Method and system for organizing and storing discrete industrial time series data based on production process

Abstract

The invention discloses an organization and storage method and system for discrete industrial time series data based on a production flow, and the method comprises the steps: firstly, representing the production flow of a single product through a production flow description language, and forming a product production flow description file; secondly, generating a product data template according to the production process description file; and then associating data of each sensor with a production process according to the product data template to generate a single product data block. And finally, integrating the plurality of product data blocks to form a data page consisting of the plurality of product data blocks, and organizing and managing the data page. According to the method, the convenience and the efficiency of data processing are greatly improved in application scenes such as digital twinning, production process decision analysis, time sequence prediction and anomaly detection.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial data storage, and specifically relates to a method for organizing and storing discrete industrial time series data based on a production process, in particular to a discrete industrial data storage method and system based on the correlation between production process and time series. Background Art

[0002] The amount of industrial data generated during factory production is growing rapidly. These data are usually generated by sensors, equipment, and control systems, and are characterized by time series and diversity. The scope of data collection covers all stages of the production process, and can provide key basis for production monitoring, quality analysis, and decision support. However, the existing data storage method is mainly non-relational data storage, which is based on sensors and lacks overall time series correlation, making the data separated from each other in independent storage and unable to reflect the intrinsic connection between different nodes in the production process.

[0003] Existing storage methods also have shortcomings in data query and utilization efficiency. When it is necessary to conduct a comprehensive analysis of production data within a certain period of time, it is often necessary to repeatedly read and process data from multiple data sources, resulting in low query efficiency. At the same time, due to the underutilization of the correlation characteristics between data, there is redundant information in the storage structure, which further increases storage costs and affects the management and use of data.

[0004] Therefore, there is an urgent need for a storage and organization method for discrete industrial data that establishes the relationship between data with the production process as the core. Organizing and storing data in a structured manner can not only improve storage efficiency, but also greatly increase the utilization value of data during query and analysis. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a method and system for organizing and storing discrete industrial time series data based on the production process, which can effectively utilize the correlation between production time series and data to achieve efficient storage and management of industrial data. This meets the needs for data correlation and analyzability in industrial scenarios and provides strong support for the optimization of industrial production and intelligent decision-making.

[0006] In order to solve the above problems, the technical solution of the present invention is as follows:

[0007] A method for organizing and storing discrete industrial time series data based on a production process comprises the following steps:

[0008] S10: Use the production process description language to represent the production process of a single product, and form a product production process description file. The product production process description file describes the sensors and data types related to the product production action according to the production sequence.

[0009] S20: Generate a product data template according to the production process description file; the product data template specifies the type, name and location of each sensor data in a single product data block.

[0010] S30: Correlate each sensor data with the production process according to the product data template to generate a single product data block.

[0011] S40: Integrate multiple product data blocks to form a data page consisting of multiple product data blocks and organize and manage the data page.

[0012] In step S10, the production process description file F is defined by the production process description language L. The production process description file is a file ending with pdesc. The production process description file includes a process action name A = {a1, a2, ..., a m}(A is a set of process action names, a m is the name of the process action), sensor number S = {s1, s2, ..., s m}(S is the sensor number set, s m is the sensor number), data attribute D={d1,d2,…,d m}(D is a data attribute set, d m Data attributes for corresponding process actions) and key action tags (K is the key action tag set, It is a key action marker, that is, the name is a i whether the process action is a key action).

[0013] The production process description file is expressed in JSON language. The process action name, sensor ID and critical action tag are represented by ActionName, SensorID and isCriticalAction fields respectively. The data attribute is represented by the key name DataAttribute, and its corresponding value includes the data type DataType and the data length DataBytes. Finally, the production process is described by the integer sequence number as the key and the set of process action name, sensor ID, data attribute and critical action tag as the value.

[0014] In step S20, the product data template M includes: the size of a single product data block BSize, which satisfies where b i It is an action i The data size of a i The English code n of each process action represented i and data type d i , and its position in the product data block p iThe product data template is a binary file that follows the above format. Its suffix is ​​pdtem. The beginning of the file contains a file header that stores the file's metadata, including the product data template identifier, file version, number of actions, and data block size. Next is the data area of ​​the product data template, which first specifies the type and length of the product ID, and then arranges the process action tuples in the order of the production process. Each tuple represents an action data, including the process action name, sensor ID, data type, data value, and its position offset in the data block.

