Steel coil data multi-dimensional alarm configuration method based on time sequence data and time window

Abstract

The application discloses a kind of steel coil data multidimensional alarm configuration methods based on time series data and time window, comprising: the time series data of each measuring point in steel coil production process is collected by sensor;Alarm configuration module is constructed, for defining and configuring alarm rule, alarm condition and alarm level;Time series data logic processing module is constructed, for receiving time series data, and according to the alarm rule of configuration issue, distinguish different alarm types, real-time analysis time series data, match data to alarm rule, obtain the alarm result data that meets rule;Time window processing module is constructed, for starting timer in the time window of configuration, continuously receive alarm result data sent by time series data logic processing module and alarm result data generated by third party service;And judge whether to meet alarm condition.The application can flexibly define and configure multidimensionally alarm data source and rule, ensure the timeliness and accuracy of alarm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing and analysis of diagnostic information, and in particular to a multi-dimensional alarm configuration method for steel coil data based on time series data and time windows. BACKGROUND

[0002] With the rapid development of modern industrial production, the safety, stability and efficiency of the production process are increasingly demanding. In this context, industrial alarm systems have emerged as a key tool to ensure production safety, avoid economic losses and maintain corporate reputation. This automated system monitors various parameters in the production process in real time and immediately alerts when abnormalities are detected, prompting operators to take action quickly to prevent accidents and avoid possible serious consequences.

[0003] The flexibility and accuracy of the alarm system are the key to its success. Through carefully designed alarm configuration, the system can promptly alert when equipment or systems are abnormal, reminding operators to take emergency measures to prevent accidents and ensure the safety of the production process. Good alarm configuration not only improves the efficiency of management and maintenance of the alarm system, but also simplifies the operation process and improves work efficiency. In addition, reasonable alarm device and parameter configuration helps to timely detect and solve equipment failures and problems on the production line, ensuring the stable operation of the production system. This efficient alarm mechanism not only ensures production safety, but also provides a solid foundation for the sustainable development and market competitiveness of the enterprise.

[0004] Therefore, there is an urgent need to design a multi-dimensional alarm configuration method for steel coil data based on time series data and time windows. SUMMARY

[0005] The technical problem to be solved by the present application is to address the technical and scenario problems in the prior art of single and inflexible alarm configuration in data analysis and diagnosis, inaccurate data source, poor real-time performance, and weak processing time window capability. A multi-dimensional alarm configuration method for steel coil data based on time series data and time windows is provided.

[0006] The technical solution adopted by the present application to solve its technical problem is:

[0007] The present application provides a multi-dimensional alarm configuration method for steel coil data based on time series data and time windows, which comprises the following steps:

[0008] Step 1, collecting time series data of each measuring point in the steel coil production process through a sensor;

[0009] Step 2, constructing an alarm configuration module for defining and configuring alarm rules, alarm conditions and alarm levels;

[0010] Step 3, a time sequence data logic processing module is constructed, used for receiving time sequence data, distinguishing different alarm types according to alarm rules configured and delivered, analyzing time sequence data in real time, matching data to alarm rules, and obtaining alarm result data meeting the rules;

[0011] Step 4, a time window processing module is constructed, used for starting a timer in a configured time window, continuously receiving alarm result data sent by the time sequence data logic processing module and alarm result data generated by a third party service, judging whether an alarm condition is met, and determining that an alarm record is truly generated at this time if the alarm result data is in an alarm state in the time range, and sending the alarm record to an alarm processing business platform; if the alarm result data is in a normal state in the time range, it is determined that the alarm is reset on the premise of having been alarmed.

[0012] Further, the time sequence data of the application includes the name ID of a measuring point, measuring point data and a time stamp.

[0013] Further, the alarm rules in step 2 of the application include:

[0014] Customized alarm: configuring an alarm formula or a multi-item expression based on multiple measuring point data, including four arithmetic operations addition, subtraction, multiplication and division, and logical symbols greater than, less than, switch true / false, 0 / 1 alarm of Boolean type, and basic functions max / min / diff;

[0015] Limit alarm: including high-high limit, high limit, low limit, low-low limit alarm of numerical type, and switch true / false, 0 / 1 alarm of Boolean type;

[0016] Third party alarm: sending alarm result data of third party alarm logic to the time window processing module, further verifying whether the alarm is valid in a certain time range, and pushing to the alarm business platform.

