Industrial Internet of Things method and system based on abnormal analysis

Abstract

The present invention relates to the technical field of industrial Internet of Things, and specifically discloses an industrial Internet of Things method and system based on abnormality analysis. The system first divides a working cycle into multiple equal time periods, processes the pressure and vibration data of each time period, and identifies the abnormal time period by calculating the data score and deviation value; then, the response speed and noise influence of the sensor are analyzed, and the sensor state is evaluated to determine whether the abnormality is caused by sensor failure or external noise; for the abnormal state, the system optimizes the accuracy of data collection by adjusting sensor parameters or replacing sensors, and evaluates the reliability of data transmission by monitoring the packet loss rate; after adjustment, the system will re-collect data to verify the effect of improvement measures, and issue an early warning when an abnormality is detected; this scheme effectively improves the accuracy of industrial Internet of Things data and the stability of the system, and provides strong support for preventive maintenance and fault prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things, and in particular to an industrial Internet of Things method and system based on abnormality analysis. Background Art

[0002] In the field of industrial Internet of Things, with the rapid development of sensor technology and network communication technology, the interconnection of a large number of devices and systems has become possible; this connectivity has brought unprecedented data collection capabilities, making real-time monitoring, predictive maintenance and optimization of production processes a reality; the processing and analysis of massive data has posed a huge challenge to traditional data analysis methods, especially in the risk assessment of edge device status. Traditional statistical methods are often unable to conduct risk assessment in a timely and accurate manner, resulting in the failure to provide timely warnings of IoT failures;

[0003] The existing technology lacks the accuracy of collected data and the assessment of sensor status, thus being unable to guarantee the accuracy of the collected data, which has become a problem that needs to be solved urgently.

[0004] Therefore, a method is provided to map the pressure and vibration data into a standardized range through real-time monitoring and data analysis, and combined with the calculation of data score deviation values, the system can distinguish between normal operation and abnormal conditions and improve the accuracy of data collection. Summary of the invention

[0005] Through real-time monitoring and data analysis, the pressure and vibration data are mapped into a standardized range, and combined with the calculation of data score deviation values, the system can distinguish between normal operation and abnormal conditions, improve the accuracy of data collection, and enhance the ability to detect abnormal equipment status. By analyzing the response speed of the sensor, it can be determined whether it is adversely affected by noise. Through real-time monitoring and necessary adjustments of the sensor device status, and the use of more powerful filters to reduce noise interference, the system can ensure the stability and accuracy of the sensor in complex environments. By re-collecting data and monitoring the packet loss rate, the effectiveness of sensor adjustments can be verified, ensuring the stability and integrity of data transmission, and optimizing the performance of the entire IoT system.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An industrial Internet of Things method and system based on abnormal analysis, comprising the following steps:

[0008] Divide the working cycle of the Internet of Things into several equal time periods, obtain the pressure data and vibration data in each time period, compare and analyze the data in each time period, and obtain the abnormal data time period;

[0009] Based on the abnormal data time period, the state of the pressure sensor and the state of the vibration sensor will affect the data accuracy in each same time period. The state of the pressure sensor and the vibration sensor is evaluated through the response speed of the pressure sensor and the vibration sensor to the data, and the impact of noise on the sensor state is determined;

[0010] Based on the judgment of the status of the sensor equipment, for abnormal sensor adjustments, adjust the sensor influencing factors or replace the sensor, and re-collect the pressure data and vibration data within a cycle, analyze the data packet loss rate, obtain the abnormal situation of the new cycle, and issue timely warnings for the abnormal time period.

[0011] As a further solution of the present invention: according to the time series, the instantaneous pressure value is collected every one minute, which is recorded as Xij;

[0012] Get the maximum instantaneous pressure value and the minimum instantaneous pressure value in each time period;

[0013] Map the instantaneous voltage data of each time period to [0, 1], denoted as Xzij;

[0014] According to the time series, the instantaneous vibration amplitude value is collected every one minute, which is recorded as Xij;

[0015] Obtain the maximum instantaneous vibration amplitude value and the minimum instantaneous vibration amplitude value in each time period;

[0016] Map the instantaneous vibration data of each time period to [0, 1], denoted as Yzij;

[0017] Among them, i represents the i-th time period collected, i=1, 2,..., n, j represents the j-th instantaneous pressure value in each time period, j=1, 2,..., n; Xzij represents the j-th instantaneous voltage value mapping data corresponding to the i-th time period; Yzij represents the j-th instantaneous vibration amplitude value mapping data corresponding to the i-th time period.

