Alarm processing method and device for industrial internet

Abstract

The invention discloses an industrial internet-oriented alarm processing method and device, and the method comprises the steps: collecting and preprocessing a sensor parameter fed back by each industrial device in real time, and obtaining a plurality of production process parameters of each industrial device; according to the plurality of production process parameters of each industrial device and a preset signal alarm condition, determining an abnormal production process parameter and a target person-in-charge identifier of an abnormal industrial device with an alarm; starting an alarm state of the abnormal industrial equipment, generating early warning information, sending the early warning information to a client corresponding to the target responsible person identifier, and timing; and when the total time length of timing is equal to the preset time length and the target parameter value in the abnormal production process parameters does not return to normal, obtaining a superior department identifier corresponding to the responsible person identifier from a preset alarm upgrading path, and sending early warning information to a client corresponding to the superior department identifier. Therefore, by adopting the embodiment of the invention, the alarm processing efficiency can be improved, the risk of production interruption is reduced, and meanwhile, the cost of manual intervention is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial internet, and in particular to an alarm processing method and device for industrial internet. BACKGROUND

[0002] With the continuous advancement of industrial automation, the complexity of production equipment and process flow is increasing. These enterprises usually rely on a large number of sensors and control systems to monitor various parameters in real time, such as temperature, pressure, vibration frequency, etc. The real-time data stream of these parameters is crucial to ensure the smoothness of the production process and the normal operation of the machine. For example, the equipment on the production line needs to monitor its running state in real time, and once an abnormal alarm signal (such as equipment overheating, excessive wear of parts, etc.) occurs, it must be able to be discovered and handled in time to avoid production interruption and equipment damage.

[0003] In related technologies, most industrial internet software systems are equipped with basic alarm functions. However, in existing systems, it is difficult to quickly find the specific responsible person when the alarm is triggered, which delays the timing of problem handling and reduces processing efficiency. Secondly, when the initial alarm is not solved in time, the problem cannot be automatically upgraded to higher-level managers or teams, which leads to critical problems being ignored and increases the risk of production interruption. Finally, after the fault is resolved, the existing system needs manual intervention to release the alarm state, increasing the cost of manual intervention. SUMMARY

[0004] The embodiments of the present application provide an alarm processing method and device for industrial internet. In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not a general review, nor does it determine the key / important components or delineate the protection scope of these embodiments. Its only purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0005] In a first aspect, the embodiments of the present application provide an alarm processing method for industrial internet, applied to a server, the method comprising: collecting and preprocessing in real time the sensor parameters fed back by each industrial equipment to obtain a plurality of production process parameters of each industrial equipment; determining the abnormal production process parameters of the abnormal industrial equipment and the target responsibility person identifier according to the plurality of production process parameters of each industrial equipment and the preset signal alarm condition; starting the alarm state of the abnormal industrial equipment and generating a warning information, and sending it to the client corresponding to the target responsibility person identifier and timing; When the total duration of the timing is equal to the preset duration and the target parameter value in the abnormal production process parameter does not return to normal, an upper department identifier corresponding to the responsibility person identifier is obtained from a preset alarm escalation path, and a warning information is sent to a client corresponding to the upper department identifier; or when the total duration of the timing is less than the preset duration and the parameter value in the abnormal production process parameter returns to normal, the alarm state of the abnormal industrial equipment is released.

[0006] In a second aspect, the embodiments of the present application provide an alarm processing device for an industrial internet, the device comprising: The data processing module is configured to collect and pre-process the sensor parameters fed back by each industrial equipment in real time to obtain a plurality of production process parameters of each industrial equipment. The determination module is configured to determine the abnormal production process parameter of the abnormal industrial equipment and the target responsibility person identifier according to the plurality of production process parameters of each industrial equipment and a preset signal alarm condition. The alarm module is configured to start an alarm state of the abnormal industrial equipment and generate a warning information, and send the warning information to a client corresponding to the target responsibility person identifier and start timing. The alarm management module is configured to obtain an upper department identifier corresponding to the responsibility person identifier from a preset alarm escalation path when the total duration of the timing is equal to the preset duration and the target parameter value in the abnormal production process parameter does not return to normal, and send a warning information to a client corresponding to the upper department identifier; or release the alarm state of the abnormal industrial equipment when the total duration of the timing is less than the preset duration and the parameter value in the abnormal production process parameter returns to normal.

[0007] The technical scheme provided by the embodiments of the present application can include the following beneficial effects: In the embodiments of the present application, on the one hand, by collecting and pre-processing the sensor parameters fed back by each industrial equipment in real time, and combining with the preset signal alarm condition, the abnormal production process parameter of the abnormal industrial equipment and the target responsibility person identifier can be accurately determined. This ensures that the specific responsibility person can be quickly located after the alarm is triggered, reduces the processing delay caused by unclear responsibility person, and significantly improves the efficiency of problem processing. On the other hand, by introducing a timing mechanism and a preset alarm escalation path, when the alarm is not processed within the preset time, the system can automatically send the alarm information to a client corresponding to the upper department identifier corresponding to the responsibility person. This ensures that critical problems can be escalated to higher-level managers or teams in a timely manner, avoids the problem being ignored, thereby reducing the risk of production interruption. In addition, when the parameter value in the abnormal production process parameter returns to normal, the system can automatically release the alarm state, reducing the need for manual intervention and reducing the cost of manual intervention.

[0008] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0010] Figure 1 This is a flowchart of an alarm processing method for the Industrial Internet provided by an embodiment of the present application; Figure 2 This is a schematic diagram of a UI interface for a client receiving an alarm provided in an embodiment of the present application; Figure 3 This is an implementation monitoring UI interface for the administrator backend provided in the embodiment of the present application; Figure 4 This is a flow chart of a method for constructing a preset signal alarm condition provided in an embodiment of the present application; Figure 5 This is a flowchart of an AI model training method provided in an embodiment of the present application; Figure 6 This is a structural diagram of an alarm processing device for the Industrial Internet provided in an embodiment of the present application; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The following description and the drawings sufficiently illustrate specific embodiments of the application to enable those skilled in the art to practice them.

