Pressure relief monitoring system based on Internet of Things

Abstract

The invention relates to the technical field of pressure relief monitoring, in particular to a pressure relief monitoring system based on the Internet of Things, which comprises the following steps: monitoring each key point location in real time to obtain each actual monitoring data; analyzing the actual monitoring data in sequence to perform primary judgment on the actual single-point condition of each key point location and the actual overall condition of the target monitoring object so as to determine whether an auxiliary verification mode is started or not; numbering and integrating the actual monitoring data according to a historical numbering sequence to obtain an actual monitoring database; based on the auxiliary verification mode, comparing the actual monitoring database with the historical monitoring database to perform risk analysis and risk marking; according to the risk marking result, carrying out secondary judgment on the actual single-point condition of each key point location and the actual overall condition of the target monitoring object; and performing association marking on each key point location by combining the primary judgment result and the secondary judgment result, and outputting a final monitoring result of each key point location according to an association marking result.

Description

Technical Field

[0001] The present invention relates to the technical field of pressure relief monitoring, and in particular to a pressure relief monitoring system based on the Internet of Things. Background Art

[0002] With the development of society and the adjustment of energy structure, more and more boiler rooms are gradually starting to use gas as the main fuel. Gas boilers are increasingly widely used in some large and medium-sized cities due to their high quality, high efficiency, cleanliness and other characteristics. However, the layout of boiler rooms is diverse, and whether their technical conditions can meet the requirements of gas design has become a very important issue. These gases themselves are flammable and explosive, which determines that fire safety must become one of the important technical parameters in the design of gas boiler rooms. According to the classification of gas boiler rooms, they are mainly divided into two types: independent gas boiler rooms and non-independent gas boiler rooms. In modern hospital buildings, boiler rooms are essential machine rooms. They are generally placed in the basement as non-independent machine rooms and are connected to the underground machine room area for joint management.

[0003] The explosion vent in the boiler room refers to the pressure relief facility inside the boiler room. During the operation of the boiler, the pressure of steam or hot water exceeds the set value, which will cause the pressure inside the boiler to rise. If the pressure is not released in time, it may cause serious accidents or even cause the boiler to explode. Therefore, the explosion vent in the boiler room is very important.

[0004] However, the explosion vent in the boiler explosion vent is usually a one-time use safety device. After completing its pressure relief task, the explosion vent usually loses its structural integrity due to rupture and cannot be used again. Therefore, the boiler explosion vent is regarded as a consumable safety element, and a pressure relief monitoring system is required to monitor the boiler room so that timely measures can be taken in case of abnormalities to reduce the number of explosions of the explosion vent.

[0005] Chinese Patent Publication No.: CN106287647A, a boiler pressure vessel monitoring and regulating system, including a control system, a pressure monitoring system and a pressure regulating system, the control system is connected to the pressure monitoring system through a line, and the control system is connected to the pressure regulating system through a transmitter; the pressure monitoring system, the control system and the pressure regulating system are all installed on the boiler pressure vessel; the boiler pressure vessel monitoring and regulating system includes a control system, a pressure monitoring system and a pressure regulating system, the pressure monitoring system monitors the internal pressure of the boiler pressure vessel in real time, the control system is used to receive the monitoring signal, and after comparing the database, sends an instruction to the pressure regulating system, and adjusts the internal pressure through the pressure regulating system.

[0006] Current monitoring systems are prone to misdiagnosis and work inefficiently. Summary of the invention

[0007] To this end, the object of the present invention is to provide a pressure relief monitoring system based on the Internet of Things, which has the advantages of improving the accuracy of measurement data, quickly realizing data processing and improving the accuracy of fault diagnosis.

[0008] To achieve the above object, the present invention provides a pressure relief monitoring system based on the Internet of Things, comprising:

[0009] A monitoring module is used to monitor the key points of the target monitoring object in real time to obtain actual monitoring data;

[0010] A first analysis module, based on any actual monitoring data, performs abnormal analysis and abnormal marking on the actual monitoring data in sequence, makes a judgment on the actual single point situation of each key point and the actual overall situation of the target monitoring object according to the abnormal marking result, and determines whether to start the auxiliary verification mode according to the judgment result;

[0011] An integration module, which is used to number and integrate each actual monitoring data according to the historical numbering sequence to obtain an actual monitoring database;

[0012] A comparison module, which compares the actual monitoring database with the historical monitoring database based on the auxiliary verification mode to perform risk analysis and risk marking;

[0013] The second analysis module is used to make a secondary determination of the actual single point situation of each key point and the actual overall situation of the target monitoring object according to the risk marking result;

[0014] The output module combines the first judgment result and the second judgment result to associate and mark each key point, and outputs the final monitoring result of each key point according to the association marking result.

[0015] Furthermore, the monitoring module includes: a deployment unit and a collection unit;

[0016] The layout unit is used to determine a plurality of monitoring areas according to the heat energy output range of the steam transmission pipeline of the target monitoring object, and the key point is set in each of the monitoring areas;

[0017] The acquisition unit is connected to the vibration sensors arranged at each key point, so as to obtain the vibration characteristics corresponding to each key point through the vibration sensors.

