Safety early warning method for high-altitude operation

Abstract

The invention discloses a safety early warning method for high-altitude operation, relates to the technical field of high-altitude operation safety protection, and solves the problems that the physical condition of a high-altitude building construction operator is uncomfortable, the operation process is not standard and cannot be found in time, and the operator can timely deal with the uncomfortable physical condition and the non-standard operation process. And the high-altitude operation has large potential safety hazards. A body abnormality coefficient is generated according to body data; generating a construction abnormal coefficient according to the construction data; generating an environment anomaly coefficient according to the working environment data; generating a comprehensive abnormal coefficient according to the body abnormal coefficient, the construction abnormal coefficient and the environment abnormal coefficient; generating an operation danger alarm signal according to the comprehensive abnormal coefficient; according to the high-altitude operation safety early warning system, corresponding prompts are made according to the alarm signals, the physical condition of an operator, the operation process and the environmental influence are considered, safety early warning is conducted through multivariate data, the accuracy of high-altitude operation safety early warning is improved, and potential safety hazards in the high-altitude operation process are reduced.

Description

Technical Field

[0001] The present application belongs to the technical field of high-altitude operation safety protection, and specifically is a safety warning method for high-altitude operation. Background Art

[0002] With the development of industry and construction, the demand for aerial work is increasing. Aerial work is widely used in building construction, power maintenance, bridge and road maintenance, dock operations, communication base station maintenance, high-altitude hoisting and other aerial work scenarios. As one of the aerial operations, high-altitude exterior wall cleaning is highly dangerous and complex, and has extremely high safety requirements for operators.

[0003] Traditional safety warning methods for high-altitude construction operations often rely on manual observation and simple protective measures, which makes it difficult to comprehensively and accurately monitor and warn of potential safety hazards. Due to the physical discomfort of high-altitude construction workers and the irregular operation process, which cannot be discovered and handled in a timely manner, there are great safety hazards in high-altitude operations. Therefore, it is particularly important to develop an efficient and intelligent safety warning method for high-altitude operations. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a safety warning method for high-altitude operations, which is used to solve the technical problem that high-altitude operations have major safety hazards due to the physical discomfort of the high-altitude construction workers themselves and the irregular working process, which cannot be discovered and handled in a timely manner.

[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides a safety warning method for high-altitude operations, comprising:

[0006] S1: Acquire some physical data, some construction data and some working environment data of the operator; the physical data include blood pressure and heart rate; the construction data include the angle between the safety rope of the working frame and the vertical direction, the rope length between the working frame and the safety rope fixing point and the maximum allowable offset of the working frame; the working environment data include working height, working temperature, working wind speed and the total height of the working floor;

[0007] S2: Generate a body abnormality coefficient based on a number of body data; generate a construction abnormality coefficient based on a number of construction data; generate an environment abnormality coefficient based on a number of working environment data;

[0008] S3: Generate a comprehensive abnormality coefficient according to the physical abnormality coefficient, the construction abnormality coefficient and the environmental abnormality coefficient; generate an operation alarm signal according to the comprehensive abnormality coefficient;

[0009] S4: Make corresponding prompts according to the alarm signal and contact relevant management personnel.

[0010] The present application obtains a number of physical data, a number of construction data and a number of working environment data of the operating personnel; generates a physical abnormality coefficient based on the a number of physical data; generates a construction abnormality coefficient based on the a number of construction data; generates an environmental abnormality coefficient based on the a number of working environment data; generates a comprehensive abnormality coefficient based on the physical abnormality coefficient, the construction abnormality coefficient and the environmental abnormality coefficient; generates an operating alarm signal based on the comprehensive abnormality coefficient; makes corresponding prompts based on the alarm signal, and contacts relevant management personnel, takes the physical condition of the operating personnel, the working process and the environmental impact into consideration, uses multivariate data for safety warning, improves the accuracy of safety warning for high-altitude operations, and reduces the potential safety hazards in the high-altitude operation process.

