A big data-based waste incineration plant leachate treatment equipment operation condition supervision method and system

Abstract

A method and system for monitoring the operational status of leachate treatment equipment in waste incineration plants based on big data, relating to the field of data monitoring technology, involves acquiring information on the leachate treatment process flow of waste incineration plants, setting leachate monitoring points according to the process flow, acquiring key leachate monitoring data and equipment operating parameters at each point, constructing a three-dimensional digital twin model of the leachate treatment process, and performing correlation analysis on the key leachate monitoring data of each process subsequence based on the three-dimensional digital twin model, obtaining the key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the correlation relationship between leachate treatment equipment in each process subsequence, and visualizing the key leachate monitoring data analysis results, providing a reliable basis for analyzing the operational status of leachate treatment equipment and improving the accuracy of the judgment results of the leachate treatment equipment's operational status.

Description

Technical Field

[0001] This invention relates to the field of data monitoring technology, specifically a method and system for monitoring the operational status of leachate treatment equipment in waste incineration plants based on big data. Background Technology

[0002] Leachate treatment equipment for waste incineration plants is used to treat leachate generated during the waste incineration process. It is mainly responsible for removing harmful substances from the leachate to meet environmental standards and ensure safe discharge.

[0003] Current technologies for monitoring the operational status of leachate treatment equipment in waste incineration plants primarily rely on analysis of the equipment's own attributes and monitoring data. However, these technologies fail to eliminate misjudgments caused by factors beyond the equipment's control, resulting in numerous false alarms and missed alarms in the equipment's alarm messages. Therefore, determining the correlation between the operational status of leachate treatment equipment and visualizing its operational status to improve accuracy is a pressing issue. This paper proposes a big data-based method and system for monitoring the operational status of leachate treatment equipment in waste incineration plants. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a method and system for monitoring the operational status of leachate treatment equipment in waste incineration plants based on big data.

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

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

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

[0009] Furthermore, the process of obtaining leachate treatment process information from waste incineration plants and setting up leachate monitoring points based on the process includes:

[0010] Based on the leachate treatment process information of the waste incineration plant, obtain all leachate data monitoring types in the leachate treatment process of the waste incineration plant, extract process characteristics based on the leachate treatment process information of the waste incineration plant, and divide the leachate treatment process of the waste incineration plant into several process sub-sequences according to the process characteristics.

[0011] Based on the process characteristics in the process subsequence, all leachate data monitoring types in the process subsequence are divided into critical leachate monitoring types and other leachate monitoring types. Importance levels are set for critical leachate monitoring types and other leachate monitoring types. Corresponding leachate monitoring points are set according to the leachate monitoring types in the process subsequence, and the number of points for each leachate monitoring type is determined according to the importance level of the leachate monitoring type.

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

[0013] The system acquires the physical entities and location information of leachate treatment equipment in several process sub-sequences of the current waste incineration plant leachate treatment process, acquires multi-source heterogeneous data of physical entities and key leachate index monitoring points of each process sub-sequence in the current waste incineration plant leachate treatment process, and performs data format preprocessing.

[0014] A virtual scene representing each process subsequence is created based on the location information of the physical entities of each process subsequence. A 3D model of the physical entity corresponding to the virtual scene is drawn in the virtual scene. The connection flow relationship between each process subsequence is obtained according to the current leachate treatment process of the waste incineration plant. The transfer path of the leachate is obtained according to the connection flow relationship. The transfer path of the leachate is animated and 3D modeled to obtain an architecture model representing the transfer path of the leachate. The architecture model is superimposed and linked with the 3D model.

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

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

[0017] The leachate transfer path connecting each process subsequence is obtained based on the three-dimensional digital twin model. A set of data association labels is constructed between the current process subsequence and the next process subsequence connected by the leachate transfer path of the current subsequence. The current process subsequence is marked as the baseline end, and the next process subsequence connected by the leachate transfer path of the current subsequence is marked as the controlled end.

