any combination of the following:

the type of data to be collected, the data collection period and the data combination period.

In a preferred embodiment, the step of generating one or more combined data includes:

and creating a json object, and packing the data sets formed in a plurality of data acquisition periods in the cache into the json object to form combined data.

In a preferred embodiment, the step of storing the combined data further includes:

and storing the newly generated combined data in a memory database, and providing data for the third-party application through the memory database.

and monitoring the data volume in the memory database, and if the data volume reaches an agreed threshold value, persisting part of combined data in the memory database to a cloud storage system.

In a preferred embodiment, the cache and the memory database are both provided with a data isolation mechanism to isolate data belonging to different numerical control systems.

In a preferred embodiment, the step of synchronously acquiring a plurality of data from a plurality of data sources from the numerical control system and storing the data in the cache comprises:

if the data loss from the numerical control system is detected, filling the storage position of the lost data with appointed characters;

the step of combining the plurality of types of data corresponding to the same G code line number information in time in the cache includes:

if the combined data comprises the appointed character, marking the data combination in the combination process;

after the step of storing the combined data, the method further comprises:

and filling lost data in the marked data combination according to the historical combination data.

In a preferred embodiment, the padding missing data in the marked data combination according to the historical data further includes:

extracting combined data which is closest to a data characteristic value of target combined data within an error range from the historical combined data, wherein the marked data are combined to be used as the target combined data;

acquiring continuous data change trend of the target combined data according to the data characteristic of the combined data with the closest data characteristic value;

and repairing the target combined data according to the continuous data change trend.

The application also discloses numerical control system industrial data gathers and storage system, including the data acquisition unit, this data acquisition unit further includes:

caching;

the acquisition module is used for synchronously acquiring various data from a plurality of data sources from the numerical control system, storing the data in the cache and synchronously acquiring real-time G code line number information from the numerical control system,

the data combination module is used for combining the various data corresponding to the same G code line number information in time in the cache to generate one or more combined data;

and the data storage module is used for storing the combined data to the storage device.

In a preferred embodiment, the method further comprises the following steps:

the memory database is used for caching the combined data output from the data acquisition unit and providing the cached combined data for the third-party application;

and the cloud storage system is used for carrying out persistent storage on the combined data in the memory database.

In a preferred embodiment, the memory database is further configured to persist, when the data volume of the memory database reaches a predetermined threshold, a part of the combined data in the memory database to the cloud storage system.

In a preferred embodiment, the method further comprises the following steps:

and the acquisition attribute configuration unit is used for providing a data acquisition attribute configuration interface for a user and outputting the configuration information input by the user to the data acquisition unit.

In a preferred example, the way that the data collection attribute configuration interface is presented to the user includes one or any combination of the following:

interactive window, XML format file, Json format file and function interface.

In a preferred embodiment, the configuration information includes one or any combination of the following:

the data acquisition method comprises the steps of data type to be acquired, data acquisition period, data combination period and single combined data amount threshold.

In a preferred embodiment, the acquisition attribute configuration unit is further configured to uniformly configure acquisition attributes of all types of data to be acquired.

In a preferred example, the data acquisition unit sends the acquisition attribute to the numerical control system, and the numerical control system completes automatic configuration of the acquisition parameters of the type of the data to be acquired.

In a preferred example, the collection attribute configuration unit is further configured to detect and automatically correct personalized configuration information input by the user.

In a preferred embodiment, the combined data includes a G code line number.

In a preferred embodiment, the data acquisition unit further comprises a lost data detection module, which is used for detecting the data loss condition from the numerical control system, and filling the storage position of the lost data with appointed characters;

the data combination module is also used for marking the data combination in the combination process if the combined data comprises the appointed character;

the cloud storage system further comprises a data padding module for padding lost data in the marked data combination according to the historical combination data.

In a preferred example, the data padding module pads the lost data by:

The application also discloses numerical control system industrial data gathers and storage system, includes:

a memory for storing computer executable instructions; and the number of the first and second groups,

a processor for implementing the steps of the method as described hereinbefore when executing the computer-executable instructions.

The present application also discloses a computer-readable storage medium having stored thereon computer-executable instructions which, when executed by a processor, implement the steps in the method as described above.

By adopting the embodiment of the application, the difficulty that the existing industrial data acquisition technology lacks a reliable data association mechanism is solved, an enabling technology is provided for reliable association analysis of various types of industrial data, and the utilization value of the industrial data is improved to the maximum extent; data transmission is carried out on the data acquisition unit, the intelligent application and the cloud storage system in a data combination mode, the probability of data loss is greatly reduced, and the completeness of data is improved.

In addition, the invention firstly provides and realizes the data reproduction of the lost sampling point, which can not be realized in the field of the existing industrial data acquisition.