[0018] As a further solution of the present invention: the process of obtaining the voltage value mapping data Xzij is as follows:

[0019] By formula Calculate and obtain the mapping data group {Xz11, Xz12, ..., Xz1j}, {Xz21, Xz22, ..., Xz2j}, ..., {Xzi1, Xzi2, ..., Xzij} for each time period;

[0020] The process of obtaining the vibration amplitude value mapping data Yzij is as follows:

[0021] By formula Calculate and obtain the mapping data groups {Yz11, Yz12, ..., Yz1j}, {Yz21, Yz22, ..., Yz2j}, ..., {Yzi1, Yzi2, ..., Yzij} for each time period.

[0022] As a further solution of the present invention: the voltage value mapping data Xzij and the vibration amplitude value mapping data Yzij at the same time are added to obtain the data score Lij at the corresponding time;

[0023] Calculate the difference between the data scores Lij at the jth moment and the j-1th moment in the i-th time period and take the absolute value to obtain the data score deviation value;

[0024] If the data score deviation value is greater than the preset data score deviation threshold, it means that the corresponding time period is an abnormal time period.

[0025] As a further solution of the present invention: the process of obtaining the data score Lij is:

[0026] The corresponding moment data score Lij is obtained through the formula Lij=Xzij+Yzij, and the data group {L11, L12,......L1j}, {L21, L22,......L2j},......, {Li1, Li2,......Lij} is obtained.

[0027] 4. As a further solution of the present invention: in the working cycle of the Internet of Things, the response speed Vx1 of the pressure sensor and the response speed Vx2 of the vibration sensor are collected in each time period;

[0028] Calculate the average response speed of the pressure sensor and the response speed of the vibration sensor in all time periods within the working cycle to obtain the average response speed Vy1 of the pressure sensor and the average response speed Vy2 of the vibration sensor;

[0029] The response speed of the pressure sensor and the response speed of the vibration sensor in all time periods within the working cycle are respectively calculated to obtain the variance value Sy1 of the response speed of the pressure sensor and the variance value Sy2 of the response speed of the vibration sensor within one cycle;

[0030] Through the function , to describe the change in the output of the pressure sensor, and establish a curve with the x-axis as the time series t and the y-axis as the output change;

[0031] Among them, Yz is the final stable value of the pressure sensor output, Yc is the initial value of the pressure sensor, and s is the time constant, which takes a value of [0,1];

[0032] By formula , to describe the output change of the vibration sensor, and establish a curve with the x-axis as the time series t and the y-axis as the output change;

[0033] Establish a straight line L1 parallel to the x-axis with y=Vy1+Sy1;

[0034] Establish a straight line L2 parallel to the x-axis with y=Vy1-Sy1;

[0035] The area between the straight lines L1 and L2 is recorded as the normal voltage sensor response speed area;

[0036] Establish a straight line L3 parallel to the x-axis with y=Vy2+Sy2;

[0037] Establish a straight line L4 parallel to the x-axis with y=Vy2-Sy2;

[0038] The area between the straight lines L3 and L4 is recorded as the normal response speed area of ​​the vibration sensor.

[0039] As a further solution of the present invention: observing the change of the final stable value output by the pressure sensor;

[0040] If the final value of the pressure sensor output appears outside the straight lines L1 and L2, it means that the pressure sensor has an abnormal state during its use time;

[0041] If the final value of the pressure sensor output appears within the straight lines L1 and L2, it means that the pressure sensor is in normal condition within the use time;

[0042] Observe the changes in the final stable value of the vibration sensor output;

[0043] If the final value of the vibration sensor output appears outside the straight lines L1 and L2, it means that the vibration sensor has an abnormal state during its use time;

[0044] If the final value of the vibration sensor output appears within the straight lines L1 and L2, it means that the vibration sensor is in normal condition within the usage time.

