Smoke detection and analysis method and system

Abstract

The embodiment of the present invention relates to the field of flue gas detection technology, and discloses a flue gas detection analysis method, including: obtaining a monitoring information set on multiple sampling detection nodes; judging whether all sampling detection nodes reporting the sampling information set cover the sampling segment to be monitored corresponding to the sampling monitoring task; the data monitoring platform judges whether the sampling information set reported by each sampling detection node includes the sensor monitoring information reported by all data acquisition sensors within the coverage range of each sampling detection node; the data monitoring platform analyzes the monitoring status of the sampling segment to be monitored based on the sensor monitoring information set reported by all sampling detection nodes and the pre-trained sampling status analysis model. It realizes efficient and accurate monitoring of flue gas emissions through comprehensive coverage monitoring, accurate judgment of sampling segment coverage, information integrity verification, model-based analysis, and efficient data processing, providing strong support for environmental management and decision-making.

Description

Technical Field

[0001] The present invention relates to the field of detection technology, and in particular to a smoke detection and analysis method and system. Background Art

[0002] At present, environmental emission standards for the industrial and power generation sectors mainly involve three pollutants: particulate matter, sulfur dioxide and nitrogen oxides. The current environmental emission monitoring method relies on a continuous emission monitoring system (CEMS), which is responsible for measuring the amount of pollutant emissions in the flue gas and transmitting the measured data in real time to a centralized environmental monitoring platform. The platform calculates the average value of the received data and compares it with the established environmental emission standards. If the data is found to exceed the prescribed standards, the platform will issue an abnormal alarm and issue corresponding treatment measures for those companies that fail to comply with the standards.

[0003] If accurate flue gas detection cannot be achieved during the actual detection process, it will make it impossible to conduct environmental protection detection on the enterprise on the one hand, and on the other hand, the detection error will affect the operation of the enterprise. Therefore, designing a method that can achieve accurate flue gas measurement has become a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0004] In view of the above-mentioned defects, an embodiment of the present invention discloses a flue gas detection and analysis method, which can perform precise flue gas sampling and monitoring, improve the accuracy of measurement results, and realize instant alarm.

[0005] The first aspect of the embodiment of the present invention discloses a flue gas detection and analysis method, which is applied to a flue gas emission monitoring system, wherein the flue gas emission monitoring system includes a particulate matter monitoring system, a pollutant monitoring system, a flue gas parameter monitoring system, and a data monitoring platform; a plurality of sampling detection nodes are arranged at the system to be monitored for collecting data at corresponding sampling points;

[0006] The smoke detection and analysis method comprises:

[0007] Acquire a set of monitoring information on multiple sampling and detection nodes, where the set of monitoring information reported by each sampling and detection node is formed by aggregating and processing the monitoring information reported by each sampling and detection node from multiple monitoring sensors within its coverage area;

[0008] Determine, according to a preset sampling monitoring task, whether all the sampling detection nodes reporting the sampling information set cover the sampling segment to be monitored corresponding to the sampling monitoring task;

[0009] When it is determined that all the sampling detection nodes cover the corresponding sampling segment to be monitored, the data monitoring platform determines whether the sampling information set reported by each of the sampling detection nodes includes the sensor monitoring information reported by all data acquisition sensors within the coverage of each of the sampling detection nodes;

[0010] When it is determined that the sensor monitoring information set reported by each of the sampling detection nodes includes the sensor monitoring information reported by all monitoring sensors within the coverage range of each of the sampling detection nodes, the data monitoring platform analyzes the monitoring status of the sampling segment to be monitored based on the sensor monitoring information set reported by all the sampling detection nodes and a pre-trained sampling status analysis model.

[0011] As an optional implementation, in the first aspect of the embodiment of the present invention, when it is determined that the sensor monitoring information set reported by each of the sampling detection nodes includes the sensor monitoring information reported by all monitoring sensors within the coverage of each of the sampling detection nodes, the data monitoring platform analyzes the monitoring state of the sampling segment to be monitored based on the sensor monitoring information set reported by all of the sampling detection nodes and the pre-trained sampling state analysis model, including:

[0012] When it is determined that the monitoring information set reported by each of the sampling monitoring nodes includes the sensor monitoring information reported by all monitoring sensors within the coverage of each of the sampling detection nodes, the data detection platform divides the sampling segment to be monitored into a plurality of sub-sampling segments to be detected according to the flow direction of the smoke in the sampling segment to be monitored;

[0013] The data monitoring platform parses the sensor monitoring information sets reported by all the sampling detection nodes to obtain the sub-sampling information sets corresponding to each sub-sampling segment to be detected;

[0014] For each of the sub-sampling segments to be detected, the data monitoring platform determines whether the sub-sampling segment to be detected is a starting sub-sampling segment. When the sub-sampling segment to be detected is a starting sub-sampling segment, the sub-sampling information set corresponding to the sub-sampling segment to be detected is input into the sub-sampling state analysis model corresponding to the sub-pipeline segment to be detected, and the sub-monitoring state corresponding to the sub-sampling segment to be detected is obtained;

[0015] When the sub-sampling segment to be detected is not the starting sub-sampling segment, the sub-sampling information set corresponding to the sub-sampling segment to be detected and the sub-monitoring state corresponding to the previous adjacent sub-sampling segment to be detected of the sub-sampling segment to be detected are input into the sub-sampling state analysis model corresponding to the sub-pipeline segment to be detected, so as to obtain the sub-monitoring state corresponding to the sub-pipeline segment to be detected;

[0016] The data monitoring platform analyzes the monitoring status of the sampling segment to be monitored according to the sub-monitoring statuses corresponding to all the sub-sampling segments to be detected.

[0017] As an optional implementation, in the first aspect of the embodiment of the present invention, the monitoring information set includes one or more of the flue gas temperature information corresponding to the sampling section to be monitored, the flue gas pressure information corresponding to the sampling section to be monitored, the flue gas flow rate information corresponding to the sampling section to be monitored, the flue gas humidity information of the sampling section to be monitored, the oxygen content information of the sampling section to be monitored, the particulate matter concentration of the sampling section to be monitored, the SO2 concentration of the sampling section to be monitored, the NOx concentration of the sampling section to be monitored, the O2 concentration of the sampling section to be monitored and the CO concentration of the sampling section to be monitored;

[0018] Before acquiring the monitoring information sets on the multiple sampling detection nodes, the method further includes:

[0019] Receive sampling type information pre-configured and associated with the sampling segment to be detected, and retrieve a sampling state analysis model associated with the sampling type information according to the sampling type information, wherein the sampling type information includes thermal power generation type information, industrial kiln type information, industrial boiler type information, civil heating boiler type information, steel and metallurgy type information, waste incineration type information, petrochemical industry type information, and cement industry type information.

