An oil fume concentration online monitoring system based on Internet of Things

Abstract

This invention belongs to the field of oil fume emission monitoring technology, specifically an online oil fume concentration monitoring system based on the Internet of Things (IoT). It includes an oil fume emission outlet monitoring module, an emission hazard analysis module, an emission outlet control classification module, a regional emission assessment module, and a back-end monitoring terminal. The invention uses the emission hazard analysis module to determine the hazard of oil fume emissions from corresponding emission outlets in real time based on emission information. The emission outlet control classification module analyzes the pollution performance of corresponding oil fume emission outlets during the monitoring period to classify emission outlets into levels, facilitating the subsequent development of matching control measures. The regional emission assessment module accurately analyzes the oil fume emission management performance of all areas requiring oil fume emission monitoring, subsequently strengthening the monitoring of areas corresponding to key inspection signals and urging improvements in oil fume emissions. This significantly reduces the management difficulty for managers, helps protect the atmospheric environment, and improves the quality of life for residents.

Description

Technical Field

[0001] This invention relates to the field of oil fume emission monitoring technology, specifically an online oil fume concentration monitoring system based on the Internet of Things. Background Technology

[0002] With the acceleration of urbanization, the catering industry has developed rapidly, but the resulting oil fume pollution problem has become increasingly serious, posing a threat to the atmospheric environment and residents' health. The catering industry generally uses oil fume purification equipment to purify the oil fumes before discharging them through the oil fume exhaust outlet, thereby reducing the adverse impact on the surrounding environment and residents' lives.

[0003] However, it is currently difficult to make a real-time and accurate judgment on the harmfulness of oil fumes emitted from each oil fume emission outlet, and it is impossible to reasonably classify each oil fume emission outlet according to the emission status and accurately assess the emission management performance of the corresponding area. This is not conducive to the scientific formulation and adjustment of subsequent management strategies, cannot effectively reduce the management difficulty for managers, and has a low level of intelligence.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an online monitoring system for oil fume concentration based on the Internet of Things, which solves the problems of existing technologies that make it difficult to make real-time and accurate judgments on the harmfulness of oil fumes emitted from each oil fume emission outlet, and that cannot reasonably classify each oil fume emission outlet according to emission status and accurately evaluate the emission management performance of the corresponding area, resulting in high management difficulty and low level of intelligence.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An Internet of Things-based online monitoring system for oil fume concentration includes an oil fume emission outlet monitoring module, an emission hazard analysis module, an emission outlet control classification module, a regional emission assessment module, and a back-end monitoring terminal.

[0008] The oil fume emission monitoring module monitors the corresponding oil fume emission outlets, collects the oil fume emission information of the corresponding oil fume emission outlets, and sends the oil fume emission information of the corresponding emission outlets to the emission hazard analysis module via the Internet of Things;

[0009] The emission hazard analysis module determines the emission hazard of the corresponding fume emission outlet based on the fume emission information. When it determines that the corresponding fume emission outlet is in a high-hazard emission state, it generates emission warning information and sends the emission warning information of the corresponding fume emission outlet to the emission outlet control classification module.

[0010] The emission outlet control classification module is used to set the detection period, analyze the emission pollution performance of the corresponding oil fume emission outlets during the detection period, mark the corresponding oil fume emission outlets as warning emission outlets or qualified emission outlets through analysis, and send the marking information of the corresponding oil fume emission outlets to the regional emission assessment module.

[0011] The regional emission assessment module is used to identify areas that require monitoring of oil fume emissions, and to mark the corresponding areas as monitoring areas i, where i is a natural number greater than 1. Through analysis, it generates key inspection signals or weakened inspection signals for monitoring area i, and sends the key inspection signals or weakened inspection signals for monitoring area i to the background monitoring terminal.

