CN118083829A - Tower crane risk prediction method and system - Google Patents
Tower crane risk prediction method and system Download PDFInfo
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Abstract
The application belongs to the technical field of tower crane equipment, and particularly relates to a tower crane risk prediction method and system; according to the application, a ranging sensor is established on each standard section, and the positions of the standard sections and the ground are obtained based on the ranging sensor, so that the actual installation position of each standard section is obtained; the weight of the goods hung by the suspension arm is calculated based on each time, the service life loss of the suspension arm and each standard section is calculated based on the stress, and therefore the service life loss of each standard section and the service life loss of the suspension arm are calculated, and data support is provided for replacing the suspension arm and the standard section; dynamically updating the service lives of the corresponding standard section and the boom based on the weight of each lifted cargo, and realizing the life prediction of the boom and the standard section under the condition of no external force; thereby providing guarantee for the safe operation of the tower crane.
Description
Technical Field
The application belongs to the technical field of tower crane equipment, and particularly relates to a tower crane risk prediction method and system.
Background
In the field of building engineering, a tower crane is an indispensable heavy hoisting machine, and mainly comprises a plurality of standard sections which are stacked and assembled to adapt to different heights, and a boom is further arranged to hoist cargoes; it plays a vital role in the vertical and horizontal transportation of materials for high-rise buildings; traditional tower crane designs focus on providing sufficient lifting capacity and stability to ensure personnel and material safety during construction; however, as the scale of construction engineering continues to expand and technology develops, the working environment and tasks of the tower crane become more and more complex, increasing the need for tower crane performance monitoring and maintenance.
Conventional monitoring methods of a tower crane generally comprise operations such as periodic visual inspection and manual recording of the number of tower crane lifts; although some technical schemes for predicting tower crane risks through a neural network exist at present; for example: the utility model discloses a neural network tower crane risk prediction method and system with the publication number of CN110032555A, which discloses that by acquiring tower crane accident influencing factors in tower crane accidents of super high-rise building engineering, accident correlation coefficients of all accident influencing factors are calculated, and three states in the construction process of the tower crane are defined; establishing a neural network model according to the accident correlation coefficient and the three running states; substituting the accident correlation coefficient of the future time period into a trained neural network model to obtain a tower crane construction running state with prediction; thereby providing powerful guarantee for the safe operation of the tower crane; although the technical scheme can obtain the construction operation state of the tower crane on a certain basis; however, the problem of life cycle loss of the tower crane is not considered, the next working operation state of the tower crane is only predicted, and in a changeable environment, a plurality of uncertain factors exist in the mode of predicting the working state based on the existing accident, so that the prediction result is not accurate enough; it is therefore necessary to replace a solution to predict the risk of the tower crane, so as to protect the safe operation of the tower crane.
Disclosure of Invention
The invention provides a method and a system for predicting risk of a tower crane, which aim to realize early warning of the tower crane by calculating life loss of the tower crane.
A tower crane risk prediction method comprises the following steps:
Step 1: acquiring the distance between a ranging sensor installed on each tower crane standard knot and the ground, and determining the position of the standard knot based on the distance between the ranging sensor and the ground;
Step 2: acquiring real-time weight of goods hung by a tower crane boom based on a sensor;
step 3: according to the weight of goods and the length of the suspension arm, calculating the stress condition of the suspension arm by adopting a mechanical principle and an engineering model;
step 4: calculating the stress condition of each standard section according to the weight of the goods, the length of the suspension arm and the position of the standard section;
step 5: calculating service life loss of the suspension arm and each standard section based on the stress condition of the suspension arm and the stress condition of each standard section;
Step 6: and summing the historical life loss, comparing the total loss obtained by summation with a safety threshold, and replacing corresponding parts if the total loss is equal to or greater than the safety threshold.
According to the invention, a ranging sensor is established on each standard section, and the positions of the standard sections and the ground are obtained based on the ranging sensor, so that the actual installation position of each standard section is obtained; the weight of the goods hung by the suspension arm is calculated based on each time, the service life loss of the suspension arm and each standard section is calculated based on the stress, and therefore the service life loss of each standard section and the service life loss of the suspension arm are calculated, and data support is provided for replacing the suspension arm and the standard section; dynamically updating the service lives of the corresponding standard section and the boom based on the weight of each lifted cargo, and realizing the life prediction of the boom and the standard section under the condition of no external force; thereby providing guarantee for the safe operation of the tower crane.
