Industrial Internet of Things intelligent equipment control method

Abstract

The invention discloses an industrial Internet of Things intelligent equipment control method. The method comprises the following steps: S1, data recording: a monitoring mechanism monitors temperature values of different time points in a closed shell in an operation process of intelligent equipment; s2, drift calibration: the driving mechanism controls the calibration mechanism to perform drift calibration on the corresponding abnormal temperature sensor; s3, data analysis: predicting the operating temperature of the equipment by the time sequence model, dynamically adjusting an alarm threshold according to historical data, and judging whether the monitoring temperature of the equipment exceeds the threshold or not; and S4, equipment starting and stopping: the driving mechanism controls the starting and stopping mechanism to perform power-off processing on the intelligent equipment. The control module and the monitoring mechanism are arranged, and the type of a data exception source is judged while the running state of the equipment is monitored through temperature; by arranging the driving mechanism and the start-stop mechanism, the intelligent equipment stops running; the calibration mechanism is arranged to carry out drift calibration on the sensor of the detection mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent device control, and in particular to an industrial Internet of Things intelligent device control method. Background Art

[0002] Industrial Internet of Things refers to the practice of applying Internet of Things technology in industrial environments. It connects industrial equipment such as sensors, equipment, machines, control systems, etc. to the Internet to collect, transmit, analyze and feedback data, thereby optimizing industrial processes, improving production efficiency, reducing costs, and improving product quality and safety.

[0003] After searching, the Chinese patent with publication number CN117092973B includes equipment operation module, primary monitoring module, information processing module, monitoring and maintenance platform, secondary monitoring module, tertiary monitoring module and safety alarm module; the monitoring and maintenance system realizes real-time monitoring of industrial equipment through primary monitoring abnormality coefficient, secondary monitoring coefficient and tertiary monitoring abnormality coefficient, and can timely discover abnormal conditions of industrial equipment to avoid production interruptions and safety accidents caused by industrial equipment failures. Moreover, through the analysis of historical data, predictive maintenance of industrial equipment can be realized, the service life of equipment can be extended, and maintenance costs can be reduced.

[0004] In this case, the parameters of the equipment are monitored through sensors of multiple monitoring modules. However, during the operation of the equipment, its various parameters are within a reasonable fluctuation range. At a certain moment, there may be a situation where the parameters temporarily exceed the threshold. Since the function of dynamic synchronization of the alarm threshold is not set, there is a problem of false alarm in this case. At the same time, taking the temperature sensor as an example, when the equipment is running, it is usually accompanied by intermittent start and stop of the cooling system to cool the equipment. At this time, the temperature change of the equipment fluctuates greatly, and there is a lag in the change of the temperature sensor's indication. This lag further accumulates to form a drift error of the sensor. At this time, the data monitored by the monitoring module in this case itself has errors, which further leads to errors in subsequent calculation results. Summary of the invention

[0005] The purpose of the present invention is to provide an industrial Internet of Things intelligent device control method, which has the advantages of dynamic monitoring and drift calibration and solves the problems raised in the background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an industrial Internet of Things intelligent device control method, comprising the following steps: S1. Data recording: Use a monitoring mechanism to monitor the temperature values ​​inside the closed shell at different time points during the operation of the smart device and form multiple time series ， The data is checked for consistency and the abnormal items are removed before averaging to obtain the device monitoring temperature; S2, drift calibration: Combined with the abnormal items eliminated in S1, the drive mechanism is used to control the calibration mechanism to perform drift calibration on the temperature sensor corresponding to the abnormality; S3. Data analysis: Use multiple sets of equipment monitoring temperature values ​​to train the time series model, and use the time series model to predict the operating temperature of the equipment. At the same time, dynamically adjust the alarm threshold based on historical data and determine whether the equipment monitoring temperature exceeds the threshold; S4, equipment start and stop: When the current temperature value of the equipment exceeds the alarm threshold, the drive mechanism is used to control the start and stop mechanism to cut off the power supply to the smart device.

