A monitoring method for judging the timeliness and authenticity of data collection in the cement industry

Abstract

The application discloses a kind of monitoring methods for judging timeliness and authenticity of data acquisition in cement industry, comprising the following steps: S1, select a number of measuring point data for judging whether the data state is abnormal;S2, build an industrial internet-of-things platform based on equipment physical model, realize model-level stream calculation;S3, establish a virtual equipment physical model for judging data state, and the virtual equipment physical model corresponds to the data acquisition link one by one;S4, establish a stream calculation task in the system, the input parameter of the task is associated with the input parameter equipment and the corresponding measuring point attribute, and whether each data acquisition link is abnormal is judged by the stream calculation task;S5, when an abnormality occurs, the abnormal interruption alarm prompt of the corresponding data acquisition link is given.The application does not need to change the existing data acquisition method, does not need to increase equipment acquisition equipment separately, reduces the measuring point data involved, and is more convenient.

Description

Technical Field

[0001] This invention belongs to the field of data monitoring technology and relates to a monitoring method for judging the timeliness and authenticity of data collection in the cement industry. Background Technology

[0002] Currently, cement industry production process data primarily originates from open data interfaces provided by industrial control systems such as DCS and PLCs on-site, with the acquisition protocols mostly being OPC_DA or OPC_UA. Most large-scale cement enterprises now collect data by deploying data acquisition devices at the factory to connect to data servers, and then transmitting the data in real-time to an industrial IoT platform via the internet. This provides crucial data support for headquarters applications such as production monitoring, equipment analysis, and performance statistics.

[0003] However, existing technologies cannot analyze and judge the timeliness and authenticity of data collection in real time. This is because the group's data management platform needs to manage data from multiple regions and production lines in multiple factories, involving a large amount of equipment, measuring points, and corresponding data. Real-time monitoring of data at each measuring point is too labor-intensive and cannot guarantee real-time performance. When the entire data collection link is abnormally interrupted, such as by data server failure, network failure, or acquisition device failure, the headquarters application side cannot detect the data anomaly in real time, which will have the following impact on business:

[0004] 1. After a data interruption, the original equipment data will remain unchanged from the value just before the data interruption. For example, if the rotary kiln host operating signal was true before the data interruption, the value perceived by the headquarters application side will still be true after the data interruption. In this case, if the rotary kiln experiences an abnormal shutdown in the factory, the headquarters will not be able to detect it in real time.

[0005] 2. After a data interruption, the original indicators will continue to be calculated. For example, indicators such as energy, quality, output, and storage location will still be calculated according to the values ​​before the data interruption, causing continuous errors in the indicators.

[0006] 3. After data anomalies and interruptions, headquarters cannot receive timely feedback and lacks real-time sensing, prompting, and alarm mechanisms, making it impossible for headquarters to monitor the timeliness and authenticity of data from various factories in real time.

[0007] Therefore, existing technologies need to be improved in this regard to promptly identify anomalies and their sources, and to notify on-site data management personnel in a timely manner to avoid or reduce data errors between the factory and the group. Summary of the Invention

[0008] The purpose of this invention is to provide a monitoring method for judging the timeliness and authenticity of data collection in the cement industry, which solves the technical problem in the prior art that it is difficult to conduct large-scale real-time monitoring of data from multiple regions or production lines in a factory, thus making it difficult to effectively monitor and alarm for anomalies in the entire data collection chain.

[0009] The monitoring method for judging the timeliness and authenticity of data collection in the cement industry includes the following steps:

[0010] S1. Select several measurement point data points to determine whether there are any abnormalities in the data status;

[0011] S2. Construct an industrial IoT platform based on equipment object models to achieve model-level stream computing;

[0012] S3. Establish a physical model of the virtual device to determine the data status. The physical model of the virtual device corresponds one-to-one with the data acquisition link.

[0013] S4. Establish a stream computing task in the system. The input parameters of this task are associated with the input parameter device and the corresponding measurement point attributes. The stream computing task is used to determine whether each data acquisition link is abnormal.

[0014] S5. When an anomaly occurs, issue an alarm to indicate an abnormal interruption of the corresponding data acquisition link.

