Power generation enterprise-based regional supervision method, device, equipment and product

Abstract

The application discloses a power generation enterprise-based regional supervision method, device, equipment and product, relates to the technical field of business supervision, and comprises the following steps: storing full-service field data of a power generation enterprise to a pre-constructed full-stack intelligent base; performing summary analysis on the power generation enterprise through the full-stack intelligent base, obtaining a plurality of regional business information, and constructing a regional supervision platform according to the plurality of business information; and performing abnormal early warning on regional power plants of the power generation enterprise through the regional supervision platform, and performing correlation alarm according to the regional power plants when it is detected that the regional power plants have abnormalities, to obtain abnormal alarms. Thus, the problem that regional power plant supervision of existing large power generation enterprises lacks intelligent means and has no exclusive platform for supervision, resulting in low supervision efficiency, is solved, and the efficiency of regional supervision is improved.

Description

Technical Field

[0001] This application relates to the field of business supervision technology, and in particular to a method, apparatus, equipment and product for regional supervision of power generation enterprises. Background Technology

[0002] In the operation and management of large power generation enterprises, the supervision of subordinate power plants by regional companies is a key link in ensuring overall operational efficiency and compliance. At present, the mainstream supervision model in the industry is still a combination of periodic reports and on-site inspections. This model lacks real-time and refined digital and intelligent supervision methods, making it difficult to achieve dynamic control over the operating status of power plants. At the same time, existing supervision solutions are generally designed with a focus on a single business dimension, failing to achieve comprehensive supervision coverage of core businesses. Furthermore, the industry has not yet built a penetrating data integration mechanism based on domestically produced digital and intelligent infrastructure, resulting in difficulties in effectively integrating data between various businesses and power plants, and insufficient comprehensiveness and coordination in supervision.

[0003] It is known that existing technologies cannot achieve integrated and penetrating supervision of different types of power generation businesses in the regional supervision of large power generation enterprises, and there is no dedicated supervision indicator system adapted to regional power generation enterprises. Moreover, the supervision process relies on manual participation, which directly leads to the problem of low supervision efficiency.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a regional supervision method, device, equipment and product for power generation enterprises, which aims to solve the technical problem that the regional power plant supervision of existing large power generation enterprises lacks digital and intelligent means and has no dedicated platform for supervision, resulting in low supervision efficiency.

[0006] To achieve the above objectives, this application proposes a regional regulatory method based on power generation enterprises, which includes: Store all business data of power generation companies in a pre-built full-stack digital intelligence foundation; The power generation enterprise is aggregated and analyzed through the full-stack digital intelligence platform to obtain several regional business information, and a regional supervision platform is constructed based on the several business information. The regional monitoring platform provides early warnings of anomalies to the regional power plants of the power generation enterprise. When an anomaly is detected in a regional power plant, an associated alarm is generated based on the regional power plant.

[0007] In one embodiment, prior to the step of storing the power generation company's full-business-domain data into a pre-built full-stack digital infrastructure, the method further includes: Receive raw data from all business operations of power generation enterprises, and parse the raw data to obtain basic data of regional power plants; Data governance was performed on the basic data of the power plants in the region to obtain a standardized base dataset; Configure the basic functionality of the initial base using the standardized base dataset; The basic functions are tested and integrated. If the test results are satisfactory, the initial base is configured with rules to obtain a full-stack digital intelligence base.

[0008] In one embodiment, the step of summarizing and analyzing the power generation enterprise through the full-stack digital intelligence platform to obtain several regional business information, and constructing a regional regulatory platform based on the several business information, includes: Extract business information and subordinate area information from the full-stack digital intelligence base; Based on the subordinate area information, the area is divided into several business areas, and the business information is mapped through the several business areas to obtain several regional business information. Based on the business information of the aforementioned regions, regulatory indicators are extracted, and anomaly analysis is performed on the full-stack digital intelligence platform according to the regulatory indicators to obtain the anomaly analysis results. The abnormal risk threshold is determined by the abnormal analysis results, and a regional regulatory platform is constructed based on the abnormal risk threshold and regulatory indicators.

[0009] In one embodiment, the step of issuing anomaly warnings to the regional power plants of the power generation enterprise through the regional monitoring platform includes: Establish a real-time data transmission channel between the regional regulatory platform and the full-stack digital infrastructure; The real-time business data of the regional power plant is sent to the regional monitoring platform through the real-time data transmission channel. Based on the aforementioned regulatory indicators and abnormal risk thresholds, the regional regulatory platform provides real-time early warnings of abnormalities in the business data.

[0010] In one embodiment, the step of generating an abnormality alarm based on the regional power plant when an anomaly is detected includes: When an anomaly is detected in the real-time business data, an anomaly indicator is determined based on the real-time business data, and the risk level corresponding to the anomaly indicator is determined through the anomaly risk threshold. Based on the aforementioned anomaly indicators, the power plant in the abnormal area is identified using the full-stack digital intelligence platform. The risk propagation value corresponding to the abnormal indicator is calculated based on the power plant information of the power plants in the abnormal area; Anomaly alarms are generated based on the risk propagation value and the power plant in the abnormal area, and then sent to the emergency response terminal.

[0011] In one embodiment, the step of generating an anomaly alarm based on the risk propagation value and the power plant in the abnormal area includes: By performing a penetrating correlation analysis on the power generation enterprises using the risk propagation value, abnormally associated power plants can be identified. The risk level corresponding to the abnormal indicator is updated by the abnormal associated power plant to obtain the final risk level; Based on the abnormal power plants in the aforementioned abnormal areas, the abnormal associated power plants, and the abnormal indicators, the root causes of the abnormalities and the information related to the abnormalities were determined. Anomaly alerts are generated based on the root cause of the anomaly, the anomaly association information, and the final risk level.