[0012] Furthermore, the specific process for analyzing and judging a high-hazard emission status is as follows:

[0013] The concentration data of the oil fume emitted from the corresponding oil fume emission outlet is obtained and marked as the oil fume concentration detection value. The concentration of particulate matter and non-methane total hydrocarbons in the emitted oil fume are marked as the oil fume particulate detection value and non-methane total hydrocarbon value, respectively. The temperature of the emitted oil fume is collected and marked as the oil fume temperature detection value.

[0014] The oil fume hazard coefficient is obtained by numerically calculating the oil fume concentration, oil fume particle count, non-methane total hydrocarbon count, and oil fume temperature. The oil fume hazard coefficient is then compared with a preset oil fume hazard coefficient threshold. If the oil fume hazard coefficient exceeds the preset oil fume hazard coefficient threshold, the corresponding oil fume emission outlet is determined to be in a high-hazard emission state.

[0015] Furthermore, the specific analysis process for the emission outlet control classification module includes:

[0016] The timing begins when the corresponding fume emission outlet is determined to be in a high-hazard emission state, and the duration of the corresponding high-hazard emission state is obtained accordingly. The duration of all high-hazard emission states of the corresponding fume emission outlet during the detection period is summed to obtain the total high-hazard emission time value. The total high-hazard emission time value is compared with the preset high-hazard emission time threshold. If the total high-hazard emission time value exceeds the preset high-hazard emission time threshold, the corresponding fume emission outlet is marked as a warning emission outlet.

[0017] Furthermore, if the total high-risk emission time exceeds the preset high-risk emission time threshold, the emission outlet classification coefficient of the corresponding fume emission outlet is obtained through analysis. The emission outlet classification coefficient is compared with the preset emission outlet classification coefficient threshold. If the emission outlet classification coefficient exceeds the preset emission outlet classification coefficient threshold, the corresponding fume emission outlet is marked as a warning emission outlet; if the emission outlet classification coefficient does not exceed the preset emission outlet classification coefficient threshold, the corresponding fume emission outlet is marked as a qualified emission outlet.

[0018] Furthermore, the specific method for obtaining the emission outlet classification coefficient is as follows:

[0019] The average value of all oil fume hazard coefficients within the duration of the corresponding high-hazard emission state is calculated to obtain the oil fume hazard analysis value, and the amount of oil fume emitted within the duration of the corresponding high-hazard emission state is marked as the emission detection value.

[0020] The high-risk status assessment value is obtained by numerically calculating the duration of the corresponding high-risk emission status, the hazard value of oil fume and the emission quantity detection value. The high-risk status assessment value is then numerically compared with the preset high-risk status assessment threshold. If the high-risk status assessment value exceeds the preset high-risk status assessment threshold, the corresponding high-risk emission status is assigned the hazard assessment symbol WP-1.

[0021] The number of times the corresponding oil fume emission outlet was assigned the hazard assessment symbol WP-1 during the detection period was obtained and marked as a high-risk frequency detection value. The average value of all high-risk status assessment values ​​of the corresponding oil fume emission outlet during the detection period was calculated to obtain the hazard assessment detection value. The emission outlet classification coefficient was obtained by numerically calculating the total high-risk emission time value, high-risk frequency detection value and hazard assessment detection value.

[0022] Furthermore, the specific operational process of the regional emissions assessment module includes:

[0023] The number of early warning emission outlets in monitoring area i is obtained and marked as early warning number detection value. The emission outlet classification coefficient of the corresponding early warning emission outlet in monitoring area i is marked as the emission over-detection value if it exceeds the preset emission outlet classification coefficient threshold. The emission over-detection value of all early warning emission outlets in monitoring area i is calculated by averaging the emission over-detection values. The emission over-detection value with the largest value in monitoring area i is marked as the emission over-amplitude value.

[0024] The regional emission assessment value is obtained by numerically calculating the warning detection value, emission over-limit value, and emission over-amplitude value. The regional emission assessment value is then compared with the preset regional emission assessment threshold. If the regional emission assessment value exceeds the preset regional emission assessment threshold, a key inspection signal is generated for monitoring area i; otherwise, a weakened inspection signal is generated for monitoring area i.