[0018] Further, the acquisition unit includes: a drawing subunit and a correction subunit;

[0019] The drawing subunit is used to draw a real-time vibration frequency diagram according to the vibration characteristics;

[0020] The correction subunit is used to judge each monitoring data point in the real-time vibration frequency diagram according to the standard fluctuation range to determine whether to correct the real-time vibration frequency diagram to a corrected vibration frequency diagram.

[0021] The drawing subunit draws a curve showing the change of vibration characteristics over time at each key point within a preset tracking and monitoring period, and generates a real-time vibration frequency diagram corresponding to each key point, wherein the real-time vibration frequency diagram includes a real-time vibration amplitude.

[0022] Furthermore, the correction subunit includes: a sampling judgment subunit and a spectrum updating subunit; for any key point;

[0023] The sampling judgment subunit is used to determine that when there is a monitoring data point that does not fall into the standard fluctuation range, draw a curve of the change of each monitoring data point that does not fall into the standard fluctuation range over time, and generate a vibration frequency diagram to be analyzed corresponding to the key point;

[0024] The spectrogram updating subunit is used to obtain the number of monitoring data points in the vibration frequency graph to be analyzed as the number of difference points, calculate the percentage of the number of difference points in the total sampling points in the real-time vibration frequency graph to obtain the difference vibration frequency, and determine whether to correct the real-time vibration frequency graph to the corrected vibration frequency graph or determine that the acquisition is unqualified according to the difference vibration frequency;

[0025] The actual monitoring data is obtained by identifying the modified vibration frequency diagram.

[0026] Furthermore, the first analysis module includes: an abnormality analysis unit, an abnormality marking unit and a single-point one-time determination unit;

[0027] The abnormality analysis unit is used to perform abnormality analysis according to the actual monitoring data and a preset monitoring threshold to determine the corresponding actual abnormality degree;

[0028] The abnormality marking unit is used to perform corresponding abnormality marking on each key point according to the actual abnormality degree to obtain the abnormality marking result;

[0029] The single-point once determination unit is used to determine the actual abnormality level of each key point according to the abnormality marking result, and to determine whether the actual single-point situation of each key point is single-point once qualified according to the actual abnormality level.

[0030] Furthermore, the first analysis module further includes: an overall primary determination unit and a verification unit;

[0031] The overall one-time determination unit is used to record the number of key points where the actual single point situation is a single point one-time qualified to obtain a one-time single point qualified number, record the number of key points where the actual single point situation is a single point one-time unqualified to obtain a one-time single point unqualified number, and judge whether the actual overall situation is an overall one-time qualified according to the one-time single point qualified number and a preset first standard number;

[0032] The verification unit is used to determine whether to start the auxiliary verification mode according to the actual overall situation;

[0033] Among them, when the actual overall situation is qualified as a whole once, the auxiliary verification mode is turned on; when the actual overall situation is unqualified as a whole once, the final monitoring result is output.

[0034] Furthermore, the comparison module includes: a history acquisition unit, a risk determination unit and a risk marking unit; for any key point;

[0035] The history acquisition unit is used to acquire the historical single point situation of the key point according to the historical monitoring database;

[0036] The risk determination unit is used to perform risk analysis according to the historical single point situation and the actual single point situation to determine an actual risk level, wherein the actual risk level includes: a high risk level and a low risk level;

[0037] The risk marking unit is used to perform corresponding risk marking based on the actual risk level.

[0038] Further, the second analysis module includes: a single-point secondary determination unit;

[0039] The single-point secondary determination unit is used to secondary determine the actual single-point situation according to the actual risk level of the key point. Based on the actual risk level being the low risk level, the actual single-point situation is determined to be a single-point secondary qualified; based on the actual risk level being the high risk level, the actual single-point situation is determined to be a secondary unqualified.

[0040] Furthermore, the second analysis module further comprises: an overall secondary determination unit;

[0041] The overall secondary judgment unit is used to record the number of key points where the actual single point situation is single point secondary qualified, so as to obtain the secondary single point qualified number, record the number of key points where the actual single point situation is single point secondary unqualified, so as to obtain the secondary single point unqualified number, and secondarily determine whether the actual overall situation is overall secondary qualified, or, overall secondary unqualified, based on the secondary single point qualified number and the secondary single point unqualified number.

[0042] Further, the output module includes: a first association marking unit, a second association marking unit, a third association marking unit and a result output unit;

[0043] The first association marking unit, based on the first determination result and the second determination result of the actual single point situation of the key point, marks the key point as finally qualified if both are qualified;

[0044] The second association marking unit, based on the first determination result of the actual single point situation of the key point and the number of unqualified items in the second determination result is equal to 1, associates and marks the key point as a type of final unqualified;

[0045] The third association marking unit, based on the number of unqualified items in the first determination result and the second determination result of the actual single point situation of the key point, is equal to 2, then the key point is associated and marked as a second-class final unqualified;

[0046] The result output unit is used to output the final monitoring result as no warning required, immediate warning, or immediate explosion relief according to the associated marking result.

