Industrial internet-oriented alarm processing method and device

Abstract

The application discloses an alarm processing method and device for an industrial internet, and the method comprises the following steps: collecting and preprocessing sensor parameters fed back by each industrial equipment in real time to obtain a plurality of production process parameters of each industrial equipment; determining abnormal production process parameters and a target responsible person identifier of an abnormal industrial equipment with an alarm according to the plurality of production process parameters of each industrial equipment and a preset signal alarm condition; starting an alarm state of the abnormal industrial equipment and generating early warning information, and sending the early warning information to a client corresponding to the target responsible person identifier and timing; when the total timing duration is equal to a preset duration and a target parameter value in the abnormal production process parameters has not returned to normal, obtaining a superior department identifier corresponding to the responsible person identifier from a preset alarm escalation path, and sending the early warning information to a client corresponding to the superior department identifier. Therefore, by adopting the embodiment of the application, the alarm processing efficiency can be improved, the risk of production interruption is reduced, and 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. Real-time data flow of these parameters is crucial to ensure the smoothness of production process and the normal operation of machines. 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 found 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 the processing efficiency. Secondly, when the initial alarm cannot be 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 failure 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:

[0006] 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;

[0007] determining 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 the preset signal alarm condition;

[0008] 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;

[0009] When the total duration of 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 acquired 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 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.

[0010] In a second aspect, the embodiments of the present application provide an alarm processing device for an industrial internet, the device comprising:

[0011] A data processing module is configured to 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.

[0012] A determination module is configured to determine abnormal production process parameters of an abnormal industrial equipment with an alarm and a target responsibility person identifier according to the plurality of production process parameters of each industrial equipment and a preset signal alarm condition.

[0013] An 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.

[0014] An alarm management module is configured to acquire an upper department identifier corresponding to the responsibility person identifier from a preset alarm escalation path when the total duration of 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 timing is less than the preset duration and the parameter value in the abnormal production process parameter returns to normal.

[0015] The technical scheme provided by the embodiments of the present application can include the following beneficial effects:

[0016] In the embodiments of the present application, on the one hand, by collecting and pre-processing sensor parameters fed back by each industrial equipment in real time and combining with a preset signal alarm condition, the abnormal production process parameters of an abnormal industrial equipment with an alarm and a 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 identifier. This ensures that critical problems can be timely escalated to higher-level managers or teams, 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 the cost of manual intervention.

[0017] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application, as claimed. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and serve to explain the principles of the application, in which, like reference numerals designate corresponding parts throughout the several views.

[0019] Figure 1 is a method flow diagram of an alarm processing method for an industrial internet provided by an embodiment of the application;

[0020] Figure 2 is a UI interface diagram of a client receiving an alarm provided by an embodiment of the application;

[0021] Figure 3 is an implementation monitoring UI interface of an administrator background provided by an embodiment of the application;

[0022] Figure 4 is a flow diagram of a construction method of a preset signal alarm condition provided by an embodiment of the application;

[0023] Figure 5 is a flow diagram of an AI model training method provided by an embodiment of the application;

[0024] Figure 6 is a structural diagram of an alarm processing device for an industrial internet provided by an embodiment of the application;

[0025] Figure 7 is a structural diagram of an electronic device provided by an embodiment of the application. DETAILED DESCRIPTION

[0026] The following description and drawings are illustrative of the specific embodiments of the present application and are not intended to limit the generality of the application as set forth in the appended claims.

[0027] It should be noted that the described embodiments are merely a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0028] The following description refers to the accompanying drawings. Unless otherwise indicated, like numbers in the various drawings of the accompanying drawings denote like or similar elements. The following detailed description does not limit the application, as claimed, to the only embodiments described, but describes embodiments, which, as provided in the appended claims, can explain some, but not all, embodiments consistent with the application.

[0029] In the description of the present application, it should be understood that the terms "first", "second" and the like are used only for descriptive purposes, 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.

[0030] At present, most industrial internet software systems are equipped with basic alarm functions.

[0031] 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.

[0032] 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 processing delay caused by unclear responsibility, 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.

[0033] 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.

[0034] 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:

[0035] 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;

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] Table 1

[0041]

[0042] 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;

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] The ternary mapping relationship is shown in Table 2, for example.

[0048] Table 2

[0049]

[0050] 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. 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.

[0051] 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; obtaining 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.

