Lampblack concentration online monitoring system based on Internet of Things

Abstract

The invention belongs to the technical field of cooking fume emission supervision, and particularly relates to an online cooking fume concentration monitoring system based on the Internet of Things, which comprises a cooking fume emission port monitoring module, an emission harmfulness analysis module, an emission port control classification module, a regional emission evaluation module and a background monitoring end, according to the invention, the emission harmfulness analysis module judges the oil smoke emission harmfulness of the corresponding oil smoke emission port in real time based on the oil smoke emission information, and the emission port control classification module analyzes the emission pollution performance of the corresponding oil smoke emission port in the detection period to realize the grade division of the emission port, so that the subsequent formulation of matched management and control measures is facilitated; the area emission evaluation module accurately analyzes the oil smoke emission management performance of all areas needing oil smoke emission supervision one by one, subsequently strengthens the investigation of the areas corresponding to key investigation signals, supervises and urges the improvement of oil smoke emission, and significantly reduces the management difficulty of managers. The atmospheric environment can be protected; and the living quality of residents can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of oil fume emission supervision, and in particular to an online monitoring system for oil fume concentration based on the Internet of Things. Background Art

[0002] With the acceleration of urbanization, the catering industry has developed rapidly, but the accompanying fume pollution problem has become increasingly serious, posing a threat to the atmospheric environment and residents' health. The catering industry generally uses fume purification equipment to purify the fume before discharging it through the fume exhaust port, thereby reducing the adverse effects on the surrounding environment and residents' lives. However, it is currently difficult to accurately judge the harmfulness of the oil fume emitted from each oil fume outlet in real time, and it is impossible to reasonably classify each oil fume outlet based on the emission status and accurately evaluate the emission management performance of the corresponding area, which is not conducive to the scientific formulation and adjustment of subsequent management strategies, and cannot effectively reduce the management difficulty of managers, and the degree of intelligence is low; In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to provide an online monitoring system for oil fume concentration based on the Internet of Things, which solves the problems that the prior art is difficult to make real-time and accurate judgments on the harmfulness of oil fume emitted from each oil fume emission outlet, and is unable to reasonably grade each oil fume emission outlet based on the emission conditions and accurately evaluate the emission management performance for the corresponding area, resulting in high management difficulty and low intelligence.

[0004] To achieve the above object, the present invention provides the following technical solutions: An online monitoring system for oil fume concentration based on the Internet of Things, including an oil fume outlet monitoring module, an emission hazard analysis module, an emission outlet control classification module, a regional emission assessment module and a background monitoring terminal; The oil fume discharge port monitoring module monitors the corresponding oil fume discharge port, collects the oil fume discharge information of the corresponding oil fume discharge port, and sends the oil fume discharge information of the corresponding discharge port to the emission hazard analysis module via the Internet of Things; The emission hazard analysis module determines the emission hazard of the corresponding oil fume emission outlet based on the oil fume emission information, generates emission warning information when it is determined that the corresponding oil fume emission outlet is in a high emission hazard state, and sends the emission warning information of the corresponding oil fume emission outlet to the emission outlet control classification module; The emission outlet control classification module is used to set the detection period, analyze the emission pollution performance of the corresponding oil fume emission outlet during the detection period, mark the corresponding oil fume emission outlet as a warning emission outlet or a qualified emission outlet through analysis, and send the marking information of the corresponding oil fume emission outlet to the regional emission assessment module; The regional emission assessment module is used to obtain the area where oil fume emission supervision is required, mark the corresponding area as monitoring area i, and i is a natural number greater than 1; generate the key visit signal or weakened visit signal of monitoring area i through analysis, and send the key visit signal or weakened visit signal of monitoring area i to the background monitoring end.