[0015] Preferably, in step S1, the measuring point is a measuring point that each production line corresponding to each data acquisition link has. The corresponding measuring point data will not be set to 0 during normal production on the production line, and will fluctuate continuously.

[0016] Preferably, the data collected by this method is relevant data of the cement kiln production line. The specific measurement point data selected in step S1 are as follows: a. Kiln feed bucket elevator - current, b. Kiln main motor - current, c. High temperature fan - current, d. Kiln head Roots blower - outlet pressure.

[0017] Preferably, in step S2, the equipment object model is set based on a unified standard. According to the region-factory-production line, the equipment ID is standardized to determine the location of the data of the input and output parameters of the flow calculation. The equipment object models of the same production line constitute a complete data acquisition link. The equipment object model contains the input parameter equipment and measurement points corresponding to the measurement point data. If the relevant measurement point data meets the judgment criteria for abnormality, the relevant measurement point attribute is abnormal; otherwise, it is normal.

[0018] Preferably, in step S3, in each complete data acquisition link, the measurement point status of the measurement point data determined in step S1 is used as an input parameter and associated with the physical model of the corresponding virtual device, and the corresponding status attribute indicates whether the data acquisition link is abnormal; when the measurement point status of each measurement point data is abnormal, the status attribute of the virtual device is abnormal, indicating that the corresponding entire data acquisition link is abnormal; when the measurement point status of the four measurement point data is not all abnormal, the status attribute of the virtual device is normal, indicating that the corresponding entire data acquisition link is normal.

[0019] Preferably, in step S4, the stream computing task includes: associating the monitoring results of each measurement point data with the object model of the virtual device corresponding to its data acquisition link; when the data of each measurement point associated with the object model of the same virtual device does not change for a certain period of time, it is determined that the data acquisition link of the production line is abnormally interrupted, and the output parameter returns "false" to the object model of the virtual device; when the data is normal, the output parameter always returns "true" to the object model.

[0020] Preferably, the stream computing automatically identifies the production line location based on the device ID basic field of the input parameters.

[0021] Preferably, the alarm module used in step S5 includes an online statistics dashboard. When a production line experiences an abnormal interruption, a pop-up window will appear on the online statistics dashboard corresponding to the abnormality, containing information on the factory-production line and the offline time. The factory-production line is determined based on the equipment ID basic field. The above alarm information is stored in the system, and the system can retrieve the information of the abnormal production line in real time as needed after the alarm is triggered.

[0022] Preferably, the alarm module used in step S5 includes an alarm push module, which is used to configure the corresponding push personnel. The push personnel correspond to the abnormality of the corresponding data acquisition link. The alarm push module can push alarm information in any one or a combination of two or more of the following methods: SMS, enterprise group, APP, and telephone group call.

[0023] The present invention has the following advantages:

[0024] 1. This solution utilizes existing data acquisition links, requiring no changes to existing data acquisition methods or the addition of new acquisition equipment. Because it uses a virtual equipment model to determine the data status of each measurement point during modeling, and the parameter association between the virtual equipment model and the stream computing task is already set within the stream computing task, only the production line model needs to be established to achieve the corresponding data timeliness and accuracy assessment. This eliminates the need to create new tasks specifically for this purpose, reducing the complexity of tasks related to adding or modifying data due to changes in the production line, making it much simpler.

[0025] 2. This solution reduces the amount of measurement data involved in judging the timeliness and authenticity of various data points. It uses a stream computing task approach to calculate and judge the continuously input relevant measurement data. Furthermore, the input and output parameters in the model-level calculation can be determined by the ID basic field to determine the location of the data in the stream computing input and output parameters.

[0026] 3. This solution uses stream computing tasks that can automatically handle data stream interruptions, retransmissions, and recovery. As a result, this solution can monitor the timeliness and authenticity of headquarters data in real time, improve the credibility of indicator calculation values, and has good real-time performance and reliability.

[0027] 4. If an anomaly occurs in the data link, this solution will immediately notify the headquarters management personnel and the data management personnel of the factory through methods such as large screen pop-ups and mobile phone text messages to ensure that the anomaly is handled as soon as possible.

