Intelligent ammonia spraying regulation and control device of SCR (Selective Catalytic Reduction) denitration system and optimal control method thereof

Abstract

The invention discloses an SCR denitration system intelligent ammonia spraying regulation and control device and an optimization control method thereof, and the method comprises the steps: dynamically adjusting parameters according to a detailed state report of a fault module to optimize resource distribution, redistributing task loads for unaffected modules, and determining a temporary scheme for continuous operation of the system; obtaining availability data of the standby module through a fault recovery mechanism, and carrying out rapid replacement or isolation processing on the fault module to obtain a recovered system operation state; according to the recovered system operation state, the working condition adaptation model is updated in real time, latest operation stability data are extracted from the working condition adaptation model, and whether a preset stability standard is met or not is judged; and dynamically adjusting the monitoring frequency and the resource allocation strategy according to the adjustment direction of further optimization in combination with the requirement of abnormal response time to obtain a final system operation optimization result.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an intelligent ammonia injection control device for an SCR denitration system and an optimization control method thereof. Background Art

[0002] In the field of environmental protection, flue gas denitrification technology, as a core means of controlling nitrogen oxide emissions, is of vital strategic significance. In particular, in the process of industrial production and energy consumption, reducing pollutant emissions plays an irreplaceable role in improving air quality and protecting the ecological environment. Selective catalytic reduction technology, as the mainstream denitrification method, is widely used in various industrial scenarios. Its operating efficiency and stability are directly related to the realization of environmental protection goals. However, the current relevant control systems still have significant shortcomings in practical applications, especially in terms of equipment integration and operational adaptability. They generally face problems such as high system complexity, difficult maintenance, and insufficient ability to cope with sudden failures. These limitations have led to a decline in the overall efficiency of the system and an increase in operating costs.

[0003] Focusing on specific challenges, the lack of flexibility in data processing and hardware configuration of the control device has become the primary problem. Due to the large differences in denitrification requirements in different scenarios, the existing system is often difficult to adjust quickly according to actual working conditions. The modularity of the equipment is low, making it difficult to achieve precise matching and efficient operation. This lack of flexibility further exacerbates another key problem, namely the system's weak ability to continue operating in the face of local failures. Once a component malfunctions, the entire system may need to be shut down for maintenance, seriously affecting the continuity and stability of the denitrification process. These two problems are interrelated. The lack of flexibility limits the system's adaptability, and the lack of adaptability directly weakens the system's resilience to failures.

[0004] Therefore, how to design a control device that can flexibly configure hardware resources under different working conditions and ensure continuous operation of the system when a fault occurs has become a key issue in improving the overall efficiency of denitrification technology. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an intelligent ammonia injection control device for an SCR denitration system and an optimization control method thereof in response to the deficiencies in the background technology.

[0006] The present invention adopts the following technical solutions to solve the above technical problems: The intelligent ammonia injection control device of the SCR denitrification system and its optimization control method specifically include the following steps: Through the pre-established operating condition database, the operating parameters and environmental data of the current flue gas denitrification scenario are obtained, and classified and stored according to different operating condition characteristics to obtain a preliminary operating condition adaptation model for subsequent dynamic matching of hardware resources; Based on the preliminary working condition adaptation model, a modular design framework is used to manage hardware resources in layers, extracting the hardware configuration solution that best matches the current working condition and determining a flexible hardware configuration allocation strategy. Based on the flexible allocation strategy of hardware configuration, the operating status of each module is monitored in real time, data fluctuation information during operation is obtained, and whether there are abnormal signals that deviate from the preset threshold; If an abnormal signal is detected, the abnormal module is located through the fault detection mechanism, and the specific fault point information is extracted from the real-time monitoring capability to obtain a detailed status report of the faulty module; Based on the detailed status report of the faulty module, dynamically adjust parameters to optimize resource allocation, redistribute task load to unaffected modules, and determine a temporary solution to ensure continuous system operation; Through the fault recovery mechanism, the availability data of the backup module is obtained, and the faulty module is quickly replaced or isolated to obtain the system operation status after recovery; Based on the restored system operating status, the operating condition adaptation model is updated in real time, from which the latest operating stability data is extracted to determine whether the preset stability standards are met; If the operational stability data does not meet the standards, we will conduct in-depth analysis of system logs through data processing efficiency improvement measures to obtain information on potential risk points and determine the direction of further optimization and adjustment; According to the adjustment direction of further optimization and combined with the requirements of abnormal response time, the monitoring frequency and resource allocation strategy are dynamically adjusted to obtain the final system operation optimization result.