[0045] As a further solution of the present invention: by formula The maximum interference noise Pz is calculated,

[0046] Among them, Py is the noise signal power, which is collected by the power meter, SNB is the signal-to-noise ratio, and the maximum noise interference corresponds to the minimum signal-to-noise ratio of 20dB;

[0047] The spectrum analyzer is used to collect the noise interference level received by the sensor in real time, which is recorded as Pzi.

[0048] If the noise interference level Pzi ≥ the maximum interference noise Pz, the noise interference is more serious. Adjust the noise interference level and use a more powerful filter.

[0049] As a further solution of the present invention: obtaining the number of data packet output messages and the number of output messages in each fixed time period;

[0050] The input message refers to the data packet arriving at the network device, and the output message refers to the data packet generated by the network device and sent to the network;

[0051] By formula, loss rate = *%100, calculate the loss rate;

[0052] Compare the loss rate in each time period with a preset loss rate threshold;

[0053] If the loss rate in each time period is greater than or equal to the loss rate threshold, it means that the data packet loss is serious and the edge data calculation is abnormal, which means that the IoT data is abnormal and the corresponding IoT system is abnormal;

[0054] If the loss rate in each time period is less than the loss rate threshold, it means that the packet loss is within the allowable range and the edge data calculation is normal, which means that the IoT data is normal and the corresponding IoT system is normal.

[0055] As a further solution of the present invention: an industrial Internet of Things system based on abnormal analysis, characterized in that it includes a data acquisition module, an abnormality detection module, a sensor state evaluation module, an abnormal state processing module, and a re-acquisition and early warning module;

[0056] A data acquisition module, wherein the data acquisition module uses a pressure sensor and a vibration sensor to collect pressure data and vibration data;

[0057] An anomaly detection module, which compares and analyzes the data in each same time period to detect the abnormal time period of the data;

[0058] A sensor status evaluation module, which analyzes data within an abnormal time period, evaluates the response speed of the pressure sensor and the vibration sensor, and thus determines the working status;

[0059] An abnormal state processing module, wherein the abnormal state processing module adjusts sensor settings and parameters according to the sensor state evaluation result;

[0060] The re-collection and early warning module uses the adjusted sensor to re-collect the pressure data and vibration data within a cycle, re-detects the data packet loss rate in the abnormal time period and triggers an early warning.

[0061] Beneficial effects of the present invention:

[0062] (1) The Industrial Internet of Things system based on abnormal analysis can quickly identify abnormal data time periods through real-time monitoring and data analysis. By mapping pressure and vibration data into a standardized range and combining the calculation of data score deviation values, the system can effectively distinguish between normal operation and abnormal conditions. This not only improves the accuracy of data collection, but also enhances the ability to detect abnormal equipment status, allowing maintenance personnel to respond in a timely manner to avoid potential production interruptions and equipment damage, thereby improving the reliability and efficiency of the entire system;

[0063] (2) It introduces in detail how to evaluate and adjust the working status of the sensor, especially by analyzing the sensor's response speed to determine whether it is adversely affected by noise. By real-time monitoring of the sensor device status and making necessary adjustments, such as using more powerful filters to reduce noise interference, the system can ensure the stability and accuracy of the sensor in complex environments. In addition, by re-collecting data and monitoring the packet loss rate, the effectiveness of sensor adjustments can be verified, ensuring the stability and integrity of data transmission, thereby optimizing the performance of the entire IoT system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The present invention will be further described below in conjunction with the accompanying drawings.