[0052] 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;

[0053] 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.

[0054] 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}, and the threshold value is {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 following figure, for example. Figure 2 The timing starts from the start of the alarm state, and the duration of the alarm is recorded.

[0055] 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 early 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.

[0056] 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 early 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.

[0057] The preset alarm escalation path includes a binary mapping relationship between the person in charge identifier and the superior department identifier.

[0058] 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.

[0059] 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 early warning information to the client of the superior department D1.

[0060] For example, at 2024-09-01 08:05:00, the temperature value drops to 78℃, which is lower than the threshold 80℃. 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℃ and does not return to normal. At 2024-09-01 08:10:00, the timing ends and the temperature value is still 82℃. The system looks up the superior department identifier D1 from the alarm escalation path table according to the responsibility person 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℃, which exceeds the threshold 80℃, please handle as soon as possible.

[0061] Specifically, the specific process of generating the preset alarm escalation path includes: obtaining each responsibility person identifier; receiving the superior department identifier configured for each responsibility person identifier; storing the 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.

[0062] Among them, the background administrator can actually inquire relevant information, for example Figure 3 as shown.

[0063] 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 person 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 identifier. It ensures that critical problems can be escalated to higher-level managers or teams in a timely manner, avoiding problems 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 human intervention and reducing the cost of human intervention.

[0064] 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:

[0065] S201, scanning each industrial device accessed in the business environment of the industrial internet;

[0066] S202, traversing each sensor identifier deployed on each industrial equipment from the industrial equipment information table to obtain;

[0067] S203, dynamically updating the initial parameter threshold value and the initial responsibility identifier corresponding to each sensor identifier to obtain the final parameter threshold value and the final responsibility identifier;

[0068] In the embodiments of the present application, the specific process of dynamically updating the initial parameter threshold value and the initial responsibility identifier corresponding to each sensor identifier to obtain the final parameter threshold value and the final responsibility identifier includes: in the case that the initial parameter threshold value and the initial responsibility identifier corresponding to each sensor identifier are empty, generating parameter configuration prompt information for display; receiving the initial parameter threshold value and the initial responsibility identifier configured for each sensor identifier in the displayed parameter configuration prompt information as the final parameter threshold value and the final responsibility identifier; or, in the case that the initial parameter threshold value and the initial responsibility identifier corresponding to each sensor identifier are not empty, querying the responsibility person information corresponding to the initial responsibility identifier and the sensor operation log corresponding to each sensor identifier from the personnel management system; updating the initial responsibility identifier according to the responsibility person information to obtain the final responsibility identifier; 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.

[0069] In a possible implementation, for each sensor identifier, it is checked whether the initial parameter threshold value and the initial responsibility identifier corresponding to the sensor identifier are empty. If the initial parameter threshold value and the initial responsibility 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 identifier through the interface. The initial parameter threshold value and the initial responsibility identifier input by the user are received, and these values are taken as the final parameter threshold value and the final responsibility identifier. If the initial parameter threshold value and the initial responsibility identifier are not empty, the responsibility person information corresponding to the initial responsibility identifier is queried from the personnel management system. The sensor operation log corresponding to each sensor identifier is queried to obtain the historical parameter value. According to the queried responsibility person information, the initial responsibility identifier is updated to obtain the final responsibility identifier. According to the sensor operation log, the initial parameter threshold value is updated to obtain the final parameter threshold value.

[0070] Before real-time collection and preprocessing of the sensor parameters fed back by each industrial equipment, the initial responsibility identifier and the initial parameter threshold value are updated, which can prevent the problem of not being able to find the responsibility person due to the resignation or job transfer of the related responsibility person, and can prevent the problem of inaccurate threshold value and false alarm 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 along with the use of the equipment, and thus reduce false alarms.

[0071] 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.

[0072] For example, the initial responsibility identifier is P1, and 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 person has left the job or has been transferred. If the responsibility person 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. The prompt information is "the responsibility person 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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 to obtain the final parameter threshold value.

[0077] 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,

[0078] The loss function of the anomaly analysis model is:

[0079]

[0080]

[0081]

[0082] wherein, is the model loss value, is the number of sensors of the industrial equipment, each sensor has historical moment data, for each historical moment of each sensor , the normal threshold range predicted by the model is , and the actually 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.

[0083] 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.