[0007] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses an intelligent hardware resource management method in a flue gas denitrification scenario. By pre-establishing a working condition database and performing classified storage, a preliminary working condition adaptation model is obtained to achieve dynamic matching of hardware resources. A modular design framework is used to manage hardware resources in a hierarchical manner and extract the most matching hardware configuration scheme. The present invention monitors the operating status of each module in real time, detects abnormal signals and locates faults, and generates a detailed status report. According to the report, parameters are dynamically adjusted to optimize resource allocation, and the faulty module is quickly replaced or isolated through a fault recovery mechanism. The present invention continuously updates the working condition adaptation model, analyzes system logs to obtain information on potential risk points, dynamically adjusts the monitoring frequency and resource allocation strategy, and ultimately achieves continuous optimization of system operation. This method can significantly improve the utilization efficiency of hardware resources and system stability in the flue gas denitrification process, and has important practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a flow chart of an intelligent ammonia injection control device for an SCR denitration system and its optimization control method according to the present invention.

[0009] Figure 2 This is a schematic diagram of an intelligent ammonia injection control device for an SCR denitrification system and its optimized control method according to the present invention.

[0010] Figure 3 Schematic diagram of an intelligent ammonia injection control device for an SCR denitrification system and its optimized control method according to the present invention Figure 2 . DETAILED DESCRIPTION

[0011] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings: The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The present invention is described in detail below based on the drawings and preferred embodiments. The purpose and effect of the present invention will become more clear. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0012] like Figure 1-3 As shown, the intelligent ammonia injection control device and optimization control method of the SCR denitration system in this embodiment may specifically include: In step S101, the operating parameters and environmental data of the current flue gas denitrification scenario are obtained through a pre-established operating condition database, and classified and stored according to different operating condition characteristics to obtain a preliminary operating condition adaptation model for subsequent dynamic matching of hardware resources.

[0013] Using a pre-built operating condition database, the operating parameters and environmental data for the current flue gas denitrification scenario are obtained. Data from various sources are initially cleaned and formatted to generate a structured basic dataset. Based on this structured basic dataset, a classified storage mechanism is employed to group the operating parameters and environmental data according to operating condition characteristics, determining the data distribution patterns under different operating condition characteristics. Based on this data distribution pattern, a preliminary operating condition adaptation model is constructed. A support vector machine algorithm is used to train the mapping relationship between operating condition characteristics and operating parameters, generating the initial parameter configuration for the adaptation model. If the matching accuracy of the initial parameter configuration on the validation set falls below a preset threshold, the adaptation model is iteratively optimized by adjusting the feature weights of the training data to obtain optimized model parameters. Based on these optimized model parameters and the current scenario analysis results, the dynamic allocation requirements between operating condition characteristics and hardware resources are determined, resulting in a preliminary resource allocation plan. This preliminary resource allocation plan is then combined with real-time operating parameter changes using a rule-matching method. If the operating parameter changes exceed the preset range, the allocation plan is fine-tuned to determine the final hardware resource allocation strategy. After obtaining the final hardware resource allocation strategy, the parameter matching results in the allocation strategy are recorded, and its application effect under different working conditions is recorded. The historical data in the working condition database is updated to form a closed-loop data feedback mechanism.

[0014] In step S102 , based on the preliminary working condition adaptation model, a modular design framework is used to perform hierarchical management of hardware resources, extract the hardware configuration solution that best matches the current working condition, and determine a flexible allocation strategy for the hardware configuration.