[0005] Furthermore, the analysis and judgment process of high-hazard emission status is as follows: The oil fume concentration data emitted from the corresponding oil fume emission port is obtained and marked as the oil fume concentration detection value, and the particulate matter concentration and non-methane total hydrocarbon concentration in the emitted oil fume are respectively marked as the oil fume particle detection value and the non-methane total hydrocarbon value, and the temperature of the emitted oil fume is collected and marked as the oil fume temperature detection value; The oil fume hazard coefficient is obtained by numerically calculating the oil fume concentration detection value, the oil fume particle detection value, the non-methane total hydrocarbon value and the oil fume temperature detection value, and the oil fume hazard coefficient is numerically compared with the preset oil fume hazard coefficient threshold value. If the oil fume hazard coefficient exceeds the preset oil fume hazard coefficient threshold value, it is judged that the corresponding oil fume emission outlet is in a high emission hazard state.

[0006] Furthermore, the specific analysis process of the discharge outlet control classification module includes: When it is determined that the corresponding oil fume emission outlet is in a high-hazard emission state, the timing starts, and the duration of the corresponding high-hazard emission state is obtained accordingly; the duration of all high-hazard emission states of the corresponding oil fume emission outlets during the detection period is summed up and calculated to obtain the total high-risk emission time value, and the total high-risk emission time value is numerically compared with the preset high-risk emission total time threshold; if the high-risk emission total time value exceeds the preset high-risk emission total time threshold, the corresponding oil fume emission outlet is marked as a warning emission outlet.

[0007] Furthermore, if the total emission high-risk time value exceeds the preset emission high-risk total time threshold, the emission port classification coefficient of the corresponding oil fume emission port is obtained through analysis, and the emission port classification coefficient is numerically compared with the preset emission port classification coefficient threshold. If the emission port classification coefficient exceeds the preset emission port classification coefficient threshold, the corresponding oil fume emission port is marked as a warning emission port; if the emission port classification coefficient does not exceed the preset emission port classification coefficient threshold, the corresponding oil fume emission port is marked as a qualified emission port.

[0008] Furthermore, the analysis and acquisition method of the discharge port classification coefficient is as follows: The oil fume hazard value is obtained by averaging all the oil fume hazard coefficients during the duration of the corresponding high-hazard emission state, and the amount of oil fume emitted during the duration of the corresponding high-hazard emission state is marked as the emission inspection value; The high-risk state assessment value is obtained by numerically calculating the duration of the corresponding high-risk emission state, the smoke hazard analysis value and the emission inspection value, and the high-risk state assessment value is numerically compared with the preset high-risk state assessment threshold. If the high-risk state assessment value exceeds the preset high-risk state assessment threshold, the corresponding high-risk emission state is assigned a hazard assessment symbol WP-1; The number of times the corresponding fume emission outlet is assigned the hazard assessment symbol WP-1 during the detection period is obtained and marked as the high-risk frequency inspection value, and the hazard assessment detection value is obtained by averaging all the high-risk status assessment values ​​of the corresponding fume emission outlet during the detection period. The emission outlet classification coefficient is obtained by numerically calculating the total high-risk emission time value, the high-risk frequency inspection value and the hazard assessment detection value.

[0009] Furthermore, the specific operation process of the regional emission assessment module includes: The number of warning emission outlets in monitoring area i is obtained and marked as the warning number inspection value, and the excess value of the emission outlet classification coefficient of the corresponding warning emission outlet in monitoring area i compared with the preset emission outlet classification coefficient threshold is marked as the emission excess inspection value, the emission excess inspection values ​​of all warning emission outlets in monitoring area i are averaged to obtain the emission excess value, and the emission excess inspection value with the largest value in monitoring area i is marked as the emission excess value; The regional emission assessment value is obtained by numerically calculating the warning value, the emission excess value and the emission excess value, and the regional emission assessment value is numerically compared with the preset regional emission assessment threshold. If the regional emission assessment value exceeds the preset regional emission assessment threshold, a key visit signal for monitoring area i is generated; if the regional emission assessment value does not exceed the preset regional emission assessment threshold, a weakened visit signal for monitoring area i is generated.