[0025] Furthermore, the back-end monitoring terminal communicates with the inspection execution monitoring module. The inspection execution monitoring module analyzes the inspection status of all areas in the next detection period, generates inspection execution deviation signals or inspection execution qualified signals through analysis, and sends the inspection execution deviation signals or inspection execution qualified signals to the back-end monitoring terminal. When the back-end monitoring terminal receives the inspection execution deviation signal, it issues a corresponding warning.

[0026] Furthermore, the specific analysis process of the execution monitoring module is as follows:

[0027] By analyzing and obtaining the regional search coefficient of monitoring area i, a corresponding preset regional search coefficient threshold is assigned to monitoring area i. The regional search coefficient is compared with the corresponding preset regional search coefficient threshold. If the regional search coefficient exceeds the preset regional search coefficient threshold, then monitoring area i is marked as a search and monitoring area.

[0028] The number of monitored areas is obtained and marked as the number of monitored areas. The ratio of the area monitoring coefficient of monitored area i to the corresponding preset area monitoring coefficient threshold is marked as the area monitoring status value. The average of the area monitoring status values ​​of all areas is calculated to obtain the comprehensive evaluation value of the monitoring.

[0029] The abnormal inspection value and the comprehensive inspection value are compared with the preset abnormal inspection threshold and the preset comprehensive inspection threshold respectively. If the abnormal inspection value or the comprehensive inspection value exceeds the corresponding preset threshold, an inspection execution deviation signal is generated; if neither the abnormal inspection value nor the comprehensive inspection value exceeds the corresponding preset threshold, an inspection execution qualified signal is generated.

[0030] Furthermore, the method for obtaining the regional survey coefficient is as follows:

[0031] When the investigation of the monitoring area i ends, the duration of the corresponding investigation process is collected and the deviation value between it and the corresponding standard duration is marked as the investigation duration value. The investigation path deviation value is obtained by comparing the actual investigation path of the corresponding investigation process with the set standard investigation path.

[0032] Obtain the query duration and query path deviation values ​​for all query processes in monitoring area i within the next detection period, and calculate the query duration value by averaging all query duration values, and calculate the query path deviation value by averaging all query path deviation values.

[0033] The interval between two adjacent visits to monitoring area i is marked as the visit interval value. The average of all visit interval values ​​for monitoring area i in the next detection period is used to calculate the visit interval status value. The regional visit coefficient is obtained by numerically calculating the visit interval analysis value, visit route analysis value and visit interval status value.

[0034] Furthermore, the specific allocation process for assigning the corresponding preset area search coefficient threshold to monitoring area i is as follows:

[0035] If the monitored area i corresponds to the key visit signal, then a preset area visit coefficient threshold LP1 is assigned to it. If the monitored area i corresponds to the weakened visit signal, then a preset area visit coefficient threshold LP2 is assigned to it, and LP2 > LP1 > 0.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. In this invention, the emission hazard analysis module judges the hazard of oil fume emissions from the corresponding oil fume emission outlets in real time based on the oil fume emission information. The emission outlet control classification module analyzes the emission pollution performance of the corresponding oil fume emission outlets during the detection period to classify the emission outlets into levels, which facilitates the formulation of matching control measures. The regional emission assessment module conducts a precise analysis of the oil fume emission management performance of all areas that need to be monitored for oil fume emissions. Subsequently, it strengthens the inspection of areas corresponding to key inspection signals and urges them to improve oil fume emissions, which significantly reduces the management difficulty for managers.