[0017] Further, the specific method of the customized alarm, limit alarm and third party alarm in step 2 of the application includes:

[0018] The method of customized alarm includes: judging whether there is speed anomaly in the steel coil processing production process, configuring a self-defined alarm rule for the speed set value and the actual value during steel coil production, determining that the speed is abnormal when the actual speed value exceeds the set value ±10%, and configuring the alarm formula or multi-item expression based on the actual speed value pv and the set speed value sv as: (pv>sv*1.1)||(pv<sv*0.9);

[0019] The method of limit alarm comprises: sensors collecting the distance data of the cylinder / strip deviation in the steel coil processing production process, threshold alarm setting high limit threshold, low limit threshold and low low limit threshold for the distance data value, and alarm information of the production strip deviation is generated when the threshold configuration range is exceeded; the process switching and flow transfer in the entry feeding and exit discharging of the steel coil production are monitored by the on-off state data of various devices / working conditions generated in the process, and the normal state of a process should be true, and the on-off alarm configuration of the process state is performed, and the on-off alarm of the process is generated when the state becomes false.

[0020] The third party alarm method comprises: process parameter data generated in the steel coil processing production process, including temperature, liquid level and pressure, and process parameter alarm is generated when the process parameter exceeds the corresponding standard range; the production line of the process is divided into multiple regions and positions, and different types of process parameters are distributed in the regions and positions, and each steel coil has corresponding process parameter values in the corresponding regions and positions, and the process parameter standards required by steel coils of different brands and different widths and thicknesses are different, and the scene of the alarm rule dynamic change is uniformly sent to the third party alarm for processing; the third party alarm obtains the upper and lower limit ranges of the corresponding process parameters according to the steel coil information in each position, and obtains the real-time value of the process parameters in the position, and if the real-time value exceeds the upper and lower limit ranges, an alarm is generated, and the alarm logic preliminary result of the third party is sent to the time window processing module.

[0021] Further, the method of step 3 of the application comprises:

[0022] The time sequence data logic processing module receives the pre-set alarm configuration message body, obtains the alarm configuration alarmId, alarm name name, alarm type type, data source eventSourceVOList information, and matches the alarm rule-formula polynomial of the custom alarm type=0, point position itemTag of the limit alarm type=1 and upper and lower limit values lower / upper.

[0023] Further, the method of step 3 of the application further comprises:

[0024] Step 3.1, the time sequence data logic processing module processes and analyzes the input time sequence data, obtains the data source by subscribing to the topic and measuring point information configured in the alarm configuration, and pre-processes the original time sequence data;

[0025] Step 3.2, according to the alarm condition set in the alarm configuration module, the data is filtered, calculated, matched and screened;

[0026] Step 3.3, the data meeting the alarm condition is aggregated, counted and forwarded to the time window processing module for analysis and judgment.

[0027] Further, the specific method of steps 3.1-3.3 of the present application comprises:

[0028] By subscribing to the topic list in the alarm configuration, all measurement point data is obtained in real time; the data includes the timestamp timestamp of accepting the message and all measurement point data information values; each measurement point data information includes id-measurement point identifier, v-data value, q-whether valid, t-timestamp; first, check whether the timestamp timestamp is within a certain minute time range, filter dirty data caused by repeated data transmission due to various reasons; then, according to the id, v, q, t rule, the message body is parsed, the data with q as false is filtered, and the parsed v data value is assigned to the point in the alarm configuration.

[0029] Further, the method of step 4 of the present application comprises:

[0030] Step 4.1, according to the time window size set by the user, the time series data is divided into several time windows;

[0031] Step 4.2, the data in each time window is statistically analyzed, and the data is derived from the result data processed by the time series data logical processing module and the alarm result data generated by the third party service;

[0032] Step 4.3, according to the alarm condition set in the alarm configuration module, combined with the alarm rule associated with the alarm result data, it is judged whether the data in the current time window meets the alarm condition;

[0033] Step 4.4, if the alarm condition is met, the corresponding alarm information is generated, and the alarm information is sent to the relevant personnel or system according to the alarm level priority.