Preferably, the step 1 includes the steps of:
And (3) establishing a mapping: labeling each sensor and each standard section, and establishing a mapping of sensor labels and standard section labels based on the standard section actually corresponding to the ranging sensor in a background database;
Data presetting: presetting standard height of each standard section in a database, and establishing a ranging error fluctuation threshold of a ranging sensor based on the whole height error of the tower crane existing during installation;
And (3) data acquisition: the distance between the current standard knot detected by the distance measuring sensor and the ground is acquired, and the position of the current standard knot is determined based on the detected distance, the height of each standard knot and an error fluctuation threshold value.
Preferably, the position of the standard knot adopts the following calculation mode:
；
Wherein: Representing the theoretical height of the standard section of the i layer; /(I) =[1、2、3、…、n]；/>Representing the standard height of a standard knot;
Traversing the theoretical height of each layer of standard knot, and calculating the difference between the theoretical height and the height measured by the ranging sensor:
；
Wherein: Representing the height value measured by the ranging sensor;
For each of Checking whether the error fluctuation threshold is less than or equal to the error fluctuation threshold; if/>And if the error fluctuation threshold value is smaller than or equal to the error fluctuation threshold value, determining the actual position of the ith layer belonging to the standard knot.
Preferably, the step 3 includes the steps of:
calculating bending moment:
；
Wherein: Representing bending moment on the cross section x of the boom; w represents the weight of the current hanging goods; l represents the length from the object hanging end to the fixed end; x represents the distance from the fixed end to the section X;
Calculating a section moment of inertia:
；
Wherein: Representing the moment of inertia of the section x,/> The width of the cross section, h the height of the cross section;
Calculating stress:
；
Wherein: Representing the stress of section x; /(I) Representing the distance of the neutral axis from the outermost edge of the cross-section.
Preferably, the step 4 includes the steps of:
Calculating the moment of the standard section of the ith section ：
；
Wherein: Representing the total weight of the standard section of the j-th section and the structure above the standard section; /(I) Representing the vertical distance from the j-1 standard section to the top end of the suspension arm; /(I)Representing the vertical distance from the j-th standard section to the top end of the suspension arm;
Calculating stress:
；
Wherein: representing the stress of the standard section of the ith section; /(I) The section modulus of the standard section of section i is shown.
Preferably, the step of determining the lifetime impairment of the boom in the step 5 is as follows:
obtaining a stress range based on the maximum stress and the minimum stress which can be borne by the suspension arm;
for a determined stress range, determining a corresponding fatigue life using an S-N curve ；
For each hoisting operation, the fatigue damage degree is calculated：
；
Wherein: The fatigue damage degree generated by the section x of the suspension arm when the mth cargo is hoisted is shown;
And (3) finding the fatigue damage degree value of the section with the largest fatigue damage degree in the mth hoisting as the service life loss of the suspension arm.
Preferably, the step of determining the life loss of the standard node in the step 5 is as follows:
Obtaining a stress range based on the maximum stress and the minimum stress which can be born by the standard section;
for the determined stress range, determining the fatigue life corresponding to the stress generated by the ith standard section of the present time by adopting an S-N curve ；
；
Wherein: And the fatigue damage degree generated by the ith standard section when the mth cargo is hoisted is shown.
Preferably, the step 6 includes the steps of:
Acquiring historical service life loss of the suspension arm, and summing the historical service life loss of the suspension arm to obtain a total service life loss value of the suspension arm at present; comparing based on the set suspension arm safety threshold, and if the total service life loss value is greater than or equal to the suspension arm safety threshold, alarming to replace the suspension arm;
Obtaining the historical life loss of each standard section, summing the historical life loss requests of each standard section, and obtaining the total life loss value of each standard section at present:
；
Wherein: indicating the total fatigue damage degree of the standard section of the ith section;
And comparing based on the set safety threshold of the standard knot, and if the total fatigue damage degree of the standard knot is greater than or equal to the safety threshold of the standard knot, alarming to replace the standard knot.
