Canoe production safety monitoring and control system based on the Internet of Things

Abstract

The present invention belongs to the technical field of kayak production supervision, and specifically is a kayak production safety monitoring and control system based on the Internet of Things, including an air and dust hazard analysis module, a personnel normative monitoring module, an equipment execution deviation detection module and a production monitoring and control terminal; the present invention analyzes the air and dust hazard status in a kayak production workshop through the air and dust hazard analysis module, monitors the kayak production workshop through the personnel normative monitoring module, and performs execution monitoring on all production equipment in operation in the kayak production workshop through the equipment execution deviation detection module, thereby ensuring efficient and high-quality production of kayaks and improving production safety, and comprehensively analyzing the degree of production safety hazards in the kayak production workshop through the production safety hazard assessment module, thereby timely strengthening production supervision of the kayak production workshop, further ensuring subsequent production safety, and significantly reducing the difficulty of kayak production safety supervision.

Description

Technical Field

[0001] The present invention relates to the technical field of kayak production supervision, and in particular to a kayak production safety monitoring and control system based on the Internet of Things. Background Art

[0002] A kayak is a watercraft that uses paddles as a propulsion tool. It is usually operated by one or more people. It is a modern competitive and leisure tool. There are many links in the production process of kayaks, and each link has potential safety hazards. Therefore, it is necessary to monitor and control the kayak production process;

[0003] At present, it is difficult to reasonably analyze the gas and dust hazards, personnel standardization and equipment operation risks in the kayak production workshop and comprehensively judge the production safety status during the kayak production safety monitoring and control. Management personnel are unable to make corresponding improvement measures in a timely and targeted manner, which is not conducive to significantly reducing the safety hazards in the kayak production process and making production supervision difficult.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a kayak production safety monitoring and control system based on the Internet of Things, which solves the problem that the prior art is difficult to reasonably analyze the gas and dust hazards, personnel standardization and equipment operation risks in the kayak production workshop and comprehensively judge the production safety status, which is not conducive to significantly reducing the safety hazards in the kayak production process and the difficulty of production supervision.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The kayak production safety monitoring and control system based on the Internet of Things includes a gas and dust hazard analysis module, a personnel normative monitoring module, an equipment execution deviation detection module, and a production monitoring and control terminal;

[0008] The gas dust hazard analysis module analyzes the gas dust hazard status in the kayak production workshop, generates a gas dust high hazard signal or a gas dust low hazard signal through analysis, and sends the gas dust high hazard signal to the production monitoring and control end;

[0009] The personnel normative monitoring module monitors the kayak production workshop through surveillance cameras, collects surveillance images of the kayak production workshop, and determines whether there are unsafe personnel based on the surveillance images and through image recognition technology, and sends the unsafe personnel to the production monitoring and control end;

[0010] The equipment execution deviation detection module obtains all the production equipment in operation in the canoe production workshop, monitors the corresponding production equipment, determines whether the corresponding production equipment is in an execution risk state through analysis, and sends the production equipment in an execution risk state to the production monitoring and control end.

[0011] Furthermore, the specific analysis process of the gas dust harmfulness analysis module is as follows:

[0012] Several detection points are set in the canoe production workshop, and the real-time dust concentration and real-time harmful gas concentration of the corresponding detection points are collected. The real-time dust concentration and the real-time harmful gas concentration are numerically compared with the preset real-time dust concentration threshold and the preset real-time harmful gas concentration threshold, respectively. If the real-time dust concentration or the real-time harmful gas concentration exceeds the corresponding preset threshold, the corresponding detection point is marked as a dust warning point;

[0013] The number of gas and dust warning points in the canoe production workshop is obtained and the ratio is calculated with the total number of detection points to obtain the gas and dust abnormality detection value, and the real-time dust concentration of all detection points is averaged to obtain the dust detection value, and the real-time harmful gas concentration of all detection points is averaged to obtain the gas risk detection value;

[0014] The gas dust harmfulness analysis value is obtained by numerically calculating the gas dust abnormality detection value, dust detection value and gas risk detection value, and the gas dust harmfulness analysis value is numerically compared with the preset gas dust harmfulness analysis threshold value. If the gas dust harmfulness analysis value exceeds the preset gas dust harmfulness analysis threshold value, a gas dust high hazard signal is generated; if the gas dust harmfulness analysis value does not exceed the preset gas dust harmfulness analysis threshold value, a gas dust low hazard signal is generated.

