Calculation method and system for index target value of power generation company
By screening and constructing target curves under optimal operating conditions, and combining them with real-time characteristic index values, the problem of accuracy and objectivity in the calculation of target values for power generation companies has been solved. This has enabled dynamic adjustment of target values and real-time performance evaluation, thereby improving the operating efficiency and equipment lifespan of power plants.
Patent Information
- Application Number
- CN202511230045.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-29
- Publication Date
- 2025-11-18
AI Technical Summary
In the existing technology, the calculation of target values for power generation companies is inaccurate, and there are subjective human factors involved. It is difficult to meet the performance changes caused by unit aging and maintenance, resulting in insufficient objectivity and accuracy in performance evaluation.
By configuring the working condition acquisition task, the working condition time period that meets the baseline and dead zone conditions is selected. The working condition model is used to extract feature indicators, construct the target curve under the optimal working condition, combine the real-time feature indicator values to determine the target value, and calculate the deviation value to determine the optimization strategy.
It enables dynamic adjustment of target values, improves the accuracy and objectivity of calculations, provides real-time and reliable performance evaluation, guides power plant optimization strategies, and promotes energy conservation, emission reduction, and equipment life extension.
Smart Images
Figure CN120975329A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, specifically to a method and system for calculating target values for power generation companies. Background Technology
[0002] In the context of the nationwide localization of smart power plants, existing solutions suffer from inaccurate calculations of target values. Traditionally, target values are determined through power plant revenue collection and provision by plant operation and management personnel. While this approach gains acceptance from power plant users, it still introduces subjective factors and struggles to meet the target value requirements of all performance evaluation indicators within the plant. Furthermore, the target value curves for indicator parameters are not static. As unit operating time increases, inevitable wear and tear on components and equipment in the production system impacts the objective target values of some indicators. When units undergo maintenance or equipment replacement, the target values of relevant operating system indicators may also change. However, previous performance evaluation systems, with their manually set target values, rarely changed or were not updated in a timely manner. Changes in target values due to technological upgrades or system wear are difficult to determine subjectively and generally require a period of operation followed by analysis. Target values are precisely one of the most critical parameters in such performance evaluation models. Inaccurate or subjective target values directly transmit errors to deviations and scores, hindering the effective improvement of the objectivity and accuracy of performance evaluations. Summary of the Invention
[0003] To address the problems existing in the prior art, this invention provides a method and system for calculating target values for power generation companies. The power plant performance system calculates the deviation between real-time indicator values and target values, using the deviation as the basis for performance evaluation. A method for calculating target values for power generation companies includes the following steps: Step 1: Collect working conditions according to the configured working condition collection task's condition logic within the set period to obtain the working condition time period that meets the set conditions. Step 2: Set the characteristic indicators of the working condition model. Based on the working condition model, output the statistical values of the characteristic indicators for the collected working condition time periods to obtain a list of working condition time periods. Each working condition in the list of working condition time periods includes multiple characteristic indicators. Step 3: Use feature indicators as filtering conditions to filter the working conditions in the working condition time period list to obtain the optimal working condition list. Step 4: Select two characteristic indicators in the optimal working condition, and use them as the independent and dependent variables of the target curve, respectively. Construct the target curve under the optimal working condition based on the statistical values of the characteristic indicators of all optimal working conditions. Step 5: Determine the target value based on the target curve and real-time values of characteristic indicators under optimal operating conditions; Step 6: Calculate the deviation between the current real-time value and the current target value, use the deviation as the cumulative score within the scoring calculation period, and determine the optimization strategy for power plant operation based on the cumulative score.
[0004] Preferably, the configuration method for the working condition data acquisition task is as follows: Set the baseline and dead zone conditions for operating condition data acquisition. Use the baseline and dead zone conditions as the condition logic for operating condition data acquisition. Execute the operating condition data acquisition task according to the sampling parameters to obtain the operating condition time period that meets the set conditions.
[0005] Preferably, the characteristic indicators of the operating condition include at least two of the following: unit load, main steam temperature, flue gas humidity, flue gas oxygen content, and average load.
[0006] Preferably, the step of filtering the operating conditions in the operating condition time period list to obtain the optimal operating condition list includes: Analyze multiple characteristic indicators to identify those strongly correlated with the operating conditions. A strongly correlated characteristic indicator can be one or more indicators. Use these strongly correlated characteristic indicators as screening criteria to select the optimal operating conditions list.
