Textile production safety intelligent monitoring alarm system based on sensor monitoring

Abstract

The invention belongs to the technical field of textile production supervision, and particularly relates to a textile production safety intelligent monitoring alarm system based on sensor monitoring, which comprises a production equipment detection analysis module, a production workshop partition module, an environment hazard analysis module, a motion normalization monitoring module and an alarm reminding module, according to the invention, all production equipment in the textile production workshop is detected and analyzed through the production equipment detection and analysis module, and the environmental hazard analysis module analyzes the environmental hazard degrees of all sub-regions in the textile production workshop. The motion normativity monitoring module monitors and analyzes motion targets in each sub-region in the textile production workshop, and reminds a manager to take corresponding improvement treatment measures when an equipment hidden danger early warning signal, an environment high hazard signal or a motion non-normativity signal is generated, so that effective monitoring and control of the textile production workshop are realized. And the production risk of a textile workshop and the textile production supervision difficulty are obviously reduced.

Description

Technical Field

[0001] The invention relates to the technical field of textile production supervision, and in particular to a textile production safety intelligent monitoring and alarm system based on sensor monitoring. Background Art

[0002] Textiles are made by twisting, combining, interweaving or knitting fiber materials through textile technology. There are many safety hazards in the textile production process, which not only affect production efficiency, but also cause serious harm to personnel safety.

[0003] A Chinese invention patent with publication number CN117171670A discloses a method, device and system for monitoring faults in a textile production process. The technical solution can timely identify abnormal equipment based on real-time textile data, significantly improving the efficiency of identifying abnormal data in the textile production process.

[0004] However, in actual use, it is unable to analyze and warn the operating conditions of each production equipment in the textile workshop, and is unable to accurately judge and timely warn the degree of environmental hazard and the control status of movement targets in each sub-area of ​​the textile production workshop, making it difficult to achieve effective monitoring and control of the textile production workshop, which is not conducive to reducing the production risk of the textile workshop and the difficulty of textile production supervision;

[0005] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent monitoring and alarm system for textile production safety based on sensor monitoring, which solves the problem that the prior art is unable to analyze and warn the operating conditions of various production equipment in a textile workshop, and is unable to accurately judge and timely warn the environmental hazard level and motion target control conditions of each sub-area in the textile production workshop, making it difficult to achieve effective monitoring and control of the textile production workshop.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The intelligent monitoring and alarm system for textile production safety based on sensor monitoring includes production equipment detection and analysis module, production workshop partition module, environmental hazard analysis module, movement normative monitoring module and alarm reminder module;

[0009] The production equipment detection and analysis module detects and analyzes all production equipment in the textile production workshop, identifies the warning objects through analysis and generates equipment hidden danger warning signals or equipment safe operation signals, and sends the equipment hidden danger warning signals to the alarm reminder module;

[0010] The production workshop partition module is used to divide the textile production workshop into several sub-areas, and mark the corresponding sub-areas as monitoring areas u, where u is a natural number greater than 1;

[0011] The environmental hazard analysis module analyzes the degree of environmental hazard of the monitoring area u, generates a high environmental hazard signal or a low environmental hazard signal of the monitoring area u through analysis, and sends the high environmental hazard signal of the monitoring area u to the alarm reminder module;

[0012] The motion norm monitoring module monitors the moving targets in the monitoring area u, generates the motion non-normative signal or the motion high-normative signal of the monitoring area u through analysis, and sends the motion non-normative signal of the monitoring area u to the alarm reminder module; the alarm reminder module issues a corresponding warning when it receives the equipment hidden danger warning signal, the environmental high hazard signal or the motion non-normative signal.

