A method and device for grading control of fuel abnormal events, electronic equipment, computer readable medium and program product

Abstract

This application provides a hierarchical control method, device, electronic equipment, computer-readable medium, and program product for fuel anomaly events, applicable to the entire fuel management process of thermal power plants. It belongs to the field of fuel management technology for thermal power plants. The hierarchical control method includes: real-time acquisition of business data from the entire fuel management process of a thermal power plant, including vehicle entry data, sampling process data, sample flow data, testing process data, inventory consumption data, and operation approval data; comparison and logical verification of the business data according to a preset set of anomaly event judgment rules to generate corresponding fuel anomaly events; determination of the event level of the fuel anomaly event according to preset hierarchical rule information; and execution of handling operations for the fuel anomaly event according to the event level and preset handling strategy information. This application avoids technical vulnerabilities such as human fraud and illegal operations, and improves the intelligence level and risk prevention capabilities of fuel management.

Description

Technical Field

[0001] This application belongs to the field of fuel management technology for thermal power plants, and specifically relates to a graded control method and device for abnormal fuel events, electronic equipment, computer-readable media and program products. Background Technology

[0002] The entire fuel management process in thermal power plants involves multiple stages, including vehicle arrival, sampling, sample preparation, sample transfer, testing, inventory management, blending, settlement, and approval. The business data generated at each stage (such as vehicle arrival data, sampling process data, sample transfer data, testing process data, inventory consumption data, and operational approval data) serves as the data basis for fuel acceptance, settlement, and quality control. However, the inventors of this application have discovered the following technical deficiencies in the identification, classification, and handling of fuel anomalies in existing fuel management systems: First, the identification of fuel anomalies relies on manual inspections or post-event verification, lacking real-time capability and predictive ability. Existing systems typically only detect problems after data is entered into the database, through manual comparison or periodic inventory checks. Anomalies such as sampling weight deviations, inconsistencies in three-card information, and laboratory indicators exceeding thresholds are often only discovered during settlement or disputes. This "post-event discovery" model prevents fuel anomalies from being captured in a timely manner during business operations and makes effective intervention difficult before their impact escalates.

[0003] Secondly, the hierarchical management of fuel anomalies lacks objective and unified standards. Different fuel anomalies significantly impact settlement fairness, sample representativeness, and compliance (for example, inconsistencies in the three-card information directly undermine the settlement basis, while exceeding thresholds in laboratory indicators can be verified through retesting). However, existing systems typically employ a "one-size-fits-all" approach, applying the same handling logic to all fuel anomalies, or relying entirely on human experience for hierarchical judgment. This approach not only leads to low-risk events consuming excessive management resources while high-risk events fail to receive sufficient attention, but also creates technical vulnerabilities that allow for arbitrary hierarchical classification due to human judgment bias.

[0004] Secondly, the handling of fuel anomaly events is disconnected from business processes, lacking process linkage and control capabilities. When a fuel anomaly occurs, the existing system typically only generates records or sends notifications, failing to integrate with business processes for control. For example, when an anomaly in sampling and weighing is detected, the system cannot automatically lock the entry point for subsequent sample preparation or testing, leaving room for human intervention or continued violations. This technical deficiency of a handling mechanism disconnected from business processes makes it difficult to effectively prevent the spread and impact of fuel anomaly events at the physical or systemic level.

[0005] The content of the background section is merely the technology known to the inventor and does not necessarily represent the prior art in this field. Summary of the Invention

[0006] This application aims to provide a method and apparatus, electronic device, computer-readable medium, and program product for graded control of fuel anomaly events, in order to solve at least one of the above-mentioned technical problems.

[0007] According to one aspect of this application, a hierarchical control method for fuel anomalies is provided, applied to the entire fuel management process of thermal power plants. The hierarchical control method includes: real-time acquisition of business data from the entire fuel management process of thermal power plants, wherein the business data includes vehicle entry data, sampling process data, sample flow data, testing process data, inventory consumption data, and operation approval data; comparison and logical verification of the business data according to a preset set of anomaly judgment rules to generate corresponding fuel anomalies; determination of the event level of the fuel anomaly according to preset hierarchical rule information; and execution of handling operations for the fuel anomaly according to the event level and preset handling strategy information.

[0008] According to some embodiments of this application, comparing and logically verifying business data based on a preset set of abnormal event judgment rules to generate corresponding fuel abnormal events includes: judging whether business data exceeds limits based on preset threshold conditions in the set of abnormal event judgment rules; generating a corresponding first fuel abnormal event when business data exceeds the threshold conditions; and / or comparing business data based on preset consistency conditions in the set of abnormal event judgment rules; generating a corresponding second fuel abnormal event when the consistency comparison result is inconsistent; and / or verifying business data based on preset rule matching conditions in the set of abnormal event judgment rules; generating a corresponding third fuel abnormal event when matching fails.