[0065] Figure 1 It is a schematic diagram of an industrial Internet of Things method based on abnormal analysis of the present invention;

[0066] Figure 2 It is a schematic diagram of an abnormal sensor judgment structure of an industrial Internet of Things method and system based on abnormal analysis in the present invention;

[0067] Figure 3 It is a schematic diagram of the structure of an industrial Internet of Things system based on abnormal analysis in the present invention; DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Example 1

[0070] See also Figure 1 As shown, the present invention is an industrial Internet of Things method based on abnormal analysis, comprising the following steps:

[0071] S1: Divide a working cycle of the Internet of Things into several equal time periods (such as every hour), obtain the pressure data and vibration data in each time period, compare and analyze the data in each time period, and obtain the data abnormal time period;

[0072] S2: Based on the abnormal data time period, the state of the pressure sensor and the state of the vibration sensor will affect the data accuracy in each same time period. The state of the pressure sensor and the state of the vibration sensor are evaluated through the response speed of the pressure sensor and the vibration sensor to the data, and the impact of noise on the sensor state is determined;

[0073] S3: Based on the judgment of the status of the sensor equipment, for abnormal sensor adjustments, adjust the sensor influencing factors or replace the sensor, and re-collect the pressure data and vibration data within a cycle, analyze the data packet loss rate, obtain the abnormal situation of the new cycle, and issue timely warnings for the abnormal time period.

[0074] Example 2

[0075] See also Figure 2 As shown,

[0076] The pressure sensor collects the pressure data in each time period during one cycle of the Internet of Things, and collects the instantaneous pressure value Xij at each moment according to the time series (every minute);

[0077] Get the maximum instantaneous pressure value and the minimum instantaneous pressure value in each time period;

[0078] Map the instantaneous voltage data of each time period to [0, 1]

[0079] By formula Calculate and obtain the mapping data group of each time period {Xz11, Xz12, ..., Xz1j}, {Xz21, Xz22, ..., Xz2j}, ..., {Xzi1, Xzi2, ..., Xzij}, where i represents the i-th time period, i=1, 2, ..., n, j represents the j-th instantaneous pressure value in each time period, j=1, 2, ..., n; Xzij represents the j-th instantaneous voltage value mapping data corresponding to the i-th time period;

[0080] The vibration sensor collects the vibration data in each time period during one cycle of the Internet of Things, and collects the instantaneous vibration amplitude value Xij at each moment according to the time series (every minute);

[0081] Obtain the maximum instantaneous vibration amplitude value and the minimum instantaneous vibration amplitude value in each time period;

[0082] Map the instantaneous vibration data of each time period to [0, 1]

[0083] By formula Calculate and obtain the mapping data group {Yz11, Yz12, ..., Yz1j}, {Yz21, Yz22, ..., Yz2j}, ..., {Yzi1, Yzi2, ..., Yzij} for each time period, wherein Yzij represents the j-th instantaneous vibration amplitude value mapping data corresponding to the i-th time period;

[0084] The corresponding moment data score Lij is calculated by the formula L1=Xzij+Yzij, and the data group {L11, L12, ... L1j}, {L21, L22, ... L2j}, ..., {Li1, Li2, ... Lij} is obtained;

[0085] Calculate the difference between the data scores at the jth moment and the j-1th moment in the i-th time period and take the absolute value to obtain the data score deviation value;

[0086] If the data score deviation value is greater than the preset data score deviation threshold, it means that the corresponding time period is an abnormal time period;

[0087] It should be noted that the data score is the value obtained by adding the voltage mapping data and the vibration amplitude mapping data, which is recorded as the data score to facilitate comparison and calculation and to facilitate judgment of abnormal time periods.

[0088] Example 3

[0089] Since the abnormal working period of IoT may be caused by abnormal pressure sensor or vibration sensor, the status of pressure sensor and vibration sensor is analyzed;

[0090] The state of the pressure sensor and the state of the vibration sensor are determined by the response speed of the output signals of the pressure sensor and the vibration sensor;

[0091] Analyze the response speed of the output signals of pressure sensors and vibration sensors;

[0092] Among them, the response speed is the time required for the sensor output to stabilize from the initial value to the final value;

[0093] During the IoT working cycle, the pressure sensor response speed Vx1 and the vibration sensor response speed Vx2 are collected in each time period;

[0094] Calculate the average response speed of the pressure sensor and the response speed of the vibration sensor in all time periods within the working cycle to obtain the average response speed Vy1 of the pressure sensor and the average response speed Vy2 of the vibration sensor;

[0095] The variance values ​​of the pressure sensor response speed and the vibration sensor response speed in all time periods within the working cycle are calculated respectively to obtain the pressure sensor response speed variance value Sy1 and the vibration sensor response speed variance value Sy2 within a cycle.