[0034] Further, the specific method of steps 4.1-4.4 of the present application comprises:

[0035] The specific method for statistically analyzing the data in each time window is:

[0036] The time series data logical processing module sends the alarm event / reset event result to the time window processing module; in a certain time window period, how many seconds or how many times the alarm event repeats, that is, continuously tracking the type 2 data in the event message body of the same alarm configuration ID, if the time window configuration condition in the alarm configuration is met, the alarm is finally generated; the specific configuration conditions include the following three cases:

[0037] N seconds continuously: from the first alarm event trigger, within N seconds, no alarm event reset occurs;

[0038] N times continuously: from the first alarm event trigger, N times of the same alarm event trigger occur continuously, and no alarm event reset occurs;

[0039] N times and above within N seconds continuously: from the first alarm event trigger, N times and above of the same alarm event trigger occur continuously within N seconds, and no alarm event reset occurs.

[0040] Further, the timing data logic processing module and the third party service of the present application perform data transmission with the time window processing module through the MQTT / HTTP mode.

[0041] The present application has the following beneficial effects: The present application can be flexibly applied to factory production systems in different fields due to dynamic configuration of data sources, time windows and alarm rules, and standardized alarm configuration methods. It is easy to implement on site, has low learning cost, weak complexity, and can be configured only, greatly shortening the implementation period and improving the debugging efficiency. Alarm configuration information can be flexibly added or modified, without modifying any code, to complete related alarm judgment, effectively reduce maintenance and operation cost, and timely and accurately complete alarm configuration. BRIEF DESCRIPTION OF DRAWINGS

[0042] The present application will be further described below in combination with the drawings and embodiments, wherein:

[0043] Figure 1 is a system module framework diagram of an embodiment of the present application;

[0044] Figure 2 is an alarm configuration module of an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0046] Embodiment 1

[0047] As Figure 1As shown, the embodiment of the present application realizes multi-dimensional definition and configuration of data source and rules through the alarm configuration module, mainly including three alarm types: custom alarm, limit alarm and third-party alarm. Among them, the real-time time series data with timestamp is sent to the logic processing module for alarm logic judgment, the result of judgment is transferred to the time window processing module to verify the validity and level of the alarm, and the alarm information is pushed to the alarm business platform. The present application can timely and accurately alarm the fault, improve the timeliness of emergency response, greatly improve the production efficiency, and ensure the normal operation of the system.

[0048] The present application can be used as a data analysis and diagnosis module, and the implementation system (see Figure 1 ) includes an alarm configuration module, a time series data logic processing module and a time window processing module.

[0049] Step 1, collecting time series data of each measuring point in the steel coil production process through a sensor;

[0050] Step 2, constructing an alarm configuration module for defining and configuring alarm rules, alarm conditions and alarm levels;

[0051] As shown in Figure 2 , it is an alarm configuration module of the embodiment of the present application. It is mainly responsible for defining and configuring alarm rules.

[0052] Custom alarm: flexible configuration of alarm formula or multiple expressions based on multi-measuring point data, including four arithmetic operations addition, subtraction, multiplication and division, and or, not, greater than, less than, logical symbols, basic functions max / min / diff, etc.

[0053] In the preferred embodiment of the present application, whether there is speed anomaly in the steel coil processing production process is judged by configuring self-defined alarm rules for the speed set value and actual value during the steel coil production, and when the speed actual value exceeds the set value ±10%, it is determined that there is speed anomaly. The alarm formula or multiple expressions based on the speed actual value (pv) and speed set value (sv) of multiple measuring points can be configured as: (pv>sv*1.1)||(pv<sv*0.9).

[0054] Limit alarm: mainly including high-high limit, high limit, low limit, low-low limit alarm of numerical type and switch true / false, 0 / 1 alarm of Boolean type.