The present invention is not limited to the embodiments described above, but rather, the embodiments described above may be implemented in a variety of forms (e.g., a variety of forms, and a variety of combinations). In order to avoid this problem, the respective technical features disclosed in the above summary of the invention of the present application, the respective technical features disclosed in the following embodiments and examples, and the respective technical features disclosed in the drawings may be freely combined with each other to constitute various new technical solutions (which are considered to have been described in the present specification) unless such a combination of the technical features is technically infeasible. For example, in one example, the feature a + B + C is disclosed, in another example, the feature a + B + D + E is disclosed, and the features C and D are equivalent technical means for performing the same function, and technically, only one feature is used, and the feature E can be technically combined with the feature C, so that the solution of a + B + C + D should not be considered as being described because the technology is not feasible, and the solution of a + B + C + E should be considered as being described.

Drawings

FIG. 1 is a schematic flow chart of a method for acquiring and storing industrial data of a numerical control system according to a first embodiment of the invention;

FIG. 2 is a schematic flow chart of a method for collecting and storing industrial data of a numerical control system according to another embodiment of the present invention;

FIG. 3 is a schematic diagram of an exemplary system architecture for a combined data implementation of the present invention for industrial data acquisition;

FIG. 4 is a flow diagram illustrating a cache manner of the combined data implementation method for industrial data acquisition of the present invention;

FIG. 5 is a flow diagram of a data collection process initiation process;

FIG. 6 is an illustration of a json object data structure and its corresponding data fields according to an embodiment of the present invention;

FIG. 7 is a diagram of a data combination scheme in which the value of the G code program line number does not jump, according to an embodiment of the present invention;

FIG. 8 is a diagram of a data combination scheme in which the value of the G code program line number is jumped, according to an embodiment of the present invention;

FIG. 9 is a flow chart diagram of a method of combining data implementation and processing according to an embodiment of the invention;

fig. 10 is a schematic structural diagram of an industrial data acquisition and storage system of a numerical control system according to a second embodiment of the invention.

In the drawings:

301: numerical control equipment

302: collection attribute configuration unit

303: data acquisition unit

304: memory database

305: cloud storage system

306: intelligent application unit

801: collection attribute configuration unit

802: data acquisition unit

803: memory database

804: cloud storage system

8021: caching

8022: acquisition module

8023: data combination module

8024: data storage module

Detailed Description

In the following description, numerous technical details are set forth in order to provide a better understanding of the present application. However, it will be understood by those skilled in the art that the technical solutions claimed in the present application may be implemented without these technical details and with various changes and modifications based on the following embodiments.

Interpretation of terms:

a numerical control system: the digital Control System is a special computer System which executes part or all of the Numerical Control functions according to the Control program stored in the memory of the computer and is provided with an interface circuit and a servo drive device. The operation control of one or more mechanical devices is realized by using digital commands composed of numbers, characters and symbols, and the mechanical quantities such as positions, angles, speeds and the like and the switching quantities are controlled.

G code: instructions in the numerical control program.

JSON: the (JavaScript Object notification) is an Object and an array in a lightweight class, so the two structures are an Object structure and an array structure, and various complex structures can be represented by the two structures, which can convert a set of data represented in the JavaScript Object into a character string, and then easily transfer the character string between functions, or transfer the character string from a Web client to a server-side program in an asynchronous application. JSON employs a text format that is completely independent of the programming language. These properties make JSON an ideal data exchange language

XML: eXtensible Markup Language (eXtensible Markup Language)

The inventor of the present invention has found through extensive and intensive research that the existing data acquisition technology still performs data acquisition in a discrete state, that is, transmission pipelines of various types of data information are mutually independent, and various types of data information generated at the same time do not form a correlation, so that effective correlation analysis cannot be performed, the completeness and reliability of data acquisition are low, and the data utilization rate is low.

In the embodiment of the application, the inventor caches various data collected from a numerical control system, wherein the caching comprises the steps of obtaining G code line number information from the numerical control system in real time, and combining various data corresponding to the same G code line number into one or more combined data according to the time when the G code line number changes, so that the data of a G code domain is realized, and the efficiency of data analysis and processing is greatly improved.

Furthermore, the combined data structure is utilized to perform uniform attribute setting on multiple types of data in the combined data, namely, the multiple types of data in the combined data automatically apply the same acquisition cycle, acquisition cycle and the like set by a user, so that a foundation is laid for effective association among the multiple types of data.

Furthermore, a two-level storage mechanism of a memory database and a cloud storage system is adopted, the memory database is used for storing recently produced combined data and providing the combined data for third-party application, and after the stored data amount of the memory database reaches a preset threshold value, a part of the combined data is duralized to the cloud storage system.

Because the requirement of industrial data on the completeness of data is very high, an erroneous decision can be formed by the application of incomplete data in an intelligent module, and a production fault can be caused when the incomplete data is seriously applied. According to the method and the device, the acquired data are detected, and if data are found to be missing, historical data are used for data reproduction, so that the completeness of the data is improved, and errors in decision making and production faults are reduced.