[0015] Furthermore, the gas and dust hazard analysis module is communicatively connected to the ventilation effectiveness detection module, and the gas and dust hazard analysis module sends a gas and dust low hazard signal to the ventilation effectiveness detection module. When the ventilation effectiveness detection module receives the gas and dust low hazard signal, it analyzes the ventilation performance of the canoe production workshop, and generates a ventilation qualified signal or a ventilation unqualified signal through the analysis, and sends the ventilation qualified signal or the ventilation unqualified signal to the production monitoring and control end.

[0016] Furthermore, the specific operation process of the ventilation effectiveness detection module includes:

[0017] All vents in the kayak production workshop are obtained, the exhaust speed and exhaust pressure of the corresponding vents are collected, the exhaust speed and exhaust pressure of the corresponding vents are numerically calculated to obtain ventilation detection values, and the ventilation detection values ​​of all vents are averaged and variance calculated to obtain ventilation decision values ​​and ventilation deviation values;

[0018] The ventilation decision value and the ventilation deviation value are numerically compared with the preset ventilation decision threshold and the preset ventilation deviation threshold respectively. If the ventilation decision value exceeds the preset ventilation decision threshold and the ventilation deviation value does not exceed the preset ventilation deviation threshold, a ventilation qualified signal is generated; if the ventilation decision value does not exceed the preset ventilation decision threshold and the ventilation deviation value does not exceed the preset ventilation deviation threshold, a ventilation unqualified signal is generated; in other cases, a precise ventilation assessment and analysis is carried out.

[0019] Furthermore, the specific analysis process of ventilation precision evaluation and analysis is as follows:

[0020] The ventilation detection value is numerically compared with the preset ventilation detection threshold. If the ventilation detection value does not exceed the preset ventilation detection threshold, the corresponding vent is marked as a non-standard vent; the number of non-standard vents in the kayak production workshop is obtained and the ratio is calculated with the total number of vents to obtain the vent number outlier, and the ventilation detection value with the smallest value is marked as the ventilation low amplitude;

[0021] The ventilation evaluation coefficient is obtained by numerically calculating the ventilation decision value, the outlier value of the number of vents and the ventilation low amplitude, and the ventilation evaluation coefficient is numerically compared with the preset ventilation evaluation coefficient threshold. If the ventilation evaluation coefficient exceeds the preset ventilation evaluation coefficient threshold, a ventilation qualification signal is generated; if the ventilation evaluation coefficient does not exceed the preset ventilation evaluation coefficient threshold, a ventilation failure signal is generated.

[0022] Furthermore, the specific operation process of the device executing the deviation detection module includes:

[0023] Obtain the operating parameters required for monitoring of the corresponding production equipment, mark the operating parameters whose real-time detection data do not meet the corresponding data requirements as risk parameters, and mark the deviation value of the real-time detection data of the corresponding risk parameters compared with the corresponding preset data requirements as the risk performance value;

[0024] Each group of operating parameters is set in advance to correspond to a group of preset safety impact values, the product of the risk manifestation value of the corresponding risk parameter and the corresponding preset safety impact value is marked as a risk judgment value, the sum of the risk judgment values ​​of all risk parameters is marked as a deviation risk value, the deviation risk value is numerically compared with the corresponding preset deviation risk threshold, if the deviation risk value exceeds the preset deviation risk threshold, it is judged that the corresponding production equipment is in an execution risk state.

[0025] Furthermore, the production monitoring and control end is communicated with the production safety hazard assessment module, which is used to set a detection period, analyze the degree of production safety hazards in the canoe production workshop during the detection period, and generate a high production safety hazard signal or a low production safety hazard signal through analysis, and send the high production safety hazard signal to the production monitoring and control end.