[0055] In the preferred embodiment of the present application, the steel strip in the steel coil processing production process will deviate abnormally, which will seriously affect the production and even stop the production, affecting the production efficiency and quality. For the deviation of the steel strip, the sensor collects the cylinder / steel strip deviation distance data in the production process, and the threshold alarm is configured with high limit 100mm, high limit 60mm, low limit-60mm, and low limit-100mm. When the deviation distance data exceeds the threshold configuration range, the alarm information of the production steel strip deviation is generated. In the process of loading at the inlet and unloading at the outlet of the steel coil production, there are a large number of process switching and circulation, and various device / working condition on-off state data are used for monitoring. The normal state of a process should be true, and the on-off alarm configuration is performed on the state of the process. When the state becomes false, the on-off alarm under the process is generated.

[0056] Third-party alarm: the third-party alarm logic preliminary result data can be sent to the time window processing module, further verifying whether the alarm is effective within a certain time range, and pushing to the alarm business platform.

[0057] In the preferred embodiment of the present application, a large amount of process parameter data will be generated in the steel coil processing production process, such as temperature, liquid level, pressure, etc. There are often corresponding standard ranges for these process parameters, and the process parameter alarm is generated when the range is exceeded. The physical length of the process production line can reach hundreds of meters or even thousands of meters, which is usually divided into regions and positions, and various types of process parameters are distributed throughout the regions and positions. Each steel coil has a process parameter value in the corresponding region and position, for example, the process parameter temperature actual value of the steel coil No. A in the heating 1 section of the furnace area is 800℃, and the process parameter temperature actual value of the steel coil No. B in the cooling 3 section of the furnace area is 300℃. Different grades and different widths and thicknesses of steel coils require different process parameter standards, so this scene with dynamic changes of alarm rules is uniformly handed over to the third-party alarm for processing. The third-party alarm obtains the corresponding process parameter upper and lower limit range according to the steel coil information in each position, and obtains the real-time value of the process parameter in the position. If the real-time value exceeds the upper and lower limit range, an alarm is generated, which is sent to the time window processing module as the third-party alarm logic preliminary result.

[0058] Step 3, constructing a time sequence data logic processing module for receiving time sequence data, and according to the alarm rules configured and issued, distinguishing different alarm types, real-time analyzing time sequence data, matching data to alarm rules, and obtaining alarm result data meeting the rules;

[0059] Time series data logic processing module. The time series data collector sends various time series data collected from PLC, switch station and other sensors to the logic processing module, wherein the time series data includes measurement point name ID, measurement point data value and timestamp. The logic processing module distinguishes different alarm types according to the alarm rules configured and delivered, analyzes the time series data in real time, matches the data to the alarm rules, and sends the recorded data meeting the alarm rules to the time window processing module.

[0060] In the preferred embodiment of the application, the time series data logic processing module receives the alarm configuration message body agreed in advance, obtains alarm configuration alarmId, alarm name name, alarm type type, data source eventSourceVOList and other information, and matches the alarm rule-formula polynomial of custom alarm type=0, point position itemTag and upper and lower limit values lower / upper of limit alarm type=1.

[0061] The specific implementation method of the time series data logic processing module includes:

[0062] Step 3.1, the time series data logic processing module processes and analyzes the input time series data, obtains the data source through subscribing the topic and measurement point information configured in the alarm configuration, and pre-processes the original time series data;

[0063] Step 3.2, according to the alarm conditions set in the alarm configuration module, filter, calculate, match and select the data;

[0064] Step 3.3, the data meeting the alarm conditions are summarized, counted and forwarded, and sent to the time window processing module for analysis and judgment.

[0065] By subscribing the topic list in the alarm configuration, all measurement point data can be obtained in real time. The data includes the timestamp timestamp of accepting the message and all measurement point data information values. Each measurement point data information includes id-measurement point identifier, v-data value, q-whether valid and t-timestamp. First, check whether the timestamp timestamp is within the time range of the last 5 minutes, filter the dirty data caused by the retransmission of data due to various reasons. Then, according to the rules of id, v, q and t, the message body is analyzed and the data is filtered, the v data value is assigned to the point position in the alarm configuration.