[0015] Using a pre-built operating condition database, the system obtains operating parameters and environmental data for the current flue gas denitrification scenario. Data standardization methods are used to process these parameters, resulting in a unified operating condition dataset. Based on this unified operating condition dataset, a clustering algorithm is used to group operating condition characteristics and determine the characteristic distribution patterns for each group. Based on these characteristic distribution patterns, a hierarchical management mechanism is implemented to divide hardware resources into computing units and storage units based on functional modules, creating a modular resource pool. If the matching degree between a computing unit in the modular resource pool and the current operating condition characteristics falls below a preset threshold, a linear regression algorithm is used to train the mapping relationship between the characteristics and computing units to obtain optimized matching parameters. Based on the optimized matching parameters, the computing unit and storage unit that best match the current operating condition characteristics are extracted from the modular resource pool to generate a preliminary hardware configuration plan. By monitoring operating parameter changes in real time, if the parameter changes exceed a preset range, a rule-based matching method is used to dynamically adjust the hardware configuration plan and determine the final allocation strategy. Based on the final allocation strategy, the correspondence between the allocation results and the operating condition characteristics is recorded, and the operating condition database is updated, resulting in an updated data feedback mechanism.

[0016] Step S103 , based on the flexible allocation strategy for hardware configuration, monitor the operating status of each module in real time, obtain data fluctuation information during operation, and determine whether there is an abnormal signal that deviates from a preset threshold.

[0017] By monitoring the operating status of each module in real time, data fluctuation information is obtained to determine whether there are abnormal signals exceeding the preset threshold. If the data fluctuation information obtained shows that there are abnormal signals exceeding the preset threshold, the abnormal signal is classified and processed to determine the source module of the abnormal signal. According to the source module of the classified abnormal signal, the historical operating status record of the module is obtained to determine whether there is a persistent deviation trend. If it is determined that a persistent deviation trend exists, the hardware configuration adjustment plan corresponding to the deviation trend is obtained through the pre-established mapping relationship table to determine the preliminary adjustment direction. According to the preliminary adjustment direction, the available module operating resources are extracted from the hardware configuration resource pool to determine whether the replacement requirements of the current module are met. If the replacement requirements are met, the extracted module operating resources are used to dynamically switch the abnormal module to obtain the updated hardware configuration status. By continuously monitoring the updated hardware configuration status, new data fluctuation information is obtained to determine whether there are still abnormal signals exceeding the preset threshold.

[0018] Step S104: If an abnormal signal is detected, the abnormal module is located through the fault detection mechanism, and specific fault point information is extracted from the real-time monitoring capability to obtain a detailed status report of the faulty module.

[0019] If an abnormal signal is detected through the fault detection mechanism, a preliminary analysis of the abnormal signal is performed, extracting relevant fault location information from the real-time monitoring data to obtain the specific identifier of the abnormal module. Based on the specific identifier of the abnormal module, the module's historical records in the monitoring data are retrieved, and the signal analysis results are analyzed to determine whether persistent abnormal signal characteristics exist. If persistent abnormal signal characteristics are determined, a pre-established fault detection model is used in conjunction with the module status data to conduct an in-depth diagnosis of the abnormal module and determine the specific category of the fault location. Based on the determined fault location category, the corresponding repair strategy resources are extracted from the data acquisition system, and the status description information is analyzed to obtain a treatment plan adapted for the current abnormal module. Using the obtained treatment plan, resources are scheduled for the abnormal module. The processed module status is verified using real-time monitoring data to determine whether it has returned to the preset threshold range. If the module status has not returned to the preset threshold range after verification, a secondary check of the monitoring data is performed using a signal analysis tool to obtain new fault location information and determine the direction for further adjustments. Based on the further adjustment direction, the backup resource configuration is retrieved from the information extraction system and dynamically updated for the abnormal module, resulting in the final module status adjustment result.

[0020] Step S105 , based on the detailed status report of the faulty module, dynamically adjust parameters to optimize resource allocation, redistribute task loads to unaffected modules, and determine a temporary solution for the continued operation of the system.