[0012] It should be clear that the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of devices and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0014] In the description of the present application, it is understood that the terms "first", "second" and the like are only for the purpose of description and cannot be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. In addition, in the description of the present application, "a plurality of" means two or more, unless otherwise specified. The association relationship of the associated objects is described, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after it.

[0015] At present, most industrial internet software systems are equipped with basic alarm functions.

[0016] The applicant of the present application realizes that it is difficult to quickly find the specific person in charge when the alarm is triggered in the existing system, which delays the timing of problem handling and reduces the handling efficiency. Secondly, when the initial alarm cannot be solved in time, the problem cannot be automatically upgraded to higher-level managers or teams, which causes critical problems to be ignored and increases the risk of production interruption. Finally, after the fault is removed, the existing system needs manual intervention to remove the alarm state, which increases the cost of manual intervention.

[0017] In order to solve the above problems, the present application provides an alarm processing method and device for industrial internet to solve the problems existing in the above related technical problems. In the embodiments of the present application, on the one hand, by collecting and preprocessing the sensor parameters fed back by each industrial equipment in real time, and combining with the preset signal alarm condition, the abnormal production process parameters of the abnormal industrial equipment with alarm and the target person in charge identifier can be accurately determined. It ensures that the specific person in charge can be quickly located after the alarm is triggered, reduces the handling delay caused by unclear person in charge, and significantly improves the efficiency of problem handling. On the other hand, by introducing a timing mechanism and a preset alarm escalation path, when the alarm is not handled within the preset time, the system can automatically send the alarm information to the client corresponding to the superior department identifier corresponding to the person in charge. It ensures that critical problems can be upgraded to higher-level managers or teams in time, avoids problems being ignored, and thus reduces the risk of production interruption. In addition, when the parameter value in the abnormal production process parameter returns to normal, the system can automatically remove the alarm state, reduce the need for manual intervention, and reduce the cost of manual intervention. The following will be described in detail by exemplary embodiments.

[0018] The following will be described in detail by exemplary embodiments. Figure 1 -Appendix Figure 5The method for alarm processing facing the industrial internet provided by the embodiment of the application is introduced in detail. The method can be realized by relying on a computer program and can run on an alarm processing device for the industrial internet based on the Von Neumann system. The computer program can be integrated in an application or can run as an independent tool application.

[0019] Please refer to Figure 1 A flowchart of a method for alarm processing facing the industrial internet provided by the embodiment of the application is provided, which is applied to a server. As shown in Figure 1 The method of the embodiment of the application includes the following steps: S101, real-time collection and preprocessing of sensor parameters fed back by each industrial device to obtain a plurality of production process parameters of each industrial device; The industrial device refers to various mechanical devices, automation devices and the like used for industrial production, such as machine tools, motors, boilers, conveyors and the like. The sensor parameter is various physical quantities or state information collected by the sensor, such as temperature, pressure, vibration frequency, current, voltage and the like. The plurality of production process parameters are the sensor data after preprocessing, reflecting the actual running state of the device in the production process.

[0020] In some embodiments of the application, a connection is established with the sensor on the industrial device through an industrial bus (such as Modbus, Profibus) or a wireless communication protocol (such as Zigbee, Wi-Fi). A data acquisition card or an industrial gateway is used to obtain data from the sensor at a set sampling frequency (such as 10 times per second). The obtained sensor data is preprocessed to obtain a characteristic parameter value reflecting the running state of the device, and the characteristic value is associated with the sensor identifier as the production process parameter of each industrial device. For example, the average value of temperature in the past 1 minute.

[0021] Specifically, the data preprocessing process includes but is not limited to checking the validity of the data and removing obviously erroneous or abnormal data points.

[0022] For example, a motor of an industrial device is equipped with a temperature sensor, a vibration sensor and a current sensor, and the plurality of production process parameters of the industrial device are shown in Table 1, for example.

[0023] Table 1

[0024] S102, determining the abnormal production process parameter of the abnormal industrial device and the target responsible person identifier according to the plurality of production process parameters of each industrial device, and the preset signal alarm condition; Each production process parameter includes a target sensor identifier and a target parameter value, and each preset signal alarm condition of the industrial equipment includes a ternary mapping relationship among a sensor identifier, a parameter threshold value, and a responsible person identifier.

[0025] In some embodiments of the present application, the specific process of determining the abnormal production process parameter and the target responsible person identifier of the abnormal industrial equipment with an alarm according to the plurality of production process parameters, the preset signal alarm condition of each industrial equipment includes: obtaining the target parameter threshold value corresponding to the target sensor identifier from the ternary mapping relationship; comparing the target parameter value with the target parameter threshold value to determine whether each production process parameter is abnormal; if so, marking each production process parameter of each industrial equipment as an abnormal production process parameter of the abnormal industrial equipment with an alarm, and obtaining the target responsible person identifier corresponding to the target sensor identifier from the ternary mapping relationship.

[0026] The ternary mapping relationship is used to quickly find the parameter threshold value of each sensor and the corresponding responsible person, for example, {sensor_id: "S1", threshold: 80, responsible_person: "P1"}. The target parameter value is used to compare with the target parameter threshold value to determine whether it is abnormal. The target parameter threshold value is the parameter threshold value associated with the target sensor identifier, which is used to determine whether the sensor parameter is abnormal. The abnormal production process parameter is the production process parameter that is marked as abnormal after comparison. The target responsible person identifier is the unique identifier of the responsible person associated with the target sensor identifier, which is used to determine the recipient of the alarm information.

[0027] In a possible implementation, in the ternary mapping relationship table, the corresponding record is found according to the target sensor identifier. The target parameter threshold value is extracted from the found record. The target parameter value is obtained from the preprocessed production process parameter. It is determined whether the target parameter value exceeds the target parameter threshold value. If the target parameter value exceeds the threshold value, the production process parameter is marked as abnormal. The abnormal information is recorded, including the sensor identifier, the parameter name, the parameter value and the state. In the ternary mapping relationship table, the responsible person identifier is found according to the target sensor identifier. The responsible person identifier is recorded for subsequent alarm notification.

[0028] The ternary mapping relationship is shown in Table 2, for example.

