Multi-parameter comprehensive monitoring method and system for wet desulphurization

Abstract

The invention relates to the technical field of data processing, in particular to a multi-parameter comprehensive monitoring method and system for wet desulphurization. The method comprises the following steps: collecting multi-parameter data in a wet desulphurization process, and obtaining the priority degree of each parameter in initial multi-parameter data according to the characteristic intensity of each parameter in the initial multi-parameter data and the similarity degree between each parameter in the initial multi-parameter data and other parameters, constructing an initial isolated tree for the initial multi-parameter data based on the priority degree of each parameter, adding the real-time data into the initial isolated tree, calculating the structural change degree of the initial isolated tree, and adaptively updating the initial isolated tree according to the structural change degree; and finally, the abnormal score of each multi-parameter data is obtained according to the final isolated tree, early warning is carried out according to the abnormal score result, and the abnormal detection result is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a multi-parameter comprehensive monitoring method and system for wet desulfurization. Background Art

[0002] Wet flue gas desulfurization technology is mainly used to control sulfur dioxide emissions from coal-fired power plants and other industrial facilities. It is one of the more common and effective desulfurization technologies. This technology converts sulfur dioxide into sulfate by contacting flue gas containing sulfur dioxide with an alkaline absorbent (usually limestone or lime solvent), thereby reducing air pollution, protecting the environment, and avoiding the negative impact caused by environmental problems such as acid rain. In the wet flue gas desulfurization system, the process of sulfur dioxide in the flue gas reacting with the absorbent to form sulfate involves multiple complex chemical and physical processes. In order to ensure the desulfurization efficiency and optimize the system performance, it is necessary to monitor the abnormal status of multiple parameters. By real-time detection of the abnormal status of these parameters, the operating conditions can be adjusted to ensure the optimal desulfurization effect, while reducing operating costs and extending equipment life.

[0003] The patent application document with the publication number CN109800900A discloses a method for modularizing and visualizing an isolation forest algorithm, including: (1) modularizing the iforest algorithm; dividing the iforest algorithm into two stages, namely training and prediction, making the training iforest process an independent module called the training iforest algorithm module, making the prediction iforest process an independent module called the prediction iforest algorithm module, and adding an evaluation indicator as a module for evaluating the quality of the iforest algorithm called the evaluation iforest algorithm module; (2) visualizing the results of each module of the iforest algorithm; visualizing the outputs corresponding to the three modules in step (1) in turn, the training iforest algorithm module stores the trained model, the prediction iforest algorithm module displays the output in a data graph, including the predicted label, and the evaluation iforest algorithm module displays the evaluation indicator.

[0004] The above-mentioned isolation forest algorithm is an algorithm for anomaly detection of multi-parameter data. Its working principle is based on the idea of ​​"isolation". It selects a parameter at each layer of the isolation tree to achieve the segmentation of data samples, so that the abnormal data is segmented in the upper layer of the isolation tree, which increases the difference between the abnormal data and the normal data. If any parameter is randomly selected at different layers of the isolation tree for segmentation, a parameter with poor discrimination may be placed in the upper layer of the isolation tree, making the distance between the abnormal data and the normal data in the isolation tree closer, resulting in inaccurate calculation of the abnormal score result. Summary of the invention

[0005] In order to solve the technical problem that when the isolation forest algorithm constructs an isolation tree, it randomly selects any parameters at different layers to split, which may place a parameter with poor discrimination degree at the upper layer of the isolation tree, making the distance between abnormal data and normal data in the isolation tree closer, resulting in inaccurate abnormal score results, the present invention provides a multi-parameter comprehensive monitoring method and system for wet flue gas desulfurization.

