A crane hook inclination monitoring and early warning system and method based on the Internet of Things

Abstract

The present invention discloses a crane hook inclination monitoring and early warning system and method based on the Internet of Things, and relates to the field of crane monitoring technology. The present invention comprises: S10: predicting the real-time deviation distance and real-time operation acceleration of the hook; S20: predicting the real-time surrounding index of the hook; S30: determining the control time of the hook operation speed; S40: regulating the real-time operation speed of the hook at the determined control time. The present invention ensures that a warning signal can be issued in time before the hook inclination exceeds the warning range, thereby improving the system's supervision effect on the hook inclination, and in the process of regulating the hook's operation speed, it can avoid a large deviation of the rope during the regulation process, and according to the determined regulation method, the hook's inclination angle can be highly close to zero, further improving the system's regulation effect on the hook inclination angle.

Description

Technical Field

[0001] The present invention relates to the technical field of crane monitoring, and in particular to a crane hook inclination monitoring and early warning system and method based on the Internet of Things. Background Art

[0002] As an important pillar of modern manufacturing, the lifting industry undertakes key tasks such as lifting and moving heavy objects.

[0003] However, the current lifting industry mainly relies on the driver's naked eye to identify the tilt of the hook during operation. This method is not only highly subjective, but also easily affected by factors such as driver experience and fatigue, and there are also major safety hazards. In addition, the current lifting industry lacks effective supervision of the tilt state of the hook, which greatly shortens the service life of the lifting equipment. In serious cases, it will lead to safety accidents. Summary of the invention

[0004] The purpose of the present invention is to provide a crane hook inclination angle monitoring and early warning system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a crane hook inclination monitoring and early warning method based on the Internet of Things, the method comprising:

[0006] S10: During the hook lifting process, a posture measurement system and a hook camera measurement system are installed at the top of the crane boom near the rope roller to measure the inclination angle of the hook at each position. Combined with the real-time height value of the hook obtained by the air pressure value measured by the barometer, the real-time deviation distance and real-time running acceleration of the hook are predicted;

[0007] S20: calculating a real-time warning coefficient of the hook based on the real-time operation acceleration of the hook, determining a hook surrounding index analysis data set based on the calculation result, analyzing the hook surrounding index analysis data set, and predicting the real-time surrounding index of the hook;

[0008] S30: predicting the real-time abnormal index of the hook, and determining the control time of the hook running speed based on the prediction result;

[0009] S40: at the determined control time, the real-time running speed of the hook is controlled.

[0010] Further, the S10 includes:

[0011] S101: A barometer is fixedly installed at the top of the rope roller and at the connection between the hook and the rope. At time t, the readings of the two barometers are measured respectively. 1t ,d 2tTo obtain, according to the formula H t =-18400*(1+Tv / 273)*ln(d 1t / d 2t ) calculates the height of the hook from the top of the rope roller at time t, where Tv represents the average virtual temperature of the air layer between the height of the hook and the rope and the height of the top of the rope roller;

[0012] S102: When the height of the hook from the top of the rope roller is H t When the hook tilt angle is measured, a posture measurement system and a hook camera measurement system are installed at the top of the crane boom near the rope roller. ,according to Predict the deviation distance of the hook at time t;

[0013] S103: During the hook lifting process, the running acceleration a of the hook at time t t To make a prediction, the specific prediction formula is:

[0014] ;

[0015] Among them, k represents the time value and time k is before time t, or ,when When it is a negative value, ,when When is positive, , L k represents the deviation distance of the hook at time k, V k It represents the running speed of the hook at time k. The real-time running acceleration of the hook is calculated by the obtained air pressure difference. There is no need to consider the wear degree of the motor in the crane, the air resistance, and the influence of the force of the rope roller on the rope on the running speed of the hook, which improves the control accuracy of the hook inclination angle.

[0016] Further, the S20 includes:

[0017] S201: The posture measurement system and the hook camera measurement system measure the inclination angle of the hook at time intervals h. t =M*a t Calculate the tension of the lifting rope at time t, where M represents the mass of the hook and the load;

[0018] S202: Obtain the maximum pressure value F' that the suspension rope can withstand, according to U t =b1*(F t / F´)+b2*[(a t -a t-h ) / at-h ] Calculate the warning coefficient of the hook at time t, where b1 and b2 represent proportional coefficients and b1+b2=1;

[0019] When 0≤U t <0.7*D, 0.6≤D≤1, put the predicted deviation distance at time t into set N, and get the surround index analysis data set. t When ≤1, the sound and light alarm installed in the cab will sound an alarm;