[0013] Furthermore, the specific analysis process of the production equipment detection and analysis module includes:

[0014] Obtain all production equipment used for textile production in the textile production workshop, mark the corresponding production equipment as i, where i is a natural number greater than 1; obtain real-time monitoring data of all parameters that need to be monitored by production equipment i, compare the real-time monitoring data of all parameters with the corresponding preset data requirements one by one, and if there are parameters whose real-time monitoring data do not meet the corresponding preset data requirements, mark the production equipment i as an early warning object;

[0015] If the real-time monitoring data of all parameters meet the corresponding preset data requirements, the time when the last maintenance of production equipment i was performed is collected and marked as the neighboring check time, and the interval between the current time and the neighboring check time is marked as the maintenance emergency coefficient;

[0016] The preset maintenance emergency coefficient threshold of production equipment i is obtained, and the maintenance emergency coefficient is numerically compared with the corresponding preset maintenance emergency coefficient threshold. If the maintenance emergency coefficient exceeds the preset maintenance emergency coefficient threshold, the production equipment i is marked as a warning object; if there is a warning object in the textile production workshop, an equipment hidden danger warning signal is generated; if there is no warning object in the textile production workshop, an equipment safe operation signal is generated.

[0017] Furthermore, the production equipment detection and analysis module is communicated with the maintenance period precise setting module, which obtains the preset maintenance emergency coefficient threshold of production equipment i through analysis, and sends the preset maintenance emergency coefficient threshold of production equipment i to the production equipment detection and analysis module.

[0018] Furthermore, the specific analysis process of the maintenance period precision setting module is as follows:

[0019] The production date of production equipment i is collected, the interval between the current date and the production date is marked as the production interval value, the total operation time of production equipment i in the historical stage is marked as the operation detection value, and the duration of the corresponding operation process of production equipment i is marked as the operation duration value, and the number of operation duration values ​​that exceed the corresponding preset operation duration threshold in the historical stage is marked as the operation abnormality value;

[0020] The average maintenance interval duration of production equipment i in the historical stage is collected and marked as the maintenance status value. The production equipment decision value is obtained by numerically calculating the production interval value, the operation detection value, the operation abnormality value and the maintenance status value.

[0021] A number of groups of preset production equipment decision value ranges are set in advance for production equipment i, and each group of preset production equipment decision value ranges corresponds to a group of preset maintenance period values; the production equipment decision values ​​are compared with all preset production equipment decision value ranges one by one, and the preset production equipment decision value ranges containing the corresponding production equipment decision values ​​are marked as the optimal period range, and the preset maintenance period value corresponding to the optimal period range is marked as the preset maintenance emergency coefficient threshold of production equipment i.

[0022] Furthermore, the specific analysis process of the environmental hazard analysis module includes:

[0023] A number of detection points are set in the monitoring area u, and the volatility harmfulness coefficient is obtained by detecting and analyzing the volatilization of hazardous gases. The volatility harmfulness coefficient is numerically compared with the preset volatility harmfulness coefficient threshold. If the volatility harmfulness coefficient exceeds the preset volatility harmfulness coefficient threshold, the corresponding detection point is marked as a harmful point;

[0024] If the volatile harmfulness coefficient does not exceed the preset volatile harmfulness coefficient threshold, the deviation value of the temperature at the corresponding detection point compared to the preset suitable temperature standard value is marked as the temperature coefficient, and the humidity coefficient is obtained in the same way; and the air smoke concentration and noise decibel value of the corresponding detection point are collected and marked as the air dust coefficient and noise coefficient respectively;

[0025] The environmental point risk value is obtained by numerically calculating the volatile harmfulness coefficient, temperature coefficient, humidity coefficient, air dust coefficient and noise coefficient, and the environmental point risk value is numerically compared with the preset environmental point risk threshold. If the environmental point risk value exceeds the preset environmental point risk threshold, the corresponding detection point is marked as a harmful point;

[0026] If there are harmful points in the monitoring area u, a high environmental hazard signal for the monitoring area is generated; if there are no harmful points in the monitoring area u, the environmental point risk values ​​of all detection points in the monitoring area u are averaged to obtain the environmental hazard coefficient, and the environmental hazard coefficient is numerically compared with the preset environmental hazard coefficient threshold. If the environmental hazard coefficient exceeds the preset environmental hazard coefficient threshold, a high environmental hazard signal for the monitoring area u is generated; if the environmental hazard coefficient does not exceed the preset environmental hazard coefficient threshold, a low environmental hazard signal for the monitoring area u is generated.