[0096] Through the function , to describe the change in the output of the pressure sensor, and establish a curve with the x-axis as the time series t and the y-axis as the output change;

[0097] Among them, Yz is the final stable value of the pressure sensor output, Yc is the initial value of the pressure sensor, and s is the time constant, which takes a value of [0,1];

[0098] By formula , to describe the change in the output of the pressure sensor, and establish a curve with the x-axis as the time series t and the y-axis as the output change;

[0099] Ya is the final stable value output by the pressure sensor, and Yb is the initial value of the pressure sensor;

[0100] Establish a straight line L1 parallel to the x-axis with y=Vy1+Sy1;

[0101] Establish a straight line L2 parallel to the x-axis with y=Vy1-Sy1;

[0102] The area between the straight lines L1 and L2 is recorded as the normal voltage sensor response speed area;

[0103] Establish a straight line L3 parallel to the x-axis with y=Vy2+Sy2;

[0104] Establish a straight line L4 parallel to the x-axis with y=Vy2-Sy2;

[0105] The area between the straight lines L3 and L4 is recorded as the normal response speed area of ​​the vibration sensor;

[0106] If the final value of the pressure sensor output appears outside the straight lines L1 and L2, it means that the pressure sensor has an abnormal state during its use time;

[0107] If the final value of the pressure sensor output appears within the straight lines L1 and L2, it means that the pressure sensor is in normal condition within the usage time.

[0108] It should be noted that: since noise will affect the device state of the sensor, thereby causing the sensor device state to be abnormal, it is necessary to determine under what circumstances the noise will cause abnormalities and reduce the impact of noise. By observing the two-dimensional curve of the sensor output value, the device state can be adjusted to keep the final value of the sensor output within the normal response time range.

[0109] Specific:

[0110] The noise interference situation is judged by the maximum interference noise that the filter can withstand.

[0111] By formula The maximum interference noise Pz is calculated,

[0112] Where Py is the noise signal power, which is collected by a power meter.

[0113] SNB is the signal-to-noise ratio, and the maximum noise interference corresponds to a minimum signal-to-noise ratio of 20dB;

[0114] The spectrum analyzer is used to collect the noise interference level received by the sensor in real time, which is recorded as Pzi.

[0115] If the noise interference level Pzi ≥ the maximum interference noise Pz, the noise interference is serious at this time, and the noise interference level should be adjusted in time, and a more powerful filter should be used to ensure that the sensor state is not affected;

[0116] pass

[0117] It should be noted that after adjusting the status of the sensor equipment, the accuracy of the collected data can be guaranteed and the efficiency of abnormality detection can be improved.

[0118] Example 4

[0119] Based on the adjusted sensor device, re-collect data,

[0120] Collect the packet loss rate within a certain fixed time through network monitoring software;

[0121] Specifically: record each fixed time period as a continuous data stream, check the continuity of the packet sequence number, and interruption or jump indicates packet loss;

[0122] Get the number of data packets in and out within each fixed time period;

[0123] The input message refers to the data packet arriving at the network device, and the output message refers to the data packet generated by the network device and sent to the network;

[0124] By formula, loss rate = *%100, calculate the loss rate;

[0125] Compare the loss rate in each time period with a preset loss rate threshold;

[0126] If the loss rate in each time period is greater than or equal to the loss rate threshold, it means that the data packet loss is serious and the edge data calculation is abnormal, which means that the IoT data is abnormal and the corresponding IoT system is abnormal;

[0127] If the loss rate in each time period is less than the loss rate threshold, it means that the packet loss is within the allowable range and the edge data calculation is normal, which means that the IoT data is normal and the corresponding IoT system is normal;

[0128] It should be noted that the packet loss rate determines the stable output of system data and the stability of IoT data transmission. By observing a new set of collected data, it can be observed that the abnormality of the adjusted data is reduced, which means that adjusting the sensor status has greatly improved the data accuracy.