[0010] Furthermore, the background monitoring end is communicated with the visit execution monitoring module, which analyzes the visit status of all areas in the next detection period, generates a visit execution deviation signal or a visit execution qualified signal through analysis, and sends the visit execution deviation signal or the visit execution qualified signal to the background monitoring end. When the background monitoring end receives the visit execution deviation signal, it issues a corresponding warning.

[0011] Furthermore, the specific analysis process of the visit execution monitoring module is as follows: By analyzing and obtaining the regional search coefficient of the monitoring area i, a corresponding preset regional search coefficient threshold is assigned to the monitoring area i, and the regional search coefficient is numerically compared with the corresponding preset regional search coefficient threshold. If the regional search coefficient exceeds the preset regional search coefficient threshold, the monitoring area i is marked as a search and abnormal monitoring area; The number of inspection and monitoring abnormal areas is obtained and marked as the inspection and monitoring abnormality value, and the ratio of the regional inspection coefficient of the monitoring area i to the corresponding preset regional inspection coefficient threshold is marked as the regional inspection occupancy value, and the regional inspection occupancy values ​​of all areas are averaged to obtain the inspection comprehensive evaluation value; The inspection value of the visit monitoring anomaly and the inspection comprehensive evaluation value are numerically compared with the preset inspection value of the visit monitoring anomaly and the preset inspection comprehensive evaluation threshold value respectively. If the inspection value of the visit monitoring anomaly or the inspection comprehensive evaluation value exceeds the corresponding preset threshold, a inspection execution deviation signal is generated; if both the inspection value of the visit monitoring anomaly and the inspection comprehensive evaluation value do not exceed the corresponding preset threshold, a inspection execution qualified signal is generated.

[0012] Furthermore, the analysis and acquisition method of the regional visit coefficient is as follows: At the end of the visit to the monitoring area i, the duration of the corresponding visit process is collected and the deviation value between it and the corresponding standard duration is marked as the visit duration value, and the actual visit path of the corresponding visit process is compared with the set standard visit path to obtain the visit path deviation value; Obtain the visit duration and visit path deviation of all visit processes in the monitoring area i in the next detection period, and calculate the average of all visit duration values ​​to obtain the visit duration value, and calculate the average of all visit path deviation values ​​to obtain the visit path value; The interval between two adjacent visits to the monitoring area i is marked as the visit interval value, and the average of all visit interval values ​​for the monitoring area i in the next detection period is calculated to obtain the visit interval value; the regional visit coefficient is obtained by numerically calculating the visit duration value, the visit path analysis value and the visit interval value.

[0013] Furthermore, the specific allocation process of allocating the corresponding preset area search coefficient threshold to the monitoring area i is as follows: If the monitoring area i corresponds to the key search signal, the preset area search coefficient threshold LP1 is allocated thereto; if the monitoring area i corresponds to the weakened search signal, the preset area search coefficient threshold LP2 is allocated thereto, and LP2>LP1>0.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, the emission hazard analysis module determines the emission hazard of the corresponding fume emission outlet in real time based on the fume emission information. The emission outlet control classification module analyzes the emission pollution performance of the corresponding fume emission outlet during the detection period to achieve the emission outlet classification, which is convenient for the subsequent formulation of matching control measures. The regional emission assessment module analyzes the fume emission management performance of all areas that need to be supervised for fume emission one by one, and subsequently strengthens the inspection of the areas corresponding to the key inspection signals and urges the improvement of fume emissions, which significantly reduces the management difficulty of management personnel; 2. In the present invention, the visit execution monitoring module is used to analyze the visit status of all areas in the next detection period. By generating a visit execution deviation signal or a visit execution qualified signal through analysis, the visit execution performance of the next detection period can be reasonably analyzed and accurately fed back. When the visit execution deviation signal is generated, the training and supervision of the visit personnel are strengthened to ensure the subsequent visit performance. It has a high degree of intelligence and further reduces the management difficulty of management personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] Embodiment 1: Figure 1 As shown, the present invention proposes an online monitoring system for oil fume concentration based on the Internet of Things, including an oil fume outlet monitoring module, an emission hazard analysis module, an emission outlet control classification module, a regional emission assessment module and a background monitoring terminal; The oil fume exhaust port monitoring module monitors the corresponding oil fume exhaust port through the corresponding sensor, collects the oil fume emission information of the corresponding oil fume exhaust port, and sends the oil fume emission information of the corresponding exhaust port to the emission hazard analysis module through the Internet of Things.