[0012] In one embodiment, after the step of issuing anomaly warnings to regional power plants of the power generation enterprise through the regional monitoring platform, and issuing associated alarms based on the regional power plants when an anomaly is detected, the method further includes: Data is extracted from the full-stack digital intelligence base through the abnormal alarm to obtain indicator operation data and abnormal handling results; The regulatory indicators and abnormal risk thresholds in the regional regulatory platform are optimized by using the operational data of the aforementioned indicators and the results of anomaly handling, resulting in an optimized regional regulatory platform.

[0013] Furthermore, to achieve the above objectives, this application also proposes a regional monitoring device based on power generation enterprises, the regional monitoring device based on power generation enterprises comprising: The storage module is used to store data from all business areas of the power generation enterprise into a pre-built full-stack digital intelligence foundation; The analysis module is used to summarize and analyze the power generation enterprise through the full-stack digital intelligence base, obtain several regional business information, and construct a regional supervision platform based on the several business information; The monitoring module is used to provide early warning of anomalies to the regional power plants of the power generation enterprise through the regional monitoring platform. When an anomaly is detected in the regional power plant, an associated alarm is generated based on the regional power plant to obtain an anomaly alarm.

[0014] In addition, to achieve the above objectives, this application also proposes a regional monitoring device based on a power generation enterprise, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the regional monitoring method based on a power generation enterprise as described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the regional supervision method based on power generation enterprises as described above.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the regional supervision method based on power generation enterprises as described above.

[0017] One or more technical solutions proposed in this application have at least the following technical effects: This application proposes a method, apparatus, equipment, and product for regional supervision of power generation enterprises. It stores all business data of the power generation enterprise in a pre-constructed full-stack digital intelligence platform. The platform aggregates and analyzes the data to obtain regional business information, and then constructs a regional supervision platform based on this information. The platform provides anomaly warnings for regional power plants of the power generation enterprise. When anomalies are detected in a regional power plant, an associated alarm is generated. Thus, by first storing all business data in the full-stack digital intelligence platform to achieve unified data aggregation, then using the platform to aggregate and analyze the data to construct a dedicated regional supervision platform, the shortcomings of digital supervision carriers are addressed. Finally, the platform enables efficient supervision of regional power plants, replacing manual methods. This solves the problem of low supervision efficiency caused by the lack of digital intelligence means and dedicated platforms for regional power plant supervision in existing large power generation enterprises, thereby improving the efficiency of regional supervision. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating an embodiment of the regional regulatory method for power generation enterprises provided in this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the regional regulatory method for power generation enterprises provided in this application; Figure 3This is a schematic diagram illustrating the abnormal early warning and related alarms involved in the regional supervision method for power generation enterprises based on this application; Figure 4 A simplified flowchart illustrating the regional regulatory method for power generation enterprises provided in Embodiment 2 of this application; Figure 5 This is a schematic diagram of the module structure of a regional monitoring device for power generation enterprises, as described in an embodiment of this application. Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the regional supervision method based on power generation enterprises in the embodiments of this application.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0024] The main solution of this application embodiment is as follows: Receive raw data from the entire business of power generation enterprises; parse the raw data to obtain regional power plant basic data; perform data governance on the regional power plant basic data to obtain a standardized base dataset; configure the basic functions of the initial base using the standardized base dataset; conduct joint debugging tests on the basic functions; if the joint debugging test results are satisfactory, configure rules on the initial base to obtain a full-stack digital intelligence base. Extract business information and subordinate regional information from the full-stack digital intelligence base; divide the region according to the subordinate regional information to obtain several business areas; map the business information to several business areas to obtain several regional business information; extract regulatory indicators based on the several regional business information; perform indicator anomaly analysis on the full-stack digital intelligence base based on the regulatory indicators to obtain anomaly analysis results; determine anomaly risk thresholds based on the anomaly analysis results; and construct a regional regulatory platform based on the anomaly risk thresholds and regulatory indicators. A real-time data transmission channel is established between the regional monitoring platform and the full-stack digital intelligence base. Real-time business data from the regional power plants is sent to the regional monitoring platform via this channel. Based on the monitoring indicators and anomaly risk thresholds, the regional monitoring platform issues anomaly warnings for the real-time business data. When anomalies are detected in the real-time business data, anomaly indicators are determined based on the data, and the risk level corresponding to these indicators is determined using the anomaly risk thresholds. Based on the anomaly indicators, the full-stack digital intelligence base identifies the abnormal regional power plants. The risk propagation value corresponding to the anomaly indicators is calculated based on the power plant information of the abnormal regional power plants. An anomaly alarm is generated based on the risk propagation value and the abnormal regional power plants, and this alarm is sent to the emergency response terminal. A penetrating correlation analysis is performed on the power generation enterprises using the risk propagation value to identify associated abnormal power plants. The risk level corresponding to the anomaly indicators is updated based on the associated abnormal power plants to obtain the final risk level. The root cause and associated information of the anomalies are determined based on the abnormal regional power plants, associated abnormal power plants, and anomaly indicators. An anomaly alarm is generated based on the root cause, associated information, and final risk level. Data is extracted from the full-stack digital infrastructure through the aforementioned anomaly alarms to obtain indicator operation data and anomaly handling results. These indicators and anomaly handling results are then used to optimize the regulatory indicators and anomaly risk thresholds in the regional regulatory platform, resulting in an optimized regional regulatory platform. This addresses the problem of low regulatory efficiency caused by the lack of digital and intelligent means and dedicated platforms for regional power plant supervision in existing large power generation enterprises, thus achieving regional supervision and improving the efficiency of regional supervision based on power generation enterprises.Based on the present invention, this invention addresses the problem that existing solutions generally focus on a single business dimension, failing to achieve comprehensive regulatory coverage of core businesses. Furthermore, the industry lacks a penetrating data integration mechanism based on a domestically developed digital infrastructure, leading to difficulties in effectively integrating data between various businesses and power plants, insufficient comprehensiveness and coordination in regulation, and consequently, low efficiency. A regional regulatory method is designed, and its effectiveness is verified during regional regulation. Finally, the efficiency of regional regulation using the present invention is significantly improved.