A tower crane risk prediction system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a tower crane risk prediction method according to the invention when executing the computer program.
The beneficial effects of the invention include:
According to the invention, a ranging sensor is established on each standard section, and the positions of the standard sections and the ground are obtained based on the ranging sensor, so that the actual installation position of each standard section is obtained; the weight of the goods hung by the suspension arm is calculated based on each time, the service life loss of the suspension arm and each standard section is calculated based on the stress, and therefore the service life loss of each standard section and the service life loss of the suspension arm are calculated, and data support is provided for replacing the suspension arm and the standard section; dynamically updating the service lives of the corresponding standard section and the boom based on the weight of each lifted cargo, and realizing the life prediction of the boom and the standard section under the condition of no external force; thereby providing guarantee for the safe operation of the tower crane.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of overall steps provided in an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It will be understood that when an element is referred to as being "mounted" or "disposed" on another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element.
It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are merely for convenience in describing and simplifying the description based on the orientation or positional relationship shown in the drawings, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Referring to fig. 1, a tower crane risk prediction method includes the following steps:
Step 1: acquiring the distance between a ranging sensor installed on each tower crane standard knot and the ground, and determining the position of the standard knot based on the distance between the ranging sensor and the ground;
As a possible implementation manner of this embodiment, the step 1 includes the following steps:
And (3) establishing a mapping: labeling each sensor and each standard section, and establishing a mapping of sensor labels and standard section labels based on the standard section actually corresponding to the ranging sensor in a background database;
Data presetting: presetting standard height of each standard section in a database, and establishing a ranging error fluctuation threshold of a ranging sensor based on the whole height error of the tower crane existing during installation;
And (3) data acquisition: the distance between the current standard knot detected by the distance measuring sensor and the ground is acquired, and the position of the current standard knot is determined based on the detected distance, the height of each standard knot and an error fluctuation threshold value.
In the embodiment, the positioning of the position of the standard node is realized by establishing the label mapping relation between the ranging sensor and the standard node and detecting the distance between the corresponding standard node and the ground through the ranging sensor; because the installation position of each standard section may change during the transition of the tower crane, the service life loss of the standard section calculated later can be corresponding to the actual standard section by establishing a mapping; for example, when used for the first time, one standard section is positioned in the second section, and after transition, one standard section is positioned in the first section; therefore, the position of the standard node after transition can be updated by establishing the mapping between the sensor and the standard node; and calculation errors caused by position disorder are prevented.
As a possible implementation manner of this embodiment, the position of the standard node adopts the following calculation manner:
；
Wherein: Representing the theoretical height of the standard section of the i layer; /(I) =[1、2、3、…、n]；/>Representing the standard height of a standard knot;
Traversing the theoretical height of each layer of standard knot, and calculating the difference between the theoretical height and the height measured by the ranging sensor:
；
Wherein: Representing the height value measured by the ranging sensor;
For each of Checking whether the error fluctuation threshold is less than or equal to the error fluctuation threshold; if/>And if the error fluctuation threshold value is smaller than or equal to the error fluctuation threshold value, determining the actual position of the ith layer belonging to the standard knot.
In the embodiment, the node number fluctuation of the standard node caused by errors can be effectively avoided by establishing the error fluctuation threshold;
For example: a base station is arranged between the first standard section (a section close to the ground) and the ground; the first standard section is fixedly arranged on the base station; at the moment, a certain height exists between the base station and the ground; the distance measuring sensor detects the distance between the standard knot and the ground; therefore, the height of the base station is an error, and the error fluctuation threshold value also needs to consider the detection precision error of the sensor; therefore, when the error fluctuation threshold value is established, the error of the base station and the error of the sensor precision should be fully considered; summing is performed based on the two errors to obtain an error fluctuation threshold; here, the error fluctuation threshold is why, since the errors will fluctuate after each transition, since the height of the base station may change in size, and therefore it is necessary to update the error fluctuation threshold based on these errors after each transition; avoiding the problem of calculation.
