Textile safety early warning method and system based on Internet of Things

Abstract

The invention relates to the technical field of safety early warning, and particularly discloses a textile safety early warning method and system based on the Internet of Things, and the method comprises the steps: obtaining a real-time safety influence data set of a textile workshop, carrying out the data preprocessing of the real-time safety influence data set of the textile workshop, and carrying out the analysis to obtain a real-time safety evaluation value of the textile workshop, the method comprises the following steps: acquiring a basic configuration information set of a textile workshop, comparing to obtain a safety evaluation deviation value of the textile workshop, and carrying out early warning on the real-time safety state of the textile workshop, thereby solving the problem that the current textile safety early warning based on the Internet of Things is limited to real-time monitoring on the working environment of textile equipment; the operation state of the textile equipment and the signal transmission state in the textile workshop are monitored in real time, the production safety is improved, the accident risk is reduced, the safety of staff and the equipment is guaranteed, the basic configuration information set of the textile workshop is acquired, the safety evaluation deviation value of the textile workshop is acquired, the resource waste is reduced, and the accident loss is reduced.

Description

Technical Field

[0001] This invention relates to the field of safety early warning technology, specifically to a textile safety early warning method and system based on the Internet of Things. Background Technology

[0002] The textile production process presents numerous safety hazards, such as high temperatures, fires, and smoke. These safety issues threaten the health of employees and the safety of production equipment. Therefore, developing a system capable of real-time monitoring, early warning, and response to these safety hazards is of great significance. With the rapid development of Internet of Things (IoT) technology and the mature application of technologies such as sensors, data processing, and cloud computing, technical support has been provided for building an IoT-based textile safety early warning system. Smart manufacturing advocates improving production efficiency and quality through informatization and automation, and an IoT-based safety early warning system is an important component of smart manufacturing. Enterprises have increasingly stringent requirements for production safety management. By introducing IoT technology, comprehensive monitoring and management of the production environment can be achieved, thereby improving the level of safety management.

[0003] Currently, research on IoT-based textile safety early warning methods and systems still has some shortcomings. Specifically, current IoT-based textile safety early warning is limited to real-time monitoring of the working environment of textile equipment, neglecting real-time monitoring of the operating status of the textile equipment itself and the signal transmission status within the textile workshop. This may lead to the failure to detect equipment faults or anomalies in a timely manner, thus affecting production efficiency and safety. The lack of real-time monitoring of equipment operating status may result in equipment faults being overlooked, requiring emergency repairs or replacement of equipment parts, increasing maintenance costs. It may also lead to poor signal transmission and information delays, affecting the timeliness and accuracy of safety warnings. Ignoring the monitoring of equipment operating status and signal transmission status may increase safety risks, such as equipment failure causing a fire. In the event of a serious accident such as an explosion, failure to acquire basic configuration information of the textile workshop may lead to deviations in IoT-based textile safety early warning systems, resulting in wasted resources, increased safety early warning costs, unnecessary interference, and potential delays in issuing safety warnings, which could lead to accidents being dealt with only after they have occurred, increasing losses. Delayed safety warnings may cause production line shutdowns or production plans to be disrupted, affecting production efficiency and corporate economic benefits. The purpose of safety early warnings is to protect employee safety. If warning information is not issued in a timely manner, employees may be placed in dangerous environments, increasing the risk of injury or accidents. Delayed safety warnings may lead to the company being perceived as having poor safety management, damaging the company's reputation and image, and affecting the company's long-term development. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the present invention provides a textile safety early warning method and system based on the Internet of Things, which can effectively solve the problems involved in the above-mentioned background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a textile safety early warning method based on the Internet of Things, comprising the following steps: acquiring a real-time safety impact dataset of a textile workshop; preprocessing the real-time safety impact dataset of the textile workshop; analyzing the preprocessed real-time safety impact dataset of the textile workshop to obtain a real-time safety assessment value of the textile workshop; acquiring a basic configuration information set of the textile workshop; comparing the acquired basic configuration information set of the textile workshop to obtain a safety assessment deviation value of the textile workshop; and combining the real-time safety assessment value of the textile workshop to issue an early warning on the real-time safety status of the textile workshop.

[0006] As a further method, a real-time safety impact dataset of the textile workshop is obtained. This dataset undergoes data preprocessing, specifically as follows: During textile production, sensor devices are installed to monitor textile equipment and the production environment in real time, acquiring a real-time safety impact dataset. This dataset includes real-time equipment impact data, real-time environmental impact data, and real-time signal impact data. Data preprocessing is performed on this dataset. The acquired data is then integrated into a unified database. Data cleaning is performed on the dataset, filling in missing values ​​through specific investigations and deleting outliers. Units are standardized to a uniform scale. Finally, the data is smoothed and then aggregated, merging or summarizing the data.