[0018] The emission hazard analysis module determines the emission hazard of the corresponding oil fume emission outlet based on the oil fume emission information, generates emission warning information when it is determined that the corresponding oil fume emission outlet is in a high emission hazard state, and sends the emission warning information of the corresponding oil fume emission outlet to the emission outlet control classification module, which is conducive to real-time and accurate grasp of the emission hazard of each oil fume emission outlet; wherein, the analysis and judgment process of the high emission hazard state is as follows: The oil fume concentration data emitted from the corresponding oil fume emission outlet is obtained and marked as the oil fume concentration detection value, and the particulate matter concentration (mainly refers to the concentration of tiny particles such as PM2.5 and PM10) and non-methane total hydrocarbon concentration (non-methane total hydrocarbons (NMHC) is a general term for a class of volatile organic compounds (VOCs), which have a certain content in oil fume. Non-methane total hydrocarbons have a certain impact on the atmospheric environment and human health, and are one of the important objects of oil fume pollution control) in the emitted oil fume are marked as the oil fume particle detection value and non-methane total hydrocarbon value, and the temperature of the emitted oil fume is collected and marked as the oil fume temperature detection value; The oil fume hazard coefficient TX is obtained by numerically calculating the oil fume concentration detection value TY, the oil fume particle detection value TL, the non-methane total hydrocarbon value TS and the oil fume temperature detection value TN through the formula TX=(eq1*TY+eq2*TL+eq3*TS+eq4*TN) / 4; wherein eq1, eq2, eq3 and eq4 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the oil fume hazard coefficient TX is, the more serious the current emission hazard of the corresponding oil fume emission outlet is; The oil fume hazard coefficient TX is numerically compared with the preset oil fume hazard coefficient threshold. If the oil fume hazard coefficient TX exceeds the preset oil fume hazard coefficient threshold, it indicates that the current emission hazard level of the corresponding oil fume emission outlet is more serious, and the corresponding oil fume emission outlet is judged to be in a high emission hazard state.

[0019] The emission outlet control classification module is used to set the detection period, preferably, the detection period is thirty days; the emission pollution performance of the corresponding oil fume emission outlet during the detection period is analyzed, and the corresponding oil fume emission outlet is marked as a warning emission outlet or a qualified emission outlet through analysis, and the marking information of the corresponding oil fume emission outlet is sent to the regional emission assessment module, which is conducive to the detailed understanding of the emission pollution of each oil fume emission outlet and the realization of the emission outlet grade classification, which is convenient for the subsequent formulation of matching control measures; the specific analysis process of the emission outlet control classification module is as follows: When it is determined that the corresponding oil fume exhaust port is in a high-hazard emission state, the timing starts, and the duration of the corresponding high-hazard emission state is obtained accordingly; the duration of all high-hazard emission states of the corresponding oil fume exhaust port during the detection period is summed up and calculated to obtain the total high-risk emission time value, and the total high-risk emission time value is numerically compared with the preset total high-risk emission time threshold; if the total high-risk emission time value exceeds the preset total high-risk emission time threshold, indicating that the emission performance of the corresponding oil fume exhaust port during the detection period is poor, the corresponding oil fume exhaust port is marked as a warning emission port.