[0011] Furthermore, the generating of the body abnormality coefficient according to the plurality of body data includes:

[0012] Obtain the heart rate XL and blood pressure XY in the body data and the working height ZG and working wind speed ZF in the working environment data;

[0013] Input the working height and working wind speed into the body parameter prediction model to obtain the standard heart rate BXL and the standard blood pressure BXY;

[0014] The heart rate abnormality coefficient is calculated by the formula YX1=k1×arctan(|(XL-BXL) / DXL|^β1);

[0015] The blood pressure abnormality coefficient was calculated by the formula YX2 = k2 × arctan (|(XL-BXY) / DXY|^β2);

[0016] The body abnormality coefficient is calculated by the formula SYX=α1×YX1+α2×YX2; wherein, k1 and k2 are proportional coefficients, k1 and k2∈(0, 2 / π); β1 and β2 are exponential coefficients, β1>1, β2>1; α1 and α2 are weight coefficients, α1 and α2∈(0, 1); DXL is the unit heart rate, and DXY is the unit blood pressure.

[0017] Furthermore, the body parameter prediction model is constructed by an artificial intelligence model, including:

[0018] Acquire the heart rate and blood pressure of several historical physical data of the operator when the operator is in normal physical condition and the working height and working temperature of the corresponding historical working environment data;

[0019] Dividing a number of historical physical data and their corresponding historical working environment data into training data, verification data and test data; performing data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set;

[0020] Select an AI model as the base model;

[0021] Train the basic model with the training set, and adjust the learning rate or other hyperparameters on the validation set to obtain the pre-trained model;

[0022] By verifying the pre-trained model on the test set, we finally obtained a body parameter prediction model with working altitude and working temperature as input and standard heart rate and standard blood pressure as output.

[0023] Furthermore, the data preprocessing includes data cleaning and data normalization.

[0024] Furthermore, the generating of the construction abnormality coefficient according to the plurality of construction data includes:

[0025] Obtain the angle θ between the work frame safety rope and the vertical direction, the rope length S between the work frame and the safety rope fixing point, the maximum allowable offset L, and the work height and the total height of the work floor in the work environment data;

[0026] Calculate the height difference between the total height of the working floor and the working height;

[0027] When the rope length is greater than the height difference, the construction abnormality coefficient is calculated by the formula SGYX=exp(-(1 / (θ / θa))^β3); otherwise, SGYX=0;

[0028] The angle threshold θa is calculated by the formula θa=arcsin(L / S); wherein β3 is an exponential coefficient, β3>1.

[0029] Furthermore, the generating of the environmental anomaly coefficient according to the plurality of operating environment data includes:

[0030] Obtaining the working height h, working temperature ZW, working wind speed ZF, and the standard temperature range for safe working and the standard safe wind speed BAF in the working environment data; the standard temperature range includes the standard maximum temperature BDW and the standard minimum temperature BXW;

[0031] Determine whether the operating temperature is within the standard temperature range: if yes, the temperature anomaly coefficient WYX = 0;

[0032] No, calculate the standard intermediate temperature value BZW by the formula, BZW = (BDW + BXW) / 2; calculate the floating deviation FP of the standard intermediate temperature value by the formula, FP = (BDW - BXW) / 2; calculate the temperature anomaly coefficient WYX by the formula, WYX = k4 × arctan (((|ZW - BZW| - FP) / DW) ^ β4); where k4 is the proportionality coefficient, k4∈(0, 2 / π); β4 is the exponential coefficient, β4>1; DW is the unit temperature;

[0033] The predicted safe wind speed value YAZF of the operator at the working height is calculated by the formula;

[0034] YAZF = BAF × exp(-b × h); where b is the proportionality coefficient and b∈(0, 1);

[0035] The wind speed anomaly coefficient FYX at the operating height of the operator is calculated by the formula;

[0036] FYX=exp(-(1 / (ZF / YAZF))^β5); β5 is the exponential coefficient, β5>1;

[0037] The environmental anomaly coefficient is calculated by the formula HYX=α3×WYX+α4×FYX; wherein α3 and α4 are weight coefficients, and α3 and α4∈(0,1).

[0038] Furthermore, generating a comprehensive abnormality coefficient according to the physical abnormality coefficient, the construction abnormality coefficient and the environmental abnormality coefficient includes:

[0039] Extract body abnormality coefficient SYX, construction abnormality coefficient SGYX and environment abnormality coefficient HYX;

[0040] The comprehensive anomaly coefficient is calculated by the formula ZYX=γ1×SYX+γ2×SGYX+γ3×HYX; wherein γ1, γ2 and γ3 are weight coefficients, and γ1, γ2 and γ3∈(0,1).