[0007] As a further method, the real-time equipment impact data in the textile workshop specifically includes the maximum continuous operating time of the real-time equipment in the textile workshop, the maximum operating power of the real-time equipment in the textile workshop, the maximum decibel value of the noise emitted by the real-time equipment in the textile workshop, and the maximum vibration intensity of the real-time equipment in the textile workshop.

[0008] As a further method, real-time environmental impact data of the textile workshop specifically includes real-time temperature, humidity, dust content, light intensity, and smoke concentration within the textile workshop during the dyeing and inspection process.

[0009] As a further method, the real-time signal impact data in the textile workshop specifically includes the real-time transmission signal delay of textile workshop equipment, the real-time transmission signal packet loss rate of textile workshop equipment, and the real-time transmission signal delay of textile workshop sensors.

[0010] As a further method, based on the preprocessed real-time safety impact dataset of the textile workshop, the real-time safety assessment value of the textile workshop is obtained. The specific analysis process is as follows: based on the preprocessed real-time safety impact dataset of the textile workshop, the preprocessed real-time equipment impact data, real-time environmental impact data, and real-time signal impact data of the textile workshop are obtained, and the real-time safety assessment value of the textile workshop is obtained. The real-time safety assessment value of the textile workshop is used as the basis for analysis to provide early warning of the real-time safety status of the textile workshop.

[0011] As a further method, the real-time safety assessment values ​​of the textile workshop are analyzed in the following process:

[0012]

[0013] In the formula, G is the real-time safety assessment value of the textile workshop, G1 is the real-time equipment safety assessment value of the textile workshop, G2 is the real-time environmental safety assessment value of the textile workshop, G3 is the real-time signal safety assessment value of the textile workshop, μ1 is the set weighting factor of the real-time equipment safety assessment value of the textile workshop, μ2 is the set weighting factor of the real-time environmental safety assessment value of the textile workshop, μ3 is the set weighting factor of the real-time signal safety assessment value of the textile workshop, β is the set correction factor of the real-time safety assessment value of the textile workshop, and e is a natural constant.

[0014] As a further method, a basic configuration information set of the textile workshop is obtained. Based on the obtained basic configuration information set, the safety assessment deviation value of the textile workshop is obtained by comparison. The specific analysis process is as follows: the basic configuration information set of the textile workshop is obtained, which specifically includes the area of ​​the textile workshop, the total number of equipment that can be used normally in the textile workshop, the total number of staff in the textile workshop, and the total number of sensors in the textile workshop; a designated label is generated for the area of ​​the textile workshop, the total number of equipment that can be used normally in the textile workshop, the total number of staff in the textile workshop, and the total number of sensors in the textile workshop; the designated label is compared with the safety assessment deviation value of the textile workshop corresponding to each designated label stored in the database to obtain the safety assessment deviation value of the textile workshop under the designated label.

[0015] As a further method, the real-time safety status of the textile workshop is given an early warning by combining the real-time safety assessment value of the textile workshop. The specific analysis process is as follows: Obtain the textile workshop safety assessment deviation value under the specified label corresponding to the textile workshop; integrate the textile workshop safety assessment deviation value under the specified label corresponding to the textile workshop with the textile workshop safety assessment reference value stored in the database to obtain the textile workshop safety assessment limit value; compare the textile workshop real-time safety assessment value with the textile workshop safety assessment limit value; if the textile workshop real-time safety assessment value is higher than or equal to the textile workshop safety assessment limit value, the real-time safety status of the textile workshop is good; if the textile workshop real-time safety assessment value is lower than the textile workshop safety assessment limit value, the textile workshop real-time safety status is bad, and an early warning is given for the real-time safety status of the textile workshop.

[0016] A second aspect of this invention provides a textile safety early warning system based on the Internet of Things (IoT), comprising a data preprocessing module, a real-time safety assessment value acquisition module, and a real-time safety status early warning module. The data preprocessing module acquires a real-time safety impact dataset of the textile workshop and performs data preprocessing on the dataset. The real-time safety assessment value acquisition module analyzes the preprocessed dataset to obtain a real-time safety assessment value for the textile workshop. The real-time safety status early warning module acquires a basic configuration information set of the textile workshop, compares the acquired basic configuration information set with the obtained safety assessment deviation value, and combines the real-time safety assessment value to issue an early warning for the real-time safety status of the textile workshop.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides a textile safety early warning method and system based on the Internet of Things, which monitors the operating status of the textile equipment itself in real time. By monitoring the operating status of the textile equipment in real time, abnormal conditions of the equipment can be detected in time, such as excessive temperature and abnormal vibration, which helps to prevent equipment failure, adjust equipment operating parameters in time, optimize production process, improve production efficiency and production quality, carry out preventive maintenance, detect equipment problems in time and repair them, reduce maintenance costs, reduce equipment downtime, help improve production safety, reduce accident risk, and protect the safety of employees and equipment. The textile safety early warning system based on the Internet of Things can realize intelligent interconnection between equipment, improve the intelligence level of the production line, and realize automated production management.