[0020] Furthermore, if the total emission high-risk time value exceeds the preset total emission high-risk time threshold, all oil fume hazard coefficients within the duration of the corresponding emission high-hazard state are averaged to obtain the oil fume hazard analysis value, and the amount of oil fume emitted within the duration of the corresponding emission high-hazard state is marked as the emission inspection value; By formula The duration NS of the corresponding high-hazard emission state, the oil smoke hazard analysis value NY and the emission inspection value NF are numerically calculated to obtain the high-hazard state assessment value NL; wherein hu1, hu2 and hu3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the high-hazard state assessment value NL is, the greater the pollution caused by the corresponding high-hazard emission state is; Compare the high-risk state assessment value NL with the preset high-risk state assessment threshold. If the high-risk state assessment value NL exceeds the preset high-risk state assessment threshold, indicating that the pollution caused by the corresponding high-risk emission state is relatively large, the corresponding high-risk emission state is assigned a risk assessment symbol WP-1; The number of times the corresponding fume exhaust outlet is assigned the risk assessment symbol WP-1 during the detection period is obtained and marked as a high-risk frequency detection value, and all high-risk state assessment values ​​of the corresponding fume exhaust outlet during the detection period are averaged to obtain a risk assessment detection value; By formula The total high-risk emission time value QR, the high-risk frequency detection value QL and the risk assessment detection value QP are numerically calculated to obtain the emission outlet classification coefficient QW; wherein fy1, fy2, and fy3 are preset proportional coefficients, fy2>fy3>fy1>0; and the larger the value of the emission outlet classification coefficient QW, the worse the emission performance of the corresponding oil fume emission outlet during the detection period is overall; The emission port classification coefficient QW is numerically compared with the preset emission port classification coefficient threshold. If the emission port classification coefficient QW exceeds the preset emission port classification coefficient threshold, it indicates that the emission performance of the corresponding oil fume emission port during the detection period is generally poor, and the corresponding oil fume emission port is marked as a warning emission port; if the emission port classification coefficient QW does not exceed the preset emission port classification coefficient threshold, it indicates that the emission performance of the corresponding oil fume emission port during the detection period is generally good, and the corresponding oil fume emission port is marked as a qualified emission port.

[0021] The regional emission assessment module is used to obtain the area that needs to be supervised for oil fume emissions, mark the corresponding area as monitoring area i, and i is a natural number greater than 1; generate a key visit signal or a weakened visit signal for monitoring area i through analysis, and send the key visit signal or the weakened visit signal for monitoring area i to the background monitoring end, and subsequently strengthen the visit to the area corresponding to the key visit signal, urge the improvement of oil fume emissions, reduce the emission pollution of each area, and significantly reduce the management difficulty of managers; the specific operation process of the regional emission assessment module is as follows: The number of warning emission outlets in monitoring area i is obtained and marked as the warning number inspection value, and the excess value of the emission outlet classification coefficient of the corresponding warning emission outlet in monitoring area i compared with the preset emission outlet classification coefficient threshold is marked as the emission excess inspection value, the emission excess inspection values ​​of all warning emission outlets in monitoring area i are averaged to obtain the emission excess value, and the emission excess inspection value with the largest value in monitoring area i is marked as the emission excess value; The regional emission assessment value SY is obtained by numerically calculating the warning value SW, the emission excess value SN and the emission excess value SF through the formula SY=ng1*SW+(ng2*SN+ng3*SF) / 2; wherein ng1, ng2 and ng3 are preset proportional coefficients, ng1>ng2>ng3>0; and the larger the value of the regional emission assessment value SY, the worse the overall performance of the oil fume emission management for the monitoring area i is; The regional emission assessment value SY is numerically compared with the preset regional emission assessment threshold. If the regional emission assessment value SY exceeds the preset regional emission assessment threshold, it indicates that the overall performance of oil fume emission management in monitoring area i is poor, and it is necessary to strengthen the oil fume emission supervision in monitoring area i in the future, then a key inspection signal for monitoring area i is generated; if the regional emission assessment value SY does not exceed the preset regional emission assessment threshold, it indicates that the overall performance of oil fume emission management in monitoring area i is good, then a weakened inspection signal for monitoring area i is generated.