[0029] Table 2

[0030] In the embodiments of the present application, by collecting and preprocessing the sensor parameters fed back by each industrial equipment in real time, and combining the preset signal alarm condition, the abnormal production process parameters and the target responsible person identifier of the abnormal industrial equipment with the alarm can be accurately determined. It ensures that the specific responsible person can be quickly located after the alarm is triggered, reduces the processing delay caused by unclear responsibility, and significantly improves the efficiency of problem handling.

[0031] Further, the specific process of generating the preset signal alarm condition includes: scanning each industrial equipment accessed in the business environment of the industrial internet; traversing each sensor identifier deployed on each industrial equipment from the industrial equipment information table; dynamically updating the initial parameter threshold value and the initial responsible person identifier corresponding to each sensor identifier to obtain the final parameter threshold value and the final responsible person identifier; storing the ternary mapping relationship between each sensor identifier deployed on each industrial equipment, the final parameter threshold value updated for each sensor identifier, and the final responsible person identifier to obtain the preset signal alarm condition of each industrial equipment.

[0032] S103, starting the alarm state of the abnormal industrial equipment and generating a warning information, and sending to the client corresponding to the target responsible person identifier and timing; The alarm state is a special state of the equipment, indicating that the equipment is running abnormally and needs to be handled in time. The warning information contains the message of the equipment abnormal information, which is used to notify the responsible person. The client is the equipment or system used by the responsible person, which is used to receive the alarm information.

[0033] In some embodiments of the present application, an alarm flag is set in the device state table, indicating that the device has entered the alarm state. The warning message is generated according to the abnormal information, including device identifier, abnormal parameter, alarm time, etc., for example, alert_message = f"device {device_id} has {parameter} parameter abnormal, the current value is {value}, which exceeds the threshold value {threshold}." The contact information of the target responsible person is obtained. The warning information is sent to the client of the responsible person using the selected notification method, and the UI interface of the client receiving the alarm is shown in the figure. Figure 2 The timing starts from the start of the alarm state, and the duration of the alarm is recorded.

[0034] For example, the temperature sensor S1 of the device D1 detects a temperature value of 82℃, which exceeds the threshold value of 80℃, at which time the alarm flag of the device D1 is set to True. The generated warning information is: the temperature sensor S1 of the device D1 detects a temperature value of 82℃, which exceeds the threshold value of 80℃, please handle as soon as possible. The mailbox of the person in charge P1 is user_P1@example.com, and the mobile phone number is 1234567890. An email is sent to user_P1@example.com with the content: the temperature sensor S1 of the device D1 detects a temperature value of 82℃, which exceeds the threshold value of 80℃, please handle. The alarm time is 2024-09-01 08:00:00, and the timer starts counting.

[0035] S104, when the total duration of the timer is equal to the preset duration and the target parameter value in the abnormal production process parameter does not recover to normal, obtaining the superior department identifier corresponding to the person in charge identifier from the preset alarm escalation path, and sending the warning information to the client corresponding to the superior department identifier; or when the total duration of the timer is less than the preset duration and the parameter value in the abnormal production process parameter recovers to normal, releasing the alarm state of the abnormal industrial equipment.

[0036] The preset alarm escalation path includes a binary mapping relationship between the person in charge identifier and the superior department identifier.

[0037] In some embodiments of the present application, the specific process of obtaining the superior department identifier corresponding to the person in charge identifier from the preset alarm escalation path includes: according to the person in charge identifier, obtaining the corresponding superior department identifier from the binary mapping relationship.

[0038] In one possible implementation, when the temperature sensor S1 detects a temperature value of 82℃, which exceeds the threshold value of 80℃, the system starts the alarm state and records the alarm time 2024-09-01 08:00:00. At the same time, the system starts the timer, and the preset alarm handling duration is 10 minutes. The system checks the current value of the temperature sensor S1 every certain time (such as every minute). If the temperature value drops to 80℃ or below within 10 minutes, the system releases the alarm state and stops the timer. If the temperature value does not recover to normal within 10 minutes, the system triggers alarm escalation after the timer ends. The system looks up the corresponding superior department identifier D1 from the alarm escalation path table according to the person in charge identifier P1. The system sends the warning information to the client of the superior department D1.

[0039] For example, at 2024-09-01 08:05:00, the temperature value drops to 78°C, which is lower than the threshold value 80°C. The system records the release time: 2024-09-01 08:05:00. The system stops timing and releases the alarm state. Within 10 minutes, the temperature value remains at 82°C, which has not returned to normal. At 2024-09-01 08:10:00, the timing ends, and the temperature value is still 82°C. The system looks up the superior department identifier D1 from the alarm escalation path table according to the responsibility identifier P1. The system sends a warning message to the client of the superior department D1, with the content: The temperature sensor S1 of the device D1 detects a temperature value of 82°C, which exceeds the threshold value of 80°C, please handle as soon as possible.

[0040] Specifically, the specific process of generating the preset alarm escalation path includes: obtaining each responsibility identifier; receiving the superior department identifier configured for each responsibility identifier; storing the binary mapping relationship between each responsibility identifier and the superior department identifier configured for each responsibility identifier to obtain the preset alarm escalation path.

[0041] Among them, the background administrator can actually inquire related information, for example Figure 3 as shown.

[0042] In the embodiments of the present application, on the one hand, by real-time acquisition and preprocessing of sensor parameters fed back by each industrial device, and combining with the preset signal alarm condition, the abnormal production process parameters and the target responsibility identifier of the abnormal industrial device with alarm can be accurately determined. It ensures that the specific responsibility person can be quickly located after the alarm is triggered, reduces the processing delay caused by unclear responsibility person, and significantly improves the efficiency of problem handling. On the other hand, by introducing a timing mechanism and a preset alarm escalation path, when the alarm is not handled within the preset time, the system can automatically send the alarm information to the client corresponding to the superior department identifier corresponding to the responsibility person. It ensures that critical problems can be escalated to higher-level managers or teams in a timely manner, avoiding the problem of being ignored, thereby reducing the risk of production interruption. In addition, when the parameter value in the abnormal production process parameter returns to normal, the system can automatically release the alarm state, reducing the need for manual intervention and reducing the cost of manual intervention.