[0026] Furthermore, the specific analysis process of the production safety hazard assessment module is as follows:

[0027] The number of times that a high-hazard signal of gas and dust is generated during the detection period is collected and marked as a gas and dust hazard warning value, and the number of times that the corresponding operator is marked as a non-safe person during the detection period is defined as a non-standard frequency, and the number of personnel marked as non-safe personnel during the detection period is defined as a non-standard personnel inspection value, and the non-standard frequencies of all operators are averaged to obtain a non-standard frequency inspection value;

[0028] Determine the abnormal equipment within the detection period through analysis, mark the number of abnormal equipment as the abnormal equipment number inspection value, and mark the ratio of the production equipment analysis value of the corresponding abnormal equipment to the corresponding preset production equipment analysis threshold as the production equipment inspection value, and calculate the average of the production equipment inspection values ​​of all production equipment running within the detection period to obtain the production equipment inspection value;

[0029] The safety hazard assessment value is obtained by numerically calculating the gas and dust hazard warning value, non-standard human inspection value, non-standard frequency inspection value, abnormal equipment number inspection value and production equipment condition value, and the safety hazard assessment value is numerically compared with the preset safety hazard assessment threshold. If the safety hazard assessment value exceeds the preset safety hazard assessment threshold, a high production safety hazard signal is generated; if the safety hazard assessment value does not exceed the preset safety hazard assessment threshold, a low production safety hazard signal is generated.

[0030] Furthermore, the specific analysis process of determining the abnormal devices within the detection period is as follows:

[0031] The duration of each time the corresponding production equipment is in the execution risk state during the detection period is obtained, and the duration of all the corresponding production equipment in the execution risk state during the detection period is summed up to obtain the production equipment risk value, and the number of occurrences when the duration exceeds the preset duration threshold is marked as a high duration frequency;

[0032] And obtain the total operating time of the corresponding production equipment during the detection period, perform numerical calculations on the total operating time, the dangerous time value of the production equipment and the high continuous frequency to obtain the production equipment analysis value, and perform numerical comparison on the production equipment analysis value with the corresponding preset production equipment analysis threshold. If the production equipment analysis value exceeds the corresponding preset production equipment analysis threshold, the corresponding production equipment will be marked as an abnormal device.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. In the present invention, by reasonably analyzing the gas and dust hazards, personnel standardization and equipment operation risks in the kayak production workshop and judging the production safety status, it is beneficial to ensure the efficient and high-quality production of kayaks and improve production safety. In addition, through the production safety hazard assessment module, the production safety hazard level of the kayak production workshop is comprehensively analyzed to strengthen the production supervision of the kayak production workshop in time, further ensure the subsequent production safety, significantly reduce the difficulty of kayak production safety supervision, and have a high degree of intelligence;

[0035] 2. In the present invention, the low-hazard signal of gas and dust is sent to the ventilation effectiveness detection module through the gas and dust hazard analysis module. The ventilation effectiveness detection module will analyze the ventilation performance of the canoe production workshop. When it is judged that the dust gas hazard inside the canoe production workshop is relatively small, it can reasonably analyze the ventilation execution status, and perform ventilation regulation and inspection of the corresponding vents when a ventilation failure signal is generated, thereby ensuring the ventilation performance of the canoe production workshop and further reducing the production risk of the canoe production workshop. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0037] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0038] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0039] 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.

[0040] Embodiment 1: Figure 1 As shown, the kayak production safety monitoring and control system based on the Internet of Things proposed by the present invention includes a gas dust hazard analysis module, a personnel normative monitoring module, an equipment execution deviation detection module and a production monitoring and control terminal; it should be noted that the materials required for kayak production are polyethylene plastic, glass fiber cloth or resin, etc., and the production method of kayaks is mainly thermoforming / blister forming, which is to heat and soften the material, put it into a mold for molding or adsorb it on the surface of the mold and then cool it for molding;

[0041] The gas dust hazard analysis module analyzes the gas dust hazard status in the canoe production workshop, generates a gas dust high hazard signal or a gas dust low hazard signal through analysis, and sends the gas dust high hazard signal to the production monitoring and control end. When the production monitoring and control end receives the gas dust high hazard signal, it issues an early warning, and the management personnel promptly speed up the ventilation efficiency of the canoe production workshop, and continue to focus on the dust gas hazard status in the canoe production workshop, reduce the harm to the health of the workshop personnel, and ensure production safety; the specific analysis process of the gas dust hazard analysis module is as follows:

[0042] Several detection points are set in the canoe production workshop, and the real-time dust concentration and real-time harmful gas concentration of the corresponding detection points are collected. The real-time dust concentration and real-time harmful gas concentration are numerically compared with the preset real-time dust concentration threshold and the preset real-time harmful gas concentration threshold. If the real-time dust concentration or the real-time harmful gas concentration exceeds the corresponding preset threshold, it indicates that the corresponding detection point poses a greater threat to personnel safety, and the corresponding detection point is marked as a dust warning point;