[0041] Furthermore, the step of generating an operation alarm signal according to the comprehensive abnormality coefficient includes:

[0042] Obtain comprehensive abnormality coefficient, physical abnormality coefficient, construction abnormality coefficient and environmental abnormality coefficient;

[0043] Determine whether the comprehensive anomaly coefficient is greater than the comprehensive anomaly threshold:

[0044] Yes, generate an operational hazard warning signal;

[0045] No, when the body abnormality coefficient is greater than the body abnormality threshold, a body danger alarm signal is generated; otherwise, when the body abnormality coefficient is greater than E times the body abnormality threshold, a body danger warning signal is generated;

[0046] When the construction abnormality coefficient is greater than the construction abnormality threshold, a construction danger alarm signal is generated; otherwise, when the construction abnormality coefficient is greater than F times the construction abnormality threshold, a construction danger warning signal is generated;

[0047] When the environmental anomaly coefficient is greater than the environmental anomaly threshold, an environmental hazard alarm signal is generated; otherwise, when the environmental anomaly coefficient is greater than G times the environmental anomaly threshold, an environmental hazard warning signal is generated; where E, F and G∈(0,1).

[0048] Compared with the prior art, the beneficial effects of this application are:

[0049] 1. This application obtains some physical data, some construction data and some working environment data of the operating personnel; generates a physical abnormality coefficient based on some physical data; generates a construction abnormality coefficient based on some construction data; generates an environmental abnormality coefficient based on some working environment data; generates a comprehensive abnormality coefficient based on the physical abnormality coefficient, the construction abnormality coefficient and the environmental abnormality coefficient; generates an operating alarm signal based on the comprehensive abnormality coefficient; makes corresponding prompts based on the alarm signal, and contacts relevant management personnel, takes the physical condition of the operating personnel, the working process and the environmental impact into consideration, uses multivariate data for safety warning, improves the accuracy of safety warning for high-altitude operations, and reduces the safety hazards in the high-altitude operation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 This is a flow chart of a safety warning method for high-altitude operations in this application. DETAILED DESCRIPTION

[0052] The technical solution of the present application will be described clearly and completely below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0053] See also Figure 1 The first embodiment of the present application provides a safety warning method for high-altitude operations, comprising:

[0054] S1: Obtaining several physical data, several construction data and several working environment data of the operator; physical data refers to the relevant data of the operator's body that may cause safety hazards, including blood pressure, heart rate, etc.; construction data refers to the relevant data of the operator that may cause safety hazards during high-altitude operations, including the angle between the safety rope of the work frame and the vertical direction, the rope length between the work frame and the safety rope fixing point, and the maximum allowable offset of the work frame, etc.; working environment data refers to the data that may cause safety hazards in the high-altitude working environment, including working height, working temperature, working wind speed and the total height of the working floor, etc.; the maximum allowable offset of the work frame refers to the maximum offset distance that the work frame can allow during the operation;

[0055] S2: Generate a body abnormality coefficient based on a number of body data; generate a construction abnormality coefficient based on a number of construction data; generate an environmental abnormality coefficient based on a number of working environment data; the body abnormality coefficient refers to the possibility that the operator's body data will cause the operator to be abnormal; the construction abnormality coefficient refers to the possibility that the operator's work on the work frame will cause the operator to be abnormal; the environmental abnormality coefficient refers to the possibility that the operator's environmental data will cause the operator to be abnormal;

[0056] S3: Generate a comprehensive abnormality coefficient according to the physical abnormality coefficient, the construction abnormality coefficient and the environmental abnormality coefficient; generate an operation alarm signal according to the comprehensive abnormality coefficient; the comprehensive abnormality coefficient refers to the possibility that the operator may encounter a dangerous situation during the entire operation process; the operation danger alarm signal includes an operation danger alarm signal, a physical danger alarm signal, a physical danger warning signal, a construction danger alarm signal, a construction danger warning signal, an environmental danger alarm signal and an environmental danger alarm signal;

[0057] S4: Make corresponding prompts according to the alarm signal and contact relevant management personnel.