[0015] The product data template M in step S20 is generated by parsing the product production process description file F.

[0016] The parsing process includes the following sub-steps:

[0017] S201: Read the process action name set A, sensor set S and its data attribute D in the production process description file F.

[0018] S202: Create a mapping f:A→N, and change the process action name a i Mapped to the action data name n in the data template i N is the set of English codes of the process action names after mapping.

[0019] S203: Determine the storage type T of each action data according to the sensor ID and data attributes d .

[0020] S204: According to the sequence of the process actions represented by the process action name set A in the production process, the offset positions of the data in the product data block are allocated to generate an offset position set P = {p1, p2, ..., p m}, p i The process action is named a i The offset position of the process action represented in the data block satisfies

[0021] S205: Generate a standardized product data template M = (N, P, T d ).

[0022] Among them, the mapping f:A→N in step S202 refers to converting the Chinese process action name with clear semantics into the corresponding English code. The mapping can be specified by the program by default or manually. In step S203, the data type that can be finally recognized by the computer will be determined based on the data type DataType and data length DataBytes in the data attribute. In step S204, the production process sequence is determined based on the key sequence number in the production process description file, and then the offset position set P is generated based on the production process sequence.

[0023] The single product data block in step S30 is a fixed-length structure and contains the following information:

[0024] Unique product ID i .

[0025] Production time range T = [t start ,t end ]; where t start is the start time of product production, t end The end time of product production.

[0026] Action data area V = {v1, v2, ..., v m}. Among them, v i Action data generated for a single process action. Each action data v i The storage area is determined by P defined by M.

[0027] Among them, the product unique identification ID i The length and type of is defined by the product data template M, and the production time range T is represented by a Unix timestamp. According to the definition of the product data template M, the production time range T can be a Unix timestamp in seconds, milliseconds, or nanoseconds. Each action data v in the action data area i The storage area is determined by the offset P defined by the product data template M.

[0028] As a further improvement, the data page containing multiple product data blocks in step S40 is composed of a page header H, a data area C and an index area I. The page header is the meta information area of ​​the data page, recording the offset position of the data area and the index area in the page, including the page number, page size, number of data blocks and index position offset. The index area is used to store the index for locating data, which is composed of a data block time sequence index, a data block ID index and a single variable time sequence index.

[0029] The data block time sequence index, data block ID index, and univariate time sequence index contained in the index area are defined as follows:

[0030] Data block ID index: uniquely identifies the product ID i Mapped to the data block location set P B , i.e. g:ID i →P B .

[0031] Data block time order index: maps the time range T to the location set P of data block B B , that is, f:T→P B .

[0032] Univariate time sequence index: records the order of variables v at different time points, forming a sequence {(t i ,v i )}, satisfying t1 <t2<...<t n . Where t i For time.

[0033] In step S40, the data page containing multiple product data blocks is organized in time sequence when generated, and the location of the data page is guided by the secondary index. The secondary index also adopts the above three index forms: data block time sequence index, data block ID index and single variable time sequence index.

[0034] Another aspect of the present invention provides a system for organizing and storing discrete industrial time series data based on a production process, comprising the following modules:

[0035] The product production process description module is used to represent the production process of a single product through a production process description language, and to form a product production process description file that describes the sensor and data types related to the product production actions according to the production sequence.

[0036] The product data module is used to generate a product data template that specifies the type, name and position of each sensor data in a single product data block according to a production process description file.

[0037] The single product data module is used to associate each sensor data with the production process according to the product data template to generate a single product data block.

[0038] The combined product data module is used to integrate multiple product data blocks to form a data page composed of multiple product data blocks and organize and manage the data page.

