WO2020107328A1 - 用于获得生产线的数据源的数据的方法和装置 - Google Patents

用于获得生产线的数据源的数据的方法和装置 Download PDF

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WO2020107328A1
WO2020107328A1 PCT/CN2018/118188 CN2018118188W WO2020107328A1 WO 2020107328 A1 WO2020107328 A1 WO 2020107328A1 CN 2018118188 W CN2018118188 W CN 2018118188W WO 2020107328 A1 WO2020107328 A1 WO 2020107328A1
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Prior art keywords
data
production line
data source
semantic
model
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PCT/CN2018/118188
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English (en)
French (fr)
Inventor
袁舟
刘奕
余明
王琪
张跃华
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西门子股份公司
袁舟
刘奕
余明
王琪
张跃华
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Application filed by 西门子股份公司, 袁舟, 刘奕, 余明, 王琪, 张跃华 filed Critical 西门子股份公司
Priority to CN201880099456.4A priority Critical patent/CN113015987A/zh
Priority to PCT/CN2018/118188 priority patent/WO2020107328A1/zh
Priority to EP18941635.7A priority patent/EP3872720A4/en
Priority to US17/297,519 priority patent/US11703842B2/en
Publication of WO2020107328A1 publication Critical patent/WO2020107328A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3013—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is an embedded system, i.e. a combination of hardware and software dedicated to perform a certain function in mobile devices, printers, automotive or aircraft systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
    • G06F11/3428—Benchmarking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31336—Store machines performance; use it to control future machining
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31449—Monitor workflow, to optimize business, industrial processes
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32366—Line performance evaluation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present disclosure relates to the field of industrial control, and more specifically, to a method, apparatus, computing device, computer-readable storage medium, and program product for obtaining data from a data source in a production line.
  • KPI Key performance indicator
  • a cloud server is usually used to complete such calculations, and the cloud server stores calculation formulas for each KPI of each production line.
  • the cloud server stores calculation formulas for each KPI of each production line.
  • the computing engine in the cloud server decomposes the KPI calculation formula according to certain calculation rules (for example, the priority of the operator, etc.), and reads it according to the corresponding model configuration file
  • the obtained data source data is substituted into the KPI calculation formula, and then calculated according to the calculation rules to obtain the KPI value.
  • the first embodiment of the present disclosure proposes a method for obtaining data of a data source in a production line, including: obtaining a semantic model, the semantic model including each of a plurality of semantic units and a corresponding data source of at least one production line The semantic relationship between the identifications; receiving the production line identification and obtaining at least one semantic unit; based on the semantic model, converting the at least one semantic unit into the corresponding data source identification of the production line represented by the production line identification; and obtaining the The data source identifies the data of the data source represented.
  • the configuration file of the virtual model of the production line can be simplified, thereby greatly reducing the configuration work.
  • the data from these data sources provides convenience.
  • the second embodiment of the present disclosure proposes an apparatus for obtaining data of a data source in a production line, including: a model obtaining unit configured to obtain a semantic model, the semantic model including each of a plurality of semantic units The semantic relationship with the corresponding data source identification of at least one production line; the receiving unit, which is configured to receive the production line identification and obtain at least one semantic unit; the conversion unit, which is configured to divide the at least one based on the semantic model The semantic unit is converted into a corresponding data source identifier of the production line indicated by the production line identifier; and a data acquisition unit configured to acquire data of the data source indicated by the data source identifier.
  • a third embodiment of the present disclosure proposes a computing device including: a processor; and a memory for storing computer-executable instructions, which causes the processor when the computer-executable instructions are executed The method described in the first embodiment is performed.
  • the fourth embodiment of the present disclosure proposes a computer-readable storage medium having computer-executable instructions stored thereon, the computer-executable instructions being used to execute the Described method.
  • a fifth embodiment of the present disclosure proposes a computer program product that is tangibly stored on a computer-readable storage medium and includes computer-executable instructions that when executed make At least one processor executes the method described in the first embodiment.
  • FIG. 1 shows a flowchart of a method for obtaining data from a data source in a production line according to an embodiment of the present disclosure
  • FIG. 2 shows a schematic diagram of a production line in a factory according to an embodiment of the present disclosure
  • FIG. 3 shows a schematic diagram of a part of the semantic model of the example production line according to FIG. 2;
  • FIG. 4 shows a block diagram of an apparatus for obtaining data from a data source in a production line according to an embodiment of the present disclosure
  • FIG. 5 shows a schematic structural diagram of a production line performance analysis system according to an embodiment of the present disclosure
  • FIG. 6 shows a schematic diagram of a part of the semantic model according to the embodiment of FIG. 5.
  • FIG. 7 shows a block diagram of a computing device for obtaining data from a data source in a production line according to one embodiment of the present disclosure.
  • the terms “including”, “including” and similar terms are open terms, that is, “including/including but not limited to”, which means that other contents can also be included.
  • the term “based on” is “based at least in part on.”
  • the term “one embodiment” means “at least one embodiment”; the term “another embodiment” means “at least one other embodiment” and so on.
  • FIG. 1 illustrates a method for obtaining data from a data source in a production line according to an embodiment of the present disclosure.
  • the method 100 starts at step 101.
  • a semantic model is obtained, and the semantic model includes a semantic relationship between each of a plurality of semantic units and a corresponding data source identifier of at least one production line.
  • Semantic units are information representation units that are easy for humans to understand, and generally have a common expression in the industrial field.
  • the variable “good_num3 (good product quantity 3) ", "good_num1 (good product quantity 1)” and “bad_num1 (defective product quantity 1)” are semantic units, respectively.
  • the semantic units involved may be "find_next_order (find next order)” and "find_material (find material)”. These semantic units usually correspond to data from data sources related to the production line.
  • the semantic units "good_num3 (good quantity 3)", “good_num1 (good quantity 1)” and “bad_num1 (bad quantity 1)” represent respectively
  • the data collected by the relevant sensing equipment in the production line is sourced from the sensing equipment.
  • semantic units "find_next_order (find next order)” and “find_material (find material)” they represent the order data and material data pre-stored in the database related to the production line, and the data source is the database.
  • the semantic model can associate the semantic unit with the data source.
  • the data source may be a sensing device in the production line or a database storing data related to the production line.
  • the data source can be identified by the data source identification.
  • the data source identification includes at least one of the following: the network address of the data source, the identification code of the data source, and the attribute of the data source.
  • the data source identification may also be any other information that can uniquely determine the data source. Therefore, as long as the data source identification is obtained through the semantic unit, data can be obtained from the corresponding data source represented by the data source identification. Therefore, the connection between the semantic unit and the data source can be provided by connecting the semantic unit and the data source identifier in the semantic model.
  • the semantic relationship is, for example, an inclusion relationship, a connection relationship, and so on.
  • the semantic model will be described in detail below with reference to FIG. 3 in a specific embodiment.
  • a unified semantic model can be established for all production lines in a factory. That is to say, the semantic model can include the semantic relationship between all the semantic units involved in all production lines in a factory and the corresponding data source identification.
  • a production line may involve more than one semantic unit, and each semantic unit may also involve more than one production line. Therefore, in the semantic model, for each semantic unit in multiple semantic units, it is connected with the corresponding data source identification of the production line involved in the semantic relationship. In this way, in the semantic model, each semantic unit is mapped to the corresponding data source identifier of the production line involving the semantic unit. Therefore, compared with the prior art, there is no need to associate the semantic unit with the data source in the configuration file for each production line.
  • Establishing a unified semantic model for a factory is not only applicable to different production lines in the factory, but also easy to expand.
  • a new production line is added to the factory, only the semantic model needs to be modified, that is, the relevant information of the new production line is added to the semantic model.
  • step 101 further includes the following steps:
  • each component may include each station of the production line and each device at each station, and the device may be, for example, a controller, a sensing device, and an action device.
  • the controller may include a programmable logic controller (PLC), an intelligent device, an industrial internet of things intelligent gateway, etc.
  • the sensing device may include a sensor, a button, a scanner, etc.
  • the action device may include a motor, a robot arm, and the like.
  • Action equipment is used to perform operations on the product or provide driving for the equipment that operates the product.
  • Sensing equipment is used to sense some variables that need to be collected during the production process, such as time, displacement, quantity, and specific characteristics of the product.
  • the controller is communicatively coupled with the sensor device and the action device, sends control signals to the sensor device and the action device, controls them, and receives the data sensed by the sensor device.
  • the establishment of the virtual model of the production line is mainly for the purpose of performance analysis of the production line. Therefore, the characteristic information of each component may include the process relationship between each station, the function and attribute of each device at each station Information related to production line performance analysis, such as network addresses and connection/control relationships between devices.
  • a context information database is established based on the virtual model, and the context information database includes context information of data sources of at least one production line.
  • a unified semantic model is established for all production lines in a factory.
  • the context information database includes context information of data sources of all production lines in the factory.
  • the context information includes device information indexed by the production line and data source information indexed by the semantic unit.
  • the equipment information indexed by the production line may specifically include characteristic information (such as attributes, connection relationships, etc.) and production sequence of each component of the production line (for example, station, equipment at the station, etc.) and the production order, indexed by the semantic unit
  • the data source information includes the association relationship between the semantic unit and the data source.
  • each of the multiple semantic units is connected to the corresponding data source identification of at least one production line with a semantic relationship to form a semantic model.