[0027] Furthermore, the specific analysis process of hazardous gas volatilization detection and analysis is as follows:

[0028] The types of harmful gases that need to be monitored in the textile production workshop are obtained, the real-time concentrations of the corresponding types of harmful gases at the corresponding detection points are collected and marked as gas detection data, and a set of preset weight values ​​are set in advance for each type of harmful gas; the product of the gas detection data of the corresponding type of harmful gas and the corresponding preset weight value is marked as gas risk data, and the gas risk data of all types of harmful gases existing in the corresponding detection points are summed and calculated to obtain the volatile toxicity coefficient.

[0029] Furthermore, the specific analysis process of the sports normative monitoring module includes:

[0030] Obtain all isolated spaces in the monitoring area u, monitor whether there are uncertified personnel and conveying equipment entering the isolated space within a unit time, and mark the uncertified personnel and conveying equipment entering the isolated space as moving targets;

[0031] The number of times a moving target appears in the isolated space of the monitoring area u per unit time is obtained and marked as a motion detection frequency value, and the appearance duration of all moving targets in the isolated space of the monitoring area u per unit time is summed up to obtain a motion detection time value, and the moving speed of all moving targets in the isolated space of the monitoring area u per unit time is averaged to obtain a motion detection speed value;

[0032] The regional motion norm coefficient is obtained by numerically calculating the motion frequency detection value, the motion time detection value and the motion speed detection value, and the regional motion norm coefficient is numerically compared with the preset regional motion norm coefficient threshold. If the regional motion norm coefficient exceeds the preset regional motion norm coefficient threshold, a motion non-normative signal of the monitoring area u is generated; if the regional motion norm coefficient does not exceed the preset regional motion norm coefficient threshold, a motion high norm signal of the monitoring area u is generated.

[0033] Furthermore, the alarm reminder module is communicatively connected to the textile production safety planning module. The textile production safety planning module is used to set a detection period, analyze the production supervision status of the textile production workshop during the detection period, generate a workshop management qualified signal or a workshop management abnormal signal through the analysis, and send the workshop management abnormal signal to the alarm reminder module. When the alarm reminder module receives the workshop management abnormal signal, it will issue a corresponding warning.

[0034] Furthermore, the specific analysis process of the textile production safety planning module is as follows:

[0035] The number of times the equipment hidden danger warning signal is generated during the detection period is obtained and marked as the equipment hidden danger warning value, and the number of times the environmental high-hazard signal is generated and the number of times the motion non-standard signal is generated in the monitoring area u are marked as the environmental high-risk detection value and the motion non-standard detection value, respectively. The environmental high-risk detection value and the motion non-standard detection value are numerically compared with the preset environmental high-risk detection threshold and the preset motion non-standard detection threshold, respectively. If the environmental high-risk detection value or the motion non-standard detection value exceeds the corresponding preset threshold, the monitoring area u is marked as a warning area;

[0036] The number of alert areas is obtained and the ratio is calculated with the total number of sub-areas in the textile production workshop to obtain the alert area occupancy value, the textile production planning coefficient is obtained by numerically calculating the equipment hidden danger warning value and the alert area occupancy value, and the textile production planning coefficient is numerically compared with the preset textile production planning coefficient threshold. If the textile production planning coefficient exceeds the preset textile production planning coefficient threshold, a workshop management abnormality signal is generated; if the textile production planning coefficient does not exceed the preset textile production planning coefficient threshold, a workshop management qualified signal is generated.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. In the present invention, all production equipment in the textile production workshop is detected and analyzed through the production equipment detection and analysis module, and the environmental hazard analysis module and the movement standardization monitoring module analyze the environmental hazard degree and movement target control status of each sub-area in the textile production workshop, and remind the management personnel to take corresponding improvement measures when the equipment hidden danger warning signal, environmental high hazard signal or movement non-standard signal is generated, so as to realize the effective monitoring and control of the textile production workshop, and significantly reduce the production risk of the textile workshop and the difficulty of textile production supervision;