[0039] Beneficial effects of the present invention:

[0040] The present invention uses a production process description language to represent the production process of a product and generates a corresponding product data template, thereby establishing a temporal relationship between data and the production process, thereby making up for the lack of neglect of the temporal relationship of the production process in traditional non-relational data storage. Thanks to the introduction of data block time sequence index and single variable time sequence index, the time complexity of time range query for single variable data and single product data block can reach O(log n ). At the same time, unlike traditional non-relational data storage that requires querying and reorganizing data when used, product data blocks based on production processes store the production data of a single product directly by process, providing a more efficient data access method. This storage method greatly improves the convenience and efficiency of data processing in application scenarios such as digital twins, production process decision analysis, time series prediction, and anomaly detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. The drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0042] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0043] Figure 2 A production process description file template provided for an embodiment of the present invention;

[0044] Figure 3 A product data template according to an embodiment of the present invention;

[0045] Figure 4 A production flow chart from a production process description file to a product data template according to an embodiment of the present invention;

[0046] Figure 5 The data block format and description examples of the embodiments of the present invention;

[0047] Figure 6 The data page format of the embodiment of the present invention;

[0048] Figure 7 In the form of a chronological index of the present invention;

[0049] Figure 8 Schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0050] The technical solution provided by the present invention will be further described below in conjunction with the accompanying drawings.

[0051] A method for organizing and storing discrete industrial time series data based on a production process comprises the following steps:

[0052] S10: Use the production process description language to represent the production process of a single product, and form a product production process description file. The product production process description file describes the sensors and data types related to the product production action according to the production sequence.

[0053] S20: Generate a product data template according to the production process description file; the product data template specifies the type, name and location of each sensor data in a single product data block.

[0054] S30: Correlate each sensor data with the production process according to the product data template to generate a single product data block.

[0055] S40: Integrate multiple product data blocks to form a data page consisting of multiple product data blocks and organize and manage the data page.

[0056] Figure 1 It is a flowchart of the present invention.

[0057] This embodiment uses a specific product production data example combined with implementation steps to illustrate the present invention in detail:

[0058] Step S10: Use a production process description language to represent the production process of a single product to form a product production process description file. The product production process description file describes the sensors and data types related to the product production action according to the production sequence.

[0059] The present invention is applicable to a process product production line. For a product produced by a process production line, it will pass through several physical devices during the production process, and each physical device will have its corresponding action meaning. The process of describing the action meaning of the physical device is the process of using the production process description language to describe the production process of a single product.

[0060] The production process description file includes a process action name A = {a1, a2, ..., a m}(A is a set of process action names, a i is the name of the process action), sensor number S = {s1, s2, ..., s m}(S is the sensor number set, s i is the sensor number), data attribute D={d1,d2,…,d m}(D is a data attribute set, d i Data attributes for corresponding process actions) and key action tags (K is the key action tag set, It is a key action marker, that is, the name is a i whether the process action is a key action).

[0061] The production process description file is expressed in JSON language. The process action name, sensor ID and critical action tag are represented by ActionName, SensorID and isCriticalAction fields respectively; the data attribute is represented by the key name DataAttribute, and its corresponding value includes the data type DataType and the data length DataBytes. For example, the feed photoelectric sensing action in a production process can be described as {"ActionName":"feed photoelectric sensing","SensorID":"I3.4","isCriticalAction":"true","DataAttribute":{"DataType":"timestamp","DataBytes":8}}. This means that the sensor ID corresponding to the feed photoelectric sensing action is I3.4, and the action is a critical action, and its data attribute is an 8-byte timestamp. Then, with the process action description as the value and the process action sequence number as the key, a representation of an action in the process is formed. For example: {1:{"ActionName":"Feeding photoelectric induction","SensorID":"I3.4","isCriticalAction":"true","DataAttribute":{"DataType":"timestamp","DataBytes":8}}} means that the feeding photoelectric induction action is the first action in the production process action sequence. A set of multiple key-value pairs with process action description as value and process action sequence number as key plus the data attribute description of the product ID defines a complete production process description file.

[0062] For example:

[0063]

[0064]

[0065] An example of a complete production process description file is shown in Figure 2 ; Figure 2 A production process description file consisting of 6 process actions is shown, in which the data type of the product ID is unsigned int and the data length is 8 bytes.

[0066] Step S20: Generate a product data template according to the production process description file; the product data template specifies the type, name and location of each sensor data in a single product data block.

