Industrial time series data analysis method based on modular configuration

Abstract

The invention discloses an industrial time series data analysis method based on modular configuration, and aims to separate time series data definition and analysis and realize efficient analysis through a time series data configuration file. The method comprises the following steps: S10, acquiring sensor data by a production line end, converting the sensor data into an original message containing a timestamp, the number of sensors and an ID, and analyzing a configuration file to generate configuration information containing a station action sequence; and S20, analyzing the original message to generate a timestamp and a sensor ID list, and matching the timestamp and the sensor ID list to each station sensor cache. And S30, determining an actually executed action sequence by each station according to the configuration information and the matched cache data, and storing the action sequence into an execution sequence cache. And S40, combining the station execution sequences according to the station execution sequence to obtain a complete action sequence and a timestamp of the product in the production line process. The method provided by the invention has configurability, can adapt to different industrial production requirements, and realizes efficient and accurate time series data analysis.

Description

Technical Field

[0001] The present invention belongs to the field of industrial time series data analysis, and in particular relates to a modularly configured industrial time series data analysis method. Background Art

[0002] In modern industrial production, the widespread use of devices such as sensors and actuators generates a large amount of time series data. These data record various states and actions in the production process, which are of great significance for monitoring production processes, detecting anomalies, and optimizing production efficiency. However, the parsing and processing of industrial time series data usually face challenges such as large and complex data volume, diverse data sources, high real-time requirements, and high parsing complexity. Existing industrial time series data parsing methods usually have the following problems: Traditional data parsing methods are usually based on a single parsing strategy, which cannot cope with diverse data sources and complex data structures, resulting in low parsing efficiency. Parsing methods are usually fixed, difficult to flexibly adjust and optimize according to different production environments and needs, and lack effective matching algorithms and optimization strategies.

[0003] In view of the shortcomings of existing technologies, a new and efficient industrial time series data analysis technology is urgently needed to solve the above problems. Summary of the invention

[0004] The present invention proposes an industrial time series data analysis method based on modular configuration, comprising the following steps:

[0005] S10: inserting a collection program at the production line end, collecting sensor information and converting it into an original message of industrial time series data; the original message of industrial time series data consists of a number of original message data points, and the format of the original message data point includes a timestamp T, the number of triggered sensors N, and the IDs of the N sensors; obtaining a time series data configuration file and parsing it to generate workstation configuration information in a structural form; the action sequence is an array composed of the sensor IDs of a number of sensors related to the execution of an action sequence arranged in sequence, and each workstation has one or more action sequences;

[0006] S20: parse the original message of industrial time series data to obtain a list L consisting of timestamps and corresponding sensor IDs.

[0007] According to the sensor ID number, the data in the list L is matched with each workstation and stored in the sensor cache of the corresponding workstation; the sensor cache includes a sensor ID cache and a timestamp cache;

[0008] S30: For each workstation, all action sequences of the workstation are obtained from the workstation configuration information, the action sequences are matched with the workstation sensor cache one by one, the action sequence executed by the workstation is obtained according to the matching result, and stored in the execution sequence cache of the workstation;

[0009] S40: According to the workstation execution order defined in the workstation configuration information, the execution sequences of different workstations are read in turn for combination and splicing, and the action sequence and timestamp of a product passing through all workstations in the production line process are obtained as the analysis result.

[0010] Preferably, the execution process of the acquisition program in S10 includes the following steps:

[0011] (1) Assign a unique identifier ID to each sensor;

[0012] (2) performing data acquisition operations at a predetermined period Δt; at the beginning of each period, recording the current timestamp T;

[0013] In the current cycle, record all triggered sensor IDs to form a list V;

[0014] (3) If the list V is not empty and contains N sensor IDs, the collected data is encapsulated into a data packet;

[0015] The format of the data packet is: {timestamp T, number of triggered sensors N, sensor ID1, sensor ID2, ...,

[0016] Sensor ID n}.

[0017] Preferably, the timing data configuration file in S10 includes the following contents: a unique number of the workstation, a name of the workstation, a list of action sequences of the workstation, and a sequential execution order relationship between the workstations.