[0021] Analyze the specific abnormal behavior of the faulty module through status reports, obtain relevant module status information from monitoring data, and identify deficiencies in the current resource configuration. Based on the deficiencies in the resource configuration, dynamically adjust the system parameters to obtain the adjusted parameter allocation results. Based on the adjusted parameter allocation results, use the task load analysis tool to identify the available capacity of the unaffected modules and determine whether the reallocation conditions are met. If the reallocation conditions are met, the task load is reallocated for the unaffected modules to obtain a new load balance distribution. Based on the new load balance distribution, extract relevant data from the system adjustment records, analyze the feasibility of continued operation, and determine the scope of application of the temporary solution. Based on the scope of application of the temporary solution, dynamically adjust the system operation strategy through the pre-established scheduling model and module status data to obtain the final system adjustment results. Based on the final system adjustment results, verify the stability of the module status through real-time monitoring tools to determine whether the preset operating standards are met.

[0022] Step S106: Obtain availability data of the standby module through the fault recovery mechanism, quickly replace or isolate the faulty module, and obtain the restored system operating status.

[0023] Through the fault recovery mechanism, available status data of backup modules is obtained from the module pool. A preliminary identification is performed on the faulty module to determine whether it meets the rapid isolation criteria, generating a preliminary identification result. Based on the preliminary identification result, if the faulty module meets the rapid isolation criteria, the isolation operation is executed, removing it from the system operating environment and obtaining the post-isolation system operating status. Based on the post-isolation system operating status, data for modules meeting available status is extracted from the backup module pool. A replacement operation is performed, connecting the backup module to the system operating environment and obtaining the post-replacement operating status. Based on the post-replacement operating status, a pre-established monitoring tool is used to obtain real-time operating status data to determine whether it meets the preset stability criteria and whether the system has entered the stable operation phase. Based on the stable operation phase, if the operating status data does not meet the preset criteria, the backup module's configuration parameters are adjusted to obtain adjusted operating data to determine whether it meets the stable operation requirements. Based on the adjusted operating data, a support vector machine algorithm is used to analyze system operating trends, identify potential instability factors, and determine subsequent optimization directions. Based on the potential instability factors, relevant status data is extracted from the system operation log and compared with the faulty module's historical records to obtain an optimized configuration strategy.

[0024] Step S107 : updating the operating condition adaptation model in real time according to the restored system operating state, extracting the latest operating stability data therefrom, and determining whether the preset stability standard is met.

[0025] Using pre-established monitoring tools, the system's operating data in the restored state is used to obtain real-time operational monitoring information to determine whether it meets the preset standards. Based on the real-time operational monitoring information, if the stability value is below the preset standard, the operating condition adaptation parameters are adjusted to obtain adjusted operational data to determine whether the stability condition is met. The adjusted operational data is analyzed using a support vector machine algorithm to analyze system operating trends, extract potential fluctuation factors, and determine the variation characteristics of the operational data. Based on the variation characteristics of the operational data, a corresponding adjustment strategy is derived from the adaptation model. Parameters are optimized to address the fluctuation factors and determine the optimized model configuration. Using the optimized model configuration, the system operating parameters are adjusted in real time, and updated stability values ​​are obtained from the operational monitoring to determine whether they meet the preset standards. If the updated stability value still does not meet the preset standard, historical information is extracted from the operational data and combined with the state judgment logic to obtain further adjustment basis and determine a new operational strategy. Based on the new operational strategy, the data structure of the adaptation model is updated to obtain the latest operational monitoring information to determine whether the system operation has entered a stable phase.

[0026] Step S108: If the operation stability data does not meet the standards, the system log is deeply analyzed through data processing efficiency improvement measures to obtain potential risk point information and determine the adjustment direction for further optimization.