[0007] Preferably, step 4 further includes dividing the target curve under the optimal working condition into intervals according to the characteristic index values to obtain multiple interval target curves.
[0008] Preferably, the deviation value is the absolute value of the difference between the real-time target value and the target value.
[0009] Preferably, the cumulative score is the sum of scores from each shift or the average score from each shift; The cumulative score for each shift is obtained by summing the scores of all cycles within a given shift. Calculate the average score of all cycles within a given shift, and use this average score as the shift average score.
[0010] A system for calculating target values for power generation companies, comprising: The configuration module is used to collect working conditions according to the conditional logic of the configured working condition collection task within a set period, and obtain the working condition time period that meets the set conditions. The data acquisition module is used to set the characteristic indicators of the working condition model, and output the statistical values of the characteristic indicators for the collected working condition time periods based on the working condition model, thereby obtaining a list of working condition time periods. Each working condition in the list of working condition time periods includes multiple characteristic indicators. The filtering module is used to filter the working conditions in the working condition time period list using feature indicators as filtering conditions, so as to obtain the optimal working condition list. The target curve module is used to select two characteristic indicators in the optimal working condition, which are used as the independent and dependent variables of the target curve, respectively. The target curve under the optimal working condition is constructed based on the statistical values of the characteristic indicator values of all optimal working conditions. The target value module is used to determine the target value based on the target curve and real-time values of characteristic indicators under optimal working conditions. The scoring optimization module is used to calculate the deviation between the current real-time value and the current target value. This deviation is used as the cumulative score within the scoring calculation period, and the optimization strategy for power plant operation is determined based on the cumulative score.
[0011] An electronic device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of a method for calculating the target value of a power generation company indicator as described above.
[0012] Compared with the prior art, the present invention has the following beneficial technical effects: The method for calculating target values for power generation company indicators provided in this application first collects operating conditions based on configured conditional logic, and then filters effective operating condition time periods by combining baseline conditions and dead zone conditions. This effectively eliminates interfering data and unstable operating conditions, laying a reliable data foundation for subsequent analysis and avoiding target value deviations caused by poor data quality in traditional methods. It then extracts a list of operating condition time periods containing multiple characteristic indicators such as unit load and main steam temperature based on the operating condition model, transforming abstract operating conditions into a quantifiable set of parameters. This achieves a precise correlation between operating conditions and key operating indicators, providing concrete data support for optimal operating condition selection. Finally, it filters the optimal operating conditions using characteristic indicators as selection criteria. By focusing on strongly correlated characteristic indicators, it ensures that subsequent analysis is based on high-quality data from the unit's optimal operating state, solving the subjectivity problem caused by the lack of objective data support when manually setting target values in traditional methods. Based on the optimal operating condition characteristic indicators... The system constructs a target curve, transforming optimal practical data into a dynamic target reference standard, replacing the static, manually set method. This ensures that the target value truly reflects the unit's optimal performance and adapts to performance changes caused by unit aging and maintenance. By combining real-time characteristic index values with the target curve to determine the current target curve, the system achieves adaptive adjustment of the target curve according to real-time operating conditions, overcoming the shortcomings of traditional target value updates that are not timely, and ensuring that the target value always fits the current operating state. Step 6 quantifies the gap between actual operation and the optimal target by calculating deviation values and accumulating scores, providing an objective quantitative basis for performance evaluation. At the same time, the system determines optimization strategies based on scores, effectively guiding operators to conduct defect self-inspection and operational improvements. Ultimately, this achieves the power plant's production optimization goals such as energy saving, emission reduction, and extended equipment lifespan, comprehensively improving the accuracy, objectivity, real-time nature, and practicality of the power generation company's target value calculation.