[0009] According to some embodiments of this application, the event level of a fuel anomaly event is determined based on preset classification rule information, including: obtaining characteristic parameters of the fuel anomaly event; matching the characteristic parameters with preset classification rule information to determine the event level of the fuel anomaly event.

[0010] According to some embodiments of this application, a handling operation for a fuel anomaly event is performed based on the event level and preset handling strategy information, including: obtaining a corresponding handling instruction sequence from the preset handling strategy information according to the event level; and sequentially performing the handling operation for the fuel anomaly event according to the handling instruction sequence, wherein the handling instruction sequence includes process control operations, approval nodes, and timing thresholds.

[0011] According to some embodiments of this application, when the event level is a preset event level, the process control operation in the handling instruction sequence includes: marking the status of at least one business process involved in the fuel abnormality event as a locked state to block the operation entry associated with the business process.

[0012] According to some embodiments of this application, the hierarchical control method further includes: generating an event handling record based on the obtained triggering information, handling operation information, approval information and timestamp of the fuel abnormality event; performing statistical analysis on the event handling record, and updating the hierarchical rule information based on the statistical analysis results.

[0013] According to another aspect of this application, a graded control device for fuel anomalies is also provided, applied to the entire fuel management process of thermal power plants. The graded control device is used to execute the graded control method described above, and includes a data acquisition module, an anomaly identification module, an event level determination module, and a graded handling module. The data acquisition module is used to acquire business data from the entire fuel management process of thermal power plants in real time. This business data includes vehicle entry data, sampling process data, sample flow data, testing process data, inventory consumption data, and operation approval data. The anomaly identification module is used to compare and logically verify the business data according to a preset set of anomaly event judgment rules, generating corresponding fuel anomalies. The event level determination module is used to determine the event level of the fuel anomaly event according to preset graded rule information. The graded handling module is used to execute handling operations for the fuel anomaly event according to the event level and preset handling strategy information.

[0014] According to another aspect of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the hierarchical control method described above.

[0015] According to another aspect of this application, a computer-readable medium having processor-executable non-volatile program code is also provided, the program code causing the processor to perform the hierarchical control method described above.

[0016] According to another aspect of this application, a computer program product is also provided, including a computer program stored on a computer-readable medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the hierarchical control method described above.

[0017] The beneficial effects of the technical solutions provided in this application are: This application achieves pre-identification of fuel anomalies by comparing and verifying real-time collected business data from the entire fuel management process, overcoming the shortcomings of low efficiency and high lag in traditional manual inspections. By matching the event level corresponding to the fuel anomaly event through a pre-set hierarchical rule base, it realizes the transformation of event level from subjective judgment to objective data feature comparison, avoiding arbitrariness in hierarchical classification. Through pre-set handling strategy information, corresponding handling operations are performed on fuel anomalies of different event levels, avoiding technical loopholes such as human fraud and illegal operations, and improving the intelligence level and risk prevention and control capabilities of fuel management. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a graded control method for fuel anomaly events according to an embodiment of this application is shown. Figure 2 A flowchart illustrating step S200 of the hierarchical control method according to an embodiment of this application is shown; Figure 3 A flowchart illustrating step S300 of the hierarchical control method according to an embodiment of this application is shown; Figure 4 A flowchart illustrating step S400 of the hierarchical control method according to an embodiment of this application is shown; Figure 5 A flowchart illustrating step S420 of the hierarchical control method according to an embodiment of this application is shown; Figure 6 This illustration shows another flowchart of the graded control method for fuel anomaly events according to an embodiment of this application; Figure 7 A schematic diagram of the structure of a graded control device for fuel anomaly events according to an embodiment of this application is shown.

[0020] Explanation of reference numerals in the attached figures: Tiered control device 1; data acquisition module 11; anomaly identification module 12; event level determination module 13; tiered handling module 14. Detailed Implementation

[0021] The present application will now be described in detail with reference to the accompanying drawings and embodiments. Various examples are provided by way of interpretation and not by way of limitation. In fact, those skilled in the art will recognize that modifications and variations can be made to the present application without departing from the scope or spirit thereof. For example, a feature represented or described as part of one embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desirable that the present application encompass such modifications and variations that fall within the scope of the appended claims and their equivalents.