[0066] Step 4, a time window processing module is constructed to start a timer within a configured time window, continuously receive alarm result data sent by the time series data logical processing module and alarm result data generated by a third party service, and determine whether an alarm condition is met. If the alarm result data is in an alarm state within the time range, it is determined that an alarm record is truly generated at this time, and is sent to an alarm processing business platform. If the alarm result data is in a normal state within the time range, it is determined that the alarm is reset on the premise that an alarm has been generated.

[0067] The time window processing module starts a timer within a configured time window, continuously receives record data that meets the alarm rule thrown by the time series data logical processing module, and determines whether the record data is in an alarm state within the time range. If the record data is in an alarm state, it is determined that an alarm record is truly generated at this time, and can be sent to an alarm processing business platform through HTTP / MQTT or other communication methods to prevent false alarms. Similarly, if the record data is in a normal state, it is determined that the alarm is reset on the premise that an alarm has been generated. Among them, whether continuous judgment is required within the time window range can be configured. If non-continuous alarm judgment is configured, some normal state data within the time window range can be automatically ignored, and an alarm can be finally generated in a timely and accurate manner.

[0068] The specific implementation method of the time window processing module includes:

[0069] Step 4.1, dividing the time series data into a plurality of time windows according to the time window size set by the user;

[0070] Step 4.2, statistically analyzing the data in each time window, which is derived from the result data processed by the time series data logical processing module and the alarm result data generated by the third party service;

[0071] Step 4.3, determining whether the data in the current time window meets the alarm condition according to the alarm condition set in the alarm configuration module and the alarm rule associated with the alarm result data;

[0072] Step 4.4, if the alarm condition is met, generating corresponding alarm information, prioritizing the alarm information according to the alarm level, and sending the alarm information to relevant personnel or systems.

[0073] The time series data logical processing module sends alarm event / reset event results to the time window processing module. Within a certain time window period, how many seconds or how many times the alarm event repeats, i.e. continuously tracking the type 2 data in the event message body of the same alarm configuration ID, if the time window configuration condition in the alarm configuration is met, an alarm is finally generated. The specific configuration conditions are divided into the following three cases:

[0074] 1. Continuous N seconds: From the first alarm event trigger, within the continuous N seconds, no alarm event reset occurs.

[0075] 2. Continuous N times: From the first alarm event trigger, the same alarm event trigger occurs continuously N times, and no alarm event reset occurs.

[0076] 3. N times and above occur within continuous N seconds: From the first alarm event trigger, within the continuous N seconds, the same alarm event trigger occurs N times and above continuously, and no alarm event reset occurs.

[0077] Embodiment 2

[0078] The embodiment of the application is mainly applied to a multi-dimensional alarm configuration method based on time series data and time window in data processing and analysis diagnosis, and the function modules include: an alarm configuration module, a time series data logical processing module, and a time window processing module.

[0079] The multi-dimensional alarm configuration method using the method of the application mainly includes the following steps:

[0080] Alarm configuration module: This module is mainly used for setting alarm rules, alarm conditions, and alarm levels. Users can flexibly customize alarm rules in multiple dimensions according to actual needs, such as setting threshold values, comparison operators, etc. At the same time, multiple alarm conditions can be set to meet the alarm needs in complex scenarios. In addition, different alarm levels can be set for each alarm condition so that appropriate measures can be taken according to the severity when an alarm occurs.

[0081] Time series data logical processing module: This module is mainly used for processing and analyzing input time series data. First, the data source is obtained by subscribing to the topic and measurement point information configured in the alarm configuration, and the original data is preprocessed and cleaned, such as removing noise, filling missing values, and non-production dirty data. Then, according to the alarm conditions set in the alarm configuration module, the data is filtered, calculated, matched, and screened. Finally, the data that meets the alarm conditions is summarized, counted, and forwarded for subsequent time window processing module analysis and judgment.

[0082] The time window processing module is mainly used for time window division and analysis of time series data. First, the time series data is divided into several time windows according to the time window size set by the user. Then, statistical analysis is performed on the data in each time window, which is derived from the result data processed by the time series data logical processing module and the alarm result data generated by the third-party service. Then, according to the alarm conditions set in the alarm configuration module, combined with the alarm rules associated with the alarm result data, it is judged whether the data in the current time window meets the alarm condition. If the alarm condition is met, the corresponding alarm information is generated, and the priority is sorted according to the alarm level. Finally, the alarm information is sent to the relevant personnel or system, so as to take timely measures for processing and solving.