[0025] In this embodiment, for ease of description, the following description uses the regional monitoring device as the implementing entity.

[0026] Due to the limitations of existing traditional regulatory models and digital technologies, the efficiency of regional supervision of large power generation enterprises needs to be improved. One issue is the lack of real-time monitoring, relying on periodic reports and on-site inspections without real-time, refined monitoring methods, making it difficult to dynamically control the operating status of power plants. This leads to a decline in the efficiency of regional supervision. Another issue is the limited business coverage; supervision focuses only on a single business dimension, failing to achieve integrated coverage of all businesses and multiple power types. This lack of comprehensiveness in supervision also significantly reduces the efficiency of regional supervision. Furthermore, there is the problem of poor data integration. The lack of a penetrating integration mechanism based on domestically developed infrastructure results in fragmented data across businesses and power plants, hindering effective collaboration and also affecting the efficiency of regional supervision. Currently, regional supervision of power generation enterprises also faces challenges in terms of domain adaptability, lacking a suitable and dedicated quantitative indicator system. The regulatory process relies on manual participation, further reducing regulatory efficiency.

[0027] This application provides a solution that first stores all business data in a full-stack digital infrastructure to achieve unified data aggregation, then uses the infrastructure to summarize and analyze the data to build a dedicated regional supervision platform, making up for the shortcomings of digital supervision carriers, and finally relies on the platform to efficiently supervise regional power plants, replacing the manual mode. This solves the problem that existing large power generation enterprises lack digital means and dedicated platforms for regional power plant supervision, resulting in low supervision efficiency, and improves the efficiency of regional supervision.

[0028] Based on this, the embodiments of this application provide a regional regulatory method based on power generation enterprises, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the regional regulatory method for power generation enterprises in this application.

[0029] In this embodiment, the regional regulatory method based on power generation enterprises includes steps S01 to S03: Step S01: Store the data of the power generation company’s entire business domain into the pre-built full-stack digital intelligence foundation; Before the implementation of this embodiment, it should be clear that existing regulatory solutions generally focus on designing a single business dimension, failing to achieve comprehensive regulatory coverage of core businesses. Furthermore, the industry has not yet built a penetrating data integration mechanism based on a domestically produced digital infrastructure, resulting in difficulties in effectively integrating data between various businesses and power plants, and insufficient comprehensiveness and coordination in regulation.

[0030] Therefore, in order to solve the above problems, this embodiment first collects multi-source heterogeneous data from the entire business areas of power generation enterprises, including production, operation, and supervision. After standardization and format unification, the compliant data is completely stored in the pre-deployed full-stack domestic digital intelligence base to achieve unified collection and management of data across the entire domain.

[0031] Step S02: The power generation enterprise is summarized and analyzed through the full-stack digital intelligence base to obtain several regional business information, and a regional supervision platform is constructed based on the several business information. Subsequently, relying on the full-stack digital intelligence platform, the collected full business data is summarized, statistically analyzed, and regional data is extracted to form business information for each region. Based on this information, a regional supervision platform is built that includes indicator supervision, risk warning, and penetrating analysis functions.

[0032] Step S03: The regional monitoring platform provides anomaly warnings for the regional power plants of the power generation enterprise. When an anomaly is detected in the regional power plant, an associated alarm is generated based on the regional power plant to obtain an anomaly alarm.

[0033] Finally, by establishing real-time data connection with regional power plants through the regional supervision platform, the production, operation and supervision data of power plants are automatically calculated and thresholded to carry out penetrating real-time supervision, identify anomalies and initiate closed-loop handling, and complete the full-process operation supervision of regional power plants.

[0034] Specifically, prior to step S01 above, which involves storing the power generation company's full-business-domain data into the pre-built full-stack digital intelligence foundation, the method further includes: Step S0101: Receive the raw data of the entire business of the power generation enterprise, and parse the raw data of the entire business to obtain the basic data of the regional power plant; Step S0102: Perform data governance on the basic data of the regional power plant to obtain a standardized base dataset; Step S0103: Configure the basic functions of the initial base using the standardized base dataset; Step S0104: Perform joint debugging tests on the basic functions. If the joint debugging test results are satisfactory, configure the rules for the initial base to obtain the full-stack digital intelligence base.