[0022] The accompanying drawings illustrate one or more examples of this application. The detailed description uses numerical and alphabetic designations to refer to features in the drawings. Similar or analogous designations in the drawings and description have been used to refer to similar or analogous portions of this application. As used herein, the terms "first," "second," "third," and "fourth," etc., are used interchangeably.

[0023] The accompanying drawings in the following embodiments clearly and completely describe the technical solutions of this application. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Figure 1 A flowchart illustrating a graded control method for fuel anomaly events according to an embodiment of this application is shown. According to an example embodiment, such as... Figure 1 As shown, the hierarchical control method includes steps S100-S400. Exemplarily, the hierarchical control method of this application can be executed in a computer system such as a set of executable instructions. Furthermore, although the logical order is shown in the flowchart, under default conditions, the steps shown or described can be executed in a different order than that shown in the flowchart. Exemplarily, the hierarchical control method of this application is applied to the entire fuel management process of a thermal power plant. The entire fuel management process of a thermal power plant includes the business processes of fuel management, which include vehicle entry, sampling, sample preparation, sample transfer, testing, inventory management, blending, settlement, and approval.

[0025] In step S100, business data from the entire fuel management process of thermal power plants is acquired in real time.

[0026] According to the example embodiment, the business data refers to structured data collected in real time by a computer system within the fuel management business process of a thermal power plant. This business data includes vehicle entry data, sampling process data, sample flow data, testing process data, inventory consumption data, and operational approval data. The computer system acquires this business data in real time through plant-side fuel information systems and IoT monitoring devices (such as RFID readers, license plate recognition devices, sampling machine sensors, electronic scales, and testing equipment) deployed at the thermal power plant's subordinate power plants.

[0027] In one specific embodiment, the business data may include the following: Vehicle entry data: vehicle license plate number, RFID card number, QR code information, entry time, transportation company, supplier name, fuel type, planned transportation volume, etc.

[0028] Sampling process data: sampling point coordinates, number of sampling points, sampling time, sampling machine operating parameters, sampling weighing curve, sampling personnel information, sampling plan number, etc.

[0029] Sample flow data: raw coal sample code, number of times the code is printed, sample handover time, handover personnel, sample weight, sample storage location, sample flow trajectory, etc.

[0030] Laboratory process data: sample code, test items (calorific value, sulfur content, ash content, etc.), test results, test time, test personnel, test equipment status, test data rollback records, test confirmation status, and test data of samples available for review.

[0031] Inventory consumption data: coal stockpile, coal type distribution, daily coal consumption, inventory change trend, coal yard temperature, fuel blending ratio and blending process data, etc.

[0032] Operation and approval data: operator information, operation records, abnormal handling applications, approval opinions, approval time, etc.

[0033] In step S200, the business data is compared and logically verified according to the preset abnormal event judgment rule set to generate the corresponding fuel abnormal event.

[0034] According to the example embodiment, the computer system has a built-in anomaly identification module, which has a preset set of anomaly event judgment rules. This set of rules is preset by the user based on risk information throughout the entire fuel management process of a thermal power plant, and includes multiple judgment rules or threshold parameters for fuel anomalies. For example, each judgment rule or threshold parameter is associated with a preset fuel anomaly event; when business data meets the judgment rule or exceeds the threshold parameter, the system generates the corresponding fuel anomaly event. The anomaly identification module acquires the business data obtained in step S100 in real time, compares and verifies the business data item by item, and generates the corresponding fuel anomaly event when the business data meets the judgment rule or exceeds the threshold parameter. For example, fuel anomalies include, but are not limited to: anomalies such as inconsistencies in the three-card information during vehicle entry into the thermal power plant's fuel management process, anomalies in sampling and weighing during the sampling process, anomalies in the manual sampling scheme during the sampling process, anomalies in sample handover weight deviation during the sample preparation process, and anomalies in laboratory indicators exceeding thresholds during the testing process.

[0035] In step S300, the event level of the fuel anomaly event is determined according to the preset classification rule information.

[0036] According to an example embodiment, the computer system has a pre-set hierarchical rule base, which stores pre-set hierarchical rule information. Each hierarchical rule is associated with an event level and defines at least one data feature condition corresponding to that event level. In a specific embodiment, event levels are divided into Level I, Level II, and Level III according to risk level from high to low. Exemplarily, the computer system performs the following steps to determine the event level of a fuel anomaly event: Feature extraction: Extract the feature parameters carried by the generated fuel anomaly events. The feature parameters include, but are not limited to: whether the preset core data modification process is triggered, and whether the preset high-risk business process is involved (such as settlement, test result replacement, etc.).

