Method and system for supervising operation condition of leachate treatment equipment of waste incineration plant based on big data

Abstract

The invention relates to the technical field of data monitoring, in particular to a garbage incineration plant leachate treatment equipment operation condition supervision method and system based on big data, and the method comprises the steps: obtaining garbage incineration plant leachate treatment process flow information, setting leachate monitoring point positions according to the process flow, and obtaining key leachate monitoring data and equipment operation parameters of each point position; constructing a three-dimensional digital twinborn model of the leachate treatment flow of the waste incineration plant, and performing correlation analysis on the key leachate monitoring data of each flow sub-sequence according to the three-dimensional digital twinborn model; the analysis result of the key percolate monitoring data is obtained through the key percolate monitoring data of each process sub-sequence and the percolate treatment equipment association relationship among the process sub-sequences, and the analysis result of the key percolate monitoring data is visually displayed, so that a reliable basis is provided for analysis of the operation condition of the percolate treatment equipment. And the judgment result accuracy of the operation condition of the leachate treatment equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring, and in particular to a method and system for supervising the operation status of leachate treatment equipment in a waste incineration plant based on big data. Background Art

[0002] The leachate treatment equipment of the waste incineration plant is used to treat the leachate generated during the waste incineration process. It is mainly responsible for removing harmful substances in the leachate so that it meets environmental protection standards and is discharged safely.

[0003] In the prior art, the detection of the operating status of leachate treatment equipment in waste incineration plants is mainly based on the analysis of the equipment's own attributes and monitoring indicator data. However, the equipment operating status detection results obtained by the prior art do not eliminate the misjudgment results caused by factors other than the equipment's own factors, resulting in a large number of false alarms and missed alarms in the alarm information of the leachate treatment equipment in the waste incineration plant. Therefore, how to determine the correlation between the operating conditions of the leachate treatment equipment in the waste incineration plant and visualize the operating conditions of the leachate treatment equipment in the incineration plant to improve the accuracy of the judgment results is a problem that we urgently need to solve. Now, a method and system for supervising the operating status of leachate treatment equipment in a waste incineration plant based on big data is provided. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method and system for supervising the operating status of leachate treatment equipment in a waste incineration plant based on big data.

[0005] The first aspect of the present invention provides a method for monitoring the operation status of leachate treatment equipment in a waste incineration plant based on big data, comprising the following steps:

[0006] Step S1: Obtain the process information of leachate treatment in the waste incineration plant, set leachate monitoring points according to the process, obtain key leachate monitoring data and equipment operating parameters of each point and mark the collection time, and set the monitoring cycle;

[0007] Step S2: construct a three-dimensional digital twin model of the leachate treatment process of the waste incineration plant, and perform correlation analysis on the key leachate monitoring data of each process subsequence based on the three-dimensional digital twin model to determine the correlation relationship between the leachate treatment equipment of each process subsequence;

[0008] Step S3: Obtain key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the association relationship between the leachate treatment equipment of each process subsequence, and visualize the key leachate monitoring data analysis results through the three-dimensional digital twin model of the leachate treatment process of the waste incineration plant.

[0009] Furthermore, the process of obtaining the process information of leachate treatment in the waste incineration plant and setting the leachate monitoring points according to the process includes:

[0010] According to the process flow information of the leachate treatment of the waste incineration plant, all leachate data monitoring types in the leachate treatment process of the waste incineration plant are obtained, and the process flow characteristics are extracted according to the process flow information of the leachate treatment of the waste incineration plant, and the leachate treatment process of the waste incineration plant is split according to the process flow characteristics and divided into a number of process sub-sequences;

[0011] According to the process characteristics in the process subsequence, all leachate data monitoring types monitored in the process subsequence are divided into key leachate monitoring types and other leachate monitoring types, and importance levels are set for key leachate monitoring types and other leachate monitoring types. According to the leachate monitoring types in the process subsequence, corresponding types of leachate monitoring points are set, and the number of each leachate monitoring type point is determined according to the importance level of the leachate monitoring type.