[0018] (2) By monitoring the signal transmission status in the textile workshop in real time, this invention can ensure the rapid transmission of information in the textile workshop, timely convey production instructions and safety alarms, improve production efficiency and work efficiency, monitor the signal transmission status to help find problems of signal loss or delay, make timely adjustments and repairs, avoid errors and loopholes in information transmission, improve work accuracy, provide real-time production data and status information, help optimize production scheduling and resource allocation, improve production efficiency and production quality, and ensure the timely transmission and response of safety warning information by monitoring the signal transmission status in real time, help employees take quick countermeasures, reduce accident risks and improve safety.

[0019] (3) This invention obtains the basic configuration information set of the textile workshop and the safety assessment deviation value of the textile workshop. It can comprehensively evaluate the equipment configuration of the textile workshop, find configuration deviations and optimize them, improve equipment operating efficiency and production quality. By analyzing the basic configuration information set and the assessment deviation value, the safety assessment deviation value of the textile workshop under the specified tag is obtained according to the comparison results. The safety assessment deviation value can reflect the degree of deviation of the textile workshop in terms of safety and is an important indicator for assessing the safety of the textile workshop. Based on the obtained safety assessment deviation value, the safety status of the textile workshop under different configuration information can be analyzed. The impact of different configuration information on the safety assessment deviation value can be compared, and the importance of configuration information to the safety of the textile workshop can be found. This provides a reference for improving and optimizing the safety of the textile workshop and also helps to reduce resource waste and accident losses. Attached Figure Description

[0020] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the method steps of the present invention.

[0022] Figure 2 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] Reference Figure 1As shown, the first aspect of the present invention provides a textile safety early warning method based on the Internet of Things, including: acquiring a real-time safety impact dataset of a textile workshop and performing data preprocessing on the real-time safety impact dataset of the textile workshop.

[0025] Specifically, the process involves acquiring a real-time safety impact dataset from a textile workshop, preprocessing the dataset, and performing the following analysis: During textile production, sensor devices are installed to monitor textile equipment and the production environment in real time, resulting in a dataset containing real-time equipment impact data, environmental impact data, and signal impact data. This dataset is preprocessed, and the data is integrated into a unified database. Data cleaning is performed, filling in missing values ​​and deleting outliers. Unit conversion is standardized to a uniform scale. Finally, the data is smoothed and aggregated.

[0026] In a specific embodiment, data cleaning and missing value imputation can improve data accuracy and completeness, reduce errors and biases, and ensure data quality. Standardizing data to a unified standard scale can guarantee data consistency and comparability, facilitating data analysis and decision-making. Deleting outliers and performing data smoothing can reduce data noise and interference, improve data reliability and stability, and ensure the accuracy of data analysis. Data aggregation can merge or summarize large amounts of data, simplify data structure, facilitate data visualization and analysis, and help managers quickly understand the real-time safety impact of textile workshops. Data preprocessed and integrated can provide managers with clearer and more intuitive information, helping to formulate more scientific and effective safety management strategies and providing decision support. Through data integration and aggregation, the complexity and workload of data processing can be reduced, the efficiency and speed of data processing can be improved, and time and labor costs can be saved. Sensor devices used for real-time monitoring of textile equipment and production environment include temperature sensors, humidity sensors, pressure sensors, vibration sensors, gas sensors, optical sensors, motion sensors, and current sensors, etc.

[0027] Furthermore, real-time equipment impact data in the textile workshop specifically includes the maximum continuous operating time of the equipment, the maximum operating power of the equipment, the maximum decibel level of noise emitted by the equipment, and the maximum vibration intensity of the equipment. The maximum continuous operating time refers to the longest time that equipment in the textile workshop can operate continuously, reflecting the stability and durability of the equipment. A longer operating time may indicate stable equipment operation, but it may also suggest the risk of excessive wear or overheating. The maximum operating power of the equipment in the textile workshop represents the maximum power consumed by a particular piece of equipment at a specific moment, reflecting the equipment's energy consumption. The conditions and load conditions indicate that high power may mean the equipment is under high load, requiring extra attention to the equipment's stability and energy consumption. The highest decibel value of real-time equipment noise in the textile workshop refers to the maximum noise level emitted by a certain piece of equipment in the textile workshop at a certain moment, reflecting the noise level of the equipment and its impact on the environment. High decibel values ​​may have a negative impact on the health and work efficiency of employees, requiring measures to reduce noise. The maximum vibration intensity of real-time equipment in the textile workshop indicates the maximum vibration intensity generated by a certain piece of equipment in the textile workshop at a certain moment, reflecting the vibration condition and stability of the equipment. Excessive vibration intensity may cause equipment damage or affect production efficiency, requiring timely inspection and maintenance.