It is of course also possible that the height of the base station is greater than the height of each standard section, i.e. when setting the error fluctuation threshold, the error fluctuation threshold is greater thanThen we can calculate this time by:
；
Wherein: f represents a preset value, and when the condition required to be met by the preset value is, Less than or equal to the error fluctuation threshold;
namely, the setting mode of F is as follows:
Acquiring the detection height of the sensor of the first standard section At this time, it is known that/>Is the first section of standard section, and the known error fluctuation threshold is greater than/>Wherein/>/>Data can be obtained by field measurements, so there are:
；
If F is not added at this time, the method comprises the following steps: ；/> to calculate, then the actual number of nodes may also be increased by 1; thus consider setting F such that/> Can be, wherein/>Representing an error fluctuation threshold, S representing an actual error; and in calculating the/>F also needs to be subtracted; therefore, the calculation result is ensured to be suitable for the error fluctuation threshold value; we only need to ensure that the set value F can be such that the actual error minus F can be less than the standard height/>, of the standard knotAnd (3) obtaining the product.
Step 2: acquiring real-time weight of goods hung by a tower crane boom based on a sensor;
step 3: according to the weight of goods and the length of the suspension arm, calculating the stress condition of the suspension arm by adopting a mechanical principle and an engineering model;
As a possible implementation manner in this embodiment, the step 3 includes the following steps:
calculating bending moment:
；
Wherein: Representing bending moment on the cross section x of the boom; w represents the weight of the current hanging goods; l represents the length from the object hanging end to the fixed end; x represents the distance from the fixed end to the section X;
Calculating a section moment of inertia:
；
Wherein: Representing the moment of inertia of the section x,/> The width of the cross section, h the height of the cross section;
Calculating stress:
；
Wherein: Representing the stress of section x; /(I) Representing the distance of the neutral axis from the outermost edge of the cross-section.
Step 4: calculating the stress condition of each standard section according to the weight of the goods, the length of the suspension arm and the position of the standard section;
As a possible implementation manner in this embodiment, the step 4 includes the following steps:
Calculating the moment of the standard section of the ith section ：
；
Wherein: Representing the total weight of the standard section of the j-th section and the structure above the standard section; /(I) Representing the vertical distance from the j-1 standard section to the top end of the suspension arm; /(I)Representing the vertical distance from the j-th standard section to the top end of the suspension arm;
Calculating stress:
；
Wherein: representing the stress of the standard section of the ith section; /(I) The section modulus of the standard section of section i is shown.
Step 5: calculating service life loss of the suspension arm and each standard section based on the stress condition of the suspension arm and the stress condition of each standard section;
As a possible implementation manner in this embodiment, the step of determining the lifetime impairment of the boom in the step 5 is as follows:
obtaining a stress range based on the maximum stress and the minimum stress which can be borne by the suspension arm;
for a determined stress range, determining a corresponding fatigue life using an S-N curve ；
For each hoisting operation, the fatigue damage degree is calculated：
；
Wherein: The fatigue damage degree generated by the section x of the suspension arm when the mth cargo is hoisted is shown;
And (3) finding the fatigue damage degree value of the section with the largest fatigue damage degree in the mth hoisting as the service life loss of the suspension arm.
In the embodiment, the fatigue damage degree value of the cross section with the maximum fatigue damage degree generated by the suspension arm is used as the service life loss of the suspension arm in one-time hoisting; in general, the boom is integrated, and therefore, in this embodiment, only the maximum fatigue damage is considered as life loss of the boom.
As a possible implementation manner of this embodiment, the determining step of the lifetime impairment of the standard node in the step 5 is as follows:
Obtaining a stress range based on the maximum stress and the minimum stress which can be born by the standard section;
for the determined stress range, determining the fatigue life corresponding to the stress generated by the ith standard section of the present time by adopting an S-N curve ；
；
Wherein: And the fatigue damage degree generated by the ith standard section when the mth cargo is hoisted is shown.
In this embodiment, by calculating the fatigue damage degree generated by each standard section during each cargo hoisting, the fatigue damage detection of each standard section is realized, so that each standard section is detected and positioned, and because the forces born by each standard section are different, the detection of each standard section is helpful to calculate the fatigue damage degree of each standard section, thereby making data support for replacing the corresponding standard section.