[0129] Example 5

[0130] See also Figure 3 As shown,

[0131] The industrial Internet of Things system based on abnormal analysis described in this embodiment includes a data acquisition module, an abnormality detection module, a sensor state evaluation module, an abnormal state processing module, and a re-acquisition and early warning module;

[0132] A data acquisition module, wherein the data acquisition module uses a pressure sensor and a vibration sensor to collect pressure data and vibration data;

[0133] An anomaly detection module, which compares and analyzes the data in each same time period to detect the abnormal time period of the data;

[0134] A sensor status evaluation module, which analyzes data within an abnormal time period, evaluates the response speed of the pressure sensor and the vibration sensor, and thus determines the working status;

[0135] An abnormal state processing module, wherein the abnormal state processing module adjusts sensor settings and parameters according to the sensor state evaluation result;

[0136] The re-collection and early warning module uses the adjusted sensor to re-collect the pressure data and vibration data within a cycle, re-detects the data packet loss rate in the abnormal time period and triggers an early warning.

[0137] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An industrial Internet of Things method based on abnormal analysis, characterized in that: The following steps are involved: Divide the IoT working cycle into several equal time periods, obtain the pressure data and vibration data in each time period, analyze the pressure data and vibration data in each time period, and obtain the data abnormal time period; Based on the data abnormal time period, since the state of the pressure sensor and the state of the vibration sensor will affect the data accuracy in each same time period, the response speed of the pressure sensor and the response speed of the vibration sensor are analyzed to evaluate the state of the pressure sensor and the state of the vibration sensor, and determine the impact of noise on the state of the sensor device, where the sensor state refers to the state of the pressure sensor and the state of the vibration sensor; Based on the judgment of sensor status, the abnormal sensors are adjusted, and the pressure data and vibration data within a cycle are recollected. The abnormal time period of the new cycle is obtained through analysis of the data packet loss rate, and an early warning is issued in time for the abnormal time period; During the IoT working cycle, the instantaneous pressure value is collected every one minute and recorded as Xij; Get the maximum instantaneous pressure value and the minimum instantaneous pressure value in each time period; Map the instantaneous voltage data of each time period to [0, 1], denoted as Xzij; During the IoT working cycle, the instantaneous vibration amplitude value is collected every one minute and recorded as Xij; Obtain the maximum instantaneous vibration amplitude value and the minimum instantaneous vibration amplitude value in each time period; Map the instantaneous vibration data of each time period to [0, 1], denoted as Yzij; Wherein, i represents the i-th time period, i=1, 2, ..., n, j represents the j-th instantaneous pressure value in each time period, j=1, 2, ..., n; Xzij represents the j-th instantaneous voltage value mapping data corresponding to the i-th time period; Yzij represents the j-th instantaneous vibration amplitude value mapping data corresponding to the i-th time period; The process of obtaining the voltage value mapping data Xzij is as follows: By formula Calculate and obtain the mapping data group {Xz11, Xz12, ..., Xz1j}, {Xz21, Xz22, ..., Xz2j}, ..., {Xzi1, Xzi2, ..., Xzij} for each time period; The process of obtaining the vibration amplitude value mapping data Yzij is as follows: By formula Calculate and obtain the mapping data group {Yz11, Yz12, ..., Yz1j}, {Yz21, Yz22, ..., Yz2j}, ..., {Yzi1, Yzi2, ..., Yzij} for each time period; Add the voltage value mapping data Xzij and the vibration amplitude value mapping data Yzij at the same time to obtain the data score Lij at the corresponding time; Calculate the difference between the data scores Lij at the jth moment and the j-1th moment in the i-th time period and take the absolute value to obtain the data score deviation value; If the data score deviation value is greater than the preset data score deviation threshold, it is recorded as a data abnormal time period; The process of obtaining the data score Lij is as follows: The corresponding moment data score Lij is obtained by formula Lij=Xzij+Yzij, and the data group {L11, L12, ...L1j}, {L21, L22, ...L2j}, ..., {Li1, Li2, ...Lij} is obtained; During the IoT working cycle, the pressure sensor response speed Vx1 and the vibration sensor response speed Vx2 are collected in each time period; Calculate the average response speed of the pressure sensor and the response speed of the vibration sensor in all time periods within the working cycle to obtain the average response speed Vy1 of the pressure sensor and the average response speed Vy2 of the vibration sensor; The response speed of the pressure sensor and the response speed of the vibration sensor in all time periods within the working cycle are respectively calculated to obtain the variance value Sy1 of the response speed of the pressure sensor and the variance value Sy2 of the response speed of the vibration sensor within one cycle; Through the function , to describe the change in the output of the pressure sensor, and establish a curve with the x-axis as the time series t and the y-axis as the output change; Among them, Yz is the final stable value of the pressure sensor output, Yc is the initial value of the pressure sensor, and s is the time constant, which takes a value of [0,1]; By formula , to describe the output change of the vibration sensor, and establish a curve with the x-axis as the time series t and the y-axis as the output change; Among them, Ya is the final stable value output by the pressure sensor, and Yb is the initial value of the pressure sensor; Establish a straight line L1 parallel to the x-axis with y=Vy1+Sy1; Establish a straight line L2 parallel to the x-axis with y=Vy1-Sy1; The area between the straight lines L1 and L2 is recorded as the normal voltage sensor response speed area; Establish a straight line L3 parallel to the x-axis with y=Vy2+Sy2; Establish a straight line L4 parallel to the x-axis with y=Vy2-Sy2; The area between the straight lines L3 and L4 is recorded as the normal response speed area of ​​the vibration sensor.