[0018] Obtain multiple sets of historical leachate monitoring data for two process subsequences in a set of data association tags, and obtain the correlation coefficient between the leachate monitoring data of the two process subsequences through data statistical analysis. Repeat the above correlation coefficient acquisition operation until the correlation coefficient of leachate monitoring data in all data association tags is obtained.

[0019] A correlation coefficient threshold is set, and the correlation coefficient of leachate monitoring data between two obtained process subsequences is compared with the correlation coefficient threshold. Leachate monitoring data with a correlation coefficient greater than or equal to the correlation coefficient threshold are classified as shared 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 correlation between leachate treatment equipment in each process subsequence includes:

[0021] Based on the process characteristics of each process subsequence, the threshold range of key leachate monitoring data indicators and the threshold range of common key leachate monitoring data indicators for each process subsequence are obtained. The key leachate monitoring data of the process subsequence is compared with the corresponding threshold range of key leachate monitoring data indicators. When the key leachate monitoring data of the process subsequence is not within the threshold range of key leachate monitoring data indicators, the key leachate monitoring data of the current process subsequence is marked as data to be tested.

[0022] Obtain the common critical leachate monitoring data of the process subsequence as the baseline end, which is the same set of data associated tags of the current process subsequence as the controlled end. Compare the common critical leachate monitoring data with the threshold range of the common critical leachate data index. When the common critical leachate monitoring data is not within the threshold range of the common critical leachate data index, obtain the correlation coefficient of the common critical leachate monitoring data between the baseline end and the controlled end. Obtain the compensation parameters of the test data based on the common critical leachate monitoring data of the process subsequence of the baseline end and the correlation coefficient of the common critical leachate monitoring data. Perform compensation parameter processing on the test data to obtain the adjusted test data.

[0023] The adjusted test data is compared with the threshold range of key leachate monitoring data indicators. If the adjusted test data is not within the threshold range of key leachate monitoring data indicators, the equipment operation parameters of the current process subsequence are tested.

[0024] Furthermore, the process of detecting equipment operating parameters for the current process sub-sequence includes:

[0025] Based on the process characteristics of each process subsequence, the preset equipment operation parameters of the current process subsequence are obtained. The equipment operation parameters of the current process subsequence are compared with the preset equipment operation parameters. If the equipment operation parameters of the current process subsequence are inconsistent with the preset equipment operation parameters, the equipment operation parameters of the current process subsequence are adjusted to be consistent with the preset equipment operation parameters.

[0026] The system then reacquires the key leachate monitoring data after adjusting the equipment operation parameters for the current process subsequence. It compares the key leachate monitoring data with the corresponding key leachate monitoring data indicator threshold range. If the key leachate monitoring data for the process subsequence is not within the key leachate monitoring data indicator threshold range, it generates leachate treatment equipment fault information for the corresponding process subsequence.

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

[0028] The key leachate monitoring data mapped on the 3D model of each process subsequence in the 3D digital twin model and the transfer path on the architecture model are set with display colors. The analysis results of the key leachate monitoring data of each process subsequence are obtained. When the analysis result of the key leachate monitoring data is to generate the fault information of the leachate treatment equipment of the corresponding process subsequence, the 3D model of the corresponding process subsequence and the transfer path connected to the process subsequence are displayed in red.

[0029] A second aspect of the present invention also provides a big data-based monitoring system for the operation of leachate treatment equipment in waste incineration plants, including 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 information on the leachate treatment process of the waste incineration plant, set leachate monitoring points according to the process, acquire key leachate monitoring data and equipment operating parameters at 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 based on the three-dimensional digital twin model, determine the correlation relationship between leachate treatment equipment in 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 leachate treatment equipment in each process subsequence.