[0043] The number of gas and dust warning points in the canoe production workshop is obtained and the ratio is calculated with the total number of detection points to obtain the gas and dust abnormality detection value, and the real-time dust concentration of all detection points is averaged to obtain the dust detection value, and the real-time harmful gas concentration of all detection points is averaged to obtain the gas risk detection value;

[0044] The gas dust hazard analysis value YH is obtained by numerically calculating the gas dust abnormality detection value QX, the dust detection value QF and the gas risk detection value QP through the formula YH=mg1*QX+(mg2*QF+mg3*QP) / mg1; wherein, mg1, mg2 and mg3 are preset proportional coefficients, mg1>mg3>mg2>0; and the larger the value of the gas dust hazard analysis value YH, the more serious the environmental hazard of the kayak production workshop;

[0045] The gas dust harmfulness analysis value YH is numerically compared with the preset gas dust harmfulness analysis threshold. If the gas dust harmfulness analysis value YH exceeds the preset gas dust harmfulness analysis threshold, it indicates that the environmental hazard of the canoe production workshop is generally serious, and a gas dust high hazard signal is generated; if the gas dust harmfulness analysis value YH does not exceed the preset gas dust harmfulness analysis threshold, it indicates that the environmental hazard of the canoe production workshop is generally light, and a gas dust low hazard signal is generated.

[0046] The personnel normative monitoring module monitors the canoe production workshop through surveillance cameras, collects surveillance images of the canoe production workshop, and determines whether there are unsafe personnel based on the surveillance images and through image recognition technology, and sends the unsafe personnel to the production monitoring and control end so as to promptly remind the relevant personnel to make adjustments and reduce the risk of the canoe production process. It should be noted that the operators in the canoe production workshop need to wear appropriate work clothes and work shoes, and wear corresponding personal protective equipment, such as safety helmets, goggles, earplugs and masks. If the corresponding operator does not meet the above wearing requirements, he or she will be judged as an unsafe person.

[0047] The equipment execution deviation detection module obtains all the production equipment (including heating equipment, cooling equipment, spraying equipment, etc.) in operation in the kayak production workshop, monitors the execution of the corresponding production equipment, and determines whether the corresponding production equipment is in an execution risk state through analysis, and sends the production equipment in the execution risk state to the production monitoring and control end. It can reasonably analyze and accurately judge the operation performance of each production equipment, and timely feedback the operation risk of each production equipment, which is conducive to the management personnel to make corresponding improvement measures, thereby ensuring the efficient and high-quality production of kayaks and improving production safety; the specific operation process of the equipment execution deviation detection module is as follows:

[0048] Obtain the operating parameters that need to be monitored for the corresponding production equipment (such as the voltage, current, temperature, etc. of the corresponding production equipment), compare the real-time detection data of the corresponding operating parameters with the corresponding preset data requirements, mark the operating parameters whose real-time detection data do not meet the corresponding data requirements as risk parameters, and mark the deviation value of the real-time detection data of the corresponding risk parameters compared with the corresponding preset data requirements as a risk performance value;

[0049] Each set of operating parameters is set in advance to correspond to a set of preset safety impact values. The greater the safety hazard caused by the deviation of the corresponding operating parameters, the greater the value of the preset safety impact value adapted thereto; the product of the risk performance value of the corresponding risk parameter and the corresponding preset safety impact value is marked as the risk judgment value, and the sum of the risk judgment values ​​of all risk parameters is marked as the biased risk value; it should be noted that the larger the value of the biased detection value, the greater the current operating risk of the corresponding production equipment;

[0050] The deviation risk value is compared with the corresponding preset deviation risk threshold. If the deviation risk value exceeds the preset deviation risk threshold, it indicates that the current operation of the corresponding production equipment is unsafe, and the corresponding production equipment is judged to be in an execution risk state.