[0012] Furthermore, it is characterized in that the process of constructing a three-dimensional digital twin model of the leachate treatment process of a waste incineration plant includes:

[0013] Obtain the physical entity and location information of the leachate treatment equipment of the current waste incineration plant corresponding to several process subsequences in the leachate treatment process of the current waste incineration plant, obtain the multi-source heterogeneous data of the physical entity and key leachate indicator monitoring points of each process subsequence in the current waste incineration plant leachate treatment process, and perform data format preprocessing;

[0014] A virtual scene representing each process subsequence is created according to the position information of the physical entity of each process subsequence, a three-dimensional model of the physical entity corresponding to the virtual scene is drawn in the virtual scene, and the connection flow relationship between each process subsequence is obtained according to the current waste incineration plant leachate treatment process, and the transfer path of the leachate is obtained according to the connection flow relationship, and the transfer path of the leachate is animated 3D modeling is performed to obtain a framework model representing the transfer path of the leachate, and the framework model is superimposed and linked with the three-dimensional model;

[0015] Obtain the three-dimensional model corresponding to the multi-source heterogeneous data of each process sub-sequence after data format preprocessing in the physical entity, map the multi-source heterogeneous data to the three-dimensional model, and generate a three-dimensional digital twin model.

[0016] Furthermore, the process of performing correlation analysis on the key leachate monitoring data of each process subsequence based on the three-dimensional digital twin model and determining the correlation relationship between the leachate treatment equipment operations of each process subsequence includes:

[0017] According to the three-dimensional digital twin model, the leachate transfer path connecting each process subsequence is obtained, and a set of data association tags is constructed between the current process subsequence and the next process subsequence connected to the leachate transfer path of the current subsequence, and the current process subsequence is marked as the reference end, and the next process subsequence connected to the leachate transfer path of the current subsequence is marked as the controlled end;

[0018] Acquire multiple groups of historical leachate monitoring data of two process subsequences in a set of data association tags, and obtain the correlation coefficient of the leachate monitoring data between the two process subsequences through data statistical analysis of the multiple groups of historical leachate monitoring data parameters of the two process subsequences, and repeat the above correlation coefficient acquisition operation until the correlation coefficient of the filtrate monitoring data in all data association tags is obtained;

[0019] A correlation coefficient threshold value is set, and the correlation coefficient of the leachate monitoring data between the two process subsequences obtained is compared with the correlation coefficient threshold value, and the leachate monitoring data whose correlation coefficient is greater than or equal to the correlation coefficient threshold value is classified as common leachate monitoring data.

[0020] Furthermore, the process of obtaining the key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the association relationship of the leachate treatment equipment between each process subsequence includes:

[0021] According to the process characteristics in each process subsequence, the key leachate monitoring data indicator threshold range and the common key leachate data indicator threshold range of each process subsequence are obtained, and the key leachate monitoring data of the process subsequence is compared with the corresponding key leachate monitoring data indicator threshold range. When the key leachate monitoring data of the process subsequence is not within the key leachate monitoring data indicator threshold range, the key leachate monitoring data of the current process subsequence is marked as data to be tested;

[0022] Acquire the common key leachate monitoring data of the process subsequence as the reference end with the same group of data association tags as the controlled end of the current process subsequence, compare the common key leachate monitoring data with the common key leachate data indicator threshold range, and when the common key leachate monitoring data is not within the common key leachate data indicator threshold range, acquire the common key leachate monitoring data correlation coefficient between the reference end and the controlled end, acquire compensation parameters of the data to be measured according to the common key leachate monitoring data of the process subsequence at the reference end and the common key leachate monitoring data correlation coefficient, perform compensation parameter processing on the data to be measured, and obtain adjusted data to be measured;

[0023] The adjusted data to be tested is compared with the threshold range of the key leachate monitoring data indicator. When the adjusted data to be tested is not within the threshold range of the key leachate monitoring data indicator, the equipment operation parameter detection is performed on the current process subsequence.

[0024] Furthermore, the process of detecting the equipment operation parameters of the current process subsequence includes:

[0025] According to the process characteristics in each process subsequence, the preset equipment operating parameters of the current process subsequence are obtained, and the equipment operating parameters of the current process subsequence are compared with the preset equipment operating parameters for parameter consistency. If the equipment operating parameters of the current process subsequence are inconsistent with the preset equipment operating parameters, the equipment operating parameters of the current process subsequence are adjusted to be consistent with the preset equipment operating parameters;

[0026] And re-acquire the key leachate monitoring data after the equipment operating parameters of the current process sub-sequence are adjusted, and compare the key leachate monitoring data with the corresponding key leachate monitoring data indicator threshold range. When the key leachate monitoring data of the process sub-sequence is not within the key leachate monitoring data indicator threshold range, the leachate treatment equipment fault information of the corresponding process sub-sequence is generated.