[0006] In the first aspect, the present invention provides a multi-parameter comprehensive monitoring method for wet flue gas desulfurization, which adopts the following technical solution: A multi-parameter comprehensive monitoring method for wet flue gas desulfurization, comprising the steps of: Collect initial multi-parameter data; obtain the priority of each parameter in the initial multi-parameter data , Represents the characteristic intensity of the i-th parameter in the initial multi-parameter data; Represents the priority of the i-th parameter in the initial multi-parameter data; represents the similarity between the i-th parameter and other parameters in the initial multi-parameter data; norm() represents a normalization function, which constructs an initial isolation tree for the initial multi-parameter data based on the priority of each parameter; Add the real-time data to the initial isolated tree to obtain the first updated isolated tree; obtain the degree of structural change of the first updated isolated tree ; Represents the level of the first update of the isolated tree; Represents the percentage of data in the first update of the a-th node in the m-th layer of the isolated tree; Represents the percentage of data in the a-th node of the m-th layer in the initial isolated tree; Represents the level number of the first update of the isolated tree; Represents the number of nodes in the mth layer of the first updated isolated tree; exp() represents an exponential function with a natural constant as the base; || represents the absolute value symbol; based on the degree of structural change of the first updated isolated tree, the initial isolated tree is adaptively updated to obtain the final isolated tree; anomaly monitoring is performed based on the final isolated tree.

[0007] The innovation of the present invention lies in obtaining the preference degree of each parameter of the initial multi-parameter data according to the change amplitude of each parameter of the initial multi-parameter data, and then constructing the initial isolated tree according to the preference degree from large to small, so that a parameter with a larger preference degree can be placed in the upper layer of the isolated tree, which can make the distance between abnormal data and normal data in the isolated tree closer, so that the abnormal score result calculation is accurate and the abnormal monitoring effect is achieved; and the real-time data is added to the initial isolated tree to obtain an updated isolated tree, and according to the comparison between the initial isolated tree and the updated isolated tree, the structural change degree of the updated isolated tree is obtained to judge whether it is necessary to update the initial isolated tree and then perform abnormal monitoring, and the isolated tree can be updated in real time, which further makes the abnormal monitoring result more accurate.

[0008] Preferably, obtaining the characteristic strength of the i-th parameter in the initial multi-parameter data includes: ; In the formula, Represents the characteristic intensity of the i-th parameter in the initial multi-parameter data; Represents the number of the i-th parameter in the initial multi-parameter data; Represents the value of the i-th parameter of the j-th item in the initial multi-parameter data; Represents all i-th parameters in the initial multi-parameter data; norm() represents the normalization function.

[0009] It reflects the variation range of each parameter, and subsequently obtains the priority of each parameter in the initial multi-parameter data according to the characteristic strength of each parameter.

[0010] Preferably, the similarity between the i-th parameter in the initial multi-parameter data and other parameters includes: The absolute value of the Pearson correlation coefficient between the i-th parameter in the initial multi-parameter data and each parameter except the i-th parameter is obtained, and the average of the absolute values ​​of the Pearson correlation coefficients between the i-th parameter in the initial multi-parameter data and all parameters except the i-th parameter is recorded as the similarity between the i-th parameter in the initial multi-parameter data and other parameters.

[0011] It is convenient to obtain the priority of each parameter in the initial multi-parameter data according to the similarity between each parameter in the initial multi-parameter data and other parameters.

[0012] Preferably, the constructing of an initial isolation tree for the initial multi-parameter data based on the priority of each parameter comprises: According to the order of priority of each parameter in the initial multi-parameter data from large to small, an initial isolation tree is constructed from top to bottom for the initial multi-parameter data.

[0013] The constructed isolation tree can make the anomaly monitoring results more accurate.

[0014] Preferably, the step of adding the real-time data to the initial isolated tree to obtain the first updated isolated tree comprises: The number of iterative update data is preset to M, the data corresponding to the Xth sampling moment in the multi-parameter data sequence is recorded as the first node in the multi-parameter data sequence, and the M data after the first node are added to the initial isolated tree to obtain the first updated isolated tree.