[0007] Preferably, the data analysis of S3 includes the following steps: S31. Temperature prediction: Combined with the equipment monitoring temperature values ​​recorded by the monitoring agency ， Calculate the corresponding predicted temperature of the equipment through the equipment temperature prediction formula; S32, dynamic threshold adjustment: combining the predicted temperature value of the device and using the confidence interval formula, calculating the dynamic threshold of the predicted temperature of the device at this time; S33, data comparison: combining the monitored temperature value of the device with the predicted temperature value of the device, and calculating whether the monitored temperature value exceeds the dynamic threshold through the abnormal judgment formula.

[0008] Preferably, the device temperature prediction formula is specifically: Q(t)=φ 1 T(t-1)+θ 1 e(t-1) ,in: T(t) : represents the temperature monitoring value of the device at time point t; Q(t) : represents the predicted temperature value of the device at time point t; f 1 : Autoregression coefficient, indicating the previous time point t-1 The impact of temperature monitoring values ​​on current forecast values; i 1 : Moving average coefficient, indicating the previous time point t-1 The correction effect of the forecast error on the current forecast value; e(t-1) : Previous time point t-1 prediction error.

[0009] Preferably, a partition for placing smart devices is fixedly connected to the middle section inside the closed shell, a fixing bolt is fixedly connected to the bottom end of the inner contour of the closed shell, the driving mechanism is rotationally connected to the middle section of the bottom end of the inner contour of the closed shell, and the start-stop mechanism penetrates and is rotationally connected to the outer contour of the fixing bolt; The driving mechanism includes a driving ring, a ratchet portion is fixedly connected to the outer contour of the driving ring, a middle section of the driving ring is screwed with a bidirectional thread, a lower cam portion is fixedly connected to the top of the bidirectional thread, and an upper cam portion of the same specification and mirror-image displaced as the lower cam portion is provided at the top of the lower cam portion.

[0010] Preferably, the monitoring mechanism includes a limit frame fixedly connected to the outer contour of the lower cam portion and the upper cam portion, and three connecting rods distributed around the top of the limit frame are fixedly connected to the outer contour of the top end of the limit frame, and a temperature sensor is fixedly connected to the top of each connecting rod away from the end of the limit frame.

[0011] Preferably, the confidence interval formula is specifically: CI(t)=Q(t)±z α / 2 *s ,in: CI(t) : Time point t The predicted confidence interval of the device temperature at time t; z α / 2 : Critical value of the standard normal distribution corresponding to the selected confidence level; s : Prediction error e(t) The standard deviation of .

[0012] Preferably, the calibration mechanism includes a positioning rod, which vertically penetrates and is fixedly connected to the inner contour of the middle section of the bottom end of the closed shell, a guide pin located between the lower cam portion and the upper cam portion is fixedly connected to the outer contour of the middle section of the positioning rod, an extension rod is fixedly connected to the outer contour of the top end of the positioning rod, the bottom of the extension rod away from one end of the positioning rod is fixedly connected to a constant temperature cavity, and the positioning rod is limitedly slidably connected to the bidirectional thread.

[0013] Preferably, the start-stop mechanism includes a deflection ring, which penetrates and is connected to the outer contour of the fixing bolt for limited rotation, and obliquely extended electric contacts are fixedly connected to both sides of the outer contour of the deflection ring, and the inclination angle of the electric contacts is forty-five degrees, and electric contact plate 1 and electric contact plate 2 are fixedly connected to the inner contour of the closed shell near the deflection ring, and electric contact plate 1 and electric contact plate 2 are both electrically connected to the smart device.

[0014] Preferably, the abnormality judgment formula is specifically: P=|T(t)-Q(t)|-z α / 2 *s, in: P : Judgment criteria for the working status of the driving mechanism; z α / 2 *s : half-width of the confidence interval; |T(t)-Q(t)| : The absolute value of the difference between the current temperature of the device and the predicted temperature.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention sets a control module and a monitoring mechanism to monitor the operating status of the temperature monitoring device while determining the type of data anomaly source, thereby reducing the probability of false alarms and improving the accuracy of monitoring results. On the other hand, a built-in algorithm is used to record and analyze the temperature changes of the device and dynamically set the corresponding alarm threshold.