[0039] 2. In the present invention, the production supervision status of the textile production workshop during the detection period is analyzed through the textile production safety planning module, and a workshop management qualified signal or a workshop management abnormal signal is generated through analysis. When the workshop management abnormal signal is generated, the management personnel are reminded to strengthen the subsequent supervision of the textile production workshop, which is conducive to the reasonable formulation and planning of the subsequent workshop supervision plan, further reducing the supervision difficulty of the management personnel, and has a high degree of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0041] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0042] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Embodiment 1: Figure 1 As shown, the textile production safety intelligent monitoring and alarm system based on sensor monitoring proposed by the present invention includes a production equipment detection and analysis module, a production workshop partition module, an environmental hazard analysis module, a movement normative monitoring module and an alarm reminder module;

[0045] The production equipment detection and analysis module detects and analyzes all production equipment in the textile production workshop, identifies the warning objects through analysis and generates equipment hidden danger warning signals or equipment safe operation signals, and sends the equipment hidden danger warning signals to the alarm reminder module. When the alarm reminder module receives the equipment hidden danger warning signal, it issues a corresponding warning to remind the management personnel to check, adjust or maintain the corresponding production equipment in time to ensure the safe operation of the production equipment, thereby reducing the risk of textile production; the specific analysis process of the production equipment detection and analysis module is as follows:

[0046] Obtain all production equipment (including spinning equipment, weaving equipment, drying equipment, etc.) used for textile production in the textile production workshop, mark the corresponding production equipment as i, and i is a natural number greater than 1; obtain the real-time monitoring data of all parameters (including operating power, real-time voltage, etc.) that need to be monitored by production equipment i (mainly through sensors to monitor the corresponding parameters of each production equipment and realize data collection), compare the real-time monitoring data of all parameters with the corresponding preset data requirements one by one, if there are parameters whose real-time monitoring data do not meet the corresponding preset data requirements, indicating that there is an operation risk of production equipment i, then the production equipment i is marked as an early warning object;

[0047] If the real-time monitoring data of all parameters meet the corresponding preset data requirements, the time when the last maintenance of production equipment i was performed is collected and marked as the neighboring check time, and the interval between the current time and the neighboring check time is marked as the maintenance emergency coefficient;

[0048] The preset maintenance emergency coefficient threshold of production equipment i is obtained, and the maintenance emergency coefficient is numerically compared with the corresponding preset maintenance emergency coefficient threshold SPi. If the maintenance emergency coefficient exceeds the preset maintenance emergency coefficient threshold SPi, it indicates that the production equipment i currently needs maintenance, and the production equipment i is marked as a warning object; if there is a warning object in the textile production workshop, an equipment hidden danger warning signal is generated; if there is no warning object in the textile production workshop, an equipment safe operation signal is generated.

[0049] Furthermore, the production equipment detection and analysis module is connected to the maintenance deadline precise setting module in communication. The maintenance deadline precise setting module obtains the preset maintenance emergency coefficient threshold value SPi of the production equipment i through analysis, and sends the preset maintenance emergency coefficient threshold value SPi of the production equipment i to the production equipment detection and analysis module, providing information support for the analysis process of the production equipment detection and analysis module to ensure the accuracy of its analysis results. The specific analysis process of the maintenance deadline precise setting module is as follows:

[0050] The production date of production equipment i is collected, the interval between the current date and the production date is marked as the production interval value, the total operation time of production equipment i in the historical stage is marked as the operation detection value, and the duration of the corresponding operation process of production equipment i is marked as the operation duration value, and the number of operation duration values ​​that exceed the corresponding preset operation duration threshold in the historical stage is marked as the operation abnormality value;

[0051] The average maintenance interval of production equipment i in the historical stage is collected and marked as the maintenance status value. The production interval value WYi, the operation detection value WLi, the operation abnormality value WSi and the maintenance status value WPi are numerically calculated to obtain the production equipment decision value SFi; wherein, eq1, eq2, eq3, eq4 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the production equipment decision value SFi, the worse the equipment condition of the production equipment i is, and the shorter the maintenance interval time needs to be relatively;