To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

The invention relates to a method for acquiring and storing industrial data of a numerical control system. FIG. 1 is a flow chart diagram of the industrial data acquisition and storage method of the numerical control system. The method comprises the following steps:

in step 101, a plurality of data sources from the numerical control system synchronously acquire a plurality of data and store the data in a cache, and synchronously acquire real-time G code line number information from the numerical control system.

Specifically, the numerical control system may be a general numerical control machine, or a numerical control machining center, or a robot, and so on.

Specifically, the type of data collected from the numerical control system may be various, and may include, for example, a program number, a command position of each motion axis, an actual position and load current, a tool number, a tool changing time, a spindle current, and the like.

Then, step 102 is entered, and various data corresponding to the same G code line number information in time in the buffer are combined to generate one or more combined data.

Optionally, the combined data includes a G code line number.

Optionally, the combined data may not include a G code line number, for example, a sequence number but no G code line number may be included, as long as it is ensured that all data in one combined data correspond to the same G code line number.

Alternatively, the packing form of the combined data may be various. Optionally, in step 102, a json object may be created, and data sets formed in a plurality of data acquisition cycles in the cache may be packed into the json object to form a combined data. For another example, the combined data may be packed in other manners, such as XML, and the like.

Then step 103 is entered for storing the combined data.

According to the embodiment, various data collected from the numerical control system are cached, and the data corresponding to the same G code line number are combined into one or more combined data according to the time when the G code line number changes, so that the data of a G code domain is conveniently generated, and the efficiency of analyzing and processing the data is greatly improved.

In the embodiment of the present application, optionally, as shown in fig. 2, before step 101, a step of configuring collection attributes of a plurality of types of data that need to be collected may also be included (step 100). On the basis, optionally, in the configuration process, unified attribute setting can be performed on multiple types of data in the combined data by using the combined data structure. Further, collecting attributes may include: the type of data to be collected, the data collection period, the data combination period, etc.

In this embodiment of the present application, optionally, step 103 may further include: and storing the newly generated combined data in a memory database, and providing data for the third-party application through the memory database. And monitoring the data volume in the memory database, and if the data volume reaches an agreed threshold value, persisting part of combined data in the memory database to a cloud storage system.

Optionally, the cache and/or the in-memory database may be provided with a data isolation mechanism to isolate data belonging to different numerical control systems.

Optionally, a step of intelligently filling the lost data can be added to ensure the integrity of the data and facilitate the use of the combined data. Specifically, the method comprises the following steps:

in step 101, further, data from the numerical control system is detected, and if data from the numerical control system is detected to be lost, the storage location of the lost data is filled with a convention character.

Based on this, in step 102, further, if the data to be combined includes a default character, the data combination is marked in the combining process.

Based on this situation, after step 103, further, in the cloud, the missing data in the marked data combination is padded according to the historical combination data. The concrete mode of filling can be various

Optionally, the combined data closest to the data feature value of the target combined data within the error range is extracted from the historical combined data, wherein the marked data combination is used as the target combined data. And obtaining the continuous data change trend of the target combined data according to the data characteristics of the combined data with the closest data characteristic values. And repairing the target combined data according to the continuous data change trend.

Alternatively, other missing data padding methods may be used, such as padding using an average of temporally adjacent data, and so forth.

The above embodiments can be realized in various ways, and the above embodiments are further described below by one of specific implementation modes.

In the specific implementation mode, a combined data structure for industrial data acquisition is provided, and various types of data in the same acquisition period are associated in a data pipeline to form combined data which are provided for users. Therefore, the blank of the existing industrial data acquisition field in the reliable data correlation technology is filled.

Specifically, as shown in fig. 3, the system according to this specific implementation includes: the system comprises a collection attribute configuration unit 302, a data collection unit 303, a memory database 304, a cloud storage system 305 and the like, wherein the collection attribute configuration unit 302 is responsible for personalized configuration of collected information, the data collection unit 303 is responsible for collecting data from the numerical control device 301 and performing data combination operation, the memory database 304 is responsible for caching combined data output from the data collection unit 303, and the cloud storage system 305 is responsible for persistent storage of the combined data in the memory database 304.

Specifically, the collection attribute configuration unit 302 provides a data collection attribute configuration interface for the user, and implements personalized configuration of data collection. Remote configuration of data collection attributes is supported. Which forms the acquisition information input by the user into a "profile" to the data acquisition unit 303. The mode of the data acquisition attribute configuration interface for showing the user comprises the following steps: interactive windows, XML format files, Json format files, function interfaces, and the like. The configurable information comprises: the type of data to be collected, the data collection period, the data combination period, the single combined data volume threshold value and the like. And supporting uniform configuration of the acquisition attributes of all data types to be acquired. The configuration information value input by the user can be detected and automatically corrected.