[0033] The data visualization module is used to visualize the analysis results of key leachate monitoring data 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 by 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. Through data statistical analysis, the correlation coefficient of leachate monitoring data between the two process subsequences is obtained, and the correlation relationship between the operating status of leachate treatment equipment in waste incineration plants is determined according to the correlation coefficient. This effectively reflects the interaction relationship between leachate treatment equipment, provides a reliable basis for the operating status of leachate treatment equipment in waste incineration plants, and plays an important role in reducing false alarms or missed alarms of the operating status of leachate treatment equipment in waste incineration plants. Attached Figure Description

[0035] Figure 1 This is a schematic diagram illustrating a method for monitoring the operational status of leachate treatment equipment in a waste incineration plant based on big data, as described in an embodiment of this application.

[0036] Figure 2 This is a schematic diagram of a big data-based monitoring system for the operation of leachate treatment equipment in a waste incineration plant, according to an embodiment of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

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

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

[0041] Step S3: Obtain the key leachate monitoring data analysis results by using the key leachate monitoring data of each process subsequence and the correlation between leachate treatment equipment in each process subsequence, and visualize the key leachate monitoring data analysis results by using a 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 leachate treatment process information from waste incineration plants and setting up leachate monitoring points according to the process includes:

[0043] Based on the leachate treatment process information of the waste incineration plant, obtain all leachate data monitoring types in the leachate treatment process of the waste incineration plant, extract process characteristics based on the leachate treatment process information of the waste incineration plant, and divide the leachate treatment process of the waste incineration plant into several process sub-sequences according to the process characteristics.

[0044] Based on the process characteristics in the process subsequence, all leachate data monitoring types in the process subsequence are divided into critical leachate monitoring types and other leachate monitoring types. Importance levels are set for critical leachate monitoring types and other leachate monitoring types. Corresponding leachate monitoring points are set according to the leachate monitoring types in the process subsequence, and the number of points for each leachate monitoring type 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 flow information includes the process flow, treatment technology, equipment operating parameters and process flow characteristics of leachate treatment in waste incineration plants;

[0046] The process includes pretreatment, intermediate treatment, advanced treatment, and solid waste treatment stages;

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

[0048] The equipment operating parameters include the amount of neutralizing agent used to adjust the pH value of the leachate, mixing time and stirring speed, the amount of activated carbon added, contact time, stirring speed, pore size of the filter medium used to remove solid particles, pressure, and 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 stage in the leachate treatment process and the treatment technology used in different waste incineration plants.

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

[0051] The system acquires the physical entities and location information of leachate treatment equipment in several process sub-sequences of the current waste incineration plant leachate treatment process, acquires multi-source heterogeneous data of physical entities and key leachate index monitoring points of each process sub-sequence in the current waste incineration plant leachate treatment process, and performs data format preprocessing.

[0052] A virtual scene representing each process subsequence is created based on the location information of the physical entities of each process subsequence. A 3D model of the physical entity corresponding to the virtual scene is drawn in the virtual scene. The connection flow relationship between each process subsequence is obtained according to the current leachate treatment process of the waste incineration plant. The transfer path of the leachate is obtained according to the connection flow relationship. The transfer path of the leachate is animated and 3D modeled to obtain an architecture model representing the transfer path of the leachate. The architecture model is superimposed and linked with the 3D model.

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

[0054] It should be further explained that, in the specific implementation process, the process of performing correlation analysis on the key leachate monitoring data of each process subsequence based on the three-dimensional digital twin model to determine the operational correlation of leachate treatment equipment among each process subsequence includes:

[0055] The leachate transfer path connecting each process subsequence is obtained based on the three-dimensional digital twin model. A set of data association labels is constructed between the current process subsequence and the next process subsequence connected by the leachate transfer path of the current subsequence. The current process subsequence is marked as the baseline end, and the next process subsequence connected by the leachate transfer path of the current subsequence is marked as the controlled end.

[0056] Obtain multiple sets of historical leachate monitoring data for two process subsequences in a set of data association tags, and obtain the correlation coefficient between the leachate monitoring data of the two process subsequences through data statistical analysis. Repeat the above correlation coefficient acquisition operation until the correlation coefficient of leachate monitoring data in all data association tags is obtained.