[0043] Please refer to Figure 4 , a flowchart of a method for constructing a preset signal alarm condition is provided for the embodiments of the present application. As Figure 4 shown, the method of the embodiments of the present application can include the following steps: S201, scanning each industrial device accessed in the business environment of the industrial internet; S202, traversing to obtain each sensor identifier deployed on each industrial device from the industrial device information table; S203, dynamically updating the initial parameter threshold value corresponding to each sensor identifier and the initial responsibility person identifier to obtain the final parameter threshold value and the final responsibility person identifier; In the embodiments of the present application, the specific process of dynamically updating the initial parameter threshold value corresponding to each sensor identifier and the initial responsibility person identifier to obtain the final parameter threshold value and the final responsibility person identifier includes: in the case that the initial parameter threshold value corresponding to each sensor identifier and the initial responsibility person identifier are empty, generating parameter configuration prompt information for display; receiving the initial parameter threshold value and the initial responsibility person identifier configured for each sensor identifier in the displayed parameter configuration prompt information as the final parameter threshold value and the final responsibility person identifier; or, in the case that the initial parameter threshold value corresponding to each sensor identifier and the initial responsibility person identifier are not empty, querying the responsibility person information corresponding to the initial responsibility person identifier and the sensor operation log corresponding to each sensor identifier from a personnel management system; updating the initial responsibility person identifier according to the responsibility person information to obtain the final responsibility person identifier; and updating the initial parameter threshold value corresponding to each sensor identifier according to the sensor operation log corresponding to each sensor identifier to obtain the final parameter threshold value.

[0044] In a possible implementation, for each sensor identifier, it is checked whether the initial parameter threshold value and the initial responsibility person identifier corresponding thereto are empty. If the initial parameter threshold value and the initial responsibility person identifier are empty, parameter configuration prompt information is generated to prompt the user to configure these parameters. The prompt information is displayed to the user, and the user can input the initial parameter threshold value and the initial responsibility person identifier through an interface. The initial parameter threshold value and the initial responsibility person identifier input by the user are received, and these values are taken as the final parameter threshold value and the final responsibility person identifier. If the initial parameter threshold value and the initial responsibility person identifier are not empty, the responsibility person information corresponding to the initial responsibility person identifier is queried from a personnel management system. The sensor operation log corresponding to each sensor identifier is queried to obtain historical parameter values. The initial responsibility person identifier is updated according to the queried responsibility person information to obtain the final responsibility person identifier. The initial parameter threshold value is updated according to the sensor operation log to obtain the final parameter threshold value.

[0045] Before real-time collection and preprocessing of the sensor parameters fed back by each industrial equipment, the initial responsibility person identifier and the initial parameter threshold value are updated, which can prevent the problem of not being able to find a responsibility person due to the resignation or job transfer of a related responsibility person, and can prevent the problem of inaccurate threshold values and false positives due to the aging of the equipment. The present application can dynamically adjust the parameter threshold value according to the sensor operation log, ensure that the threshold value adapts to the actual operation state of the equipment as the equipment is used, and thus reduce false positives.

[0046] In some embodiments of the present application, the specific process of updating the initial responsibility identifier according to the responsibility information to obtain the final responsibility identifier includes: parsing the responsibility information to obtain the structured data corresponding to the initial responsibility identifier; in the case that the field used to represent the employee status in the structured data indicates that the employee has left the job or has been transferred, generating a prompt information to inform the administrator that the responsibility identifier needs to be re-assigned; and in response to the responsibility identifier configuration instruction, receiving the re-configured responsibility identifier for the initial responsibility identifier as the final responsibility identifier.

[0047] For example, the initial responsibility identifier is P1, the responsibility information is {"id": "P1", "name": "Zhang San", "status": "left", "new_position": null}, the parsed structured data is {"id": "P1", "name": "Zhang San", "status": "left"}, the value of the status field in the structured data is checked to determine whether the responsibility identifier has left the job or has been transferred. If the responsibility identifier has left the job or has been transferred, a prompt information is generated to inform the administrator that the responsibility identifier needs to be re-assigned, and the prompt information is "the responsibility identifier P1 (Zhang San) has left the job, and the responsibility identifier needs to be re-assigned". The administrator inputs a new responsibility identifier P2, and the final responsibility identifier is P2.

[0048] In some embodiments of the present application, the specific process of updating the initial parameter threshold corresponding to each sensor identifier according to the sensor operation log corresponding to each sensor identifier to obtain the final parameter threshold includes: preprocessing the sensor operation log corresponding to each sensor identifier to obtain the historical parameter value sequence corresponding to each sensor identifier; performing statistical analysis on the historical parameter value sequence corresponding to each sensor identifier to calculate the statistical index corresponding to each sensor identifier; in the case that the statistical index corresponding to each sensor identifier deviates from the initial parameter threshold corresponding to each sensor identifier, adjusting the initial parameter threshold corresponding to each sensor identifier to the statistical index corresponding to each sensor identifier to obtain the final parameter threshold; or analyzing the change trend of the historical parameter value sequence corresponding to each sensor identifier over time to obtain the change trend corresponding to each sensor identifier; in the case that the change trend corresponding to each sensor identifier presents an upward trend, increasing the initial parameter threshold corresponding to each sensor identifier by a preset step to obtain the final parameter threshold; or in the case that the change trend corresponding to each sensor identifier presents a downward trend, decreasing the initial parameter threshold corresponding to each sensor identifier by a preset step to obtain the final parameter threshold.

[0049] For example, a temperature sensor (S1) and a vibration sensor (S2) are installed on the motor. Each sensor has an initial parameter threshold value that needs to be adjusted according to the historical running log of the sensor. The historical running log of sensor S1: [78, 80, 82, 81, 83, 84, 85]; the historical running log of sensor S2: [2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7]; statistical analysis is performed on the historical parameter value sequence of each sensor, and statistical indicators (such as mean, standard deviation, etc.) are calculated, for example, the mean of sensor S1: 82.14; the mean of sensor S2: 2.4. The initial parameter threshold value of sensor S1 is 80. The initial parameter threshold value of sensor S2 is 2.5. If the statistical indicator deviates from the initial parameter threshold value, adjust the initial parameter threshold value to the statistical indicator. The final parameter threshold value of sensor S1 can be 82.14, and the final parameter threshold value of sensor S2 can be 2.4.