2. The industrial Internet of Things method based on abnormal analysis according to claim 1 is characterized in that: Observe the changes in the final stable value of the pressure sensor output; If the final value of the pressure sensor output appears outside the straight lines L1 and L2, it means that the pressure sensor has an abnormal state during its use time; If the final value of the pressure sensor output appears within the straight lines L1 and L2, it means that the pressure sensor is in normal condition within the use time; Observe the changes in the final stable value of the vibration sensor output; If the final value of the vibration sensor output appears outside the straight lines L3 and L4, it means that the vibration sensor has an abnormal state during its use time; If the final value of the vibration sensor output appears within the straight lines L3 and L4, it means that the vibration sensor is in normal condition within the usage time.

3. The industrial Internet of Things method based on abnormal analysis according to claim 1 is characterized in that , through the formula The maximum interference noise Pz is calculated, Among them, Py is the noise signal power, which is collected by the power meter, SNB is the signal-to-noise ratio, and the maximum noise interference corresponds to the minimum signal-to-noise ratio of 20dB; The spectrum analyzer is used to collect the noise interference level received by the sensor in real time, which is recorded as Pzi. If the noise interference level Pzi ≥ the maximum interference noise Pz, the noise interference is more serious at this time, and the noise interference level should be adjusted.

4. The industrial Internet of Things method based on abnormal analysis according to claim 1 is characterized in that ,Get the number of data packets output and output in each fixed time period; The input message refers to the data packet arriving at the network device, and the output message refers to the data packet generated by the network device and sent to the network; By formula, loss rate = *%100, calculate the loss rate; Compare the loss rate in each time period with a preset loss rate threshold; If the loss rate in each time period is greater than or equal to the loss rate threshold, it is recorded as an abnormal time period of the IoT system; If the loss rate in each time period is less than the loss rate threshold, it is recorded as a normal time period of the IoT system.