[0018] Preferably, the workstation configuration information in the structural form of S10 includes the following contents: the workstation unique number unique_id, the data comes from the unique number of the workstation in the timing data configuration file, the workstation name pos_name, the data comes from the name of the workstation in the timing data configuration file, the action sequence list possible_exc_seqs, the data comes from the action sequence list of the workstation in the timing data configuration file, the order of the workstation in the production line process template_info, the action sequence list length possible_exc_seqs_length, used to record the length of the action sequence list possible_exc_seqs, the workstation sensor ID cache fifo_id, the workstation timestamp cache fifo_time, and the parsed execution sequence cache fifo_excute_seq of the workstation.

[0019] Preferably, step S20 includes the following sub-steps:

[0020] S201: parse the original message of industrial time series data sent by the acquisition program, extract the sensor ID and timestamp contained in each original message data point, and encapsulate each original message data point into a key-value pair {timestamp, sensor ID}, so as to construct a list L;

[0021] S202: constructing a hash table H through the workstation configuration information in S10, wherein the key of the hash table H is the sensor ID and the value is the workstation ID that needs the sensor data; this hash table is used for subsequent data distribution;

[0022] S203: Based on the hash table H constructed in S202, the list L is traversed. For each key-value pair {timestamp, sensor ID} in the list L, the hash table H is searched to see whether there is a workstation with the sensor ID of the key-value pair as the key; if so, the sensor ID of the key-value pair is added to the workstation sensor ID cache fifo_id of the corresponding workstation, and the timestamp of the key-value pair is added to the timestamp cache fifo_time of the corresponding workstation; if not, the key-value pair is discarded.

[0023] Preferably, step S30 includes the following sub-steps:

[0024] S301: Based on the sensor ID cache fifo_id and the timestamp cache fifo_time of the current workstation, a time series data list received by the current workstation is constructed; each element of the time series list is a key-value pair {timestamp, sensor ID}, and the action sequence list possible_exc_seqs of the current workstation is obtained from the workstation configuration information;

[0025] S302: Traverse each action sequence in the action sequence list possible_exc_seqs of the current workstation; for each action sequence, perform the following matching operations:

[0026] a. Match the current action sequence with the time series data list received by the workstation. The matching rule is: check whether the sensor ID in the action sequence exists in the time series data list in order; the timestamp is not a necessary condition for matching, but is used for the association of subsequent results.

[0027] b. If yes, record the unique identifier of the action sequence that matches successfully, and extract the timestamp list corresponding to the sensor ID in the action sequence from the time series data list; combine the unique identifier of the action sequence and the corresponding timestamp list into a result item; for example, {action sequence identifier: X, timestamp list: [T1, T2, T3, ...]}.

[0028] S303: All result items of the successfully matched action sequences are stored in the execution sequence cache fifo_excute_seq of the workstation;

[0029] Preferably, step S40 includes the following sub-steps:

[0030] S401: Obtaining the order template_info of all workstations in the production line process from the workstation configuration information to form a global execution order between workstations; the execution order between global workstations refers to the order of a product passing through all workstations in the production line process;

[0031] S402: traverse each workstation in turn according to the execution order between the global workstations; for the currently traversed workstation, read the action sequence result item with the smallest timestamp from its execution sequence cache fifo_excute_seq, and add it to the product analysis result list; the product analysis result list refers to a list of action sequences and timestamps of all workstations that a product passes through in the production line process;

[0032] S403: After all workstations have been traversed, the final product parsing result list will contain the action sequences of all workstations arranged in execution order and their corresponding timestamp information;

[0033] S404: Convert the format of the final product analysis result list for subsequent analysis or use.

[0034] Compared with the prior art, the present invention completely separates the definition of time series data (through configuration files) from the parsing process, and uses configuration files in TOML format to describe production line and workstation information, achieving high flexibility and configurability. By modifying the configuration files, users can easily adapt to different production environments, adjust the order of workstations, add or delete workstations and sensors, modify action sequences, etc., without modifying the code, which greatly simplifies system configuration and maintenance and reduces deployment costs. This modular configuration method enables the present invention to be quickly deployed to different production lines, with good versatility and scalability;

[0035] In the data preprocessing stage, the present invention introduces a hash table to maintain the mapping relationship between the sensor ID and the workstation ID. The average search time complexity of the hash table O(1) is used to accelerate the speed of data filtering and distribution, effectively reducing the time consumption of the data preprocessing stage, which is significantly better than the traditional linear search method.