  • Multiple semantic units can come from the summary and summary of existing production line performance analysis methods.
  • each of the multiple semantic units is connected to the corresponding data source identification of the related production line with a semantic relationship To form a semantic model.
  • step 102 the production line identification is received and at least one semantic unit is obtained.
  • the production line logo indicates a specific production line.
  • the step of obtaining at least one semantic unit further includes: receiving a production line data acquisition request; and parsing the production line data acquisition request to obtain at least one semantic unit.
  • obtaining the semantic unit requires parsing the data acquisition request of the production line.
  • at least one semantic unit may also be directly received.
  • the production line data acquisition request includes a KPI calculation formula, and the semantic unit is a variable in the KPI calculation formula.
  • Parsing the calculation formula of the KPI includes decomposing the calculation formula into multiple semantic units according to the operation rules in the calculation formula (for example, the operator priority of the four operations).
  • the production line data acquisition request may include calculation formulas or data acquisition requests related to other production lines. For example, in a data acquisition request for "finding the next order's material information" for a production line, it is parsed out The semantic units of are "Find Next Order” and "Find Materials".
  • step 103 based on the semantic model, at least one semantic unit is converted into a corresponding data source identifier of the production line represented by the production line identifier.
  • this step through the semantic relationship between the semantic unit in the semantic model and the specific data source identification of the specific production line, at least one obtained semantic unit can be converted into the data source identification of the production line represented by the production line identification.
  • step 104 further includes: obtaining data of the data source represented by the data source identifier from the database, wherein the database stores data of each data source of at least one production line in a unified structure.
  • the controller at each station causes the data of the sensor devices communicatively coupled to it to be stored in a unified database.
  • the method 100 further includes (not shown in the figure): receiving data from each data source of at least one production line and storing it in a database in a unified structure.
  • receiving data from each data source of the production line, and converting these data into a unified data structure and storing it in the database so that the data of the data source represented by the data source ID can be obtained from the database, so that the data of the data source can be Unified management and storage, which also improves the efficiency of data analysis and calculation, and can be flexibly applied to different applications, for example, for other platforms or applications.
  • the method 100 further includes (not shown in the figure): based on predetermined analysis rules and data from the data source, analyze the performance of the production line to obtain an analysis result.
  • the predetermined analysis rule may be any set rule.
  • the KPI calculation formula may be calculated based on a predetermined calculation rule (for example, four calculation rules) according to the KPI calculation formula and the obtained data from the data source.
  • the data of the data source of the production line is obtained to calculate the KPI of the production line.
  • Each production line can have one or more KPIs, for example, the yield rate YR (Yield Rate), the actual average cycle time RACT (Real Average Cycle Time), the through rate FPY (First Pass Yield), etc.
  • a factory has a unified semantic model.
  • FIG. 2 shows a schematic diagram 200 of a production line in a factory according to an embodiment of the present disclosure.
  • the production line L1 has three stations S1, S2, and S3, and each station has a controller and a sensing device.
  • the production line may have any other number of stations, and each station may have any other number of controllers and sensing devices.
  • Each controller can control any number of sensing devices and receive data from the sensing devices.
  • the sensing devices can also have different functions, for example, to detect the quantity, time, displacement, a certain characteristic of the product and so on. Collecting these data can be used for different purposes, for example, the number of inspections can be used to calculate the yield KPI, the inspection time can be used to analyze the production line capacity, and so on.
  • the product process of the production line L1 is to enter the station S2 from the station S1 and then enter the station S3.
  • controllers c1, c2, c3 and sensor devices t1, t2, and t3 that are communicatively connected to these controllers, respectively.
  • These sensing devices t1, t2, and t3 can be used to sense whether the products produced at the corresponding stations S1, S2, and S3, respectively, meet the production standards at the stations, and to meet and not meet the production standards at the corresponding stations.
  • the products are counted separately, and the corresponding data is sent to the controllers c1, c2, and c3.
  • the counting function may be performed by the controllers c1, c2, and c3, that is, the sensing devices t1, t2, and t3 send the outputs at the corresponding stations S1, S2, and S3 to the controllers c1, c2, and c3
  • the signal of whether the product meets the production standard at the station, the controllers c1, c2 and c3 respectively count the products that meet the production standard and the products that do not meet the production standard sensed by the sensing devices t1, t2 and t3, thus generating Corresponding data.
  • Good_num1 (good product quantity 1) and bad_num1 (defective product quantity 1) represent the number of products that meet the production standard at station S1 and do not meet the production standard at station S1 within a predetermined period of time.
  • good_num3 (good product number 3) represents the number of products that meet the production standards at station S3 within a predetermined period of time.
  • the number of products that meet the production standards at station S3 represents the number of good products in the entire production line L1. Therefore, the yield rate is in line with The ratio of the number of standard production products at station S3 to the total number of products produced at station S1, that is, the number of good products 3/(the number of good products 1+the number of defective products 1).
  • the following describes the specific process of the method for obtaining data from the data source of the production line.
  • the first step in establishing a semantic model is to establish a virtual model of the production line.
  • a factory has a unified semantic model, so accordingly, a virtual model of all production lines in the factory needs to be established.
  • the virtual model can be established by any existing modeling tool and can be presented on the user interface.
  • the following takes the production line L1 as an example.
  • a virtual model of the production line L1 is established.
  • the data of the data source in the production line is obtained in order to calculate the KPI of the production line.
  • the virtual model includes the stations S1, S2, S3 of the production line L1, and the process relationship between them (ie, the output of the station S1 is the input of the station S2, and the output of the station S2 is the station S3 Input), connection and control relationship, attribute, function and network address of the controller c1 at the station S1 and the sensing device t1, connection and control relationship, attribute of the controller c2 at the station S2 and the sensing device t2 , Function and network address, and the connection and control relationship, attribute, function and network address of the controller c3 and the sensing device t3 at the station S3.
  • a context information database is established based on the virtual model of the production line, which includes context information of each data source of at least one production line.
  • a factory has a unified semantic model. Therefore, accordingly, a context information library including context information of each data source of all production lines in the factory needs to be established.
  • the following uses the production line L1 as an example to illustrate these contextual information.
  • the data of the sensor devices t1, t2, and t3 collected by the controllers c1, c2, and c3, respectively, are continuously received at predetermined time intervals (for example, 1s), and unified
  • the format is stored in the database. Therefore, the data sources are sensing devices t1, t2, and t3, and their context information includes:
  • the above context information indicates that the production line L1 has three stations S1, S2, and S3, and L1 has KPI definitions of YR (yield rate), RACT (actual average cycle duration), and FPY (through rate). Since the KPI calculation of the yield rate is related to the data within a predetermined time period of the data source, the context information also includes KPI-related parameters, which include the start time of the target time period that needs to be input when performing the calculation And end time. However, in other embodiments, if the KPI to be calculated is independent of the time period, such a parameter may not be included.
  • the station S1 has a controller c1 and a sensing device t1
  • the station S2 has a controller c2 and a sensing device t2
  • the station S3 has a controller c3 and a sensing device t3.
  • the controller c1 has data points: point1 (data point 1), point2 (data point 2) and point3 (data point 3).
  • the data point point1 is in communication with the sensor device t1 and receives data from the sensor device t1.
  • the above only illustrates a part of the KPI definition and device information indexed by the production line L1.
  • the KPI definition and device information indexed by the production line L1 also include the data point point2 of the controller c1, the connection relationship information of the data point point3, the information of the controller c2, other KPI definition information of the production line L1, and so on.
  • the above context information indicates that the semantic unit good_num1 (good quantity 1) corresponds to the data collected by the sensing device t1, and bad_num1 (the defective quantity 1) corresponds to the data collected by the sensing device t1, good_num3 (good quantity 3 ) Corresponding to the data collected by the sensing device t3, these data are stored in the database b1.
  • the above only illustrates a part of the data source information indexed by the semantic unit.
  • the data source information indexed by the semantic unit also includes information related to other semantic units involved in the production line L1.
  • the definition of the KPI indexed by the production line L1 may not be included in the context information.
  • each of the multiple semantic units is connected to the corresponding data source identification of the production line with a semantic relationship to form a semantic model.
  • the components of the production line L1 using the context information in the context information library, the components of the production line L1, their attributes, and semantic units good_num1 (good quantity 1), bad_num1 (Number of defective products 1), good_num3 (number of good products 3), and the data sources involved in the semantic unit are connected with semantic relations respectively.
  • FIG. 3 shows a part of the semantic model of the example production line according to FIG. 2.
  • the various components of the production line L1 including station and controller, sensing equipment), their attributes (including KPI definitions, data points possessed by the controller, database storing data),
  • the semantic units good_num1 (good quantity 1), bad_num1 (defective quantity 1) and good_num3 (good quantity 3), and the data sources involved in the semantic unit are connected through semantic relations.
  • L1 has three stations S1, S2 and S3, and has a KPI definition of YR (yield rate) and two other KPI definitions RACT (actual average period duration) and FPY (Through rate).
  • YR has parameters for start time and end time.
  • the station S1 has a controller c1 and a sensor device t1, and the controller c1 has a data point 1, a data point 2 and a data point 3, wherein the data point 1 is connected to the sensor device t1 and receives data from the sensor device t1.
  • the controller c1 stores the data received from data point 1 to data point 3 in the database with ID b1.