[0027] Furthermore, the process of visualizing the key leachate monitoring data analysis results through the three-dimensional digital twin model of the waste incineration plant leachate treatment process includes:

[0028] The display colors of the key leachate monitoring data mapped on the three-dimensional model of each process subsequence in the three-dimensional digital twin model and the transfer paths on the architecture model are set to obtain the key leachate monitoring data analysis results of each process subsequence. When the key leachate monitoring data analysis results generate the fault information of the leachate treatment equipment of the corresponding process subsequence, the three-dimensional model of the corresponding process subsequence and the transfer path connected to the process subsequence are displayed in red.

[0029] The second aspect of the present invention also provides a big data-based monitoring system for the operation status of leachate treatment equipment in a waste incineration plant, comprising a monitoring center, wherein the monitoring center is communicatively connected to a data acquisition module, a data processing module, a data analysis module and a data visualization module;

[0030] The data acquisition module is used to obtain the process information of leachate treatment in the waste incineration plant, set leachate monitoring points according to the process, obtain key leachate monitoring data and equipment operating parameters of each point, mark the collection time, and set the monitoring cycle;

[0031] The data processing module is used to construct a three-dimensional digital twin model of the leachate treatment process of a waste incineration plant;

[0032] The data analysis module is used to perform correlation analysis on the key leachate monitoring data of each process subsequence according to the three-dimensional digital twin model, determine the correlation relationship between the leachate treatment equipment of each process subsequence, and obtain the key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the correlation relationship between the leachate treatment equipment of each process subsequence;

[0033] The data visualization module is used to visualize the key leachate monitoring data analysis results through a three-dimensional digital twin model of the leachate treatment process of a waste incineration plant.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention constructs a set of data association tags between the current process subsequence and the next process subsequence connected to the leachate transfer path of the current subsequence, and obtains multiple sets of historical leachate index data of the two process subsequences in the data association tags, obtains the correlation coefficient of the leachate monitoring data between the two process subsequences through data statistical analysis, and determines the correlation relationship between the operating conditions of the leachate treatment equipment of the waste incineration plant according to the correlation coefficient, effectively reflects the functional relationship between the leachate treatment equipment, provides a reliable basis for the operating conditions of the leachate treatment equipment of the waste incineration plant, and plays an important role in reducing the false alarm or missed alarm of the operating conditions of the leachate treatment equipment of the waste incineration plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram of a method for supervising the operating status of leachate treatment equipment in a waste incineration plant based on big data according to an embodiment of the present application.

[0036] Figure 2 This is a schematic diagram of a big data-based waste incineration plant leachate treatment equipment operation status monitoring system according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0038] like Figure 1 As shown, the first aspect of the present invention provides a method for supervising the operation status of leachate treatment equipment in a waste incineration plant based on big data, comprising the following steps:

[0039] Step S1: Obtain the process information of leachate treatment in the waste incineration plant, set leachate monitoring points according to the process, obtain key leachate monitoring data and equipment operating parameters of each point and mark the collection time, and set the monitoring cycle;

[0040] Step S2: construct a three-dimensional digital twin model of the leachate treatment process of the waste incineration plant, and perform correlation analysis on the key leachate monitoring data of each process subsequence based on the three-dimensional digital twin model to determine the correlation relationship between the leachate treatment equipment of each process subsequence;

[0041] Step S3: Obtain key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the association relationship between the leachate treatment equipment of each process subsequence, and visualize the key leachate monitoring data analysis results through the three-dimensional digital twin model of the leachate treatment process of the waste incineration plant.

[0042] It should be further explained that, in the specific implementation process, the process of obtaining the process information of leachate treatment in the waste incineration plant and setting the leachate monitoring points according to the process includes:

[0043] According to the process flow information of the leachate treatment of the waste incineration plant, all leachate data monitoring types in the leachate treatment process of the waste incineration plant are obtained, and the process flow characteristics are extracted according to the process flow information of the leachate treatment of the waste incineration plant, and the leachate treatment process of the waste incineration plant is split according to the process flow characteristics and divided into a number of process sub-sequences;

[0044] According to the process characteristics in the process subsequence, all leachate data monitoring types monitored in the process subsequence are divided into key leachate monitoring types and other leachate monitoring types, and importance levels are set for key leachate monitoring types and other leachate monitoring types. According to the leachate monitoring types in the process subsequence, corresponding types of leachate monitoring points are set, and the number of each leachate monitoring type point is determined according to the importance level of the leachate monitoring type.