[0047] Compared with the prior art, the beneficial effect of the present invention is that by real-time monitoring of the key points of the boiler, the operating status of the boiler can be quickly obtained. Once abnormal data is monitored, abnormal analysis and marking can be performed immediately, thereby achieving real-time early warning and preventing accidents from occurring, so that timely measures can be taken in case of abnormalities to reduce the number of explosion venting of the explosion venting plate. Abnormal analysis and judgment can be performed based on real-time monitoring data, which is helpful to timely discover problems in boiler operation and take corresponding measures. The monitoring data are numbered and integrated into a database, which is convenient for long-term storage and analysis of boiler operation trends. By comparing the actual monitoring database with the historical monitoring database, risk analysis and marking can be performed, which is helpful to identify potential risk points. When there is no problem in the first judgment result, the opening of the auxiliary verification mode can confirm the risk through more in-depth data comparison and analysis, and improve the accuracy of the judgment. The second judgment further ensures the reliability of the monitoring results, which is helpful to accurately identify the actual conditions of each key point, thereby more effectively controlling the risk. Combining the first and second judgment results, each key point is associated and marked, and the final monitoring result is output, which can significantly improve the operating safety of the boiler, reduce safety accidents, and help ensure the safe, stable and efficient operation of the boiler.

[0048] By arranging vibration sensors at key points, the vibration characteristics of the steam transmission pipeline can be accurately obtained, thereby improving the accuracy of the monitoring data. The drawing of the real-time vibration frequency diagram can timely reflect the vibration state of the boiler steam transmission pipeline, which is helpful for real-time monitoring and early warning of potential safety problems. The correction subunit can judge and correct the monitoring data according to the standard fluctuation range, ensuring the quality of the data and reducing the data distortion caused by acquisition errors. By analyzing the vibration frequency diagram to be analyzed, the abnormal vibration frequency can be identified, which is helpful for diagnosing specific problems in the operation of the boiler. Considering the intervals between key points, missed monitoring can be avoided. By increasing the layout density, the monitoring area can be covered more comprehensively and the comprehensiveness of monitoring can be improved. By accurately evaluating the boiler pressure relief situation, abnormalities can be discovered in advance and measures can be taken in time, thereby improving the guarantee of safe operation of the boiler. Accurate monitoring and timely abnormality handling can reduce downtime and maintenance costs caused by equipment failure. Through the storage and management of historical data, long-term trend analysis can be carried out to provide data support for the maintenance and optimization of the boiler. Automated monitoring and data processing reduce manual intervention, improve the automation level of the monitoring system, and ensure the safe and stable operation of the boiler under high temperature and high pressure environments.

[0049] Accurate abnormality analysis based on actual monitoring data and preset monitoring thresholds can help to promptly discover potential problems in boiler operation, and clear abnormal marking of each key point can make it possible to quickly identify the problem, thereby improving the efficiency of problem handling. The actual abnormality level of each key point can be determined based on the abnormal marking results, and it can be judged whether the single point situation is qualified, which helps to independently evaluate each key point. By recording the number of qualified and unqualified key points and calculating the second difference, the actual overall situation of the entire monitored object can be evaluated to ensure the safety of the overall operation. It can decide whether to start the auxiliary verification mode based on whether the overall situation is qualified once, which helps to conduct more in-depth analysis and verification when necessary, and improves the intelligence level of the monitoring system. Through accurate abnormal monitoring and grading, it can effectively prevent the occurrence of safety accidents, ensure the reliability of the monitoring system, and reduce false alarms and missed alarms.

[0050] By making full use of historical monitoring data, providing reference and comparison for current monitoring, and enhancing the depth and breadth of data analysis, by comparing historical and actual data, the actual risk level of each key point can be accurately determined, which helps to take targeted preventive measures. By marking different risk levels, high-risk areas can be intuitively identified, facilitating rapid response and processing. A secondary judgment is made for each key point to ensure the accuracy of the monitoring results and reduce the possibility of misjudgment. By counting the number of qualified and unqualified key points after the secondary judgment, the risk status of the boiler can be evaluated as a whole, taking into account the combined impact of historical data and current data, and reducing the deviation that may be caused by a single data source. By making a secondary judgment on the risk level of key points, guidance is provided for preventive maintenance, which helps to ensure the overall safe operation of the boiler and prevent accidents by timely discovering and handling potential risks.

[0051] Through clear association marking, the final state of each point can be clearly identified. By distinguishing key points with different unqualified levels, a hierarchical early warning mechanism is realized. Appropriate measures can be taken according to the risk level, which improves the pertinence of emergency response. Different monitoring results can be output according to the association marking results, thereby effectively controlling risks and ensuring that emergency measures are taken when necessary, such as immediate early warning or explosion relief, to prevent accidents. The points that are finally qualified do not need early warning, which reduces unnecessary interference and ensures that the normal operation of the system is not affected. For a type of points that are ultimately unqualified, immediate early warning can notify inspections in time to prevent problems from worsening. For a type of points that are ultimately unqualified, immediate explosion relief instructions can quickly reduce system pressure and avoid serious accidents such as explosions. The automated processing of the output module reduces manual intervention and improves the automation level of the entire monitoring system. Through timely early warning and explosion relief measures, potential safety accidents are effectively prevented and the safety of personnel and equipment is guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a system block diagram of the pressure relief monitoring system based on the Internet of Things;