Step 6: and summing the historical life loss, comparing the total loss obtained by summation with a safety threshold, and replacing corresponding parts if the total loss is equal to or greater than the safety threshold.
As a possible implementation manner of this embodiment, the step 6 includes the following steps:
Acquiring historical service life loss of the suspension arm, and summing the historical service life loss of the suspension arm to obtain a total service life loss value of the suspension arm at present; comparing based on the set suspension arm safety threshold, and if the total service life loss value is greater than or equal to the suspension arm safety threshold, alarming to replace the suspension arm;
Obtaining the historical life loss of each standard section, summing the historical life loss requests of each standard section, and obtaining the total life loss value of each standard section at present:
；
Wherein: indicating the total fatigue damage degree of the standard section of the ith section;
And comparing based on the set safety threshold of the standard knot, and if the total fatigue damage degree of the standard knot is greater than or equal to the safety threshold of the standard knot, alarming to replace the standard knot.
The safety threshold of the present application is 1, or may be set to 0.9 or the like according to actual conditions, which is merely exemplary;
the above-described S-N curve (a graph used to represent the fatigue life of a material) reflects the number of cycles (N) a material can withstand at a particular stress level until fatigue failure occurs. The following data are typically required to build an S-N curve:
A material type;
Stress level: in experiments, samples were tested at different stress levels to determine the number of cycles a material can withstand at these stress levels; for the experiment, the data can be obtained by a manufacturer;
Number of cycles : For each stress level, the number of cycles the material can withstand before breaking is recorded.
Stress ratio (R): a stress ratio (r=minimum stress/maximum stress) is also typically specified in the S-N experiment, which affects fatigue life.
Stress type: it is necessary to determine whether to use tensile stress, compressive stress or alternating stress for testing.
Test frequency: the frequency at which the test is performed may also affect the results, as some materials are sensitive to the loading rate.
Environmental conditions: temperature, humidity, corrosive environment, etc. can affect fatigue life.
Sample size and shape: the geometry and shape of the experimental sample may affect the stress distribution and thus the fatigue life.
Surface treatment: the treatment of the material surface (e.g., polishing, plating, heat treatment, etc.) also affects its fatigue properties.
The S-N curve can be obtained by a manufacturer, and the S-N curve can be directly obtained by the manufacturer because the S-N curve is established by the test usually performed in the research and development stage of the tower crane.
According to the invention, a ranging sensor is established on each standard section, and the positions of the standard sections and the ground are obtained based on the ranging sensor, so that the actual installation position of each standard section is obtained; the weight of the goods hung by the suspension arm is calculated based on each time, the service life loss of the suspension arm and each standard section is calculated based on the stress, and therefore the service life loss of each standard section and the service life loss of the suspension arm are calculated, and data support is provided for replacing the suspension arm and the standard section; dynamically updating the service lives of the corresponding standard section and the boom based on the weight of each lifted cargo, and realizing the life prediction of the boom and the standard section under the condition of no external force; thereby providing guarantee for the safe operation of the tower crane.
A tower crane risk prediction system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a tower crane risk prediction method according to the invention when executing the computer program.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
Claims (9)
1. The tower crane risk prediction method is characterized by comprising the following steps of:
Step 1: acquiring the distance between a ranging sensor installed on each tower crane standard knot and the ground, and determining the position of the standard knot based on the distance between the ranging sensor and the ground;
Step 2: acquiring real-time weight of goods hung by a tower crane boom based on a sensor;
step 3: according to the weight of goods and the length of the suspension arm, calculating the stress condition of the suspension arm by adopting a mechanical principle and an engineering model;
step 4: calculating the stress condition of each standard section according to the weight of the goods, the length of the suspension arm and the position of the standard section;
step 5: calculating service life loss of the suspension arm and each standard section based on the stress condition of the suspension arm and the stress condition of each standard section;
Step 6: and summing the historical life loss, comparing the total loss obtained by summation with a safety threshold, and replacing corresponding parts if the total loss is equal to or greater than the safety threshold.