Specifically, the data acquisition unit 303 may read acquisition attribute configuration information in the configuration file, send the acquisition attribute configuration information to the numerical control device 301, and the numerical control device 301 completes automatic configuration of acquisition parameters. Data combination operation can be carried out on data in multiple acquisition periods, and the data are provided for third-party intelligent application in a 'combined data' mode. And a data isolation mechanism is arranged for different numerical control devices 301.

Based on the above system, a combined data implementation method for industrial data acquisition can be implemented, the method is mainly executed by the data acquisition unit 303, as shown in fig. 4, the main steps include:

step 401: the data acquisition unit 303 acquires data to a local cache according to a set data acquisition cycle.

Step 402: the data acquisition unit 303 periodically fetches the data in the local cache according to a set data combination period.

Step 403: the data acquisition unit 303 performs data combination operations on the data retrieved from the local cache, and forms one or more sets of combined data.

Specifically, the combined data is packed in a json data format.

Specifically, the combined data is implemented based on the current G code program line number.

According to this particular implementation, the combined data is provided with a combined threshold to avoid that the data size of the single combined data is too large to reduce the data operation efficiency.

Specifically, the invention provides a data completeness automatic check function for the system.

In addition, in the implementation mode, a sampling point data 'recurrence' method for combined data is provided, and the method can realize recurrence and filling of the sampling point data lost in the acquisition or combination process.

Specifically, in this method, the system automatically detects the missing sample points.

Specifically, the "recurrence" operation of the sample point data is performed at the cloud.

Specifically, the cloud end can automatically provide a suitable data characteristic analysis algorithm for the target combined data according to the data characteristics of the target combined data.

Specifically, the combined data closest to the data characteristic value of the target combined data within the error range can be quickly extracted from the massive historical combined data.

Specifically, according to the extracted data characteristics of the historical combined data, the continuous data change trend of the target combined data is judged in advance, and the data 'reproduction' of the lost sampling points is realized.

According to the specific implementation mode, the difficulty that the existing industrial data acquisition technology lacks a reliable data association mechanism is solved, an enabling technology is provided for reliable association analysis of various types of industrial data, the utilization value of the industrial data is improved to the maximum extent, data transmission is performed on the data acquisition unit 303, the intelligent application unit 306 and the cloud storage system 305 in a data combination mode, the probability of data loss is greatly reduced, and the completeness of the data is improved. In addition, data "reproduction" of missing sampling points is proposed and implemented for the first time, which cannot be achieved in the existing industrial data acquisition field.

This particular implementation is further described below in more detail.

The industrial big data is from the numerical control equipment 301 in production, and the range of the industrial big data can cover numerical control system information (such as numerical control system version number, PLC information, G code information, etc.), machining process data (such as moving axis state information, tracking error, current/voltage information, cutting speed information, machining program information, etc.), external sensor information (such as force, heat, vibration, etc.) and so on.

The industrial big data plays a key role in the aspects of real-time monitoring of production state, fault diagnosis and early warning of the numerical control equipment 301, analysis and optimization of product processing quality and the like, and can help managers to better analyze and decide production information, predict and solve production faults in time and realize intelligent management of the production process of an intelligent factory.

Therefore, various data generated in the same section of processing are subjected to correlation analysis, comprehensive judgment on the processing condition of the section of processing is facilitated, comprehensive guide information is provided for intelligent application, and the accuracy of intelligent service is improved.

However, through the careful research of the inventor, it is found that the conventional common data acquisition technology still performs discrete acquisition of various types of data, different types of data transmission channels are independent of each other, and intelligent application also performs analysis on various types of data in a discrete manner, so that correlation analysis among various types of data cannot be realized.

Further research by the inventor shows that the existing literature discloses an industrial data association mechanism implemented in an intelligent application layer, the mechanism is implemented based on synchronous acquisition, and the intelligent application defaults various types of acquired data acquired at the same time as state data corresponding to the same processing time.

However, data collection under the mechanism is still discrete, and in the process of each type of data from the numerical control device 301 to the intelligent application unit 306, inconsistency of transmission speed is caused by uncertain factors such as signal interference and network delay, so that even if different types of data are transmitted from the numerical control device 301 at the same time, the time for reaching the intelligent application unit 306 may be different.

Therefore, the data association mechanism implemented based on synchronous acquisition is not reliable.

Through the above research, the inventors intend to find a solution to overcome the above difficulties of the existing industrial data acquisition technology, such as lack of reliable data association mechanism and low data completeness.

For this reason, through continuous research, the inventors propose the combined data implementation method for industrial data acquisition of the present application, which re-associates and combines the processing state data generated in one or more data acquisition cycles to form a group of associated data sets to be provided to the intelligent application unit 306.