[0037] Matching rules: The extracted feature parameters are compared one by one with the data feature conditions corresponding to the event level in the hierarchical rule base.

[0038] Output Event Level: The event level corresponding to the successfully matched data feature conditions is determined as the event level of the fuel anomaly event. In a specific embodiment, Level I fuel anomaly events include inconsistencies in the three-card information during vehicle entry and sampling weighing anomalies during the sampling process. Level II fuel anomaly events include manual sampling scheme anomalies during the sampling process and sample handover weight deviation anomalies during the sample preparation process. Level III anomaly events include laboratory indicators exceeding thresholds during the testing process.

[0039] In step S400, the handling operation for the fuel abnormality event is performed according to the event level and the preset handling strategy information.

[0040] According to the example embodiment, the preset handling strategy information refers to configuration data pre-stored by the user in the computer system, used to define the handling operations corresponding to the event level. Each piece of handling strategy information is associated with an event level and specifies the handling operation to be performed for the corresponding fuel anomaly event.

[0041] For example, the handling operation refers to the specific handling actions performed by the computer system in response to a fuel anomaly event, including but not limited to: modifying the status flag of the business process (such as setting it to "locked", "paused", or "normal"), creating an approval node, pushing a message to the person responsible for the fuel anomaly event, starting a timer to monitor the handling time limit, and generating a handling record, etc. Taking the event level mentioned in step S300 as divided into Level I, Level II, and Level III as an example, the handling operation is exemplified as follows: (1) The procedures for handling Level I fuel anomaly events are as follows: The computer system sets the status flag of the business process involved in the Level I fuel anomaly event to "locked". In the "locked" state, all access points associated with this business process are disabled, and subsequent business operations cannot continue. Once the Level I fuel anomaly event has been resolved, the computer system can restore the corresponding status flag to "normal".

[0042] (2) The procedures for handling Level II fuel anomaly events are as follows: The computer system sets the status flag of the business process involved in the Level II fuel anomaly event to "paused" and creates an approval node in the approval process. In the "paused" state, related business operations for this Level II fuel anomaly event are prohibited. Once the Level II fuel anomaly event has been resolved, the computer system can restore the corresponding status flag to "normal".

[0043] (3) The procedures for handling Level III fuel anomaly events are as follows: The computer system does not change the status flags of the relevant business processes for the Level III fuel anomaly event; it only generates an event record and pushes the event record to the corresponding responsible person according to the preset responsible person mapping table.

[0044] Through the above embodiments, this application achieves pre-identification of fuel anomaly events by comparing and verifying the business data of the entire fuel management process collected in real time, overcoming the shortcomings of low efficiency and strong lag in traditional manual inspections; by matching the event level corresponding to the fuel anomaly event through a preset hierarchical rule base, it realizes the transformation of event level from subjective judgment to objective data feature comparison, avoiding arbitrariness in hierarchical classification; and by using preset handling strategy information, it performs corresponding handling operations for fuel anomaly events of different event levels, avoiding technical loopholes such as human fraud and illegal operations, and improving the intelligence level and risk prevention and control capabilities of fuel management.

[0045] Figure 2 A flowchart illustrating step S200 of the hierarchical control method according to an embodiment of this application is shown. According to an example embodiment, such as... Figure 2 As shown, step S200 includes at least one of steps S210-230.

[0046] In step S210, the business data is judged to exceed the limit according to the preset threshold conditions in the abnormal event judgment rule set. When the business data exceeds the threshold conditions, the corresponding first fuel abnormal event is generated.

[0047] According to the example embodiment, the preset threshold condition is a preset threshold or allowable deviation range of the business data.

[0048] In one specific embodiment, for sampling and weighing anomaly events, a preset threshold condition is that the deviation between the actual sampled weight and the preset sampled weight exceeds the allowable deviation range of ±5%. When the sampled weight obtained by the computer system is compared with this threshold condition and determined to exceed the allowable deviation range, a fuel anomaly event corresponding to the sampling and weighing anomaly is immediately generated as the first fuel anomaly event. For another example, for laboratory indicator exceeding threshold anomaly events, when the calorific value, sulfur content, or other indicators in the laboratory results obtained by the computer system exceed preset thresholds, the system generates a corresponding fuel anomaly event as the first fuel anomaly event.

[0049] In step S220, the business data is compared for consistency according to the preset consistency conditions in the abnormal event judgment rule set. When the consistency comparison result is inconsistent, the corresponding second fuel abnormal event is generated.

[0050] According to the example embodiment, the preset consistency condition is a judgment principle used to verify whether business data matches each other or whether business data matches a preset standard.