[0058] This embodiment obtains a number of physical data, a number of construction data, and a number of working environment data of the operating personnel; generates a physical abnormality coefficient based on the a number of physical data; generates a construction abnormality coefficient based on the a number of construction data; generates an environmental abnormality coefficient based on the a number of working environment data; generates a comprehensive abnormality coefficient based on the physical abnormality coefficient, the construction abnormality coefficient, and the environmental abnormality coefficient; generates an operating alarm signal based on the comprehensive abnormality coefficient; makes corresponding prompts based on the alarm signal, and contacts relevant management personnel, takes the physical condition of the operating personnel, the working process, and the environmental impact into consideration, and uses multivariate data for safety warnings, thereby improving the accuracy of safety warnings for high-altitude operations and reducing potential safety hazards in the high-altitude operation process.

[0059] The physical abnormality coefficient is generated based on several physical data, including:

[0060] Obtain the heart rate XL and blood pressure XY in the body data and the working height ZG and working temperature in the working environment data; the working height refers to the height of the working frame on which the operator rides from the ground; the working temperature refers to the temperature data detected by the working frame at high altitude;

[0061] The working height and working temperature are input into the body parameter prediction model to obtain the standard heart rate BXL and the standard blood pressure BXY; the standard heart rate and the standard blood pressure refer to the heart rate and blood pressure values ​​that the operator can achieve at the working height and working temperature;

[0062] The heart rate abnormality coefficient is calculated by the formula YX1=k1×arctan(|(XL-BXL) / DXL|^β1); the greater the difference between the operator's measured heart rate and the standard heart rate, the greater the change in the operator's heart rate, and the greater the possibility of abnormal heart rate, so the heart rate abnormality coefficient will also increase;

[0063] The blood pressure abnormality coefficient is calculated by the formula YX2=k2×arctan(|(XY-BXY) / DXY|^β2); the greater the difference between the operator's measured blood pressure and the standard blood pressure, the greater the change in the operator's blood pressure, and the greater the possibility of blood pressure abnormality, so the blood pressure abnormality coefficient will also increase;

[0064] The body abnormality coefficient is calculated by the formula SYX = α1 × YX1 + α2 × YX2; wherein, k1 and k2 are proportional coefficients, k1 and k2∈(0, 2 / π), set according to experience, and the setting of k1 and k2 is to make the heart rate abnormality coefficient and blood pressure abnormality coefficient YX1 and YX2∈(0, 1); β1 and β2 are exponential coefficients, β1>1, β2>1, set according to experience; α1 and α2 are weight coefficients, α1 and α2∈(0, 1), set according to experience; DXL is the unit heart rate, DXY is the unit blood pressure, set according to experience; as the heart rate and blood pressure change, the greater the possibility of abnormal heart rate and blood pressure, the greater the possibility of abnormal body of the operator, and therefore the body abnormality coefficient increases accordingly.

[0065] The body parameter prediction model is constructed through an artificial intelligence model, including:

[0066] Acquire the heart rate and blood pressure of several historical physical data of the operator when the operator is in normal physical condition and the working height and working temperature of the corresponding historical working environment data;

[0067] Divide a number of historical physical data and their corresponding historical working environment data into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set; the ratio between the training set, the test set and the verification set is 7:2:1;

[0068] Select an AI model as the base model;

[0069] Train the basic model with the training set, and adjust the learning rate or other hyperparameters on the validation set to obtain the pre-trained model;

[0070] By verifying the pre-trained model on the test set, we finally obtained a body parameter prediction model with working altitude and working temperature as input and standard heart rate and standard blood pressure as output.

[0071] Data preprocessing includes data cleaning and data normalization;

[0072] Data cleaning: directly remove missing values ​​from the acquired historical physical data and their corresponding historical working environment data to obtain normal data;

[0073] Data normalization: Normal data is normalized using the maximum and minimum normalization method to obtain several preprocessed historical body data and their corresponding historical working environment data.

[0074] The construction anomaly coefficient is generated based on several construction data, including:

[0075] Obtain the angle θ between the safety rope of the work frame and the vertical direction, the rope length S between the work frame and the safety rope fixing point, the maximum allowable offset L in the construction data, and the working height and the total height of the working floor in the working environment data; the maximum allowable offset L is a fixed value set based on experience; the total height of the working floor is the floor height of the high-altitude work;

[0076] Calculate the height difference between the total height of the working floor and the working height;

[0077] When the rope length is greater than the height difference, the construction abnormality coefficient is calculated by the formula SGYX=exp(-(1 / (θ / θa))^β3); otherwise, SGYX=0; as the angle offset of the work frame increases, the offset of the work frame exceeds the maximum allowable offset, which will cause the construction abnormality coefficient of the high-altitude operation to increase accordingly;

[0078] The angle threshold is calculated by the formula θa=arcsin(L / S); as the rope length increases, the angle threshold decreases accordingly; wherein β3 is an exponential coefficient, β3>1, and β3 is set based on experience.