[0016] The present invention provides a driving mechanism and a start-stop mechanism. When the data abnormality comes from the operation of the device, the driving mechanism controls the start-stop mechanism to stop the operation of the intelligent device, thereby achieving the purpose of controlling the operating state of the intelligent device.

[0017] The present invention provides a calibration mechanism. When the data anomaly is caused by sensor drift, the driving mechanism controls the calibration mechanism to work and performs drift calibration on the sensor of the detection mechanism to further improve the accuracy of the monitoring result. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the main structure of the present invention; Figure 2 It is a cross-sectional view of the main structure of the present invention; Figure 3 It is a schematic diagram of the connection relationship between the housing and the start-stop mechanism of the present invention; Figure 4 It is an exploded schematic diagram of the driving mechanism structure of the present invention; Figure 5 This is an exploded schematic diagram of the monitoring mechanism structure of the present invention; Figure 6 It is a schematic diagram of the calibration mechanism structure of the present invention; Figure 7 This is a graph showing the relationship between the temperature and operating time of smart devices without interference factors; Figure 8 The temperature-operation time relationship diagram of smart devices when working for the cooling system; Figure 9 It is a flow chart of the working method of the present invention.

[0019] In the figure: 1. closed shell; 11. partition; 12. intelligent device; 13. fixing bolt; 2. deflection ring; 21. electric contact plate 1; 22. electric contact plate 2; 23. electric contact; 3. drive ring; 31. ratchet portion; 32. bidirectional thread; 33. lower cam portion; 34. upper cam portion; 4. limit frame; 41. connecting rod; 42. temperature sensor; 5. positioning rod; 51. guide pin; 52. extension rod; 53. constant temperature cavity. DETAILED DESCRIPTION

[0020] 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. Embodiment 1

[0021] See also Figure 1 to Figure 9 The present invention provides a technical solution: an industrial Internet of Things intelligent device control method, comprising the following steps: S1. Data recording: Using a monitoring mechanism to monitor the temperature values ​​of the inside of the closed housing 1 at different time points during the operation of the smart device 12 and forming multiple groups of time series ， The data is checked for consistency and the abnormal items are removed before averaging to obtain the device monitoring temperature; S2, drift calibration: combining the abnormal items eliminated in S1, using the driving mechanism to control the calibration mechanism to perform drift calibration on the temperature sensor 42 corresponding to the abnormality; S3. Data analysis: Use multiple sets of equipment monitoring temperature values ​​to train the time series model, and use the time series model to predict the operating temperature of the equipment. At the same time, dynamically adjust the alarm threshold based on historical data and determine whether the equipment monitoring temperature exceeds the threshold; S4, device start and stop: when the current temperature value of the device exceeds the alarm threshold, the drive mechanism is used to control the start and stop mechanism to cut off the power supply of the smart device 12.

[0022] First, when the smart device 12 is running, the temperature sensor 42 automatically records the temperature change data inside the closed shell 1, and automatically performs consistency detection on the data of the three temperature sensors 42. When the readings of the three temperature sensors 42 are inconsistent, the abnormal items are eliminated through difference calculation and the corresponding temperature sensor 42 with abnormal reading is found. At this time, the driving mechanism starts to rotate clockwise and drives the monitoring mechanism and the calibration mechanism to work synchronously.

[0023] The monitoring mechanism starts to rise and fall, causing the relative distance between the temperature sensor 42 and the smart device 12 to change. At this time, since the smart device 12 is in operation, the smart device 12 bears the heat source inside the closed housing 1. When the temperature sensor 42 is away from the smart device 12, the reading of the temperature sensor 42 changes synchronously and shows a downward trend. At this time, observe whether the decline ratio of the reading of the abnormal temperature sensor 42 is the same as the decline ratio of the readings of the other temperature sensors 42. If they decline at the same ratio, the problem of span drift of the abnormal temperature sensor 42 can be ruled out, and the abnormal temperature sensor 42 is locked as zero drift; Along with the movement of the monitoring mechanism, the calibration mechanism rotates synchronously and intermittently, and each rotation angle is ninety degrees. When the calibration mechanism rotates to the top of the abnormal temperature sensor 42, the monitoring mechanism rises and inserts the abnormal temperature sensor 42 into the calibration mechanism. At this time, the calibration mechanism starts to perform zero point drift detection on the abnormal temperature sensor 42 and perform calibration operations.