[0028] Specifically, real-time environmental impact data for textile workshops includes real-time temperature, humidity, dust content, light intensity during textile dyeing and inspection, and smoke concentration. Real-time temperature refers to the actual temperature level within the workshop, reflecting the ambient temperature conditions. Controlling and monitoring the workshop temperature ensures that production equipment and employees operate in a suitable working environment, avoiding the impact of overheating or excessive cold on production. Real-time humidity indicates the actual humidity level within the workshop, reflecting the level of moisture. Appropriate humidity levels help maintain textile quality and the normal operation of production equipment; excessively high or low humidity may negatively impact textile production. The real-time dust content in the textile workshop refers to the level of dust in the air within the workshop, reflecting the cleanliness and air quality. High dust content may affect the health of employees and the normal operation of equipment, requiring cleaning and ventilation measures. The real-time light intensity in the textile workshop during the dyeing and inspection process indicates the real-time light intensity level in the workshop. Appropriate light intensity helps employees perform their work and ensures product quality. Insufficient or excessive light may affect work efficiency and product quality. The real-time smoke concentration in the textile workshop refers to the real-time smoke concentration level in the air within the workshop, reflecting the cleanliness of the air. High smoke concentration may affect the health of employees and the normal operation of production equipment, requiring timely smoke removal and improved ventilation.

[0029] Furthermore, the real-time signal impact data in the textile workshop includes, specifically, the real-time transmission signal delay of textile workshop equipment, the real-time transmission signal packet loss rate of textile workshop equipment, and the real-time transmission signal delay of textile workshop sensors. The real-time transmission signal delay of textile workshop equipment represents the time delay required for signal transmission between textile workshop equipment. This indicator reflects the efficiency and speed of communication between devices. Lower transmission signal delay means faster communication between devices, helping to improve production efficiency and response speed. The real-time transmission signal packet loss rate of textile workshop equipment refers to the proportion of lost or damaged signals among the total transmitted signals during signal transmission between textile workshop equipment. A high packet loss rate may lead to incomplete or erroneous data transmission, affecting the communication quality and data accuracy between devices. It is necessary to reduce the packet loss rate to ensure the integrity and accuracy of data transmission. The real-time transmission signal delay of textile workshop sensors represents the time delay required for textile workshop sensors to transmit data to the monitoring system. The magnitude of the sensor's real-time transmission signal delay affects the monitoring system's real-time perception capability of the production environment. Lower transmission delay helps to monitor the production process in real time, improving production efficiency and safety.

[0030] Based on the preprocessed real-time safety impact dataset of the textile workshop, the real-time safety assessment value of the textile workshop is obtained through analysis.

[0031] Furthermore, based on the preprocessed real-time safety impact dataset of the textile workshop, the real-time safety assessment value of the textile workshop is obtained. The specific analysis process is as follows: based on the preprocessed real-time safety impact dataset of the textile workshop, the preprocessed real-time equipment impact data, real-time environmental impact data, and real-time signal impact data of the textile workshop are obtained, and the real-time safety assessment value of the textile workshop is obtained. The real-time safety assessment value of the textile workshop serves as the basis for early warning of the real-time safety status of the textile workshop.

[0032] In one specific embodiment, by providing an IoT-based textile safety early warning method and system, the operating status of textile equipment itself can be monitored in real time. By monitoring the operating status of textile equipment in real time, abnormal conditions of the equipment can be detected in a timely manner, such as excessive temperature or abnormal vibration, which helps to prevent equipment failures, adjust equipment operating parameters in a timely manner, optimize production processes, improve production efficiency and quality, enable preventive maintenance, detect equipment problems in a timely manner and repair them, reduce maintenance costs, reduce equipment downtime, help improve production safety, reduce accident risks, and protect the safety of employees and equipment. The IoT-based textile safety early warning system can realize intelligent interconnection between equipment, improve the intelligence level of the production line, and realize automated production management.

[0033] In one specific embodiment, real-time monitoring of signal transmission status within the textile workshop ensures rapid information transmission, timely delivery of production instructions and safety alarms, and improves production and work efficiency. Monitoring signal transmission status helps identify signal loss or delays, allowing for timely adjustments and repairs, avoiding errors and loopholes in information transmission, and improving work accuracy. It also provides real-time production data and status information, which helps optimize production scheduling and resource allocation, thereby improving production efficiency and quality. Furthermore, real-time monitoring of signal transmission status ensures timely transmission and response to safety warnings, helping employees quickly take countermeasures, reducing accident risks, and enhancing safety.

[0034] Specifically, the real-time safety assessment value of the textile workshop can not only be obtained through further analysis using machine learning ensemble models, combining the prediction results of multiple base models using ensemble methods such as Gradient Boosting Machine or Random Forest to obtain a more accurate real-time safety assessment value, but it can also be calculated in the following way: The formula for calculating the real-time safety assessment value of the textile workshop is as follows:

[0035]

[0036] In the formula, G is the real-time safety assessment value of the textile workshop, G1 is the real-time equipment safety assessment value of the textile workshop, G2 is the real-time environmental safety assessment value of the textile workshop, G3 is the real-time signal safety assessment value of the textile workshop, μ1 is the set weighting factor of the real-time equipment safety assessment value of the textile workshop, μ2 is the set weighting factor of the real-time environmental safety assessment value of the textile workshop, μ3 is the set weighting factor of the real-time signal safety assessment value of the textile workshop, β is the set correction factor of the real-time safety assessment value of the textile workshop, and e is a natural constant.