[0013] This application also proposes a system for calculating target values of power generation company indicators, an electronic device, and a computer storage medium, which possess all the advantages of the aforementioned method for calculating target values of power generation company indicators. Attached Figure Description
[0014] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0015] Figure 1 This is a flowchart illustrating the method for calculating the target values of power generation companies according to the present invention. Figure 2 This is a list of operating times for Embodiment 1 of the present invention; Figure 3 This is a working condition model diagram of Embodiment 1 of the present invention; Figure 4 This is a list of characteristic indicators corresponding to the working condition in Embodiment 1 of the present invention; Figure 5 This is the list of operating conditions optimized in Embodiment 1 of the present invention; Figure 6 This is a target steam temperature curve for Embodiment 1 of the present invention; Figure 7 This is the target curve diagram under the combined variable conditions of Embodiment 1 of the present invention; Figure 8 This is a schematic diagram illustrating the exact optimal target value in Embodiment 1 of the present invention; Figure 9 This is a schematic diagram of the scoring rules in Embodiment 1 of the present invention; Figure 10 This is a ranking diagram of Embodiment 1 of the present invention. Detailed Implementation
[0016] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0017] A method for calculating target values for power generation companies includes the following steps: Step 1: Collect working conditions according to the configured working condition collection task's condition logic within the set period to obtain the working condition time period that meets the set conditions. By setting baseline conditions (thresholds for triggering data collection) and dead zone conditions (filtering invalid data), combined with set time periods (such as sampling intervals and durations), the system filters out operating time periods that meet stability requirements from real-time operational data. This process eliminates interfering data and unstable operating conditions, obtaining initial, valid operating time periods that meet the basic conditions, providing a reliable data foundation for subsequent accurate analysis.
[0018] Step 2: Calculate the characteristic indicators of each working condition time period based on the working condition model to obtain a list of working condition time periods. Each working condition in the list of working condition time periods includes multiple characteristic indicators and their statistical values.
[0019] Based on the pre-defined operating condition model, key characteristic indicators (such as unit load, main steam temperature, and flue gas oxygen content) are extracted from the operating condition time period obtained in step 1 according to the sampling frequency, forming an operating condition record containing multiple operating parameters. Then, the statistical value of each characteristic indicator is calculated, which can be the extreme value or mean of each characteristic indicator. The abstract operating condition is transformed into a set of quantifiable and analyzable characteristic parameters, establishing the correlation between the operating condition and key operating indicators, and providing specific data support for subsequent selection of the optimal operating condition.
[0020] Step 3: Use feature indicators as filtering conditions to filter the working conditions in the working condition time period list to obtain the optimal working condition list. By analyzing the correlation between characteristic indicators and operating condition performance, strongly correlated characteristic indicators are selected. These are then used as criteria to perform a secondary filter on the list of operating conditions, retaining those with superior performance. Non-optimal operating conditions are eliminated, focusing on high-quality data that reflects the unit's best operating state. This ensures that the subsequent construction of target curves is based on "best practices," improving the objectivity and accuracy of target values.
[0021] Step 4: Select two characteristic indicators in the optimal working condition, and use them as the independent and dependent variables of the target curve, respectively. Construct the target curve under the optimal working condition based on the characteristic indicator values of all optimal working conditions.
[0022] The target curve under optimal working conditions is divided into intervals according to the characteristic index values to obtain multiple interval target curves.
[0023] It should be noted that when segmenting the target curve, any feature index can be selected, and the target curve can be segmented according to the fluctuation characteristics of the feature index value to form multiple interval target curves.
[0024] For example, the target curve can be segmented based on the time nodes of the characteristic index values, such as summer or winter. Taking load as an example, the target curve is segmented according to the load threshold. Since the summer or winter environment will cause fluctuations in load, segmenting the target curve can more accurately determine the target value.
[0025] By using one characteristic indicator of the optimal operating condition as the independent variable (X value) and another characteristic indicator value as the dependent variable (Y value), a mapping relationship is established between the two to form a target curve. This replaces the traditional method of manually setting target values, generating a dynamic and accurate target reference standard through optimal operating condition data, thus linking the target value with the actual optimal performance of the unit and solving the problems of subjectivity and staticity in manual setting.
[0026] Step 5: Determine the optimal target value based on the target curve and real-time values of characteristic indicators under optimal operating conditions; The system extracts current operational characteristic indicators from the real-time database as variables and inputs them into the constructed target curve range variable, which represents the target curve to be adopted under the current operating conditions. The target curve outputs the optimal target value. This enables dynamic updating of the target curve, allowing the target value to adaptively adjust with changes in real-time operating conditions (such as unit load fluctuations), ensuring that the target value always closely matches the current optimal operating state and improving the real-time performance and accuracy of the assessment.
[0027] Step 6: Calculate the difference between the current real-time target value and the optimal target value. Use this difference as the deviation value and the deviation value as the cumulative score within the scoring calculation period. Determine the optimization strategy for power plant operation based on the cumulative score.
[0028] (1) (2) In the above formula, xi is the real-time value of indicator x at time i, and xi,T is the target value of indicator x at time i.