[0051] In one specific embodiment, for an anomaly event of inconsistency between the three information (license plate, RFID card, and QR code) during vehicle entry, the preset consistency condition is: the license plate recognition information, RFID card reading information, and QR code scanning information of the vehicle entering the factory must match each other and be consistent with the pre-entered vehicle information. If the above consistency condition is not met, the consistency comparison result is considered inconsistent, and the computer system generates a corresponding anomaly event of inconsistency between the three information (license plate, RFID card, and QR code) as a second fuel anomaly event.

[0052] In step S230, the business data is matched and verified according to the preset rule matching conditions in the abnormal event judgment rule set. When the matching fails, the corresponding third fuel abnormal event is generated.

[0053] According to the example implementation, the rule matching conditions are used to verify the compliance of the business process, such as whether the business process is authorized and whether it is executed in a preset order.

[0054] In a specific embodiment, for abnormal events in the manual sampling scheme, the preset rule matching condition can be: the manual sampling process is allowed to start only when the manual sampling trigger condition is met (such as sampler failure or excessive fuel particle size). If the above rule matching condition is not met, it indicates that the matching has failed, and the system generates an abnormal event in the manual sampling scheme as the third fuel abnormal event.

[0055] Through the above embodiments, this application constructs an automatic identification system for fuel anomaly events by using multiple logics such as threshold judgment, consistency comparison and rule matching, ensuring that all kinds of potential violations or anomalies of different natures can be accurately and in real time captured.

[0056] Figure 3 A flowchart illustrating step S300 of the hierarchical control method according to an embodiment of this application is shown. According to an example embodiment, such as... Figure 3 As shown, step S300 includes steps S310-S320.

[0057] In step S310, the characteristic parameters of the fuel anomaly event are obtained.

[0058] According to the example embodiment, the feature parameters are parameter information used to quantitatively assess the severity and scope of impact of fuel anomaly events. Exemplarily, feature parameters include, but are not limited to: whether a preset core data modification process is triggered, and whether it involves preset high-risk business processes (such as settlement, replacement of test results, etc.). Exemplarily, the computer system extracts feature parameters from fuel anomaly events by reading field names.

[0059] In step S320, the feature parameters are matched with preset classification rule information to determine the event level of the fuel anomaly event. The specific matching method is described in the aforementioned step S300 and will not be repeated here.

[0060] Through the above embodiments, this application adopts a hierarchical approach of rule pre-setting and precise matching to ensure the scientific, reasonable and flexible nature of event level classification, which can adapt to the risk management needs in different scenarios.

[0061] Figure 4 A flowchart illustrating step S400 of the hierarchical control method according to an embodiment of this application is shown. According to an example embodiment, such as... Figure 4 As shown, step S400 includes steps S410-S420.

[0062] In step S410, the corresponding sequence of handling instructions is obtained from the preset handling strategy information according to the event level.

[0063] According to the example embodiment, the preset handling strategy information includes a sequence of handling instructions corresponding to the event level.

[0064] For example, for a Level I fuel anomaly event, the sequence of handling instructions may include: locking the business process; generating a non-skipable record; issuing a red alert; awaiting rectification confirmation; and unlocking the process. For a Level II fuel anomaly event, the sequence of handling instructions may include: suspending related business processes; submitting materials; initiating the company's approval process; executing the unlocking instruction; and archiving the record. For a Level III fuel anomaly event, the sequence of handling instructions may include: generating an event record and issuing a notification to the responsible person; requiring an explanation of the cause; and initiating a tiered approval process.

[0065] In step S420, the handling operations for the fuel anomaly event are executed sequentially according to the handling instruction sequence, wherein the handling instruction sequence includes process control operations, approval nodes, and timing thresholds.

[0066] According to the example embodiment, the computer system executes process control operations, approval nodes, and timing thresholds according to the processing instructions in the acquired processing instruction sequence.

[0067] For example, when handling a Level II fuel anomaly event (such as a data modification request event anomaly), the computer system executes the following actions in sequence according to the handling instructions in the handling instruction sequence: Process control operation: Suspend the settlement function for this batch of fuel; Approval node: Open the material submission portal to the power plant and set a submission time limit; Timing threshold: Set a 24-hour approval timing threshold for this approval node. If the approval is not completed within the time limit, the computer system sends a reminder notice to the reminder personnel.

[0068] Through the above embodiments, this application decomposes the approval and handling logic into standardized instruction sequences, thereby achieving standardized and automated processing of abnormal events of different levels, ensuring the rigor and efficiency of the handling process.