[0015] Preferably, the obtaining of the final isolated tree comprises: The number of iterative update data is preset M, and the change threshold T1 is preset. If the degree of structural change of the first updated isolated tree is greater than or equal to T1, the isolated tree is rebuilt for all data in the first updated isolated tree according to the construction method of the initial isolated tree to obtain the first final isolated tree; otherwise, the first updated isolated tree is recorded as the first final isolated tree; the Mth data after the first node in the multi-parameter data sequence is recorded as the second node, and the M data after the second node are added to the first final isolated tree to obtain the second updated isolated tree; the degree of structural change of the second updated isolated tree is obtained to determine whether the second updated isolated tree needs to be rebuilt; and so on, until there is no data in the multi-parameter data sequence, and the pth final isolated tree is recorded as the final isolated tree.

[0016] Whether the initial isolated tree needs to be updated and then abnormal monitoring is determined according to the degree of structural change of the updated isolated tree. The isolated tree can be updated in real time, further making the abnormal monitoring result more accurate.

[0017] Preferably, the abnormality monitoring according to the final isolated tree includes: The abnormal threshold T2 is preset, and the abnormal score of each multi-parameter data is obtained according to the final isolation tree. If the mean of the abnormal scores of all multi-parameter data is greater than the preset abnormal threshold T2, the system issues an early warning.

[0018] In a second aspect, the present invention provides a multi-parameter comprehensive monitoring system for wet flue gas desulfurization, which adopts the following technical solution: A multi-parameter comprehensive monitoring system for wet flue gas desulfurization comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the multi-parameter comprehensive monitoring method for wet flue gas desulfurization is implemented.

[0019] By adopting the above technical solution, the above-mentioned multi-parameter comprehensive monitoring method for wet desulfurization is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0020] The present invention has the following technical effects: the purpose of the present invention is to obtain the preference degree of each parameter of the initial multi-parameter data, and then construct an initial isolated tree according to the preference degree from large to small, so that a parameter with a larger preference degree can be placed in the upper layer of the isolated tree, and the distance between abnormal data and normal data in the isolated tree can be made closer, so that the abnormal score result calculation is accurate and the abnormal monitoring effect is achieved; and real-time data is added to the initial isolated tree to obtain an updated isolated tree, and according to the comparison between the initial isolated tree and the updated isolated tree, the degree of structural change of the updated isolated tree is obtained to determine whether it is necessary to update the initial isolated tree and then perform abnormal monitoring, and the isolated tree can be updated in real time, further making the abnormal monitoring result more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 It is a method flow chart of a multi-parameter comprehensive monitoring method for wet desulfurization according to an embodiment of the present invention.

[0023] Figure 2 Schematic diagram of the percentage of representative data. DETAILED DESCRIPTION

[0024] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0025] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0026] The embodiment of the present invention discloses a multi-parameter comprehensive monitoring method for wet desulfurization, referring to Figure 1 , comprising steps S1 to S3: S1: Collect multi-parameter data during wet flue gas desulfurization process.

[0027] It should be noted that the wet flue gas desulfurization process is a method for removing sulfur dioxide from the gas. It is mainly used in industrial waste gas treatment, flue gas desulfurization in coal-fired power plants and other fields. The basic principle of this process is to use liquid absorbent to react with sulfur dioxide in the gas, thereby reducing the concentration of sulfur dioxide in the waste gas. The wet flue gas desulfurization process includes multiple parameters, and monitoring these parameters is crucial to ensure the efficiency, safety and environmental protection of the wet flue gas desulfurization process. By monitoring these parameters, the desulfurization efficiency can be effectively improved, the operating costs can be reduced, and the negative impact on the environment can be reduced.

[0028] In the embodiment of the present invention, the sampling time is preset to 2 seconds / time. During the wet flue gas desulfurization process, a spectrometer is arranged to collect the sulfur dioxide concentration in and out of the reaction tower, a pH meter is arranged to collect the pH value of the absorption liquid; a flow meter is arranged to collect the flue gas flow rate; an infrared thermometer is arranged to collect the flue gas temperature, and the parameters such as the sulfur dioxide concentration in and out of the reaction tower, the pH value of the absorption liquid, the flue gas flow rate and the flue gas temperature collected at the same sampling time are recorded as a multi-parameter data. The multi-parameter data at each sampling time is taken as a multi-parameter data sequence.