[0067] The product data template M contains: the size of a single product data block BSize, satisfying where b i It is an actioni The data size of each action data a i Name i , data type d i , and its position in the product data block p i The product data template is a binary file that follows the above format. Its suffix is ​​pdtem. The beginning of the file contains a file header that stores the file's metadata, including the product data template identifier, file version, number of actions, and data block size. Next is the template data area, which first specifies the type and length of the product ID, and then arranges the process action tuples in the order of the production process. Each tuple represents an action data, including the action name, data type, data value, and its position offset in the data block. Figure 3 The format of the product data template is Figure 3 The order from left to right and from top to bottom is the organization order of the elements in the product data template in the binary file. The size of each element in bytes is Figure 3 Already marked in.

[0068] The product data template M in step S20 is generated by parsing the product production process description file F.

[0069] The parsing process includes the following sub-steps:

[0070] S201: Read the process action name set A, sensor set S and its data attribute D in the production process description file F.

[0071] S202: Create a mapping f:A→N, and change the process action name a i Mapped to the action data name n in the data template i N is the set of English codes of the process action names after mapping.

[0072] S203: Determine the storage type T of each action data according to the sensor ID and data attributes d .

[0073] S204: According to the sequence of the process actions represented by the process action name set A in the production process, the offset positions of the data in the product data block are allocated to generate an offset position set P = {p1, p2, ..., p m}, p i The process action is named a i The offset position of the process action represented in the data block satisfies

[0074] S205: Generate a standardized product data template M = (N, P, T d ).

[0075] Figure 4 The mapping f:A→N in step S202 refers to converting the Chinese action name with clear semantics into the corresponding English code, and the mapping can be specified by the program by default or manually. Figure 4 In the manual assignment, each action is given a more readable English code. If the default assignment is used, the generated code is in the form of P01, P02, etc., which starts with the letter P and then a number.

[0076] In step S203, the data type that can be finally recognized by the computer is determined based on the data type DataType and the data length DataBytes in the data attribute. Figure 4 The timestamp type plus the 8-byte length ultimately determines that the data type of the action is UINT64, which is an 8-byte unsigned integer.

[0077] In step S204, the production process sequence is determined according to the key serial number in the production process description file, and then an offset position set P is generated according to the production process sequence. The position offset is related to the production sequence and data type. For example, the action data types in 4 are all 8-byte unsigned integers, so their position offset is the action serial number multiplied by the data length plus the starting position.

[0078] Finally, the product data template is generated. The product data template is in binary byte form. Figure 4 The product data template (byte form) shown in . For ease of presentation, hexadecimal is used to represent binary data.

[0079] Step S30: associating each sensor data with the production process according to the product data template to generate a single product data block;

[0080] A single product data block contains the product's unique ID i , production time range T = [t start ,t end ] and action data area V = {v1, v2, ..., v m}Three parts.

[0081] Among them, the product unique identification ID i The length and type of is defined by the product data template M, and the production time range T is represented by a Unix timestamp. According to the definition of the product data template M, the production time range T can be a Unix timestamp in seconds, milliseconds, or nanoseconds. Each action data v in the action data area i The storage area is determined by the offset P defined by the product data template M.

[0082] Figure 5It shows the form of a product data block. The first 16 bytes are the product ID, which is the ID set for the product by the factory during the actual production process. If the factory does not set an ID for the product, a default ID is set for the product in the form of an integer auto-increment; the following 16 bytes are the production time range of the product. The first 8 bytes of the production time range are the production start time, and the last 8 bytes are the production end time. Next is the action data area, which stores the actual action data. Figure 5 In the data block example shown, the product ID of the product is 100001410402668; the UNIX timestamp of the production start time is 1732979729860, which is 23:15:29 on November 30, 2024; the UNIX timestamp of the production end time is 1732980131960, which is 23:22:11 on November 30, 2024. Corresponding to the product data template, the first action of the data block example is the InSense action, which is the photoelectric sensing action for feeding; in the data block example, the trigger time of the InSense action is 1732979739860, which is 23:15:39 on November 30, 2024.

[0083] Step S40: Integrate multiple product data blocks to form a data page consisting of multiple product data blocks and organize and manage the data page.

[0084] The data page consists of a page header H, a data area C, and an index area I. The page header is the metadata area of ​​the data page, which records the offset position of the data area and the index area in the page, including the page number, page size, number of data blocks, and index position offset. The index area is used to store indexes for locating data, which consists of a data block time sequence index, a data block ID index, and a univariate time sequence index.