[0084] 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 escalate and 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 in 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.

[0085] 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:

[0086] S301, obtaining historical running data of each sensor of an industrial equipment collected at each historical moment;

[0087] S302, calculating historical statistical correlation features of each sensor at each historical moment according to the historical running data of each sensor at each historical moment;

[0088] S303, receiving a normal threshold range labeled for the historical statistical correlation features of each sensor at each historical moment, to obtain a model training sample;

[0089] S304, creating an anomaly analysis model by using a machine learning algorithm;

[0090] S305, inputting the model training sample into the anomaly analysis model, and outputting a model loss value;

[0091] S306, generating a pre-trained anomaly analysis model when the model loss value reaches a minimum.

[0092] In the embodiments of the present application, by systematically collecting and analyzing the historical running data of the sensors of the industrial equipment, and using statistical features and machine learning algorithms to construct an anomaly analysis model, accurate monitoring and abnormal prediction of the equipment running state can be realized, and the accuracy and reliability of the alarm system can be improved. At the same time, through model training and optimization, the system can continuously learn and improve, further improving the detection ability of equipment abnormalities, thereby effectively reducing the risk of equipment failure and ensuring the stable operation of industrial production.

[0093] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the apparatus embodiments of the present application, please refer to the method embodiments of the present application.

[0094] Please refer to Figure 6 which shows a structure diagram of an alarm processing apparatus for industrial internet provided by an exemplary embodiment of the present application. The alarm processing apparatus for industrial internet can be realized by software, hardware or a combination of both to become all or part of an electronic device. The apparatus 1 comprises a data processing module 10, a determination module 20, an alarm module 30 and an alarm management module 40.

[0095] 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.

[0096] The determination module 20 is configured to determine abnormal production process parameters and target responsible person identifiers of abnormal industrial devices with alarms according to the plurality of production process parameters of each industrial device and preset signal alarm conditions.

[0097] 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 count time.

[0098] 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 the counting time is equal to a preset time length and a target parameter value in the abnormal production process parameters has not recovered 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 the counting time is less than the preset time length and the parameter value in the abnormal production process parameters has recovered to normal.

[0099] It should be noted that the alarm processing apparatus for industrial internet provided by the above embodiments 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 apparatus for industrial internet provided by the above embodiments and the alarm processing method for industrial internet embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0100] The above sequence numbers of the embodiments of the present application are only for description, not representing the advantages and disadvantages of the embodiments.

[0101] 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, avoiding problems 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.

[0102] 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 method embodiments.

[0103] 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 method embodiments.

[0104] Please refer to Figure 7 , the present application provides a structural schematic diagram of an electronic device. As Figure 7 shown, 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.

[0105] The communication bus 1002 is used to realize the connection and communication between the components.

[0106] The user interface 1003 can include a display screen (Display), a camera (Camera), and optionally a standard wired interface, a wireless interface.

[0107] The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).

[0108] 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.

[0109] 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

[0110] In Figure 7 ​The user interface 1003 in the electronic device 1000 shown 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:

[0111] Real-time collection and preprocessing of sensor parameters fed back by each industrial equipment to obtain a plurality of production process parameters of each industrial equipment;

[0112] According to the plurality of production process parameters of each industrial equipment and the preset signal alarm condition, determining the abnormal production process parameter of the abnormal industrial equipment and the target responsible person identifier existing alarm;

[0113] Starting the alarm state of the abnormal industrial equipment and generating a warning information, and sending the warning information to the client corresponding to the target responsible person identifier and timing;

[0114] When the total timing duration is equal to the preset duration and the target parameter value in the abnormal production process parameter is not restored to normal, obtaining the superior department identifier corresponding to the responsible person 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 timing duration 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 released.

[0115] In one embodiment, the processor 1001, when executing the determination of the abnormal production process parameter of the abnormal industrial equipment and the target responsible person identifier existing alarm according to the plurality of production process parameters of each industrial equipment and the preset signal alarm condition, specifically performs the following operations:

[0116] From the ternary mapping relationship, obtaining the target parameter threshold value corresponding to the target sensor identifier;

[0117] Comparing the target parameter value with the target parameter threshold value to determine whether each production process parameter is abnormal;

[0118] If so, mark each production process parameter of each industrial equipment as an abnormal production process parameter of an abnormal industrial equipment existing alarm, and from the ternary mapping relationship, obtain the target responsible person identifier corresponding to the target sensor identifier.