[0051] Further, the production monitoring and control terminal is connected to the production safety hazard assessment module in communication, and the production safety hazard assessment module is used to set a detection period, preferably, the detection period is forty-eight hours; the degree of production safety hazard of the kayak production workshop during the detection period is analyzed, and a production safety high hazard signal or a production safety low hazard signal is generated through analysis;

[0052] The high-risk production safety signal is sent to the production monitoring and control end. When the production monitoring and control end receives the high-risk production safety signal, it issues an early warning to remind management personnel to promptly strengthen production supervision in the kayak production workshop to further ensure subsequent production safety. The specific analysis process of the production safety hazard assessment module is as follows:

[0053] The number of times that a high-hazard signal of gas and dust is generated during the detection period is collected and marked as a gas and dust hazard warning value, and the number of times that the corresponding operator is marked as a non-safe person during the detection period is defined as a non-standard frequency, wherein the larger the value of the non-standard frequency, the greater the safety hazard brought by the corresponding operator; and the number of personnel marked as non-safe personnel during the detection period is defined as a non-standard person inspection value, and the non-standard frequencies of all operators are averaged to obtain a non-standard frequency inspection value;

[0054] The duration of each time the corresponding production equipment is in an execution risk state during the detection period is obtained, and the duration of all the execution risk states of the corresponding production equipment during the detection period is calculated by summing up to obtain the production equipment risk value, and the duration of the corresponding execution risk state is numerically compared with the corresponding preset duration threshold, and the number of occurrences when the duration exceeds the preset duration threshold is marked as a high duration frequency;

[0055] And obtain the total operating time of the corresponding production equipment during the detection period (that is, the total time the corresponding production equipment is in operation during the detection period), through the formula The total operation time LS, the production equipment risk time value LD and the high continuous frequency LF are numerically calculated to obtain the production equipment analysis value LK; wherein, uw1, uw2 and uw3 are preset proportional coefficients, uw3>uw2>uw1>0; and the larger the value of the production equipment analysis value LK, the more abnormal the operation of the corresponding production equipment during the detection period is, and the greater the safety hazard is;

[0056] The production equipment analysis value LK is numerically compared with the corresponding preset production equipment analysis threshold. If the production equipment analysis value LK exceeds the corresponding preset production equipment analysis threshold, it indicates that the operation of the corresponding production equipment during the detection period is relatively abnormal and the potential safety hazard is relatively large, then the corresponding production equipment is marked as abnormal equipment;

[0057] The number of abnormal equipment is marked as the abnormal equipment number inspection value, and the ratio of the production equipment analysis value of the corresponding abnormal equipment to the corresponding preset production equipment analysis threshold is marked as the production equipment inspection value, and the production equipment inspection values ​​of all production equipment running within the inspection period are averaged to obtain the production equipment inspection value;

[0058] By formula The gas dust hazard warning value PY, the non-standard human inspection value PN, the non-standard frequency inspection value PW, the abnormal equipment number inspection value PX and the production equipment inspection value PK are numerically calculated to obtain the safety hazard assessment value FM; wherein c1, c2, c3, c4, c5 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the safety hazard assessment value FM is, the greater the safety hazard existing in the kayak production workshop during the detection period is, and the more production supervision needs to be strengthened;

[0059] The safety hazard assessment value FM is numerically compared with the preset safety hazard assessment threshold. If the safety hazard assessment value FM exceeds the preset safety hazard assessment threshold, it indicates that the safety hazards existing in the canoe production workshop during the detection period are generally large, and production supervision needs to be strengthened in time, then a high production safety hazard signal is generated; if the safety hazard assessment value FM does not exceed the preset safety hazard assessment threshold, it indicates that the safety hazards existing in the canoe production workshop during the detection period are generally small, then a low production safety hazard signal is generated.

[0060] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the gas dust harmfulness analysis module is communicatively connected to the ventilation effectiveness detection module, the gas dust harmfulness analysis module sends a gas dust low hazard signal to the ventilation effectiveness detection module, and when the ventilation effectiveness detection module receives the gas dust low hazard signal, it analyzes the ventilation performance of the canoe production workshop and generates a ventilation qualified signal or a ventilation unqualified signal through the analysis;

[0061] And the ventilation qualified signal or ventilation unqualified signal is sent to the production monitoring and control end. When the production monitoring and control end receives the ventilation unqualified signal, it will issue an early warning. When it is judged that the dust gas hazard inside the kayak production workshop is relatively small, it can reasonably analyze the ventilation execution performance, so as to remind the management personnel to conduct cause investigation and analysis in time, and conduct ventilation control and inspection of the corresponding vents, so as to ensure the ventilation performance of the kayak production workshop and further reduce the production risk of the kayak production workshop. The specific operation process of the ventilation effectiveness detection module is as follows:

[0062] All the vents in the kayak production workshop are obtained, and the exhaust speed and exhaust pressure of the corresponding vents are collected, where the exhaust speed refers to the data value of the exhaust speed of the corresponding vent, and the exhaust pressure is the data value indicating the pressure of the exhausted gas;

[0063] The exhaust speed TS and exhaust pressure TY of the corresponding vent are numerically calculated by the formula TP=a1*TS+a2*TY to obtain the ventilation detection value TP, wherein a1 and a2 are preset weight coefficients with values ​​greater than zero, and the smaller the value of the ventilation detection value TP is, the worse the ventilation condition of the corresponding vent is; the ventilation detection values ​​of all vents are mean calculated and variance calculated to obtain the ventilation decision value and ventilation deviation value;

[0064] The ventilation decision value and the ventilation deviation value are numerically compared with the preset ventilation decision threshold and the preset ventilation deviation threshold, respectively; if the ventilation decision value exceeds the preset ventilation decision threshold and the ventilation deviation value does not exceed the preset ventilation deviation threshold, indicating that the ventilation performance of the kayak production workshop is generally good, then a ventilation qualified signal is generated; if the ventilation decision value does not exceed the preset ventilation decision threshold and the ventilation deviation value does not exceed the preset ventilation deviation threshold, indicating that the ventilation performance of the kayak production workshop is generally poor, then a ventilation unqualified signal is generated;

[0065] For other cases, precise ventilation assessment and analysis is carried out, specifically: the ventilation detection value is compared with the preset ventilation detection threshold. If the ventilation detection value does not exceed the preset ventilation detection threshold, the corresponding vent is marked as a non-standard vent; the number of non-standard vents in the kayak production workshop is obtained and the ratio is calculated with the total number of vents to obtain the vent number outlier, and the ventilation detection value with the smallest value is marked as the ventilation low amplitude;

[0066] By formula The ventilation decision value RF, the ventilation opening number difference value GN and the ventilation low amplitude value YS are numerically calculated to obtain the ventilation evaluation coefficient TW; wherein, sh1, sh2 and sh3 are preset proportional coefficients, sh2>sh1>sh3>0; and the larger the value of the ventilation evaluation coefficient TW is, the better the ventilation condition of the kayak production workshop is in general.

[0067] The ventilation evaluation coefficient TW is numerically compared with the preset ventilation evaluation coefficient threshold. If the ventilation evaluation coefficient TW exceeds the preset ventilation evaluation coefficient threshold, it indicates that the ventilation condition of the canoe production workshop is generally good, and a ventilation qualified signal is generated; if the ventilation evaluation coefficient TW does not exceed the preset ventilation evaluation coefficient threshold, it indicates that the ventilation condition of the canoe production workshop is generally poor, and a ventilation unqualified signal is generated.

[0068] The working principle of the present invention is as follows: when in use, the harmfulness of dust in the kayak production workshop is analyzed through the harmfulness analysis module of dust and gas. When a high-hazard signal of dust and gas is generated, the ventilation efficiency of the kayak production workshop is accelerated to reduce the harm to the health of workshop personnel. The personnel normative monitoring module monitors the kayak production workshop through the monitoring camera. Based on the monitoring image and image recognition technology, it is judged whether there are unsafe personnel. The equipment execution deviation detection module monitors all the production equipment in operation in the kayak production workshop. By analyzing and judging whether the corresponding production equipment is in an execution risk state, it is helpful for the management personnel to make corresponding improvement measures to ensure the efficient and high-quality production of kayaks and improve production safety. The production safety hazard assessment module comprehensively analyzes the degree of production safety hazard in the kayak production workshop during the detection period. When a high-hazard signal of production safety is generated, the production supervision of the kayak production workshop is strengthened in time to further ensure the subsequent production safety, significantly reduce the difficulty of kayak production safety supervision, and has a high degree of intelligence.