[0083] In specific implementation, first, the alarm related information is configured in the alarm configuration module in multiple dimensions, mainly including: alarm name, alarm type, alarm level, alarm rule, alarm point, alarm description, etc. The time series data collected by the time series data logical processing module is transmitted through the MQTT / HTTP mode. The time series data logical processing module judges the received data according to the preset alarm rule and threshold. If the data exceeds the threshold or meets the alarm rule, the alarm is triggered. At the same time, the time window processing module will receive the alarm result data from the logical processing module according to the set time window, and process the alarm result in a specified time range, to ensure the accuracy and timeliness of the alarm. Finally, the alarm result data that meets the alarm configuration is sent to the business platform by the time window processing module, and the alarm information is pushed to the relevant departments and personnel on the system platform for timely processing.

[0084] It has been proved that the multi-dimensional alarm method of the present application solves the technical and scene problems of single and inflexible alarm configuration, inaccurate data source, poor real-time performance and weak processing time window capability in existing data analysis and diagnosis. The alarm data source and rule are defined and configured in multiple dimensions, and the alarm judgment is based on the time series data logical processing module and the time window processing module, to ensure the timeliness and accuracy of the alarm, ensure the safety and smooth progress of the production process, and avoid huge economic losses and reputation damage to the factory.

[0085] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0086] It should be understood that those skilled in the art can improve or change according to the above description, and all these improvements and changes should belong to the protection scope of the appended claims of the present application.

Claims

1. A method for configuring multi-dimensional alarms of a steel coil based on time-series data and a time window, characterized in that, The method comprises the following steps: Step 1, collecting time series data of each measuring point in the steel coil production process through a sensor; Step 2, constructing an alarm configuration module for defining and configuring alarm rules, alarm conditions and alarm levels; the alarm rules in step 2 comprise: Customized alarm: configuring an alarm formula or a multi-item expression based on multiple measuring point data, including four arithmetic operations, logical symbols greater than, less than, and basic functions max / min / diff; Limit alarm: including high-high limit, high limit, low limit, low-low limit alarm of numerical type, and switch quantity true / false, 0 / 1 alarm of Boolean type; Third-party alarm: sending alarm result data of the alarm logic of a third party to a time window processing module, further verifying whether the alarm is valid within a certain time range, and pushing to an alarm business platform; Step 3, constructing a time series data logic processing module for receiving time series data and distinguishing different alarm types according to the alarm rules configured and issued, real-time analyzing time series data, matching data to alarm rules, and obtaining alarm result data meeting the rules; Step 4, constructing a time window processing module for starting a timer within a configured time window, continuously receiving alarm result data sent by the time series data logic processing module and alarm result data generated by a third-party service; determining whether the alarm condition is met, and if the alarm result data is in an alarm state within the time range, determining that an alarm record is truly generated at this time and sending it to an alarm processing business platform; if the alarm result data is in a normal state within the time range, determining alarm reset on the premise of having an alarm. 2.The method of claim 1, wherein, The time series data comprises the name ID of the measuring point, the measuring point data, and the timestamp. 3.The method of claim 1, wherein, The specific method of the customized alarm, the limit alarm, and the third-party alarm in step 2 comprises: The method of the customized alarm comprises: determining whether there is a speed anomaly in the steel coil processing and production process, configuring a self-defined alarm rule for the speed set value and the actual value during steel coil production, determining that the speed is abnormal when the actual speed value exceeds the set value ±10%, and configuring the alarm formula or multi-item expression based on the actual speed value pv and the set speed value sv as: (pv > sv * 1.1) || (pv < sv * 0.9); The method of the limit alarm comprises: the sensor continuously collects the cylinder / strip deviation distance data during the steel coil processing and production process, the threshold alarm configures high-high limit, high limit, low limit, and low-low limit thresholds for the deviation distance data, and alarm information is generated when the threshold configuration range is exceeded; the switch quantity state data of various devices / working conditions generated during the process of the steel coil production inlet feeding and outlet discharging is used for monitoring, the normal state of a process should be true, and switch quantity alarm is configured for the process state, and switch quantity alarm is generated when the state becomes false. The third-party alarm method comprises: process parameter data generated in the process of steel coil processing production, including temperature, liquid level, pressure, process parameter alarm is generated when the process parameter exceeds the corresponding standard range; the production line of process processing is divided into multiple regions and positions, wherein different types of process parameters are distributed, each steel coil has corresponding process parameter values in the corresponding region and position, and the process parameter standards required for steel coils of different brands and different widths and thicknesses are different; the scene of dynamic change of the alarm rule is uniformly given to the third-party alarm for processing; the third-party alarm obtains the upper and lower limit ranges of the corresponding process parameters according to the steel coil information on each position, and simultaneously obtains the real-time value of the process parameter on the position; if the real-time value exceeds the upper and lower limit ranges, an alarm is generated, and the alarm logic preliminary result as a third party is sent to the time window processing module. 4.The method of claim 1, wherein, The step 3 data analysis method comprises: The time sequence data logic processing module receives a pre-set alarm configuration message body, obtains alarm configuration alarmId, alarm name name, alarm type type, data source eventSourceVOList information, and matches alarm rule-self-defined alarm type=0 formula polynomial, limit alarm type=1 point itemTag and upper and lower limit values lower / upper.