[0069] Figure 5 A flowchart illustrating step S420 of the hierarchical control method according to an embodiment of this application is shown. According to an example embodiment, such as... Figure 5 As shown, step S420 includes step S421.

[0070] When the event level is the preset event level, the flow control operation in the processing instruction sequence in step S420 includes step S421.

[0071] In step S421, the status of at least one business process involved in the fuel anomaly event is marked as locked to block the operation entry point associated with the business process.

[0072] For example, in the case of a fuel anomaly event with a preset event level of I, where the three-card information inconsistency is an anomaly event, the computer system marks the status of at least one business process involved in the three-card information inconsistency anomaly event as locked. For example, the computer system sets the operation entry of the sampling link corresponding to the three-card information inconsistency anomaly event to locked (i.e., disabled).

[0073] Through the above embodiments, this application fundamentally prevents fraud and operational risks caused by lax process control by forcibly locking the business process.

[0074] Figure 6 This illustration shows another flowchart of a graded control method for fuel anomaly events according to an embodiment of this application. According to an example embodiment, such as... Figure 6 As shown, the hierarchical control method also includes steps S500-S600.

[0075] In step S500, an event handling record is generated based on the obtained triggering information, handling operation information, approval information and timestamp of the fuel abnormality event.

[0076] According to the example embodiment, after the computer system completes the handling operation in step S400, it acquires and records the trigger information, handling operation information, approval information, and timestamps of the trigger information, handling operation information, and approval information of the corresponding fuel abnormality event, and generates an event handling record.

[0077] In step S600, the event handling records are statistically analyzed, and the hierarchical rule information is updated based on the statistical analysis results.

[0078] According to the example embodiment, the computer system performs statistical analysis on the event handling records obtained in step S500 according to a preset cycle, calculates statistical indicators such as the frequency of fuel abnormal events and the average handling time, and obtains statistical analysis results.

[0079] In one specific embodiment, when the frequency of a fuel anomaly event exceeds a frequency threshold and / or the average handling time exceeds a handling time threshold, the computer system raises the event level of the type of fuel anomaly event in the classification rule information by one level.

[0080] For example, if a fuel anomaly is classified as a Level II event, and the computer system determines that the monthly frequency of the fuel anomaly exceeds 10 times and / or the average handling time exceeds 48 hours, the computer system will adjust the event level of the fuel anomaly in the classification rule information from Level II to Level I in order to update the classification rule information.

[0081] Through the above embodiments, the data-driven approach of this application continuously optimizes the identification rules and classification strategies for abnormal events, thereby improving the precision and intelligence of fuel management.

[0082] Figure 7 A schematic diagram of a graded control device for fuel anomaly events according to an embodiment of this application is shown. According to an example embodiment, such as… Figure 7 As shown, the hierarchical control device 1 includes a data acquisition module 11, an anomaly identification module 12, an event level determination module 13, and a hierarchical handling module 14. Exemplarily, the hierarchical control device 1 is applied to the entire fuel management process of a thermal power plant, and is used to execute the hierarchical control method described above.

[0083] The data acquisition module 11 is used to acquire business data in real time throughout the entire fuel management process of a thermal power plant. This business data includes vehicle entry data, sampling process data, sample flow data, testing process data, inventory consumption data, and operation approval data. According to the example embodiment, the business data is structured data collected in real time by the data acquisition module 11 within the business process of fuel management in a thermal power plant. The computer system acquires this business data in real time through the plant-side fuel information system and IoT monitoring equipment (such as RFID readers, license plate recognition devices, sampling machine sensors, electronic scales, and testing equipment) deployed at the thermal power plant's subordinate power plants.

[0084] In one specific embodiment, the business data may include the following: Vehicle entry data: vehicle license plate number, RFID card number, QR code information, entry time, transportation company, supplier name, fuel type, planned transportation volume, etc.

[0085] Sampling process data: sampling point coordinates, number of sampling points, sampling time, sampling machine operating parameters, sampling weighing curve, sampling personnel information, sampling plan number, etc.

[0086] Sample flow data: raw coal sample code, number of times the code is printed, sample handover time, handover personnel, sample weight, sample storage location, sample flow trajectory, etc.

[0087] Laboratory process data: sample code, test items (calorific value, sulfur content, ash content, etc.), test results, test time, test personnel, test equipment status, test data rollback records, test confirmation status, and test data of samples available for review.

[0088] Inventory consumption data: coal stockpile, coal type distribution, daily coal consumption, inventory change trend, coal yard temperature, fuel blending ratio and blending process data, etc.

[0089] Operation and approval data: operator information, operation records, abnormal handling applications, approval opinions, approval time, etc.