[0035] In this embodiment, the system first receives raw data from the regional side of the power generation enterprise and its subordinate thermal power, new energy, and hydropower plants. This raw data encompasses heterogeneous data from multiple sources, including the production DCS system, the operation ERP system, and the supervision OA system. A multi-source heterogeneous data adaptation and parsing algorithm is used to split key information such as power plant identification, business type, and data attributes from the data, resulting in basic regional power plant data including dimensions such as unit operation, revenue cost, and task progress. During data parsing, the multi-source heterogeneous data adaptation and parsing algorithm matches the transmission protocol and data format corresponding to the raw data through its protocol identification module. Then, it calls field splitting rules to extract core fields such as the power plant's unique code, unit number, business domain identifier, and collection timestamp. Subsequently, the split data is categorized and integrated according to three major business dimensions: production operation, business management, and task supervision. Meaningless redundant characters and garbled data are automatically removed, ultimately outputting standardized regional power plant basic data including unit operation parameters, revenue cost, and task progress.

[0036] Subsequently, the obtained regional power plant basic data was subjected to hierarchical management. First, invalid data such as null values, outliers, and duplicate values ​​were removed. Then, in accordance with the adaptation requirements of the full-stack domestic digital intelligent base, the data was converted into a unified format. Combined with the adaptation specifications of the Renmin University Kingbase database and Eastcom middleware, the data standardization process was completed to obtain a standardized base dataset.

[0037] Through this standardized base dataset, the basic functions of the initial base, such as data storage, data retrieval, and basic calculation, are configured to adapt to the underlying architecture of the "Ruisi" domestic digital intelligence base, and domestic servers are deployed and software and hardware debugging is completed.

[0038] The basic functions that have been configured are subjected to full-process joint debugging and testing, simulating scenarios such as data transmission and function calls, and checking for compatibility vulnerabilities between the base and subsequent data collection and indicator calculation modules. After the joint debugging and testing results are passed, data collection rules, access control rules, etc. are configured, and finally a full-stack digital intelligence base based on a full-stack domestic architecture is obtained.

[0039] More specifically, step S02 above, which involves summarizing and analyzing the power generation enterprise through the full-stack digital intelligence platform to obtain several regional business information, and constructing a regional regulatory platform based on the several business information, includes: Step S021: Extract the business information and subordinate area information from the full-stack digital intelligence base; Step S022: Divide the area according to the subordinate area information to obtain several business areas, and map the business information through the several business areas to obtain several regional business information. Step S023: Extract regulatory indicators based on the business information of the several regions, and perform indicator anomaly analysis on the full-stack digital intelligence base according to the regulatory indicators to obtain the anomaly analysis results; Step S024: Determine the abnormal risk threshold based on the abnormality analysis results, and construct a regional regulatory platform based on the abnormal risk threshold and regulatory indicators.

[0040] First, extract all business information on production, operation, and supervision of each power plant from the full-stack digital intelligence platform, as well as information on the geographical area and administrative affiliation of each power plant, to ensure that the extracted data covers all business dimensions of multiple power sources such as thermal power, new energy, and hydropower.

[0041] Subsequently, based on the geographical boundaries and power plant affiliations in the subordinate regional information, standardized regional divisions are performed to obtain several regulatory regions. Then, a data mapping algorithm is used to associate the extracted full business information with the corresponding regulatory regions, achieving regional classification of business information and obtaining regional business information for each regulatory region. Specifically, the data mapping algorithm first constructs a regional dimension index table based on the standardized regional division results, including regulatory region number, geographical boundaries, power plant affiliation, unique power plant identifier, and power source type. This creates a one-to-one index association between each regulatory region and its subordinate power plants. Then, the extracted full business information... The system automatically extracts unique identifiers such as power plant codes, unit numbers, and business affiliation fields. Using these identifiers as matching keys, it performs precise key-value matching with the regional dimension index table, and binds various business data such as production operation, business management, and task supervision to their respective regulatory regions. For scattered business data without clear affiliation, it performs mapping completion according to the power plant's administrative affiliation rules. At the same time, it performs consistency verification on the mapping results, removes abnormal data that fails to match, and finally completes the aggregation and classification of all business information according to the regulatory dimension, outputting standardized regional business information covering all dimensions of production, operation, and supervision for each regulatory region.

[0042] Following this, based on the obtained regional business information, three major categories of regulatory indicators were extracted: production, operation, and supervision. The calculation methods and statistical periods for each indicator were clarified. Among them, production indicators include unit load rate and equipment health, operation indicators include unit power generation cost and electricity bill collection rate, and supervision indicators include task completion rate and rectification completion rate.

[0043] Based on the extracted regulatory indicators, the regional business data in the full-stack digital intelligence foundation is analyzed in layers. The actual values ​​of each indicator are compared with the preset reference values ​​to identify abnormal indicators and obtain anomaly analysis results including abnormal indicators, abnormal data, and abnormal ranges.

[0044] By combining the frequency of occurrence and scope of impact of each abnormal indicator in the anomaly analysis results, the abnormal risk thresholds corresponding to different indicators are determined, and the judgment criteria for general anomalies and severe anomalies are distinguished. Then, regulatory indicators, abnormal risk thresholds, and penetrating analysis rules are embedded into the platform architecture to build a regional regulatory platform that includes panoramic business supervision, real-time indicator monitoring, and early risk warning, and achieves real-time data linkage with the full-stack digital intelligence foundation.

[0045] This embodiment, through the aforementioned steps, specifically stores data from all business areas of a power generation enterprise into a pre-constructed full-stack digital intelligence platform; it then aggregates and analyzes the power generation enterprise's data using this platform to obtain regional business information, and constructs a regional monitoring platform based on this information; finally, it uses this regional monitoring platform to issue anomaly warnings for the power plants within the power generation enterprise's region. When anomalies are detected in a regional power plant, it generates associated alarms based on that power plant. Thus, by first storing all business data in the full-stack digital intelligence platform to achieve unified data aggregation, then using the platform to aggregate and analyze the data to construct a dedicated regional monitoring platform, it addresses the shortcomings of digital monitoring carriers. Finally, it relies on the platform to efficiently monitor regional power plants, replacing manual methods. This solves the problem of low monitoring efficiency caused by the lack of digital intelligence tools and dedicated platforms for monitoring regional power plants in existing large power generation enterprises, thereby improving the efficiency of regional monitoring.