  • the station S3 has a controller c3 and a sensing device t3, and the controller c3 has a data point 1, which is connected to the sensing device t3 and receives data from the sensing device t3.
  • the controller c3 stores the data received by data point 1 in the database with ID b1.
  • the station S2 has a controller c2 and a sensing device t2.
  • the data corresponding to the semantic unit good_num1 (quantity 1) and bad_num1 (quantity 1) are measured by the sensing device t1, and the data corresponding to the semantic unit good_num3 (quantity 3) are measured by the sensing device t3.
  • the data source is identified as the data point 1 of the controller c1 at the station S1 and the data point 1 of the controller c3 at the station S3, that is, the data source sensing devices t1 and t3 and the controller c1 and Connection properties of c3. Therefore, the semantic model includes the semantic unit good_num1 (good quantity 1), bad_num1 (defective quantity 1) and the data point 1 of the controller c1 at the station S1, and good_num3 (good quantity 3) and the station S3 The semantic relationship between data point 1 of the controller c3 at.
  • the data source identification may also include the network address of the data source, the identification code of the data source, a combination thereof, or other identification information that may uniquely determine the data source.
  • the production line L1 is taken as an example to introduce the process of establishing the semantic model. It should be noted that FIG. 3 shows only a part of the semantic model. Since a unified semantic model is established in a factory in this embodiment, the semantic model includes each of the semantic units involved in all production lines in the entire factory. The semantic relationship between each semantic unit and the corresponding data source identification of the production line involved in the semantic unit.
  • the data of the desired data source can be obtained based on the semantic model.
  • the above step of establishing the semantic model is not a necessary step.
  • Time parameter Including the start time and the end time (for example, 10:00-11:00), it means that the data of the data source in the time period between these two times needs to be obtained.
  • the parameters may be other types The parameters may or may not have such parameters.
  • the calculation formula of the KPI is analyzed. After decomposing the formula according to the calculation rules of the four operations, the semantic units good_num1 (good quantity 1), bad_num1 (defective quantity 1) and good_num3 (good quantity 3) are extracted. Then, based on the semantic model shown in FIG. 3, these semantic units are converted into the corresponding data source identifiers of the production line L1, namely the data point 1 of the controller c1 at the station S1 and the controller c3 at the station S3. Data point 1, and get the database ID b1.
  • the database of the unified data structure can be constructed by receiving data from various data sources of the production line at predetermined time intervals (for example, 1 second), and converting these data into a unified structure. For example, several fields such as production line identification, data source type, data source identification code, data point, controller network address, data source network address, data type, data, time stamp, etc., can be established to store data from the data source as a unified structure .
  • a part of the example database b1 is shown in Table 1 below.
  • Table 1 Only a part of the example database b1 is shown in Table 1.
  • Table 1 the data collected by the sensing device t1 at the station S1, the sensing device t2 at the station S2 and the sensing device t3 at the station S3 meets and does not meet the production standards at the corresponding station The number of products, so the data type is the number of good products or defective products, respectively.
  • the data is stored in the database b1 at 1 second intervals.
  • the KPI-related time parameters are also received (for example, start time 10:00:00, end time 11:00:00). Therefore, according to the data source identification obtained through the semantic model conversion, from the database b1 Get the data between the start time and the end time.
  • the "data source type” field may also include other sensing devices, such as buttons, scanners, or may include other devices that generate data.
  • the field of "Data Type” can be different according to the type of data collected by the data source.
  • the yield rate is calculated according to the calculation rule (for example, operator priority) based on the KPI calculation formula. Specifically, the data whose data point is "S1, c1, data point 1" and whose data type is “good product number” are added separately as the value of the semantic unit good_num1 (good product quantity 1), and the data point is "S1, c1" , The data type of the data point 1 is "the number of defective products” are added separately, as the value of the semantic unit bad_num1 (the number of defective products 1), the data type of the data point is "S3, c3, data point 1" is " The "good product number” data are added separately as the value of the semantic unit good_num3 (good product number 3).
  • the calculation rule for example, operator priority
  • the time parameter may include a time interval in addition to the start and end times.
  • a predetermined interval time for example, 5 s
  • the time parameter may also include a specific time to obtain data from the database at a specific time.
  • the data in the database may also be data independent of time, for example, the database may be a static database. Therefore, in such an embodiment, when acquiring data in the database, there is no need to set time parameters.
  • FIG. 4 shows an apparatus for obtaining data from a data source in a production line according to an embodiment of the present disclosure.
  • the device 400 includes a model acquisition unit 411, a reception unit 412, a conversion unit 413 and a data acquisition unit 414.
  • the model obtaining unit 411 is configured to obtain a semantic model, and the semantic model includes a semantic relationship between each of the plurality of semantic units and a corresponding data source identifier of at least one production line.
  • the receiving unit 412 is configured to receive the production line identification and obtain at least one semantic unit.
  • the conversion unit 413 is configured to convert at least one semantic unit to the corresponding data source identification of the production line represented by the production line identification based on the semantic model.
  • the data acquiring unit 414 is configured to acquire data of the data source represented by the data source identifier.
  • Each unit in FIG. 4 may be implemented by software, hardware (such as integrated circuits, FPGAs, etc.) or a combination of software and hardware.
  • the model acquisition unit 411 is further configured to: build a virtual model of at least one production line based on the feature information and production sequence of each component of at least one production line; establish a context information database based on the virtual model, the context information database includes The context information of each data source of at least one production line; and based on the virtual model, multiple semantic units and context information base, connect each of the multiple semantic units to the corresponding data source identification of at least one production line with a semantic relationship to Form a semantic model.
  • the data acquiring unit 414 is further configured to acquire data of the data source represented by the data source identifier from the database, wherein the database stores data of each data source of at least one production line in a unified structure.
  • the apparatus 400 further includes an interaction unit (not shown).
  • the interaction unit is configured to receive data from each data source of at least one production line and store it in a database in a unified structure.
  • the device 400 further includes an analysis unit (not shown) configured to analyze the performance of the production line based on predetermined analysis rules and data from the data source to obtain analysis results.
  • the receiving unit 412 is further configured to: receive a production line data acquisition request; and parse the production line data acquisition request to obtain at least one semantic unit.
  • the production line data acquisition request includes the calculation formula of the key performance index
  • the analysis unit is configured to calculate the key performance index based on the calculation formula and the data of the data source to obtain the calculation result.
  • the data source identification includes at least one of the following: the network address of the data source, the identification code of the data source, and the attributes of the data source.
  • FIG. 5 shows a schematic structural diagram of a production line performance analysis system 500 according to an embodiment of the present disclosure.
  • the production line performance analysis system 500 of FIG. 5 uses the method for obtaining data of the data source in the production line shown in FIG. 1 to calculate the KPI of the production line in the factory.
  • the entire system is divided into three layers: a cloud server 50, a local server 51, and a device layer 53.
  • the equipment layer 53 includes data sources of various production lines in the factory, and they provide data to the local server 51 of the factory.
  • the local server 51 performs the method described with reference to FIG. 1 and interacts with the cloud server 50 and the device layer 53.
  • the cloud server 51 is used for unified data storage and management of multiple factories.
  • the local server 51 is divided into two platforms: a middleware platform 510 and a device service platform 520.
  • the middleware platform 510 includes a model acquisition unit 511, a reception unit 512, a conversion unit 513, an acquisition unit 514, and an analysis unit 515.
  • the device service platform 520 includes an interaction unit 521.
  • the model obtaining unit 511 is used to obtain a semantic model.
  • the step of obtaining a semantic model further includes establishing a semantic model.
  • the process of establishing a semantic model is similar to the steps described with reference to FIGS. 1 to 3.
  • the cloud server 50 is used in the system 500 of FIG.
  • the model acquisition unit 511 establishes a virtual model of the production line at the local server 51 of the factory, it is uploaded to the cloud server 50 .
  • the KPI calculation formula related to the production line is also pre-configured at the local server 51, and it is also uploaded to the cloud server 50 together.
  • the cloud server After receiving the virtual model and the KPI calculation formula, the cloud server stores them in the KPI calculation formula memory 502 and the production line virtual model storage 503 in a unified manner.
  • the cloud server 50 also includes a dashboard 501 for displaying the virtual model of the production line, KPI calculation formulas and KPI calculation results, and can interact with the user.
  • the interaction unit 521 of the equipment service platform 520 is in communication with the data sources 531, 532, and 533 of at least one production line of the equipment layer 53, which receives data from each data source 531, 532, and 533 and stores them in a database in a unified structure in.
  • the device service platform 520 also provides a series of data service interfaces 522, 523, and 524. These data service interfaces are called by the middleware platform 510 to locate data in the database. Therefore, the format of the unified structure database is similar to Table 1, but the "data service” field is added.
  • the data service may include, for example, “getGoodNum (get good quantity)", “getBadNum (get bad quantity)", and “getTotalNum (get total quantity)", and so on.
  • a data service indexed by the semantic unit is also defined in the context information database. Still taking the production line L1 shown in FIG. 2 as an example, an example of context information of a data service indexed by a semantic unit is shown.
  • the semantic unit good_num1 (good quantity 1) corresponds to the data service "getGoodNum (get good quantity)"
  • the semantic unit bad_num1 (defective quantity 1)
  • the semantic unit good_num3 (good product quantity 1) corresponds to the "getGoodNum (get good product quantity)" data service
  • the data they involve is stored in the data server (Ie data service platform) d1.