[0119] In one embodiment, the processor 1001, before executing the real-time collection and preprocessing of the sensor parameters fed back by each industrial equipment, also performs the following operations:

[0120] Scanning each industrial equipment accessed in the business environment of the industrial internet;

[0121] From the industrial equipment information table, traversing to obtain each sensor identifier deployed on each industrial equipment;

[0122] updating the initial parameter threshold value and the initial responsible person identifier corresponding to each sensor identifier to obtain a final parameter threshold value and a final responsible person identifier;

[0123] storing a 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 a preset signal alarm condition of each industrial equipment.

[0124] In one embodiment, the processor 1001, when performing the operation of updating the initial parameter threshold value and the initial responsible person identifier corresponding to each sensor identifier to obtain a final parameter threshold value and a final responsible person identifier, specifically performs the following operations:

[0125] generating parameter configuration prompt information for display in a case where the initial parameter threshold value and the initial responsible person identifier corresponding to each sensor identifier are empty;

[0126] receiving the initial parameter threshold value and the initial responsible person identifier configured for each sensor identifier in the displayed parameter configuration prompt information as the final parameter threshold value and the final responsible person identifier; or,

[0127] in a case where the initial parameter threshold value and the initial responsible person identifier corresponding to each sensor identifier are not empty, querying, from a personnel management system, the responsible person information corresponding to the initial responsible person identifier and the sensor operation log corresponding to each sensor identifier;

[0128] updating the initial responsible person identifier according to the responsible person information to obtain a final responsible person identifier;

[0129] updating the initial parameter threshold value corresponding to each sensor identifier according to the sensor operation log corresponding to each sensor identifier to obtain a final parameter threshold value.

[0130] In one embodiment, the processor 1001, when performing the operation of updating the initial responsible person identifier according to the responsible person information to obtain a final responsible person identifier, specifically performs the following operations:

[0131] parsing the responsible person information to obtain structured data corresponding to the initial responsible person identifier;

[0132] generating prompt information to notify the administrator that the responsible person needs to be reassigned in a case where a field used to represent the employee state in the structured data indicates that the employee has resigned or has been transferred;

[0133] In response to a responsible person identifier configuration instruction, receiving a reconfigured responsible person identifier for the initial responsible person identifier as a final responsible person identifier.

[0134] 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, obtains the final parameter threshold, specifically performs the following operations:

[0135] preprocessing the sensor running log corresponding to each sensor identifier to obtain a historical parameter value sequence corresponding to each sensor identifier;

[0136] statistical analysis is performed on the historical parameter value sequence corresponding to each sensor identifier to calculate a 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, the initial parameter threshold corresponding to each sensor identifier is adjusted to the statistical indicator corresponding to each sensor identifier to obtain the final parameter threshold; or,

[0137] analysis is performed on the historical parameter value sequence corresponding to each sensor identifier 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, the initial parameter threshold corresponding to each sensor identifier is increased by a preset step to obtain the final parameter threshold; or in a case where the change trend corresponding to each sensor identifier presents a downward trend, the initial parameter threshold corresponding to each sensor identifier is decreased by a preset step to obtain the final parameter threshold.

[0138] 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, obtains the final parameter threshold, specifically performs the following operations:

[0139] preprocessing the sensor running log corresponding to each sensor identifier to obtain a historical parameter value sequence corresponding to each sensor identifier;

[0140] extracting statistical correlation features from the historical parameter value sequence;

[0141] inputting the statistical correlation features into a pre-trained anomaly analysis model to output a normal threshold range corresponding to each sensor identifier at a current time;

[0142] comparing 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, and obtaining the final parameter threshold.

[0143] In an embodiment, the processor 1001, when performing obtaining the superior department identifier corresponding to the responsibility identifier from the preset alarm escalation path, specifically performs the following operations:

[0144] According to the responsibility person identifier, the corresponding superior department identifier is obtained from the binary mapping relationship;

[0145] The preset alarm escalation path is generated according to the following steps, including:

[0146] Obtain each responsibility person identifier;

[0147] Receive the superior department identifier configured for each responsibility person identifier;

[0148] Store the 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.