[0027] Through in-depth mining of system logs, information on potential risk points is obtained and preliminary optimization and adjustment directions are determined. Based on the preliminary optimization and adjustment directions, historical data records related to potential risks are extracted from the system logs to obtain the distribution characteristics of risk points. If the distribution characteristics of risk points show a centralized trend, the centralized trend areas are prioritized using pre-established analysis tools to determine high-priority risk points. For high-priority risk points, the support vector machine algorithm is used to classify and process the relevant data to obtain a classified risk feature set. Based on the classified risk feature set, the corresponding adjustment parameter scheme is matched from the preset strategy library to determine the targeted optimization configuration. Through targeted optimization configuration, the system operating parameters are updated in real time, and the updated stability index is obtained from the operation monitoring information to determine whether it meets the preset standards. If the updated stability index still does not meet the preset standard, the differential features are extracted based on the comparison of historical data records with the current operation monitoring information to obtain new adjustment basis.

[0028] Step S109 , according to the further optimized adjustment direction and in combination with the requirement of abnormal response time, the monitoring frequency and resource allocation strategy are dynamically adjusted to obtain the final system operation optimization result.

[0029] By collecting system operation data in real time and analyzing the response time distribution, the abnormal response time is determined. If the response time exceeds the preset threshold, the monitoring frequency adjustment coefficient is calculated based on the degree of abnormal response time to obtain a new monitoring frequency. Based on the adjusted monitoring frequency, high-frequency monitoring data is collected, the system load distribution is analyzed, and resource allocation requirements are determined. Using the K-means clustering algorithm in the machine learning algorithm, resource allocation is divided into multiple priorities based on the system load distribution to obtain a resource allocation strategy. Through the resource allocation strategy, the computing node allocation ratio is dynamically adjusted to balance the system load and obtain the optimized system operation status. If the response time of the system operation status still exceeds the threshold, the monitoring frequency and resource allocation strategy are iteratively adjusted, the performance change trend is analyzed, and the final optimization strategy is determined.

[0030] The above embodiment is only one of the preferred implementation methods of the present invention and should not be used to limit the scope of protection of the present invention. Any changes or modifications that have no substantive meaning made to the main design concept and spirit of the present invention, as long as the technical problems they solve are still consistent with the present invention, should be included in the scope of protection of the present invention.

Claims

1. An intelligent ammonia injection control device for an SCR denitration system and an optimization control method thereof, characterized in that: The specific steps include: Through the pre-established operating condition database, the operating parameters and environmental data of the current flue gas denitrification scenario are obtained, and classified and stored according to different operating condition characteristics to obtain a preliminary operating condition adaptation model for subsequent dynamic matching of hardware resources; Based on the preliminary working condition adaptation model, a modular design framework is used to manage hardware resources in layers, extracting the hardware configuration solution that best matches the current working condition and determining a flexible hardware configuration allocation strategy. Based on the flexible allocation strategy of hardware configuration, the operating status of each module is monitored in real time, data fluctuation information during operation is obtained, and whether there are abnormal signals that deviate from the preset threshold; If an abnormal signal is detected, the abnormal module is located through the fault detection mechanism, and the specific fault point information is extracted from the real-time monitoring capability to obtain a detailed status report of the faulty module; Based on the detailed status report of the faulty module, dynamically adjust parameters to optimize resource allocation, redistribute task load to unaffected modules, and determine a temporary solution to ensure continuous system operation; Through the fault recovery mechanism, the availability data of the backup module is obtained, and the faulty module is quickly replaced or isolated to obtain the system operation status after recovery; Based on the restored system operating status, the operating condition adaptation model is updated in real time, from which the latest operating stability data is extracted to determine whether the preset stability standards are met; If the operational stability data does not meet the standards, we will conduct in-depth analysis of system logs through data processing efficiency improvement measures to obtain information on potential risk points and determine the direction of further optimization and adjustment; According to the adjustment direction of further optimization and combined with the requirements of abnormal response time, the monitoring frequency and resource allocation strategy are dynamically adjusted to obtain the final system operation optimization result.