[0052] A plurality of preset production equipment decision value ranges are set in advance for production equipment i, and each preset production equipment decision value range corresponds to a set of preset maintenance period values; wherein the values ​​of the preset maintenance period values ​​are all positive numbers; and, the larger the value of the preset production equipment decision value range is, the smaller the value of the preset maintenance period value adapted thereto is;

[0053] The production equipment decision value SFi is compared with all preset production equipment decision value ranges one by one, the preset production equipment decision value range containing the corresponding production equipment decision value SFi is marked as the optimal deadline range, and the preset maintenance deadline value corresponding to the optimal deadline range is marked as the preset maintenance emergency coefficient threshold SPi of production equipment i.

[0054] The production workshop partition module is used to divide the textile production workshop into several sub-areas, and mark the corresponding sub-areas as monitoring areas u, where u is a natural number greater than 1; the environmental hazard analysis module analyzes the degree of environmental hazard of the monitoring area u, and generates a high environmental hazard signal or a low environmental hazard signal of the monitoring area u through analysis;

[0055] And the high environmental hazard signal of the monitoring area u is sent to the alarm reminder module. When the alarm reminder module receives the high environmental hazard signal, it issues a corresponding warning to remind the management personnel to conduct environmental inspection and regulation in the monitoring area in a timely manner to ensure the environmental suitability of each location in the production workshop, which is conducive to ensuring the safety of personnel and production stability in the production workshop; the specific analysis process of the environmental hazard analysis module is as follows:

[0056] A number of detection points are set in the monitoring area u, and the volatile harmfulness coefficient is obtained through the detection and analysis of the volatilization of harmful gases, specifically: the types of harmful gases that need to be monitored in the textile production workshop are obtained, the real-time concentrations of the corresponding types of harmful gases at the corresponding detection points are collected and marked as gas detection data, and each type of harmful gas is set in advance to correspond to a set of preset weight values; wherein the value of the preset weight value is greater than zero, and the greater the harm caused by the corresponding type of harmful gas, the greater the value of the preset weight value matching it;

[0057] The product of the gas detection data of the corresponding type of harmful gas and the corresponding preset weight value is marked as gas risk data, and the gas risk data of all types of harmful gases existing at the corresponding detection point are summed up to obtain the volatile harmfulness coefficient; it should be noted that the larger the value of the volatile harmfulness coefficient, the higher the degree of air hazard at the corresponding detection point;

[0058] Compare the volatile harmfulness coefficient with the preset volatile harmfulness coefficient threshold. If the volatile harmfulness coefficient exceeds the preset volatile harmfulness coefficient threshold, it indicates that the air hazard level of the corresponding detection point is high, which is not conducive to ensuring the safety of workshop personnel, and the corresponding detection point is marked as a harmful point.

[0059] If the volatile harmfulness coefficient does not exceed the preset volatile harmfulness coefficient threshold, the deviation value of the temperature at the corresponding detection point compared to the preset suitable temperature standard value is marked as the temperature coefficient, and the deviation value of the humidity at the corresponding detection point compared to the preset suitable humidity standard value is marked as the humidity coefficient; and the air smoke concentration and noise decibel value at the corresponding detection point are collected and marked as the air dust coefficient and the noise coefficient respectively;

[0060] The environmental point risk value XLu is obtained by numerically calculating the volatile harmfulness coefficient GYu, the temperature coefficient LHu, the humidity coefficient NFu, the air dust coefficient TWu and the noise coefficient ZFu through the formula XLu=(b1*GYu+b2*LHu+b3*NFu+b4*TWu+b5*ZFu) / 5; wherein b1, b2, b3, b4 and b5 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the environmental point risk value XLu is, the higher the degree of environmental hazard of the corresponding detection point in the monitoring area u is in general;

[0061] The environmental point risk value XLu is numerically compared with the preset environmental point risk threshold. If the environmental point risk value XLu exceeds the preset environmental point risk threshold, it indicates that the environmental hazard level of the corresponding detection point in the monitoring area u is generally high, and the corresponding detection point is marked as a harmful point;