[0038] 2. In this invention, the inspection execution monitoring module analyzes the inspection status of all areas in the next inspection period. By generating inspection execution deviation signals or inspection execution qualified signals, the module can reasonably analyze and accurately reflect the inspection execution performance in the next inspection period. When an inspection execution deviation signal is generated, the module strengthens the training and supervision of inspection personnel to ensure subsequent inspection performance. The invention has a high degree of intelligence and further reduces the management difficulty for managers. Attached Figure Description

[0039] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0040] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0041] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1: As 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 emission outlet monitoring module, an emission hazard analysis module, an emission outlet control classification module, a regional emission assessment module, and a back-end monitoring terminal;

[0044] The oil fume emission monitoring module monitors the corresponding oil fume emission outlets through corresponding sensors, collects the oil fume emission information of the corresponding oil fume emission outlets, and sends the oil fume emission information of the corresponding emission outlets to the emission hazard analysis module via the Internet of Things.

[0045] The emission hazard analysis module determines the emission hazard of corresponding fume emission outlets based on fume emission information. When a fume emission outlet is determined to be in a high-hazard state, an emission warning is generated and sent to the emission outlet control classification module. This facilitates real-time and accurate monitoring of the emission hazard of each fume emission outlet. The specific analysis and judgment process for a high-hazard state is as follows:

[0046] The concentration data of oil fume emitted from the corresponding oil fume emission outlet is obtained and marked as oil fume concentration detection value. The concentration of particulate matter (mainly referring to the concentration of fine particles such as PM2.5 and PM10) and non-methane total hydrocarbon (NMHC) in the emitted oil fume are marked as oil fume particulate detection value and non-methane total hydrocarbon value, respectively. The temperature of the emitted oil fume is collected and marked as oil fume temperature detection value.

[0047] The oil fume hazard coefficient TX is obtained by numerically calculating the oil fume concentration value TY, oil fume particle value TL, non-methane total hydrocarbon value TS, and oil fume temperature value TN using the formula TX=(eq1*TY+eq2*TL+eq3*TS+eq4*TN) / 4. Among them, eq1, eq2, eq3, and eq4 are preset proportional coefficients with values ​​greater than zero. Furthermore, the larger the value of the oil fume hazard coefficient TX, the more serious the current emission hazard level of the corresponding oil fume emission outlet.

[0048] The hazard coefficient TX of oil fume is compared with the preset threshold value of oil fume hazard coefficient. If the hazard coefficient TX of oil fume exceeds the preset threshold value, it indicates that the current emission hazard level of the corresponding oil fume emission outlet is relatively serious, and the corresponding oil fume emission outlet is judged to be in a high emission hazard state.

[0049] The emission outlet control classification module is used to set the detection period, preferably thirty days. During the detection period, it analyzes the emission pollution performance of the corresponding oil fume emission outlets. Based on the analysis, the corresponding oil fume emission outlets are marked as either warning emission outlets or qualified emission outlets. This marking information is then sent to the regional emission assessment module. This facilitates a detailed understanding of the pollution characteristics of each oil fume emission outlet and enables the classification of emission outlet levels, making it easier to formulate corresponding control measures. The specific analysis process of the emission outlet control classification module is as follows:

[0050] The timing begins when the corresponding fume emission outlet is determined to be in a high-hazard emission state, and the duration of the high-hazard emission state is obtained accordingly. The duration of all high-hazard emission states of the corresponding fume emission outlet during the detection period is summed to obtain the total high-hazard emission time value. The total high-hazard emission time value is compared with the preset high-hazard emission time threshold. If the total high-hazard emission time value exceeds the preset high-hazard emission time threshold, it indicates that the emission performance of the corresponding fume emission outlet is poor during the detection period, and the corresponding fume emission outlet is marked as a warning emission outlet.

[0051] Furthermore, if the total duration of high-risk emissions exceeds the preset threshold for high-risk emissions, the average value of all oil fume hazard coefficients within the duration of the corresponding high-risk emission state will be calculated to obtain the oil fume hazard analysis value, and the amount of oil fume emitted within the duration of the corresponding high-risk emission state will be marked as the emission detection value.