[0050] For example, analyze the trend of the historical parameter value sequence of each sensor over time. Assume that the trend of sensor S1 is: upward trend. The trend of sensor S2 is upward trend. The initial parameter threshold value of sensor S2 is 2.5. The preset step size is 0.1. The final parameter threshold value of sensor S2 can be adjusted to 2.6.

[0051] In some embodiments of the present application, the specific process of updating the initial parameter threshold value corresponding to each sensor identifier according to the corresponding sensor running log of each sensor identifier to obtain the final parameter threshold value includes: preprocessing the sensor running log corresponding to each sensor identifier to obtain the historical parameter value sequence corresponding to each sensor identifier; extracting statistical correlation features from the historical parameter value sequence; inputting the statistical correlation features into a pre-trained anomaly analysis model to output the normal threshold range corresponding to each sensor identifier at the current time; comparing the initial parameter threshold value corresponding to each sensor identifier with the normal threshold range corresponding to each sensor identifier at the current time to adjust the initial parameter threshold value corresponding to each sensor identifier to be within the normal threshold range corresponding to each sensor identifier at the current time, thereby obtaining the final parameter threshold value.

[0052] Specifically, the specific process of generating the pre-trained anomaly analysis model comprises: acquiring historical running data of each historical moment collected by each sensor of the industrial equipment; calculating historical statistical correlation features of each historical moment of each sensor according to the historical running data of each historical moment of each sensor; receiving a normal threshold range labeled for the historical statistical correlation features of each historical moment of each sensor to obtain a model training sample; creating an anomaly analysis model by using a machine learning algorithm; inputting the model training sample into the anomaly analysis model to output a model loss value; generating the pre-trained anomaly analysis model when the model loss value reaches a minimum value; wherein, The loss function of the anomaly analysis model is:

[0053]

[0054]

[0055] wherein, is the model loss value, is the number of sensors of the industrial equipment, each sensor has historical moment data, for each sensor each historical moment , the normal threshold range predicted by the model is , and the actual labeled normal threshold range is , is used to measure the difference between the predicted lower bound and the actual lower bound, is used to measure the difference between the predicted upper bound and the actual upper bound.

[0056] S204, store the ternary mapping relationship between each sensor identifier deployed on each industrial equipment, the final parameter threshold updated for each sensor identifier, and the final person identifier to obtain a preset signal alarm condition of each industrial equipment.

[0057] In the embodiments of the present application, on the one hand, by collecting and preprocessing the sensor parameters fed back by each industrial equipment in real time, and combining with the preset signal alarm condition, the abnormal production process parameters and the target responsible person identifier of the abnormal industrial equipment with alarm can be accurately determined. After the alarm is triggered, the specific responsible person can be quickly located, the processing delay caused by unclear responsibility is reduced, and the efficiency of problem processing is significantly improved. On the other hand, by introducing a timing mechanism and a preset alarm escalation path, when the alarm is not processed within the preset time, the system can automatically send the alarm information to the client corresponding to the superior department identifier of the responsible person. This ensures that critical problems can be escalated to higher-level managers or teams in a timely manner, avoiding the problem of being ignored, thereby reducing the risk of production interruption. In addition, when the parameter value of the abnormal production process parameter returns to normal, the system can automatically remove the alarm state, reducing the need for manual intervention and reducing the cost of manual intervention.

[0058] Please refer to Figure 5 A flowchart of an AI model training method is provided for the embodiments of the present application. As shown in Figure 5 The method of the embodiments of the present application can include the following steps: S301, obtaining historical running data of each sensor of the industrial equipment collected at each historical time; S302, calculating historical statistical correlation features of each sensor at each historical time according to the historical running data of each sensor at each historical time; S303, receiving a normal threshold range labeled for the historical statistical correlation features of each sensor at each historical time, obtaining a model training sample; S304, creating an anomaly analysis model using a machine learning algorithm; S305, inputting the model training sample into the anomaly analysis model, and outputting a model loss value; S306, generating a pre-trained anomaly analysis model when the model loss value reaches a minimum.

[0059] In the embodiments of the present application, by systematically collecting and analyzing the historical running data of the industrial equipment sensors, an anomaly analysis model is constructed using statistical features and machine learning algorithms, which can realize accurate monitoring and abnormal prediction of the equipment running state, improve the accuracy and reliability of the alarm system. At the same time, through model training and optimization, the system can continuously learn and improve, further improve the detection ability of equipment anomalies, thereby effectively reducing the risk of equipment failure and ensuring the stable operation of industrial production.

[0060] The following is an embodiment of the device of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0061] Please refer to Figure 6 , which shows a structure diagram of an alarm processing device for industrial internet provided by an example embodiment of the present application. The alarm processing device for industrial internet can be realized by software, hardware or a combination of both to become all or part of an electronic device. The device 1 includes a data processing module 10, a determination module 20, an alarm module 30 and an alarm management module 40.

[0062] The data processing module 10 is configured to collect and pre-process sensor parameters fed back by each industrial device in real time to obtain a plurality of production process parameters of each industrial device. The determination module 20 is configured to determine abnormal production process parameters and a target responsible person identifier of an abnormal industrial device with an alarm according to the plurality of production process parameters of each industrial device and a preset signal alarm condition. The alarm module 30 is configured to start an alarm state of the abnormal industrial device and generate a warning information, and send the warning information to a client corresponding to the target responsible person identifier and time. The alarm management module 40 is configured to acquire a superior department identifier corresponding to the responsible person identifier from a preset alarm escalation path when a total time length of timing is equal to a preset time length and a target parameter value in the abnormal production process parameters is not restored to normal, and send the warning information to a client corresponding to the superior department identifier; or release the alarm state of the abnormal industrial device when the total time length of timing is less than the preset time length and the parameter value in the abnormal production process parameters is restored to normal.

[0063] It should be noted that the alarm processing device for industrial internet provided by the above embodiment is only exemplified by the division of the above functional modules when executing the alarm processing method for industrial internet. In actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the alarm processing device for industrial internet and the alarm processing method for industrial internet provided by the above embodiment belong to the same concept, and the implementation process is detailed in the method embodiment. Here, it is not repeated.