[0037] In a specific embodiment, the real-time equipment safety assessment value in the textile workshop is used to evaluate the real-time safety status of the equipment in the textile workshop, serving as the analytical basis for early warning of the real-time safety status of the textile workshop. The real-time equipment safety assessment value can not only be obtained through further analysis using machine learning ensemble models, combining the prediction results of multiple basic models using ensemble methods such as K-means clustering, support vector machine, or CatBoost to obtain a more accurate real-time equipment safety assessment value, but it can also be calculated in the following way. The specific calculation formula for the real-time equipment safety assessment value in the textile workshop is as follows:

[0038]

[0039] In the formula, G1 is the real-time safety assessment value of the textile workshop equipment, LT is the maximum continuous operating time of the textile workshop equipment, LP is the maximum operating power of the textile workshop equipment, ZB is the maximum decibel value of the noise emitted by the textile workshop equipment, ZD is the maximum vibration intensity of the textile workshop equipment, LT0 is the reference continuous operating time of the textile workshop equipment stored in the database, LP0 is the reference operating power of the textile workshop equipment stored in the database, ZB0 is the decibel value of the noise emitted by the textile workshop equipment stored in the database, ZD0 is the permissible vibration intensity of the textile workshop equipment stored in the database, ε1 is the compensation factor for the maximum continuous operating time of the textile workshop equipment, ε2 is the compensation factor for the maximum operating power of the textile workshop equipment, ε3 is the compensation factor for the maximum decibel value of the noise emitted by the textile workshop equipment, and ε4 is the compensation factor for the maximum vibration intensity of the textile workshop equipment.

[0040] It should be explained that the above-mentioned real-time equipment safety assessment values ​​in the textile workshop are calculated based on the maximum continuous operating time of the equipment, the maximum operating power of the equipment, the maximum decibel level of noise emitted by the equipment, and the maximum vibration intensity of the equipment. By comprehensively considering factors such as the maximum continuous operating time, maximum operating power, maximum noise level, and maximum vibration intensity of the equipment, a more comprehensive assessment of the equipment's operating status and safety can be achieved, going beyond a single indicator evaluation and contributing to a holistic understanding of the equipment's working condition. Furthermore, by monitoring parameters such as continuous operating time, power, noise, and vibration of equipment, signs of abnormal equipment operation or potential problems can be detected in a timely manner, providing early warnings and reducing the likelihood of malfunctions. Based on comprehensive assessment values, more reasonable equipment maintenance plans can be developed, including regular inspections, maintenance, and upkeep, to improve equipment reliability and stability, ensure equipment safety and stability, help improve production efficiency, reduce downtime caused by equipment failures or problems, and improve the continuity and stability of the production line. The equipment safety assessment value reflects the degree of impact of the equipment on the surrounding environment and operators. By comprehensively considering various factors, the safety and health of employees can be guaranteed.

[0041] It should be explained that the above-mentioned real-time environmental safety assessment value of the textile workshop is used to evaluate the real-time environmental safety status of the textile workshop and serves as the analytical basis for early warning of the real-time safety status of the textile workshop. The real-time environmental safety assessment value of the textile workshop can not only be obtained through further analysis using machine learning ensemble models, combining the prediction results of multiple basic models using ensemble methods such as Graph Boosting Machine or AdaBoost to obtain a more accurate real-time environmental safety assessment value, but it can also be calculated in the following way: The formula for calculating the real-time environmental safety assessment value of the textile workshop is as follows:

[0042]

[0043] In the formula, G2 is the real-time environmental safety assessment value of the textile workshop, CT is the real-time temperature inside the textile workshop, CS is the real-time humidity inside the textile workshop, FC is the real-time dust content inside the textile workshop, GZ is the real-time light intensity inside the textile workshop during the dyeing and inspection process, YW is the real-time smoke concentration inside the textile workshop, CT0 is the reference temperature inside the textile workshop stored in the database, CS0 is the reference humidity inside the textile workshop stored in the database, FC0 is the permissible dust content inside the textile workshop stored in the database, GZ0 is the reference light intensity inside the textile workshop during the dyeing and inspection process, YW0 is the defined smoke concentration inside the textile workshop stored in the database, σ1 is the set compensation factor for the real-time temperature inside the textile workshop, σ2 is the set compensation factor for the real-time humidity inside the textile workshop, σ3 is the set compensation factor for the real-time dust content inside the textile workshop, σ4 is the set compensation factor for the real-time light intensity inside the textile workshop during the dyeing and inspection process, σ5 is the set compensation factor for the real-time smoke concentration inside the textile workshop, and e is a natural constant.