5. An industrial Internet of Things system based on abnormal analysis, characterized in that ,Including data acquisition module, anomaly detection module, sensor state evaluation module, abnormal state processing module, re-acquisition and early warning module; A data acquisition module, wherein the data acquisition module uses a pressure sensor and a vibration sensor to collect pressure data and vibration data; An anomaly detection module, which compares and analyzes the data in each same time period to detect the abnormal time period of the data; A sensor status evaluation module, which analyzes data within an abnormal time period, evaluates the response speed of the pressure sensor and the vibration sensor, and thus determines the working status; An abnormal state processing module, wherein the abnormal state processing module adjusts sensor settings and parameters according to the sensor state evaluation result; A re-collection and early warning module, which uses the adjusted sensor to re-collect pressure data and vibration data within a cycle, re-detects the data packet loss rate during the abnormal time period and triggers an early warning; During the IoT working cycle, the instantaneous pressure value is collected every one minute and recorded as Xij; Get the maximum instantaneous pressure value and the minimum instantaneous pressure value in each time period; Map the instantaneous voltage data of each time period to [0, 1], denoted as Xzij; During the IoT working cycle, the instantaneous vibration amplitude value is collected every one minute and recorded as Xij; Obtain the maximum instantaneous vibration amplitude value and the minimum instantaneous vibration amplitude value in each time period; Map the instantaneous vibration data of each time period to [0, 1], denoted as Yzij; Wherein, i represents the i-th time period, i=1, 2, ..., n, j represents the j-th instantaneous pressure value in each time period, j=1, 2, ..., n; Xzij represents the j-th instantaneous voltage value mapping data corresponding to the i-th time period; Yzij represents the j-th instantaneous vibration amplitude value mapping data corresponding to the i-th time period; The process of obtaining the voltage value mapping data Xzij is as follows: By formula Calculate and obtain the mapping data group {Xz11, Xz12, ..., Xz1j}, {Xz21, Xz22, ..., Xz2j}, ..., {Xzi1, Xzi2, ..., Xzij} for each time period; The process of obtaining the vibration amplitude value mapping data Yzij is as follows: By formula Calculate and obtain the mapping data group {Yz11, Yz12, ..., Yz1j}, {Yz21, Yz22, ..., Yz2j}, ..., {Yzi1, Yzi2, ..., Yzij} for each time period; Add the voltage value mapping data Xzij and the vibration amplitude value mapping data Yzij at the same time to obtain the data score Lij at the corresponding time; Calculate the difference between the data scores Lij at the jth moment and the j-1th moment in the i-th time period and take the absolute value to obtain the data score deviation value; If the data score deviation value is greater than the preset data score deviation threshold, it is recorded as a data abnormal time period; The process of obtaining the data score Lij is as follows: The corresponding moment data score Lij is obtained by formula Lij=Xzij+Yzij, and the data group {L11, L12, ...L1j}, {L21, L22, ...L2j}, ..., {Li1, Li2, ...Lij} is obtained; During the IoT working cycle, the pressure sensor response speed Vx1 and the vibration sensor response speed Vx2 are collected in each time period; Calculate the average response speed of the pressure sensor and the response speed of the vibration sensor in all time periods within the working cycle to obtain the average response speed Vy1 of the pressure sensor and the average response speed Vy2 of the vibration sensor; The response speed of the pressure sensor and the response speed of the vibration sensor in all time periods within the working cycle are respectively calculated to obtain the variance value Sy1 of the response speed of the pressure sensor and the variance value Sy2 of the response speed of the vibration sensor within one cycle; Through the function , to describe the change in the output of the pressure sensor, and establish a curve with the x-axis as the time series t and the y-axis as the output change; Among them, Yz is the final stable value of the pressure sensor output, Yc is the initial value of the pressure sensor, and s is the time constant, which takes a value of [0,1]; By formula , to describe the output change of the vibration sensor, and establish a curve with the x-axis as the time series t and the y-axis as the output change; Establish a straight line L1 parallel to the x-axis with y=Vy1+Sy1; Establish a straight line L2 parallel to the x-axis with y=Vy1-Sy1; The area between the straight lines L1 and L2 is recorded as the normal voltage sensor response speed area; Establish a straight line L3 parallel to the x-axis with y=Vy2+Sy2; Establish a straight line L4 parallel to the x-axis with y=Vy2-Sy2; The area between the straight lines L3 and L4 is recorded as the normal response speed area of ​​the vibration sensor.

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