[0020] As an optional implementation, in the first aspect of the embodiment of the present invention, the SO2 concentration of the sampling section to be monitored is obtained by the following steps:

[0021] A pre-processing device is arranged at the front end of the flue gas emission monitoring system, and the humidity in the flue gas entering the flue gas emission monitoring system is reduced by the pre-processing device;

[0022] The SO2 concentration in the pretreated flue gas is measured by the flue gas emission monitoring system, and the measured SO2 concentration in the pretreated flue gas, the flue gas humidity and the pre-constructed humidity-loss rate mapping table are matched to obtain the standard SO2 concentration at the sampling section to be monitored.

[0023] As an optional implementation manner, in the first aspect of the embodiment of the present invention, before acquiring the monitoring information sets on the multiple sampling detection nodes, the method further includes:

[0024] Acquire sampling detection nodes within a set range, select a sampling detection node from the sampling detection nodes as a first node, and set the other sampling detection nodes as second nodes;

[0025] Establishing a communication network between the first node and each of the second nodes, wherein the first node allocates data reporting time slots to the multiple second nodes;

[0026] The second nodes send sampled reporting data to the first node in their respective allocated reporting time slots. The first node receives the sampled reporting data from each second node, processes and integrates the sampled reporting data, and sends the integrated data to the data upload node within the preset range for uploading the sampled data.

[0027] As an optional implementation, in the first aspect of the embodiment of the present invention, the particle concentration of the sampling section to be monitored is obtained by the following steps:

[0028] Obtain the particle concentration of the corresponding sampling section to be monitored under the detected actual state;

[0029] The particle concentration of the corresponding sampling section to be monitored under the actual state is input into the preset concentration calibration formula for calculation to obtain the calibrated particle concentration; the concentration calibration formula is:

[0030]

[0031] Among them, e is the calibrated particle concentration value, e S is the particle concentration value measured under actual conditions, is the average temperature of the flue gas at the sampling point, p a is the atmospheric pressure during sampling, p s is the flue gas static pressure at the sampling point, X sw is the moisture content at the sampling point.

[0032] As an optional implementation, in the first aspect of the embodiment of the present invention, the detection and analysis method further includes:

[0033] Acquire the running state of the device at the current sampling section to be detected, and determine the working state of the device according to the running state of the device, wherein the working state is a first working state, a second working state and a third working state;

[0034] The various parameters in the sampling state analysis model are updated according to the working state to perform subsequent monitoring and comparison.

[0035] A second aspect of an embodiment of the present invention discloses a smoke detection and analysis system, comprising:

[0036] Acquisition module: used to acquire monitoring information sets on multiple sampling detection nodes, where the monitoring information set reported by each sampling detection node is formed by aggregating and processing the monitoring information reported by each sampling detection node from multiple monitoring sensors within its coverage area;

[0037] A first judgment module: used to judge whether all the sampling detection nodes reporting the sampling information set cover the sampling segment to be monitored corresponding to the sampling monitoring task according to a preset sampling monitoring task;

[0038] The second judgment module is used for, when it is judged that all the sampling detection nodes cover the corresponding sampling segment to be monitored, the data monitoring platform to judge whether the sampling information set reported by each sampling detection node includes the sensor monitoring information reported by all data acquisition sensors within the coverage range of each sampling detection node;

[0039] Analysis module: used to analyze the monitoring status of the sampling segment to be monitored based on the sensor monitoring information set reported by all the sampling detection nodes and a pre-trained sampling status analysis model when it is determined that the sensor monitoring information set reported by each of the sampling detection nodes includes the sensor monitoring information reported by all monitoring sensors within the coverage area of ​​each of the sampling detection nodes.

[0040] The third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the smoke detection and analysis method disclosed in the first aspect of the embodiment of the present invention.

[0041] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the smoke detection and analysis method disclosed in the first aspect of an embodiment of the present invention.

[0042] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0043] In the embodiment of the present invention, by setting up multiple sampling detection nodes at the system to be monitored, the method can realize comprehensive data collection of multiple sampling points in the entire monitoring area. Each sampling detection node is responsible for aggregating and processing the monitoring information reported by multiple monitoring sensors within its coverage area to ensure the integrity of the information. The above-mentioned flue gas detection and analysis method realizes efficient and accurate monitoring of flue gas emissions through technical means in multiple aspects such as comprehensive coverage monitoring, accurate judgment of sampling segment coverage, information integrity verification, model-based analysis, and efficient data processing, providing strong support for environmental management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 It is a flow chart of the smoke detection and analysis method disclosed in the embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of a process for further performing state analysis disclosed in an embodiment of the present invention;

[0047] Figure 3 It is a schematic diagram of the flow of sampling node communication networking disclosed in an embodiment of the present invention;

[0048] Figure 4 It is a structural schematic diagram of a smoke detection and analysis system provided by an embodiment of the present invention;

[0049] Figure 5 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] 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.

[0051] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0052] If accurate flue gas detection cannot be achieved during the actual detection process, it will make it impossible to conduct environmental protection detection on the enterprise on the one hand, and on the other hand, the enterprise operation will be affected due to detection errors. Based on this, the embodiment of the present invention discloses a flue gas detection and analysis method, system, electronic device and storage medium. By setting multiple sampling and detection nodes at the system to be monitored, the method can realize comprehensive data collection of multiple sampling points in the entire monitoring area. Each sampling and detection node is responsible for aggregating and processing the monitoring information reported by multiple monitoring sensors within its coverage area to ensure the integrity of the information. The above-mentioned flue gas detection and analysis method realizes efficient and accurate monitoring of flue gas emissions through technical means in multiple aspects such as comprehensive coverage monitoring, accurate judgment of sampling segment coverage, information integrity verification, model-based analysis and efficient data processing, providing strong support for environmental management and decision-making.

[0053] Embodiment 1

[0054] See also Figure 1 , Figure 1 It is a flow chart of the smoke detection and analysis method disclosed in the embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices.