2. The intelligent ammonia injection control device and optimization control method of the SCR denitration system according to claim 1 is characterized in that: The operating parameters and environmental data under the current flue gas denitrification scenario are obtained through the pre-established operating condition database, and classified and stored according to different operating condition characteristics to obtain a preliminary operating condition adaptation model for subsequent dynamic matching of hardware resources, including: Through the pre-built operating condition database, the operating parameters and environmental data of the current flue gas denitrification scenario are obtained. The data from different sources are preliminarily cleaned and formatted to obtain a structured basic data set. Based on the structured basic data set, a classified storage mechanism is adopted to group the operating parameters and environmental data according to the working condition characteristics, and determine the data distribution pattern under different working condition characteristics; Based on the data distribution pattern, a preliminary working condition adaptation model is constructed. The support vector machine algorithm is used to train the mapping relationship between working condition characteristics and operating parameters to obtain the initial parameter configuration of the adaptation model. If the matching accuracy of the initial parameter configuration on the validation set is lower than the preset threshold, the adaptation model is iteratively optimized, and the optimized model parameters are obtained by adjusting the feature weights of the training data; Based on the optimized model parameters and the current scenario analysis results, the dynamic allocation requirements between the working condition characteristics and hardware resources are determined, and a preliminary resource allocation plan is obtained; Based on the preliminary resource allocation plan, combined with the real-time changes in operating parameters, a rule matching method is used. If the operating parameter changes exceed the preset range, the allocation plan is fine-tuned to determine the final hardware resource allocation strategy. After obtaining the final hardware resource allocation strategy, the parameter matching results in the allocation strategy are recorded, and its application effect under different working conditions is recorded. The historical data in the working condition database is updated to form a closed-loop data feedback mechanism.

3. The intelligent ammonia injection control device and optimization control method of the SCR denitration system according to claim 1 is characterized in that: Based on the preliminary working condition adaptation model, a modular design framework is used to manage hardware resources in layers, extract the hardware configuration solution that best matches the current working condition, and determine a flexible hardware configuration allocation strategy, including: Through the pre-built operating condition database, the operating parameters and environmental data of the current flue gas denitrification scenario are obtained, and the parameters are processed using data standardization methods to obtain an operating condition data set in a unified format; Based on the working condition data set in a unified format, clustering algorithms are used to group working condition characteristics and determine the characteristic distribution pattern of each group of working conditions; Based on the characteristic distribution pattern, a hierarchical management mechanism is adopted to divide hardware resources into computing units and storage units according to functional modules, thus obtaining a modular resource pool; If the matching degree between the computing unit in the modular resource pool and the current working condition characteristics is lower than the preset threshold, the mapping relationship between the characteristics and the computing unit is trained through the linear regression algorithm to obtain the optimized matching parameters; Based on the optimized matching parameters, the computing units and storage units that best match the current working conditions are extracted from the modular resource pool to generate a preliminary hardware configuration plan; By monitoring the changes in operating parameters in real time, if the parameter changes exceed the preset range, the hardware configuration plan is dynamically adjusted using the rule matching method to determine the final allocation strategy; According to the final allocation strategy, the correspondence between the allocation results and the working condition characteristics is recorded, the working condition database is updated, and an updated data feedback mechanism is obtained.

4. The intelligent ammonia injection control device and optimization control method of the SCR denitration system according to claim 1 is characterized in that: The flexible allocation strategy for hardware configuration monitors the operating status of each module in real time, obtains data fluctuation information during operation, and determines whether there are abnormal signals that deviate from the preset threshold, including: By monitoring the operating status of each module in real time, data fluctuation information can be obtained to determine whether there are abnormal signals exceeding the preset threshold; If the acquired data fluctuation information shows that there is an abnormal signal exceeding the preset threshold, the abnormal signal is classified and processed to determine the source module of the abnormal signal; According to the abnormal signal source module of the classification processing, obtain the historical operation status record of the module to determine whether there is a continuous deviation trend; If a persistent deviation trend is determined to exist, the hardware configuration adjustment plan corresponding to the deviation trend is obtained through the pre-established mapping relationship table to determine the initial adjustment direction; Based on the preliminary adjustment direction, extract available module operation resources from the hardware configuration resource pool to determine whether the replacement requirements of the current module are met; If the replacement requirements are met, the extracted module operation resources are used to dynamically switch the abnormal module to obtain the updated hardware configuration status; By continuously monitoring the updated hardware configuration status, new data fluctuation information is obtained to determine whether there are still abnormal signals exceeding the preset threshold.