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

[0058] It should be further explained that, in the specific implementation process, when obtaining the correlation coefficient of leachate monitoring data between the two process subsequences through statistical analysis of multiple sets of historical leachate index data parameters of the two process subsequences, the statistical analysis formula used is as follows:

[0059]

[0060] Where T i P represents the correlation coefficient between two process subsequences in the i-th data group's associated labels. a This represents the leachate monitoring data for process subsequence 'a' in the data association label;

[0061] This represents the average value of leachate monitoring data for process subsequence 'a' in the data association label;

[0062] P b This represents the leachate monitoring data for process subsequence b in the data association label; This represents the average value of leachate monitoring data for process subsequence b in the data association tag; n represents the total number of data association tags; a represents the baseline end in a set of data association tags; b represents the controlled end in a set of data association tags;

[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 correlation between leachate treatment equipment of each process subsequence includes:

[0064] Based on the process characteristics of each process subsequence, the threshold range of key leachate monitoring data indicators and the threshold range of common key leachate monitoring data indicators for each process subsequence are obtained. The key leachate monitoring data of the process subsequence is compared with the corresponding threshold range of key leachate monitoring data indicators. When the key leachate monitoring data of the process subsequence is not within the threshold range of key leachate monitoring data indicators, the key leachate monitoring data of the current process subsequence is marked as data to be tested.

[0065] Obtain the common critical leachate monitoring data of the process subsequence as the baseline end, which is the same set of data associated tags of the current process subsequence as the controlled end. Compare the common critical leachate monitoring data with the threshold range of the common critical leachate data index. When the common critical leachate monitoring data is not within the threshold range of the common critical leachate data index, obtain the correlation coefficient of the common critical leachate monitoring data between the baseline end and the controlled end. Obtain the compensation parameters of the test data based on the common critical leachate monitoring data of the process subsequence of the baseline end and the correlation coefficient of the common critical leachate monitoring data. Perform compensation parameter processing on the test data to obtain the adjusted test data.

[0066] The adjusted test data is compared with the threshold range of key leachate monitoring data indicators. If the adjusted test data is not within the threshold range of key leachate monitoring data indicators, the equipment operation parameters of the current process subsequence are tested.

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

[0068] Based on the process characteristics of each process subsequence, the preset equipment operation parameters of the current process subsequence are obtained. The equipment operation parameters of the current process subsequence are compared with the preset equipment operation parameters. If the equipment operation parameters of the current process subsequence are inconsistent with the preset equipment operation parameters, the equipment operation parameters of the current process subsequence are adjusted to be consistent with the preset equipment operation parameters.

[0069] The system then reacquires the key leachate monitoring data after adjusting the equipment operation parameters for the current process subsequence. It compares the key leachate monitoring data with the corresponding key leachate monitoring data indicator threshold range. If the key leachate monitoring data for the process subsequence is not within the key leachate monitoring data indicator threshold range, it generates leachate treatment equipment fault information for the corresponding process subsequence.

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

[0071] The key leachate monitoring data mapped on the 3D model of each process subsequence in the 3D digital twin model and the transfer path on the architecture model are set with display colors. The analysis results of the key leachate monitoring data of each process subsequence are obtained. When the analysis result of the key leachate monitoring data is to generate the fault information of the leachate treatment equipment of the corresponding process subsequence, the 3D model of the corresponding process subsequence and the transfer path connected to the process subsequence are displayed in red.

[0072] A second aspect of the present invention also provides a big data-based monitoring system for the operation of leachate treatment equipment in waste incineration plants, including 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 information on the leachate treatment process of the waste incineration plant, set leachate monitoring points according to the process, acquire key leachate monitoring data and equipment operating parameters at 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 based on the three-dimensional digital twin model, determine the correlation relationship between leachate treatment equipment in 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 leachate treatment equipment in each process subsequence.