[0055] like Figure 1 As shown, the smoke detection and analysis method includes the following steps:

[0056] S101: Acquire a set of monitoring information on multiple sampling detection nodes, where the set of monitoring information reported by each sampling detection node is formed by aggregating and processing monitoring information reported by multiple monitoring sensors within its coverage area by each sampling detection node;

[0057] S102: judging, according to a preset sampling monitoring task, whether all the sampling detection nodes reporting the sampling information set cover the sampling segment to be monitored corresponding to the sampling monitoring task;

[0058] S103: When it is determined that all the sampling detection nodes cover the corresponding sampling segment to be monitored, the data monitoring platform determines whether the sampling information set reported by each sampling detection node includes the sensor monitoring information reported by all data acquisition sensors within the coverage of each sampling detection node;

[0059] S104: When it is determined that the sensor monitoring information set reported by each of the sampling detection nodes includes the sensor monitoring information reported by all monitoring sensors within the coverage area of ​​each of the sampling detection nodes, the data monitoring platform analyzes the monitoring status of the sampling segment to be monitored based on the sensor monitoring information set reported by all of the sampling detection nodes and a pre-trained sampling status analysis model.

[0060] The method is applied to a flue gas emission monitoring system, which includes a particulate matter monitoring system, a pollutant monitoring system, a flue gas parameter monitoring system and a data monitoring platform; a plurality of sampling detection nodes are arranged at the system to be monitored for collecting data from corresponding sampling points; the technical effects of the above-mentioned flue gas detection and analysis method are mainly reflected in the following aspects: by setting a plurality of sampling detection nodes at the system to be monitored, the method can realize comprehensive data collection of multiple sampling points in the entire monitoring area.

[0061] Each sampling detection node is responsible for aggregating and processing the monitoring information reported by multiple monitoring sensors within its coverage area to ensure the integrity of the information. According to the pre-set sampling and monitoring tasks, the system can accurately determine whether the sampling detection node that reports the sampling information set completely covers the sampling segment to be monitored. This judgment mechanism ensures the pertinence and effectiveness of monitoring, and avoids repeated sampling of non-critical areas or omissions in monitoring of critical areas. When the sampling detection node covers the corresponding sampling segment to be monitored, the data monitoring platform will further verify whether the sensor monitoring information reported by each sampling detection node is complete, that is, whether it contains the sensor monitoring information reported by all data acquisition sensors within its coverage area. This step ensures the comprehensiveness and accuracy of the monitoring data.

[0062] When all monitoring information is verified to be correct, the data monitoring platform will use the pre-trained sampling state analysis model to analyze the collected sensor monitoring information. This model-based analysis method can more accurately evaluate the monitoring status of the sampling section to be monitored, providing strong support for subsequent decision-making and management. The entire flue gas detection and analysis method adopts a distributed data collection and centralized data processing mode, which not only ensures the comprehensiveness and real-time nature of data collection, but also improves the efficiency and accuracy of data processing through centralized processing of the data monitoring platform. Because this method adopts a modular design, the entire flue gas emission monitoring system has strong scalability and adaptability. Whether it is adding new sampling detection nodes or adjusting the monitoring area, it can be achieved through simple configuration and update.

[0063] More preferably, when it is determined that the sensor monitoring information set reported by each sampling detection node includes the sensor monitoring information reported by all monitoring sensors within the coverage of each sampling detection node, the data monitoring platform analyzes the monitoring status of the sampling segment to be monitored based on the sensor monitoring information set reported by all the sampling detection nodes and the pre-trained sampling status analysis model, including:

[0064] S1041: when it is determined that the monitoring information set reported by each of the sampling monitoring nodes includes the sensor monitoring information reported by all monitoring sensors within the coverage of each of the sampling detection nodes, the data detection platform divides the sampling segment to be monitored into a plurality of sub-sampling segments to be detected according to the flow direction of the smoke in the sampling segment to be monitored;

[0065] S1042: The data monitoring platform parses the sensor monitoring information sets reported by all the sampling detection nodes to obtain the sub-sampling information sets corresponding to each of the sub-sampling segments to be detected;

[0066] S1043: For each of the sub-sampling segments to be detected, the data monitoring platform determines whether the sub-sampling segment to be detected is a starting sub-sampling segment. When the sub-sampling segment to be detected is a starting sub-sampling segment, the sub-sampling information set corresponding to the sub-sampling segment to be detected is input into the sub-sampling state analysis model corresponding to the sub-pipeline segment to be detected, and the sub-monitoring state corresponding to the sub-sampling segment to be detected is obtained.

[0067] S1044: When the sub-sampling segment to be detected is not the starting sub-sampling segment, the sub-sampling information set corresponding to the sub-sampling segment to be detected and the sub-monitoring state corresponding to the previous adjacent sub-sampling segment to be detected of the sub-sampling segment to be detected are input into the sub-sampling state analysis model corresponding to the sub-pipeline segment to be detected, so as to obtain the sub-monitoring state corresponding to the sub-pipeline segment to be detected;

[0068] S1045: The data monitoring platform analyzes the monitoring status of the sampling segment to be monitored according to the sub-monitoring statuses corresponding to all the sub-sampling segments to be detected.

[0069] The above method can further improve the overall refined monitoring. By subdividing the sampling segment to be monitored into multiple sub-sampling segments to be detected and assigning a corresponding sub-sampling state analysis model to each sub-sampling segment, the system can realize refined monitoring of the sampling segment. This helps to more accurately identify the specific location where the problem occurs, thereby improving the efficiency and accuracy of monitoring. When dividing the sampling segment to be monitored into multiple sub-sampling segments, the flow direction of the flue gas is considered. This consideration makes the analysis more in line with actual working conditions, because the flow state of the flue gas may directly affect the accuracy and reliability of the monitoring data of each segment. For non-starting sub-sampling segments, the system considers not only the sub-sampling information set of the segment itself, but also the sub-monitoring state of its previous adjacent sub-sampling segments. This dynamic analysis method can more comprehensively reflect the overall state of the sampling segment and improve the comprehensiveness and accuracy of the monitoring results. Finally, the system comprehensively analyzes the overall monitoring state of the sampling segment to be monitored based on the sub-monitoring state of all sub-sampling segments to be detected. This comprehensive evaluation method can ensure an accurate and comprehensive evaluation of the entire sampling segment, providing strong support for subsequent decision-making and actions. Through the pre-trained sampling state analysis model, the system can quickly and accurately analyze the monitoring status of each sub-sampling segment, thereby greatly improving the efficiency of the monitoring work.