[0044] It should be explained that the aforementioned real-time environmental safety assessment values ​​for textile workshops are calculated based on real-time temperature, humidity, dust content, light intensity during textile dyeing and inspection, and smoke concentration within the workshop. By comprehensively considering factors such as temperature, humidity, dust content, light intensity during textile dyeing and inspection, and smoke concentration, the assessment can determine whether the working environment of the textile workshop meets health standards, ensuring employee health and reducing the risk of work-related injuries. Health problems caused by environmental factors can be identified in advance by monitoring parameters such as temperature, humidity, dust content, and smoke concentration. This can help prevent accidents and ensure production safety. A good working environment helps improve employee work efficiency and comfort, reduce production interruptions and employee fatigue, and increase production efficiency. Reasonable control of environmental factors such as temperature and humidity helps to save energy and reduce emissions, lower production costs, and meet environmental protection requirements. A good working environment also helps to improve the accuracy and stability of textile dyeing and inspection processes, improve product quality and pass rate. By comprehensively considering multiple environmental factors, it is possible to more comprehensively assess whether the working environment of the textile workshop meets relevant safety standards and ensure that the enterprise operates in compliance with regulations.

[0045] It should be explained that the above-mentioned real-time signal safety assessment value for textile workshops is used to evaluate the real-time signal safety status of textile workshops and serves as the analytical basis for early warning of the real-time safety status of textile workshops. The real-time signal safety assessment value for textile workshops can not only be obtained through further analysis using machine learning ensemble models, such as LightGBM, which combines the prediction results of multiple basic models to obtain a more accurate value, but it can also be calculated using the following formula:

[0046]

[0047] In the formula, G3 is the real-time signal security assessment value of the textile workshop, ST is the real-time transmission signal delay of the textile workshop equipment, SD is the packet loss rate of the real-time transmission signal of the textile workshop equipment, GT is the real-time transmission signal delay of the textile workshop sensor, ST0 is the permitted real-time transmission signal delay of the textile workshop equipment stored in the database, SD0 is the permitted packet loss rate of the real-time transmission signal of the textile workshop equipment stored in the database, GT0 is the reference delay of the real-time transmission signal of the textile workshop sensor stored in the database, τ1 is the set compensation factor for the real-time transmission signal delay of the textile workshop equipment, τ2 is the set compensation factor for the packet loss rate of the real-time transmission signal of the textile workshop equipment, τ3 is the set compensation factor for the real-time transmission signal delay of the textile workshop sensor, and e is the natural constant.

[0048] It should be explained that the aforementioned real-time signal security assessment value in the textile workshop is calculated based on the real-time signal transmission delay of textile workshop equipment, the real-time signal loss rate of textile workshop equipment, and the real-time signal transmission delay of textile workshop sensors. By comprehensively considering factors such as the real-time signal transmission delay, packet loss rate, and sensor signal transmission delay, the real-time operating status of the equipment can be understood, equipment faults can be detected and resolved in a timely manner, and normal equipment operation can be ensured. By comprehensively considering factors such as sensor signal transmission delay, communication quality can be assessed, communication parameters can be adjusted in a timely manner, and the accuracy and reliability of sensor data can be improved. By monitoring parameters such as the packet loss rate, the possibility of communication failures can be predicted, and corresponding measures can be taken to avoid communication interruptions, ensuring the stability of data transmission. Stable equipment communication and sensor data transmission help improve production efficiency, reduce production interruptions caused by communication failures, and improve the stability of the production line. Based on parameters such as the real-time signal transmission delay, reasonable equipment maintenance plans can be formulated to extend equipment life and reduce maintenance costs. By comprehensively considering multiple factors in the signal security assessment value, the security and privacy of data transmission can be ensured, preventing data leakage and tampering.

[0049] The system acquires a basic configuration information set for the textile workshop, compares the acquired basic configuration information set to obtain the safety assessment deviation value of the textile workshop, and combines the real-time safety assessment value of the textile workshop to issue an early warning on the real-time safety status of the textile workshop.

[0050] Specifically, the basic configuration information set of the textile workshop is obtained. Based on the obtained basic configuration information set, the safety assessment deviation value of the textile workshop is obtained by comparison. The specific analysis process is as follows: The basic configuration information set of the textile workshop is obtained, which specifically includes the area of ​​the textile workshop, the total number of equipment that can be used normally in the textile workshop, the total number of staff in the textile workshop, and the total number of sensors in the textile workshop; the area of ​​the textile workshop, the total number of equipment that can be used normally in the textile workshop, the total number of staff in the textile workshop, and the total number of sensors in the textile workshop are generated into designated tags; the designated tags are compared with the safety assessment deviation values ​​of the textile workshop corresponding to each designated tag stored in the database to obtain the safety assessment deviation value of the textile workshop under the designated tag.