[0064] The above sequence numbers of the embodiments of the present application are only for description, not representing the advantages and disadvantages of the embodiments.

[0065] In the embodiments of the present application, on the one hand, by collecting and preprocessing the sensor parameters fed back by each industrial equipment in real time, and combining with the preset signal alarm condition, the abnormal production process parameters and the target responsible person identifier of the abnormal industrial equipment with alarm can be accurately determined. After the alarm is triggered, the specific responsible person can be quickly located, the processing delay caused by unclear responsibility can be reduced, and the efficiency of problem processing can be significantly improved. On the other hand, by introducing a timing mechanism and a preset alarm escalation path, when the alarm is not processed within the preset time, the system can automatically send the alarm information to the client corresponding to the superior department identifier corresponding to the responsible person. Key problems can be escalated to higher-level managers or teams in a timely manner, and the risk of production interruption can be reduced. In addition, when the parameter value in the abnormal production process parameter returns to normal, the system can automatically release the alarm state, reducing the need for manual intervention and reducing the cost of manual intervention.

[0066] The present application also provides a computer readable medium having program instructions stored thereon, which, when executed by a processor, implement the industrial internet-oriented alarm processing method provided by each of the above method embodiments.

[0067] The present application also provides a computer program product containing instructions, which, when running on a computer, causes the computer to execute the industrial internet-oriented alarm processing method of each of the above method embodiments.

[0068] Please refer to Figure 7 The present application provides a structural schematic diagram of an electronic device. As shown in Figure 7 The electronic device 1000 can include at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0069] The communication bus 1002 is used to realize the connection and communication between the components.

[0070] The user interface 1003 can include a display, a camera, and optionally a standard wired interface and a wireless interface.

[0071] The network interface 1004 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0072] The processor 1001 can include one or more processing cores. The processor 1001 connects various parts within the entire electronic device 1000 through various interfaces and lines, and performs various functions of the electronic device 1000 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Alternatively, the processor 1001 can be implemented in at least one of a hardware form of a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic array (PLA). The processor 1001 can integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes an operating system, a user interface, and an application program; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 1001, but can be realized by a separate chip.

[0073] The memory 1005 can include a random access memory (RAM) and can also include a read-only memory (ROM). Alternatively, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 1005 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 1005 can alternatively be at least one storage system located away from the aforementioned processor 1001. As shown in the figure, the memory 1005 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an alarm processing application program facing the industrial internet. Figure 7

[0074] In Figure 7 ​In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an interface for user input and obtain data input by the user, and the processor 1001 can be used to call an industrial internet-oriented alarm processing application stored in the memory 1005 and specifically perform the following operations: collect and pre-process sensor parameters fed back by each industrial equipment in real time to obtain a plurality of production process parameters of each industrial equipment; determine abnormal production process parameters and target responsible person identifiers of abnormal industrial equipment with alarms according to the plurality of production process parameters of each industrial equipment and preset signal alarm conditions; start an alarm state of the abnormal industrial equipment and generate a warning information, and send the warning information to a client corresponding to the target responsible person identifier and time; When the total duration of timing is equal to the preset duration and the target parameter value in the abnormal production process parameter is not restored to normal, obtain a superior department identifier corresponding to the responsible person identifier from the preset alarm escalation path, and send the warning information to a client corresponding to the superior department identifier; or when the total duration of timing is less than the preset duration and the parameter value in the abnormal production process parameter is restored to normal, the alarm state of the abnormal industrial equipment is removed.

[0075] In one embodiment, when the processor 1001 performs determining abnormal production process parameters and target responsible person identifiers of abnormal industrial equipment with alarms according to a plurality of production process parameters of each industrial equipment and preset signal alarm conditions, the processor 1001 specifically performs the following operations: obtain a target parameter threshold value corresponding to the target sensor identifier from the ternary mapping relationship; compare the target parameter value with the target parameter threshold value to determine whether each production process parameter is abnormal; If so, mark each production process parameter of each industrial equipment as an abnormal production process parameter of abnormal industrial equipment with alarms, and obtain a target responsible person identifier corresponding to the target sensor identifier from the ternary mapping relationship.

[0076] In one embodiment, before the processor 1001 performs real-time collection and pre-processing of sensor parameters fed back by each industrial equipment, the processor 1001 also performs the following operations: scan each industrial equipment accessed in the business environment of the industrial internet; obtain each sensor identifier deployed on each industrial equipment by traversing the industrial equipment information table; dynamically update the initial parameter threshold value and the initial responsible person identifier corresponding to each sensor identifier to obtain the final parameter threshold value and the final responsible person identifier; store a ternary mapping relationship between each sensor identifier deployed on each industrial equipment, an updated final parameter threshold for each sensor identifier, and a final person identifier, to obtain a preset signal alarm condition of each industrial equipment.

[0077] In one embodiment, the processor 1001, when performing the dynamic updating of the initial parameter threshold corresponding to each sensor identifier and the initial person identifier to obtain the final parameter threshold and the final person identifier, specifically performs the following operations: In the case that the initial parameter threshold corresponding to each sensor identifier and the initial person identifier are empty, generating parameter configuration prompt information for display; receiving the initial parameter threshold and the initial person identifier configured for each sensor identifier in the displayed parameter configuration prompt information as the final parameter threshold and the final person identifier; or, In the case that the initial parameter threshold corresponding to each sensor identifier and the initial person identifier are not empty, querying the person information corresponding to the initial person identifier and the sensor operation log corresponding to each sensor identifier from the personnel management system; updating the initial person identifier according to the person information to obtain the final person identifier; updating the initial parameter threshold corresponding to each sensor identifier according to the sensor operation log corresponding to each sensor identifier to obtain the final parameter threshold.

[0078] In one embodiment, the processor 1001, when performing the updating of the initial person identifier according to the person information to obtain the final person identifier, specifically performs the following operations: parsing the person information to obtain structured data corresponding to the initial person identifier; in the case that the field used to represent the employee state in the structured data indicates that the employee has left the job or has been transferred, generating prompt information to notify the administrator that the person identifier needs to be reconfigured; in response to the person identifier configuration instruction, receiving the reconfigured person identifier for the initial person identifier as the final person identifier.