[0024] Subsequently, the data recorded by the remaining temperature sensors 42 are used to calculate the predicted temperature of the smart device 12 and the corresponding dynamic threshold. When the reading of the normal temperature sensor 42 exceeds the dynamic threshold, the driving mechanism rotates counterclockwise and drives the start-stop mechanism to work synchronously. The start-stop mechanism further releases the electrical connection between it and the smart device 12. At this time, the smart device 12 is powered off and stops working. Embodiment 2

[0025] See also Figure 7-Figure 9 This embodiment further illustrates the following on the basis of the first embodiment: The following steps are involved in the data analysis of S3: S31. Temperature prediction: Combined with the equipment monitoring temperature values ​​recorded by the monitoring agency ， Calculate the corresponding predicted temperature of the equipment through the equipment temperature prediction formula; S32, dynamic threshold adjustment: combining the predicted temperature value of the device and using the confidence interval formula, calculating the dynamic threshold of the predicted temperature of the device at this time; S33, data comparison: combining the monitored temperature value of the device with the predicted temperature value of the device, and calculating whether the monitored temperature value exceeds the dynamic threshold through the abnormal judgment formula.

[0026] In the process of predicting the temperature of the smart device 12, historical data and the normal temperature sensor 42 indication changes are combined to perform time series model training and fitting through multiple sets of data, so that the difference between the predicted value and the actual monitoring value is minimized, thereby minimizing the error through repeated iterations; When the smart device 12 is not disturbed by external factors, that is, when the cooling system is not working, the relationship between the temperature change inside the closed housing 1 and the operation time of the smart device 12 is as follows: Figure 7As shown, at the initial stage of operation of the smart device 12, due to the lag of temperature, the temperature rises slowly in the initial stage; then as the heat of the smart device 12 accumulates, the temperature will accelerate in the middle stage of the operation of the smart device 12; finally, after the smart device 12 has been running for a long time, the temperature inside the closed shell 1 may tend to stabilize and reach a thermal equilibrium state.

[0027] When the cooling system is working, the relationship between the temperature change inside the closed housing 1 and the operation time of the smart device 12 is as follows: Figure 8 As shown, the temperature drop range corresponds to the working range of the cooling system of the smart device 12. Under the cooling effect of the cooling system, the temperature inside the closed shell 1 fluctuates. At this time, there is a lag accumulation in the reading change of the temperature sensor 42, which further easily forms a drift error of the temperature sensor 42. Embodiment 3

[0028] See also Figure 4-Figure 5 This embodiment further illustrates the following on the basis of the second embodiment: The middle section inside the closed shell 1 is fixedly connected with a partition 11 for placing the smart device 12, the bottom end of the inner contour of the closed shell 1 is fixedly connected with a fixing bolt 13, the driving mechanism is limitedly rotatably connected to the middle section of the bottom end of the inner contour of the closed shell 1, and the start-stop mechanism penetrates and is rotatably connected to the outer contour of the fixing bolt 13; The driving mechanism includes a driving ring 3, a ratchet portion 31 is fixedly connected to the outer contour of the driving ring 3, a bidirectional thread 32 is screwed on the middle section of the driving ring 3, a lower cam portion 33 is fixedly connected to the top end of the bidirectional thread 32, and an upper cam portion 34 of the same specification as the lower cam portion 33 and mirror-imaged is arranged at the top end of the lower cam portion 33.

[0029] The monitoring mechanism includes a limit frame 4 fixedly connected to the outer contours of the lower cam portion 33 and the upper cam portion 34, and three connecting rods 41 distributed around the top of the limit frame 4 are fixedly connected, and a temperature sensor 42 is fixedly connected to the top of each connecting rod 41 away from the end of the limit frame 4.