[0028] 5. This solution also provides the headquarters with an online data status statistics method, enabling abnormal log statistics, chart analysis, and real-time monitoring of data from various regions, factories, and production lines. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the data flow computing task structure in this invention.

[0030] Figure 2 This is a schematic diagram of an IoT platform system that applies the present invention. Detailed Implementation

[0031] The following detailed description of the embodiments, with reference to the accompanying drawings, will further illustrate the specific implementation of the present invention, in order to help those skilled in the art to have a more complete, accurate, and in-depth understanding of the inventive concept and technical solution of the present invention.

[0032] like Figure 1-2 As shown, this invention discloses a monitoring method for judging the timeliness and authenticity of data collection in the cement industry, comprising the following steps:

[0033] S1. Select several measurement point data points to determine whether there are any abnormalities in the data status.

[0034] In this embodiment, four measurement points are selected as the basis for judging whether there are any abnormalities in the data from the data acquisition link, and as the benchmark for judging the data status. These measurement points are as follows: a. Kiln bucket elevator - current, b. Kiln main motor - current, c. High temperature fan - current, d. Kiln head Roots blower - outlet pressure.

[0035] The reason for choosing the above measurement point data is that the above four measurement point data meet the following requirements:

[0036] 1. Uniformity: All cement kiln production lines have the above-mentioned measuring points.

[0037] 2. Importance: The above-mentioned measurement data are all important parameters in the cement kiln process, and the data will not be set to 0 during normal production of the cement kiln system.

[0038] 3. High frequency: In a normal production environment, the data from the above measurement points will inevitably fluctuate continuously.

[0039] Therefore, the system can determine whether the data from the aforementioned measuring points are zero or have stopped changing, thereby identifying any anomalies in the data and ultimately the entire data acquisition chain. By monitoring the data from these measuring points, it is possible to monitor various cement kiln production systems and the corresponding entire data acquisition chain for any abnormal interruptions.

[0040] S2. Construct an industrial IoT platform based on equipment object models to achieve model-level stream computing.

[0041] In this embodiment, the equipment model is set based on a unified cement industry or group system standard to facilitate data interoperability. Equipment models from the same cement kiln production line or cement kiln plant constitute a complete data acquisition link.

[0042] For example: Figure 2 As shown, the entire data acquisition chain is divided into 5 areas: Area 1 is the OPC server for each factory; Area 2 is the industrial security isolation equipment; Area 3 is the data acquisition device, which is mainly responsible for data acquisition and forwarding; Area 4 is the industrial IoT platform; and Area 5 is the online statistical dashboard and SMS alarm function module. Areas 1-3 are located in the factory, while areas 4-5 are located in the cloud (headquarters).

[0043] This function can analyze anomalies in the data acquisition link between zones 1-4, complete the analysis and judgment in the IoT platform (zone 4), and provide the results for application in zone 5. This enables the assessment of the timeliness and authenticity of data acquisition in the cement industry.

[0044] Based on the measurement point data determined in the previous step, the equipment model corresponding to the above data acquisition link should include at least the following four input devices and measurement points, and the model is shown in Table 1.

[0045] Table 1: Modeling Data Table of Input Equipment and Measurement Points

[0046]

[0047] The input parameters in this scheme include rotary kiln, kiln feed bucket elevator, high-temperature blower and kiln head Roots blower. The input parameters of the corresponding physical model include the corresponding production line number and equipment ID. If the relevant measurement point data meets the judgment criteria for abnormality, the relevant measurement point attribute is abnormal; otherwise, it is normal.

[0048] The basic field setting specification for factory and production line equipment IDs is as follows: Based on region-factory-production line, standardize the equipment ID to determine the location of the data for stream computing input and output parameters. See Table 2 for a specific example.

[0049] Table 2: Example of Basic Field Settings for Factories and Production Lines

[0050]

[0051] S3. Establish a virtual device object model to determine the data status. The virtual device object model corresponds one-to-one with the data acquisition link.

[0052] The virtual device model and attributes established in this step are shown in Table 3.