[0085] Figure 6 The following is the data page format, where the page number is represented by an integer and increases automatically as the number of data pages increases; the page size is the size of the data page itself, which includes the page header, data area, and index area; the number of data blocks is the number of data blocks contained in the page, and all data blocks can be located by combining the data block size recorded in the product data template. The index position offset is the offset of the start position of the index area relative to the start position of the data page. Since the index area is at the end of the data page, the index position offset is used to locate the index area.

[0086] The data block time sequence index, data block ID index, and univariate time sequence index contained in the index area are defined as follows:

[0087] Data block ID index: uniquely identifies the product ID i Mapped to the data block location set P B , i.e. g:IDi →P B .

[0088] Data block time order index: maps the time range T to the location set P of data block B B , that is, f:T→P B .

[0089] Univariate time sequence index: records the order of variables v at different time points, forming a sequence {(t i ,v i )}, satisfying t1 <t2<…<t n .

[0090] The data block ID index is in the form of a key-value pair. Through the data block ID index hash function, a hash calculation is performed on a specific data block ID. The hash calculation is used as the key and the corresponding position offset is used as the value to quickly locate the index of the data block ID. If a hash collision occurs, that is, the hash values ​​corresponding to different data block IDs are the same, the chain address method is used to avoid it.

[0091] The index structure used by the data block time sequence index and the univariate time sequence index is consistent, both of which are time sequence indexes. The data block time sequence index is a time sequence index for the production time range field in the data block, and the univariate time sequence index is a time sequence index for the same action data in different data blocks.

[0092] The time-order index is a tree structure in which nodes are connected sequentially. Figure 7 This is an example of a single variable time sequence index. First, the data points of the same variable are indexed in time sequence to form a linear sequence index at the bottom of the index tree. Then, a tree-shaped time index is established based on the linear sequence index. Its characteristic is that it uses a binary method to establish indexes layer by layer. The node of the tree-shaped time index is a time range, and a single variable node can be quickly located through the tree-shaped time index. For example, locating Figure 7 The data point 1732979749860 in the figure can be located through the time sequence structure via the nodes [1732979729860,1732979749860] and [1732979739860,1732979749860]. At the same time, the data can also be located sequentially from the data point 1732979749860.

[0093] As production progresses, product data will gradually increase, and the number of data pages will also increase. Multiple data pages are organized in chronological order when they are generated, and the location of data pages is guided by secondary indexes. The secondary index also uses the three aforementioned index forms: data block chronological index, data block ID index, and single variable chronological index. Its principle is the same as the above index principle.

[0094] like Figure 8 As shown, another aspect of the present invention provides a system for organizing and storing discrete industrial time series data based on a production process, comprising the following modules:

[0095] The product production process description module is used to represent the production process of a single product through a production process description language, and to form a product production process description file that describes the sensor and data types related to the product production actions according to the production sequence.

[0096] The product data module is used to generate a product data template that specifies the type, name and position of each sensor data in a single product data block according to a production process description file.

[0097] The single product data module is used to associate each sensor data with the production process according to the product data template to generate a single product data block.

[0098] The combined product data module is used to integrate multiple product data blocks to form a data page composed of multiple product data blocks and organize and manage the data page.

Claims

1. A method for organizing and storing discrete industrial time series data based on production processes, characterized in that: The following steps are involved: S10: Use the production process description language to represent the production process of a single product and form a product production process description file; S20: Generate a product data template according to the production process description file; S30: Associating each sensor data with the production process according to the product data template to generate a single product data block; S40: Integrate multiple product data blocks to form a data page consisting of multiple product data blocks and organize and manage the data page.

2. The method for organizing and storing discrete industrial time series data based on production processes according to claim 1 is characterized in that: The specific implementation process of step S10 is as follows: The production process description file F is defined by the production process description language L. The production process description file includes a process action name set A = {a1, a2, ..., a m }, a m is the process action name, the sensor number set S = {s1, s2, ..., s m },s m is the sensor number, the data attribute set D = {d1, d2, ..., d m }, d m is the data attribute corresponding to the process action, key action set It is a key action marker, that is, the name is a i Whether the process action is a key action; The production process description file is expressed in JSON language, and the process action name, sensor ID and critical action tag are represented by ActionName, SensorID and isCriticalAction fields respectively; The data attribute is represented by the key name DataAttribute, and its corresponding value includes the data type DataType and the data length DataBytes; finally, the production process is described by an integer serial number as the key and the set of process action name, sensor ID, data attribute and key action tag as the value.