[0029] It should be noted that in the embodiment of the present invention, parameters such as the concentration of sulfur dioxide entering and exiting the reaction tower, the pH value of the absorption liquid, the flue gas flow rate, and the flue gas temperature are collected simultaneously.

[0030] S2: Obtain the characteristic strength of each parameter in the initial multi-parameter data, obtain the priority of each parameter in the initial multi-parameter data according to the characteristic strength of each parameter in the initial multi-parameter data and the similarity between each parameter in the initial multi-parameter data and other parameters, and construct an initial isolation tree for the initial multi-parameter data based on the priority of each parameter.

[0031] It should be noted that the isolation forest algorithm is an algorithm for anomaly detection of multi-parameter data. Its working principle is based on the idea of ​​"isolation". It selects a parameter at each layer of the isolation tree to achieve the segmentation of data samples, so that the abnormal data is segmented in the upper layer of the isolation tree, widening the difference between the abnormal data and the normal data. If any parameter is randomly selected at different layers of the isolation tree for segmentation, a parameter with poor discrimination will be placed in the upper layer of the isolation tree, making the distance between the abnormal data and the normal data in the isolation tree closer, resulting in inaccurate calculation of the anomaly score result. Therefore, it is necessary to calculate the priority of each parameter in the initial multi-parameter data according to the feature strength of each parameter in the initial multi-parameter data, and construct an isolation tree based on the preferred degree of each parameter. The parameter items with good discrimination can be placed above the isolation tree, making the subsequent anomaly results more accurate.

[0032] Step S2 includes steps S20 and S21, which are specifically as follows: S20: Obtain the characteristic strength of each parameter in the initial multi-parameter data.

[0033] It should be noted that in the wet flue gas desulfurization process, different parameters in the multi-parameter data have different variation ranges, and different variation ranges will affect the segmentation effect of the parameter in the isolated tree. Therefore, it is necessary to first analyze the variation range of each parameter in the multi-parameter data, calculate the feature strength of each parameter in the multi-parameter data, and then obtain the priority of each parameter in the multi-parameter data to construct an isolated tree. In this way, the features with good discrimination can be placed above the isolated tree, which is convenient for subsequent abnormal monitoring. It should be further explained that, since the initial multi-parameter data provides the baseline state of various parameters in the wet flue gas desulfurization process in the initial stage of the reaction, these parameters reflect the typical performance of the system during normal operation. Therefore, in the present invention, it is necessary to first obtain the initial multi-parameter data to construct an isolation tree.

[0034] In the embodiment of the present invention, the number of sampling moments X is preset, and the multi-parameter data of the first X sampling moments of the multi-parameter data sequence is used as the initial multi-parameter data. In the embodiment of the present invention, the preset sampling moment X=100. In other embodiments, the implementer can preset the value of the number of sampling moments X according to the specific implementation situation.

[0035] Get the characteristic strength of each parameter in the initial multi-parameter data: ; In the formula, Represents the characteristic intensity of the i-th parameter in the initial multi-parameter data; Represents the number of the i-th parameter in the initial multi-parameter data; Represents the value of the i-th parameter of the j-th item in the initial multi-parameter data; Represents all i-th parameters in the initial multi-parameter data; norm() represents the normalization function; in, Represents the difference between each i-th parameter in the initial multi-parameter data and the mean of all i-th parameters. The larger its value is, the greater the difference between the i-th parameters in the initial multi-parameter data is, and the greater its characteristic strength is; Represents the standard deviation of the i-th parameter in the initial multi-parameter data. Dividing by the standard deviation and performing standardization operations can avoid the impact of different orders of magnitude between parameters of different items.

[0036] S21: Obtain the priority of each parameter in the initial multi-parameter data according to the feature strength of each parameter in the initial multi-parameter data and the similarity between each parameter in the initial multi-parameter data and other parameters, and construct an initial isolation tree for the initial multi-parameter data based on the priority of each parameter.