[0053] Figure 2 It is a system block diagram of the monitoring module in the pressure relief monitoring system based on the Internet of Things;

[0054] Figure 3 It is a system block diagram of the first analysis module in the pressure relief monitoring system based on the Internet of Things;

[0055] Figure 4 It is a system block diagram of the second analysis module in the pressure relief monitoring system based on the Internet of Things. DETAILED DESCRIPTION

[0056] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0058] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0059] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0060] See also Figure 1-Figure 4 As shown, Figure 1 This is a system block diagram of the pressure relief monitoring system based on the Internet of Things; Figure 2 It is a system block diagram of the monitoring module in the pressure relief monitoring system based on the Internet of Things; Figure 3 It is a system block diagram of the first analysis module in the pressure relief monitoring system based on the Internet of Things; Figure 4 It is a system block diagram of the second analysis module in the pressure relief monitoring system based on the Internet of Things.

[0061] The embodiment of the present invention provides a pressure relief monitoring system based on the Internet of Things, comprising:

[0062] A monitoring module is used to monitor the key points of the target monitoring object in real time to obtain actual monitoring data;

[0063] A first analysis module, based on any actual monitoring data, performs abnormal analysis and abnormal marking on the actual monitoring data in sequence, makes a judgment on the actual single point situation of each key point and the actual overall situation of the target monitoring object according to the abnormal marking result, and determines whether to start the auxiliary verification mode according to the judgment result;

[0064] An integration module, which is used to number and integrate each actual monitoring data according to the historical numbering sequence to obtain an actual monitoring database;

[0065] A comparison module, which compares the actual monitoring database with the historical monitoring database based on the auxiliary verification mode to perform risk analysis and risk marking;

[0066] The second analysis module is used to make a secondary determination of the actual single point situation of each key point and the actual overall situation of the target monitoring object according to the risk marking result;

[0067] The output module combines the first judgment result and the second judgment result to associate and mark each key point, and outputs the final monitoring result of each key point according to the association marking result.

[0068] In this embodiment, the target monitoring object is a boiler.

[0069] Specifically, the embodiment of the present invention can quickly obtain the operating status of the boiler by real-time monitoring of the key points of the boiler. Once abnormal data is monitored, abnormal analysis and marking can be performed immediately to achieve real-time early warning and prevent accidents from occurring, so that timely measures can be taken in case of abnormalities to reduce the number of explosion venting of the explosion venting plate. Abnormal analysis and judgment can be performed based on real-time monitoring data, which helps to timely discover problems in boiler operation and take corresponding measures. The monitoring data is numbered and integrated into a database for long-term storage and analysis of boiler operation trends. By comparing the actual monitoring database with the historical monitoring database, risk analysis and marking can be performed, which helps to identify potential risk points. When there is no problem in the first judgment result, the activation of the auxiliary verification mode can confirm the risk through more in-depth data comparison and analysis, thereby improving the accuracy of the judgment. The second judgment further ensures the reliability of the monitoring results, helps to accurately identify the actual conditions of each key point, thereby more effectively controlling the risk. Combining the first and second judgment results, each key point is associated and marked, and the final monitoring result is output, which can significantly improve the operating safety of the boiler, reduce safety accidents, and help to ensure the safe, stable and efficient operation of the boiler.

[0070] Specifically, the monitoring module in this embodiment includes: a deployment unit and a collection unit;

[0071] The layout unit is used to determine a plurality of monitoring areas according to the heat energy output range of the steam transmission pipeline of the target monitoring object, and the key point is set in each of the monitoring areas;

[0072] The acquisition unit is connected to the vibration sensors arranged at each key point, so as to obtain the vibration characteristics corresponding to each key point through the vibration sensors.

[0073] The acquisition unit includes: a drawing subunit and a correction subunit;

[0074] The drawing subunit is used to draw a real-time vibration frequency diagram according to the vibration characteristics;

[0075] The correction subunit is used to judge each monitoring data point in the real-time vibration frequency diagram according to the standard fluctuation range to determine whether to correct the real-time vibration frequency diagram to a corrected vibration frequency diagram.

[0076] The drawing subunit draws a curve of the change of vibration characteristics over time at each key point within a preset tracking and monitoring period, and generates a real-time vibration frequency diagram corresponding to each key point, wherein the real-time vibration frequency diagram includes a real-time vibration amplitude, and each real-time vibration amplitude represents each monitoring data point;

[0077] The correction subunit includes: a sampling judgment subunit and a spectrum update subunit; for any key point;

[0078] The sampling judgment subunit is used to determine that when there is a monitoring data point that does not fall into the standard fluctuation range, draw a curve of the change of each monitoring data point that does not fall into the standard fluctuation range over time, and generate a vibration frequency diagram to be analyzed corresponding to the key point;

[0079] The spectrogram updating subunit is used to obtain the number of monitoring data points in the vibration frequency graph to be analyzed as the number of difference points, calculate the percentage of the number of difference points in the total sampling points in the real-time vibration frequency graph to obtain the difference vibration frequency, and determine whether to correct the real-time vibration frequency graph to the corrected vibration frequency graph or determine that the acquisition is unqualified according to the difference vibration frequency;

[0080] In the specific implementation process, the spectrum updating subunit is provided with a first standard vibration frequency and a second standard vibration frequency. When the sampling judgment subunit determines that there is a monitoring data point that does not fall into the standard fluctuation interval, it draws a change curve of each monitoring data point that does not fall into the standard fluctuation interval over time to generate a vibration frequency diagram to be analyzed. The spectrum updating subunit determines the difference vibration frequency according to the first standard vibration frequency and the second standard vibration frequency.