[0052] Through formula The high-risk state assessment value NL is obtained by numerically calculating the duration NS of the corresponding high-risk emission state, the hazard value NY of the oil fume, and the emission quantity detection value NF. Among them, hu1, hu2, and hu3 are preset proportional coefficients with values ​​greater than zero. Furthermore, the larger the value of the high-risk state assessment value NL, the greater the pollution caused by the corresponding high-risk emission state.

[0053] The high-risk status assessment value NL is compared with the preset high-risk status assessment threshold. If the high-risk status assessment value NL exceeds the preset high-risk status assessment threshold, it indicates that the pollution caused by the corresponding high-risk emission status is relatively large. Then, the corresponding high-risk emission status is assigned the risk assessment symbol WP-1.

[0054] The number of times the corresponding fume emission outlet was assigned the hazard assessment symbol WP-1 during the detection period was obtained and marked as a high-risk frequency detection value. The average value of all high-risk status assessment values ​​of the corresponding fume emission outlet during the detection period was calculated to obtain the hazard assessment detection value.

[0055] Through formula The emission outlet classification coefficient QW is obtained by numerically calculating the total high-risk emission value QR, the high-risk frequency detection value QL, and the hazard assessment detection value QP. Among them, fy1, fy2, and fy3 are preset proportional coefficients, fy2>fy3>fy1>0. Furthermore, the larger the value of the emission outlet classification coefficient QW, the worse the overall emission performance of the corresponding oil fume emission outlet during the detection period.

[0056] The emission outlet classification coefficient QW is compared with the preset emission outlet classification coefficient threshold. If the emission outlet classification coefficient QW exceeds the preset emission outlet classification coefficient threshold, it indicates that the emission performance of the corresponding fume emission outlet is poor overall during the detection period, and the corresponding fume emission outlet is marked as a warning emission outlet. If the emission outlet classification coefficient QW does not exceed the preset emission outlet classification coefficient threshold, it indicates that the emission performance of the corresponding fume emission outlet is good overall during the detection period, and the corresponding fume emission outlet is marked as a qualified emission outlet.

[0057] The regional emission assessment module is used to identify areas requiring monitoring of oil fume emissions, marking these areas as monitoring areas i, where i is a natural number greater than 1. Through analysis, it generates either a key inspection signal or a weakened inspection signal for monitoring area i, and sends these signals to the backend monitoring terminal. Subsequent monitoring of areas corresponding to key inspection signals is strengthened to urge improvements in oil fume emissions, reduce pollution levels in each area, and significantly reduce the management difficulty for administrators. The specific operation process of the regional emission assessment module is as follows:

[0058] The number of early warning emission outlets in monitoring area i is obtained and marked as early warning number detection value. The emission outlet classification coefficient of the corresponding early warning emission outlet in monitoring area i is marked as the emission over-detection value if it exceeds the preset emission outlet classification coefficient threshold. The emission over-detection value of all early warning emission outlets in monitoring area i is calculated by averaging the emission over-detection values. The emission over-detection value with the largest value in monitoring area i is marked as the emission over-amplitude value.

[0059] The regional emission assessment value SY is obtained by numerically calculating the warning detection value SW, the emission over-limit value SN, and the emission over-amplitude value SF using the formula SY=ng1*SW+(ng2*SN+ng3*SF) / 2. Among them, ng1, ng2, and ng3 are preset proportional coefficients, where ng1>ng2>ng3>0. Furthermore, the larger the value of the regional emission assessment value SY, the worse the overall performance of oil fume emission management for monitoring area i.

[0060] The regional emission assessment value SY is 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 further supervision of oil fume emissions in monitoring area i is needed. In this case, 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. In this case, a weakened inspection signal for monitoring area i is generated.