3. The method for organizing and storing discrete industrial time series data based on production processes according to claim 2 is characterized in that: The generated product data template is generated by parsing the product production process description file F; The product data template M contains: the size of a single product data block BSize, satisfying where b i It is action a i The data size of a i The English code n of each process action represented i and data type d i , and its position in the product data block p i ; The product data template is a binary file that follows the above format. The beginning of the file contains a file header that stores the file's metadata, including the product data template identifier, file version, number of actions, and data block size; Data area of ​​the product data template: This area first specifies the type and length of the product ID, and then arranges the process action tuples in the order of the production process. Each tuple represents an action data, including the process action name, sensor ID, data type, data value and its position offset in the data block.

4. The method for organizing and storing discrete industrial time series data based on production processes according to claim 3 is characterized in that: The specific process of parsing the product production process description file F is as follows S201: Reading the process action name set A, sensor set S and its data attribute D in the production process description file F; S202: Create a mapping f:A→N, and change the process action name a i Mapped to the action data name n in the data template i , N is the set of English codes of the process action names after mapping; S203: Determine the storage type T of each action data according to the sensor ID and data attributes d ; S204: According to the sequence of the process actions represented by the process action name set A in the production process, the offset positions of the data in the product data block are allocated to generate an offset position set P = {p1, p2, ..., p m }, p i The process action is named a i The offset position of the process action represented in the data block satisfies S205: Generate a standardized product data template M = (N, P, T d ).

5. The method for organizing and storing discrete industrial time series data based on production processes according to claim 4 is characterized in that: The single product data block is a fixed-length structure and contains the following information: Unique product ID i ; Production time range T = [t start ,t end ]; where t start is the start time of product production, t end The end time of product production; Action data area V = {v1, v2, ..., v m }; where v i Action data generated for a single process action; each action data v i The storage area is determined by P defined by M; Among them, the product unique identification ID i The length and type of are defined by the product data template M, and the production time range T is represented by a Unix timestamp.

6. The method for organizing and storing discrete industrial time series data based on production processes according to claim 5 is characterized in that: The specific implementation process of step S40 is as follows: The data page containing multiple product data blocks is composed of a page header H, a data area C and an index area I, wherein the page header is the meta information area of ​​the data page, recording the offset position of the data area and the index area in the page, including the page number, page size, number of data blocks and index position offset; the index area is used to store the index used to locate the data, and is composed of a data block time sequence index, a data block ID index and a single variable time sequence index; The data block time sequence index, data block ID index, and univariate time sequence index contained in the index area are defined as follows: Data block ID index: uniquely identifies the product ID i Mapped to the data block location set P B ; Data block time order index: maps the time range T to the location set P of data block B B ; Univariate time sequence index: records the order of variables v at different time points, forming a sequence {(t i ,v i )}, satisfying t1 <t2<...<t n , where t i For time.

7. The method for organizing and storing discrete industrial time series data based on production processes according to claim 6 is characterized in that: In step S40, the data page containing multiple product data blocks is organized in chronological order when generated, and the positioning of the data page is guided by a secondary index; the secondary index also adopts the three index forms mentioned above: data block chronological index, data block ID index and univariate time sequence index.

8. A system for organizing and storing discrete industrial time series data based on a production process, used to implement the organization and storage method according to any one of claims 1 to 7, characterized in that: Includes the following modules: The product production process description module is used to express the production process of a single product through the production process description language, and form a product production process description file that describes the sensors and data types related to the product production actions according to the production sequence; A product data module is used to generate a product data template that specifies the type, name and location of each sensor data in a single product data block according to a production process description file; Single product data module, used to associate each sensor data with the production process according to the product data template to generate a single product data block; The combined product data module is used to integrate multiple product data blocks to form a data page composed of multiple product data blocks and organize and manage the data page.

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