In particular, fig. 3 shows a typical system architecture of the combined data implementation method for industrial data acquisition.

The numerical control device 301 is a source of industrial data and is also a final service object of the industrial data, and the numerical control device 301 applicable to the present application includes a general numerical control machine, a machining center, a robot, and the like.

Further, the numerical control system configurable by the numerical control apparatus 301 may be, but is not limited to, numerical control in China, numerical control in Guangzhou, SIEMENSE, FUNAC, etc.

The collection attribute configuration unit 302 is responsible for reading collection attribute information set by a user and sending the collection attribute information to the data collection unit 303.

The data acquisition unit 303 acquires data from the numerical control device 301 according to the user requirements, performs combination processing, and writes the data into the memory database 304.

The intelligent application unit 306 acquires the required data from the database and provides intelligent services for the numerical control device 301 according to the analysis result.

Specifically, the connection manner between the modules in fig. 1 includes, but is not limited to: wireless network, wired network, bluetooth, radio frequency, etc.

The following describes a specific implementation method based on the above system architecture in detail.

The data acquisition unit 303 can acquire various types of data information including a program number, a G code line number, an instruction position of each motion axis, an actual position, load current, a tool number, tool changing time, spindle current, and the like from the same numerical control device 301 in different acquisition cycles. At the same time, different intelligent applications have different data requirements.

Specifically, the data acquisition unit 303 has a function of configurable data acquisition attributes, and is implemented by the acquisition attribute configuration unit 302, which includes at least two interfaces: one is a collection attribute configuration interface provided to the user to allow the user to configure data collection information, and the other interface is responsible for sending the configuration information to the data collection unit 303.

Note that in the existing data acquisition technology, a user needs to set an acquisition attribute for each type of data individually, the operation is complicated, the embodiment of the present application improves this part, and a combined data structure is used to perform uniform attribute setting on multiple types of data in combined data, that is, multiple types of data in combined data automatically apply the same acquisition cycle, and the like set by the user, so as to lay a foundation for effective association among various types of data.

Specifically, the manner in which the collection attribute configuration interface is presented to the user includes, but is not limited to: interactive windows, XML formatted files, Json formatted files, functional interfaces, etc., the collection attributes that allow user configuration may include, but are not limited to: the data acquisition method comprises the steps of data type to be acquired, a data acquisition cycle, a data combination cycle and the like, wherein the data acquisition cycle is a time interval when the data acquisition unit 303 requests data from the numerical control device 301, and the data combination cycle is a time interval when the data acquisition unit 303 performs combination operation on data sets formed in a plurality of data acquisition cycles. Therefore, the data demand period set by the user cannot be smaller than the data acquisition period.

If the data requirement period set by the user is smaller than the data acquisition period, the acquisition attribute configuration unit 302 automatically optimizes the data requirement period and corrects the value to the data acquisition period value.

Specifically, the acquisition attribute configuration unit 302 and the data acquisition unit 303 may be integrated on the same hardware device, and support localized configuration of data acquisition attributes, that is, once acquisition attribute configuration information input by a user is confirmed, the acquisition attribute configuration unit 302 reads the information and forms a "configuration file" to be stored locally; the collection attribute configuration unit 302 and the data collection unit 303 may also be installed on different hardware devices, and in this mode, the collection attribute configuration unit 302 remotely sends collection attribute configuration information to the hardware device where the data collection unit 303 is located, and also stores the collection attribute configuration information in a "configuration file" manner.

The data acquisition unit 303 provides an interactive window for a user to set information (e.g., IP address, port number, etc.) of the numerical control devices 301 that need to be connected, one data acquisition unit 303 may correspond to one or more numerical control devices 301, and the plurality of numerical control devices 301 may share the same "profile", or may use respective "profiles".

Specifically, the process of starting the data collection process is shown in fig. 5:

step 501: the user triggers an acquisition process for a certain numerical control device 301.

Step 502-step 504: the data acquisition unit 303 requests the numerical control device 301 to establish a communication connection.

Step 505: after the connection is successful, the data acquisition unit 303 sends the configuration file information to the numerical control system.

Step 506-step 507: the numerical control system analyzes the information, performs automatic configuration, and sends a feedback signal to the data acquisition unit 303 after the configuration is successful.

Step 508-step 513: the data acquisition unit 303 analyzes the feedback signal, and notifies the user that the data acquisition process is started through an interactive window. The data acquisition unit 303 acquires data to a local cache according to a data acquisition cycle set by a user, and periodically fetches and combines the data in the cache according to a data combination cycle set by the user.

The implementation manner of the combined data of this implementation manner is: designing a sampling class in a data acquisition program, creating a json object based on the sampling class, and finally packaging a data set formed in a plurality of data acquisition periods in a local cache into the json object to form a group of 'combined data'.