[0081] If the difference vibration frequency is less than the first standard vibration frequency, the spectrum updating subunit determines not to correct the real-time vibration frequency graph;

[0082] If the difference vibration frequency is between the first standard vibration frequency and the second standard vibration frequency, the spectrum updating subunit corrects the real-time vibration frequency graph, that is, deletes each monitoring data point that does not fall into the standard fluctuation range to obtain the corrected vibration frequency graph;

[0083] If the difference vibration frequency is greater than the second standard vibration frequency, the spectrum updating subunit determines that the acquisition is unqualified;

[0084] The first standard vibration frequency set in this embodiment is close to zero, and is generally set between 0.01-0.02. The second standard vibration frequency represents the limit of the number of difference points, that is, the difference vibration frequency is higher than the second standard vibration frequency, which is a high acquisition error, and is generally set between 0.5-0.7, and is selected according to actual safety requirements;

[0085] Among them, identifying the corrected vibration frequency diagram means obtaining the actual monitoring data. The actual monitoring data refers to the accurate and reliable monitoring data after the above-mentioned correction process. In the corrected vibration frequency diagram, the vibration amplitude represented by each monitoring data point is within the standard fluctuation range, which reflects the actual vibration condition of the boiler steam transmission pipeline. The monitored vibration amplitude, after correction, excludes distorted data and reflects the normal vibration amplitude of the pipeline. In this embodiment, the actual monitoring data is the actual amplitude average value obtained according to the corrected vibration frequency diagram.

[0086] In this embodiment, it is necessary to consider that the intervals between key points should not be too large to avoid anomalies appearing between two points and being missed. The intervals between key points can be shortened by increasing the arrangement density of key points.

[0087] By deploying multiple key points for monitoring the steam transmission pipeline during boiler operation, we can avoid a small monitoring coverage area. By setting vibration sensors at key points to obtain the vibration characteristics of the steam transmission pipeline, we can analyze the vibration characteristics to achieve real-time monitoring of whether the boiler is depressurizing normally, ensure accurate assessment of the pressure relief situation, so as to detect abnormalities in advance and in time, and improve the accuracy of monitoring the safe operation of the boiler.

[0088] Specifically, if all monitoring data points fall within the standard displacement fluctuation range, the sampling judgment subunit determines that no abnormality in collection occurs; if there are monitoring data points that do not fall within the standard displacement fluctuation range, the sampling judgment subunit determines that abnormality in collection occurs, and a judgment will be made based on the difference vibration frequency to determine whether to correct the real-time vibration frequency diagram or determine that the collected data is unqualified;

[0089] The standard fluctuation range in this embodiment is an allowable vibration amplitude range, indicating a vibration amplitude range in which no distortion of collected data occurs in the steam delivery pipeline.

[0090] Specifically, the embodiment of the present invention can accurately obtain the vibration characteristics of the steam delivery pipeline by arranging vibration sensors at key points, thereby improving the accuracy of the monitoring data. The drawing of the real-time vibration frequency diagram can timely reflect the vibration state of the boiler steam delivery pipeline, which is helpful for real-time monitoring and early warning of potential safety problems. The correction subunit can determine and correct the monitoring data according to the standard fluctuation range, ensure the quality of the data, and reduce the data distortion caused by the acquisition error. By analyzing the vibration frequency diagram to be analyzed, the abnormal vibration frequency can be identified, which is helpful for diagnosing specific problems in the operation of the boiler. Considering the interval between key points, missed monitoring is avoided. By increasing the arrangement density, the monitoring area can be more comprehensively covered, and the comprehensiveness of monitoring is improved. By accurately evaluating the boiler pressure relief situation, anomalies can be discovered in advance and measures can be taken in time, thereby improving the guarantee of safe operation of the boiler. Accurate monitoring and timely abnormality handling can reduce downtime and maintenance costs caused by equipment failure. Through the storage and management of historical data, long-term trend analysis can be performed to provide data support for the maintenance and optimization of the boiler. Automated monitoring and data processing reduce manual intervention, improve the automation level of the monitoring system, and ensure the safe and stable operation of the boiler under high temperature and high pressure environment.