  • the data server d1 has an IP address, and the provided data services include getGoodNum (get good product quantity) and getBadNum (get bad product quantity).
  • FIG. 6 a part of the established semantic model is shown in FIG. 6.
  • the data service corresponding to the semantic unit good_num1 (quantity of good 1) is getGoodNum (quantity of good product)
  • the data service corresponding to the semantic unit bad_num1 (quantity of bad product 1) is getBadNum (quantity of bad product) , They are all data services provided by the data server.
  • FIG. 6 only shows a part of the semantic model.
  • the semantic model should include every semantic in the semantic units involved in all production lines The semantic relationship between the unit and the corresponding data source identification and data service of the production line involved in the semantic unit.
  • the semantic model may be stored in a local memory (not shown) of the local server 51.
  • the model acquisition unit 511 can directly read the semantic model from the memory when it needs to acquire the semantic model.
  • the user when calculating the specified KPI of the specified production line, the user can select the specified production line and the specified KPI calculation formula via the dashboard 501 of the cloud server 50.
  • the cloud server 50 sends the specified production line identifier and KPI calculation formula to the receiving unit 512 of the middleware platform 510.
  • the receiving unit 512 parses the KPI calculation formula to obtain at least one semantic unit.
  • the KPI calculation formula can be parsed according to a predetermined rule (for example, the priority of the operator of the four operations).
  • the conversion unit 513 can convert at least one semantic unit into a corresponding data source identification and data service based on the semantic model acquired by the model acquisition unit 511, and obtain the IP address of the data server.
  • the data obtaining unit 314 calls the corresponding data service interface from the IP address of the data server, locates the corresponding data source in the database according to the data source identification, and obtains its data. Similar to the embodiment described with reference to FIGS. 2 and 3, when the user selects the production line and the KPI calculation formula, the time parameter related to the selected KPI calculation formula can also be input accordingly to select the corresponding from the database according to the time parameter data.
  • the analysis unit 315 calculates the value of the KPI using the obtained data according to the KPI calculation formula, and sends it to the cloud server for display on the dashboard 301.
  • the KPI calculation is performed at the local server, and only the calculation result is displayed on the cloud server's dashboard 301. Therefore, compared with the prior art, it is avoided that the data of the data source involved in the calculation is transmitted to Cloud server, thereby improving the efficiency and cost of data transmission.
  • the local server 51 also has a dashboard (not shown). The user can select a specified production line and KPI calculation formula at the local server 51. The local server 51 calculates the KPI value and displays it on the dashboard.
  • the computing device 700 for obtaining data of the data source in the production line includes a processor 701 and a memory 702 coupled to the processor 701.
  • the memory 702 is used to store computer-executable instructions.
  • the processor 701 executes the method in the above embodiments.
  • the above method can be implemented by a computer-readable storage medium.
  • the computer-readable storage medium is loaded with computer-readable program instructions for performing various embodiments of the present disclosure.
  • the computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device.
  • the computer-readable storage medium may be, for example but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), and erasable programmable read only memory (EPROM (Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical coding device, such as a computer on which instructions are stored
  • RAM random access memory
  • ROM read only memory
  • EPROM erasable programmable read only memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick floppy disk
  • mechanical coding device such as a computer on which instructions are stored
  • the convex structure in the hole card or the groove and any suitable combination of the above.
  • the computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, optical pulses through fiber optic cables), or through wires The transmitted electrical signal.