[0036] The various parsing steps (configuration file parsing, data preprocessing, action sequence matching, product parsing result generation) of the present invention are relatively independent and highly modularized. This modular design makes the system structure clear, easy to understand and maintain, and facilitates subsequent functional expansion and upgrading. For example, it is easy to add new data processing modules, modify matching algorithms, expand output formats, etc. without affecting other parts of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific implementation of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the specific implementation or the prior art description. Some specific embodiments of the present invention will be described in detail in an exemplary but not restrictive manner with reference to the drawings. The same reference numerals in the drawings indicate the same or similar components or parts. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0038] Figure 1 Flow chart of the present invention

[0039] Figure 2 The original time series data format and conversion diagram of the present invention

[0040] Figure 3 Schematic diagram of data flow of each module of the present invention DETAILED DESCRIPTION

[0041] The technical solution provided by the present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0042] The present invention proposes an industrial time series data analysis method based on modular configuration, the flow chart is as follows Figure 1 The steps in the flowchart are further described below:

[0043] S10: Insert the acquisition program at the production line end, collect sensor information and convert it into industrial time series data original message, obtain the time series data configuration file and parse it, and generate the workstation configuration information in the form of a structure. The following is the specific structure of the workstation information structure:

[0044]

[0045]

[0046] The core of this step is to use a time series data configuration file (TOML language is used in this invention) to describe the production process of a production line, so as to realize the digitization and standardization of production line information. A production line usually consists of one or more workstations, each of which performs one or more action sequences. Each sensor is assigned a unique ID number. Through the time series data configuration file, complex production processes can be clearly expressed, and subsequent analysis and processing are convenient. In the embodiment, the time series data configuration file is as follows:

[0047] [line]

[0048] name="Main Production Line"

[0049] id=1

[0050] [line.workstations.workstation1]

[0051] name="Assembly Station"

[0052] id=1

[0053] action_sequences=[[101,102,103],[104,105],

[106] ]

[0054] [line.workstations.workstation2]

[0055] name="Packaging Station"

[0056] id=2

[0057] action_sequences=[[201,202],[203,204],[205,206,207]]

[0058] [line.workstation_order]

[0059] order=[1,2]

[0060] The functions of the main fields of the above time series data configuration file are as follows: [line] represents the root node of the production line, where the name field defines the name of the production line, which is "Main Production Line" here, and the id field defines the unique identifier of the production line, which is 1 here. [line.workstations.workstation1] represents the first workstation in the production line, where the name field defines the name of the workstation, which is "Assembly Station" here, and the id field defines the unique identifier of the workstation, which is 1 here. The action_sequences field defines the action sequence array of the workstation. Each action sequence is an array composed of sensor IDs, including three action sequences here: [101,102,103], [104,105] and

[106] . [line.workstations.workstation2] represents the second workstation in the production line. The name field defines the name of the workstation, which is "Packaging Station" here. The id field defines the unique identifier of the workstation, which is 2 here. The action_sequences field defines the action sequence array of the workstation. Each action sequence is an array composed of action IDs. Here, there are three action sequences: [201,202], [203,204], and [205,206,207]. The order in [line.workstation_order] represents the execution order between the workstations in the production line. [1,2] means that workstation 1 is executed first, and then workstation 2. This time series data configuration file describes a production line named "Main Production Line" with an ID of 1. The production line contains two workstations, namely "Assembly Station" and "Packaging Station", whose IDs are 1 and 2 respectively. Each workstation has one or more action sequences, and the action sequence is represented by an array composed of sensor IDs. According to the production line configuration file described by TOML, the workstation configuration information can be parsed.

[0061] S20: parse the original message of industrial time series data, obtain a list L consisting of timestamps and corresponding sensor IDs, match according to the sensor ID number, correspond the data in list L to each workstation and store them in the sensor cache of the corresponding workstation. The data stream conversion process is as follows: Figure 3 The detailed steps are as follows:

[0062] S201: Receive and parse the original message of industrial time series data sent by the acquisition program, extract the sensor ID and timestamp contained in each original message data point, and encapsulate each original message data point into a key-value pair {timestamp, sensor ID} to construct a list L. The original message format of industrial time series data is: {timestamp T, number of triggered sensors N, sensor ID1, sensor ID2, ..., sensor ID N}, and the specific conversion process is as follows: Figure 2 For example, if the received industrial timing raw data message is: {1635678900,3,101,102,103}, the parsed list L is:

[0063] [{1635678900,101},{1635678900,102},{1635678900,103}], if two consecutive messages {1678886400,5,201,202,203,204,205} and {1678886401,2,206,207} are received, the list L generated after parsing is: [{1678886400,201},{1678886400,202},{1678886400,203},

[0064] {1678886400,204},{1678886400,205},{1678886401,206},{1678886401,207}].