Further, in order to make the implementation manner easier to understand, for example, it is assumed that the type of data to be collected set by the user for a certain numerical control device 301 is a command position, an actual position and a load current of each motion axis, an actual speed and a load current of the main shaft, a channel 0 state, a tool number, a tool changing time, and the like, a data collection period is 1ms, a data combination period is 100ms, and fig. 6 shows a json object data structure and a data field description corresponding to the json object data structure for the above embodiment. Please note that the drawings are only for explaining the present invention, but not for limiting the present invention.

Specifically, a single machining process of one numerical control device 301 is uniquely identified by information combinations such as "numerical control device 301 identification, G code name, G code start running time" and the like, the machining process is segmented according to 100ms, the data collection unit 303 performs data combination operation on the data set collected in the time period every 100ms, and the combination basis is "G code program line number".

Specifically, the Data corresponding to the type of the Data to be collected set by the user is "Data" in the implementation manner, and the Data is assembled in a three-dimensional array: the first dimension represents the shaft number, the second dimension represents the data type, and the third position corresponds to the continuous data value of the current time of the current type of the current shaft number.

Since the industrial Data is strictly time-sequenced, the Data of each combined Data is time-sequenced, and many combined Data corresponding to one processing procedure keep the time-sequence thereof with the combined Data count (SedID) in the implementation mode.

The following further describes how the combined data is implemented in this implementation.

The method comprises the following steps: the data acquisition unit 303 continuously acquires information such as the type of data to be acquired and the corresponding line number of the G code program, which are set by a user, to a local cache according to an acquisition cycle of 1 ms.

Optionally, as shown in fig. 7 and 8, the cache manner of this implementation is that each type of data forms an array, which is called a cache array for descriptive convenience, and the data formed in each acquisition cycle is continuously added to the corresponding cache array.

Note that fig. 7 and 8 only illustrate one of the data caching methods, and the cache array numbers and values in the figures are only used to explain the present invention, and do not limit the present invention.

Step two: the data acquisition unit 303 fetches the data in all the cache arrays every 100ms, performs data combination operation, and the cache arrays continue to receive new data.

Step three: the data acquisition unit 303 performs data combination operations according to the values in the "G code program line number" buffer array, and two typical combination cases are shown below:

the first combination case:

the line number values of the G code program are all the same, and a group of combined data is formed after data in all the cache arrays are written into the json object, as shown in fig. 4.

The second combination case:

if the current G code program line number value has a jump, a plurality of combined data are correspondingly formed, as shown in fig. 5.

In this implementation, in order to avoid an excessive amount of data in the combined data from degrading the efficiency of data operation, a threshold is set for the combined data.

Specifically, when the G code program line number value detection operation is performed, the data acquisition unit 303 calculates the data amount of the corresponding data to be combined in real time, and if the data amount reaches the set threshold value and the G code program line number value still does not jump, performs a data combination operation first, and then continues to form the next combined data.

In one implementation, the combined data processing method is as follows: after a combined data is formed, the data acquisition unit 303 stores the combined data in the memory database 304, and a third party application can acquire the data from the memory database 304. The data acquisition unit 303 monitors the data amount in the in-memory database 304 in real time, sets a corresponding threshold, and persists the combined data in the cloud storage system 305 when the data amount reaches the set threshold.

The above steps can be implemented by a plurality of threads as shown in fig. 9, which are not described herein.

In this implementation manner, one data acquisition unit 303 may perform data acquisition operations on multiple pieces of numerical control equipment 301 at the same time, and data isolation mechanisms are respectively disposed in the cache array and the memory database 304, so as to implement effective isolation of data of different pieces of numerical control equipment 301.

This implementation has the following significant technical advantages:

(1) the data association is not influenced by different data transmission delays, and the association reliability is high;

(2) the continuous data set is provided for third-party intelligent application in a data combination mode, and the completeness of data is high;

(3) through data caching, the data acquisition unit 303 has ms-level data acquisition capability, which is difficult to implement by the existing data acquisition technology.

Because the requirement of industrial data on the completeness of data is very high, an erroneous decision can be formed by the application of incomplete data in an intelligent module, and a production fault can be caused when the incomplete data is seriously applied.

Therefore, the implementation further designs a completeness (i.e., continuity) check program for the combined data, which is divided into the following three stages:

stage one: data acquisition process

The process is to carry out real-time monitoring on the continuous acquisition of the native data, and the checking method comprises the following steps: in the implementation mode, the data type is called as "heartbeat data", when the raw data is continuously transmitted to the data acquisition unit 303, the heartbeat data value should be continuously increased, and the data acquisition unit 303 can judge whether the raw data is continuous through the "heartbeat data".

And a second stage: data combining process

As described above, the raw data is buffered (e.g., buffered in an array) after reaching the data acquisition unit 303, so that on one hand, the data acquisition unit 303 ensures normal data acquisition in an offline buffering manner when being away from the cloud, and on the other hand, based on the buffering mechanism, data combination operation and combined data continuity check (i.e., "heartbeat data" generated in each acquisition cycle) are implemented.