[0076] The data visualization module is used to visualize the analysis results of key leachate monitoring data 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 methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for monitoring the operational status of leachate treatment equipment in waste incineration plants based on big data, characterized in that, Includes the following steps: Step S1: Obtain the leachate treatment process information of the waste incineration plant, set leachate monitoring points according to the process, obtain key leachate monitoring data and equipment operating parameters at 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 leachate treatment equipment in each process subsequence; Step S3: Obtain the key leachate monitoring data analysis results through the key leachate monitoring data of each process subsequence and the correlation between leachate treatment equipment in each process subsequence, and visualize the key leachate monitoring data analysis results through a three-dimensional digital twin model of the leachate treatment process of the waste incineration plant. The process of obtaining key leachate monitoring data analysis results by using key leachate monitoring data from each process subsequence and the correlation between leachate treatment equipment in each process subsequence includes: Based on the process characteristics of each process subsequence, the threshold range of key leachate monitoring data indicators and the threshold range of common key leachate monitoring data indicators for each process subsequence are obtained. The key leachate monitoring data of the process subsequence is compared with the corresponding threshold range of key leachate monitoring data indicators. When the key leachate monitoring data of the process subsequence is not within the threshold range of key leachate monitoring data indicators, the key leachate monitoring data of the current process subsequence is marked as data to be tested. Obtain the common critical leachate monitoring data of the process subsequence as the baseline end, which is the same set of data associated tags of the current process subsequence as the controlled end. Compare the common critical leachate monitoring data with the threshold range of the common critical leachate data index. When the common critical leachate monitoring data is not within the threshold range of the common critical leachate data index, obtain the correlation coefficient of the common critical leachate monitoring data between the baseline end and the controlled end. Obtain the compensation parameters of the test data based on the common critical leachate monitoring data of the process subsequence of the baseline end and the correlation coefficient of the common critical leachate monitoring data. Perform compensation parameter processing on the test data to obtain the adjusted test data. The adjusted test data is compared with the threshold range of key leachate monitoring data indicators. If the adjusted test data is not within the threshold range of key leachate monitoring data indicators, the equipment operation parameters of the current process subsequence are tested.

2. The method for monitoring the operational status of leachate treatment equipment in a waste incineration plant based on big data, as described in claim 1, is characterized in that... The process of obtaining leachate treatment process information from a waste incineration plant and setting up leachate monitoring points based on the process includes: Based on the leachate treatment process information of the waste incineration plant, obtain all leachate data monitoring types in the leachate treatment process of the waste incineration plant, extract process characteristics based on the leachate treatment process information of the waste incineration plant, and divide the leachate treatment process of the waste incineration plant into several process sub-sequences according to the process characteristics. Based on the process characteristics in the process subsequence, all leachate data monitoring types in the process subsequence are divided into critical leachate monitoring types and other leachate monitoring types. Importance levels are set for critical leachate monitoring types and other leachate monitoring types. Corresponding leachate monitoring points are set according to the leachate monitoring types in the process subsequence, and the number of points for each leachate monitoring type is determined according to the importance level of the leachate monitoring type.

3. The method for monitoring the operational status of leachate treatment equipment in a waste incineration plant based on big data, as described in claim 2, is characterized in that... The process of constructing a three-dimensional digital twin model of the leachate treatment process in a waste incineration plant includes: The system acquires the physical entities and location information of leachate treatment equipment in several process sub-sequences of the current waste incineration plant leachate treatment process, acquires multi-source heterogeneous data of physical entities and key leachate index monitoring points of each process sub-sequence in the current waste incineration plant leachate treatment process, and performs data format preprocessing. A virtual scene representing each process subsequence is created based on the location information of the physical entities of each process subsequence. A 3D model of the physical entity corresponding to the virtual scene is drawn in the virtual scene. The connection flow relationship between each process subsequence is obtained according to the current leachate treatment process of the waste incineration plant. The transfer path of the leachate is obtained according to the connection flow relationship. The transfer path of the leachate is animated and 3D modeled to obtain an architecture model representing the transfer path of the leachate. The architecture model is superimposed and linked with the 3D model. Obtain the three-dimensional models corresponding to the multi-source heterogeneous data of each process sub-sequence after data format preprocessing in the physical entity, and map the multi-source heterogeneous data onto the three-dimensional models to generate a three-dimensional digital twin model.