[0091] Specifically, in this embodiment, the first analysis module includes: an abnormality analysis unit, an abnormality marking unit and a single-point one-time determination unit;

[0092] The abnormality analysis unit is used to perform abnormality analysis according to the actual monitoring data and a preset monitoring threshold to determine the corresponding actual abnormality degree;

[0093] Calculate a first difference absolute value according to the actual monitoring data and the monitoring threshold;

[0094] The absolute value of the first difference = |actual monitoring data - monitoring threshold|;

[0095] If the first difference is greater than or equal to a preset first evaluation value, the actual abnormality level is determined to be the high-level abnormality;

[0096] If the first difference is less than the first evaluation value, the actual abnormality level is determined to be the low-level abnormality;

[0097] In this embodiment, the first evaluation value is used to measure the abnormal change intensity of the key point;

[0098] The abnormality marking unit is used to perform corresponding abnormality marking on each key point according to the actual abnormality degree to obtain the abnormality marking result;

[0099] The single-point once determination unit is used to determine the actual abnormality level of each key point according to the abnormality marking result, and determine whether the actual single-point situation of each key point is single-point once qualified according to the actual abnormality level;

[0100] The first analysis module further includes: an overall primary determination unit and a verification unit;

[0101] The overall one-time determination unit is used to record the number of key points where the actual single point situation is a single point one-time qualified, so as to obtain a one-time single point qualified number, record the number of key points where the actual single point situation is a single point one-time unqualified, so as to obtain a one-time single point qualified number, and judge whether the actual overall situation is an overall one-time qualified number according to the one-time single point qualified number and a preset first standard number;

[0102] Calculate a second difference based on the one-time single-point qualified quantity and the one-time single-point qualified quantity;

[0103] The second difference = the number of qualified points at one time - the number of qualified points at one time;

[0104] If the second difference is greater than or equal to a preset second evaluation value, the actual overall situation is determined to be overall qualified once;

[0105] If the second difference is less than the second evaluation value, determining whether the actual overall situation is unqualified as a whole;

[0106] In this embodiment, the second evaluation value is used to measure the qualified proportion of each key point after a determination;

[0107] The verification unit is used to determine whether to start the auxiliary verification mode according to the actual overall situation;

[0108] Among them, when the actual overall situation is qualified as a whole once, the auxiliary verification mode is turned on; when the actual overall situation is unqualified as a whole once, the final monitoring result is output.

[0109] Specifically, the embodiment of the present invention performs accurate abnormality degree analysis through actual monitoring data and preset monitoring thresholds, which helps to timely discover potential problems in boiler operation, and clearly marks the abnormalities of each key point, so that the problem can be quickly identified, improving the efficiency of problem handling, and can determine the actual abnormality level of each key point based on the abnormal marking results, and judge whether the single point situation is qualified, which helps to independently evaluate each key point. By recording the number of qualified and unqualified key points and calculating the second difference, the actual overall situation of the entire monitored object can be evaluated to ensure the safety of the overall operation. It can decide whether to turn on the auxiliary verification mode based on whether the overall one is qualified. This helps to conduct more in-depth analysis and verification when necessary, improves the intelligence level of the monitoring system, and through accurate abnormal monitoring and grading, it can effectively prevent the occurrence of safety accidents, ensure the reliability of the monitoring system, and reduce false alarms and omissions.

[0110] Specifically, the comparison module in this embodiment includes: a history acquisition unit, a risk determination unit and a risk marking unit; for any key point;

[0111] The history acquisition unit is used to acquire the historical single point situation of the key point according to the historical monitoring database;

[0112] The risk determination unit is used to perform risk analysis according to the historical single point situation and the actual single point situation to determine an actual risk level, wherein the actual risk level includes: a high risk level and a low risk level;

[0113] The risk marking unit is used to perform corresponding risk marking based on the actual risk level.

[0114] In this embodiment, the historical monitoring database is queried, the historical monitoring data of the key point is extracted, the current actual single point situation (i.e., the actual amplitude average) is compared with the historical data, and the difference between the actual single point situation and the historical data is calculated. It can be a simple difference or a more complex statistical indicator (such as standard deviation, coefficient of variation, etc.), which is not specifically limited in this embodiment.

[0115] Specifically, the second analysis module in this embodiment includes: a single-point secondary determination unit;

[0116] The single point secondary determination unit is used to secondary determine the actual single point situation according to the actual risk level of the key point, and determine the actual single point situation as a single point secondary qualified based on the actual risk level being the low risk level, and determine the actual single point situation as a single point secondary unqualified based on the actual risk level being the high risk level;

[0117] The second analysis module further includes: an overall secondary determination unit;

[0118] The overall secondary determination unit is used to record the number of key points where the actual single point situation is a single point secondary qualified number to obtain a secondary single point qualified number, record the number of key points where the actual single point situation is a single point secondary unqualified number to obtain a secondary single point unqualified number, and secondarily determine whether the actual overall situation is an overall secondary qualified or an overall secondary unqualified number based on the secondary single point qualified number and the secondary single point unqualified number;

[0119] Calculate a third difference based on the secondary single point qualified number and the secondary single point qualified number;

[0120] The third difference = the number of qualified secondary single points - the number of qualified secondary single points;

[0121] If the third difference is greater than or equal to the preset second evaluation value, the actual overall situation is determined to be overall second qualified;

[0122] If the third difference is less than the second evaluation value, determining whether the actual overall situation is overall secondarily unqualified;

[0123] In this embodiment, the second evaluation value is used to measure the qualified proportion of each key point after the second determination.