And a third stage: cloud check

And a combined data counting (SedID) item exists in the combined data, and the continuity check of the combined data corresponding to the cloud end section of the processing process is realized through the data item.

However, once the data of the sampling point is lost, the numerical control device 301 cannot reproduce the data, and the inventor finds that, in the prior art, if the data acquired in a section of processing process has faults such as interruption, loss and the like, all the acquired data can only be discarded, and then the processing process is restarted and the data is acquired again, that is, due to the loss of data of one sampling point, other high-quality data acquired synchronously cannot be used.

In the present implementation, this technical gap is filled with the combined data. Specifically, the method comprises the following steps:

(1) a data acquisition stage: when the data formed in each acquisition period is written into the corresponding cache array, if data of one or more sampling points are lost, filling the data at the corresponding position of the cache array by using a special character.

(2) A data combination stage: and automatically marking the special characters read from the cache array in the data combination process, forming records and sending the records to the cloud.

(3) Cloud data "recurrence" stage: the cloud comprehensively analyzes various data in the corresponding combined data according to the records to form various data characteristics, and for convenience of description, the data combination is called as target combined data; the cloud extracts a similar historical data combination by using information such as the name of the G code, the identification of the numerical control device 301 and the like, namely the historical data combination and the target combination data are formed in the process of processing the same G code program by the same numerical control device 301; the cloud side performs parallel analysis on all the taken historical combined data according to the data characteristic types of the target combined data, and extracts the combined data which is closest to the characteristic value of the target data within an error range; performing further matching analysis on the target combined data and the extracted historical combined data, for example, filling missing data in the target combined data according to data fluctuation features of corresponding types in the historical combined data; and forming a record to be displayed to a user. The industrial data has the characteristic of 'mass', so that the invention solves the problem that the invention has a high-efficiency data extraction and analysis mechanism in the 'reproduction' stage of cloud data. Different types of combined data also need different feature extraction algorithms, and the realization mode can provide automatic matching of array combination and the feature extraction algorithms, which is also a difficulty solved by the invention.

Compared with the prior art, the embodiment has at least the following differences and effects: various data generated by the numerical control equipment can be effectively correlated, and the efficiency of further analysis and processing is improved.

The second embodiment of the invention relates to an industrial data acquisition and storage system of a numerical control system. FIG. 10 is a schematic diagram of the construction of the numerical control system industrial data acquisition and storage system. The numerical control system industrial data acquisition and storage system includes a data acquisition unit 802.

Specifically, the data acquisition unit 802 further includes: the 8021 is cached. The obtaining module 8022 is configured to synchronously acquire a plurality of data from a plurality of data sources of the numerical control system, store the acquired data in the cache 8021, and synchronously obtain real-time G code line number information from the numerical control system. The data combination module 8023 is configured to combine multiple types of data corresponding to the same G code line number information in time in the cache 8021, and generate one or more combined data. A data saving module 8024, configured to save the combined data to the storage device.

Optionally, in this embodiment, the numerical control system industrial data collecting and storing system may further include: an in-memory database 803 and a cloud storage system 804.

Specifically, the in-memory database 803 is used for caching the combined data output from the data acquisition unit 802 and providing the cached combined data to a third-party application.

Specifically, the cloud storage system 804 is configured to perform persistent storage on the combined data in the memory database 803.

Optionally, the memory database 803 is further configured to persist, when the data volume of the memory database 803 reaches an agreed threshold, a part of the combined data in the memory database 803 to the cloud storage system 804.

Optionally, in this embodiment, the numerical control system industrial data collecting and storing system may further include: the collection attribute configuration unit 801 is configured to provide a data collection attribute configuration interface for a user, and output configuration information input by the user to the data collection unit 802.

Optionally, the data collection attribute configuration interface presents the user with a mode including one of the following or any combination thereof:

interactive window, XML format file, Json format file and function interface.

Optionally, the configuration information includes one or any combination of the following:

Further, the acquisition attribute configuration unit 801 is further configured to perform unified configuration on acquisition attributes of all types of data to be acquired.

In this case, the data acquisition unit 802 sends the acquisition attribute to the numerical control system, and the numerical control system completes automatic configuration of the acquisition parameter of the data type to be acquired.

In this case, the collected attribute configuration unit 801 is also used for detecting and automatically correcting personalized configuration information input by the user.

Optionally, the combined data includes a G code line number.

The numerical control system is a common numerical control machine tool, or a machining center, or a robot.

The plurality of data includes one or any combination of the following:

The cache 8021 and the memory database 803 are both provided with a data isolation mechanism to isolate data belonging to different numerical control systems.

Optionally, the data acquisition unit 802 further includes a missing data detection module, configured to detect a data loss condition from the numerical control system, and fill the storage location of the missing data with an appointed character.