[0065] S202: Construct a hash table H according to the workstation configuration information parsed in step S10, which is used to maintain the mapping relationship between the sensor ID and the workstation ID that needs the sensor data. The key of the hash table is the sensor ID, and the value is the workstation ID that needs the sensor data. This hash table is used for subsequent data distribution. Assume that the workstation configuration information parsed according to S10 is as follows:

[0066] Station 1 (Assembly Station) requires sensor IDs: 101, 102, 103;

[0067] Station 2 (Packaging Station) requires sensor IDs: 201, 203, 206.

[0068] Then the constructed hash table H is {H

[101] =[1],H

[102] =[1],H

[103] =[1],H

[201] =[2],H

[202] =[2],H

[203] =[2]}.

[0069] S203: Distribute and filter data based on the hash table constructed in S202, traverse the list L, and for each key-value pair {timestamp, sensor ID} in the list, perform the following operations:

[0070] 1. Check in the hash table H whether there is a workstation with the sensor ID of the key-value pair as the key.

[0071] 2. If it exists, add the sensor ID of the data point to the fifo_id cache of the corresponding station, and add the timestamp of the key-value pair to the fifo_time cache of the corresponding station.

[0072] 3. If the mapping for the sensor ID does not exist in the hash table, the key-value pair is discarded.

[0073] Assume there is a list L:

[0074] [{1678886400,101},{1678886400,103},{1678886400,105},{1678886402,102},{1678886402,201},{1678886405,203},{1678886405,202},{1678 886405,207},{1678886405,101},{1678886406,300}; the hash table H is {H

[101] =[1],H

[102] =[1],H

[103] =[1],H

[201] =[2],H

[202] =[2],H

[203] =[2]}, the execution results are as follows:

[0076] Station 1 fifo_time: [1678886400,1678886400,1678886402,1678886405]

[0077] Station 1 fifo_id: [101,103,102,101]

[0078] Station 2 fifo_time: [1678886402,1678886405,1678886405]

[0079] Station 2 fifo_id: [201,203,202]

[0080] S30: For each workstation, all action sequences of the workstation are obtained from the workstation configuration information, and the action sequences are matched with the workstation sensor cache one by one. The action sequence executed by the workstation is obtained according to the matching result, and the result is stored in the execution sequence cache of the workstation. The data stream conversion process is as follows: Figure 3 As shown, the specific steps are as follows:

[0081] S301: construct a time series data list received by the current workstation from the sensor ID cache fifo_id and timestamp cache fifo_time of the current workstation. Each element of the list is a key-value pair {timestamp, sensor ID}, and obtain the action sequence list possible_exc_seqs of the current workstation from the workstation configuration information;

[0082] S302: Traverse each action sequence in the action sequence list possible_exc_seqs of the current workstation. For each action sequence, perform the following matching operations:

[0083] a. Match the current action sequence (consisting of a series of sensor IDs) with the time series data list received by the station. The matching rule is: check whether the sensor IDs in the action sequence exist in the time series data list in order. The timestamp is not a necessary condition for matching, but is used for the association of subsequent results.

[0084] b. If the match is successful (that is, all sensor IDs in the action sequence appear in the time series data list in order

[0085] In the example above, the unique identifier of the successfully matched action sequence is recorded, and the timestamp list corresponding to the sensor ID in the action sequence is extracted from the time series data list. The action sequence identifier and the corresponding timestamp list are combined into a result item, such as {action sequence identifier: X, timestamp list: [T1, T2, T3, ...]}.

[0086] S303: Store all result items of successfully matched action sequences (including action sequence identifiers and corresponding timestamp lists) into the execution sequence cache fifo_excute_seq of the workstation;

[0087] Specifically, suppose there is a workstation with the following data in its sensor cache: [{16001,101},{16003,102},{16005,104},{16007,103}], where the first item of each data tuple is the timestamp and the second item is the sensor ID.