[0053] Table 3: Modeling Data Table for Virtual Devices to Determine Data Status

[0054] Serial Number Equipment Name Device ID physical model State Attributes 1 Data status Online dtml:HLSN-Online-01-00 State

[0055] In each complete data acquisition link, the measurement point status of the four measurement points determined in step S1 is used as input parameters and associated with the physical model of the corresponding virtual device. The corresponding status attribute indicates whether the data acquisition link is abnormal. When the measurement point status of all four measurement points is abnormal, the status attribute of the virtual device is abnormal, indicating that the entire data acquisition link is abnormal. When the measurement point status of the four measurement points is not all abnormal, the status attribute of the virtual device is normal, indicating that the entire data acquisition link is normal, and the abnormality is in the corresponding measurement point or related line.

[0056] S4. Establish a stream computing task in the system. The input parameters of this task are associated with the input parameter devices and the corresponding measurement point attributes. The stream computing task is used to determine whether each data acquisition link is abnormal.

[0057] The streaming computation task includes: associating the monitoring results of four measurement points with the object model of the virtual device corresponding to their data acquisition link. If the data of the four measurement points a, b, c, and d associated with the object model of the same virtual device do not change for 30 consecutive seconds, it is determined that the data acquisition link of the production line is abnormally interrupted, and the output parameter returns "false" to the object model of the virtual device (dtml: HLSN-Online-01-00 / State); when the data is normal, the output parameter always returns "true" to the object model (dtml: HLSN-Online-01-00 / State).

[0058] The specific data stream computation task structure is as follows: Figure 1As shown, the input and output parameters of stream computing are associated with corresponding models. Under this model, relevant object instance data will automatically participate in the computation. Stream computing automatically identifies the production line location based on the device ID field of the input parameters. For example, the input parameters are:

[0059] 01_02_01_512 (Current data of the main motor of the rotary kiln in production line 1 of factory 2 in Anhui);

[0060] 01_02_01_428 (Current data of the kiln feed bucket elevator in production line 1 of factory 2 in Anhui);

[0061] 01_02_01_506 (High-temperature fan - current data for high-temperature fans in production line 1 of factory #2 in Anhui);

[0062] 01_02_01_825 (Outlet pressure data of the kiln head Roots blower in production line 1 of factory 2 in Anhui);

[0063] When the stream computing task is executed, it can identify the data status of the Anhui-2# factory-1# production line based on the device ID basic field, and then return the output parameters to the virtual device's object model 01_02_01_Online. When all four input parameters (i.e., the measurement point status of the corresponding four measurement points) of the virtual device's object model 01_02_01_Online are abnormal, the status attribute of the virtual device is abnormal, indicating that the corresponding entire data acquisition link is abnormal.

[0064] S5. When an anomaly occurs, issue an alarm to indicate an abnormal interruption of the corresponding data acquisition link.

[0065] The headquarters application features an online statistical dashboard that provides chart statistics and analysis of online data status for each region, factory, and production line, as well as data anomaly logs. When a virtual device returns an abnormal status attribute, it indicates an abnormal interruption of a corresponding production line. A pop-up window will then appear on the online statistical dashboard for that abnormality, containing information such as a factory / production line description (e.g., Ningguo Cement Plant Line 2 is offline) and the offline time. The factory / production line is determined based on the device ID field. The alarm information is stored in the system, and the system can retrieve information about the abnormal production line in real time upon receiving an alarm. The dashboard system also integrates a logging function to statistically analyze basic information such as the offline and online times and the number of times each production line has been offline.

[0066] In addition to the large-screen alarm function mentioned above, the system can also be configured with an SMS alarm module as an important tool for alarm push notifications. This method, after associating the data status with the industrial IoT platform and setting up stream computing tasks, allows configuration of corresponding push personnel, each corresponding to an anomaly in the data acquisition link. When data anomalies occur, an SMS notification is immediately sent to the mobile phones of the relevant personnel. When the alarm module receives a "true" value from the IoT platform for a given field, an alarm is pushed. Besides SMS notifications, other push methods include enterprise groups, apps, and mass phone calls, which can be accomplished by setting up corresponding group alarm modules, app alarm modules, or mass phone call modules.

[0067] After obtaining offline information through large screens, alarm text messages, and other means, relevant personnel immediately analyzed and determined the cause of the anomaly on-site and took timely action.