  • the present disclosure proposes a computer-readable storage medium having computer-executable instructions stored thereon, the computer-executable instructions being used to perform various implementations of the present disclosure The method in the example.
  • the present disclosure proposes a computer program product that is tangibly stored on a computer-readable storage medium and includes computer-executable instructions that when executed make At least one processor performs the method in various embodiments of the present disclosure.
  • various example embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, firmware, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device.
  • firmware or software that may be executed by a controller, microprocessor, or other computing device.
  • the computer readable program instructions or computer program products used to execute the various embodiments of the present disclosure can also be stored in the cloud, and when needed, users can access the stored on the cloud for execution through the mobile Internet, fixed network or other networks
  • the computer-readable program instructions of one embodiment of the present disclosure implement the technical solutions disclosed according to the various embodiments of the present disclosure.

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Abstract

本公开的实施例提供了一种用于获得生产线中数据源的数据的方法，包括：获得语义模型，语义模型包括多个语义单元中的每个与至少一条生产线的相应数据源标识之间的语义关系；接收生产线标识并获得至少一个语义单元；基于语义模型，将至少一个语义单元转换为生产线标识所表示的生产线的对应数据源标识；以及获取数据源标识所表示的数据源的数据。根据本公开的实施例，简化了生产线的虚拟模型的配置文件，从而大量减少了配置工作，为获得数据源的数据提供了便利性。

Description

用于获得生产线的数据源的数据的方法和装置 技术领域
本公开涉及工业控制领域，更具体地说，涉及用于获得生产线中数据源的数据的方法、装置、计算设备、计算机可读存储介质和程序产品。
背景技术
关键性能指标(KPI)是评价过程和绩效的经济技术指标，它可以作为生产过程的基准和参考，用于对生产过程进行评估和优化，能够不断完善工厂的生产过程。通常，针对工厂中的每条生产线都定义了许多种类的KPI，例如，良品率、生产率、设备负荷率等等。根据KPI的计算公式计算得到的KPI值可以与设定的KPI基准值进行比较，从而评估是否达到预期目标。
在现有技术中，通常使用云服务器来完成这样的计算，云服务器中存储针对每条生产线的各个KPI的计算公式。在需要计算KPI时，首先针对每条生产线建立虚拟模型，并在模型配置文件中，根据KPI计算公式中的变量与生产线中的数据源(例如，传感器)的数据的关系，将KPI计算公式中的变量与相关数据源进行绑定，并将所建立的生产线虚拟模型上传到云服务器。
之后，云服务器中的计算引擎在计算某条生产线的某个KPI时，按照一定的计算规则(例如，运算符的优先级等)将KPI计算公式进行分解，根据对应的模型配置文件来读取所需要的数据源的数据，将所获得的数据源的数据代入KPI计算公式中，再根据计算规则进行计算，从而获得KPI值。
发明内容
在目前的生产线KPI计算方法中，针对工厂中不同的生产线，都需要在对生产线进行建模时，在配置文件中将KPI公式中的变量与数据源进行关联，以此来获得数据源的数据进行KPI计算。因此，在生产线数量较多时，需要在建模阶段进行大量的复杂配置工作，而且不能足够灵活地适用不同应 用。
本公开的第一实施例提出了一种用于获得生产线中数据源的数据的方法，包括：获得语义模型，所述语义模型包括多个语义单元中的每个与至少一条生产线的相应数据源标识之间的语义关系；接收生产线标识并获得至少一个语义单元；基于所述语义模型，将所述至少一个语义单元转换为所述生产线标识所表示的生产线的对应数据源标识；以及获取所述数据源标识所表示的数据源的数据。
在该实施例中，通过建立语义模型，在语义模型中建立语义单元与生产线的数据源的数据源标识的语义关系，能够简化生产线的虚拟模型的配置文件，从而大量减少了配置工作，为获得这些数据源的数据提供了便利性。
本公开的第二实施例提出了一种用于获得生产线中数据源的数据的装置，包括：模型获取单元，其被配置为获得语义模型，所述语义模型包括多个语义单元中的每个与至少一条生产线的相应数据源标识之间的语义关系；接收单元，其被配置为接收生产线标识并获得至少一个语义单元；转换单元，其被配置为基于所述语义模型，将所述至少一个语义单元转换为所述生产线标识所表示的生产线的对应数据源标识；以及数据获取单元，其被配置为获取所述数据源标识所表示的数据源的数据。
本公开的第三实施例提出了一种计算设备，所述计算设备包括：处理器；以及存储器，其用于存储计算机可执行指令，当所述计算机可执行指令被执行时使得所述处理器执行第一实施例中所述的方法。
本公开的第四实施例提出了一种计算机可读存储介质，所述计算机可读存储介质具有存储在其上的计算机可执行指令，所述计算机可执行指令用于执行第一实施例中所述的方法。
本公开的第五实施例提出了一种计算机程序产品，所述计算机程序产品被有形地存储在计算机可读存储介质上，并且包括计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行第一实施例中所述的方法。
附图说明
结合附图并参考以下详细说明，本公开的各实施例的特征、优点及其他 方面将变得更加明显，在此以示例性而非限制性的方式示出了本公开的若干实施例，在附图中：
图1示出了根据本公开的一个实施例的用于获得生产线中数据源的数据的方法流程图；
图2示出了根据本公开的一个实施例的工厂中的一条生产线的示意图；
图3示出了根据图2的示例生产线的语义模型的一部分的示意图；
图4示出了根据本公开的一个实施例的用于获得生产线中数据源的数据的装置的框图；
图5示出了根据本公开的一个实施例的生产线性能分析系统的架构示意图；
图6示出了根据图5的实施例的语义模型的一部分的示意图；以及
图7示出了根据本公开的一个实施例的用于获得生产线中数据源的数据的计算设备的框图。
具体实施方式
以下参考附图详细描述本公开的各个示例性实施例。虽然以下所描述的示例性方法、装置包括在其它组件当中的硬件上执行的软件和/或固件，但是应当注意，这些示例仅仅是说明性的，而不应看作是限制性的。例如，考虑在硬件中独占地、在软件中独占地、或在硬件和软件的任何组合中可以实施任何或所有硬件、软件和固件组件。因此，虽然以下已经描述了示例性的方法和装置，但是本领域的技术人员应容易理解，所提供的示例并不用于限制用于实现这些方法和装置的方式。
此外，附图中的流程图和框图示出了根据本公开的各个实施例的方法和系统的可能实现的体系架构、功能和操作。应当注意，方框中所标注的功能也可以按照不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，或者它们有时也可以按照相反的顺序执行，这取决于所涉及的功能。同样应当注意的是，流程图和/或框图中的每个方框、以及流程图和/或框图中的方框的组合，可以使用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以使用专用硬件与计算机指令的组合来实现。
本文所使用的术语“包括”、“包含”及类似术语是开放性的术语，即“包括/包含但不限于”，表示还可以包括其它内容。术语“基于”是“至少部分地基于”。术语“一个实施例”表示“至少一个实施例”；术语“另一实施例”表示“至少一个另外的实施例”等等。
图1示出了根据本公开的一个实施例的用于获得生产线中数据源的数据的方法。参考图1，方法100从步骤101开始。在步骤101中，获得语义模型，该语义模型包括多个语义单元中的每个与至少一条生产线的相应数据源标识之间的语义关系。语义单元是便于人类理解的信息表示单元，通常在工业领域中具有通用的表达方式。例如，在关键性能指标(KPI)的计算公式yield_rate＝good_num3/(good_num1+bad_num1)(良品率＝良品数量3/(良品数量1+次品数量1))中，变量“good_num3(良品数量3)”、“good_num1(良品数量1)”和“bad_num1(次品数量1)”都分别是语义单元。又例如，在查找工厂中某条生产线的下一个订单所需的物料信息时，所涉及的语义单元可以是“find_next_order(查找下一订单)”和“find_material(查找物料)”。这些语义单元通常与生产线相关的数据源的数据相对应。例如，在上述yield_rate＝good_num3/(good_num1+bad_num1)的KPI计算公式中，语义单元“good_num3(良品数量3)”、“good_num1(良品数量1)”和“bad_num1(次品数量1)”分别表示生产线中的相关传感设备所采集的数据，数据源为传感设备。而对于语义单元“find_next_order(查找下一订单)”和“find_material(查找物料)”，它们表示预先存储在生产线相关的数据库中的订单数据和物料数据，数据源为数据库。
语义模型可以将语义单元与数据源关联起来。如上面提及的，数据源可以是生产线中的传感设备或者存储生产线相关数据的数据库等。数据源可以通过数据源标识来识别，在一些实施例中，数据源标识包括以下各项中的至少一项：数据源的网络地址、数据源的识别码、以及数据源的属性。在其它实施例中，数据源标识还可以是能够唯一确定数据源的任何其它信息。因此，只要通过语义单元获得数据源标识，就可以从数据源标识表示的相应数据源获得数据。因而，在语义模型中用语义关系将语义单元和数据源标识连接起来，就可以提供语义单元与数据源的联系。语义关系例如为包含关系、连接关系等等。下文中将参照图3以一个具体的实施例详细介绍语义模型。