[0037] It should be noted that the greater the characteristic strength of a parameter, the greater the difference between the parameters, which means that the parameter can effectively distinguish data. Placing it in the upper layer of the isolation tree can segment the data earlier, so that abnormal data can be isolated faster. At the same time, due to the mutual influence between some parameters, the similarity between the parameters needs to be considered. If the similarity between any parameter and other parameters is low, it means that the parameter has independent information and is not affected by other parameters. Placing the parameter in the upper layer of the isolation tree can help better identify abnormal data. Therefore, the present invention obtains the priority of each parameter in the initial multi-parameter data according to the characteristic strength of each parameter in the initial multi-parameter data and the similarity between each parameter in the initial multi-parameter data and other parameters.

[0038] In an embodiment of the present invention, the absolute value of the Pearson correlation coefficient between the i-th parameter in the initial multi-parameter data and each parameter except the i-th parameter is obtained, and the average of the absolute values ​​of the Pearson correlation coefficients between the i-th parameter in the initial multi-parameter data and all parameters except the i-th parameter is recorded as the similarity between the i-th parameter in the initial multi-parameter data and other parameters.

[0039] Get the priority of each parameter in the initial multi-parameter data: ; In the formula, Represents the characteristic intensity of the i-th parameter in the initial multi-parameter data; Represents the priority of the i-th parameter in the initial multi-parameter data; Represents the similarity between the i-th parameter and other parameters in the initial multi-parameter data; the greater the characteristic strength of the i-th parameter and the smaller the similarity with other parameters, the i-th parameter will be used as the upper dimension of the isolated tree, that is, the higher the priority of the characteristic strength of the parameter; conversely, the smaller the characteristic strength of the i-th parameter and the greater the similarity with other parameters, the parameter will be used as the lower dimension of the isolated tree, that is, the lower the priority of the parameter.

[0040] According to the order of priority of each parameter in the initial multi-parameter data from large to small, an initial isolation tree is constructed from top to bottom for the initial multi-parameter data. It should be noted that the parameter with the highest priority is at the highest level of the initial isolation tree. Construction of the isolation tree is a prior art and will not be described in detail in the embodiments of the present invention.

[0041] S3: Add the real-time data to the initial isolated tree, calculate the degree of structural change of the initial isolated tree, and adaptively update the initial isolated tree according to the degree of structural change to obtain the final isolated tree; obtain the anomaly score of each multi-parameter data according to the final isolated tree, and issue an early warning based on the anomaly score result.

[0042] Step S3 includes steps S30 and S31, which are specifically as follows: S30: adding the real-time data to the initial isolated tree, calculating the degree of structural change of the initial isolated tree, and adaptively updating the initial isolated tree according to the degree of structural change to obtain a final isolated tree.

[0043] It should be noted that during the wet flue gas desulfurization process, if the feed to the absorber changes (such as changes in absorbent concentration, flue gas temperature, and flow rate), the distribution of the initial multi-parameter data will no longer represent the current process state, resulting in inaccurate abnormal results obtained using the initial isolation tree constructed using the initial multi-parameter data. Therefore, it is necessary to add real-time data to the initial isolation tree and analyze the degree of structural change of the initial isolation tree. The initial isolation tree can then be adaptively determined based on the degree of structural change to determine whether it is necessary to update the initial isolation tree. This can ensure that the abnormal detection results are accurate after the real-time data is added to the initial isolation tree.

[0044] In the embodiment of the present invention, the number of iteratively updated data M is preset, the data corresponding to the Xth sampling moment in the multi-parameter data sequence is recorded as the first node in the multi-parameter data sequence, and the M data after the first node in the multi-parameter data sequence are added to the initial isolated tree to obtain the first updated isolated tree, and the ratio of the data of each node in any layer of the first updated isolated tree to the total number of data in the layer is obtained to obtain the proportion of the number of data of each node in each layer of the first updated isolated tree. Similarly, the proportion of the number of data of each node in each layer of the initial isolated tree is obtained; the proportion of the number of data refers to Figure 2 It should be noted that X is the number of preset sampling moments.