[0070] In the embodiment of the present invention, the flue gas in the starting section comes directly from the emission source without being mixed and diluted in the chimney. Therefore, sampling and analyzing the starting section can more directly understand the original emission situation of the emission source, including the type and concentration of pollutants. This is of great significance for understanding the characteristics of the emission source, evaluating its environmental impact, and formulating targeted emission reduction measures. In certain emergency situations, such as when the emission source leaks or emits abnormally, sampling and analyzing the starting section can quickly respond and diagnose the problem. By comparing the sampling data of the starting section with that under normal conditions, it is possible to quickly determine whether there is abnormal emission and take corresponding measures to deal with it. There are also some limitations to the sampling and analysis of the starting section. Since the flue gas state in the starting section may be unstable and easily affected by the operating conditions of the emission source, the accuracy and reliability of the sampling results may be affected to a certain extent. Therefore, when carrying out specific implementation, the sampling of the starting section usually needs to take into account the influence of higher temperature and higher pressure conditions.

[0071] More preferably, the monitoring information set includes one or more of the flue gas temperature information corresponding to the sampling section to be monitored, the flue gas pressure information corresponding to the sampling section to be monitored, the flue gas flow rate information corresponding to the sampling section to be monitored, the flue gas humidity information of the sampling section to be monitored, the oxygen content information of the sampling section to be monitored, the particulate matter concentration of the sampling section to be monitored, the SO2 concentration of the sampling section to be monitored, the NOx concentration of the sampling section to be monitored, the O2 concentration of the sampling section to be monitored and the CO concentration of the sampling section to be monitored;

[0072] Before acquiring the monitoring information sets on the multiple sampling detection nodes, the method further includes:

[0073] Receive sampling type information pre-configured and associated with the sampling segment to be detected, and retrieve a sampling state analysis model associated with the sampling type information according to the sampling type information, wherein the sampling type information includes thermal power generation type information, industrial kiln type information, industrial boiler type information, civil heating boiler type information, steel and metallurgy type information, waste incineration type information, petrochemical industry type information, and cement industry type information.

[0074] The monitoring information set in the embodiment of the present invention includes a variety of key parameters, such as flue gas temperature, pressure, flow rate, humidity, oxygen content, particulate matter concentration, SO2 concentration, NOx concentration, O2 concentration and CO concentration, etc. These parameters can fully reflect the state and emission of flue gas, and help to more accurately evaluate its impact on the environment.

[0075] By receiving the pre-configured sampling type information associated with the sampling segment to be detected, the system can retrieve the sampling state analysis model associated with specific industry types (such as thermal power generation, industrial kilns, industrial boilers, civil heating boilers, iron and steel metallurgy, waste incineration, petrochemical industry and cement industry, etc.). This customized analysis method can more accurately reflect the characteristics and emissions of flue gas in different industries, and improve the pertinence and accuracy of monitoring. Collecting data for different industries can facilitate subsequent data management sampling.

[0076] The system can automatically select the corresponding sampling state analysis model according to different sampling type information, without manual adjustment or setting. This not only improves the flexibility of monitoring, but also reduces the difficulty and complexity of operation. At the same time, the system can support the configuration and update of multiple sampling type information, so that it can adapt to changes in different industries and different monitoring needs.

[0077] Through the sampling state analysis model associated with specific industry types, the system can process and analyze monitoring data more quickly and accurately. This helps to promptly discover and solve potential problems and improve the timeliness and effectiveness of monitoring. At the same time, the system can also automatically adjust monitoring parameters or issue alarms based on the analysis results to achieve real-time control and optimization of flue gas emissions.

[0078] More preferably, the SO2 concentration of the sampling section to be monitored is obtained by the following steps:

[0079] A pre-processing device is arranged at the front end of the flue gas emission monitoring system, and the humidity in the flue gas entering the flue gas emission monitoring system is reduced by the pre-processing device;

[0080] The SO2 concentration in the pretreated flue gas is measured by the flue gas emission monitoring system, and the measured SO2 concentration in the pretreated flue gas, the flue gas humidity and the pre-constructed humidity-loss rate mapping table are matched to obtain the standard SO2 concentration at the sampling section to be monitored.

[0081] By configuring a pre-processing device at the front end of the flue gas emission monitoring system, the humidity of the flue gas entering the system can be effectively reduced. Since a high humidity environment will interfere with the measurement of SO2 concentration, the application of the pre-processing device significantly improves the accuracy of SO2 concentration measurement. Combining the flue gas humidity and the pre-built humidity-loss rate mapping table, the SO2 concentration lost due to humidity can be more accurately estimated. This method effectively compensates for the influence of humidity on SO2 concentration measurement and further improves the accuracy of measurement. By matching the actual measured SO2 concentration, flue gas humidity and the humidity-loss rate mapping table, the standard SO2 concentration at the sampling section to be monitored can be automatically calculated. This automated data processing process not only improves work efficiency, but also reduces the possibility of human error. Due to the application of the pre-processing device and the humidity-loss rate mapping table, this method can adapt to SO2 concentration measurement under different humidity conditions. Accurate and reliable measurement results can be obtained in both low humidity and high humidity environments.

[0082] More preferably, before acquiring the monitoring information sets on the plurality of sampling detection nodes, the method further includes:

[0083] S100a: Acquire sampling detection nodes within a set range, select a sampling detection node from the sampling detection nodes as a first node, and set the other sampling detection nodes as second nodes;

[0084] S100b: establishing a communication network between the first node and each of the second nodes, wherein the first node allocates data reporting time slots to the multiple second nodes;

[0085] S100c: The second nodes send sampled reporting data to the first node in their respective allocated reporting time slots. The first node receives the sampled reporting data from each second node, processes and integrates the sampled reporting data, and sends the integrated data to the data upload node within the preset range for uploading the sampled data.

[0086] During the specific implementation, selecting a sampling detection node as the first node from the sampling detection nodes, setting the other sampling detection nodes as the second nodes also includes determining the data sending frequency band of each sampling detection node; performing statistics on the data sending frequency band to determine the frequency band interval, dividing the frequency band interval into multiple sub-frequency band groups, each of the sub-frequency band groups occupying a different frequency band; and setting a sampling detection node for each of the sub-frequency band groups.