[0079] Generate environmental anomaly coefficients based on several operating environment data, including:

[0080] Obtain the working height h, working temperature ZW, working wind speed ZF, standard temperature range for safe working and standard safe wind speed BAF in the working environment data; the standard temperature range includes the standard maximum temperature BDW and the standard minimum temperature BXW; the working wind speed refers to the wind speed at the height of the working frame, the standard temperature range refers to the temperature range suitable for workers to perform high-altitude work, and the standard safe wind speed refers to the maximum wind speed for high-altitude work under safe conditions; the standard maximum temperature refers to the maximum value in the standard temperature range, and the standard minimum temperature refers to the minimum value in the standard temperature range;

[0081] Determine whether the operating temperature is within the standard temperature range: if yes, the temperature anomaly coefficient WYX = 0;

[0082] No, the standard intermediate temperature value BZW is calculated by the formula, BZW = (BDW + BXW) / 2; the floating deviation FP of the standard intermediate temperature value is calculated by the formula, FP = (BDW - BXW) / 2; the larger the standard temperature range, the greater the floating deviation of the standard intermediate temperature value; the temperature anomaly coefficient WYX is calculated by the formula, WYX = k4 × arctan (((|ZW - BZW| - FP) / DW) ^ β4); the greater the difference between the operating temperature and the standard temperature range, the more the operating temperature deviates from the standard temperature range, resulting in an increase in the temperature anomaly coefficient; wherein k4 is a proportional coefficient, k4∈(0, 2 / π), which is set according to experience, and the setting of k4 is to make the temperature anomaly coefficient WYX∈(0, 1); β4 is an exponential coefficient, β4>1, which is set according to experience; DW is a unit temperature, which is set according to experience, and the unit temperature is set to 1°C in this embodiment;

[0083] The predicted safe wind speed value YAZF of the operator at the working height is calculated by the formula;

[0084] YAZF = BAF × exp (-b × h); As the working height increases, the higher the working height, the greater the impact of wind speed on high-altitude work, and the predicted safe wind speed value will decrease accordingly; where b is the proportionality coefficient, and b∈(0, 1), which is set according to experience;

[0085] The wind speed anomaly coefficient FYX at the operating height of the operator is calculated by the formula;

[0086] FYX=exp(-(1 / (ZF / YAZF))^β5); As the working height increases, the working wind speed will increase accordingly, and the impact of the working wind speed on the high-altitude operation will increase. The gap between the working wind speed and the predicted safe wind speed value will increase, resulting in an increase in the wind speed anomaly coefficient; where β5 is an exponential coefficient, β5>1, which is set based on experience;

[0087] The environmental anomaly coefficient is calculated by the formula HYX=α3×WYX+α4×FYX; wherein α3 and α4 are weight coefficients, and α3 and α4∈(0,1), which are set according to experience; as the temperature anomaly coefficient and the wind speed anomaly coefficient increase, it will make it more likely for operators to encounter abnormal conditions when working at high altitudes, so the environmental anomaly coefficient will also increase accordingly.

[0088] Generate a comprehensive abnormality coefficient based on the physical abnormality coefficient, construction abnormality coefficient and environmental abnormality coefficient, including:

[0089] Extract body abnormality coefficient SYX, construction abnormality coefficient SGYX and environment abnormality coefficient HYX;

[0090] The comprehensive abnormality coefficient is calculated by the formula ZYX=γ1×SYX+γ2×SGYX+γ3×HYX; with the increase of the physical abnormality coefficient, the construction abnormality coefficient and the environmental abnormality coefficient, the possibility of safety hazards in high-altitude operations on the work frame increases, and the comprehensive abnormality coefficient will also increase accordingly; among them, γ1, γ2 and γ3 are weight coefficients, γ1, γ2 and γ3∈(0,1) and are set according to experience.