[0079] In one embodiment, the processor 1001, when performing the updating of the initial parameter threshold corresponding to each sensor identifier according to the sensor operation log corresponding to each sensor identifier to obtain the final parameter threshold, specifically performs the following operations: preprocessing the sensor operation log corresponding to each sensor identifier to obtain a historical parameter value sequence corresponding to each sensor identifier; perform statistical analysis on the historical parameter value sequence corresponding to each sensor identifier to calculate a statistical indicator corresponding to each sensor identifier; adjust the initial parameter threshold corresponding to each sensor identifier to the statistical indicator corresponding to each sensor identifier in a case where the statistical indicator corresponding to each sensor identifier deviates from the initial parameter threshold corresponding to each sensor identifier, to obtain a final parameter threshold; or analyze a change trend of the historical parameter value sequence corresponding to each sensor identifier over time to obtain a change trend corresponding to each sensor identifier; in a case where the change trend corresponding to each sensor identifier presents an upward trend, increase the initial parameter threshold corresponding to each sensor identifier by a preset step to obtain a final parameter threshold; or in a case where the change trend corresponding to each sensor identifier presents a downward trend, decrease the initial parameter threshold corresponding to each sensor identifier by a preset step to obtain a final parameter threshold.

[0080] In an embodiment, the processor 1001, when performing updating the initial parameter threshold corresponding to each sensor identifier according to the sensor running log corresponding to each sensor identifier to obtain a final parameter threshold, specifically performs the following operations: preprocess the sensor running log corresponding to each sensor identifier to obtain a historical parameter value sequence corresponding to each sensor identifier; extract statistical features from the historical parameter value sequence; input the statistical features into a pre-trained anomaly analysis model to output a normal threshold range corresponding to each sensor identifier at a current time; compare the initial parameter threshold corresponding to each sensor identifier with the normal threshold range corresponding to each sensor identifier at the current time to adjust the initial parameter threshold corresponding to each sensor identifier to be within the normal threshold range corresponding to each sensor identifier at the current time, to obtain a final parameter threshold.

[0081] In an embodiment, the processor 1001, when performing obtaining the superior department identifier corresponding to the responsibility person identifier from the preset alarm escalation path, specifically performs the following operations: obtain the superior department identifier corresponding to the responsibility person identifier from the binary mapping relationship; generate the preset alarm escalation path according to the following steps, including: obtain each responsibility person identifier; receive a superior department identifier configured for each responsibility person identifier; store a binary mapping relationship between each responsibility person identifier and the superior department identifier configured for each responsibility person identifier to obtain the preset alarm escalation path.

[0082] In the embodiments of the present application, on the one hand, by collecting and preprocessing the sensor parameters fed back by each industrial equipment in real time, and combining with the preset signal alarm condition, the abnormal production process parameters and the target responsible person identifier of the abnormal industrial equipment with alarm can be accurately determined. After the alarm is triggered, the specific responsible person can be quickly located, the processing delay caused by unclear responsibility can be reduced, and the efficiency of problem processing can be significantly improved. On the other hand, by introducing a timing mechanism and a preset alarm escalation path, when the alarm is not processed within the preset time, the system can automatically escalate and send the alarm information to the client corresponding to the superior department identifier corresponding to the responsible person. It ensures that critical problems can be escalated to higher-level managers or teams in a timely manner, avoids problems being ignored, and thus reduces the risk of production interruption. In addition, when the parameter value in the abnormal production process parameter returns to normal, the system can automatically release the alarm state, reducing the need for manual intervention and reducing the cost of manual intervention.

[0083] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The program for alarm processing for industrial internet can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium of the program for alarm processing for industrial internet can be a disk, an optical disc, a read-only memory, a random access memory, etc.

[0084] The above only discloses the preferred embodiments of the present application, and of course cannot limit the scope of the rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. An alarm processing method for the industrial Internet, characterized in that: Applied to the server, the method includes: Real-time collection and pre-processing of sensor parameters fed back by each industrial equipment to obtain multiple production process parameters of each industrial equipment; According to the multiple production process parameters of each industrial device and the preset signal alarm conditions, determining the abnormal production process parameters of the abnormal industrial device with an alarm and the identification of the target responsible person; Activate the alarm state of the abnormal industrial equipment and generate early warning information, send it to the client corresponding to the target responsible person identification and time it; When the total timing time is equal to the preset time and the target parameter value in the abnormal production process parameter has not returned to normal, the superior department identifier corresponding to the responsible person identifier is obtained from the preset alarm escalation path, and the early warning information is sent to the client corresponding to the superior department identifier; or when the total timing time is less than the preset time and the parameter value in the abnormal production process parameter returns to normal, the alarm status of the abnormal industrial equipment is released.

2. The method according to claim 1, characterized in that Each production process parameter includes a target sensor identifier and a target parameter value, and the preset signal alarm condition of each industrial device includes a ternary mapping relationship between the sensor identifier, the parameter threshold, and the responsible person identifier; The determining, based on the multiple production process parameters of each industrial device and the preset signal alarm conditions, the abnormal production process parameters of the abnormal industrial device with an alarm and the identification of the target responsible person includes: Obtaining a target parameter threshold corresponding to the target sensor identifier from the ternary mapping relationship; comparing the target parameter value with the target parameter threshold to determine whether each production process parameter is abnormal; If so, each production process parameter of each industrial device is marked as an abnormal production process parameter of an abnormal industrial device with an alarm, and the target responsible person identifier corresponding to the target sensor identifier is obtained from the ternary mapping relationship.

3. The method according to claim 1, characterized in that Before the real-time collection and pre-processing of sensor parameters fed back by each industrial device, the following steps are also included: Scan each industrial device connected to the business environment of the Industrial Internet; From the industrial equipment information table, traverse and obtain the identifier of each sensor deployed on each industrial equipment; Dynamically update the initial parameter threshold and the initial responsible person identification corresponding to each sensor identification to obtain the final parameter threshold and the final responsible person identification; A ternary mapping relationship between each sensor identifier deployed on each industrial device, a final parameter threshold updated for each sensor identifier, and a final responsible person identifier is stored to obtain a preset signal alarm condition for each industrial device.