4. The method for monitoring the operational status of leachate treatment equipment in a waste incineration plant based on big data, as described in claim 3, is characterized in that... The process of determining the operational correlation of leachate treatment equipment among different process subsequences by performing correlation analysis on key leachate monitoring data of each process subsequence based on a three-dimensional digital twin model includes: The leachate transfer path connecting each process subsequence is obtained based on the three-dimensional digital twin model. A set of data association labels is constructed between the current process subsequence and the next process subsequence connected by the leachate transfer path of the current subsequence. The current process subsequence is marked as the baseline end, and the next process subsequence connected by the leachate transfer path of the current subsequence is marked as the controlled end. Obtain multiple sets of historical leachate monitoring data for two process subsequences in a set of data association tags, and obtain the correlation coefficient between the leachate monitoring data of the two process subsequences through data statistical analysis. Repeat the above correlation coefficient acquisition operation until the correlation coefficient of leachate monitoring data in all data association tags is obtained. A correlation coefficient threshold is set, and the correlation coefficient of leachate monitoring data between two obtained process subsequences is compared with the correlation coefficient threshold. Leachate monitoring data with a correlation coefficient greater than or equal to the correlation coefficient threshold are classified as shared leachate monitoring data.

5. The method for monitoring the operational status of leachate treatment equipment in a waste incineration plant based on big data, as described in claim 4, is characterized in that... The process of detecting equipment operation parameters for the current process subsequence includes: Based on the process characteristics of each process subsequence, the preset equipment operation parameters of the current process subsequence are obtained. The equipment operation parameters of the current process subsequence are compared with the preset equipment operation parameters. If the equipment operation parameters of the current process subsequence are inconsistent with the preset equipment operation parameters, the equipment operation parameters of the current process subsequence are adjusted to be consistent with the preset equipment operation parameters. The system then reacquires the key leachate monitoring data after adjusting the equipment operation parameters for the current process subsequence. It compares the key leachate monitoring data with the corresponding key leachate monitoring data indicator threshold range. If the key leachate monitoring data for the process subsequence is not within the key leachate monitoring data indicator threshold range, it generates leachate treatment equipment fault information for the corresponding process subsequence.

6. The method for monitoring the operational status of leachate treatment equipment in a waste incineration plant based on big data, as described in claim 5, is characterized in that... The process of visualizing the analysis results of key leachate monitoring data using a three-dimensional digital twin model of the leachate treatment process at a waste incineration plant includes: The key leachate monitoring data mapped on the 3D model of each process subsequence in the 3D digital twin model and the transfer path on the architecture model are set with display colors. The analysis results of the key leachate monitoring data of each process subsequence are obtained. When the analysis result of the key leachate monitoring data is to generate the fault information of the leachate treatment equipment of the corresponding process subsequence, the 3D model of the corresponding process subsequence and the transfer path connected to the process subsequence are displayed in red.

7. A system for monitoring the operational status of leachate treatment equipment in a waste incineration plant, based on big data analysis as described in any one of claims 1 to 6, characterized in that... This includes a monitoring 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 information on the leachate treatment process of the waste incineration plant, set leachate monitoring points according to the process, acquire key leachate monitoring data and equipment operating parameters at 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 based on the three-dimensional digital twin model, determine the correlation relationship between leachate treatment equipment in 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 leachate treatment equipment in each process subsequence. The data visualization module is used to visualize the analysis results of key leachate monitoring data through a three-dimensional digital twin model of the leachate treatment process of a waste incineration plant.

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