[0091] Generate operation alarm signals based on comprehensive abnormality coefficients, including:

[0092] Obtain comprehensive abnormality coefficient, physical abnormality coefficient, construction abnormality coefficient and environmental abnormality coefficient;

[0093] Determine whether the comprehensive anomaly coefficient is greater than the comprehensive anomaly threshold. The comprehensive anomaly threshold is set based on experience:

[0094] Yes, generate an operational hazard warning signal;

[0095] No, when the body abnormality coefficient is greater than the body abnormality threshold, a body danger alarm signal is generated; otherwise, when the body abnormality coefficient is greater than E times the body abnormality threshold, a body danger warning signal is generated; the body abnormality threshold is set according to the body state;

[0096] When the construction abnormality coefficient is greater than the construction abnormality threshold, a construction danger alarm signal is generated; otherwise, when the construction abnormality coefficient is greater than F times the construction abnormality threshold, a construction danger warning signal is generated; the construction abnormality threshold is set according to the construction status;

[0097] When the environmental anomaly coefficient is greater than the environmental anomaly threshold, an environmental hazard alarm signal is generated; otherwise, when the environmental anomaly coefficient is greater than G times the environmental anomaly threshold, an environmental hazard warning signal is generated; the environmental anomaly threshold is set according to the environmental state, where E, F and G∈(0, 1) are set according to experience. In this embodiment, E, F and G are all set to 0.7.

[0098] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0099] The working principle of this application is as follows: by obtaining a number of physical data, a number of construction data and a number of working environment data of the workers; generating a physical abnormality coefficient based on the a number of physical data; generating a construction abnormality coefficient based on a number of construction data; generating an environmental abnormality coefficient based on a number of working environment data; generating a comprehensive abnormality coefficient based on the physical abnormality coefficient, the construction abnormality coefficient and the environmental abnormality coefficient; generating an operation alarm signal based on the comprehensive abnormality coefficient; making corresponding prompts based on the alarm signal, and contacting relevant management personnel, taking the physical condition of the workers, the working process and the environmental impact into consideration, and using multivariate data for safety warnings, thereby improving the accuracy of safety warnings for high-altitude operations, reducing safety hazards in the high-altitude operation process, and avoiding the problem that the high-altitude construction workers themselves are in poor physical condition and the working process is not standardized and cannot be discovered and handled in a timely manner, resulting in major safety hazards in high-altitude operations.

[0100] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. A safety warning method for high-altitude operations, characterized in that: include: S1: Acquire some physical data, some construction data and some working environment data of the operator; the physical data include blood pressure and heart rate; the construction data include the angle between the safety rope of the working frame and the vertical direction, the rope length between the working frame and the safety rope fixing point and the maximum allowable offset of the working frame; the working environment data include working height, working temperature, working wind speed and the total height of the working floor; S2: Generate a body abnormality coefficient based on a number of body data; generate a construction abnormality coefficient based on a number of construction data; generate an environment abnormality coefficient based on a number of working environment data; S3: Generate a comprehensive abnormality coefficient according to the physical abnormality coefficient, the construction abnormality coefficient and the environmental abnormality coefficient; generate an operation alarm signal according to the comprehensive abnormality coefficient; S4: Make corresponding prompts according to the alarm signal and contact relevant management personnel.

2. A safety warning method for high-altitude operations according to claim 1, characterized in that: The method of generating a body abnormality coefficient according to a plurality of body data includes: Obtain the heart rate XL and blood pressure XY in the body data and the working height ZG and working wind speed ZF in the working environment data; Input the working height and working wind speed into the body parameter prediction model to obtain the standard heart rate BXL and the standard blood pressure BXY; The heart rate abnormality coefficient is calculated by the formula YX1=k1×arctan(|(XL-BXL) / DXL|^β1); The blood pressure abnormality coefficient was calculated by the formula YX2 = k2 × arctan (|(XL-BXY) / DXY|^β2); The body abnormality coefficient is calculated by the formula SYX=α1×YX1+α2×YX2; wherein, k1 and k2 are proportional coefficients, k1 and k2∈(0, 2 / π); β1 and β2 are exponential coefficients, β1>1, β2>1; α1 and α2 are weight coefficients, α1 and α2∈(0, 1); DXL is the unit heart rate, and DXY is the unit blood pressure.

3. A safety warning method for high-altitude operations according to claim 2, characterized in that: The body parameter prediction model is constructed by an artificial intelligence model, including: Acquire the heart rate and blood pressure of several historical physical data of the operator when the operator is in normal physical condition and the working height and working temperature of the corresponding historical working environment data; Dividing a number of historical physical data and their corresponding historical working environment data into training data, verification data and test data; performing data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set; Select an AI model as the base model; Train the basic model with the training set, and adjust the learning rate or other hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally obtained a body parameter prediction model with working altitude and working temperature as input and standard heart rate and standard blood pressure as output.