[0020] According to the wraparound index analysis data set, the wraparound index of the hook at time t is predicted. The specific method is as follows:

[0021] According to the order of the prediction time of each deviation distance, each deviation distance stored in the surround index analysis data set is numbered, and the numbering result is: j=1,2,…,n; n represents the total number of deviation distances stored in the surround index analysis data set;

[0022] Obtain the tilt angles that match each deviation distance. If the sign of the tilt angle that matches the deviation distance numbered j is opposite to the sign of the tilt angle that matches the deviation distance numbered j+1, place a split symbol between the deviation distance numbered j and the deviation distance numbered j+1, and before the deviation distance numbered 1, and obtain the maximum value of the deviation distances stored between adjacent split symbols.

[0023] according to Predict the hook's encirclement index at time t, where i=1,2,…,p, indicating that each maximum value is numbered in the order in which the maximum value is obtained, p indicates the total number, and y i Indicates the maximum value of number i. hour, ,when hour, By predicting the hook's encirclement index, the driver can control the hook's running speed before the rope falls off due to multiple rotations, thereby enhancing the system's regulatory safety.

[0024] Furthermore, the specific method for determining the control time of the hook running speed in S30 is:

[0025] According to B t =U t *R t Predict the abnormal index of the hook at time t;

[0026] If B t< set threshold and the sound and light alarm installed in the cab does not sound an alarm, then there is no need to control the operating speed of the hook at time t;

[0027] If B t <If the threshold is set and the sound and light alarm in the cab sounds an alarm, the speed of the hook needs to be regulated at time t;

[0028] If B t ≥ set threshold, then the running speed of the hook needs to be regulated at time t, 0.8≤set threshold≤1.

[0029] Furthermore, the S40 includes: the driver in the cab controls the running speed of the hook at the determined control time, and the specific control method is:

[0030] At time t+x*h, adjust the running speed of the rope to V t+x*h , until the hook's inclination angle is 0, , the driver controls the hook to move at a constant speed according to the corresponding running speed when the hook tilt angle is 0, V t represents the running speed of the hook at time t, x represents a positive integer and x=1,2,….

[0031] A crane hook inclination monitoring and early warning system based on the Internet of Things, the system comprising a hook inclination monitoring and analysis module, a surround index prediction module, a control time determination module and a control analysis module;

[0032] The hook inclination monitoring and analysis module is used to predict the real-time deviation distance and real-time running acceleration of the hook;

[0033] The wraparound index prediction module is used to predict the real-time wraparound index of the hook;

[0034] The control time determination module is used to determine the control time of the hook running speed;

[0035] The control and analysis module is used to control the real-time running speed of the hook at a determined control time.

[0036] Further, the hook inclination monitoring and analysis module includes a hook height calculation unit, a deviation distance prediction unit and an operation acceleration prediction unit;

[0037] The hook height calculation unit calculates the real-time height value of the hook by using the difference between the air pressure values ​​measured by the barometer;

[0038] The deviation distance calculation unit monitors the real-time tilt angle of the hook through a posture measurement system and a hook camera measurement system installed at the top of the crane boom near the rope roller, and predicts the real-time deviation distance of the hook based on the monitoring results;

[0039] During the hook lifting process, the operating acceleration prediction unit predicts the real-time operating acceleration of the hook based on the constructed prediction formula.

[0040] Further, the wrapping index prediction module includes a rope tension calculation unit, a data set determination unit and a wrapping index prediction unit;

[0041] The rope tension calculation unit calculates the real-time tension value of the rope based on the real-time running acceleration of the hook;

[0042] The data set determination unit calculates the real-time warning coefficient of the hook based on the real-time tension value of the lifting rope, and determines the hook's surrounding index analysis data set in combination with the real-time deviation distance value of the hook;

[0043] The surround index prediction unit analyzes the surround index analysis data set of the hook, determines the target analysis data of the surround index, and predicts the real-time surround index of the hook based on the determination result.

[0044] Further, the control time determination module includes an abnormality index prediction unit and a control time determination unit;

[0045] The abnormality index prediction unit predicts the real-time abnormality index of the hook according to the real-time warning coefficient of the lifting rope and the real-time surrounding index of the hook;

[0046] The control time determination unit determines the control time of the hook running speed according to the real-time abnormal index of the hook and the alarm status of the sound and light alarm arranged in the cab.