[0045] According to the comparison between the first updated isolated tree and the initial isolated tree, the degree of structural change of the first updated isolated tree is obtained: ; In the formula, Represents the degree of structural change of the isolated tree in the first update; Represents the level of the first update of the isolated tree; Represents the percentage of data in the first update of the a-th node in the m-th layer of the isolated tree; Represents the percentage of data in the a-th node of the m-th layer in the initial isolated tree; Represents the level number of the first update of the isolated tree; Represents the number of nodes in the mth layer of the first update of the isolated tree; exp() represents an exponential function with a natural constant as the base; || represents the absolute value symbol; Represents the difference between the corresponding nodes of the initial isolated tree and the first updated isolated tree in all layers. The larger the value, the greater the structural change of the first updated isolated tree. The weight representing the difference between the corresponding nodes of the initial isolated tree and the first updated isolated tree at the mth layer. The larger the value, the closer the mth layer is to the root node. If the nodes at the mth layer change, the greater the impact on the lower layer of the first updated isolated tree. Therefore, the greater the degree of structural change of the first updated isolated tree. The larger the value of , the greater the degree of structural change of the first updated isolation tree, and the isolation tree needs to be rebuilt based on the data in the first updated isolation tree.

[0046] The preset change threshold value T1=0.5. In other embodiments, the implementer may preset the value of the change threshold value according to the specific implementation situation. If the structural change degree of the first updated isolated tree is greater than or equal to the change threshold value T1, the isolated tree is reconstructed for all data in the first updated isolated tree according to the construction method of the initial isolated tree to obtain the first final isolated tree; if the structural change degree of the first updated isolated tree is less than the change threshold value T1, the first updated isolated tree is recorded as the first final isolated tree; The Mth data after the first node in the multi-parameter data sequence is recorded as the second node, and then the M data after the second node in the multi-parameter data sequence are added to the first final isolated tree to obtain the second updated isolated tree; according to the first final isolated tree and the second updated isolated tree, the degree of structural change of the second updated isolated tree is obtained to determine whether the second updated isolated tree needs to be rebuilt; And so on, the process stops when there is no data in the multi-parameter data sequence. At this time, the pth final isolated tree is recorded as the final isolated tree, and the adaptive update of the isolated tree is completed.

[0047] S31: Obtain anomaly scores for each multi-parameter data according to the final isolated tree, and issue an early warning based on the anomaly score results.

[0048] In the embodiment of the present invention, the abnormality score of each multi-parameter data is obtained according to the final isolation tree. If the mean of the abnormality scores of all multi-parameter data is greater than the preset abnormality threshold T2, the system issues an early warning.

[0049] The abnormal threshold value T2 is preset to be 0.8. In other embodiments, the implementer may preset the value of the abnormal threshold value T2 according to specific implementation conditions.

[0050] It should be noted that obtaining the abnormality score of each multi-parameter data according to the final isolated tree is a prior art, and will not be described in detail in the embodiments of the present invention.

[0051] An embodiment of the present invention also discloses a multi-parameter comprehensive monitoring system for wet desulfurization, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a multi-parameter comprehensive monitoring method for wet desulfurization according to the present invention is implemented.

[0052] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0053] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high bandwidth memory, a hybrid storage cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0054] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0055] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-parameter comprehensive monitoring method for wet flue gas desulfurization, characterized in that: Includes steps: Collect initial multi-parameter data; obtain the priority of each parameter in the initial multi-parameter data , Represents the characteristic intensity of the i-th parameter in the initial multi-parameter data; Represents the priority of the i-th parameter in the initial multi-parameter data; represents the similarity between the i-th parameter and other parameters in the initial multi-parameter data; norm() represents a normalization function, which constructs an initial isolation tree for the initial multi-parameter data based on the priority of each parameter; Add the real-time data to the initial isolation tree to obtain the first updated isolation tree; Get the extent of structural changes of the first updated isolated tree ; Represents the level of the first update of the isolated tree; Represents the percentage of data in the first update of the a-th node in the m-th layer of the isolated tree; Represents the number of nodes in the mth layer of the isolated tree updated for the first time; Represents the percentage of data in the a-th node of the m-th layer in the initial isolated tree; Represents the level number of the first update of the isolated tree; exp() represents an exponential function with a natural constant as the base; || represents the absolute value symbol; Adaptively update the initial isolated tree based on the degree of structural change of the first updated isolated tree to obtain the final isolated tree; Anomaly monitoring is performed based on the final isolation tree.