2. The tower crane risk prediction method according to claim 1, wherein the step 1 comprises the steps of:
And (3) establishing a mapping: labeling each sensor and each standard section, and establishing a mapping of sensor labels and standard section labels based on the standard section actually corresponding to the ranging sensor in a background database;
Data presetting: presetting standard height of each standard section in a database, and establishing a ranging error fluctuation threshold of a ranging sensor based on the whole height error of the tower crane existing during installation;
And (3) data acquisition: the distance between the current standard knot detected by the distance measuring sensor and the ground is acquired, and the position of the current standard knot is determined based on the detected distance, the height of each standard knot and an error fluctuation threshold value.
3. The tower crane risk prediction method according to claim 2, wherein the position of the standard knot adopts the following calculation mode:
；
Wherein: Representing the theoretical height of the standard section of the i layer; /(I) =[1、2、3、…、n]；/>Representing the standard height of a standard knot;
Traversing the theoretical height of each layer of standard knot, and calculating the difference between the theoretical height and the height measured by the ranging sensor:
；
Wherein: Representing the height value measured by the ranging sensor;
For each of Checking whether the error fluctuation threshold is less than or equal to the error fluctuation threshold; if/>And if the error fluctuation threshold value is smaller than or equal to the error fluctuation threshold value, determining the actual position of the ith layer belonging to the standard knot.
4. The tower crane risk prediction method according to claim 1, wherein the step 3 comprises the steps of:
calculating bending moment:
；
Wherein: Representing bending moment on the cross section x of the boom; w represents the weight of the current hanging goods; l represents the length from the object hanging end to the fixed end; x represents the distance from the fixed end to the section X;
Calculating a section moment of inertia:
；
Wherein: Representing the moment of inertia of the section x,/> The width of the cross section, h the height of the cross section;
Calculating stress:
；
Wherein: Representing the stress of section x; /(I) Representing the distance of the neutral axis from the outermost edge of the cross-section.
5. The tower crane risk prediction method according to claim 1, wherein the step 4 comprises the steps of:
Calculating the moment of the standard section of the ith section ：
；
Wherein: Representing the total weight of the standard section of the j-th section and the structure above the standard section; /(I) Representing the vertical distance from the j-1 standard section to the top end of the suspension arm; /(I)Representing the vertical distance from the j-th standard section to the top end of the suspension arm;
Calculating stress:
；
Wherein: representing the stress of the standard section of the ith section; /(I) The section modulus of the standard section of section i is shown.
6. The tower crane risk prediction method according to claim 1, wherein the step of determining the lifetime impairment of the boom in step 5 comprises the steps of:
obtaining a stress range based on the maximum stress and the minimum stress which can be borne by the suspension arm;
for a determined stress range, determining a corresponding fatigue life using an S-N curve ；
For each hoisting operation, the fatigue damage degree is calculated：
；
Wherein: The fatigue damage degree generated by the section x of the suspension arm when the mth cargo is hoisted is shown;
And (3) finding the fatigue damage degree value of the section with the largest fatigue damage degree in the mth hoisting as the service life loss of the suspension arm.
7. The tower crane risk prediction method according to claim 1, wherein the step of determining the life loss of the standard node in step 5 comprises the following steps:
Obtaining a stress range based on the maximum stress and the minimum stress which can be born by the standard section;
for the determined stress range, determining the fatigue life corresponding to the stress generated by the ith standard section of the present time by adopting an S-N curve ；
；
Wherein: And the fatigue damage degree generated by the ith standard section when the mth cargo is hoisted is shown.
8. The tower crane risk prediction method according to claim 1, wherein the step 6 includes the steps of:
Acquiring historical service life loss of the suspension arm, and summing the historical service life loss of the suspension arm to obtain a total service life loss value of the suspension arm at present; comparing based on the set suspension arm safety threshold, and if the total service life loss value is greater than or equal to the suspension arm safety threshold, alarming to replace the suspension arm;
Obtaining the historical life loss of each standard section, summing the historical life loss requests of each standard section, and obtaining the total life loss value of each standard section at present:
；
Wherein: indicating the total fatigue damage degree of the standard section of the ith section;
And comparing based on the set safety threshold of the standard knot, and if the total fatigue damage degree of the standard knot is greater than or equal to the safety threshold of the standard knot, alarming to replace the standard knot.
9. A tower risk prediction system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a tower risk prediction method according to any one of claims 1 to 8 when the computer program is executed by the processor.
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