[0047] Furthermore, the control and analysis module controls the real-time operating speed of the hook according to the real-time abnormality index of the hook and the operating acceleration of the hook at a determined control time.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention calculates the real-time running acceleration and the real-time deviation distance of the hook through the air pressure difference. This process eliminates the influence of external factors such as air resistance and motor wear on the calculation results, ensuring that the early warning signal can be issued in time before the hook inclination angle exceeds the early warning range, thereby improving the system's supervision effect on the hook inclination angle.

[0050] 2. The present invention predicts the real-time encirclement index of the hook through the real-time deviation distance of the hook, and combines the real-time early warning coefficient of the hook to ensure that the driver can adjust the running speed of the hook before the rope falls off due to multiple rotations, thereby enhancing the supervision safety of the system.

[0051] 3. In the process of regulating the running speed of the hook, the present invention can prevent the suspension rope from deviating significantly during the regulation process, and according to the determined regulation method, the inclination angle of the hook can be highly close to zero, further improving the regulation effect of the system on the inclination angle of the hook. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of the workflow of a crane hook inclination monitoring and early warning system and method based on the Internet of Things of the present invention;

[0053] Figure 2 The present invention is a schematic structural diagram of the working principle of a crane hook inclination monitoring and early warning system and method based on the Internet of Things. DETAILED DESCRIPTION

[0054] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0055] Example: Figure 1-Figure 2 As shown, the present invention provides a crane hook inclination monitoring and early warning system and method technical solution based on the Internet of Things;

[0056] A crane hook inclination monitoring and early warning method based on the Internet of Things, the method comprising:

[0057] S10: During the hook lifting process, a posture measurement system and a hook camera measurement system are installed at the top of the crane boom near the rope roller to measure the inclination angle of the hook at various positions. The method for measuring the inclination angle of the hook at various positions here belongs to the prior art (a cooperation mark is installed on the hook, and the inclination posture of the hook camera measurement system is measured in real time by the posture measurement system, and then the position of the cooperation mark on the hook relative to the hook camera measurement system is measured in real time by the double-camera intersection of the hook camera measurement system, and finally the hook position is converted to a horizontal coordinate system according to the inclination posture, and the hook swing angle is calculated in real time using the hook position represented by the horizontal coordinate system), combined with the real-time height value of the hook obtained by the air pressure value measured by the barometer, the real-time deviation distance and real-time running acceleration of the hook are predicted;

[0058] S10 includes:

[0059] S101: A barometer is fixedly installed at the top of the rope roller and at the connection between the hook and the rope. At time t, the readings of the two barometers are measured respectively. 1t ,d 2t To obtain, according to the formula H t =-18400*(1+Tv / 273)*ln(d 1t / d 2t ) is used to calculate the height of the hook from the top of the rope roller at time t, where Tv represents the average virtual temperature of the air layer between the height of the hook and the rope and the height of the top of the rope roller. The virtual temperature represents the temperature of dry air with the same pressure and density as the wet air, and H t It indicates the height of the hook from the top of the rope roller at time t;

[0060] S102: When the height of the hook from the top of the rope roller is H t When the hook tilt angle is measured, a posture measurement system and a hook camera measurement system are installed at the top of the crane boom near the rope roller. The inclination angle refers to the angle between the rope and the rope roller compared to the vertical line of the horizontal plane. When the inclination angle is on the right side of the rope roller, the sign of the inclination angle is positive, and when the inclination angle is on the left side of the rope roller, the sign of the inclination angle is negative. Predict the deviation distance of the hook at time t. The deviation distance refers to the minimum distance between the hook position and the rope roller relative to the vertical line of the horizontal plane.

[0061] S103: During the hook lifting process, the running acceleration a of the hook at time t t To make a prediction, the specific prediction formula is:

[0062] ;

[0063] Among them, k represents the time value and time k is before time t, or ,when When it is a negative value, ,when When is positive, , similarly, or ,when When it is a negative value, ,when When is positive, , L k represents the deviation distance of the hook at time k, V k represents the running speed of the hook at time k. If the hook is in the initial position at time k, then V k =0, if the hook is not in the initial position at time k, then , the hook drives the object upward from the initial position, and the motor of the crane is always in rotation during the lifting process, H k represents the height of the hook from the top of the rope roller at time k, and H0 represents the initial position of the hook;

[0064] S20: calculating a real-time warning coefficient of the hook based on the real-time operation acceleration of the hook, determining a hook surrounding index analysis data set based on the calculation result, analyzing the hook surrounding index analysis data set, and predicting the real-time surrounding index of the hook;

[0065] The S20 includes:

[0066] S201: The posture measurement system and the hook camera measurement system measure the inclination angle of the hook at time intervals h. t =M*a t Calculate the tension of the rope at time t, where M represents the mass of the hook and the load, and a t+h Indicates the running acceleration of the hook at time t+h;

[0067] S202: Obtain the maximum pressure value F' that the suspension rope can withstand, according to U t =b1*(F t / F´)+b2*[(a t -a t-h ) / a t-h ] Calculate the warning coefficient of the hook at time t, where b1 and b2 represent proportional coefficients and b1+b2=1;

[0068] When 0≤U t <0.7*D, 0.6≤D≤1, put the predicted deviation distance at time t into the set N, and get the surround index analysis data set. t When ≤1, the sound and light alarm installed in the cab will sound an alarm;

[0069] According to the wraparound index analysis data set, the wraparound index of the hook at time t is predicted. The specific method is as follows:

[0070] According to the order of the prediction time of each deviation distance, each deviation distance stored in the surround index analysis data set is numbered, and the numbering result is: j=1,2,…,n; n represents the total number of deviation distances stored in the surround index analysis data set;

[0071] Obtain the tilt angles that match the deviation distances. When the deviation distance numbered j is equal to the tilt angle When the calculation is done, it is considered that the deviation distance and inclination angle of the number j are If they match, then the deviation distance and inclination angle of j are considered to match. If there is no match, if the sign of the tilt angle that matches the offset distance numbered j is opposite to the sign of the tilt angle that matches the offset distance numbered j+1, a split symbol is placed between the offset distance numbered j and the offset distance numbered j+1, and before the offset distance numbered 1, and the maximum value of the offset distances stored between adjacent split symbols is obtained. When the number of the obtained maximum values ​​is an odd number, the last obtained maximum value is deleted. When the number of the obtained maximum values ​​is an even number, there is no need to delete the last obtained maximum value.

[0072] according to Predict the hook's encirclement index at time t, where i=1,2,…,p, indicating that each maximum value is numbered in the order in which the maximum value is obtained, p indicates the total number, and y i Indicates the maximum value of number i. hour, ,when hour, ;

[0073] S30: predicting the real-time abnormal index of the hook, and determining the control time of the hook running speed based on the prediction result, the specific method is:

[0074] According to B t =U t *R t Predict the abnormal index of the hook at time t;

[0075] If B t < set threshold and the sound and light alarm installed in the cab does not sound an alarm, then there is no need to control the operating speed of the hook at time t;

[0076] If B t <If the threshold is set and the sound and light alarm in the cab sounds an alarm, the speed of the hook needs to be regulated at time t;

[0077] If B t ≥ set threshold, then the running speed of the hook needs to be regulated at time t, 0.8≤set threshold≤1;

[0078] S40: regulating the real-time running speed of the hook at the determined regulation time;

[0079] S40 includes: the driver in the cab controls the running speed of the hook at a determined control time, and the specific control method is:

[0080] At time t+x*h, adjust the running speed of the rope to V t+x*h, until the hook's inclination angle is 0, , the driver controls the hook to move at a constant speed according to the corresponding running speed when the hook tilt angle is 0, V t represents the running speed of the hook at time t, x represents a positive integer and x=1,2,….

[0081] A crane hook inclination monitoring and early warning system based on the Internet of Things, the system includes a hook inclination monitoring and analysis module, a surround index prediction module, a control time determination module and a control analysis module;

[0082] The hook inclination monitoring and analysis module is used to predict the real-time deviation distance and real-time operation acceleration of the hook;

[0083] The hook inclination monitoring and analysis module includes a hook height calculation unit, a deviation distance prediction unit and an operation acceleration prediction unit;

[0084] The hook height calculation unit calculates the real-time height value of the hook through the difference between the air pressure values ​​measured by the barometer;

[0085] The deviation distance calculation unit monitors the real-time tilt angle of the hook through a posture measurement system and a hook camera measurement system installed at the top of the crane boom near the rope roller, and predicts the real-time deviation distance of the hook based on the monitoring results;

[0086] During the hook lifting process, the running acceleration prediction unit predicts the real-time running acceleration of the hook based on the constructed prediction formula;

[0087] The wraparound index prediction module is used to predict the real-time wraparound index of the hook;

[0088] The wrapping index prediction module includes a rope tension calculation unit, a data set determination unit and a wrapping index prediction unit;

[0089] The rope tension calculation unit calculates the real-time tension value of the rope based on the real-time running acceleration of the hook;