[0061] Example 2: Figure 2As shown, the difference between this embodiment and Embodiment 1 is that the background monitoring terminal is connected to the inspection execution monitoring module. The inspection execution monitoring module will analyze the inspection status of all areas in the next inspection period, and generate inspection execution deviation signals or inspection execution qualified signals through analysis. It can reasonably analyze and accurately reflect the inspection execution performance in the next inspection period.

[0062] Furthermore, the system sends deviation signals or compliance signals for inspection execution to the backend monitoring terminal. Upon receiving a deviation signal, the backend monitoring terminal issues a corresponding warning and strengthens the training and supervision of inspection personnel to ensure subsequent inspection performance. The specific analysis process of the inspection execution monitoring module is as follows:

[0063] When the investigation of the monitoring area i ends, the duration of the corresponding investigation process is collected and the deviation value between it and the corresponding standard duration is marked as the investigation duration value. The investigation path deviation value (i.e. the proportion of non-overlapping paths) is obtained by comparing the actual investigation path of the corresponding investigation process with the set standard investigation path.

[0064] Obtain the query duration and query path deviation values ​​for all query processes in monitoring area i within the next detection period, and calculate the query duration value by averaging all query duration values, and calculate the query path deviation value by averaging all query path deviation values.

[0065] And mark the interval between two adjacent visits to the monitoring area i as the visit interval value, and calculate the average of all visit interval values ​​for the monitoring area i in the next detection period to obtain the visit interval status value.

[0066] Through formula The regional search coefficient CYi is obtained by numerically calculating the search analysis value CWi, the search path analysis value CFi, and the search interval value CPi. Among them, sg1, sg2, and sg3 are preset proportional coefficients with values ​​greater than zero. Furthermore, the larger the value of the regional search coefficient CYi, the worse the search performance for the monitored area i.

[0067] Assign a corresponding preset area search coefficient threshold to monitoring area i. Specifically, if monitoring area i corresponds to a key search signal, assign a preset area search coefficient threshold LP1 to it; if monitoring area i corresponds to a weakened search signal, assign a preset area search coefficient threshold LP2 to it, and LP2 > LP1 > 0. By assigning appropriate preset area search coefficient thresholds to different areas, it is beneficial to improve the accuracy of the corresponding analysis results.

[0068] The regional search coefficient i is compared with the corresponding preset regional search coefficient threshold. If the regional search coefficient i exceeds the preset regional search coefficient threshold, it indicates that the search performance for the monitored region i is poor. Then, the monitored region i is marked as a search and monitoring abnormal region.

[0069] The number of monitored areas is obtained and marked as the number of monitored areas. The ratio of the area monitoring coefficient of monitored area i to the corresponding preset area monitoring coefficient threshold is marked as the area monitoring status value. The average of the area monitoring status values ​​of all areas is calculated to obtain the comprehensive evaluation value of the monitoring.

[0070] The outlier detection value and the overall assessment value of the investigation are compared with the preset outlier detection threshold and the preset overall assessment threshold, respectively. If the outlier detection value or the overall assessment value exceeds the corresponding preset threshold, it indicates that the overall performance of the investigation in all areas is poor, and an investigation execution deviation signal is generated. If neither the outlier detection value nor the overall assessment value exceeds the corresponding preset threshold, it indicates that the overall performance of the investigation in all areas is good, and an investigation execution qualified signal is generated.

[0071] The working principle of this invention is as follows: During use, the oil fume emission monitoring module collects oil fume emission information from the corresponding oil fume emission outlets. The emission hazard analysis module judges the hazard of oil fume emissions from the corresponding outlets in real time based on this information. When an outlet is determined to be in a high-hazard state, an emission warning is generated, which helps to accurately grasp the emission hazard of each outlet in real time. Furthermore, the emission outlet control classification module analyzes the pollution performance of the corresponding outlets during the monitoring period to identify warning outlets and qualified outlets, achieving outlet classification and facilitating the development of matching control measures. Additionally, the regional emission assessment module precisely analyzes the oil fume emission management performance of all areas requiring oil fume emission monitoring, generating key inspection signals or weakened inspection signals for each area. Subsequent monitoring of areas corresponding to key inspection signals is strengthened, and improvements in oil fume emissions are urged, significantly reducing the management difficulty for managers. This contributes to promoting the green development of the catering industry, protecting the atmospheric environment, and improving the quality of life for residents, demonstrating a high degree of intelligence.