在一些实施例中，可以对一个工厂中的所有生产线建立统一的语义模型。也就是说，语义模型中可以包括一个工厂中所有生产线所涉及的全部语义单元与相应数据源标识之间的语义关系。一条生产线可能涉及多于一个语义单元，而每个语义单元也可能涉及多于一条生产线。因此，在语义模型中，针对多个语义单元中的每个语义单元，用语义关系将其与所涉及的生产线的相应数据源标识相连接。这样，在语义模型中，每个语义单元都被映射到涉及该语义单元的生产线的相应数据源标识。因此，相比于现有技术，不需要再针对每条生产线分别在配置文件中将语义单元与数据源进行关联。为一个工厂建立统一的语义模型，不仅能够适用于工厂中不同生产线，而且也易于扩展。当工厂中增加新的生产线时，仅需要对语义模型进行修改即可，即在语义模型中增加新的生产线的相关信息。
在其它实施例中，步骤101进一步包括以下步骤：
首先，基于至少一条生产线的各组成部分的特征信息和生产顺序，建立至少一条生产线的虚拟模型。在实际生产线中，产品在生产线中通过各组成部分进行流转生产、检测等一系列操作。因此，为了使得虚拟模型能够与实际生产线相对应，需要获得生产线的各组成部分的特征信息以及生产线对产品的生产顺序(即工序)。在一些实施例中，各组成部分可以包括生产线的各个工位和各个工位处的各个设备，设备例如可以为控制器、传感设备和动作设备。控制器可以包括可编程逻辑控制器(PLC)、智能设备、工业物联网智能网关等，传感设备可以包括传感器、按钮、扫码器等，动作设备可以包括电机、机械手臂等。动作设备用于执行对产品的操作或为操作产品的设备提供驱动，传感设备用于感测生产过程中需要收集的一些变量，例如时间、位移、数量、产品的特定特征等。控制器与传感设备和动作设备通信耦合，向传感设备和动作设备发送控制信号，对它们进行控制，并且接收传感设备所感测到的数据。在一些实施例中，建立生产线的虚拟模型主要是为了生产线性能分析的目的，因此，各组成部分的特征信息可以包括各个工位之间的工序关系、各个工位处的各设备的功能、属性和网络地址、各设备之间的连接/控制关系等与生产线性能分析有关的信息。
接下来，基于虚拟模型建立上下文信息库，上下文信息库包括至少一条生产线的各数据源的上下文信息。在一些实施例中，对一个工厂中的所有生 产线建立一个统一的语义模型，相应地，上下文信息库包括工厂中所有生产线的各数据源的上下文信息。具体来说，上下文信息包括以生产线为索引的设备信息以及以语义单元为索引的数据源信息。其中，以生产线为索引的设备信息具体可以包括生产线各组成部分(例如，工位、工位处的设备等)的特征信息(例如属性、连接关系等)和生产顺序，以语义单元为索引的数据源信息包括语义单元与数据源的关联关系。
之后，基于虚拟模型、多个语义单元和上下文信息库，将多个语义单元中的每个分别与至少一条生产线的相应数据源标识用语义关系连接，以形成语义模型。多个语义单元可以来自于现有的生产线性能分析方法的总结和归纳。在至少一条生产线的虚拟模型的基础上，利用多个语义单元和上下文信息库中的各索引关系，将多个语义单元中的每个分别与所涉及的生产线的相应数据源标识用语义关系连接，从而形成语义模型。
语义模型的建立过程将在下文中参照图3和图4以一个具体的实施例来详细描述。应当指出，当语义模型建立之后，上述步骤并非是执行本方法的必须步骤。
继续参考图1，接下来，方法100行进到步骤102。在步骤102中，接收生产线标识并获得至少一个语义单元。生产线标识表示某一条具体的生产线。在其它实施例中，获得至少一个语义单元的步骤进一步包括：接收生产线数据获取请求；以及对生产线数据获取请求进行解析，以获得至少一个语义单元。在这些实施例中，语义单元的获得需要对生产线数据获取请求进行解析。在其它实施例中，也可以直接接收至少一个语义单元。在一些实施例中，生产线数据获取请求包括KPI的计算公式，语义单元为KPI计算公式中的变量。对KPI的计算公式进行解析包括按照计算公式中的运算规则(例如，四则运算的运算符优先级)将计算公式分解为多个语义单元。然而，在其它实施例中，生产线数据获取请求可以包括其它生产线相关的计算公式或数据获取请求，例如，在针对某条生产线的“查找下一订单的物料信息”的数据获取请求中，解析出的语义单元为“查找下一订单”和“查找物料”。
接着，在步骤103中，基于语义模型，将至少一个语义单元转换为生产线标识所表示的生产线的对应数据源标识。在该步骤中，通过语义模型中的语义单元与具体生产线的具体数据源标识的语义关系，能够将所获得的至少 一个语义单元转换为生产线标识所表示的生产线的数据源标识。
接着，方法100转到步骤104，获取数据源标识所表示的数据源的数据。至此，便可以根据数据源标识，获取对应数据源的数据。在其它实施例中，步骤104进一步包括：从数据库中获取数据源标识所表示的数据源的数据，其中，数据库以统一的结构存储至少一条生产线的各数据源的数据。例如，每个工位处的控制器使与其通信耦合的传感设备的数据存储在统一结构的数据库中。
在其它实施例中，方法100进一步包括(图中未示出)：接收至少一条生产线的各数据源的数据，并以统一的结构存储在数据库中。通过接收生产线的各数据源的数据，并将这些数据转换为统一的数据结构后存储在数据库中，以便能从数据库中获取数据源标识所表示的数据源的数据，使得对数据源的数据进行统一的管理和存储，这也提高了对数据进行分析和计算的效率，并且还能够灵活地适用于不同的应用，例如，供其它平台或应用使用。
在其它实施例中，方法100进一步包括(图中未示出)：基于预定的分析规则和数据源的数据，对生产线的性能进行分析，以获得分析结果。预定的分析规则可以是任意设定的规则。例如，在一些实施例中，可以根据KPI的计算公式和所获得的数据源的数据，基于预定的运算规则(例如，四则运算的规则)对KPI计算公式进行计算来获得KPI的计算结果。
接下来将参照一个具体的实施例来说明图1所示的用于获得生产线的数据源的数据的方法。在本实施例中，获得生产线的数据源的数据是为了计算生产线的KPI。每条生产线都可以具有一个或多个KPI，例如，良品率YR(Yield Rate)、实际平均周期时长RACT(Real Average Cycle Time)、直通率FPY(First Pass Yield)等。在本实施例中，一个工厂具有统一的语义模型。
图2示出了根据本公开的一个实施例的工厂中的一条生产线的示意图200。为了说明的简单起见，如图2中示出的，生产线L1具有三个工位S1、S2、S3，并且每个工位处分别具有一个控制器和一个传感设备。在其它实施例中，生产线可以具有其它任意数量的工位，而每个工位处可以具有其它任意数量的控制器和传感设备。每个控制器可以对任意数量的传感设备进行控制并接收传感设备的数据，传感设备也可以具有不同的功能，例如，检测数 量、时间、位移、产品的某个特性等等。收集这些数据可以用于不同的目的，例如，检测数量可用于计算良品率KPI，检测时间可用于分析生产线的产能，等等。
在本实施例中，生产线L1的产品工序为从工位S1进入工位S2，再进入工位S3。在各个工位处分别具有控制器c1、c2、c3以及分别与这些控制器通信连接的传感设备t1、t2和t3。这些传感设备t1、t2和t3可用于分别感测相应工位S1、S2和S3处产出的产品是否符合该工位处的生产标准，并对符合和不符合相应工位处的生产标准的产品分别进行计数，并向控制器c1、c2和c3发送相应的数据。在其它实施例中，计数功能可以由控制器c1、c2和c3来完成，即，传感设备t1、t2和t3向控制器c1、c2和c3发送相应工位S1、S2和S3处产出的产品是否符合该工位处的生产标准的信号，控制器c1、c2和c3对传感设备t1、t2和t3所感测到的符合生产标准和不符合生产标准的产品分别进行计数，从而生成相应数据。
在本实施例中，对于图2中示出的生产线L1，良品率的计算公式定义为：yield_rate＝good_num3/(good_num1+bad_num1)(良品率＝良品数量3/(良品数量1+次品数量1)。good_num1(良品数量1)和bad_num1(次品数量1)分别表示在某段预定的时间内，符合工位S1处的生产标准和不符合工位S1处的生产标准的产品的数量。类似地，good_num3(良品数量3)表示在某段预定的时间内，符合工位S3处的生产标准的产品的数量。不符合工位S1处的生产标准的产品将不会进入工位S2，而不符合工位S2处的生产标准的产品将不会进入工位S3，因此，符合工位S3处的生产标准的产品数量就代表了整条生产线L1的良品数量。所以，良品率即为符合工位S3处的生产标准的产品数量与工位S1处所生产的产品总数量的比率，即，良品数量3/(良品数量1+次品数量1)。
以上仅介绍了工厂中的一条生产线L1及其良品率KPI的计算公式。在一个工厂中还可以具有其它生产线，并且每条生产线还可以具有其它KPI定义，这些KPI分别有相应的计算公式。
下面介绍用于获得生产线的数据源的数据的方法的具体过程。首先，建立语义模型。建立语义模型的第一步是建立生产线的虚拟模型。如上面提及的，一个工厂具有统一的语义模型，因此相应地，需要建立工厂中所有生产 线的虚拟模型，虚拟模型可以通过任何现有的建模工具来建立，并可以呈现在用户界面上。下面以生产线L1为例进行说明。在本实施例中，基于生产线L1的各组成部分的特征信息和生产顺序，建立该生产线L1的虚拟模型。由于在本实施例中，获得生产线中的数据源的数据是为了计算生产线的KPI，因此仅与传感设备的数据有关，不需要各工位处的动作设备的相关信息。因此，也不需要在虚拟模型中包括动作设备。在本实施例中，虚拟模型中包括生产线L1的工位S1、S2、S3，它们之间的工序关系(即工位S1的输出为工位S2的输入、工位S2的输出为工位S3的输入)，工位S1处的控制器c1和传感设备t1的连接和控制关系、属性、功能和网络地址，工位S2处的控制器c2和传感设备t2的连接和控制关系、属性、功能和网络地址，以及工位S3处的控制器c3和传感设备t3的连接和控制关系、属性、功能和网络地址。
接下来，基于生产线的虚拟模型建立上下文信息库，其包括至少一条生产线的各数据源的上下文信息。如上面提及的，在本实施例中，一个工厂具有统一的语义模型，因此，相应地，需要建立包括工厂中所有生产线的各数据源的上下文信息的上下文信息库。下面以生产线L1为例说明这些上下文信息。
在本实施例中，在生产线的生产过程期间，以预定的时间间隔(例如，1s)不断地接收控制器c1、c2和c3分别收集的传感设备t1、t2和t3的数据，并以统一的格式存储在数据库中。因此，数据源是传感设备t1、t2和t3，其上下文信息包括：
1)以生产线L1为索引的KPI定义和设备信息。
Figure PCTCN2018118188-appb-000001
Figure PCTCN2018118188-appb-000002
Figure PCTCN2018118188-appb-000003
以上上下文信息表示生产线L1具有S1、S2和S3这三个工位，L1具有YR(良品率)、RACT(实际平均周期时长)和FPY(直通率)的KPI定义。由于良品率的KPI计算与数据源的预定时间段内的数据相关，因此，在该上下文信息中，还包括了KPI相关的参数，这些参数包括在进行计算时需要输入的目标时间段的开始时间和结束时间。然而，在其它实施例中，如果需要计算的KPI与时间段无关，也可以不包括这样的参数。工位S1具有控制器c1和传感设备t1，工位S2具有控制器c2和传感设备t2，工位S3具有控制器c3和传感设备t3。控制器c1具有数据点：point1(数据点1)、point2(数据点2)和point3(数据点3)，数据点point1与传感设备t1通信连接，接收传感设备t1的数据。为了说明的目的，以上仅例示了以生产线L1为索引的KPI定义和设备信息的一部分。类似地，以生产线L1为索引的KPI定义和设备信息还包括控制器c1的数据点point2、数据点point3的连接关系的信息、控制器c2的信息、生产线L1的其它KPI定义的信息等等。