[0090] The data set determination unit calculates the real-time warning coefficient of the hook based on the real-time tension value of the lifting rope, and determines the hook's surrounding index analysis data set in combination with the real-time deviation distance value of the hook;

[0091] The surround index prediction unit analyzes the surround index analysis data set of the hook, determines the target analysis data of the surround index, the target analysis data refers to the maximum value obtained, and predicts the real-time surround index of the hook based on the determination result;

[0092] The control time determination module is used to determine the control time of the hook running speed;

[0093] The control time determination module includes an abnormality index prediction unit and a control time determination unit;

[0094] The abnormal index prediction unit predicts the real-time abnormal index of the hook according to the real-time warning coefficient of the lifting rope and the real-time surrounding index of the hook;

[0095] The control time determination unit determines the control time of the hook running speed according to the real-time abnormal index of the hook and the alarm situation of the sound and light alarm set in the cab;

[0096] The control analysis module is used to control the real-time running speed of the hook at a determined control time;

[0097] The control and analysis module controls the real-time operating speed of the hook according to the real-time abnormal index of the hook and the operating acceleration of the hook at the determined control time.

[0098] Example 1: Assume that the running speed of the hook at time t is 1 m / s, and the running acceleration of the hook at time t is 0.5 m / s 2 , h=1s, the abnormal index of the hook at time t is 0.8, then the running speed of the hook at time t+h is:

[0099] ;

[0100] Then the running speed of the hook at time t+h is 0.6m / s;

[0101] Assuming that the abnormal index of the hook at time t+h is 0.6, the running speed of the hook at time t+2*h is:

[0102] ;

[0103] Then the running speed of the hook at time t+3*h is 0.42m / s.

[0104] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A crane hook inclination monitoring and early warning method based on the Internet of Things, characterized in that: The method comprises: S10: During the hook lifting process, a posture measurement system and a hook camera measurement system are installed at the top of the crane boom near the rope roller to measure the inclination angle of the hook at each position. Combined with the real-time height value of the hook obtained by the air pressure value measured by the barometer, the real-time deviation distance and real-time running acceleration of the hook are predicted; The S10 includes: S101: A barometer is fixedly installed at the top of the rope roller and at the connection between the hook and the rope. At time t, the readings of the two barometers are measured respectively. 1t ,d 2t To obtain, according to the formula H t =-18400*(1+Tv / 273)*ln(d 1t / d 2t ) calculates the height of the hook from the top of the rope roller at time t, where Tv represents the average virtual temperature of the air layer between the height of the hook and the rope and the height of the top of the rope roller; S102: When the height of the hook from the top of the rope roller is H t When the hook tilt angle is measured, a posture measurement system and a hook camera measurement system are installed at the top of the crane boom near the rope roller. according to Predict the deviation distance of the hook at time t; S103: During the hook lifting process, the running acceleration a of the hook at time t t To make a prediction, the specific prediction formula is: Among them, k represents the time value and time k is before time t, or when When it is a negative value, when When is positive, L k represents the deviation distance of the hook at time k, V k represents the running speed of the hook at time k; S20: calculating a real-time warning coefficient of the hook based on the real-time operation acceleration of the hook, determining a hook surrounding index analysis data set based on the calculation result, analyzing the hook surrounding index analysis data set, and predicting the real-time surrounding index of the hook; S30: predicting the real-time abnormal index of the hook, and determining the control time of the hook running speed based on the prediction result; S40: at the determined control time, the real-time running speed of the hook is controlled.