[0087] By allocating different data reporting time slots to the sampling detection nodes within the set range, network congestion and data conflicts caused by multiple nodes sending data at the same time can be avoided, thereby significantly improving the efficiency of data transmission. Since each node sends data in its assigned reporting time slot, the timing of data transmission is effectively controlled, reducing the possibility of data loss due to network congestion. The first node is not only responsible for receiving the sampling reporting data of each second node, but also processes and integrates these data. This centralized processing method not only improves the data integration capability, but also helps to analyze and process the data at a higher level. Since the data is uploaded through the data upload node within the preset range, this design can improve the transmission stability of the data. Even if some nodes fail or the data transmission is disturbed, the entire system can still maintain normal operation. The above method can greatly improve the convenience of communication networking of the sampling detection nodes. The solution of the present application also reasonably optimizes the networking mechanism, improves the data transmission efficiency, and avoids the interference between devices during high concurrent data transmission.

[0088] More preferably, the particle concentration of the sampling section to be monitored is obtained by the following steps:

[0089] Obtain the particle concentration of the corresponding sampling section to be monitored under the detected actual state;

[0090] The particle concentration of the corresponding sampling section to be monitored under the actual state is input into the preset concentration calibration formula for calculation to obtain the calibrated particle concentration; the concentration calibration formula is:

[0091]

[0092] Among them, e is the calibrated particle concentration value, e s is the particle concentration value measured under actual conditions, is the average temperature of the flue gas at the sampling point, p a is the atmospheric pressure during sampling, p s is the flue gas static pressure at the sampling point, X sw is the moisture content at the sampling point.

[0093] By applying the concentration calibration formula, the particle concentration measured under actual conditions can be calibrated to eliminate the influence of environmental factors (such as temperature, pressure, moisture content, etc.) on the measurement results. This can significantly improve the accuracy of particle concentration measurement and provide more reliable data support for environmental monitoring and pollution control. The concentration calibration formula contains multiple parameters related to environmental conditions, such as temperature, pressure, moisture content, etc. These parameters can reflect changes in the actual monitoring environment, so the calibrated particle concentration can more accurately reflect the actual emission situation. This enables the monitoring system to adapt to different environmental conditions and improve the adaptability and reliability of monitoring.

[0094] More preferably, the detection and analysis method further comprises:

[0095] Acquire the running state of the device at the current sampling section to be detected, and determine the working state of the device according to the running state of the device, wherein the working state is a first working state, a second working state and a third working state;

[0096] The various parameters in the sampling state analysis model are updated according to the working state to perform subsequent monitoring and comparison.

[0097] The equipment operating status here actually refers to the operating load, which refers to the maximum load that the equipment or system bears under actual operating conditions. The level of the operating condition directly reflects the operating intensity of the equipment or system and the potential level of particulate matter emissions.

[0098] High operating condition: When the equipment or system is in a high-load operating state, it can be considered to be in a high operating condition. At this time, due to the high operating intensity of the equipment or system, the particulate matter emissions also increase accordingly. Under high operating conditions, special attention should be paid to the monitoring and control of particulate matter to prevent excessive emissions. Medium operating condition: The equipment or system is in a medium-load operating state, that is, medium operating condition. At this time, the emission of particulate matter is at a medium level, but monitoring and control are still required to ensure that emissions meet relevant standards. Low operating condition: The equipment or system is in a low-load operating state, that is, low operating condition. At this time, the emission of particulate matter is relatively low, but monitoring is still required to understand the emission characteristics of the equipment or system and provide a reference for subsequent optimized operation.

[0099] When determining specific working conditions, you can use the operating parameters of the equipment or system: such as current, voltage, power, temperature, pressure, etc. These parameters can directly reflect the operating status and load level of the equipment or system. Particulate emission data: By continuously monitoring the particulate emission data of the equipment or system, you can understand its emission characteristics and changing trends, and then judge its working condition level. Other related factors: such as the age of the equipment or system, maintenance status, environmental factors (such as temperature, humidity, atmospheric pressure, etc.), etc., may also affect the determination of the working conditions.

[0100] In the flue gas monitoring process of the present invention, the openness of the parameters mainly depends on whether they involve specific commercial or environmental sensitive information. The following is a classification of the openness of some common flue gas monitoring parameters:

[0101] Private information parameters: Company-specific data: Some flue gas monitoring data may be internal information of a specific company, such as the detailed location of emission sources, emission data for a specific time period, etc. This information may have a significant impact on the company's operations and strategies, and is therefore generally considered private information. Technical details: The technical details of certain flue gas monitoring systems, such as specific sensor models, calibration methods, data processing algorithms, etc., may also be considered private information of the company.

[0102] Public information parameters: Pollutant concentrations: such as the concentrations of sulfur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), particulate matter (PM), etc. These are usually monitoring indicators required by environmental protection laws and regulations and need to be reported publicly. Flue gas parameters: including flue gas temperature, pressure, flow rate (flow), humidity, etc. These parameters are very important for understanding the physical state and environmental impact of flue gas, and usually need to be disclosed. Total emissions: The total amount of pollutant emissions in a certain period of time. This is an important indicator for assessing the impact of emission sources on the environment and also needs to be disclosed. Since the above parameters have a more far-reaching impact on the subsequent carbon trading, different applications and implementations are required for different data during the specific implementation, which can improve the scalability of the system to obtain parameters, and also make the accuracy of the parameters obtained have more far-reaching application scenarios.

[0103] It should be noted that even if some parameters are generally considered public information, they may need to be kept confidential in specific circumstances. For example, if the pollutant concentration data of a certain emission source is abnormally high, it may have a negative impact on the company's reputation and operations. The company may choose not to disclose this data until the problem is resolved. When implementing it, different encryption algorithms need to be selected for different parameters: First, you need to choose a suitable encryption algorithm. Common encryption algorithms include symmetric encryption algorithms (such as AES, DES) and asymmetric encryption algorithms (such as RSA). Symmetric encryption algorithms use the same key for encryption and decryption, which is suitable for encryption of large amounts of data. Asymmetric encryption algorithms use a pair of keys (public key and private key), the public key is used to encrypt data, and the private key is used to decrypt data, which is suitable for scenarios where keys need to be transmitted securely. Generate keys: For symmetric encryption algorithms, a key needs to be generated. This key can be randomly generated or generated through a specific algorithm. The complexity and length of the key will affect the strength of the encryption. For asymmetric encryption algorithms, a pair of keys (public key and private key) needs to be generated. The public key can be made public, but the private key must be kept confidential. Encrypt data: Encrypt the parameters that need to be encrypted using the selected encryption algorithm and key. This can be achieved programmatically, using various cryptographic libraries and tools to simplify the encryption process. The encrypted data will become unreadable ciphertext that can only be decrypted by those who hold the corresponding key. Transmit and store encrypted data: Transmit the encrypted data through a secure channel (such as HTTPS) or store it in a secure storage medium (such as an encrypted hard drive or cloud storage service). During the transmission and storage process, ensure that the data cannot be accessed or tampered with by unauthorized persons. Decrypt data: When you need to access the encrypted data, use the corresponding key to decrypt it. The decryption process is the opposite of the encryption process, using the same algorithm and key to restore the ciphertext to readable plaintext.