[0124] Specifically, the embodiments of the present invention make full use of historical monitoring data to provide reference and comparison for current monitoring, thereby enhancing the depth and breadth of data analysis. By comparing historical and actual data, the actual risk level of each key point can be accurately determined, which helps to take targeted preventive measures. By marking different risk levels, high-risk areas can be intuitively identified, which facilitates rapid response and processing. A secondary judgment is made on each key point, which ensures the accuracy of the monitoring results and reduces the possibility of misjudgment. By counting the number of qualified and unqualified key points after the secondary judgment, the risk status of the boiler can be evaluated as a whole, taking into account the combined impact of historical data and current data, and reducing the deviation that may be caused by a single data source. By making a secondary judgment on the risk level of the key points, guidance is provided for preventive maintenance, which helps to ensure the overall safe operation of the boiler and prevent accidents by promptly discovering and handling potential risks.

[0125] Specifically, the output module in this embodiment includes: a first association marking unit, a second association marking unit, a third association marking unit and a result output unit;

[0126] The first association marking unit, based on the first determination result and the second determination result of the actual single point situation of the key point, marks the key point as finally qualified if both are qualified;

[0127] The second association marking unit, based on the first determination result of the actual single point situation of the key point and the number of unqualified items in the second determination result is equal to 1, associates and marks the key point as a type of final unqualified;

[0128] The third association marking unit, based on the number of unqualified items in the first determination result and the second determination result of the actual single point situation of the key point, is equal to 2, then the key point is associated and marked as a second-class final unqualified;

[0129] The result output unit is used to output the final monitoring result as no warning required, immediate warning, or immediate explosion relief according to the associated marking result.

[0130] During the specific implementation process, when the result based on the association mark is finally qualified, the final monitoring result is output as no warning is required; when the result based on the association mark is a type of final unqualified, the final monitoring result is output as an immediate warning; when the result based on the association mark is a type of final unqualified, the final monitoring result is output as immediate explosion relief.

[0131] Specifically, the embodiment of the present invention uses clear association marks so that the final state of each point can be clearly identified. By distinguishing key points with different unqualified levels, a hierarchical warning mechanism is implemented, and corresponding measures can be taken according to the risk level, thereby improving the pertinence of emergency response. Different monitoring results can be output according to the association marking results, thereby effectively controlling risks and ensuring that emergency measures are taken when necessary, such as immediate warning or explosion relief, to prevent accidents. The points that are ultimately qualified do not require warnings, reducing unnecessary interference and ensuring that the normal operation of the system is not affected. For a type of points that are ultimately unqualified, immediate warnings can notify inspections in time to prevent problems from worsening. For a type of points that are ultimately unqualified, immediate explosion relief instructions can quickly reduce system pressure and avoid serious accidents such as explosions. The automated processing of the output module reduces manual intervention and improves the automation level of the entire monitoring system. Through timely warnings and explosion relief measures, potential safety accidents are effectively prevented and the safety of personnel and equipment is guaranteed.

[0132] The calculation compensation parameters and the calculation adjustment parameters described in the present invention have two functions: one is to balance the left and right dimensions of the formula, and the other is to adjust the numerical results. No specific assignment is performed in this embodiment. In addition, the calculation formulas in this embodiment are used to intuitively reflect the adjustment relationship between the various numerical values, such as positive correlation and negative correlation. Unless otherwise specified, the parameter values ​​that are not specifically limited to numerical values ​​are all positive.

[0133] The present invention is mainly applied to the operation monitoring and control method of abnormal vibration and high temperature of tower-type oil pumping units. It has the advantages of improving the accuracy of measurement data, quickly realizing data processing and improving the accuracy of fault diagnosis. It is of great significance for ensuring the safe operation of the oil pumping units and improving work efficiency. When the central control module senses abnormal operation vibration or temperature of the tower-type oil pumping units, it can quickly process the data and issue an alarm to prevent mechanical accidents of the equipment, thus solving the many defects of the existing monitoring methods, such as high cost, complicated operation, difficult measurement and poor reliability.

[0134] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A pressure relief monitoring system based on the Internet of Things, characterized in that: include, A monitoring module is used to monitor the key points of the target monitoring object in real time to obtain actual monitoring data; A first analysis module, based on any actual monitoring data, performs abnormal analysis and abnormal marking on the actual monitoring data in sequence, makes a judgment on the actual single point situation of each key point and the actual overall situation of the target monitoring object according to the abnormal marking result, and determines whether to start the auxiliary verification mode according to the judgment result; An integration module, which is used to number and integrate each actual monitoring data according to the historical numbering sequence to obtain an actual monitoring database; A comparison module, which compares the actual monitoring database with the historical monitoring database based on the auxiliary verification mode to perform risk analysis and risk marking; A second analysis module is used to make a secondary determination of the actual single point situation of each key point and the actual overall situation of the target monitoring object according to the risk marking result; The output module combines the first judgment result and the second judgment result to associate and mark each key point, and outputs the final monitoring result of each key point according to the association marking result.

2. The pressure relief monitoring system based on the Internet of Things according to claim 1 is characterized in that: The monitoring module includes: a deployment unit and a collection unit; The layout unit is used to determine a plurality of monitoring areas according to the heat energy output range of the steam transmission pipeline of the target monitoring object, and the key point is set in each of the monitoring areas; The acquisition unit is connected to the vibration sensors arranged at each key point, so as to obtain the vibration characteristics corresponding to each key point through the vibration sensors.