[0022] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the background monitoring terminal is connected to the visit execution monitoring module in communication, and the visit execution monitoring module analyzes the visit status of all areas in the next detection period, and generates a visit execution deviation signal or a visit execution qualified signal through analysis, so as to reasonably analyze and accurately feedback the visit execution performance in the next detection period; And the inspection execution deviation signal or inspection execution qualified signal is sent to the background monitoring end. When the background monitoring end receives the inspection execution deviation signal, it issues a corresponding warning. When receiving the warning, it strengthens the training and supervision of the inspection personnel to ensure the subsequent inspection performance. The specific analysis process of the inspection execution monitoring module is as follows: At the end of the visit to the monitoring area i, the duration of the corresponding visit process is collected and the deviation value between it and the corresponding standard duration is marked as the visit duration value, and the actual visit path of the corresponding visit process is compared with the set standard visit path to obtain the visit path deviation value (i.e. the proportion of non-overlapping paths); Obtain the visit duration and visit path deviation of all visit processes in the monitoring area i in the next detection period, and calculate the average of all visit duration values ​​to obtain the visit duration value, and calculate the average of all visit path deviation values ​​to obtain the visit path value; And the interval between two consecutive visits to the monitoring area i is marked as the visit interval value, and the average of all the visit interval values ​​of the monitoring area i in the next detection period is calculated to obtain the visit interval value; By formula The visit duration value CWi, the visit path value CFi and the visit interval value CPi are numerically calculated to obtain the regional visit coefficient CYi; wherein sg1, sg2, and sg3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the regional visit coefficient CYi, the worse the visit execution performance for the monitoring area i; Assigning a corresponding preset regional search coefficient threshold to the monitoring area i, specifically: if the monitoring area i corresponds to the key search signal, assigning the preset regional search coefficient threshold LP1 to it; if the monitoring area i corresponds to the weakened search signal, assigning the preset regional search coefficient threshold LP2 to it, and LP2>LP1>0; by assigning the appropriate preset regional search coefficient threshold to different areas, it is helpful to improve the accuracy of the corresponding analysis results; Compare the regional search coefficient i with the corresponding preset regional search coefficient threshold. If the regional search coefficient i exceeds the preset regional search coefficient threshold, indicating that the search execution performance of the monitoring area i is poor, the monitoring area i is marked as a search abnormality area. The number of inspection and monitoring abnormal areas is obtained and marked as the inspection and monitoring abnormality value, and the ratio of the regional inspection coefficient of the monitoring area i to the corresponding preset regional inspection coefficient threshold is marked as the regional inspection occupancy value, and the regional inspection occupancy values ​​of all areas are averaged to obtain the inspection comprehensive evaluation value; The inspection and monitoring anomaly check value and the inspection and comprehensive evaluation value are numerically compared with the preset inspection and monitoring anomaly check threshold and the preset inspection and comprehensive evaluation threshold respectively. If the inspection and monitoring anomaly check value or the inspection and comprehensive evaluation value exceeds the corresponding preset threshold, indicating that the inspection and execution performance of all areas is generally poor, then a inspection and execution deviation signal is generated; if the inspection and monitoring anomaly check value and the inspection and comprehensive evaluation value do not exceed the corresponding preset threshold, indicating that the inspection and execution performance of all areas is generally good, then a inspection and execution qualified signal is generated.

[0023] The working principle of the present invention is as follows: when in use, the fume emission information of the corresponding fume emission outlet is collected through the fume emission outlet monitoring module, and the emission hazard analysis module determines the fume emission hazard of the corresponding fume emission outlet in real time based on the fume emission information, and generates emission warning information when it is determined that the corresponding fume emission outlet is in a high-hazard emission state, which is conducive to real-time and accurate grasp of the emission hazard of each fume emission outlet, and the emission pollution performance of the corresponding fume emission outlet during the detection period is analyzed through the emission outlet control classification module to determine the warning emission outlet and the qualified emission outlet, so as to realize the emission outlet grade division, facilitate the subsequent formulation of matching management and control measures, and the regional emission assessment module accurately analyzes the fume emission management performance of all areas that need to be supervised for fume emission one by one, generates key visit signals or weakened visit signals for the corresponding areas, and subsequently strengthens the visits to the areas corresponding to the key visit signals and urges the improvement of fume emissions, significantly reduces the management difficulty of management personnel, helps to promote the green development of the catering industry, protect the atmospheric environment and improve the quality of life of residents, and has a high degree of intelligence.