[0045] It should be further explained that, in the specific implementation process, the process information includes the process, treatment technology, equipment operating parameters and process characteristics of the leachate treatment of the waste incineration plant;

[0046] The process includes pre-treatment, intermediate treatment, deep treatment and solid waste treatment;

[0047] The treatment technologies include leachate microbial treatment technology, flotation sludge treatment technology, membrane separation of four-season flowers, heavy metal chemical treatment technology, acid-base neutralization technology and activated carbon adsorption technology.

[0048] The equipment operating parameters include the amount of neutralizer used to adjust the pH value of the leachate, the mixing time and stirring speed, the amount of activated carbon added, the contact time, the stirring speed, the pore size of the filter medium used to remove solid particles, the pressure and the cleaning cycle;

[0049] The process characteristics refer to the types of leachate pollutants that need to be removed and the concentration of leachate pollutants that meet the requirements for entering the next link during the leachate treatment process and treatment technology used by the leachate from the waste incineration plant;

[0050] It should be further explained that, in the specific implementation process, the process of building a three-dimensional digital twin model of the leachate treatment process of a waste incineration plant includes:

[0051] Obtain the physical entity and location information of the leachate treatment equipment of the current waste incineration plant corresponding to several process subsequences in the leachate treatment process of the current waste incineration plant, obtain the multi-source heterogeneous data of the physical entity and key leachate indicator monitoring points of each process subsequence in the current waste incineration plant leachate treatment process, and perform data format preprocessing;

[0052] A virtual scene representing each process subsequence is created according to the position information of the physical entity of each process subsequence, a three-dimensional model of the physical entity corresponding to the virtual scene is drawn in the virtual scene, and the connection flow relationship between each process subsequence is obtained according to the current waste incineration plant leachate treatment process, and the transfer path of the leachate is obtained according to the connection flow relationship, and the transfer path of the leachate is animated 3D modeling is performed to obtain a framework model representing the transfer path of the leachate, and the framework model is superimposed and linked with the three-dimensional model;

[0053] Obtain the three-dimensional model corresponding to the multi-source heterogeneous data of each process sub-sequence after data format preprocessing in the physical entity, map the multi-source heterogeneous data to the three-dimensional model, and generate a three-dimensional digital twin model.

[0054] It should be further explained that, in the specific implementation process, the key leachate monitoring data of each process subsequence is correlated and analyzed according to the three-dimensional digital twin model, and the process of determining the correlation relationship between the leachate treatment equipment operations of each process subsequence includes:

[0055] According to the three-dimensional digital twin model, the leachate transfer path connecting each process subsequence is obtained, and a set of data association tags is constructed between the current process subsequence and the next process subsequence connected to the leachate transfer path of the current subsequence, and the current process subsequence is marked as the reference end, and the next process subsequence connected to the leachate transfer path of the current subsequence is marked as the controlled end;

[0056] Acquire multiple groups of historical leachate monitoring data of two process subsequences in a set of data association tags, and obtain the correlation coefficient of the leachate monitoring data between the two process subsequences through data statistical analysis of the multiple groups of historical leachate monitoring data parameters of the two process subsequences, and repeat the above correlation coefficient acquisition operation until the correlation coefficient of the filtrate monitoring data in all data association tags is obtained;

[0057] A correlation coefficient threshold value is set, and the correlation coefficient of the leachate monitoring data between the two process subsequences obtained is compared with the correlation coefficient threshold value, and the leachate monitoring data whose correlation coefficient is greater than or equal to the correlation coefficient threshold value is classified as common leachate monitoring data.