[0046] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 In step S03, the step of issuing anomaly warnings to the regional power plants of the power generation enterprise through the regional monitoring platform, the regional monitoring method based on the power generation enterprise further includes steps S031 to S033: Step S031: Establish a real-time data transmission channel between the regional supervision platform and the full-stack digital intelligence base; Step S032: Send the real-time business data of the regional power plant to the regional monitoring platform through the real-time data transmission channel; Step S033: Based on the regulatory indicators and abnormal risk thresholds, issue anomaly warnings for the real-time business data through the regional regulatory platform.

[0047] In this embodiment, a dedicated real-time data transmission channel is first established between the regional monitoring platform and the full-stack digital intelligent base. The domestic SM4 encryption algorithm is used to encrypt the transmitted data to prevent data leakage and tampering, ensuring the security and stability of cross-level data transmission. At the same time, it is compatible with the transmission protocol of the domestic digital intelligent base to achieve seamless connection between the channel and the base and platform.

[0048] Through power plant-level data acquisition nodes, real-time business data of regional power plants is collected. Production data is collected at the second level and operational data at the hour level. The data is transmitted to the real-time data transmission channel through standardized interfaces. The channel classifies and transmits data according to data type and power plant identification, accurately sending the real-time business data of each regional power plant to the regional supervision platform.

[0049] After receiving real-time business data, the regional monitoring platform invokes the regulatory indicator calculation model to automatically calculate the data. The calculation results are then compared with preset regulatory indicators and anomaly risk thresholds. This enables real-time monitoring of all aspects of power plant production, operation, management, and task supervision in each region. It supports drilling down from regional aggregated data to specific power plants and business processes to accurately pinpoint data anomalies. Specifically, the regulatory indicator calculation model first loads pre-defined indicator calculation formulas and parameter configurations, then reads key fields such as power plant identification, data timestamp, and business type from the real-time business data, and distributes the real-time data to the corresponding data types. The indicator calculation unit performs calculations on a second-level cycle for real-time production data, an hourly cycle for operational data, and a daily cycle for supervisory data. It strictly follows preset formulas to complete the calculation of single indicator values. First, it verifies the detailed indicator results at the power plant level and equipment / position level, and then summarizes them upwards according to regional aggregation rules to generate regional-level summary indicators. Simultaneously, it compares the calculation results with preset abnormal risk thresholds item by item, marking them as normal, yellow warning, and red warning. Finally, it generates an indicator result set containing four dimensions: region, power plant, business process, and equipment / position, providing computational support for drill-down queries and anomaly location.

[0050] Specifically, step S03 above, which involves generating an alarm based on the regional power plant when an anomaly is detected, includes the following steps: Step S034: When an anomaly is detected in the real-time business data, an anomaly indicator is determined based on the real-time business data, and the risk level corresponding to the anomaly indicator is determined through the anomaly risk threshold. Step S035: Based on the abnormal indicators, determine the power plant in the abnormal area using the full-stack digital intelligence base. Step S036: Calculate the risk propagation value corresponding to the abnormal indicator based on the power plant information of the power plant in the abnormal area; Step S037: Generate an abnormal alarm based on the risk propagation value and the power plant in the abnormal area, and send the abnormal alarm to the emergency response terminal.

[0051] In this embodiment, the regional supervision platform monitors the comparison results of real-time business data with supervision indicators and abnormal risk thresholds. When it detects that the real-time business data exceeds the preset threshold range, it automatically locks the corresponding abnormal indicator and extracts information such as the calculation data, collection time, and business area to which the abnormal indicator belongs.

[0052] Based on the classification standard of abnormal risk thresholds, the actual deviation value of the abnormal indicator is compared with the threshold range to determine the risk level corresponding to the abnormal indicator. Among them, the deviation value with a narrow range of influence is judged as general risk, and the deviation value with a wide range of influence is judged as severe risk.

[0053] Based on the identification information of abnormal indicators, the penetrating data query function of the full-stack digital intelligence platform is used to trace the source of abnormal data, pinpoint the power plants in the region that generated the abnormal indicators, and extract power plant information such as power plant type, equipment configuration, and business scale. Combining the power plant information in the abnormal region, the risk propagation value corresponding to the abnormal indicators is calculated. Taking into account factors such as the business relevance of the power plants, regional location, and equipment linkage, the potential scope and degree of risk spread are quantified to obtain an accurate risk propagation value.

[0054] Finally, by integrating information on power plants in abnormal areas, details of abnormal indicators, risk levels, and risk propagation values, standardized abnormal alerts are generated. According to the emergency response level, the abnormal alerts are pushed to the corresponding emergency response terminals. The push channels include system in-system messages, WeChat for enterprises, and SMS reminders to ensure that emergency personnel receive them in a timely manner.

[0055] More specifically, step S037 above, the step of generating an abnormal alarm through the risk propagation value and the power plant in the abnormal area, includes: Step S0371: Perform a penetrating correlation analysis on the power generation enterprise using the risk propagation value to obtain abnormally associated power plants; Step S0372: Update the risk level corresponding to the abnormal indicator through the abnormal associated power plant to obtain the final risk level; Step S0373: Determine the root cause of the anomaly and the associated information based on the power plant in the abnormal area, the associated power plant, and the abnormal indicators. Step S0374: Generate an anomaly alert based on the anomaly root cause, anomaly association information, and final risk level.