[0030] The calibration mechanism includes a positioning rod 5, which vertically penetrates and is fixedly connected to the inner contour of the middle section of the bottom end of the closed shell 1, and a guide pin 51 located between the lower cam portion 33 and the upper cam portion 34 is fixedly connected to the outer contour of the middle section of the positioning rod 5. An extension rod 52 is fixedly connected to the outer contour of the top end of the positioning rod 5, and a constant temperature cavity 53 is fixedly connected to the bottom of the extension rod 52 away from one end of the positioning rod 5. The positioning rod 5 is limitedly slidably connected to the bidirectional thread 32.

[0031] First, the three temperature sensors 42 record TtA consistency test is performed to remove the abnormal ones and find out the corresponding position of the temperature sensor 42. At this time, the drive ring 3 starts to rotate clockwise, and the bidirectional thread 32 further starts to drive the lower cam portion 33 to reciprocate up and down under the screw connection of the drive ring 3. Since the lower cam portion 33 and the upper cam portion 34 are fixedly connected by the limit frame 4, that is, the limit frames 4 and 35 synchronously reciprocate and rise and fall with the lower cam portion 33, a single rise and fall is regarded as a cycle. In a cycle, the relative positions of the lower cam portion 33, the upper cam portion 34 and the guide pin 51 change synchronously; When the lower cam portion 33 and the upper cam portion 34 descend, the guide pin 51 is squeezed by the downward arc surface of the upper cam portion 34 and further drives the positioning rod 5 to deflect, and the deflection angle is fixed at forty-five degrees. Subsequently, when the lower cam portion 33 and the upper cam portion 34 rise, the guide pin 51 is squeezed by the upward arc surface of the lower cam portion 33 and again drives the positioning rod 5 to deflect forty-five degrees, that is, within a single lifting cycle of the lower cam portion 33 and the upper cam portion 34, the intermittent rotation of the positioning rod 5 is achieved and the rotation angle is ninety degrees.

[0032] While the positioning rod 5 rotates, the extension rod 52 and the constant temperature cavity 53 rotate synchronously. At the same time, the lifting and lowering of the lower cam portion 33 and the upper cam portion 34 drive the limit frame 4, the connecting rod 41 and the temperature sensor 42 to lift and lower synchronously, thereby avoiding the temperature sensor 42 from blocking the rotation path of the constant temperature cavity 53; and during the lifting and lowering process of the temperature sensor 42, the relative distance between the temperature sensor 42 and the smart device 12 changes, causing the reading of the temperature sensor 42 to drop synchronously. At this time, the drift type of the abnormal temperature sensor 42 can be determined by observing the decrease ratio of the reading of the abnormal temperature sensor 42 and the decrease ratio of the readings of the other temperature sensors 42, and the abnormal temperature sensor 42 can be adaptively adjusted using the constant temperature cavity 53 according to the drift type.

[0033] By repeatedly lifting and lowering the lower cam portion 33 and the upper cam portion 34, the constant temperature cavity 53 is rotated to the corresponding position of the abnormal temperature sensor 42. At this time, the lower cam portion 33 and the upper cam portion 34 are lifted for the last time, and the abnormal temperature sensor 42 is lifted synchronously and inserted into the constant temperature cavity 53; if the abnormal temperature sensor 42 has a zero drift, since the interior of the constant temperature cavity 53 is in a constant temperature state, the zero drift fixed value of the abnormal temperature sensor 42 can be obtained by observing the difference between the reading of the abnormal temperature sensor 42 and the temperature of the constant temperature cavity 53, and electrical adjustment can be performed to restore the reading of the abnormal temperature sensor 42 to normal; if the abnormal temperature sensor 42 has a span drift, the drift span value of the abnormal temperature sensor 42 can be obtained by the change ratio of the reading during the lifting and lowering process of the temperature sensor 42, and the temperature sensor 42 can be further electrically adjusted to restore the temperature sensor 42 to normal. Embodiment 4

[0034] See also Figure 9 This embodiment further illustrates the following on the basis of the third embodiment: The equipment temperature prediction formula is specifically: Q(t)=φ 1 T(t-1)+θ 1 e(t-1) ,in: T(t) : represents the temperature monitoring value of the device at time point t; Q(t) : represents the predicted temperature value of the device at time point t; f 1 : Autoregression coefficient, indicating the previous time point t-1 The impact of temperature monitoring values ​​on current forecast values; i 1 : Moving average coefficient, indicating the previous time point t-1 The correction effect of the forecast error on the current forecast value; e (t-1) : Previous time point t-1 prediction error.