4. A safety warning method for high-altitude operations according to claim 3, characterized in that: The data preprocessing includes data cleaning and data normalization.

5. A safety warning method for high-altitude operations according to claim 1, characterized in that: The generating of the construction abnormality coefficient according to the plurality of construction data comprises: Obtain the angle θ between the work frame safety rope and the vertical direction, the rope length S between the work frame and the safety rope fixing point, the maximum allowable offset L, and the work height and the total height of the work floor in the work environment data; Calculate the height difference between the total height of the working floor and the working height; When the rope length is greater than the height difference, the construction abnormality coefficient is calculated by the formula SGYX=exp(-(1 / (θ / θa))^β3); otherwise, SGYX=0; The angle threshold θa is calculated by the formula θa=arcsin(L / S); wherein β3 is an exponential coefficient, β3>1.

6. A safety warning method for high-altitude operations according to claim 1, characterized in that: The generating of the environmental anomaly coefficient according to a number of operating environment data includes: Obtaining the working height h, working temperature ZW, working wind speed ZF, and the standard temperature range for safe working and the standard safe wind speed BAF in the working environment data; the standard temperature range includes the standard maximum temperature BDW and the standard minimum temperature BXW; Determine whether the operating temperature is within the standard temperature range: if yes, the temperature anomaly coefficient WYX = 0; No, calculate the standard intermediate temperature value BZW by the formula, BZW = (BDW + BXW) / 2; calculate the floating deviation FP of the standard intermediate temperature value by the formula, FP = (BDW - BXW) / 2; calculate the temperature anomaly coefficient WYX by the formula, WYX = k4 × arctan (((|ZW - BZW| - FP) / DW) ^ β4); where k4 is the proportionality coefficient, k4∈(0, 2 / π); β4 is the exponential coefficient, β4>1; DW is the unit temperature; The predicted safe wind speed value YAZF of the operator at the working height is calculated by the formula; YAZF = BAF × exp(-b × h); where b is the proportionality coefficient and b∈(0, 1); The wind speed anomaly coefficient FYX at the operating height of the operator is calculated by the formula; FYX=exp(-(1 / (ZF / YAZF))^β5); β5 is the exponential coefficient, β5>1; The environmental anomaly coefficient is calculated by the formula HYX=α3×WYX+α4×FYX; wherein α3 and α4 are weight coefficients, and α3 and α4∈(0,1).

7. A safety warning method for high-altitude operations according to claim 1, characterized in that: The method of generating a comprehensive abnormality coefficient according to the physical abnormality coefficient, the construction abnormality coefficient and the environmental abnormality coefficient includes: Extract body abnormality coefficient SYX, construction abnormality coefficient SGYX and environment abnormality coefficient HYX; The comprehensive anomaly coefficient is calculated by the formula ZYX=γ1×SYX+γ2×SGYX+γ3×HYX; wherein γ1, γ2 and γ3 are weight coefficients, and γ1, γ2 and γ3∈(0,1).

8. A safety warning method for high-altitude operations according to claim 1, characterized in that: The step of generating an operation alarm signal according to the comprehensive abnormality coefficient comprises: Obtain comprehensive abnormality coefficient, physical abnormality coefficient, construction abnormality coefficient and environmental abnormality coefficient; Determine whether the comprehensive anomaly coefficient is greater than the comprehensive anomaly threshold: Yes, generate an operational hazard warning signal; No, when the body abnormality coefficient is greater than the body abnormality threshold, a body danger alarm signal is generated; otherwise, when the body abnormality coefficient is greater than E times the body abnormality threshold, a body danger warning signal is generated; When the construction abnormality coefficient is greater than the construction abnormality threshold, a construction danger alarm signal is generated; otherwise, when the construction abnormality coefficient is greater than F times the construction abnormality threshold, a construction danger warning signal is generated; When the environmental anomaly coefficient is greater than the environmental anomaly threshold, an environmental hazard alarm signal is generated; otherwise, when the environmental anomaly coefficient is greater than G times the environmental anomaly threshold, an environmental hazard warning signal is generated; where E, F and G∈(0,1).

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