2. According to the method of claim 1, the method is characterized by: The S20 includes: S201: The posture measurement system and the hook camera measurement system measure the inclination angle of the hook at time intervals h. t =M*a t Calculate the tension of the lifting rope at time t, where M represents the mass of the hook and the load; S202: Obtain the maximum pressure value F' that the suspension rope can withstand, according to U t =b1*(F t / F′)+b2*[(a t -a t-h ) / a t-h ] Calculate the warning coefficient of the hook at time t, where b1 and b2 represent proportional coefficients and b1+b2=1; When 0≤U t <0.7*D, 0.6≤D≤1, put the predicted deviation distance at time t into the set N, and get the surround index analysis data set. t When ≤1, the sound and light alarm installed in the cab will sound an alarm; According to the wraparound index analysis data set, the wraparound index of the hook at time t is predicted. The specific method is as follows: According to the order of the prediction time of each deviation distance, each deviation distance stored in the surround index analysis data set is numbered, and the numbering result is: j=1, 2, ..., n; n represents the total number of deviation distances stored in the surround index analysis data set; Obtain the tilt angles that match each deviation distance. If the sign of the tilt angle that matches the deviation distance numbered j is opposite to the sign of the tilt angle that matches the deviation distance numbered j+1, place a split symbol between the deviation distance numbered j and the deviation distance numbered j+1, and before the deviation distance numbered 1, and obtain the maximum value of the deviation distances stored between adjacent split symbols. according to Predict the hook's wraparound index at time t, where i = 1, 2, ..., p, indicating that each maximum value is numbered in the order in which the maximum value is obtained, p indicates the total number, and y i Indicates the maximum value of number i. hour, when hour, 3. The method for monitoring and early warning of crane hook inclination angle based on the Internet of Things according to claim 2 is characterized in that: The specific method for determining the control time of the hook running speed in S30 is: According to B t =U t *R t Predict the abnormal index of the hook at time t; If B t < set threshold and the sound and light alarm installed in the cab does not sound an alarm, then there is no need to control the operating speed of the hook at time t; If B t <If the threshold is set and the sound and light alarm in the cab sounds an alarm, the speed of the hook needs to be regulated at time t; If B t ≥ set threshold, then the running speed of the hook needs to be regulated at time t, 0.8≤set threshold≤1.

4. The method for monitoring and early warning of crane hook inclination angle based on the Internet of Things according to claim 3 is characterized in that: The step S40 includes: the driver in the cab controls the running speed of the hook at a determined control time, and the specific control method is: At time t+x*h, adjust the running speed of the rope to V t+x*h , until the hook's inclination angle is 0, V t+x*h =V t -a t *B t+(x-1)*h x *h, the driver controls the hook to move at a constant speed according to the corresponding running speed when the hook tilt angle is 0, V t represents the running speed of the hook at time t, x represents a positive integer and x=1,2,….

5. A crane hook inclination monitoring and early warning system based on the Internet of Things applied to the crane hook inclination monitoring and early warning method based on the Internet of Things according to any one of claims 1 to 4, characterized in that: The system includes a hook inclination monitoring and analysis module, a surround index prediction module, a control time determination module and a control analysis module; The hook inclination monitoring and analysis module is used to predict the real-time deviation distance and real-time running acceleration of the hook; The wraparound index prediction module is used to predict the real-time wraparound index of the hook; The control time determination module is used to determine the control time of the hook running speed; The control and analysis module is used to control the real-time running speed of the hook at a determined control time.

6. The IoT-based crane hook inclination monitoring and early warning system according to claim 5 is characterized by: The hook inclination monitoring and analysis module includes a hook height calculation unit, a deviation distance prediction unit and an operation acceleration prediction unit; The hook height calculation unit calculates the real-time height value of the hook by using the difference between the air pressure values ​​measured by the barometer; The deviation distance calculation unit monitors the real-time tilt angle of the hook through a posture measurement system and a hook camera measurement system installed at the top of the crane boom near the rope roller, and predicts the real-time deviation distance of the hook based on the monitoring results; During the hook lifting process, the operating acceleration prediction unit predicts the real-time operating acceleration of the hook based on the constructed prediction formula.

7. The IoT-based crane hook inclination monitoring and early warning system according to claim 6 is characterized by: The wrapping index prediction module includes a rope tension calculation unit, a data set determination unit and a wrapping index prediction unit; The rope tension calculation unit calculates the real-time tension value of the rope based on the real-time running acceleration of the hook; The data set determination unit calculates the real-time warning coefficient of the hook based on the real-time tension value of the lifting rope, and determines the hook's surrounding index analysis data set in combination with the real-time deviation distance value of the hook; The surround index prediction unit analyzes the surround index analysis data set of the hook, determines the target analysis data of the surround index, and predicts the real-time surround index of the hook based on the determination result.

8. The IoT-based crane hook inclination monitoring and early warning system according to claim 7 is characterized by: The control time determination module includes an abnormality index prediction unit and a control time determination unit; The abnormality index prediction unit predicts the real-time abnormality index of the hook according to the real-time warning coefficient of the lifting rope and the real-time surrounding index of the hook; The control time determination unit determines the control time of the hook running speed according to the real-time abnormal index of the hook and the alarm status of the sound and light alarm arranged in the cab.

9. The IoT-based crane hook inclination monitoring and early warning system according to claim 8 is characterized by: The control and analysis module controls the real-time running speed of the hook according to the real-time abnormal index of the hook and the running acceleration of the hook at a determined control time.

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