2)以语义单元为索引的数据源信息。
Figure PCTCN2018118188-appb-000004
Figure PCTCN2018118188-appb-000005
以上上下文信息表示语义单元good_num1(良品数量1)对应的是由传感设备t1所采集的数据，bad_num1(次品数量1)对应的也是由传感设备t1所采集的数据，good_num3(良品数量3)对应的是传感设备t3所采集的数据，这些数据都存储在数据库b1中。为了说明的目的，以上仅例示了以语义单元为索引的数据源信息的一部分。类似地，以语义单元为索引的数据源信息还包括生产线L1所涉及的其它语义单元相关的信息。
在其它实施例中，如果获得数据源的数据的目的并非为了计算KPI，也可以在上下文信息中不包括以生产线L1为索引的KPI的定义。
之后，基于虚拟模型、多个语义单元和上下文信息库，将多个语义单元中的每个分别与生产线的相应数据源标识用语义关系连接，形成语义模型。仍以生产线L1为例，在该步骤中，基于生产线L1的虚拟模型，利用上下文信息库中的上下文信息，将生产线L1的各组成部分、它们的属性、语义单元good_num1(良品数量1)，bad_num1(次品数量1)和good_num3(良品数量3)、语义单元涉及的数据源分别用语义关系连接起来。图3示出了根据图2的示例生产线的语义模型的一部分。如图3中可以看到的，生产线L1的各组成部分(包括工位和控制器、传感设备)、它们的属性(包括KPI定义、控制器所具有的数据点、存储数据的数据库)、语义单元good_num1(良品数量1)、bad_num1(次品数量1)和good_num3(良品数量3)、以及语义单元涉及的数据源通过语义关系被连接起来。
从图3的一部分的语义模型中可以看到，L1具有S1、S2和S3三个工位，并具有YR(良品率)的KPI定义以及另外两个KPI定义RACT(实际平均周期时长)和FPY(直通率)。YR具有开始时间和结束时间的参数。工位S1具有控制器c1和传感设备t1，控制器c1又具有数据点1、数据点2和数据点3，其中数据点1与传感设备t1连接，接收传感设备t1的数据。 控制器c1将数据点1至数据点3所接收到的数据存储在ID为b1的数据库中。类似地，工位S3具有控制器c3和传感设备t3，控制器c3具有数据点1，其连接到传感设备t3，接收传感设备t3的数据。控制器c3将数据点1接收到的数据存储在ID为b1的数据库中。工位S2具有控制器c2和传感设备t2。语义单元good_num1(良品数量1)和bad_num1(次品数量1)对应的数据由传感设备t1测量，而语义单元good_num3(良品数量3)对应的数据由传感设备t3测量。
在本实施例中，数据源标识为工位S1处的控制器c1的数据点1和工位S3处的控制器c3的数据点1，即数据源传感设备t1和t3与控制器c1和c3的连接属性。因此，在该语义模型中包括了语义单元good_num1(良品数量1)、bad_num1(次品数量1)与工位S1处的控制器c1的数据点1、以及good_num3(良品数量3)与工位S3处的控制器c3的数据点1之间的语义关系。
在其它实施例中，除了数据源的属性，数据源标识还可以包括数据源的网络地址、数据源的识别码、它们的组合，或者可以唯一确定数据源的其它标识信息。
以上以生产线L1为例介绍了语义模型的建立过程。应当指出，图3仅示出了语义模型的一部分，由于在本实施例中，在一个工厂中建立统一的语义模型，因此，语义模型中包括整个工厂中所有生产线所涉及的语义单元中的每个语义单元与该语义单元涉及的生产线的相应数据源标识之间的语义关系。
在语义模型被建立起来之后，就可以基于该语义模型获得期望的数据源的数据。如上文中提及的，当语义模型被确定之后，以上建立语义模型的步骤并非是必须的步骤。
仍以生产线L1的上述良品率KPI的计算公式为例，继续说明获得生产线中数据源的数据的方法。在本实施例中，接收生产线标识L1、良品率KPI的计算公式yield_rate＝good_num3/(good_num1+bad_num1)(良品率＝良品数量3/(良品数量1+次品数量1)以及时间参数。时间参数包括开始时间和结束时间(例如，10：00-11：00)，表示需要获得在这两个时间之间的时间段内的数据源的数据。在其它实施例中，参数可以为其它类型的参数，或者，也 可以不具有这样的参数。
然后，对该KPI的计算公式进行解析。按照四则运算的计算规则对该公式进行分解后，提取出语义单元good_num1(良品数量1)，bad_num1(次品数量1)和good_num3(良品数量3)。之后，基于图3中示出的语义模型，将这些语义单元分别转换为生产线L1的对应数据源标识，即工位S1处的控制器c1的数据点1和工位S3处的控制器c3的数据点1，并获得数据库ID为b1。
接下来，根据时间参数，从具有统一数据结构的数据库b1中获得与这些数据点对应的数据。统一数据结构的数据库可以通过以预定时间间隔(例如，1秒)接收生产线的各数据源的数据，并将这些数据转换为统一的结构来构建。例如，可以建立生产线标识、数据源类型、数据源识别码、数据点、控制器网络地址、数据源网络地址、数据类型、数据、时间戳等若干字段，来作为统一的结构存储数据源的数据。下面的表1中示出了示例的数据库b1的一部分。
表1数据库b1示例
Figure PCTCN2018118188-appb-000006
表1中仅示出了示例数据库b1的一部分。在表1中，工位S1处的传感设备t1、工位S2处的传感设备t2和工位S3处的传感设备t3所采集的数据为符合及不符合相应工位处的生产标准的产品数量，因此，数据类型分别为良品数或次品数。此外，还可以看到，数据以1秒的时间间隔存储在数据库b1中。在本实施例中，还接收KPI有关的时间参数(例如，开始时间10：00：00，结束时间11：00：00)，因此，根据通过语义模型转换得到的数据源标识，从数据库b1中获取在开始时间与结束时间之间的数据。例如，可以读取时间在10：00：00-11：00：00之间、数据点为“S1，c1，数据点1”的数据类型为“良品数”和“次品数”、数据点“S3，c3，数据点1”的数据类型为“良品数”的所有数据，这些数据为计算良品率KPI所需要的数据。
在示例的数据库中，除了传感器之外，“数据源类型”的字段中还可以包括其它传感设备，例如按钮、扫码器，或者可以包括产生数据的其它设备。相应地，“数据类型”的字段中可以按照数据源采集数据的类型不同而不同。
在获得计算良品率KPI所需的相应数据之后，基于KPI的计算公式，按照计算规则(例如，运算符优先级)来对良品率进行计算。具体来说，将数据点为“S1，c1，数据点1”的数据类型为“良品数”的数据分别相加，作为语义单元good_num1(良品数量1)的数值，数据点为“S1，c1，数据点1”的数据类型为“次品数”的数据分别相加，作为语义单元bad_num1(次品数量1)的数值，将数据点为“S3，c3，数据点1”的数据类型为“良品数”的数据分别相加，作为语义单元good_num3(良品数量3)的数值。接下来，将good_num1(良品数量1)的数值与bad_num1(次品数量1)的数值相加，再用good_num3(良品数量3)的数值除以相加得到的数值，从而获得良品率的值。
在其它实施例中，时间参数除了起止时间之外，还可以包含时间间隔。例如，对于测量型(例如，测量位移)的数据，可以在时间参数中设置预定的间隔时间(例如，5s)。也就是说，可以根据需要从数据库中获取预定时间间隔的数据，而不是从数据库中获取数据源的所有时间的数据。此外，在其它实施例中，时间参数还可以包括某个具体的时间，以从数据库中获取数据源的某个具体时间的数据。或者，在其它实施例中，数据库中的数据也可以是与时间无关的数据，例如，数据库可以是静态的数据库。因此，在这样 的实施例中，当获取数据库中的数据时，不需要设置时间参数。
图4示出了根据本公开的一个实施例的获得生产线中数据源的数据的装置。参照图4，装置400包括模型获取单元411、接收单元412、转换单元413和数据获取单元414。模型获取单元411被配置为获得语义模型，语义模型包括多个语义单元中的每个与至少一条生产线的相应数据源标识之间的语义关系。接收单元412被配置为接收生产线标识并获得至少一个语义单元。转换单元413被配置为基于语义模型，将至少一个语义单元转换为生产线标识所表示的生产线的对应数据源标识。数据获取单元414被配置为获取所述数据源标识所表示的数据源的数据。图4中的各单元可以利用软件、硬件(例如集成电路、FPGA等)或者软硬件结合的方式来实现。
在其它实施例中，模型获取单元411被进一步配置为：基于至少一条生产线的各组成部分的特征信息和生产顺序，建立至少一条生产线的虚拟模型；基于虚拟模型建立上下文信息库，上下文信息库包括至少一条生产线的各数据源的上下文信息；以及基于虚拟模型、多个语义单元和上下文信息库，将多个语义单元中的每个分别与至少一条生产线的对应数据源标识用语义关系连接，以形成语义模型。
在其它实施例中，数据获取单元414被进一步配置为：从数据库中获取数据源标识所表示的数据源的数据，其中，数据库以统一的结构存储至少一条生产线的各数据源的数据。
在其它实施例中，装置400还包括交互单元(未示出)，交互单元被配置为接收至少一条生产线的各数据源的数据，并以统一的结构存储在数据库中。在其它实施例中，装置400还包括分析单元(未示出)，分析单元被配置为基于预定的分析规则和数据源的数据，对生产线的性能进行分析，以获得分析结果。
在其它实施例中，接收单元412被进一步配置为：接收生产线数据获取请求；以及对生产线数据获取请求进行解析，以获得至少一个语义单元。在一些实施例中，生产线数据获取请求包括关键性能指标的计算公式，并且，分析单元被配置为基于计算公式和数据源的数据，对关键性能指标进行计算以获得计算结果。
在其它实施例中，数据源标识包括以下各项中的至少一项：数据源的网 络地址、数据源的识别码、以及数据源的属性。
现在参考图5，其示出了根据本公开的一个实施例的生产线性能分析系统500的架构示意图。图5的生产线性能分析系统500使用图1示出的用于获得生产线中数据源的数据的方法来计算工厂中的生产线的KPI。如图5中示出的，整个系统分为三个层：云服务器50、本地服务器51和设备层53。设备层53包括工厂中各条生产线的数据源，它们向工厂的本地服务器51提供数据。本地服务器51执行参照图1所描述的方法，并与云服务器50和设备层53进行交互。云服务器51用于对多个工厂进行统一的数据存储和管理。