[0104] The method of the embodiment of the present invention further includes:

[0105] For each sub-sampling segment to be optimized, the data monitoring platform determines whether the sampling optimization strategy corresponding to the sub-sampling segment to be optimized includes the sampling self-optimization strategy corresponding to the sub-sampling segment to be optimized. When it is determined that the sampling optimization strategy corresponding to the sub-sampling segment to be optimized includes the sampling self-optimization strategy corresponding to the sub-sampling segment to be optimized, the sampling optimization control parameters corresponding to the sub-sampling segment to be optimized are generated according to the sampling self-optimization strategy corresponding to the sub-sampling segment to be optimized, and the sub-sampling segment to be optimized is controlled to perform the corresponding sampling optimization operation based on the sampling optimization control parameters corresponding to the sub-sampling segment to be optimized.

[0106] The CEMS system first configures the corresponding sampling optimization strategy for each sub-sampling segment to be optimized. These strategies include conventional optimization methods and more advanced self-optimization strategies. The core of the self-optimization strategy is to automatically adjust the sampling parameters according to real-time flue gas data, environmental conditions and equipment status. When the CEMS system recognizes that a sub-sampling segment needs to be optimized, it will first determine whether the sampling optimization strategy corresponding to the sub-sampling segment contains a self-optimization strategy. If it does, the system will calculate the best sampling optimization control parameters based on the algorithm and real-time data of the self-optimization strategy.

[0107] These control parameters include but are not limited to sampling frequency, sampling point location, sampling time, etc., which are precisely adjusted to suit the current flue gas emission and environmental conditions. Based on these parameters, the CEMS system can intelligently control the sampling equipment to perform corresponding sampling optimization operations. For example, when a high concentration of particulate matter is detected in a certain area, the system can automatically increase the sampling frequency and number of sampling points in that area to obtain more accurate data; at the same time, when environmental conditions change, the system can also automatically adjust the sampling time and sampling point location to adapt to the new environmental conditions.

[0108] This automated optimization process not only reduces the need for human intervention, reduces operational errors and the impact of human factors on data, but also enables the CEMS system to respond more quickly to environmental changes and emission fluctuations. By adjusting sampling parameters in real time, the system can capture key data more accurately and improve the representativeness and accuracy of sampling. At the same time, this optimization strategy can also optimize resource allocation, such as the reasonable allocation of sampling equipment and human resources, and improve the overall operating efficiency of the system.

[0109] Specific optimization strategies include: Automatically adjust the sampling frequency according to the fluctuation of flue gas emissions and the concentration changes of pollutants. For example, during the peak period of pollutant emissions, increase the sampling frequency to obtain more intensive data points; during the period of low or stable emissions, reduce the sampling frequency to save resources. Automatically select or adjust the location of the sampling point according to the flue gas flow characteristics and the distribution law of pollutants. By optimizing the location of the sampling point, it can be ensured that the sampling data is more representative and can more accurately reflect the actual situation of flue gas emissions. Automatically select or adjust the sampling time according to environmental conditions and equipment status. For example, when the parameters such as ambient temperature and humidity change greatly, adjust the sampling time in time to avoid the influence of interference factors; during equipment maintenance or fault handling, suspend sampling to avoid data distortion. Automatically select or adjust the sampling method according to different monitoring needs and pollutant characteristics. For example, different sampling methods and processing methods may be required for different types of pollutants (such as gases, particulate matter, etc.). Through the self-optimization strategy, the system can automatically select the sampling method that best suits the current situation. By monitoring the operating status and performance parameters of the equipment in real time, possible faults can be predicted and repaired in advance. This can reduce the impact of faults on the monitoring system and ensure the continuous and stable operation of the system.

[0110] In the embodiment of the present invention, by setting up multiple sampling detection nodes at the system to be monitored, the method can realize comprehensive data collection of multiple sampling points in the entire monitoring area. Each sampling detection node is responsible for aggregating and processing the monitoring information reported by multiple monitoring sensors within its coverage area to ensure the integrity of the information. The above-mentioned flue gas detection and analysis method realizes efficient and accurate monitoring of flue gas emissions through technical means in multiple aspects such as comprehensive coverage monitoring, accurate judgment of sampling segment coverage, information integrity verification, model-based analysis, and efficient data processing, providing strong support for environmental management and decision-making.

[0111] Embodiment 2

[0112] See also Figure 4 , Figure 4 Schematic diagram of the structure of the smoke detection and analysis system disclosed in the embodiment of the present invention. Figure 4 As shown, the smoke detection and analysis system may include:

[0113] Acquisition module 21: used to acquire monitoring information sets on multiple sampling detection nodes, where the monitoring information set reported by each sampling detection node is formed by aggregating and processing monitoring information reported by multiple monitoring sensors within its coverage area by each sampling detection node;

[0114] The first judgment module 22 is used to judge whether all the sampling detection nodes reporting the sampling information set cover the sampling segment to be monitored corresponding to the sampling monitoring task according to the preset sampling monitoring task;

[0115] The second judgment module 23 is used for, when it is judged that all the sampling detection nodes cover the corresponding sampling segment to be monitored, the data monitoring platform to judge whether the sampling information set reported by each sampling detection node includes the sensor monitoring information reported by all data acquisition sensors within the coverage range of each sampling detection node;

[0116] Analysis module 24: used for analyzing the monitoring status of the sampling segment to be monitored based on the sensor monitoring information set reported by all the sampling detection nodes and a pre-trained sampling status analysis model when it is determined that the sensor monitoring information set reported by each of the sampling detection nodes includes the sensor monitoring information reported by all monitoring sensors within the coverage area of ​​each of the sampling detection nodes.