[0035] Before this process, combining historical data and T(t) , use the time series model to perform multiple data fitting and repeated iterations to find the optimal f 1 、the 1 as well as e(t) , so that the temperature prediction formula is Qt The error is minimal, and during the operation of the smart device 12, f 1 、the 1 as well as e(t) The value is constantly adjusted.

[0036] among them 1 It measures the influence of the temperature value at the previous time point on the predicted value at the current time point. f 1 The value of is close to 1, which means that the current temperature value is highly dependent on the temperature value at the previous time point. At this time, the temperature prediction will show strong inertia, that is, the temperature value at the previous time point will be almost completely transferred to the current prediction value; if f 1 The value of is close to 0, which means that the temperature value at the previous time point has little influence on the current prediction, and the predicted value will be more dependent on the influence of other coefficient factors.

[0037] And th1 It measures the degree to which the forecast error at the previous time point corrects the forecast value at the current time point. i 1 A larger value of means that past forecast errors have a stronger corrective effect on the current forecast value; that is, if the forecast error at the previous time point is large, then the current forecast value will be adjusted significantly to correct the previous error; i 1 The value of is close to 0, which means that the past forecast errors have little influence on the current forecast. At this time, the forecast value depends more on the autoregressive part.

[0038] on the other hand, e(t-1) is the forecast error at the previous time point, which represents the difference between the actual value and the predicted value. e(t-1) A large value means that the previous forecast has a significant deviation. In this case, the moving average coefficient i 1 Adjustments will be made based on this deviation to make the current forecast value more reasonable. e(t-1) A smaller value means that the previous forecast was more accurate and the revision is smaller at this time. The current forecast value is mainly dominated by the autoregressive part. Embodiment 5

[0039] See also Figure 3 and Figure 6 This embodiment further illustrates the following on the basis of the second embodiment: The confidence interval formula is specifically: CI(t)=Q(t)±z α / 2 *s ,in: CI(t) : Time point t The predicted confidence interval of the device temperature at time t; z α / 2 : Critical value of the standard normal distribution corresponding to the selected confidence level; s : Prediction error e(t) The standard deviation of .

[0040] After calculating the predicted temperature of the smart device 12 using the device temperature prediction formula, substitute the result into the confidence interval formula to calculate the alarm threshold at this time. This threshold range is dynamically adjusted according to the running time of the smart device 12 combined with the confidence interval formula. The position of the confidence interval is determined by the predicted value. Q(t) The higher the predicted value, the higher the confidence interval moves upward; the lower the predicted value, the lower the confidence interval moves downward. z α / 2 Depends on the selected confidence level. The higher the confidence level, zα / 2 The larger the value, the wider the confidence interval. This means that at a higher confidence level, the model has greater uncertainty about future predictions, providing a wider confidence interval to cover possible actual values, while choosing a lower confidence level will narrow the confidence interval, but also increase the probability that the predicted value is not within the interval. Usually, a 95% confidence level is selected, and the corresponding z α / 2 ≈1.96.

[0041] The abnormality judgment formula is specifically: P=|T(t)-Q(t)|-z α / 2 *s, in: P : Judgment criteria for the working status of the driving mechanism; z α / 2 *s : half-width of the confidence interval; |T(t)-Q(t)| : The absolute value of the difference between the current temperature of the device and the predicted temperature.

[0042] The start-stop mechanism includes a deflection ring 2, which penetrates and is limitedly rotated on the outer contour of the fixing bolt 13, and the two sides of the outer contour of the deflection ring 2 are fixedly connected with obliquely extended electric contacts 23, and the inclination angle of the electric contacts 23 is forty-five degrees. The inner contour of the closed shell 1 near the deflection ring 2 is fixedly connected with electric contact plate 1 21 and electric contact plate 2 22, and the electric contact plate 1 21 and electric contact plate 2 22 are both electrically connected to the smart device 12.