[0062] If there are harmful points in the monitoring area u, it means that the environment of the monitoring area u has a greater adverse impact on the production safety of the workshop, and a high environmental hazard signal of the monitoring area is generated;

[0063] If there is no harmful point in the monitoring area u, the environmental point risk values ​​of all detection points in the monitoring area u are averaged to obtain the environmental hazard coefficient, and the environmental hazard coefficient is numerically compared with the preset environmental hazard coefficient threshold;

[0064] If the environmental hazard coefficient exceeds the preset environmental hazard coefficient threshold, it indicates that the environment of the monitoring area u has a greater adverse impact on the workshop production safety, and a high environmental hazard signal for the monitoring area u is generated; if the environmental hazard coefficient does not exceed the preset environmental hazard coefficient threshold, it indicates that the environment of the monitoring area u has a smaller adverse impact on the safety of the workshop personnel, and a low environmental hazard signal for the monitoring area u is generated.

[0065] The motion normative monitoring module monitors the moving targets in the monitoring area u, generates the motion non-normative signal or the motion high-normative signal of the monitoring area u through analysis, and sends the motion non-normative signal of the monitoring area u to the alarm reminder module. When the alarm reminder module receives the motion non-normative signal, it issues a corresponding warning to promptly remind the management personnel to strengthen the control of the moving targets in the monitoring area u, reduce potential risks and hidden dangers, and ensure the safety of textile production; the specific analysis process of the motion normative monitoring module is as follows:

[0066] Obtain all isolated spaces in the monitoring area u (i.e., the space involved in the production process of the corresponding production equipment, the isolated space has an isolation boundary, and in principle, transportation equipment and workshop personnel not involved in the equipment process are not allowed to enter). Through monitoring, whether there are uncertified personnel (i.e., operators who are not operating the corresponding production equipment) and transportation equipment entering the isolated space within a unit time, the uncertified personnel and transportation equipment entering the isolated space are marked as moving targets;

[0067] The number of times a moving target appears in the isolated space of the monitoring area u per unit time is obtained and marked as a motion detection frequency value, and the appearance duration of all moving targets in the isolated space of the monitoring area u per unit time is summed up to obtain a motion detection time value, and the moving speed of all moving targets in the isolated space of the monitoring area u per unit time is averaged to obtain a motion detection speed value;

[0068] By formula The motion detection frequency value MLu, the motion detection time value MRu and the motion detection speed value MPu are numerically calculated to obtain the regional motion specification coefficient FYu; wherein np1, np2 and np3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the regional motion specification coefficient FYu is, the worse the control status of the moving target in the monitoring area u is, and the greater the potential hidden danger is;

[0069] The regional motion norm coefficient FYu is numerically compared with the preset regional motion norm coefficient threshold. If the regional motion norm coefficient FYu exceeds the preset regional motion norm coefficient threshold, it indicates that the control status of the moving target in the monitoring area u is poor and the potential hidden dangers are large, then a non-normative motion signal is generated for the monitoring area u; if the regional motion norm coefficient FYu does not exceed the preset regional motion norm coefficient threshold, it indicates that the control status of the moving target in the monitoring area u is good and the potential hidden dangers are small, then a high-normative motion signal is generated for the monitoring area u.

[0070] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the alarm reminder module is communicatively connected to the textile production safety planning module, and the textile production safety planning module is used to set a detection period, preferably, the detection period is twenty days; the production supervision status of the textile production workshop during the detection period is analyzed, and a workshop management qualified signal or a workshop management abnormal signal is generated through the analysis;

[0071] And the abnormal workshop management signal is sent to the alarm reminder module. When the alarm reminder module receives the abnormal workshop management signal, it issues a corresponding warning. When the management personnel receive the corresponding warning, they strengthen the subsequent supervision of the textile production workshop, which is conducive to the reasonable formulation and planning of the subsequent workshop supervision plan, reducing the supervision difficulty of the management personnel, and has a high degree of intelligence; the specific analysis process of the textile production safety planning module is as follows:

[0072] The number of times the equipment hidden danger warning signal is generated during the detection period is obtained and marked as the equipment hidden danger warning value, and the number of times the environmental high-hazard signal is generated and the number of times the motion non-standard signal is generated in the monitoring area u are marked as the environmental high-risk detection value and the motion non-standard detection value, respectively. The environmental high-risk detection value and the motion non-standard detection value are numerically compared with the preset environmental high-risk detection threshold and the preset motion non-standard detection threshold, respectively. If the environmental high-risk detection value or the motion non-standard detection value exceeds the corresponding preset threshold, indicating that the management status of the monitoring area u during the detection period is poor, the monitoring area u is marked as a warning area;

[0073] The number of alert areas is obtained and the ratio is calculated with the total number of sub-areas in the textile production workshop to obtain the alert area occupancy value. The equipment hidden danger warning value QS and the alert area occupancy value QF are numerically calculated by the formula QL=uy1*QS+uy2*QF to obtain the textile production planning coefficient QL; wherein, uy1 and uy2 are preset proportional coefficients, uy2>uy1>0.15; and the larger the value of the textile production planning coefficient QL, the worse the overall management performance of the textile production workshop during the detection period;

[0074] The textile production planning coefficient is numerically compared with the preset textile production planning coefficient threshold. If the textile production planning coefficient exceeds the preset textile production planning coefficient threshold, it indicates that the management performance of the textile production workshop during the detection period is generally poor, and it is necessary to strengthen the control of the textile production workshop in the future, then a workshop management abnormality signal is generated; if the textile production planning coefficient does not exceed the preset textile production planning coefficient threshold, it indicates that the management performance of the textile production workshop during the detection period is generally good, then a workshop management qualified signal is generated.

[0075] The working principle of the present invention is as follows: when in use, all production equipment in a textile production workshop is detected and analyzed through a production equipment detection and analysis module to identify warning objects and generate equipment hidden danger warning signals or equipment safe operation signals; a production workshop partitioning module is used to divide the textile production workshop into regions; an environmental hazard analysis module analyzes the degree of environmental hazard of all sub-regions in the textile production workshop to generate environmental high hazard signals or environmental low hazard signals for the corresponding sub-regions; a motion normative monitoring module monitors the motion targets of each sub-region in the textile production workshop and generates motion non-normative signals or motion high-normative signals for the corresponding sub-regions; when an equipment hidden danger warning signal, an environmental high hazard signal or a motion non-normative signal is generated, an early warning is issued to remind management personnel, which is conducive to management personnel to make corresponding improvement measures in a timely manner, realize effective monitoring and control of textile production workshops, significantly reduce the production risk of textile workshops and the difficulty of textile production supervision, have a high degree of intelligence, are suitable for various types of textile production enterprises, and provide a strong guarantee for improving production safety levels and production efficiency.

[0076] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. The intelligent monitoring and alarm system for textile production safety based on sensor monitoring is characterized by: It includes a production equipment detection and analysis module, a production workshop partition module, an environmental hazard analysis module, a motion normative monitoring module and an alarm reminder module; the production equipment detection and analysis module detects and analyzes all production equipment in the textile production workshop, identifies warning objects through analysis and generates equipment hidden danger warning signals or equipment safe operation signals; The production workshop partition module is used to divide the textile production workshop into several sub-areas, and mark the corresponding sub-areas as monitoring areas u, where u is a natural number greater than 1; The environmental hazard analysis module analyzes the degree of environmental hazard of the monitoring area u, and generates a high environmental hazard signal or a low environmental hazard signal of the monitoring area u through analysis; The motion norm monitoring module monitors the moving targets in the monitoring area u, and generates motion non-normative signals or motion high-normative signals in the monitoring area u through analysis; the alarm reminder module issues a corresponding warning when it receives equipment hidden danger warning signals, environmental high hazard signals or motion non-normative signals.