[0149] In the embodiments of the present application, on the one hand, by real-time acquisition and preprocessing of the sensor parameters fed back by each industrial equipment, and combining with the preset signal alarm condition, the abnormal production process parameters of the abnormal industrial equipment with alarm and the target responsibility person identifier 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 escalate and send the alarm information to the client corresponding to the superior department identifier corresponding to the responsibility person identifier. It ensures that critical problems can be escalated 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 release the alarm state, reducing the need for manual intervention and reducing the cost of manual intervention.

[0150] Those skilled 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 disc, an optical disc, a read-only memory or a random access memory, etc.

[0151] The above disclosure is only the preferred embodiments of the present application, and of course cannot limit the scope of the rights of the present application, so the equivalent changes made according to the claims of the present application still fall 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 side, the method includes: The sensor parameters fed back by each industrial device are collected and preprocessed in real time to obtain multiple production process parameters for each industrial device; Based on multiple production process parameters and preset signal alarm conditions for each industrial device, the abnormal production process parameters and target responsible person identifiers of the abnormal industrial devices with alarms are determined; each production process parameter includes a target sensor identifier and a target parameter value, and the preset signal alarm conditions for each industrial device include a ternary mapping relationship between the sensor identifier, parameter threshold, and responsible person identifier; The step of determining the abnormal production process parameters and target responsible person identifiers of the abnormal industrial equipment with alarms based on multiple production process parameters and preset signal alarm conditions for each industrial equipment includes: From the ternary mapping relationship, obtain the target parameter threshold corresponding to the target sensor identifier; The target parameter value is compared with the target parameter threshold to determine whether each production process parameter is abnormal; If so, mark each production process parameter of each industrial device as an abnormal production process parameter of an abnormal industrial device with an alarm, and obtain the target responsible person identifier corresponding to the target sensor identifier from the ternary mapping relationship; The alarm status of the abnormal industrial equipment is activated and a warning message is generated, which is then sent to the client corresponding to the target responsible person's identifier and timed. When the total duration of the timekeeping is equal to the preset duration and the target parameter value in the abnormal production process parameters 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 warning information is sent to the client corresponding to the superior department identifier; or when the total duration of the timekeeping is less than the preset duration and the parameter value in the abnormal production process parameters has returned to normal, the alarm status of the abnormal industrial equipment is lifted.

2. The method according to claim 1, characterized in that, Before the real-time acquisition and preprocessing of sensor parameters fed back by each industrial device, the process also includes: Scan each industrial device connected to the industrial internet business environment; From the industrial equipment information table, iterate through the table to obtain the identifier of each sensor deployed on each industrial equipment; The initial parameter threshold and initial responsible person identifier corresponding to each sensor identifier are dynamically updated to obtain the final parameter threshold and final responsible person identifier. The system stores a ternary mapping relationship between each sensor identifier deployed on each industrial device, the final parameter threshold updated for each sensor identifier, and the final responsible person identifier, thereby obtaining the preset signal alarm conditions for each industrial device.

3. The method according to claim 2, characterized in that, The dynamic updating of the initial parameter threshold and initial responsible person identifier corresponding to each sensor identifier to obtain the final parameter threshold and final responsible person identifier includes: If the initial parameter threshold and the initial responsible person identifier corresponding to each sensor identifier are empty, a parameter configuration prompt message is generated and displayed. Receive the initial parameter threshold and initial responsible person identifier configured for each sensor identifier in the displayed parameter configuration prompt information, and use them as the final parameter threshold and final responsible person identifier; or, If the initial parameter threshold and the initial responsible person identifier corresponding to each sensor identifier are not empty, query 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; Based on the information of the responsible person, update the initial responsible person identifier to obtain the final responsible person identifier; Based on the sensor operation log corresponding to each sensor identifier, update the initial parameter threshold corresponding to each sensor identifier to obtain the final parameter threshold.

4. The method according to claim 3, characterized in that, The step of updating the initial responsible person identifier based on the responsible person information to obtain the final responsible person identifier includes: Parse the information of the responsible person to obtain the structured data corresponding to the initial responsible person identifier; If the field representing the employee's status in the structured data indicates that the employee has left the company or been transferred to another position, a notification message will be generated to inform the administrator that the person in charge needs to be reassigned. In response to the responsible person identifier configuration instruction, the responsible person identifier reconfigured for the initial responsible person identifier is received as the final responsible person identifier.