[0069] 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 kayak production safety monitoring and control system based on the Internet of Things is characterized by: It includes gas and dust harmfulness analysis module, personnel normative monitoring module, equipment execution deviation detection module, production safety hazard assessment module and production monitoring and control terminal; The gas dust hazard analysis module analyzes the gas dust hazard status in the kayak production workshop, generates a gas dust high hazard signal or a gas dust low hazard signal through analysis, and sends the gas dust high hazard signal to the production monitoring and control end; The personnel normative monitoring module monitors the kayak production workshop through surveillance cameras, collects surveillance images of the kayak production workshop, and determines whether there are unsafe personnel based on the surveillance images and through image recognition technology, and sends the unsafe personnel to the production monitoring and control end; The equipment execution deviation detection module obtains all the production equipment in operation in the kayak production workshop, monitors the corresponding production equipment, determines whether the corresponding production equipment is in an execution risk state through analysis, and sends the production equipment in an execution risk state to the production monitoring and control end; The production safety hazard assessment module is used to set the detection period, analyze the degree of production safety hazards in the canoe production workshop during the detection period, generate a high production safety hazard signal or a low production safety hazard signal through analysis, and send the high production safety hazard signal to the production monitoring and control end; The specific analysis process of the gas dust harmfulness analysis module is as follows: Several detection points are set in the canoe production workshop, and the real-time dust concentration and real-time harmful gas concentration of the corresponding detection points are collected. The real-time dust concentration and the real-time harmful gas concentration are numerically compared with the preset real-time dust concentration threshold and the preset real-time harmful gas concentration threshold, respectively. If the real-time dust concentration or the real-time harmful gas concentration exceeds the corresponding preset threshold, the corresponding detection point is marked as a dust warning point; The number of gas and dust warning points in the canoe production workshop is obtained and the ratio is calculated with the total number of detection points to obtain the gas and dust abnormality detection value, and the real-time dust concentration of all detection points is averaged to obtain the dust detection value, and the real-time harmful gas concentration of all detection points is averaged to obtain the gas risk detection value; The gas dust harmfulness analysis value YH is obtained by numerically calculating the gas dust abnormality detection value QX, the dust detection value QF and the gas risk detection value QP through the formula YH=mg1*QX+(mg2*QF+mg3*QP) / mg1; wherein mg1, mg2 and mg3 are preset proportional coefficients, and mg1>mg3>mg2>0; the gas dust harmfulness analysis value is numerically compared with the preset gas dust harmfulness analysis threshold value, and if the gas dust harmfulness analysis value exceeds the preset gas dust harmfulness analysis threshold value, a gas dust high hazard signal is generated; If the gas dust harmfulness analysis value does not exceed the preset gas dust harmfulness analysis threshold value, a gas dust low hazard signal is generated.

2. According to the Internet of Things-based kayak production safety monitoring and control system of claim 1, it is characterized in that: The air and dust harmfulness analysis module is communicatively connected to the ventilation effectiveness detection module. The air and dust harmfulness analysis module sends an air and dust low hazard signal to the ventilation effectiveness detection module. When the ventilation effectiveness detection module receives the air and dust low hazard signal, it analyzes the ventilation performance of the canoe production workshop, generates a ventilation qualified signal or a ventilation unqualified signal through the analysis, and sends the ventilation qualified signal or the ventilation unqualified signal to the production monitoring and control end.

3. According to the Internet of Things-based kayak production safety monitoring and control system of claim 2, it is characterized in that: The specific operation process of the ventilation effectiveness detection module includes: All the vents in the kayak production workshop are obtained, and the exhaust speed and exhaust pressure of the corresponding vents are collected. The exhaust speed TS and exhaust pressure TY of the corresponding vents are numerically calculated by the formula TP=a1*TS+a2*TY to obtain the ventilation detection value TP, where a1 and a2 are preset weight coefficients with values ​​greater than zero; the ventilation detection values ​​of all vents are averaged and variances are calculated to obtain the ventilation decision value and ventilation deviation value; If the ventilation decision value exceeds the preset ventilation decision threshold and the ventilation deviation value does not exceed the preset ventilation deviation threshold, a ventilation qualified signal is generated; if the ventilation decision value does not exceed the preset ventilation decision threshold and the ventilation deviation value does not exceed the preset ventilation deviation threshold, a ventilation unqualified signal is generated; in other cases, a precise ventilation assessment and analysis is performed.