[0056] Based on the calculated risk propagation value, a penetrating correlation analysis process is initiated to retrieve business correlation data, equipment linkage data, and data transmission link information of power plants in each region within the full-stack digital intelligence foundation. The business correlation and data interaction relationship between the abnormal regional power plant and its subordinate power plants are analyzed to screen out abnormally related power plants that may be affected by the anomaly, and to clarify the degree of correlation and scope of impact of each related power plant.

[0057] Subsequently, the initial risk level is updated based on the number of abnormally associated power plants, business scale, and degree of association. If there are many abnormally associated power plants and a large business scale, the risk level is raised by one level. If there are only a few associations and the impact is small, the original risk level is maintained, thus obtaining the accurate final risk level.

[0058] Next, historical operating data and real-time monitoring data of the power plants in the abnormal area and the power plants associated with the abnormality were retrieved. Combined with the calculation process of the abnormal indicators, the core cause of the abnormality was traced to clarify whether the root cause of the abnormality was equipment failure, data abnormality or human operation problem. At the same time, the abnormal related information was sorted out, including the abnormal impact points of the associated power plants and the abnormal links in data interaction.

[0059] Finally, information such as the root cause of the anomaly, related information, final risk level, power plants in the anomaly area, and associated power plants are integrated and anomaly alarms are generated according to a standardized format. The alarm number, anomaly description, handling requirements, urgency level, and other contents are clearly defined to ensure that the alarm information is complete and accurate, providing a clear basis for emergency response.

[0060] The overall abnormal alarm generation process is as follows: Figure 3 As shown, the data acquisition process begins by comprehensively collecting real-time business data from regional power plants. Once the data acquisition is complete, anomaly detection is performed to determine if any anomalies exist in the collected real-time business data.

[0061] If the test result is normal, the process maintains the data monitoring state and continues to collect and test data. If the test result is abnormal, an abnormal warning is triggered directly, and the process enters the data correlation analysis stage to further determine whether there is a correlation between the abnormal data and other abnormalities. If there is no correlation, only basic warning information is generated and the process of this stage is completed. If there is a correlation, a complete abnormal warning is triggered, and standardized alarm information is generated by combining abnormal data and correlation information, thus completing the entire abnormal alarm generation process.

[0062] Further, in the above embodiments, after step S03, which involves issuing anomaly warnings to the regional power plants of the power generation enterprise through the regional monitoring platform, and issuing associated alarms based on the regional power plants when anomalies are detected, the method further includes: Step S04: Extract data from the full-stack digital intelligence base through the abnormal alarm to obtain indicator operation data and abnormal handling results; Step S05: Optimize the regulatory indicators and abnormal risk thresholds in the regional regulatory platform using the indicator operation data and abnormal handling results to obtain the optimized regional regulatory platform.

[0063] In this embodiment, based on the identification information of the abnormal alarm, the full-cycle indicator operation data corresponding to the abnormal alarm is extracted through the data query interface of the full-stack digital intelligence base. This includes indicator change data before, during, and after the abnormality occurs. At the same time, relevant data in the abnormality handling process is extracted, covering information such as handling measures, handling time, handling personnel, and handling results, forming a complete indicator operation data and abnormality handling result ledger.

[0064] The extracted indicator operation data are subjected to stratified analysis to statistically analyze the frequency of occurrence, deviation range, and duration of impact of abnormal indicators. The implementation effects of different abnormal handling measures are compared, and the rationality of the calculation caliber and abnormal risk threshold of existing regulatory indicators is analyzed to identify the unreasonable aspects of the setting of regulatory indicators and thresholds.

[0065] Based on the analysis results, the regulatory indicators in the regional regulatory platform were optimized by supplementing missing business indicators and adjusting the calculation methods. Simultaneously, based on the patterns of anomalies and the effectiveness of their handling, the anomaly risk thresholds were updated, and the threshold grading standards were optimized to ensure that the regulatory indicators and thresholds better align with the actual regulatory needs of regional power generation enterprises. The optimized regulatory indicators and anomaly risk thresholds were then re-embedded into the regional regulatory platform, completing the platform optimization and forming a closed-loop mechanism of "regulation-anomaly-handling-optimization." This continuously improves the accuracy and efficiency of regional regulation while adapting to the iterative upgrade requirements of the fully domestically produced digital infrastructure, ensuring the continuous compatibility of the platform and infrastructure.

[0066] This embodiment, through the above-described scheme, specifically establishes a real-time data transmission channel between the regional monitoring platform and the full-stack digital intelligence base; the real-time business data of the regional power plant is sent to the regional monitoring platform through this channel; based on the monitoring indicators and abnormal risk thresholds, the regional monitoring platform provides abnormal warnings for the real-time business data. Thus, all business data is first stored in the full-stack digital intelligence base to achieve unified data aggregation; then, the base aggregates and analyzes the data to build a dedicated regional monitoring platform, addressing the shortcomings of digital monitoring carriers; and finally, the platform enables efficient monitoring of regional power plants, replacing manual methods. This solves the problem of low monitoring efficiency caused by the lack of digital intelligence tools and dedicated platforms for monitoring regional power plants in existing large power generation enterprises, thereby improving the efficiency of regional monitoring.