3. The pressure relief monitoring system based on the Internet of Things according to claim 2 is characterized in that: The acquisition unit includes: a drawing subunit and a correction subunit; The drawing subunit is used to draw a real-time vibration frequency diagram according to the vibration characteristics; The correction subunit is used to judge each monitoring data point in the real-time vibration frequency diagram according to the standard fluctuation range to determine whether to correct the real-time vibration frequency diagram to a corrected vibration frequency diagram. The drawing subunit draws a curve showing the change of vibration characteristics over time at each key point within a preset tracking and monitoring period, and generates a real-time vibration frequency diagram corresponding to each key point, wherein the real-time vibration frequency diagram includes a real-time vibration amplitude.

4. The pressure relief monitoring system based on the Internet of Things according to claim 3 is characterized in that: The correction subunit includes: a sampling judgment subunit and a spectrum update subunit; for any key point; The sampling judgment subunit is used to determine that when there is a monitoring data point that does not fall into the standard fluctuation range, draw a curve of the change of each monitoring data point that does not fall into the standard fluctuation range over time, and generate a vibration frequency diagram to be analyzed corresponding to the key point; The spectrogram updating subunit is used to obtain the number of monitoring data points in the vibration frequency graph to be analyzed as the number of difference points, calculate the percentage of the number of difference points in the total sampling points in the real-time vibration frequency graph to obtain the difference vibration frequency, and determine whether to correct the real-time vibration frequency graph to the corrected vibration frequency graph or determine that the acquisition is unqualified according to the difference vibration frequency; The actual monitoring data is obtained by identifying the modified frequency graph.

5. The pressure relief monitoring system based on the Internet of Things according to claim 4 is characterized in that: The first analysis module includes: an abnormality analysis unit, an abnormality marking unit and a single point one-time determination unit; The abnormality analysis unit is used to perform abnormality analysis according to the actual monitoring data and a preset monitoring threshold to determine the corresponding actual abnormality degree; The abnormality marking unit is used to perform corresponding abnormality marking on each key point according to the actual abnormality degree to obtain the abnormality marking result; The single-point once determination unit is used to determine the actual abnormality level of each key point according to the abnormality marking result, and to determine whether the actual single-point situation of each key point is single-point once qualified according to the actual abnormality level.

6. The pressure relief monitoring system based on the Internet of Things according to claim 5 is characterized in that: The first analysis module further includes: an overall primary determination unit and a verification unit; The overall one-time determination unit is used to record the number of key points where the actual single point situation is a single point one-time qualified to obtain a one-time single point qualified number, record the number of key points where the actual single point situation is a single point one-time unqualified to obtain a one-time single point unqualified number, and judge whether the actual overall situation is an overall one-time qualified according to the one-time single point qualified number and a preset first standard number; The verification unit is used to determine whether to start the auxiliary verification mode according to the actual overall situation; Among them, when the actual overall situation is qualified as a whole once, the auxiliary verification mode is turned on; when the actual overall situation is unqualified as a whole once, the final monitoring result is output.

7. The pressure relief monitoring system based on the Internet of Things according to claim 6, characterized in that: The comparison module includes: a history acquisition unit, a risk determination unit and a risk marking unit; for any key point; The history acquisition unit is used to acquire the historical single point situation of the key point according to the historical monitoring database; The risk determination unit is used to perform risk analysis according to the historical single point situation and the actual single point situation to determine an actual risk level, wherein the actual risk level includes: a high risk level and a low risk level; The risk marking unit is used to perform corresponding risk marking based on the actual risk level.

8. The pressure relief monitoring system based on the Internet of Things according to claim 7, characterized in that: The second analysis module includes: a single point secondary determination unit; The single-point secondary determination unit is used to secondary determine the actual single-point situation according to the actual risk level of the key point. Based on the actual risk level being the low risk level, the actual single-point situation is determined to be a single-point secondary qualified; based on the actual risk level being the high risk level, the actual single-point situation is determined to be a secondary unqualified.

9. The pressure relief monitoring system based on the Internet of Things according to claim 8, characterized in that: The second analysis module further includes: an overall secondary determination unit; The overall secondary judgment unit is used to record the number of key points where the actual single point situation is single point secondary qualified, so as to obtain the secondary single point qualified number, record the number of key points where the actual single point situation is single point secondary unqualified, so as to obtain the secondary single point unqualified number, and secondarily determine whether the actual overall situation is overall secondary qualified, or, overall secondary unqualified, based on the secondary single point qualified number and the secondary single point unqualified number.

10. The pressure relief monitoring system based on the Internet of Things according to claim 9, characterized in that: The output module includes: a first association marking unit, a second association marking unit, a third association marking unit and a result output unit; The first association marking unit, based on the first determination result and the second determination result of the actual single point situation of the key point, marks the key point as finally qualified if both are qualified; The second association marking unit, based on the first determination result of the actual single point situation of the key point and the number of unqualified items in the second determination result is equal to 1, associates and marks the key point as a type of final unqualified; The third association marking unit, based on the number of unqualified items in the first determination result and the second determination result of the actual single point situation of the key point, is equal to 2, then the key point is associated and marked as a second-class final unqualified; The result output unit is used to output the final monitoring result as no warning required, immediate warning, or immediate explosion relief according to the associated marking result.

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