4. According to the Internet of Things-based kayak production safety monitoring and control system of claim 3, it is characterized in that: The specific analysis process of ventilation precision assessment and analysis is as follows: The ventilation detection value is numerically compared with the preset ventilation detection threshold. If the ventilation detection value does not exceed the preset ventilation detection threshold, the corresponding vent is marked as a non-standard vent; the number of non-standard vents in the kayak production workshop is obtained and the ratio is calculated with the total number of vents to obtain the vent number outlier, and the ventilation detection value with the smallest value is marked as the ventilation low amplitude; By formula The ventilation decision value RF, the ventilation opening number outlier GN and the ventilation low amplitude YS are numerically calculated to obtain the ventilation evaluation coefficient TW; wherein, sh1, sh2, sh3 are preset proportional coefficients, sh2>sh1>sh3>0; the ventilation evaluation coefficient is numerically compared with the preset ventilation evaluation coefficient threshold, if the ventilation evaluation coefficient exceeds the preset ventilation evaluation coefficient threshold, a ventilation qualified signal is generated; if the ventilation evaluation coefficient does not exceed the preset ventilation evaluation coefficient threshold, a ventilation unqualified signal is generated.

5. The kayak production safety monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: The specific operation process of the device executing the deviation detection module includes: Obtain the operating parameters required for monitoring of the corresponding production equipment, mark the operating parameters whose real-time detection data do not meet the corresponding data requirements as risk parameters, and mark the deviation value of the real-time detection data of the corresponding risk parameters compared with the corresponding preset data requirements as the risk performance value; Each set of operating parameters is set in advance to correspond to a set of preset safety impact values, the product of the risk manifestation value of the corresponding risk parameter and the corresponding preset safety impact value is marked as the risk judgment value, and the sum of the risk judgment values ​​of all risk parameters is marked as the bias risk value. If the bias risk value exceeds the preset bias risk threshold, the corresponding production equipment is judged to be in an execution risk state.

6. The kayak production safety monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: The specific analysis process of the production safety hazard assessment module is as follows: The number of times that a high-hazard signal of gas and dust is generated during the detection period is collected and marked as a gas and dust hazard warning value, and the number of times that the corresponding operator is marked as a non-safe person during the detection period is defined as a non-standard frequency, and the number of personnel marked as non-safe personnel during the detection period is defined as a non-standard personnel inspection value, and the non-standard frequencies of all operators are averaged to obtain a non-standard frequency inspection value; Determine the abnormal equipment within the detection period through analysis, mark the number of abnormal equipment as the abnormal equipment number inspection value, and mark the ratio of the production equipment analysis value of the corresponding abnormal equipment to the corresponding preset production equipment analysis threshold as the production equipment inspection value, and calculate the average of the production equipment inspection values ​​of all production equipment running within the detection period to obtain the production equipment inspection value; By formula The gas and dust hazard warning value PY, the non-standard human inspection value PN, the non-standard frequency inspection value PW, the abnormal equipment number inspection value PX and the production equipment inspection condition value PK are numerically calculated to obtain the safety hazard assessment value FM; among them, c1, c2, c3, c4, c5 are preset proportional coefficients whose values ​​are greater than zero; the safety hazard assessment value is numerically compared with the preset safety hazard assessment threshold value, if the safety hazard assessment value exceeds the preset safety hazard assessment threshold value, a high production safety hazard signal is generated; if the safety hazard assessment value does not exceed the preset safety hazard assessment threshold value, a low production safety hazard signal is generated.

7. The kayak production safety monitoring and control system based on the Internet of Things according to claim 6 is characterized in that: The specific analysis process of determining the abnormal devices within the detection period through analysis is as follows: The duration of each time the corresponding production equipment is in the execution risk state during the detection period is obtained, and the duration of all the corresponding production equipment in the execution risk state during the detection period is summed up to obtain the production equipment risk value, and the number of occurrences when the duration exceeds the preset duration threshold is marked as a high duration frequency; and the total operation time of the corresponding production equipment during the detection period is obtained; By formula The total operation time LS, the production equipment risk time LD and the high continuous frequency LF are numerically calculated to obtain the production equipment analysis value LK; wherein, uw1, uw2, uw3 are preset proportional coefficients, uw3>uw2>uw1>0; the production equipment analysis value is numerically compared with the corresponding preset production equipment analysis threshold value, if the production equipment analysis value exceeds the corresponding preset production equipment analysis threshold value, the corresponding production equipment will be marked as abnormal equipment.

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