4. The method according to claim 3, characterized in that The dynamically updating the initial parameter threshold and the initial responsible person identification corresponding to each sensor identification to obtain the final parameter threshold and the final responsible person identification includes: When the initial parameter threshold and the initial responsible person identifier corresponding to each sensor identifier are empty, parameter configuration prompt information is generated for display; Receive the initial parameter threshold and initial responsible person identifier configured for each sensor identifier in the displayed parameter configuration prompt information as the final parameter threshold and final responsible person identifier; or, When the initial parameter threshold and the initial responsible person identifier corresponding to each sensor identifier are not empty, querying the responsible person information corresponding to the initial responsible person identifier and the sensor operation log corresponding to each sensor identifier from the personnel management system; According to the responsible person information, the initial responsible person identifier is updated to obtain the final responsible person identifier; According to the sensor operation log corresponding to each sensor identifier, the initial parameter threshold corresponding to each sensor identifier is updated to obtain a final parameter threshold.

5. The method according to claim 4, characterized in that The updating of the initial responsible person identification according to the responsible person information to obtain the final responsible person identification includes: Parsing the responsible person information to obtain structured data corresponding to the initial responsible person identifier; When the field representing the employee status in the structured data indicates that the employee has resigned or has been transferred, a prompt message is generated to notify the administrator that a responsible person needs to be reassigned; In response to the principal identification configuration instruction, a principal identification reconfigured for the initial principal identification is received as a final principal identification.

6. The method according to claim 4, characterized in that The updating of the initial parameter threshold corresponding to each sensor identifier according to the sensor operation log corresponding to each sensor identifier to obtain the final parameter threshold includes: Preprocessing the sensor operation log corresponding to each sensor identifier to obtain a historical parameter value sequence corresponding to each sensor identifier; performing statistical analysis on the historical parameter value sequence corresponding to each sensor identifier to calculate the statistical index corresponding to each sensor identifier; if the statistical index corresponding to each sensor identifier deviates from the initial parameter threshold corresponding to each sensor identifier, adjusting the initial parameter threshold corresponding to each sensor identifier to the statistical index corresponding to each sensor identifier to obtain a final parameter threshold; or Analyze the change trend of the historical parameter value sequence corresponding to each sensor identifier over time to obtain the change trend corresponding to each sensor identifier; when the change trend corresponding to each sensor identifier shows an upward trend, increase the initial parameter threshold corresponding to each sensor identifier according to a preset step size to obtain a final parameter threshold; or when the change trend corresponding to each sensor identifier shows a downward trend, reduce the initial parameter threshold corresponding to each sensor identifier according to a preset step size to obtain the final parameter threshold.

7. The method according to claim 4, characterized in that The updating of the initial parameter threshold corresponding to each sensor identifier according to the sensor operation log corresponding to each sensor identifier to obtain the final parameter threshold includes: Preprocessing the sensor operation log corresponding to each sensor identifier to obtain a historical parameter value sequence corresponding to each sensor identifier; extracting statistically relevant features from the historical parameter value sequence; Inputting the statistically relevant features into a pre-trained anomaly analysis model, and outputting the normal threshold range corresponding to each sensor identifier at the current moment; The initial parameter threshold corresponding to each sensor identifier is compared with the normal threshold range corresponding to each sensor identifier at the current moment, so as to adjust the initial parameter threshold corresponding to each sensor identifier to be within the normal threshold range corresponding to each sensor identifier at the current moment, thereby obtaining a final parameter threshold.

8. The method according to claim 7, characterized in that Follow these steps to generate a pre-trained anomaly analysis model, including: Obtain historical operating data collected by each sensor of industrial equipment at each historical moment; Calculating historical statistical related features of each sensor at each historical moment based on the historical operation data of each sensor at each historical moment; Receiving a normal threshold range of historical statistical related feature annotations for each historical moment of each sensor to obtain a model training sample; Use machine learning algorithms to create anomaly analysis models; Inputting the model training sample into the anomaly analysis model and outputting a model loss value; When the model loss value reaches the minimum, a pre-trained anomaly analysis model is generated; wherein, The loss function of the anomaly analysis model is: in, is the model loss value, is the number of sensors in industrial equipment, each sensor has Data at historical moments, for each sensor Every historical moment , the normal threshold range predicted by the model is , the actual marked normal threshold range is , It is used to measure the difference between the predicted lower bound and the actual lower bound. Measures the difference between the predicted upper bound and the actual upper bound.

9. The method according to claim 1, characterized in that The preset alarm escalation path includes a binary mapping relationship between the responsible person identifier and the superior department identifier; The step of obtaining the superior department identifier corresponding to the responsible person identifier from the preset alarm escalation path includes: According to the responsible person identifier, obtain the corresponding superior department identifier from the binary mapping relationship; Follow these steps to create a preset alarm escalation path, including: Obtain the ID of each responsible person; Receiving the superior department identifier configured for each responsible person identifier; The binary mapping relationship between each responsible person identifier and the superior department identifier configured for each responsible person identifier is stored to obtain a preset alarm escalation path.

10. An alarm processing system for the industrial Internet, characterized in that: The system comprises: The data processing module is used to collect and pre-process the sensor parameters fed back by each industrial device in real time to obtain multiple production process parameters of each industrial device; a determination module, configured to determine, based on the plurality of production process parameters of each industrial device and the preset signal alarm conditions, abnormal production process parameters of the abnormal industrial device with an alarm and an identifier of a target responsible person; An alarm module is used to activate the alarm state of the abnormal industrial equipment and generate early warning information, which is sent to the client corresponding to the target responsible person identifier and is timed; The alarm management module is used to obtain the superior department identifier corresponding to the responsible person identifier from the preset alarm escalation path and send the early warning information to the client corresponding to the superior department identifier when the total timing time is equal to the preset time and the target parameter value in the abnormal production process parameter has not returned to normal; or to cancel the alarm status of the abnormal industrial equipment when the total timing time is less than the preset time and the parameter value in the abnormal production process parameter has returned to normal.

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