[0051] It should be explained that the above-mentioned acquisition of basic configuration information set of textile workshops and obtaining safety assessment deviation values ​​allows for a comprehensive evaluation of the equipment configuration of textile workshops, identifying configuration deviations and making optimizations to improve equipment operating efficiency and production quality. By analyzing the basic configuration information set and assessment deviation values, and based on the comparison results, the safety assessment deviation value under the corresponding specified label of the textile workshop can be obtained. The safety assessment deviation value reflects the degree of deviation in safety of the textile workshop and is an important indicator for assessing the safety of the textile workshop. Based on the obtained safety assessment deviation value, the safety status of the textile workshop under different configuration information can be analyzed. The impact of different configuration information on the safety assessment deviation value can be compared, and the importance of configuration information to the safety of the textile workshop can be identified. This provides a reference for improving and optimizing the safety of the textile workshop and also helps to reduce resource waste and accident losses.

[0052] Furthermore, the real-time safety assessment values ​​of the textile workshop are combined to issue early warnings for the real-time safety status of the textile workshop. The specific analysis process is as follows: Obtain the textile workshop safety assessment deviation value under the specified label corresponding to the textile workshop; integrate the textile workshop safety assessment deviation value under the specified label with the textile workshop safety assessment reference value stored in the database to obtain the textile workshop safety assessment threshold value; compare the textile workshop real-time safety assessment value with the textile workshop safety assessment threshold value; if the textile workshop real-time safety assessment value is higher than or equal to the textile workshop safety assessment threshold value, the real-time safety status of the textile workshop is good; if the textile workshop real-time safety assessment value is lower than the textile workshop safety assessment threshold value, the real-time safety status of the textile workshop is poor, and an early warning is issued for the real-time safety status of the textile workshop.

[0053] It should be explained that by comparing the real-time safety assessment value and the safety assessment limit value of the textile workshop, the system can achieve real-time monitoring of the safety status of the textile workshop, promptly identify safety risks and problems, and issue timely warnings when the real-time safety assessment value is lower than the safety assessment limit value. This helps to prevent accidents. Through the real-time warning system, managers can respond quickly to safety issues, take corresponding measures and emergency responses, effectively reduce accident risks, and ensure production and employee safety. Real-time safety status warnings can help managers quickly understand the safety status of the textile workshop, reduce information transmission and decision-making time, and improve the efficiency of responding to emergencies. Through the warning system, resources can be better planned and allocated, production plans and personnel can be adjusted in a timely manner, production interruptions and resource waste can be avoided, and resource utilization can be optimized. Through the warnings and feedback on the real-time safety status of the textile workshop, lessons learned can be continuously summarized, and safety management can be continuously improved and optimized to enhance the safety and production efficiency of the textile workshop.

[0054] Reference Figure 2 As shown, the second aspect of the present invention provides a textile safety early warning system based on the Internet of Things, including a data preprocessing module, a real-time safety assessment value acquisition module, and a real-time safety status early warning module, wherein: the data preprocessing module is used to acquire a real-time safety impact dataset of the textile workshop and perform data preprocessing on the real-time safety impact dataset of the textile workshop; the real-time safety assessment value acquisition module is used to analyze and obtain a real-time safety assessment value of the textile workshop based on the preprocessed real-time safety impact dataset of the textile workshop; the real-time safety status early warning module is used to acquire a basic configuration information set of the textile workshop, compare the acquired basic configuration information set of the textile workshop to obtain a safety assessment deviation value of the textile workshop, and combine the real-time safety assessment value of the textile workshop to issue an early warning on the real-time safety status of the textile workshop.

[0055] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A textile safety early warning method based on the Internet of Things, characterized in that, Includes the following steps: Obtain the real-time safety impact dataset of the textile workshop and perform data preprocessing on the dataset. Based on the preprocessed real-time safety impact dataset of the textile workshop, the real-time safety assessment value of the textile workshop is obtained through analysis. The system acquires a basic configuration information set for the textile workshop, compares the acquired basic configuration information set to obtain the safety assessment deviation value of the textile workshop, and combines the real-time safety assessment value of the textile workshop to issue an early warning on the real-time safety status of the textile workshop.

2. The textile safety early warning method based on the Internet of Things according to claim 1, characterized in that: The process of obtaining the real-time safety impact dataset of the textile workshop and preprocessing the dataset is as follows: In the textile production process, sensor devices are installed to monitor textile equipment and the production environment in real time, and to obtain real-time safety impact datasets of the textile workshop. The real-time safety impact dataset for textile workshops specifically includes real-time equipment impact data, real-time environmental impact data, and real-time signal impact data. Data preprocessing is performed on the real-time safety impact dataset of the textile workshop; Obtain the real-time safety impact dataset of the textile workshop and integrate the data from the obtained real-time safety impact dataset of the textile workshop into a unified database; Data cleaning was performed on the dataset of real-time safety impacts of textile workshops in the database. Missing data in the dataset were filled with missing values ​​through specific investigations, and outliers were deleted. The content units were uniformly converted, and the data in the real-time safety impact dataset of the textile workshop was standardized to a unified standard scale. The data in the real-time safety impact dataset of the textile workshop is smoothed and then aggregated to merge or summarize the data.