5. The intelligent ammonia injection control device and optimization control method of the SCR denitration system according to claim 1 is characterized in that: If an abnormal signal is detected, the abnormal module is located through the fault detection mechanism, and the specific fault point information is extracted from the real-time monitoring capability to obtain a detailed status report of the faulty module, including: If an abnormal signal is obtained through the fault detection mechanism, a preliminary analysis is performed on the abnormal signal, and relevant fault point information is extracted from the real-time monitoring data to obtain the specific identification of the abnormal module; According to the specific identification of the abnormal module, obtain the historical records of the module in the monitoring data, analyze the signal analysis results, and determine whether there are persistent abnormal signal characteristics; If persistent abnormal signal characteristics are detected, the pre-established fault detection model, combined with module status data, is used to conduct in-depth diagnosis of the abnormal module and determine the specific type of the fault point. According to the determined fault point category, the corresponding repair strategy resources are extracted from the data acquisition system, the status description information is analyzed, and the processing solution adapted to the current abnormal module is obtained; The obtained processing solution is used to schedule resources for abnormal modules, and the status of the processed modules is verified through real-time monitoring data to determine whether it has recovered to the preset threshold range; If the module status does not return to the preset threshold range after verification, the monitoring data is rechecked through the signal analysis tool to obtain new fault point information and determine the direction of further adjustments; According to the direction of further adjustment, the backup resource configuration is obtained from the information extraction system, and the abnormal module is dynamically updated to obtain the final module status adjustment result.

6. The intelligent ammonia injection control device and optimization control method of the SCR denitration system according to claim 1 is characterized in that: Based on the detailed status report of the faulty module, the system dynamically adjusts parameters to optimize resource allocation, redistributes task loads to unaffected modules, and determines a temporary solution for the system's continued operation, including: Analyze the specific abnormal performance of the faulty module through status reports, obtain relevant module status information from monitoring data, and determine the shortcomings of the current resource configuration; According to the insufficiency of resource allocation, the system parameters are optimized by dynamic adjustment to obtain the adjusted parameter allocation result; Based on the adjusted parameter allocation results, use the task load analysis tool to identify the available capacity of the unaffected modules and determine whether the reallocation conditions are met; If the redistribution conditions are met, the task load is redistributed for the unaffected modules to obtain a new load balance distribution state; Based on the new load balancing distribution status, extract relevant data from the system adjustment records, analyze the feasibility of continued operation, and determine the applicability of the temporary solution; Based on the applicable scope of the temporary solution, the system operation strategy is dynamically adjusted through the pre-established scheduling model and combined with the module status data to obtain the final system adjustment result; Based on the final system adjustment results, the real-time monitoring tool is used to verify whether the module status is stable and whether the preset operating standards are met.

7. The intelligent ammonia injection control device and optimization control method for an SCR denitration system according to claim 1, characterized in that: The fault recovery mechanism is used to obtain availability data of the backup module, quickly replace or isolate the faulty module, and obtain the restored system operating status, including: Through the fault recovery mechanism, the available status data of the backup module is obtained from the module pool, and the faulty module is preliminarily identified to determine whether it meets the rapid isolation conditions and obtain a preliminary identification result; Based on the preliminary identification results, if the faulty module meets the rapid isolation conditions, the isolation operation is performed to remove it from the system operating environment and obtain the system operating status after isolation; Based on the isolated system operating status, the module data that meets the available status is extracted from the spare module pool, and the replacement operation is performed. The spare module is connected to the system operating environment to obtain the operating status after replacement; Based on the operating status after replacement, pre-established monitoring tools are used to obtain real-time operating status data to determine whether the preset stability standards are met and whether the system has entered the stable operation stage; According to the judgment result of the stable operation stage, if the operating status data does not meet the preset standard, the configuration parameters of the standby module are adjusted to obtain the adjusted operating data to determine whether the stable operation requirements are met; Based on the adjusted operating data, the support vector machine algorithm is used to analyze the system operation trend, obtain potential unstable factors, and determine the subsequent optimization direction; Based on potential unstable factors, relevant status data is extracted from the system operation log, and compared and analyzed with the historical records of the faulty module to obtain the optimized configuration strategy.