[0024] 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. An online monitoring system for oil fume concentration based on the Internet of Things, characterized in that: It includes oil fume outlet monitoring module, emission hazard analysis module, emission outlet control classification module, regional emission assessment module and background monitoring terminal; The oil fume discharge port monitoring module monitors the corresponding oil fume discharge port, collects the oil fume discharge information of the corresponding oil fume discharge port, and sends the oil fume discharge information of the corresponding discharge port to the emission hazard analysis module via the Internet of Things; The emission hazard analysis module determines the emission hazard of the corresponding oil fume emission outlet based on the oil fume emission information, generates emission warning information when it is determined that the corresponding oil fume emission outlet is in a high emission hazard state, and sends the emission warning information of the corresponding oil fume emission outlet to the emission outlet control classification module; The emission outlet control classification module is used to set the detection period, analyze the emission pollution performance of the corresponding oil fume emission outlet during the detection period, mark the corresponding oil fume emission outlet as a warning emission outlet or a qualified emission outlet through analysis, and send the marking information of the corresponding oil fume emission outlet to the regional emission assessment module; The regional emission assessment module is used to obtain the area where oil fume emission supervision is required, mark the corresponding area as monitoring area i, and i is a natural number greater than 1; generate the key visit signal or weakened visit signal of monitoring area i through analysis, and send the key visit signal or weakened visit signal of monitoring area i to the background monitoring end.

2. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 1 is characterized in that: The analysis and judgment process of high-hazard emission status is as follows: The oil fume concentration data emitted from the corresponding oil fume emission port is obtained and marked as the oil fume concentration detection value, and the particulate matter concentration and non-methane total hydrocarbon concentration in the emitted oil fume are respectively marked as the oil fume particle detection value and the non-methane total hydrocarbon value, and the temperature of the emitted oil fume is collected and marked as the oil fume temperature detection value; The oil fume hazard coefficient is obtained by numerically calculating the oil fume concentration detection value, the oil fume particle detection value, the non-methane total hydrocarbon value and the oil fume temperature detection value. If the oil fume hazard coefficient exceeds the preset oil fume hazard coefficient threshold, the corresponding oil fume emission outlet is judged to be in a high-hazard emission state.

3. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 1 is characterized in that: The specific analysis process of the discharge outlet control classification module includes: When it is determined that the corresponding oil fume exhaust outlet is in a high-hazard emission state, the timing starts, and the duration of the corresponding high-hazard emission state is obtained accordingly; the duration of all high-hazard emission states of the corresponding oil fume exhaust outlet during the detection period is summed up and calculated to obtain the total high-risk emission time value; if the total high-risk emission time value exceeds the preset high-risk emission total time threshold, the corresponding oil fume exhaust outlet will be marked as a warning exhaust outlet.

4. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 3 is characterized in that: If the total emission high-risk time value exceeds the preset emission high-risk total time threshold, the emission outlet classification coefficient of the corresponding oil fume emission outlet is obtained through analysis. If the emission outlet classification coefficient exceeds the preset emission outlet classification coefficient threshold, the corresponding oil fume emission outlet is marked as a warning emission outlet; If the emission port classification coefficient does not exceed the preset emission port classification coefficient threshold, the corresponding oil fume emission port will be marked as a qualified emission port.

5. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 4 is characterized in that: The analysis and acquisition method of the discharge outlet classification coefficient is as follows: The oil fume hazard value is obtained by averaging all the oil fume hazard coefficients during the duration of the corresponding high-hazard emission state, and the amount of oil fume emitted during the duration of the corresponding high-hazard emission state is marked as the emission inspection value; The high-risk state assessment value is obtained by numerically calculating the duration of the corresponding high-risk emission state, the smoke hazard analysis value and the emission inspection value. If the high-risk state assessment value exceeds the preset high-risk state assessment threshold, the corresponding high-risk emission state is assigned a hazard assessment symbol WP-1; The number of times the corresponding fume emission outlet is assigned the hazard assessment symbol WP-1 during the detection period is obtained and marked as the high-risk frequency inspection value, and the hazard assessment detection value is obtained by averaging all the high-risk status assessment values ​​of the corresponding fume emission outlet during the detection period. The emission outlet classification coefficient is obtained by numerically calculating the total high-risk emission time value, the high-risk frequency inspection value and the hazard assessment detection value.

6. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 1 is characterized in that: The specific operation process of the regional emission assessment module includes: The number of warning emission outlets in monitoring area i is obtained and marked as the warning number inspection value, and the excess value of the emission outlet classification coefficient of the corresponding warning emission outlet in monitoring area i compared with the preset emission outlet classification coefficient threshold is marked as the emission excess inspection value, the emission excess inspection values ​​of all warning emission outlets in monitoring area i are averaged to obtain the emission excess value, and the emission excess inspection value with the largest value in monitoring area i is marked as the emission excess value; The regional emission assessment value is obtained by numerically calculating the warning value, emission excess value and emission excess value. If the regional emission assessment value exceeds the preset regional emission assessment threshold, a key visit signal for monitoring area i is generated; if the regional emission assessment value does not exceed the preset regional emission assessment threshold, a weakened visit signal for monitoring area i is generated.

7. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 1 is characterized in that: The background monitoring end is communicated with the visit execution monitoring module, which analyzes the visit status of all areas in the next detection period, generates a visit execution deviation signal or a visit execution qualified signal through analysis, and sends the visit execution deviation signal or the visit execution qualified signal to the background monitoring end. When the background monitoring end receives the visit execution deviation signal, it will issue a corresponding warning.

8. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 7 is characterized in that: The specific analysis process of the visit execution monitoring module is as follows: By analyzing and obtaining the regional search coefficient of the monitoring area i, a corresponding preset regional search coefficient threshold is assigned to the monitoring area i. If the regional search coefficient exceeds the preset regional search coefficient threshold, the monitoring area i is marked as a search and abnormal monitoring area; The number of inspection and monitoring abnormal areas is obtained and marked as the inspection and monitoring abnormality value, and the ratio of the regional inspection coefficient of the monitoring area i to the corresponding preset regional inspection coefficient threshold is marked as the regional inspection occupancy value, and the regional inspection occupancy values ​​of all areas are averaged to obtain the inspection comprehensive evaluation value; If the visit monitoring anomaly check value or the visit comprehensive evaluation value exceeds the corresponding preset threshold, a visit execution deviation signal is generated; if the visit monitoring anomaly check value and the visit comprehensive evaluation value do not exceed the corresponding preset threshold, a visit execution qualified signal is generated.

9. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 8 is characterized in that: The analysis and acquisition method of the regional visit coefficient is as follows: At the end of the visit to the monitoring area i, the duration of the corresponding visit process is collected and the deviation value between it and the corresponding standard duration is marked as the visit duration value, and the actual visit path of the corresponding visit process is compared with the set standard visit path to obtain the visit path deviation value; Obtain the visit duration and visit path deviation of all visit processes in the monitoring area i in the next detection period, and calculate the average of all visit duration values ​​to obtain the visit duration value, and calculate the average of all visit path deviation values ​​to obtain the visit path value; The interval between two adjacent visits to the monitoring area i is marked as the visit interval value, and the average of all visit interval values ​​for the monitoring area i in the next detection period is calculated to obtain the visit interval value; the regional visit coefficient is obtained by numerically calculating the visit duration value, the visit path analysis value and the visit interval value.

10. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 8 is characterized in that: The specific allocation process of the preset regional access coefficient threshold is as follows: If the monitoring area i corresponds to the key search signal, the preset area search coefficient threshold LP1 is allocated thereto; if the monitoring area i corresponds to the weakened search signal, the preset area search coefficient threshold LP2 is allocated thereto, and LP2>LP1>0.

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