5. The method according to claim 3, characterized in that, The step of updating the initial parameter threshold corresponding to each sensor identifier based on the sensor operation log corresponding to each sensor identifier to obtain the final parameter threshold includes: Preprocess the sensor operation logs corresponding to each sensor identifier to obtain a sequence of historical parameter values ​​corresponding to each sensor identifier; Statistical analysis is performed 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, the initial parameter threshold corresponding to each sensor identifier is adjusted to the statistical index corresponding to each sensor identifier to obtain the final parameter threshold; or... Analyze the historical parameter value sequence corresponding to each sensor identifier over time to obtain the change trend corresponding to each sensor identifier; if the change trend corresponding to each sensor identifier shows an upward trend, increase the initial parameter threshold corresponding to each sensor identifier by a preset step size to obtain the final parameter threshold; or if the change trend corresponding to each sensor identifier shows a downward trend, decrease the initial parameter threshold corresponding to each sensor identifier by a preset step size to obtain the final parameter threshold.

6. The method according to claim 3, characterized in that, The step of updating the initial parameter threshold corresponding to each sensor identifier based on the sensor operation log corresponding to each sensor identifier to obtain the final parameter threshold includes: Preprocess the sensor operation logs corresponding to each sensor identifier to obtain a sequence of historical parameter values ​​corresponding to each sensor identifier; Extract statistically relevant features from the historical parameter value sequence; The statistically relevant features are input into a pre-trained anomaly analysis model, which outputs the normal threshold range corresponding to each sensor identifier at the current time. The initial parameter threshold corresponding to each sensor identifier is compared with the normal threshold range corresponding to each sensor identifier at the current time, so as to adjust the initial parameter threshold corresponding to each sensor identifier to the normal threshold range corresponding to each sensor identifier at the current time, and thus obtain the final parameter threshold.

7. The method according to claim 6, characterized in that, Generate a pre-trained anomaly analysis model by following these steps: Acquire historical operating data collected by each sensor of industrial equipment at various historical moments; Based on the historical operating data of each sensor at each historical moment, calculate the historical statistical correlation characteristics of each sensor at each historical moment; Receive the normal threshold range of historical statistically relevant feature annotations for each historical moment of each sensor to obtain model training samples; Machine learning algorithms are used to create an anomaly analysis model; Input the training samples of the model into the anomaly analysis model, and output the model loss value; When the model loss value reaches its minimum, a pre-trained anomaly analysis model is generated; wherein, The loss function of the anomaly analysis model is: in, This represents the model loss value. The number of sensors in industrial equipment, each sensor has Data from a historical moment, for each sensor Every historical moment The normal threshold range predicted by the model is The actual normal threshold range for annotation is: , Used to measure the difference between the predicted lower bound and the actual lower bound. It measures the difference between the upper bound of the prediction and the actual upper bound.

8. The method according to claim 1, characterized in that, The preset alarm escalation path includes a binary mapping relationship between the responsible person's identifier and the superior department's identifier; The step of obtaining the superior department identifier corresponding to the responsible person identifier from the preset alarm escalation path includes: Based on the responsible person's identifier, obtain the corresponding superior department identifier from the binary mapping relationship; Generate a preset alarm escalation path by following these steps: Obtain the identifier of each responsible person; Receive the superior department identifier configured for each of the responsible persons; The binary mapping relationship between each responsible person's identifier and the superior department identifier configured for each responsible person's identifier is stored to obtain the preset alarm escalation path.

9. An alarm processing system for the Industrial Internet implemented using the method described in any one of claims 1-8, characterized in that, The system includes: The data processing module is used to collect and preprocess the sensor parameters fed back by each industrial device in real time to obtain multiple production process parameters for each industrial device. The determination module is used to determine the abnormal production process parameters and the target responsible person identifier of the abnormal industrial equipment that has alarms, based on multiple production process parameters and preset signal alarm conditions of each industrial equipment. The alarm module is used to activate the alarm status of the abnormal industrial equipment, generate early warning information, send it to the client corresponding to the target responsible person identifier, and time the alarm. 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 warning information to the client corresponding to the superior department identifier when the total duration of the timekeeping is equal to the preset duration and the target parameter value in the abnormal production process parameters has not returned to normal; or to release the alarm status of the abnormal industrial equipment when the total duration of the timekeeping is less than the preset duration and the parameter value in the abnormal production process parameters has returned to normal.

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