[0088] There are two action sequences that can be executed at this station, defined as follows: Sequence 1: [101,102], Sequence 2: [104,103]. Extracting actions from the sensor cache and trying to match them against the above action sequence, we can get:

[0089] Action sequence 1 matches the actions {16001,101} and {16003,102} in the sensor cache in sequence, which conforms to the definition of sequence 1. Therefore, it is confirmed that the action sequence executed by the workstation is sequence 1, and the corresponding timestamp is extracted as [16001,16003]. Action sequence 2 matches the actions {16005,104} and {16007,103} in the sensor cache in sequence, which conforms to the definition of sequence 2. Therefore, it is confirmed that the action sequence executed by the workstation is sequence 2, and the corresponding timestamp is extracted as [16005,16007]. The parsing results can be listed as follows:

[0090]

[0091] This result shows that the workstation has executed two action sequences, sequence 1 and sequence 2, in sequence 1, where the timestamp corresponding to the sequence 1 is [16001, 16003]; the timestamp corresponding to the sequence 2 is [16005, 16007].

[0092] S40: According to the station execution order defined in the station configuration information, read the execution sequences of different stations in turn for combination and splicing, and obtain the action sequence and timestamp of a product passing through all stations in the production line process as the analysis result. The analysis result of a product refers to a list of action sequences and timestamps of the product passing through all stations in the production line process, which represents the assembly process of the product when passing through different stations. Figure 3 As shown, after completing steps S10 to S30, the action sequence of each workstation and its corresponding timestamp list have been obtained. Next, the action sequences of these workstations need to be combined in the order of the product production process to form a complete product analysis result. The specific steps are as follows:

[0093] S401: Obtain the order template_info of the production line process of all workstations from the workstation configuration information to form a global execution order between workstations. This information defines the execution order of each workstation in the product production process.

[0094] S402: traverse each workstation in turn according to the acquired workstation execution order. For the currently traversed workstation, read the first matching action sequence result from its execution sequence cache fifo_excute_seq and add it to the product analysis result list;

[0095] S403: After all workstations have been traversed, the final product parsing result list will contain the action sequences of all workstations arranged in execution order and their corresponding timestamp information;

[0096] S404: Format or convert the final product analysis result list for subsequent analysis or use.

[0097] Assume that there is the following execution sequence cache for the workstation (obtained from the result of step S30)

[0098] Station 1 (Assembly Station):

[0099]

[0100] Station 2 (Packaging Station):

[0101]

[0102] And according to the [line.workstation_order]order=[1,2] field in the production line TOML configuration file, the execution order of station 1 should be earlier than that of station 2. Therefore, the execution sequence cache of station 1 and station 2 is read in turn according to the execution order, and two products can be parsed, one of which executes sequence 1 on the Assembly Station and action sequence 3 on the Packaging Station; the other product executes action sequence 2 on the Assembly Station and action sequence 4 on the Packaging Station.

[0103] Product 1

[0104]

[0105] Product 2

[0106]

[0107] Finally, the parsing results of the product are stored in a suitable data structure (in this embodiment, a csv table is used). The storage content of product 1 is shown in the following table:

[0108] Workstation Sequence ID Timestamp 1 Timestamp 2 Assembly Station Sequence 1 16001 16003 Packaging Station Sequence 3 16010 16012

[0109] The storage contents of product 2 are shown in the following table:

[0110] Workstation Sequence ID Timestamp 1 Timestamp 2 Assembly Station Sequence 2 16005 16007 Packaging Station Sequence 4 16015 16017

[0111] The above descriptions are only some specific implementation methods of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person familiar with the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. An industrial time series data analysis method based on modular configuration, characterized in that: The following steps are involved: S10: inserting a collection program at the production line end to collect sensor information and convert it into an original message of industrial time series data; the original message of industrial time series data consists of a number of original message data points, and the format of the original message data point includes a timestamp T, the number of triggered sensors N, and the IDs of the N sensors; Obtain and parse the time series data configuration file to generate workstation configuration information in the form of a structure; The action sequence is an array consisting of sensor IDs of several sensors related to executing an action sequence arranged in sequence, and each workstation has one or more action sequences; S20: parse the original message of industrial time series data to obtain a list L consisting of timestamps and corresponding sensor IDs. According to the sensor ID number, the data in the list L is matched with each workstation and stored in the sensor cache of the corresponding workstation; the sensor cache includes a sensor ID cache and a timestamp cache; S30: For each workstation, obtain all action sequences of the workstation from the workstation configuration information. Match the action sequence with the workstation sensor cache one by one, obtain the action sequence executed by the workstation according to the matching result, and store it in the execution sequence cache of the workstation; S40: According to the workstation execution order defined in the workstation configuration information, the execution sequences of different workstations are read in turn for combination and splicing, and the action sequence and timestamp of a product passing through all workstations in the production line process are obtained as the analysis result.