8. The intelligent ammonia injection control device for an SCR denitration system and its optimized control method according to claim 1 is characterized in that: The operating condition adaptation model is updated in real time based on the restored system operating status, and the latest operating stability data is extracted from it to determine whether the preset stability standard is met, including: By using the system operation data in the restored state and pre-established monitoring tools, real-time operation monitoring information can be obtained to determine whether the preset standards are met; Based on real-time operation monitoring information, if the monitoring information shows that the stability value is lower than the preset standard, the operating condition adaptation parameters are adjusted to obtain the adjusted operation data to determine whether the stability conditions are met; For the adjusted operating data, the support vector machine algorithm is used to analyze the system operation trend, extract potential fluctuation factors, and obtain the change characteristics of the operating data; Based on the changing characteristics of the operating data, the corresponding adjustment strategy is obtained from the adaptation model, the parameters are optimized for the fluctuation factors, and the optimized model configuration is determined; Through the optimized model configuration, the system operating parameters are adjusted in real time, and the updated stability value is obtained from the operation monitoring to determine whether it meets the requirements of the preset standards; If the updated stability value still does not meet the preset standard, historical information is extracted from the operating data and combined with the status judgment logic to obtain further adjustment basis and determine the new operating strategy; According to the new operation strategy, the data structure of the adaptation model is updated, the latest operation monitoring information is obtained from it, and it is determined whether the system operation has entered a stable stage.

9. The intelligent ammonia injection control device and optimization control method for an SCR denitration system according to claim 1, characterized in that: If the operational stability data does not meet the standards, the system logs will be deeply analyzed through data processing efficiency improvement measures to obtain information on potential risk points and determine the direction of further optimization adjustments, including: Through in-depth mining of system logs, we can obtain information on potential risk points and determine preliminary optimization and adjustment directions; Based on the preliminary optimization and adjustment direction, historical data records related to potential risks are extracted from the system log to obtain the distribution characteristics of risk points; If the distribution characteristics of risk points show a concentration trend, the pre-established analysis tools are used to prioritize the concentration trend areas and identify high-priority risk points; For high-priority risk points, the support vector machine algorithm is used to classify the relevant data and obtain the classified risk feature set; Based on the classified risk feature set, the corresponding adjustment parameter scheme is matched from the preset strategy library to determine the targeted optimization configuration; Through targeted optimization configuration, the system operating parameters are updated in real time, and the updated stability indicators are obtained from the operation monitoring information to determine whether they meet the preset standards; If the updated stability index still does not meet the preset standard, the differentiated features are extracted based on the comparison between historical data records and current operation monitoring information to obtain new adjustment basis.

10. The intelligent ammonia injection control device and optimization control method for an SCR denitration system according to claim 1, characterized in that: The above-mentioned further optimized adjustment direction, combined with the requirements of abnormal response time, dynamically adjusts the monitoring frequency and resource allocation strategy to obtain the final system operation optimization results, including: By collecting system operation data in real time, analyzing response time distribution, and determining abnormal response time; If the response time exceeds the preset threshold, the monitoring frequency adjustment coefficient is calculated based on the degree of abnormal response time to obtain a new monitoring frequency; Based on the adjusted monitoring frequency, collect high-frequency monitoring data, analyze system load distribution, and determine resource allocation requirements; Using the K-means clustering algorithm in machine learning algorithms, resource allocation is divided into multiple priorities based on system load distribution to obtain a resource allocation strategy; Through resource allocation strategies, dynamically adjust the computing node allocation ratio, balance the system load, and obtain the optimized system operation status; If the response time of the system operation status still exceeds the threshold, iteratively adjust the monitoring frequency and resource allocation strategy, analyze the performance change trend, and determine the final optimization strategy.

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