[0058] It should be further explained that, in the specific implementation process, when multiple groups of historical leachate index data parameters of two process subsequences are analyzed by data statistics to obtain the leachate monitoring data correlation coefficient between the two process subsequences, the data statistical analysis formula used is:

[0059]

[0060] Where T i represents the correlation coefficient of two process subsequences in the i-th group of data association labels, P a Represents the leachate monitoring data of the process subsequence a in the data association tag;

[0061] Represents the average value of leachate monitoring data of process subsequence a in the data association label;

[0062] P b Indicates the leachate monitoring data of the b process subsequence in the data association tag; represents the average value of the leachate monitoring data of the b process subsequence in the data association label; n represents the total number of data association labels; a represents the reference end in a set of data association labels; b represents the controlled end in a set of data association labels;

[0063] It should be further explained that, in the specific implementation process, the process of obtaining the key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the association relationship between the leachate treatment equipment of each process subsequence includes:

[0064] According to the process characteristics in each process subsequence, the key leachate monitoring data indicator threshold range and the common key leachate data indicator threshold range of each process subsequence are obtained, and the key leachate monitoring data of the process subsequence is compared with the corresponding key leachate monitoring data indicator threshold range. When the key leachate monitoring data of the process subsequence is not within the key leachate monitoring data indicator threshold range, the key leachate monitoring data of the current process subsequence is marked as data to be tested;

[0065] Acquire the common key leachate monitoring data of the process subsequence as the reference end with the same group of data association tags as the controlled end of the current process subsequence, compare the common key leachate monitoring data with the common key leachate data indicator threshold range, and when the common key leachate monitoring data is not within the common key leachate data indicator threshold range, acquire the common key leachate monitoring data correlation coefficient between the reference end and the controlled end, acquire compensation parameters of the data to be measured according to the common key leachate monitoring data of the process subsequence at the reference end and the common key leachate monitoring data correlation coefficient, perform compensation parameter processing on the data to be measured, and obtain adjusted data to be measured;

[0066] The adjusted data to be tested is compared with the threshold range of the key leachate monitoring data indicator. When the adjusted data to be tested is not within the threshold range of the key leachate monitoring data indicator, the equipment operation parameter detection is performed on the current process subsequence.

[0067] It should be further explained that, in the specific implementation process, the process of detecting the equipment operation parameters of the current process subsequence includes:

[0068] According to the process characteristics in each process subsequence, the preset equipment operating parameters of the current process subsequence are obtained, and the equipment operating parameters of the current process subsequence are compared with the preset equipment operating parameters for parameter consistency. If the equipment operating parameters of the current process subsequence are inconsistent with the preset equipment operating parameters, the equipment operating parameters of the current process subsequence are adjusted to be consistent with the preset equipment operating parameters;

[0069] And re-acquire the key leachate monitoring data after the equipment operating parameters of the current process sub-sequence are adjusted, and compare the key leachate monitoring data with the corresponding key leachate monitoring data indicator threshold range. When the key leachate monitoring data of the process sub-sequence is not within the key leachate monitoring data indicator threshold range, the leachate treatment equipment fault information of the corresponding process sub-sequence is generated.

[0070] It should be further explained that, in the specific implementation process, the process of visualizing the key leachate monitoring data analysis results through the three-dimensional digital twin model of the leachate treatment process of the waste incineration plant includes:

[0071] The display colors of the key leachate monitoring data mapped on the three-dimensional model of each process subsequence in the three-dimensional digital twin model and the transfer paths on the architecture model are set to obtain the key leachate monitoring data analysis results of each process subsequence. When the key leachate monitoring data analysis results generate the fault information of the leachate treatment equipment of the corresponding process subsequence, the three-dimensional model of the corresponding process subsequence and the transfer path connected to the process subsequence are displayed in red.

[0072] The second aspect of the present invention also provides a big data-based monitoring system for the operation status of leachate treatment equipment in a waste incineration plant, comprising a monitoring center, wherein the monitoring center is communicatively connected to a data acquisition module, a data processing module, a data analysis module and a data visualization module;

[0073] The data acquisition module is used to obtain the process information of leachate treatment in the waste incineration plant, set leachate monitoring points according to the process, obtain key leachate monitoring data and equipment operating parameters of each point, mark the collection time, and set the monitoring cycle;

[0074] The data processing module is used to construct a three-dimensional digital twin model of the leachate treatment process of a waste incineration plant;

[0075] The data analysis module is used to perform correlation analysis on the key leachate monitoring data of each process subsequence according to the three-dimensional digital twin model, determine the correlation relationship between the leachate treatment equipment of each process subsequence, and obtain the key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the correlation relationship between the leachate treatment equipment of each process subsequence;

[0076] The data visualization module is used to visualize the key leachate monitoring data analysis results through a three-dimensional digital twin model of the leachate treatment process of a waste incineration plant.