[0090] The anomaly identification module 12 is used to compare and logically verify business data according to a preset set of anomaly event judgment rules, and generate corresponding fuel anomaly events. According to the example embodiment, the anomaly event judgment module has a preset set of anomaly event judgment rules. This set of rules is preset by the user based on risk information in the entire fuel management process of a thermal power plant, and includes multiple judgment rules or threshold parameters for fuel anomalies. For example, each judgment rule or threshold parameter is associated with a preset fuel anomaly event; when business data meets the judgment rule or exceeds the threshold parameter, the system generates the corresponding fuel anomaly event. The anomaly identification module 12 acquires the business data obtained in step S100 in real time, compares and verifies the business data item by item, and generates the corresponding fuel anomaly event when the business data meets the judgment rule or exceeds the threshold parameter. For example, fuel-related abnormal events include, but are not limited to: abnormal events in the vehicle entry process of thermal power plants where the three cards are inconsistent; abnormal events in the sampling and weighing process; abnormal events in the manual sampling scheme; abnormal events in the sample handover weight deviation process; and abnormal events in the laboratory indicators exceeding the threshold in the laboratory testing process.

[0091] The event level determination module 13 is used to determine the event level of a fuel anomaly event based on preset classification rule information. According to an example embodiment, the event level determination module 13 has a preset classification rule library, which stores preset classification rule information. Each classification rule is associated with an event level and defines at least one data feature condition corresponding to that event level. In a specific embodiment, the event levels are divided into Level I, Level II, and Level III according to risk level from high to low. Exemplarily, the computer system performs the following steps to determine the event level of the fuel anomaly event: Feature extraction: Extract the feature parameters carried by the generated fuel anomaly events. The feature parameters include, but are not limited to: whether the preset core data modification process is triggered, and whether the preset high-risk business process is involved (such as settlement, test result replacement, etc.).

[0092] Matching rules: The extracted feature parameters are compared one by one with the data feature conditions corresponding to the event level in the hierarchical rule base.

[0093] Output Event Level: The event level corresponding to the successfully matched data feature conditions is determined as the event level of the fuel anomaly event. In a specific embodiment, Level I fuel anomaly events include inconsistencies in the three-card information during vehicle entry and sampling weighing anomalies during the sampling process. Level II fuel anomaly events include manual sampling scheme anomalies during the sampling process and sample handover weight deviation anomalies during the sample preparation process. Level III anomaly events include laboratory indicators exceeding thresholds during the testing process.

[0094] The graded handling module 14 is used to perform handling operations on fuel anomaly events based on the event level and preset handling strategy information. According to the example embodiment, the preset handling strategy information refers to configuration data pre-stored by the user in the graded handling module 14, used to define the handling operations corresponding to the event level. Each handling strategy information is associated with an event level and specifies the handling operation to be performed for the corresponding fuel anomaly event.

[0095] For example, the handling operation refers to the specific handling actions performed by the graded handling module 14 for fuel anomaly events, including but not limited to: modifying the status flag of the business process (such as setting it to "locked", "paused", or "normal"), creating an approval node, pushing a message to the person responsible for the fuel anomaly event, starting a timer to monitor the handling time limit, and generating handling records, etc. Taking the event level mentioned in step S300 as divided into Level I, Level II, and Level III as an example, the handling operation is exemplified as follows: (1) The procedures for handling Level I fuel anomaly events are as follows: The tiered handling module 14 sets the status flag of the business process involved in the Level I fuel anomaly event to "locked". In the "locked" state, all operation entry points associated with this business process are prohibited from access, and subsequent business operations cannot continue. After the Level I fuel anomaly event is handled, the computer system can restore the corresponding status flag to "normal".

[0096] (2) The procedures for handling Level II fuel anomaly events are as follows: The tiered handling module 14 sets the status flag of the business process involved in the Level II fuel anomaly event to "paused" and creates an approval node in the approval process. In the "paused" state, related business operations for the Level II fuel anomaly event are prohibited. Once the Level II fuel anomaly event has been handled, the computer system can restore the corresponding status flag to "normal".

[0097] (3) The procedures for handling Level III fuel anomaly events are as follows: The graded handling module 14 does not change the status flag of the relevant business process of the Level III fuel anomaly event, but only generates an event record and pushes the event record to the corresponding responsible person according to the preset responsible person mapping table.