In this case, the data combination module 8023 is also used to mark the data combination in the combination process if the contracted character is included in the combined data.

In this case, the cloud storage system 804 further includes a data padding module, configured to pad missing data in the marked data combination according to the historical combination data.

Specifically, the data padding module pads the lost data in the following manner:

and extracting the combined data which is closest to the data characteristic value of the target combined data within an error range from the historical combined data, wherein the marked data combination is used as the target combined data.

And obtaining continuous data change trend of the target combined data according to the data characteristics of the combined data with the closest data characteristic values.

The first embodiment is a method embodiment corresponding to the present embodiment, and the present embodiment can be implemented in cooperation with the first embodiment. The related technical details mentioned in the first embodiment are still valid in this embodiment, and are not described herein again in order to reduce repetition. Accordingly, the related-art details mentioned in the present embodiment can also be applied to the first embodiment.

It should be noted that, each unit mentioned in each system embodiment of the present invention is a logical unit, and physically, one logical unit may be one physical unit, or may be a part of one physical unit, or may be implemented by a combination of multiple physical units, and the physical implementation manner of these logical units itself is not the most important, and the combination of the functions implemented by these logical units is the key to solve the technical problem provided by the present invention. In addition, in order to highlight the innovative part of the present invention, the above-mentioned system embodiments of the present invention do not introduce elements that are not so closely related to solving the technical problems proposed by the present invention, which does not indicate that other elements do not exist in the above-mentioned system embodiments.

It should be noted that, as will be understood by those skilled in the art, the implementation functions of the modules shown in the embodiment of the numerical control system industrial data acquisition and storage system can be understood by referring to the related description of the numerical control system industrial data acquisition and storage method. The functions of the modules shown in the embodiment of the industrial data acquisition and storage system of the numerical control system can be realized by a program (capable of executing instructions) running on a processor, and can also be realized by a specific logic circuit. The user equipment can also be stored in a computer readable storage medium if the user equipment is implemented in the form of a software functional module and sold or used as an independent product. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.

Accordingly, the embodiment of the present invention also provides a computer storage medium, in which computer executable instructions are stored, and the computer executable instructions are executed by a processor to realize the method embodiments of the present invention.

In addition, the embodiment of the invention also provides a numerical control system industrial data acquisition and storage system, which comprises a memory for storing computer executable instructions and a processor; the processor is configured to implement the steps of the method embodiments described above when executing the computer-executable instructions in the memory.

It is noted that, in the present patent application, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the use of the verb "comprise a" to define an element does not exclude the presence of another, same element in a process, method, article, or apparatus that comprises the element. In the present patent application, if it is mentioned that a certain action is executed according to a certain element, it means that the action is executed according to at least the element, and two cases are included: performing the action based only on the element, and performing the action based on the element and other elements. The expression of a plurality of, a plurality of and the like includes 2, 2 and more than 2, more than 2 and more than 2.

All documents referred to in this application are incorporated by reference into this application as if each document were individually incorporated by reference. Further, it should be understood that various changes or modifications can be made to the present application by those skilled in the art after reading the above teachings of the present application, and these equivalents also fall within the scope of the claimed application.

Claims

1. A method for acquiring and storing industrial data of a numerical control system is characterized by comprising the following steps:

and storing the combined data.

2. The numerical control system industrial data collection and storage method of claim 1, wherein the combined data includes a G code line number.

3. The numerical control system industrial data collection and storage method of claim 1, wherein the numerical control system is a general numerical control machine, or a machining center, or a robot.

4. The numerical control system industrial data collection and storage method of claim 1, wherein the plurality of data comprises one or any combination of the following:

5. The numerical control system industrial data collection and storage method according to claim 1, wherein before the step of synchronously collecting and storing a plurality of data from a plurality of data sources from the numerical control system into the cache, the method further comprises:

and configuring the collection attributes of the various data to be collected.

6. The numerical control system industrial data acquisition and storage method according to claim 5, wherein the configuring of the acquisition attributes of the plurality of data to be acquired comprises:

and carrying out uniform attribute setting on multiple types of data in the combined data by utilizing a combined data structure.

7. The numerical control system industrial data collection and storage method of claim 5, wherein the collection attributes comprise one or any combination of the following:

8. The utility model provides a numerical control system industrial data gathers and memory system which characterized in that, includes the data acquisition unit, the data acquisition unit further includes:

caching;

and the data storage module is used for storing the combined data to a storage device.

9. The utility model provides a numerical control system industrial data gathers and memory system which characterized in that includes:

a processor for implementing the steps of the method of any one of claims 1 to 7 when executing the computer-executable instructions.

10. A computer-readable storage medium having stored thereon computer-executable instructions which, when executed by a processor, implement the steps in the method of any one of claims 1 to 7.