[0077] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for monitoring the operation status of leachate treatment equipment in a waste incineration plant based on big data, characterized in that: The following steps are involved: Step S1: Obtain the process information of leachate treatment in the waste incineration plant, set leachate monitoring points according to the process, obtain key leachate monitoring data and equipment operating parameters of each point and mark the collection time, and set the monitoring cycle; Step S2: construct a three-dimensional digital twin model of the leachate treatment process of the waste incineration plant, and perform correlation analysis on the key leachate monitoring data of each process subsequence based on the three-dimensional digital twin model to determine the correlation relationship between the leachate treatment equipment of each process subsequence; Step S3: Obtain key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the association relationship between the leachate treatment equipment of each process subsequence, and visualize the key leachate monitoring data analysis results through the three-dimensional digital twin model of the leachate treatment process of the waste incineration plant.

2. According to a method for monitoring the operation status of leachate treatment equipment in a waste incineration plant based on big data according to claim 1, it is characterized in that: The process of obtaining the process information of leachate treatment in the waste incineration plant and setting the leachate monitoring points according to the process includes: According to the process flow information of the leachate treatment of the waste incineration plant, all leachate data monitoring types in the leachate treatment process of the waste incineration plant are obtained, and the process flow characteristics are extracted according to the process flow information of the leachate treatment of the waste incineration plant, and the leachate treatment process of the waste incineration plant is split according to the process flow characteristics and divided into a number of process sub-sequences; According to the process characteristics in the process subsequence, all leachate data monitoring types monitored in the process subsequence are divided into key leachate monitoring types and other leachate monitoring types, and importance levels are set for key leachate monitoring types and other leachate monitoring types. According to the leachate monitoring types in the process subsequence, corresponding types of leachate monitoring points are set, and the number of each leachate monitoring type point is determined according to the importance level of the leachate monitoring type.

3. According to a method for monitoring the operation status of leachate treatment equipment in a waste incineration plant based on big data according to claim 2, it is characterized in that: The process of building a 3D digital twin model of the leachate treatment process at a waste incineration plant includes: Obtain the physical entity and location information of the leachate treatment equipment of the current waste incineration plant corresponding to several process subsequences in the leachate treatment process of the current waste incineration plant, obtain the multi-source heterogeneous data of the physical entity and key leachate indicator monitoring points of each process subsequence in the current waste incineration plant leachate treatment process, and perform data format preprocessing; A virtual scene representing each process subsequence is created according to the position information of the physical entity of each process subsequence, a three-dimensional model of the physical entity corresponding to the virtual scene is drawn in the virtual scene, and the connection flow relationship between each process subsequence is obtained according to the current waste incineration plant leachate treatment process, and the transfer path of the leachate is obtained according to the connection flow relationship, and the transfer path of the leachate is animated 3D modeling is performed to obtain a framework model representing the transfer path of the leachate, and the framework model is superimposed and linked with the three-dimensional model; Obtain the three-dimensional model corresponding to the multi-source heterogeneous data of each process sub-sequence after data format preprocessing in the physical entity, map the multi-source heterogeneous data to the three-dimensional model, and generate a three-dimensional digital twin model.

4. The method for monitoring the operation status of leachate treatment equipment in a waste incineration plant based on big data according to claim 3 is characterized in that: The process of performing correlation analysis on the key leachate monitoring data of each process subsequence based on the three-dimensional digital twin model and determining the correlation relationship between the leachate treatment equipment operations of each process subsequence includes: According to the three-dimensional digital twin model, the leachate transfer path connecting each process subsequence is obtained, and a set of data association tags is constructed between the current process subsequence and the next process subsequence connected to the leachate transfer path of the current subsequence, and the current process subsequence is marked as the reference end, and the next process subsequence connected to the leachate transfer path of the current subsequence is marked as the controlled end; Acquire multiple groups of historical leachate monitoring data of two process subsequences in a set of data association tags, and obtain the correlation coefficient of the leachate monitoring data between the two process subsequences through data statistical analysis of the multiple groups of historical leachate monitoring data parameters of the two process subsequences, and repeat the above correlation coefficient acquisition operation until the correlation coefficient of the filtrate monitoring data in all data association tags is obtained; A correlation coefficient threshold value is set, and the correlation coefficient of the leachate monitoring data between the two process subsequences obtained is compared with the correlation coefficient threshold value, and the leachate monitoring data whose correlation coefficient is greater than or equal to the correlation coefficient threshold value is classified as common leachate monitoring data.