[0117] In the embodiment of the present invention, by setting up multiple sampling detection nodes at the system to be monitored, the method can realize comprehensive data collection of multiple sampling points in the entire monitoring area. Each sampling detection node is responsible for aggregating and processing the monitoring information reported by multiple monitoring sensors within its coverage area to ensure the integrity of the information. The above-mentioned flue gas detection and analysis method realizes efficient and accurate monitoring of flue gas emissions through technical means in multiple aspects such as comprehensive coverage monitoring, accurate judgment of sampling segment coverage, information integrity verification, model-based analysis, and efficient data processing, providing strong support for environmental management and decision-making.

[0118] Embodiment 3

[0119] See also Figure 5 , Figure 5 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a smart device such as a mobile phone, a tablet computer, a monitoring terminal, and an image acquisition device with processing functions. Figure 5 As shown, the electronic device may include:

[0120] A memory 510 storing executable program codes;

[0121] a processor 520 coupled to the memory 510;

[0122] The processor 520 calls the executable program code stored in the memory 510 to execute part or all of the steps in the smoke detection and analysis method in the first embodiment.

[0123] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the smoke detection and analysis method in the first embodiment.

[0124] The embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is enabled to execute part or all of the steps in the smoke detection and analysis method in the first embodiment.

[0125] An embodiment of the present invention further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the smoke detection and analysis method in embodiment one.

[0126] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0128] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the method described in each embodiment of the present invention.

[0130] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0131] A person of ordinary skill in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0132] The smoke detection and analysis method, system, electronic device and storage medium disclosed in the embodiments of the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A smoke detection and analysis method, characterized in that: It is applied to a flue gas emission monitoring system, which includes a particulate matter monitoring system, a pollutant monitoring system, a flue gas parameter monitoring system and a data monitoring platform; a plurality of sampling detection nodes are arranged at the system to be monitored for data collection at the corresponding sampling points; The smoke detection and analysis method comprises: Receive sampling type information pre-configured and associated with the sampling segment to be detected, and retrieve a sampling state analysis model associated with the sampling type information according to the sampling type information, wherein the sampling type information includes thermal power generation type information, industrial kiln type information, industrial boiler type information, civil heating boiler type information, iron and steel metallurgy type information, waste incineration type information, petrochemical industry type information, and cement industry type information; Acquire sampling detection nodes within a set range, select a sampling detection node from the sampling detection nodes as a first node, and set the other sampling detection nodes as second nodes; Establishing a communication network between the first node and each of the second nodes, wherein the first node allocates data reporting time slots to the plurality of second nodes; The second nodes send the sampled report data to the first node in the respective allocated report time slots, the first node receives the sampled report data from each second node, processes and integrates the sampled report data, and sends the integrated data to the data upload node within the set range for uploading the sampled data; Acquire a set of monitoring information on multiple sampling and detection nodes, wherein the set of monitoring information reported by each sampling and detection node is formed by aggregating and processing the monitoring information reported by multiple monitoring sensors within its coverage area by each sampling and detection node; the set of monitoring information includes one or more of the flue gas temperature information corresponding to the sampling section to be monitored, the flue gas pressure information corresponding to the sampling section to be monitored, the flue gas flow rate information corresponding to the sampling section to be monitored, the flue gas humidity information of the sampling section to be monitored, the oxygen content information of the sampling section to be monitored, the particulate matter concentration of the sampling section to be monitored, the SO2 concentration of the sampling section to be monitored, the NOx concentration of the sampling section to be monitored, the O2 concentration of the sampling section to be monitored, and the CO concentration of the sampling section to be monitored; the particulate matter concentration of the sampling section to be monitored is obtained by the following steps: Obtain the particle concentration of the corresponding sampling section to be monitored under the detected actual state; The particle concentration of the corresponding sampling section to be monitored under the actual state is input into the preset concentration calibration formula for calculation to obtain the calibrated particle concentration; the concentration calibration formula is: ， in, is the calibrated particle concentration value, is the particle concentration value measured under actual conditions, is the average temperature of the flue gas at the sampling point, is the atmospheric pressure during sampling, is the flue gas static pressure at the sampling point, is the moisture content at the sampling point; Determine, according to a preset sampling monitoring task, whether all the sampling detection nodes reporting the sampling information set cover the sampling segment to be monitored corresponding to the sampling monitoring task; When it is determined that all the sampling detection nodes cover the corresponding sampling segment to be monitored, the data monitoring platform determines whether the sampling information set reported by each of the sampling detection nodes includes the sensor monitoring information reported by all data acquisition sensors within the coverage of each of the sampling detection nodes; When it is determined that the sensor monitoring information set reported by each sampling detection node includes the sensor monitoring information reported by all monitoring sensors within the coverage of each sampling detection node, the data monitoring platform analyzes the monitoring status of the sampling segment to be monitored based on the sensor monitoring information set reported by all the sampling detection nodes and the pre-trained sampling status analysis model; when it is determined that the sensor monitoring information set reported by each sampling detection node includes the sensor monitoring information reported by all monitoring sensors within the coverage of each sampling detection node, the data monitoring platform analyzes the monitoring status of the sampling segment to be monitored based on the sensor monitoring information set reported by all the sampling detection nodes and the pre-trained sampling status analysis model, including: When it is determined that the monitoring information set reported by each sampling detection node includes the sensor monitoring information reported by all monitoring sensors within the coverage of each sampling detection node, the data detection platform divides the sampling segment to be monitored into a plurality of sub-sampling segments to be detected according to the flow direction of the smoke in the sampling segment to be monitored; The data monitoring platform parses the sensor monitoring information sets reported by all the sampling detection nodes to obtain the sub-sampling information sets corresponding to each sub-sampling segment to be detected; For each of the sub-sampling segments to be detected, the data monitoring platform determines whether the sub-sampling segment to be detected is a starting sub-sampling segment. When the sub-sampling segment to be detected is a starting sub-sampling segment, the sub-sampling information set corresponding to the sub-sampling segment to be detected is input into the sub-sampling state analysis model corresponding to the sub-pipeline segment to be detected, and the sub-monitoring state corresponding to the sub-sampling segment to be detected is obtained; When the sub-sampling segment to be detected is not the starting sub-sampling segment, the sub-sampling information set corresponding to the sub-sampling segment to be detected and the sub-monitoring state corresponding to the previous adjacent sub-sampling segment to be detected of the sub-sampling segment to be detected are input into the sub-sampling state analysis model corresponding to the sub-pipeline segment to be detected, so as to obtain the sub-monitoring state corresponding to the sub-pipeline segment to be detected; The data monitoring platform analyzes the monitoring status of the sampling segment to be monitored according to the sub-monitoring statuses corresponding to all the sub-sampling segments to be detected.