[0067] For example, to help understand the implementation process of the regional supervision method for power generation enterprises obtained by combining this embodiment with the above embodiment one, please refer to... Figure 4 , Figure 4 A simplified flowchart of a regional regulatory approach for power generation enterprises is provided, specifically: The first step is regional division, which involves standardizing the regulatory scope based on the geographical attributes, administrative affiliation attributes, and business function attributes of the power plants under the power generation enterprise, clarifying the boundaries of each regulatory region, the power plants under its jurisdiction, and the regulatory responsibilities, thus laying the foundation for subsequent full-business data collection and hierarchical supervision.

[0068] The second step is data collection, which involves collecting data from all power plants in each regulatory area across all business areas, including production, operation, and supervision, through various methods such as real-time sensor collection and manual supplementary collection. This includes multi-source heterogeneous data such as real-time parameters from the production DCS system, revenue data from the operation ERP system, and task data from the supervision OA system.

[0069] The third step is risk assessment, which involves transmitting the collected full-business data to the full-stack digital intelligence platform, performing automated calculations through the regulatory indicator calculation model, and comparing and analyzing the data with preset abnormal risk thresholds to determine the risk level of each regulatory region and each power plant.

[0070] The fourth step is to implement the measures. Differentiated control measures are adopted based on the different risk levels determined by the risk assessment. For general risk levels, only early warning prompts are sent and continuous monitoring is carried out. For severe risk levels, an abnormal alarm is immediately triggered and pushed to the emergency handling terminal to initiate the emergency response process.

[0071] The fifth step is to collect data on the effectiveness of various control measures, including the completion rate of abnormal handling, the effectiveness of risk and hazard rectification, and the optimization of regulatory indicators.

[0072] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the regional regulatory method for power generation enterprises in this application. Any simple modifications based on this technical concept are within the scope of protection of this application.

[0073] This application also provides a regional monitoring device for power generation enterprises; please refer to [reference needed]. Figure 5 The regional monitoring device based on power generation enterprises includes: Storage module 10 is used to store data from all business areas of the power generation enterprise to a pre-built full-stack digital intelligence foundation; Analysis module 20 is used to summarize and analyze the power generation enterprise through the full-stack digital intelligence base, obtain several regional business information, and construct a regional supervision platform based on the several business information; The monitoring module 30 is used to provide early warning of anomalies to the regional power plants of the power generation enterprise through the regional monitoring platform. When an anomaly is detected in the regional power plant, an associated alarm is generated based on the regional power plant to obtain an anomaly alarm.

[0074] The regional monitoring device based on power generation enterprises provided in this application, employing the regional monitoring method based on power generation enterprises in the above embodiments, can solve the technical problem of low monitoring efficiency caused by the lack of digital and intelligent means and the absence of a dedicated platform for monitoring regional power plants of existing large power generation enterprises. Compared with the prior art, the beneficial effects of the regional monitoring device based on power generation enterprises provided in this application are the same as those of the regional monitoring method based on power generation enterprises provided in the above embodiments, and other technical features in the regional monitoring device based on power generation enterprises are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0075] This application provides a regional monitoring device based on a power generation enterprise. The regional monitoring device based on a power generation enterprise includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the regional monitoring method based on the power generation enterprise in the above embodiment 1.

[0076] The following is for reference. Figure 6 The diagram illustrates a structural schematic suitable for implementing the regional monitoring equipment based on power generation enterprises in the embodiments of this application. The regional monitoring equipment based on power generation enterprises in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The regional monitoring equipment based on power generation enterprises shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0077] like Figure 6As shown, the regional monitoring equipment based on power generation enterprises may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows regional monitoring equipment based on a power generation enterprise to communicate wirelessly or wiredly with other equipment to exchange data. While the figure shows regional monitoring equipment based on a power generation enterprise with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0078] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0079] The regional monitoring equipment for power generation enterprises provided in this application, employing the regional monitoring method for power generation enterprises described in the above embodiments, can solve the technical problem of low monitoring efficiency caused by the lack of digital and intelligent means and dedicated platforms for monitoring regional power plants in existing large power generation enterprises. Compared with the prior art, the beneficial effects of the regional monitoring equipment for power generation enterprises provided in this application are the same as those of the regional monitoring method for power generation enterprises provided in the above embodiments, and other technical features of the regional monitoring equipment for power generation enterprises are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0080] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0082] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the regional regulatory method based on power generation enterprises in the above embodiments.

[0083] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0084] The aforementioned computer-readable storage medium may be included in the regional monitoring equipment based on the power generation enterprise; or it may exist independently and not be assembled into the regional monitoring equipment based on the power generation enterprise.

[0085] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a regional monitoring device based on a power generation enterprise, the regional monitoring device enables the following: the power generation enterprise to store all business domain data of the power generation enterprise into a pre-built full-stack digital intelligence platform; the power generation enterprise to be aggregated and analyzed through the full-stack digital intelligence platform to obtain several regional business information, and to construct a regional monitoring platform based on the several business information; and the power generation enterprise to receive anomaly warnings for its regional power plants through the regional monitoring platform, and to issue associated alarms based on the regional power plants when anomalies are detected, thereby obtaining an anomaly alarm.

[0086] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0088] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0089] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the aforementioned regional supervision method based on power generation enterprises. This addresses the technical problem of low supervision efficiency caused by the lack of digital and intelligent means and dedicated platforms for regional power plant supervision in existing large power generation enterprises. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the regional supervision method based on power generation enterprises provided in the above embodiments, and will not be elaborated upon here.