5. The method for monitoring the operation status of leachate treatment equipment in a waste incineration plant based on big data according to claim 4 is characterized in that: The process of obtaining the key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the association relationship between the leachate treatment equipment of each process subsequence includes: According to the process characteristics in each process subsequence, the key leachate monitoring data indicator threshold range and the common key leachate data indicator threshold range of each process subsequence are obtained, and the key leachate monitoring data of the process subsequence is compared with the corresponding key leachate monitoring data indicator threshold range. When the key leachate monitoring data of the process subsequence is not within the key leachate monitoring data indicator threshold range, the key leachate monitoring data of the current process subsequence is marked as data to be tested; Acquire the common key leachate monitoring data of the process subsequence as the reference end with the same group of data association tags as the controlled end of the current process subsequence, compare the common key leachate monitoring data with the common key leachate data indicator threshold range, and when the common key leachate monitoring data is not within the common key leachate data indicator threshold range, acquire the common key leachate monitoring data correlation coefficient between the reference end and the controlled end, acquire compensation parameters of the data to be measured according to the common key leachate monitoring data of the process subsequence at the reference end and the common key leachate monitoring data correlation coefficient, perform compensation parameter processing on the data to be measured, and obtain adjusted data to be measured; The adjusted data to be tested is compared with the threshold range of the key leachate monitoring data indicator. When the adjusted data to be tested is not within the threshold range of the key leachate monitoring data indicator, the equipment operation parameter detection is performed on the current process subsequence.

6. A method for monitoring the operation status of leachate treatment equipment in a waste incineration plant based on big data according to claim 5, characterized in that: The process of detecting the equipment operating parameters of the current process subsequence includes: According to the process characteristics in each process subsequence, the preset equipment operating parameters of the current process subsequence are obtained, and the equipment operating parameters of the current process subsequence are compared with the preset equipment operating parameters for parameter consistency. If the equipment operating parameters of the current process subsequence are inconsistent with the preset equipment operating parameters, the equipment operating parameters of the current process subsequence are adjusted to be consistent with the preset equipment operating parameters; And re-acquire the key leachate monitoring data after the equipment operating parameters of the current process sub-sequence are adjusted, and compare the key leachate monitoring data with the corresponding key leachate monitoring data indicator threshold range. When the key leachate monitoring data of the process sub-sequence is not within the key leachate monitoring data indicator threshold range, the leachate treatment equipment fault information of the corresponding process sub-sequence is generated.

7. A method for monitoring the operation status of leachate treatment equipment in a waste incineration plant based on big data according to claim 6, characterized in that: The process of visualizing the key leachate monitoring data analysis results through the 3D digital twin model of the waste incineration plant leachate treatment process includes: The display colors of the key leachate monitoring data mapped on the three-dimensional model of each process subsequence in the three-dimensional digital twin model and the transfer paths on the architecture model are set to obtain the key leachate monitoring data analysis results of each process subsequence. When the key leachate monitoring data analysis results generate the fault information of the leachate treatment equipment of the corresponding process subsequence, the three-dimensional model of the corresponding process subsequence and the transfer path connected to the process subsequence are displayed in red.

8. A system for monitoring the operation status of leachate treatment equipment in a waste incineration plant according to a method for monitoring the operation status of leachate treatment equipment in a waste incineration plant based on big data according to any one of claims 1 to 7, characterized in that: It includes a supervision center, which is communicatively connected to a data acquisition module, a data processing module, a data analysis module and a data visualization module; The data acquisition module is used to obtain the process information of leachate treatment in the waste incineration plant, set leachate monitoring points according to the process, obtain key leachate monitoring data and equipment operating parameters of each point, mark the collection time, and set the monitoring cycle; The data processing module is used to construct a three-dimensional digital twin model of the leachate treatment process of a waste incineration plant; The data analysis module is used to perform correlation analysis on the key leachate monitoring data of each process subsequence according to the three-dimensional digital twin model, determine the correlation relationship between the leachate treatment equipment of each process subsequence, and obtain the key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the correlation relationship between the leachate treatment equipment of each process subsequence; The data visualization module is used to visualize the key leachate monitoring data analysis results through a three-dimensional digital twin model of the leachate treatment process of a waste incineration plant.

Images