2. The smoke detection and analysis method according to claim 1, characterized in that: The SO2 concentration of the sampling section to be monitored is obtained by the following steps: A pre-processing device is arranged at the front end of the flue gas emission monitoring system, and the humidity in the flue gas entering the flue gas emission monitoring system is reduced by the pre-processing device; The SO2 concentration in the pretreated flue gas is measured by the flue gas emission monitoring system, and the measured SO2 concentration in the pretreated flue gas, the flue gas humidity and the pre-constructed humidity-loss rate mapping table are matched to obtain the standard SO2 concentration at the sampling section to be monitored.

3. The smoke detection and analysis method according to claim 1, characterized in that: The detection and analysis method also includes: Acquire the running state of the device at the current sampling section to be detected, and determine the working state of the device according to the running state of the device, wherein the working state is a first working state, a second working state and a third working state; The various parameters in the sampling state analysis model are updated according to the working state to perform subsequent monitoring and comparison.

4. A smoke detection and analysis system, characterized in that: include: Receive sampling type information pre-configured and associated with the sampling segment to be detected, and retrieve a sampling state analysis model associated with the sampling type information according to the sampling type information, wherein the sampling type information includes thermal power generation type information, industrial kiln type information, industrial boiler type information, civil heating boiler type information, iron and steel metallurgy type information, waste incineration type information, petrochemical industry type information, and cement industry type information; Acquire sampling detection nodes within a set range, select a sampling detection node from the sampling detection nodes as a first node, and set the other sampling detection nodes as second nodes; Establishing a communication network between the first node and each of the second nodes, wherein the first node allocates data reporting time slots to the plurality of second nodes; The second nodes send the sampled report data to the first node in the respective allocated report time slots, the first node receives the sampled report data from each second node, processes and integrates the sampled report data, and sends the integrated data to the data upload node within the set range for uploading the sampled data; Acquisition module: used to acquire monitoring information sets on multiple sampling and detection nodes. The monitoring information set reported by each sampling and detection node is formed by aggregating and processing the monitoring information reported by multiple monitoring sensors within its coverage area by each sampling and detection node; the monitoring information set includes one or more of the flue gas temperature information corresponding to the sampling section to be monitored, the flue gas pressure information corresponding to the sampling section to be monitored, the flue gas flow rate information corresponding to the sampling section to be monitored, the flue gas humidity information of the sampling section to be monitored, the oxygen content information of the sampling section to be monitored, the particulate matter concentration of the sampling section to be monitored, the SO2 concentration of the sampling section to be monitored, the NOx concentration of the sampling section to be monitored, the O2 concentration of the sampling section to be monitored and the CO concentration of the sampling section to be monitored; the particulate matter concentration of the sampling section to be monitored is obtained by the following steps: Obtain the particle concentration of the corresponding sampling section to be monitored under the detected actual state; The particle concentration of the corresponding sampling section to be monitored under the actual state is input into the preset concentration calibration formula for calculation to obtain the calibrated particle concentration; the concentration calibration formula is: ， in, is the calibrated particle concentration value, is the particle concentration value measured under actual conditions, is the average temperature of the flue gas at the sampling point, is the atmospheric pressure during sampling, is the flue gas static pressure at the sampling point, is the moisture content at the sampling point; The first judgment module is used to judge whether all the sampling detection nodes reporting the sampling information set cover the sampling segment to be monitored corresponding to the sampling monitoring task according to the preset sampling monitoring task; The second judgment module is used for, when it is judged that all the sampling detection nodes cover the corresponding sampling segment to be monitored, the data monitoring platform to judge whether the sampling information set reported by each sampling detection node includes the sensor monitoring information reported by all data acquisition sensors within the coverage range of each sampling detection node; Analysis module: when it is determined that the sensor monitoring information set reported by each sampling detection node includes the sensor monitoring information reported by all monitoring sensors within the coverage of each sampling detection node, the data monitoring platform analyzes the monitoring status of the sampling segment to be monitored based on the sensor monitoring information set reported by all the sampling detection nodes and the pre-trained sampling status analysis model; when it is determined that the sensor monitoring information set reported by each sampling detection node includes the sensor monitoring information reported by all monitoring sensors within the coverage of each sampling detection node, the data monitoring platform analyzes the monitoring status of the sampling segment to be monitored based on the sensor monitoring information set reported by all the sampling detection nodes and the pre-trained sampling status analysis model, including: When it is determined that the monitoring information set reported by each sampling detection node includes the sensor monitoring information reported by all monitoring sensors within the coverage of each sampling detection node, the data detection platform divides the sampling segment to be monitored into a plurality of sub-sampling segments to be detected according to the flow direction of the smoke in the sampling segment to be monitored; The data monitoring platform parses the sensor monitoring information sets reported by all the sampling detection nodes to obtain the sub-sampling information sets corresponding to each sub-sampling segment to be detected; For each of the sub-sampling segments to be detected, the data monitoring platform determines whether the sub-sampling segment to be detected is a starting sub-sampling segment. When the sub-sampling segment to be detected is a starting sub-sampling segment, the sub-sampling information set corresponding to the sub-sampling segment to be detected is input into the sub-sampling state analysis model corresponding to the sub-pipeline segment to be detected, and the sub-monitoring state corresponding to the sub-sampling segment to be detected is obtained; When the sub-sampling segment to be detected is not the starting sub-sampling segment, the sub-sampling information set corresponding to the sub-sampling segment to be detected and the sub-monitoring state corresponding to the previous adjacent sub-sampling segment to be detected of the sub-sampling segment to be detected are input into the sub-sampling state analysis model corresponding to the sub-pipeline segment to be detected, so as to obtain the sub-monitoring state corresponding to the sub-pipeline segment to be detected; The data monitoring platform analyzes the monitoring status of the sampling segment to be monitored according to the sub-monitoring statuses corresponding to all the sub-sampling segments to be detected.

5. An electronic device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the smoke detection and analysis method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program enables a computer to execute the smoke detection and analysis method according to any one of claims 1 to 3.

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