[0090] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the regional supervision method based on power generation enterprises as described above.

[0091] The computer program product provided in this application can solve the technical problem of low regulatory efficiency caused by the lack of digital and intelligent means and the absence of a dedicated platform for regional power plant supervision in existing large power generation enterprises. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the regional supervision method based on power generation enterprises provided in the above embodiments, and will not be repeated here.

[0092] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A regional regulatory method based on power generation enterprises, characterized in that, The regional regulatory approach based on power generation enterprises includes: Store all business data of power generation companies in a pre-built full-stack digital intelligence foundation; The power generation enterprise is aggregated and analyzed through the full-stack digital intelligence platform to obtain several regional business information, and a regional supervision platform is constructed based on the several business information. The regional monitoring platform provides early warnings of anomalies to the regional power plants of the power generation enterprise. When an anomaly is detected in a regional power plant, an associated alarm is generated based on the regional power plant.

2. The regional regulatory method based on power generation enterprises as described in claim 1, characterized in that, Before the step of storing the power generation company's full-business-domain data into a pre-built full-stack digital infrastructure, the method further includes: Receive raw data from all business operations of power generation enterprises, and parse the raw data to obtain basic data of regional power plants; Data governance was performed on the basic data of the power plants in the region to obtain a standardized base dataset; Configure the basic functionality of the initial base using the standardized base dataset; The basic functions are tested and integrated. If the test results are satisfactory, the initial base is configured with rules to obtain a full-stack digital intelligence base.

3. The regional regulatory method based on power generation enterprises as described in claim 1, characterized in that, The steps of summarizing and analyzing the power generation enterprise through the full-stack digital intelligence platform to obtain several regional business information, and constructing a regional supervision platform based on the several business information, include: Extract business information and subordinate area information from the full-stack digital intelligence base; Based on the subordinate area information, the area is divided into several business areas, and the business information is mapped through the several business areas to obtain several regional business information. Based on the business information of the aforementioned regions, regulatory indicators are extracted, and anomaly analysis is performed on the full-stack digital intelligence platform according to the regulatory indicators to obtain the anomaly analysis results. The abnormal risk threshold is determined by the abnormal analysis results, and a regional regulatory platform is constructed based on the abnormal risk threshold and regulatory indicators.

4. The regional regulatory method based on power generation enterprises as described in claim 3, characterized in that, The steps for issuing abnormal early warnings for regional power plants of the power generation enterprise through the regional monitoring platform include: Establish a real-time data transmission channel between the regional regulatory platform and the full-stack digital infrastructure; The real-time business data of the regional power plant is sent to the regional monitoring platform through the real-time data transmission channel. Based on the aforementioned regulatory indicators and abnormal risk thresholds, the regional regulatory platform provides real-time early warnings of abnormalities in the business data.

5. The regional regulatory method based on power generation enterprises as described in claim 4, characterized in that, The step of generating an abnormality alarm by issuing a related alarm based on the regional power plant when an abnormality is detected includes: When an anomaly is detected in the real-time business data, an anomaly indicator is determined based on the real-time business data, and the risk level corresponding to the anomaly indicator is determined through the anomaly risk threshold. Based on the aforementioned anomaly indicators, the power plant in the abnormal area is identified using the full-stack digital intelligence platform. The risk propagation value corresponding to the abnormal indicator is calculated based on the power plant information of the power plants in the abnormal area; Anomaly alarms are generated based on the risk propagation value and the power plant in the abnormal area, and then sent to the emergency response terminal.

6. The regional regulatory method based on power generation enterprises as described in claim 5, characterized in that, The step of generating an anomaly alarm based on the risk propagation value and the power plant in the abnormal area includes: By performing a penetrating correlation analysis on the power generation enterprises using the risk propagation value, abnormally associated power plants can be identified. The risk level corresponding to the abnormal indicator is updated by the abnormal associated power plant to obtain the final risk level; Based on the abnormal power plants in the aforementioned abnormal areas, the abnormal associated power plants, and the abnormal indicators, the root causes of the abnormalities and the information related to the abnormalities were determined. Anomaly alerts are generated based on the root cause of the anomaly, the anomaly association information, and the final risk level.

7. The regional regulatory method based on power generation enterprises as described in claim 4, characterized in that, After the step of issuing anomaly warnings to regional power plants of the power generation enterprise through the regional monitoring platform, and issuing associated alarms based on the regional power plants when anomalies are detected, the method further includes: Data is extracted from the full-stack digital intelligence base through the abnormal alarm to obtain indicator operation data and abnormal handling results; The regulatory indicators and abnormal risk thresholds in the regional regulatory platform are optimized by using the operational data of the aforementioned indicators and the results of anomaly handling, resulting in an optimized regional regulatory platform.

8. A regional monitoring device based on a power generation enterprise, characterized in that, The regional monitoring device based on power generation enterprises includes: The storage module is used to store data from all business areas of the power generation enterprise into a pre-built full-stack digital intelligence foundation; The analysis module is used to summarize and analyze the power generation enterprise through the full-stack digital intelligence base, obtain several regional business information, and construct a regional supervision platform based on the several business information; The monitoring module is used to provide early warning of anomalies to the regional power plants of the power generation enterprise through the regional monitoring platform. When an anomaly is detected in the regional power plant, an associated alarm is generated based on the regional power plant to obtain an anomaly alarm.

9. A regional monitoring device for power generation enterprises, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the regional regulatory method based on a power generation enterprise as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the regional regulatory method based on a power generation enterprise as described in any one of claims 1 to 7.

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