[0043] Furthermore, the monitoring value of the normal temperature sensor 42 T(t) The calculated predicted value Q(t) Substitute into the abnormality judgment formula and calculate the result. P＞0 When T(t) The calculated predicted value Q(t) The distance between them exceeds the half-width of the confidence interval z α / 2 *s, The monitoring value at this time T(t) If the value does not fall within the confidence interval, the data is abnormal. However, the problem of the drift of the temperature sensor 42 causing the result to be erroneous has been eliminated by the third embodiment. That is, the abnormal data at this time corresponds to a malfunction in the operation of the smart device 12.

[0044] At this time, the driving ring 3 drives the ratchet portion 31 to rotate counterclockwise, and the ratchet portion 31 squeezes and causes the electric contact 23 and the deflection ring 2 to deflect around the fixing bolt 13. At this time, the contact between the electric contact 23 in the opposite direction and the electric contact plate 1 21 is disengaged and further adheres to the electric contact plate 2 22, and the electric contact plate 1 21 and the electric contact plate 2 22 are both electrically connected to the smart device 12, that is, the contact switching of the electric contact 23 causes the power supply circuit of the smart device 12 to switch synchronously. At this time, the power supply circuit of the smart device 12 is cut off, causing the smart device 12 to be powered off and stop working; further, while the electric contact 23 is adhered to the electric contact plate 2 22, the contact between the electric contact 23 and the ratchet portion 31 is disengaged, and at this time the electric contact 23 and the deflection ring 2 stop deflecting.

[0045] And during the counterclockwise rotation of the driving ring 3, the monitoring mechanism and the calibration mechanism work synchronously, and the working process is the same as the description process of the second embodiment, thereby realizing the regular calibration and inspection operation of the temperature sensor 42 when the temperature sensor 42 does not drift.

[0046] During the clockwise rotation of the drive ring 3, the squeezing of the electric contact 23 by the ratchet portion 31 can ensure that the electric contact 23 is always in contact with the electric contact plate 1 21, thereby avoiding damage to the smart device 12 caused by accidental power failure during the operation of the smart device 12.

[0047] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The method for controlling an industrial IoT intelligent device is characterized in that: The following steps are involved: S1. Data recording: using a monitoring mechanism to monitor the temperature values ​​of the inside of the closed housing (1) at different time points during the operation of the smart device (12) and forming multiple groups of time series ， The data is checked for consistency and the abnormal items are removed before averaging to obtain the device monitoring temperature; S2, drift calibration: combining the abnormal items eliminated in S1, using the driving mechanism to control the calibration mechanism to perform drift calibration on the temperature sensor (42) corresponding to the abnormality; S3. Data analysis: Use multiple sets of equipment monitoring temperature values ​​to train the time series model, and use the time series model to predict the operating temperature of the equipment. At the same time, dynamically adjust the alarm threshold based on historical data and determine whether the equipment monitoring temperature exceeds the threshold; S4, device start and stop: When the current temperature value of the device exceeds the alarm threshold, the drive mechanism is used to control the start and stop mechanism to cut off the power supply of the intelligent device (12).

2. The method for controlling an industrial IoT intelligent device according to claim 1, characterized in that: The following steps are involved in the data analysis of S3: S31. Temperature prediction: Combined with the equipment monitoring temperature values ​​recorded by the monitoring agency ， Calculate the corresponding predicted temperature of the equipment through the equipment temperature prediction formula; S32, dynamic threshold adjustment: combining the predicted temperature value of the device and using the confidence interval formula, calculating the dynamic threshold of the predicted temperature of the device at this time; S33, data comparison: combining the monitored temperature value of the device with the predicted temperature value of the device, and calculating whether the monitored temperature value exceeds the dynamic threshold through the abnormal judgment formula.