[0029] The deviation score function f(δ) supports common function expressions as well as piecewise functions. It also supports the definition of a deviation-score curve, allowing the score to be calculated from the deviation through linear interpolation of the curve.
[0030] By calculating the difference (deviation value) between real-time operating indicators and optimal target values, the deviation value is converted into a score, which is then accumulated over a period of time. Based on the scores, different shifts / duties are ranked, and the ranking differences are analyzed to pinpoint operational problems. Quantifying the gap between actual operation and the optimal target provides objective data support for performance evaluation. Through score ranking and deviation analysis, operators are guided to conduct self-inspection and optimization of defects, ultimately achieving improvements in production processes such as energy saving, emission reduction, and extended equipment lifespan.
[0031] A method for calculating target values for power generation companies includes the following steps: Step 1: Configure the working condition data collection task, collect working conditions according to the condition logic within the set period, and obtain the working condition time period that meets the set conditions.
[0032] Set the baseline and dead zone conditions for operating condition data acquisition, use the baseline and dead zone conditions as the operating condition data acquisition logic, execute the operating condition data acquisition task according to the set time period, and acquire the operating condition time period.
[0033] For example, setting "N1BS_F_FGDIn>1400" as the baseline condition triggers the threshold for data acquisition under working conditions, and setting "N1BS_F_FGDIn<20" as the dead zone condition filters out invalid data under this dead zone condition.
[0034] Set the time period, i.e., the sampling parameters, including the sampling interval (e.g., 0 seconds, i.e., real-time acquisition) and the duration (e.g., at least 5 minutes to ensure operational stability). The final result is the time period where N1BS_F_FGDIn > 1400 and the N1BS_F_FGDIn value fluctuates relatively little. Figure 2 ).
[0035] Step 2: Configure the working condition model ( Figure 3 Based on the working condition model, the working condition data is collected for the working condition time period set in step 1 to obtain a working condition time period list. Each working condition in the working condition time period list includes multiple characteristic indicators. Figure 4 ).
[0036] Characteristic indicators are the key features of unit operation under various operating conditions, such as unit load (generator power), main steam temperature, flue gas humidity, flue gas oxygen content, average load and other operating parameters.
[0037] By configuring a working condition model, relevant feature indicators are matched to the working conditions. Within the working condition time period, the working condition model is automatically triggered every time a working condition is collected, based on the sampling frequency, to obtain the values of each feature indicator for that working condition. Figure 4 The output shows a single operating condition record containing six characteristic values.
[0038] Step 3: Using feature indicators as screening criteria, filter the working conditions generated in Step 1 based on the screening results of feature indicators, and output secondary working conditions to achieve the purpose of working condition optimization.
[0039] Analyze multiple characteristic indicators to identify those that are strongly correlated with the operating conditions. A strongly correlated characteristic indicator can be one or more indicators. Use these strongly correlated characteristic indicators as screening criteria to select the optimal operating conditions.
[0040] For example: if the flue gas humidity is >0 and the flue gas oxygen content is <7, then the operating conditions that simultaneously satisfy both >0 and <7 are selected. In this case, the remaining 5 operating conditions are called the optimal operating conditions after optimization. Figure 5 ).
[0041] Step 4: After filtering, the optimal working conditions list is obtained. The characteristic index value of the optimal working condition is used as the X value of the target curve to determine the target value. Based on all the target values, the target curve under the optimal working condition is constructed, thus obtaining the target curve corresponding to a certain characteristic index under the optimal working condition.
[0042] S4.1, according to Figure 5 The output list of operating conditions uses generator power as a characteristic indicator, the target characteristic as the target value of main steam temperature, the characteristic indicator as the X value of the target curve, and the target value as the Y value of the target curve.
[0043] With generator power as value X and main steam temperature target value as value Y: When the X value is 86.97, the target value for the main steam temperature is 520.14. When X is 186.78, the target main steam temperature is 588.13. When X is 1575.31, the target main steam temperature is 534.12. When X is 3466.6, the target value for main steam temperature is 554.36. When X is 4039.01, the target value for main steam temperature is 499.24.
[0044] S4.2 The final target curve is 86.97:520.14, 186.78:588.13, 1575.31:534.12, 3466.6:554.36, 4039.01:499.24 ( Figure 6 The target curve is a two-dimensional curve based on generator power.