[0068] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution of the present invention to other occasions without modification, are all within the protection scope of the present invention.

Claims

1. A monitoring method for judging the timeliness and authenticity of data collection in the cement industry, characterized in that: Includes the following steps: S1. Select several measurement point data points to determine whether there are any abnormalities in the data status; S2. Construct an industrial IoT platform based on equipment object models to achieve model-level stream computing; S3. Establish a physical model of the virtual device to determine the data status. The physical model of the virtual device corresponds one-to-one with the data acquisition link. S4. Establish a stream computing task in the system. The input parameters of this task are associated with the input parameter device and the corresponding measurement point attributes. The stream computing task is used to determine whether each data acquisition link is abnormal. S5. When an anomaly occurs, provide an alarm for the abnormal interruption of the corresponding data acquisition link; In step S3, in each complete data acquisition link, the measurement point status of the measurement point data determined in step S1 is used as an input parameter and associated with the physical model of the corresponding virtual device. The corresponding status attribute indicates whether the data acquisition link is abnormal. When the measurement point status of each measurement point data is abnormal, the status attribute of the virtual device is abnormal, indicating that the corresponding entire data acquisition link is abnormal. When the measurement point status of each measurement point data is not abnormal, the status attribute of the virtual device is normal, indicating that the corresponding entire data acquisition link is normal.

2. The monitoring method for judging the timeliness and authenticity of data collection in the cement industry according to claim 1, characterized in that: In step S1, the measuring point is a measuring point that each production line corresponding to each data acquisition link has. The corresponding measuring point data will not be set to 0 when the production line is in normal production, and will fluctuate continuously.

3. The monitoring method for judging the timeliness and authenticity of data collection in the cement industry according to claim 2, characterized in that: The data collected by this method are relevant data of the cement kiln production line. The specific measurement points selected in step S1 are as follows: a. Kiln feed bucket elevator - current, b. Kiln main motor - current, c. High temperature blower - current, d. Kiln head Roots blower - outlet pressure.

4. The monitoring method for judging the timeliness and authenticity of data collection in the cement industry according to claim 1, characterized in that: In step S2, the equipment object model is set based on a unified standard. According to the region-factory-production line, the equipment ID is standardized to determine the location of the data of the input and output parameters of the flow calculation. The equipment object models of the same production line constitute a complete data acquisition link. The equipment object model contains the input parameter equipment and measurement points corresponding to the measurement point data. If the relevant measurement point data meets the judgment criteria for abnormality, the relevant measurement point attribute is abnormal; otherwise, it is normal.

5. The monitoring method for judging the timeliness and authenticity of data collection in the cement industry according to claim 2, characterized in that: In step S4, the stream computing task includes: associating the monitoring results of each measuring point data with the object model of the virtual device corresponding to its data acquisition link; when the data of each measuring point associated with the object model of the same virtual device does not change for a certain period of time, it is determined that the data acquisition link corresponding to the production line is abnormally interrupted, and the output parameter returns "false" to the object model of the virtual device; when the data is normal, the output parameter always returns "true" to the object model.

6. The monitoring method for judging the timeliness and authenticity of data collection in the cement industry according to claim 5, characterized in that: Stream computing automatically identifies the production line location based on the device ID field of the input parameters.

7. The monitoring method for judging the timeliness and authenticity of data collection in the cement industry according to claim 1, characterized in that: The alarm module used in step S5 includes an online statistics dashboard. When a production line experiences an abnormal interruption, a pop-up window will appear on the online statistics dashboard corresponding to the abnormality, containing alarm prompts about the factory and production line and the offline time. The factory and production line are determined based on the equipment ID basic field. The above alarm prompt information is stored in the system. After the system issues an alarm, it can retrieve the information of the abnormal production line in real time as needed.

8. The monitoring method for judging the timeliness and authenticity of data collection in the cement industry according to claim 7, characterized in that: The alarm module used in step S5 includes an alarm push module, which is used to configure the push personnel to respond. The push personnel correspond to the abnormality of the corresponding data acquisition link. The alarm push module can push alarm prompt information in any one or a combination of two or more of the following methods: SMS, enterprise group, APP, and telephone group call.

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