3. The textile safety early warning method based on the Internet of Things according to claim 2, characterized in that: The real-time equipment impact data in the textile workshop specifically includes the maximum continuous operating time of the real-time equipment in the textile workshop, the maximum operating power of the real-time equipment in the textile workshop, the maximum decibel value of the noise emitted by the real-time equipment in the textile workshop, and the maximum vibration intensity of the real-time equipment in the textile workshop.

4. The textile safety early warning method based on the Internet of Things according to claim 2, characterized in that: The real-time environmental impact data of the textile workshop specifically includes the real-time temperature, humidity, dust content, light intensity, and smoke concentration within the textile workshop during the dyeing and inspection process.

5. The textile safety early warning method based on the Internet of Things according to claim 2, characterized in that: The real-time signal impact data of the textile workshop specifically includes the real-time transmission signal delay of textile workshop equipment, the real-time transmission signal packet loss rate of textile workshop equipment, and the real-time transmission signal delay of textile workshop sensors.

6. The textile safety early warning method based on the Internet of Things according to claim 2, characterized in that: Based on the preprocessed real-time safety impact dataset of the textile workshop, the real-time safety assessment value of the textile workshop is obtained through analysis. The specific analysis process is as follows: Based on the preprocessed real-time safety impact dataset of the textile workshop, we obtain the preprocessed real-time equipment impact data, real-time environmental impact data, and real-time signal impact data of the textile workshop. We then analyze these data to obtain the real-time safety assessment value of the textile workshop, which serves as the basis for early warning of the real-time safety status of the textile workshop.

7. The textile safety early warning method based on the Internet of Things according to claim 6, characterized in that: The specific analysis process for the real-time safety assessment values ​​of the textile workshop is as follows: In the formula, G is the real-time safety assessment value of the textile workshop, G1 is the real-time equipment safety assessment value of the textile workshop, G2 is the real-time environmental safety assessment value of the textile workshop, G3 is the real-time signal safety assessment value of the textile workshop, μ1 is the set weighting factor of the real-time equipment safety assessment value of the textile workshop, μ2 is the set weighting factor of the real-time environmental safety assessment value of the textile workshop, μ3 is the set weighting factor of the real-time signal safety assessment value of the textile workshop, β is the set correction factor of the real-time safety assessment value of the textile workshop, and e is a natural constant.

8. The textile safety early warning method based on the Internet of Things according to claim 1, characterized in that: The process of obtaining the basic configuration information set of the textile workshop, comparing it with the obtained basic configuration information set to obtain the safety assessment deviation value of the textile workshop, is as follows: Obtain a basic configuration information set for the textile workshop, which specifically includes the textile workshop's floor area, the total number of equipment that can be used normally in the textile workshop, the total number of staff members in the textile workshop, and the total number of sensors in the textile workshop. Generate designated tags for the area of ​​the textile workshop, the total number of equipment that can be used normally in the textile workshop, the total number of staff in the textile workshop, and the total number of sensors in the textile workshop. The specified label is compared with the safety assessment deviation values ​​of the textile workshop corresponding to each specified label stored in the database to obtain the safety assessment deviation value of the textile workshop under the specified label.

9. The textile safety early warning method based on the Internet of Things according to claim 8, characterized in that: The process of combining real-time safety assessment values ​​of the textile workshop to provide early warning of the real-time safety status of the textile workshop is as follows: Obtain the safety assessment deviation value of the textile workshop under the specified label corresponding to the textile workshop, and integrate the safety assessment deviation value of the textile workshop under the specified label corresponding to the textile workshop with the safety assessment reference value of the textile workshop stored in the database to obtain the safety assessment definition value of the textile workshop. Compare the real-time safety assessment values ​​of the textile workshop with the defined safety assessment values ​​of the textile workshop; If the real-time safety assessment value of the textile workshop is higher than or equal to the safety assessment limit value of the textile workshop, then the real-time safety status of the textile workshop is good. If the real-time safety assessment value of the textile workshop is lower than the safety assessment limit value of the textile workshop, the real-time safety status of the textile workshop is poor, and an early warning will be issued for the real-time safety status of the textile workshop.

10. A textile safety early warning system based on the Internet of Things, characterized in that, It includes a data preprocessing module, a real-time security assessment value acquisition module, and a real-time security status early warning module, among which: The data preprocessing module is used to acquire the real-time safety impact dataset of the textile workshop and perform data preprocessing on the real-time safety impact dataset of the textile workshop. The real-time safety assessment value acquisition module is used to analyze and obtain the real-time safety assessment value of the textile workshop based on the pre-processed real-time safety impact dataset of the textile workshop. The real-time safety status early warning module is used to acquire the basic configuration information set of the textile workshop, compare the acquired basic configuration information set of the textile workshop to obtain the safety assessment deviation value of the textile workshop, and combine the real-time safety assessment value of the textile workshop to issue an early warning on the real-time safety status of the textile workshop.

Images