2. The industrial time series data analysis method according to claim 1, characterized in that: The execution process of the acquisition program in S10 includes the following steps: (1) Assign a unique identifier ID to each sensor; (2) Perform data acquisition operations at a predetermined period Δt; at the beginning of each period, record the current timestamp T; within the current period, record all triggered sensor IDs to form a list V; (3) If the list V is not empty and contains N sensor IDs, the collected data is encapsulated into a data packet; the format of the data packet is: {timestamp T, number of triggered sensors N, sensor ID1, sensor ID2, ..., sensor ID n }.

3. The industrial time series data analysis method according to claim 1, characterized in that: The timing data configuration file in S10 includes the following contents: a unique number of the workstation, a name of the workstation, a list of action sequences of the workstation, and a sequential execution order relationship between the workstations.

4. The industrial time series data analysis method according to claim 3, characterized in that: The workstation configuration information in the structural form of S10 includes the following contents: The unique number of the workstation is unique_id. The data comes from the unique number of the workstation in the time series data configuration file. The workstation name pos_name, the data comes from the name of the workstation in the time series data configuration file, The action sequence list possible_exc_seqs, the data comes from the action sequence list of the workstation in the time series data configuration file, the order of the workstation in the production line process template_info, The action sequence list length possible_exc_seqs_length is used to record the length of the action sequence list possible_exc_seqs, the workstation sensor ID cache fifo_id, the workstation timestamp cache fifo_time, and the parsed execution sequence cache fifo_excute_seq of the workstation.

5. The industrial time series data analysis method according to claim 4, characterized in that: Step S20 includes the following sub-steps: S201: parse the original message of industrial time series data sent by the acquisition program, extract the sensor ID and timestamp contained in each original message data point, and encapsulate each original message data point into a key-value pair {timestamp, sensor ID}, so as to construct a list L; S202: constructing a hash table H through the workstation configuration information in S10, wherein the key of the hash table H is the sensor ID and the value is the workstation ID that requires the sensor data; S203: Based on the hash table H constructed in S202, the list L is traversed. For each key-value pair {timestamp, sensor ID} in the list L, the hash table H is searched to see whether there is a workstation with the sensor ID of the key-value pair as the key; if so, the sensor ID of the key-value pair is added to the workstation sensor ID cache fifo_id of the corresponding workstation, and the timestamp of the key-value pair is added to the timestamp cache fifo_time of the corresponding workstation; if not, the key-value pair is discarded.

6. The industrial time series data analysis method according to claim 4, characterized in that: Step S30 includes the following sub-steps: S301: constructing a time series data list received by the current workstation based on the sensor ID cache fifo_id and the timestamp cache fifo_time of the current workstation; Each element of the time sequence list is a key-value pair {timestamp, sensor ID}, and the action sequence list possible_exc_seqs of the current workstation is obtained from the workstation configuration information; S302: Traverse each action sequence in the action sequence list possible_exc_seqs of the current workstation; for each action sequence, perform the following matching operations: a. Match the current action sequence with the time series data list received by the workstation. The matching rule is: check whether the sensor ID in the action sequence exists in the time series data list in order; b. If yes, record the unique identifier of the successfully matched action sequence, and extract the timestamp list corresponding to the sensor ID in the action sequence from the time series data list; combine the unique identifier of the action sequence and the corresponding timestamp list into a result item; S303: All result items of the successfully matched action sequences are stored in the execution sequence cache fifo_excute_seq of the workstation.

7. The industrial time series data analysis method according to claim 6, characterized in that: Step S40 includes the following sub-steps: S401: Obtaining the order template_info of all workstations in the production line process from the workstation configuration information to form a global execution order between workstations; the execution order between global workstations refers to the order of a product passing through all workstations in the production line process; S402: traverse each workstation in turn according to the execution order between the global workstations; for the currently traversed workstation, read the action sequence result item with the smallest timestamp from its execution sequence cache fifo_excute_seq, and add it to the product analysis result list; the product analysis result list refers to a list of action sequences and timestamps of all workstations that a product passes through in the production line process; S403: After all workstations have been traversed, the final product parsing result list will contain the action sequences of all workstations arranged in execution order and their corresponding timestamp information; S404: Convert the format of the final product analysis result list.

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