[0098] Through the above embodiments, this application achieves pre-identification of fuel anomaly events by comparing and verifying the business data of the entire fuel management process collected in real time, overcoming the shortcomings of low efficiency and strong lag in traditional manual inspections; by matching the event level corresponding to the fuel anomaly event through a preset hierarchical rule base, it realizes the transformation of event level from subjective judgment to objective data feature comparison, avoiding arbitrariness in hierarchical classification; and by using preset handling strategy information, it performs corresponding handling operations for fuel anomaly events of different event levels, avoiding technical loopholes such as human fraud and illegal operations, and improving the intelligence level and risk prevention and control capabilities of fuel management.

[0099] According to another aspect of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the hierarchical control method described above.

[0100] According to another aspect of this application, a computer-readable medium having processor-executable non-volatile program code is also provided, the program code causing the processor to perform the hierarchical control method described above.

[0101] According to another aspect of this application, a computer program product is also provided, including a computer program stored on a computer-readable medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the hierarchical control method described above.

[0102] As is known from common technical knowledge, this application can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this application or equivalent to this application are included in this application.

Claims

1. A method for grading and controlling fuel abnormal events, applied to the whole process of fuel management in a thermal power enterprise, characterized in that, The hierarchical control method includes: Real-time acquisition of business data from the entire fuel management process of the thermal power plant, including vehicle entry data, sampling process data, sample transfer data, laboratory testing process data, inventory consumption data, and operation approval data; Based on a preset set of abnormal event judgment rules, the business data is compared and logically verified to generate corresponding fuel abnormal events; The event level of the fuel anomaly event is determined based on the preset classification rules. Based on the event level and the preset handling strategy information, the handling operation for the fuel anomaly event is executed.

2. The hierarchical pipe and control method of claim 1, wherein, The step of comparing and logically verifying the business data according to a preset set of abnormal event judgment rules to generate a corresponding fuel abnormal event includes: According to the preset threshold conditions in the set of abnormal event judgment rules, the business data is judged to exceed the limit. When the business data exceeds the threshold conditions, a corresponding first fuel abnormal event is generated. And / or, according to the consistency conditions preset in the set of abnormal event judgment rules, the business data is compared for consistency. When the consistency comparison result is inconsistent, a corresponding second fuel abnormal event is generated. And / or, according to the preset rule matching conditions in the abnormal event judgment rule set, the business data is matched and verified, and when the matching fails, a corresponding third fuel abnormal event is generated.

3. The hierarchical pipe and control method of claim 1, wherein, The step of determining the event level of the fuel anomaly event based on preset classification rules includes: Obtain the characteristic parameters of the fuel anomaly event; The feature parameters are matched with preset classification rules to determine the event level of the fuel anomaly event.

4. The hierarchical pipe and control method of claim 1, wherein, The step of performing the handling operation for the fuel anomaly event based on the event level and preset handling strategy information includes: Based on the event level, obtain the corresponding sequence of handling instructions from the preset handling strategy information; The handling operations for the fuel anomaly event are executed sequentially according to the handling instruction sequence, wherein the handling instruction sequence includes process control operations, approval nodes, and timing thresholds.

5. The hierarchical pipe and control method of claim 4, wherein, When the event level is a preset event level, the flow control operations in the handling instruction sequence include: Mark the status of at least one business process involved in the fuel anomaly event as locked to block the operation entry points associated with the business process.

6. The hierarchical pipe and control method of claim 1, wherein, Also includes: Based on the obtained triggering information, handling operation information, approval information, and timestamp of the fuel anomaly event, an event handling record is generated; Perform statistical analysis on the event handling records, and update the hierarchical rule information based on the statistical analysis results.

7. A device for grading and controlling fuel abnormal events, applied to the whole process of fuel management in a thermal power enterprise, characterized in that, The hierarchical control device is used to execute the hierarchical control method according to any one of claims 1-6, and the hierarchical control device includes: The data acquisition module is used to acquire business data in the entire process of fuel management of the thermal power enterprise in real time. The business data includes vehicle entry data, sampling process data, sample circulation data, testing process data, inventory consumption data, and operation approval data. The anomaly identification module is used to compare and logically verify the business data according to a preset set of anomaly event judgment rules, and generate corresponding fuel anomaly events. The event level determination module is used to determine the event level of the fuel abnormality event based on preset classification rule information; The graded handling module is used to perform handling operations on the fuel abnormality event according to the event level and preset handling strategy information.

8. An electronic device, comprising: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the hierarchical control method according to any one of claims 1-6.

9. A computer readable medium having a non-transitory program code executable by a processor, the program code comprising instructions for: The program code causes the processor to execute the hierarchical control method according to any one of claims 1-6.

10. A computer program product, characterised in that, It includes a computer program stored on a computer-readable medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the hierarchical control method as described in any one of claims 1-6.

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