接下来参考图5说明在该生产线性能分析系统500的各组成部分的动作流程。如图5中示出的，本地服务器51被划分为两个平台：中间件平台510和设备服务平台520。中间件平台510包括模型获取单元511、接收单元512、转换单元513、获取单元514和分析单元515。设备服务平台520包括交互单元521。模型获取单元511用于获取语义模型，获取语义模型的步骤进一步包括建立语义模型，建立语义模型的过程与参照图1至图3所描述的步骤类似。在这里，由于图5的系统500中使用云服务器50来对多个工厂进行统一管理，因此在模型获取单元511在工厂的本地服务器51处建立生产线的虚拟模型之后，将其上传至云服务器50。同时，在本地服务器51处还事先配置了生产线相关的KPI计算公式，也将其一并上传至云服务器50。云服务器在接收到虚拟模型和KPI计算公式之后，将它们统一存储在KPI计算公式存储器502和生产线虚拟模型存储器503中。云服务器50还包括仪表板501，其用于显示生产线的虚拟模型、KPI计算公式和KPI计算结果，并可以与用户进行交互。
另外，设备服务平台520的交互单元521与设备层53的至少一条生产线的数据源531、532和533通信连接，其接收各数据源531、532和533的数据，并以统一的结构存储在数据库中。在图5的实施例中，设备服务平台520还提供了一系列数据服务接口522、523和524。这些数据服务接口供中间件平台510进行调用，以在数据库中定位数据。因此，统一结构的数据库的格式与表1类似，但增加了“数据服务”的字段。数据服务例如可以包括“getGoodNum(获得良品数量)”、“getBadNum(获得次品数量)”和“getTotalNum(获得总数量)”、等等。
因此，在该实施例中，当模型获取单元511在建立语义模型的步骤中建立上下文信息库时，还在上下文信息库中定义了以语义单元为索引的数据服务。仍以图2中示出的生产线L1为例，示出以语义单元为索引的数据服务的上下文信息的示例。
Figure PCTCN2018118188-appb-000007
如从以上示例的上下文信息中可以看到的，在本实施例中，语义单元good_num1(良品数量1)对应的是“getGoodNum(获得良品数量)”的数据服务，语义单元bad_num1(次品数量1)对应的是“getBadNum(获得次 品数量)”的数据服务，语义单元good_num3(良品数量1)对应的是“getGoodNum(获得良品数量)”的数据服务，并且它们所涉及的数据存储在数据服务器(即数据服务平台)d1中。数据服务器d1具有IP地址，并且提供的数据服务包括getGoodNum(获得良品数量)和getBadNum(获得次品数量)。
因此，相应地，所建立的语义模型的一部分如图6所示。如图6中看到的，语义单元good_num1(良品数量1)对应的数据服务为getGoodNum(获得良品数量)，而语义单元bad_num1(次品数量1)对应的数据服务为getBadNum(获得次品数量)，它们都属于数据服务器提供的数据服务。
应当指出，图6仅示出了语义模型的一部分，在本实施例中，对于一个工厂的各条生产线建立统一的语义模型，因此语义模型应当包括所有生产线所涉及的语义单元中的每个语义单元与该语义单元涉及的生产线的相应数据源标识及数据服务之间的语义关系。在建立语义模型之后，语义模型可以被存储在本地服务器51的本地存储器(未示出)中。模型获取单元511在需要获取语义模型时，可以直接从存储器中读取该语义模型。
继续参考图5，在对指定生产线的指定KPI进行计算时，用户可以经由云服务器50的仪表板501选择指定的生产线和指定的KPI计算公式。云服务器50将所指定的生产线标识和KPI计算公式发送给中间件平台510的接收单元512。接收单元512对KPI计算公式进行解析，以获得至少一个语义单元。可以根据预定的规则(例如，四则运算的运算符的优先级)来对KPI计算公式进行解析。
转换单元513基于模型获取单元511所获取的语义模型，可以将至少一个语义单元转换为对应的数据源标识和数据服务，并获得数据服务器的IP地址。数据获取单元314从数据服务器的IP地址调用相应的数据服务接口，根据数据源标识，在数据库定位对应的数据源，并获取其数据。与参照图2和图3所描述的实施例类似，在用户选择生产线和KPI计算公式时，还可以相应地输入与所选择的KPI计算公式有关的时间参数，以根据时间参数从数据库中选择对应数据。
在获得数据源的数据之后，分析单元315根据KPI计算公式，使用所获得的数据计算出KPI的值，并发送给云服务器，以在仪表板301上显示。在 该实施例中，使KPI计算在本地服务器处进行，而仅在云服务器的仪表板301上显示计算结果，因此与现有技术相比，避免了将计算所涉及的数据源的数据传送给云服务器，从而提高了数据传输的效率和成本。
在其它实施例中，本地服务器51也具有仪表板(未示出)，用户可以在本地服务器51处选择指定的生产线和KPI计算公式，本地服务器51计算出KPI值后显示在仪表板上。
图7示出了根据本公开的一个实施例的用于获得生产线中数据源的数据的计算设备700的框图。从图7中可以看出，用于获得生产线中数据源的数据的计算设备700包括处理器701和与处理器701耦接的存储器702。存储器702用于存储计算机可执行指令，当计算机可执行指令被执行时使得处理器701执行以上实施例中的方法。
此外，替代地，上述方法能够通过计算机可读存储介质来实现。计算机可读存储介质上载有用于执行本公开的各个实施例的计算机可读程序指令。计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是但不限于电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
因此，在另一个实施例中，本公开提出了一种计算机可读存储介质，该计算机可读存储介质具有存储在其上的计算机可执行指令，计算机可执行指令用于执行本公开的各个实施例中的方法。
在另一个实施例中，本公开提出了一种计算机程序产品，该计算机程序产品被有形地存储在计算机可读存储介质上，并且包括计算机可执行指令， 该计算机可执行指令在被执行时使至少一个处理器执行本公开的各个实施例中的方法。
一般而言，本公开的各个示例实施例可以在硬件或专用电路、软件、固件、逻辑，或其任何组合中实施。某些方面可以在硬件中实施，而其他方面可以在可以由控制器、微处理器或其他计算设备执行的固件或软件中实施。当本公开的实施例的各方面被图示或描述为框图、流程图或使用某些其他图形表示时，将理解此处描述的方框、装置、系统、技术或方法可以作为非限制性的示例在硬件、软件、固件、专用电路或逻辑、通用硬件或控制器或其他计算设备，或其某些组合中实施。
用于执行本公开的各个实施例的计算机可读程序指令或者计算机程序产品也能够存储在云端，在需要调用时，用户能够通过移动互联网、固网或者其他网络访问存储在云端上的用于执行本公开的一个实施例的计算机可读程序指令，从而实施依据本公开的各个实施例所公开的技术方案。
虽然已经参考若干具体实施例描述了本公开的实施例，但是应当理解，本公开的实施例并不限于所公开的具体实施例。本公开的实施例旨在涵盖在所附权利要求的精神和范围内所包括的各种修改和等同布置。权利要求的范围符合最宽泛的解释，从而包含所有这样的修改及等同结构和功能。

Claims (15)

  1. 用于获得生产线的数据源的数据的方法，包括：
    获得语义模型，所述语义模型包括多个语义单元中的每个与至少一条生产线的相应数据源标识之间的语义关系；
    接收生产线标识并获得至少一个语义单元；
    基于所述语义模型，将所述至少一个语义单元转换为所述生产线标识所表示的生产线的对应数据源标识；以及
    获取所述数据源标识所表示的数据源的数据。
  2. 根据权利要求1所述的方法，其中，获得语义模型包括：
    基于所述至少一条生产线的各组成部分的特征信息和生产顺序，建立所述至少一条生产线的虚拟模型；
    基于所述虚拟模型建立上下文信息库，所述上下文信息库包括所述至少一条生产线的各数据源的上下文信息；以及
    基于所述虚拟模型、所述多个语义单元和所述上下文信息库，将所述多个语义单元中的每个分别与所述至少一条生产线的相应数据源标识用语义关系连接，以形成所述语义模型。
  3. 根据权利要求1所述的方法，其中，获取所述数据源标识所表示的数据源的数据包括：
    从数据库中获取所述数据源标识所表示的数据源的数据，其中，所述数据库以统一的结构存储所述至少一条生产线的各数据源的数据。
  4. 根据权利要求3所述的方法，还包括：
    接收所述至少一条生产线的各数据源的数据，并以统一的结构存储在所述数据库中。
  5. 根据权利要求1所述的方法，还包括：
    基于预定的分析规则和所获取的数据源的数据，对所述生产线的性能进 行分析，以获得分析结果。
  6. 根据权利要求1所述的方法，其中，获得至少一个语义单元包括：
    接收生产线数据获取请求；以及
    对所述生产线数据获取请求进行解析，以获得所述至少一个语义单元。
  7. 根据权利要求6所述的方法，其中，所述生产线数据获取请求包括关键性能指标的计算公式，并且，所述方法还包括：
    基于所述计算公式和所获取的数据源的数据，对所述关键性能指标进行计算以获得计算结果。
  8. 根据权利要求1所述的方法，其中，所述数据源标识包括以下各项中的至少一项：数据源的网络地址、所述数据源的识别码、以及所述数据源的属性。
  9. 用于获得生产线的数据源的数据的装置，包括：
    模型获取单元，其被配置为获得语义模型，所述语义模型包括多个语义单元中的每个与至少一条生产线的相应数据源标识之间的语义关系；
    接收单元，其被配置为接收生产线标识并获得至少一个语义单元；
    转换单元，其被配置为基于所述语义模型，将所述至少一个语义单元转换为所述生产线标识所表示的生产线的对应数据源标识；以及
    数据获取单元，其被配置为获取所述数据源标识所表示的数据源的数据。
  10. 根据权利要求9所述的装置，其中，所述模型获取单元被进一步配置为：
    基于所述至少一条生产线的各组成部分的特征信息和生产顺序，建立所述至少一条生产线的虚拟模型；
    基于所述虚拟模型建立上下文信息库，所述上下文信息库包括所述至少一条生产线的各数据源的上下文信息；以及
    基于所述虚拟模型、所述多个语义单元和所述上下文信息库，将所述多个语义单元中的每个分别与所述至少一条生产线的对应数据源标识用语义关系连接，以形成所述语义模型。
  11. 根据权利要求9所述的装置，其中，所述数据获取单元被进一步配置为：
    从数据库中获取所述数据源标识所表示的数据源的数据，其中，所述数据库以统一的结构存储所述至少一条生产线的各数据源的数据。
  12. 根据权利要求11所述的装置，还包括：
    交互单元，其被配置为接收所述至少一条生产线的各数据源的数据，并以统一的结构存储在所述数据库中。
  13. 计算设备，所述计算机备包括：
    处理器；以及
    存储器，其用于存储计算机可执行指令，当所述计算机可执行指令被执行时使得所述处理器执行根据权利要求1-8中任一项所述的方法。
  14. 计算机可读存储介质，所述计算机可读存储介质具有存储在其上的计算机可执行指令，所述计算机可执行指令用于执行根据权利要求1-8中任一项所述的方法。
  15. 计算机程序产品，所述计算机程序产品被有形地存储在计算机可读存储介质上，并且包括计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行根据权利要求1-8中任一项所述的方法。
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