2. The intelligent monitoring and alarm system for textile production safety based on sensor monitoring according to claim 1 is characterized in that: The specific analysis process of the production equipment detection and analysis module includes: All production equipment used for textile production in the textile production workshop is obtained, and the corresponding production equipment is marked as i, where i is a natural number greater than 1; if there is a parameter whose real-time monitoring data does not meet the corresponding preset data requirements, the production equipment i is marked as an early warning object; If the real-time monitoring data of all parameters meet the corresponding preset data requirements, the maintenance emergency coefficient will be numerically compared with the corresponding preset maintenance emergency coefficient threshold. If the maintenance emergency coefficient exceeds the preset maintenance emergency coefficient threshold, the production equipment i will be marked as a warning object; if there is a warning object in the textile production workshop, an equipment hidden danger warning signal is generated; if there is no warning object in the textile production workshop, an equipment safe operation signal is generated.

3. The intelligent monitoring and alarm system for textile production safety based on sensor monitoring according to claim 2 is characterized in that: The production equipment detection and analysis module is communicated with the maintenance period precise setting module. The maintenance period precise setting module obtains the preset maintenance emergency coefficient threshold of production equipment i through analysis, and sends the preset maintenance emergency coefficient threshold of production equipment i to the production equipment detection and analysis module.

4. The intelligent monitoring and alarm system for textile production safety based on sensor monitoring according to claim 3 is characterized in that: The specific analysis process of the maintenance period precision setting module is as follows: The production equipment decision value of production equipment i is obtained by numerically calculating the production interval value, operation detection value, operation abnormality value and maintenance status value; the production equipment decision value is compared with all corresponding preset production equipment decision value ranges one by one to determine the optimal range of the deadline, and the preset maintenance deadline value corresponding to the optimal range of the deadline is marked as the preset maintenance emergency coefficient threshold of production equipment i.

5. The intelligent monitoring and alarm system for textile production safety based on sensor monitoring according to claim 1 is characterized in that: The specific analysis process of the environmental hazard analysis module includes: The volatile harmfulness coefficient is obtained by detecting and analyzing the volatilization of hazardous gases. If the volatile harmfulness coefficient exceeds the preset volatile harmfulness coefficient threshold, the corresponding detection point is marked as a harmful point; if the volatile harmfulness coefficient does not exceed the preset volatile harmfulness coefficient threshold, the environmental point risk value is obtained by numerically calculating the volatile harmfulness coefficient, temperature coefficient, humidity coefficient, air dust coefficient and noise coefficient. If the environmental point risk value exceeds the preset environmental point risk threshold, the corresponding detection point is marked as a harmful point; If there are harmful points in the monitoring area u, a high environmental hazard signal of the monitoring area is generated; if there are no harmful points in the monitoring area u, the environmental point risk values ​​of all detection points in the monitoring area u are averaged to obtain the environmental hazard coefficient. If the environmental hazard coefficient exceeds the preset environmental hazard coefficient threshold, a high environmental hazard signal of the monitoring area u is generated; if the environmental hazard coefficient does not exceed the preset environmental hazard coefficient threshold, a low environmental hazard signal of the monitoring area u is generated.

6. The intelligent monitoring and alarm system for textile production safety based on sensor monitoring according to claim 5 is characterized in that: The specific analysis process of hazardous gas volatilization detection and analysis is as follows: The types of harmful gases that need to be monitored in the textile production workshop are obtained, the real-time concentrations of the corresponding types of harmful gases at the corresponding detection points are collected and marked as gas detection data, the product of the gas detection data of the corresponding type of harmful gases and the corresponding preset weight value is marked as gas risk data, and the gas risk data of all types of harmful gases existing in the corresponding detection points are summed up and calculated to obtain the volatile harmfulness coefficient.

7. The intelligent monitoring and alarm system for textile production safety based on sensor monitoring according to claim 1 is characterized in that: The specific analysis process of the sports normative monitoring module includes: The regional motion norm coefficient is obtained by numerically calculating the motion frequency detection value, motion timing detection value and motion speed detection value. If the regional motion norm coefficient exceeds the preset regional motion norm coefficient threshold, a motion non-normative signal of the monitoring area u is generated; if the regional motion norm coefficient does not exceed the preset regional motion norm coefficient threshold, a motion high norm signal of the monitoring area u is generated.

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