[0072] The above formulas are all dimensionless numerical calculations. These formulas are derived from software simulations using collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to actual conditions. The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. The preferred embodiments do not describe all details exhaustively, nor do they limit the invention to specific implementations. Obviously, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only 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 a fume emission monitoring module, an emission hazard analysis module, an emission control classification module, a regional emission assessment module, and a back-end monitoring terminal; The oil fume emission monitoring module monitors the corresponding oil fume emission outlets, collects the oil fume emission information of the corresponding oil fume emission outlets, and sends the oil fume emission information of the corresponding emission outlets to the emission hazard analysis module via the Internet of Things; The emission hazard analysis module determines the emission hazard of the corresponding fume emission outlet based on the fume emission information. When it determines that the corresponding fume emission outlet is in a high-hazard emission state, it generates emission warning information and sends the emission warning information of the corresponding 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 outlets during the detection period, mark the corresponding oil fume emission outlets as warning emission outlets or qualified emission outlets through analysis, and send the marking information of the corresponding oil fume emission outlets to the regional emission assessment module. The regional emission assessment module is used to identify areas that require monitoring of oil fume emissions, mark the corresponding areas as monitoring areas i, where i is a natural number greater than 1; analyze the data to generate key inspection signals or weakened inspection signals for monitoring area i, and send the key inspection signals or weakened inspection signals for monitoring area i to the backend monitoring terminal. The specific process for analyzing and judging high-hazard emission conditions is as follows: The concentration data of the oil fume emitted from the corresponding oil fume emission outlet is obtained and marked as the oil fume concentration detection value. The concentration of particulate matter and non-methane total hydrocarbons in the emitted oil fume are marked as the oil fume particulate detection value and non-methane total hydrocarbon value, respectively. 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, oil fume particle count, non-methane total hydrocarbon count, and oil fume temperature. 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. The specific analysis process of the emission outlet control classification module includes: The timing begins when the corresponding fume emission outlet is determined to be in a high-hazard emission state, and the duration of the corresponding high-hazard emission state is obtained accordingly. The duration of all high-hazard emission states of the corresponding fume emission outlet during the detection period is summed to obtain the total high-hazard emission time value. If the total high-hazard emission time value exceeds the preset high-hazard emission time threshold, the corresponding fume emission outlet is marked as a warning emission outlet. If the total time value of high-risk emissions exceeds the preset threshold for high-risk emissions, the emission classification coefficient of the corresponding fume emission outlet is obtained through analysis. If the emission outlet classification coefficient exceeds the preset threshold for emission outlet classification coefficient, the corresponding fume emission outlet is marked as a warning emission outlet; if the emission outlet classification coefficient does not exceed the preset threshold for emission outlet classification coefficient, the corresponding fume emission outlet is marked as a qualified emission outlet. The specific method for obtaining the emission outlet classification coefficient is as follows: The average value of all oil fume hazard coefficients within the duration of the corresponding high-hazard emission state is calculated to obtain the oil fume hazard analysis value, and the amount of oil fume emitted within the duration of the corresponding high-hazard emission state is marked as the emission detection value. The high-risk status assessment value is obtained by numerically calculating the duration of the corresponding high-risk emission status, the hazard analysis value of oil fume, and the emission quantity detection value. If the high-risk status assessment value exceeds the preset high-risk status assessment threshold, the corresponding high-risk emission status is assigned the hazard assessment symbol WP-1. The number of times the corresponding oil fume emission outlet was assigned the hazard assessment symbol WP-1 during the detection period was obtained and marked as a high-risk frequency detection value. The average value of all high-risk status assessment values ​​of the corresponding oil fume emission outlet during the detection period was calculated to obtain the hazard assessment detection value. The emission outlet classification coefficient was obtained by numerically calculating the total high-risk emission time value, high-risk frequency detection value and hazard assessment detection value.

2. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 1, characterized in that, The specific operational process of the regional emissions assessment module includes: The number of early warning emission outlets in monitoring area i is obtained and marked as early warning number detection value. The emission outlet classification coefficient of the corresponding early warning emission outlet in monitoring area i is marked as the emission over-detection value if it exceeds the preset emission outlet classification coefficient threshold. The emission over-detection value of all early warning emission outlets in monitoring area i is calculated by averaging the emission over-detection values. The emission over-detection value with the largest value in monitoring area i is marked as the emission over-amplitude value. The regional emission assessment value is obtained by numerically calculating the warning detection value, emission over-limit value, and emission over-amplitude value. If the regional emission assessment value exceeds the preset regional emission assessment threshold, a key inspection signal for monitoring area i is generated; if the regional emission assessment value does not exceed the preset regional emission assessment threshold, a weakened inspection signal for monitoring area i is generated.

3. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 1, characterized in that, The backend monitoring terminal communicates with the inspection execution monitoring module. The inspection execution monitoring module analyzes the inspection status of all areas in the next inspection period, generates inspection execution deviation signals or inspection execution qualified signals through analysis, and sends the inspection execution deviation signals or inspection execution qualified signals to the backend monitoring terminal. When the backend monitoring terminal receives the inspection execution deviation signal, it issues a corresponding warning.

4. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 3, characterized in that, The specific analysis process of the execution monitoring module is as follows: By analyzing the regional search coefficient of monitoring area i, a corresponding preset regional search coefficient threshold is assigned to monitoring area i. If the regional search coefficient exceeds the preset regional search coefficient threshold, monitoring area i is marked as a search and monitoring area. The number of monitored areas is obtained and marked as the number of monitored areas. The ratio of the area monitoring coefficient of monitored area i to the corresponding preset area monitoring coefficient threshold is marked as the area monitoring status value. The average of the area monitoring status values ​​of all areas is calculated to obtain the comprehensive evaluation value of the monitoring. If the abnormal value of the inspection and monitoring or the comprehensive evaluation value of the inspection exceeds the corresponding preset threshold, a deviation signal of inspection execution is generated; if neither the abnormal value of the inspection and monitoring nor the comprehensive evaluation value of the inspection exceeds the corresponding preset threshold, a qualified signal of inspection execution is generated.

5. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 4, characterized in that, The method for obtaining the regional survey coefficient is as follows: When the investigation of the monitoring area i ends, the duration of the corresponding investigation process is collected and the deviation value between it and the corresponding standard duration is marked as the investigation duration value. The investigation path deviation value is obtained by comparing the actual investigation path of the corresponding investigation process with the set standard investigation path. Obtain the query duration and query path deviation values ​​for all query processes in monitoring area i within the next detection period, and calculate the query duration value by averaging all query duration values, and calculate the query path deviation value by averaging all query path deviation values. The interval between two adjacent visits to monitoring area i is marked as the visit interval value. The average of all visit interval values ​​for monitoring area i in the next detection period is used to calculate the visit interval status value. The regional visit coefficient is obtained by numerically calculating the visit interval analysis value, visit route analysis value and visit interval status value.

6. The online monitoring system for oil fume concentration based on the Internet of Things according to claim 4, characterized in that, The specific allocation process for the preset area survey coefficient threshold is as follows: If the monitored area i corresponds to the key visit signal, then a preset area visit coefficient threshold LP1 is assigned to it. If the monitored area i corresponds to the weakened visit signal, then a preset area visit coefficient threshold LP2 is assigned to it, and LP2 > LP1 > 0.

Images