3. The industrial Internet of Things intelligent device control method according to claim 2 is characterized in that: The equipment temperature prediction formula is specifically: Q(t)=φ 1 T(t-1)+θ 1 ε(t-1) ,in: T(t) : represents the temperature monitoring value of the device at time point t; Q(t) : represents the predicted temperature value of the device at time point t; φ 1 : Autoregressive coefficient, indicating the previous time point t-1 The impact of temperature monitoring values ​​on current forecast values; θ 1 : Moving average coefficient, indicating the previous time point t-1 The correction effect of the forecast error on the current forecast value; ε(t-1) : Previous time point t-1 prediction error.

4. The industrial Internet of Things intelligent device control method according to claim 3 is characterized in that: A partition (11) for placing an intelligent device (12) is fixedly connected to the middle section inside the closed shell (1), a fixing bolt (13) is fixedly connected to the bottom end of the inner contour of the closed shell (1), the driving mechanism is rotationally connected to the middle section at the bottom end of the inner contour of the closed shell (1), and the start-stop mechanism penetrates and is rotationally connected to the outer contour of the fixing bolt (13); The driving mechanism comprises a driving ring (3), a ratchet portion (31) being fixedly connected to the outer contour of the driving ring (3), a bidirectional thread (32) being screwed to the middle section of the driving ring (3), a lower cam portion (33) being fixedly connected to the top end of the bidirectional thread (32), and an upper cam portion (34) having the same specifications as the lower cam portion (33) and being mirror-image-displaced is provided at the top end of the lower cam portion (33); The calibration mechanism comprises a positioning rod (5), the positioning rod (5) vertically penetrating through and fixedly connected to the inner contour of the middle section of the bottom end of the closed shell (1), a guide pin (51) located between a lower cam portion (33) and an upper cam portion (34) is fixedly connected to the outer contour of the middle section of the positioning rod (5), an extension rod (52) is fixedly connected to the outer contour of the top end of the positioning rod (5), a constant temperature cavity (53) is fixedly connected to the bottom of the extension rod (52) away from one end of the positioning rod (5), and the positioning rod (5) is slidably connected to the bidirectional thread (32) in a limited position.

5. The industrial Internet of Things intelligent device control method according to claim 1 is characterized in that: The monitoring mechanism comprises a limit frame (4) fixedly connected to the outer contours of the lower cam portion (33) and the upper cam portion (34), and three connecting rods (41) distributed around the top end of the limit frame (4) are fixedly connected to the outer contour.

6. The industrial Internet of Things intelligent device control method according to claim 5 is characterized in that: A temperature sensor (42) is fixedly connected to the top of one end of each connecting rod (41) away from the limiting frame (4).

7. The industrial Internet of Things intelligent device control method according to claim 2 is characterized in that: The confidence interval formula is specifically: CI(t)=Q(t)±z α / 2 *σ ,in: CI(t) : Time point t The predicted confidence interval of the device temperature at time t; z α / 2 : Critical value of the standard normal distribution corresponding to the selected confidence level; σ : Prediction error ε(t) The standard deviation of .

8. The industrial Internet of Things intelligent device control method according to claim 1 is characterized in that: The start-stop mechanism comprises a deflection ring (2), the deflection ring (2) penetrates and is connected to the outer contour of the fixing bolt (13) in a limited rotation manner, obliquely extending electric contacts (23) are fixedly connected to both sides of the outer contour of the deflection ring (2), and the inclination angle of the electric contacts (23) is forty-five degrees, and the inner contour of the closed shell (1) near the deflection ring (2) is fixedly connected to an electric contact plate 1 (21) and an electric contact plate 2 (22), and the electric contact plate 1 (21) and the electric contact plate 2 (22) are both electrically connected to the smart device (12).

9. According to the industrial Internet of Things intelligent device control method of claim 2, the abnormality judgment formula is specifically: P= |T(t)-Q(t)|-z α / 2 *σ, in: P : Judgment criteria for the working status of the driving mechanism; z α / 2 *σ : half-width of the confidence interval; |T(t)-Q(t)| : The absolute value of the difference between the current temperature of the device and the predicted temperature.

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