5. The method of claim 1, wherein, The step 3 method further comprises: Step 3.1, the time sequence data logic processing module processes and analyzes the input time sequence data, obtains the data source by subscribing to the topic and measurement point information configured in the alarm configuration, and pre-processes the original time sequence data; Step 3.2, according to the alarm condition set in the alarm configuration module, filter, calculate, match and select the data; Step 3.3, the data meeting the alarm condition is summarized, counted and forwarded, and sent to the time window processing module for analysis and judgment. 6.The method of claim 5, wherein, The specific method of steps 3.1-3.3 comprises: By subscribing to the topic list in the alarm configuration, all measurement point data is obtained in real time; the data includes the timestamp timestamp of accepting the message, all measurement point data information values; wherein each measurement point data information includes id-measurement point identifier, v-data value, q-whether valid, t-timestamp; first, check whether the timestamp timestamp is within a certain minute time range, filter the dirty data caused by repeated data transmission due to various reasons; then, according to the id, v, q, t rule, the message body is data-analyzed, the data with q as false is filtered, and the v data value obtained by analysis is assigned to the point in the alarm configuration.

7. The method of claim 4, wherein, The step 4 method comprises: Step 4.1, according to the time window size set by the user, the time sequence data is divided into several time windows; Step 4.2, statistical analysis is performed on the data in each time window, and the data is derived from the result data processed by the time sequence data logic processing module and the alarm result data generated by the third-party service. Step 4.

3. According to the alarm condition set in the alarm configuration module, combined with the alarm rule associated with the alarm result data, determine whether the data in the current time window meets the alarm condition; Step 4.

4. If the alarm condition is met, generate the corresponding alarm information, and prioritize the alarm information according to the alarm level, and send the alarm information to the relevant personnel or system. 8.The method of claim 7, wherein, The specific methods of steps 4.1-4.4 include: The specific method for statistical analysis of data in each time window is: The time series data logic processing module sends the alarm event / reset event result to the time window processing module; within a certain time window period, how many seconds or how many times the alarm event repeats, that is, continuously tracking the type 2 data in the event message body of the same alarm configuration ID, meeting the time window configuration condition in the alarm configuration, then finally a real alarm is generated; The specific configuration conditions include the following three cases: Continuous N seconds: from the first alarm event trigger, within N seconds, no alarm event reset occurs; Continuous N times: from the first alarm event trigger, continuously occur N times of the same alarm event trigger, and no alarm event reset occurs; N times and above occur within N seconds: from the first alarm event trigger, within N seconds, N times and above of the same alarm event trigger occur continuously, and no alarm event reset occurs. 9.The method of claim 1, wherein, The time series data logic processing module and the third party service perform data transmission with the time window processing module through MQTT / HTTP mode.

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