2. A multi-parameter comprehensive monitoring method for wet flue gas desulfurization according to claim 1, characterized in that: The acquisition of the characteristic strength of the i-th parameter in the initial multi-parameter data includes: ； In the formula, Represents the characteristic intensity of the i-th parameter in the initial multi-parameter data; Represents the number of the i-th parameter in the initial multi-parameter data; Represents the value of the i-th parameter of the j-th item in the initial multi-parameter data; Represents all i-th parameters in the initial multi-parameter data; norm() represents the normalization function.

3. A multi-parameter comprehensive monitoring method for wet flue gas desulfurization according to claim 1, characterized in that: The similarity between the i-th parameter in the initial multi-parameter data and other parameters includes: The absolute value of the Pearson correlation coefficient between the i-th parameter in the initial multi-parameter data and each parameter except the i-th parameter is obtained, and the average of the absolute values ​​of the Pearson correlation coefficients between the i-th parameter in the initial multi-parameter data and all parameters except the i-th parameter is recorded as the similarity between the i-th parameter in the initial multi-parameter data and other parameters.

4. A multi-parameter comprehensive monitoring method for wet flue gas desulfurization according to claim 1, characterized in that: The constructing of an initial isolation tree for the initial multi-parameter data based on the priority of each parameter includes: According to the order of priority of each parameter in the initial multi-parameter data from large to small, an initial isolation tree is constructed from top to bottom for the initial multi-parameter data.

5. The multi-parameter comprehensive monitoring method for wet flue gas desulfurization according to claim 1, characterized in that: The step of adding the real-time data to the initial isolation tree to obtain the first updated isolation tree includes: The number of iterative update data is preset to M, the data corresponding to the Xth sampling moment in the multi-parameter data sequence is recorded as the first node in the multi-parameter data sequence, and the M data after the first node are added to the initial isolated tree to obtain the first updated isolated tree.

6. A multi-parameter comprehensive monitoring method for wet flue gas desulfurization according to claim 1, characterized in that: The obtaining of the final isolated tree comprises: The number of iterative update data is preset M, and the change threshold T1 is preset. If the degree of structural change of the first updated isolated tree is greater than or equal to T1, the isolated tree is rebuilt for all data in the first updated isolated tree according to the construction method of the initial isolated tree to obtain the first final isolated tree; otherwise, the first updated isolated tree is recorded as the first final isolated tree; the Mth data after the first node in the multi-parameter data sequence is recorded as the second node, and the M data after the second node are added to the first final isolated tree to obtain the second updated isolated tree; the degree of structural change of the second updated isolated tree is obtained to determine whether the second updated isolated tree needs to be rebuilt; and so on, until there is no data in the multi-parameter data sequence, and the pth final isolated tree is recorded as the final isolated tree.

7. A multi-parameter comprehensive monitoring method for wet flue gas desulfurization according to claim 1, characterized in that: The abnormality monitoring according to the final isolated tree includes: The abnormal threshold T2 is preset, and the abnormal score of each multi-parameter data is obtained according to the final isolation tree. If the mean of the abnormal scores of all multi-parameter data is greater than the preset abnormal threshold T2, the system issues an early warning.

8. A multi-parameter comprehensive monitoring system for wet flue gas desulfurization, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a multi-parameter comprehensive monitoring method for wet desulfurization according to any one of claims 1 to 7 is implemented.

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