[0045] S4.3 The characteristic index results of different working conditions can be substituted into the curve as variables to automatically match a suitable target curve. Figure 7 When the main steam temperature is evaluated as low, the selected curve is 50:243.7, 100.26:273.64, 50.33:246.8, 105.32:261.27, 52.46:245.46, 50.82:241.95. When the main steam temperature is evaluated as normal, the selected curve is curve S4.2: 86.97:521.14, 186.78:588.13, 1575.31:534.12, 3466.6:554.36, 4039.01:499.24. This target curve is a multi-interval two-dimensional curve based on generator power.
[0046] Step 5: Calculate the optimal target value based on the target curve. Extract the latest snapshot value of the current generator power from the real-time database as X and substitute it into the target curve to obtain the optimal target value Y, which is the latest target value at this moment. Figure 8 ).
[0047] Step 6: Calculate the difference between the current real-time target value and the optimal target value. Use this difference as the deviation value, and use the deviation value as the score, or use it as a benchmark to input into the score curve, and finally calculate the score.
[0048] Extract the latest snapshot value corresponding to the main steam temperature index from the real-time database, i.e., the real-time value of the main steam temperature. Calculate the absolute value of the difference between the real-time value and the optimal target value, i.e., |actual value - target value|, to ensure that the deviation value is non-negative.
[0049] The deviation value is used as the score (e.g., a deviation of 10 corresponds to a final score of 10).
[0050] Alternatively, the deviation can be used as a benchmark value and substituted into the scoring function to calculate the score, supporting common functions (such as linear functions) and flexibly adapting to the scoring rules of different indicators. Figure 9 ).
[0051] The deviation and score can be set freely according to business needs, such as every 1 minute or every 10 minutes.
[0052] Step 7: Calculate and summarize the scores of all classes within the period to obtain the class score.
[0053] The score in step 6 is the periodic score (every 1 minute or every 10 minutes). The calculation of the shift score includes two methods, as follows: 1. Accumulate the periodic scores within the set shifts to obtain the shift's cumulative score.
[0054] For example, if a cycle is 30 days, the scores for the 30-day cycle are summed to obtain the cumulative score for each shift.
[0055] 2. Calculate the average score of all cycles within the set shift, and use this average score as the shift average score.
[0056] For example, if a cycle is 30 days, the scores for the 30-day cycle are summed and averaged to obtain the average score for each shift.
[0057] It should be noted that when calculating cumulative indicators such as electricity consumption, coal consumption, and emissions, the cumulative scores for each shift can be used for comparison; however, when calculating scores for non-cumulative indicators such as temperature, rotational speed, and efficiency, the average scores for each shift must be used for comparison. The total score for each shift (e.g., morning, afternoon, and evening shifts) reflects the operational performance of that shift.
[0058] Step 8: Sort the scores of different shifts from high to low, develop optimization strategies for the shifts with lower scores, and improve operational efficiency based on the optimization strategies.
[0059] For example, a day can be divided into three shifts, each lasting 8 hours. The deviation is calculated every hour, resulting in 8 segmented unit scores. The 8 segmented unit scores are then summed to obtain the periodic score for that shift. The periodic scores of the three shifts are then sorted to characterize the operational efficiency of each shift.
[0060] Ranking results can be used for real-time inter-value competition and defect self-inspection (locating operational problems through ranking differences), ultimately promoting optimization of production processes such as energy conservation, emission reduction, and equipment life extension. Real-time deviations between actual and target values can also provide real-time operational guidance. Figure 10 ).
[0061] Example 2 Correspondingly, a system for calculating the target values of a power generation company includes: The configuration module is used to collect working conditions according to the conditional logic of the configured working condition collection task within a set period, and obtain the working condition time period that meets the set conditions. The data acquisition module is used to set the characteristic indicators of the working condition model, and output the statistical values of the characteristic indicators for the collected working condition time periods based on the working condition model, thereby obtaining a list of working condition time periods. Each working condition in the list of working condition time periods includes multiple characteristic indicators. The filtering module is used to filter the working conditions in the working condition time period list using feature indicators as filtering conditions, so as to obtain the optimal working condition list. The target curve module is used to select two characteristic indicators in the optimal working condition, which are used as the independent and dependent variables of the target curve, respectively. The target curve under the optimal working condition is constructed based on the statistical values of the characteristic indicator values of all optimal working conditions. The target value module is used to determine the target value based on the target curve and real-time values of characteristic indicators under optimal working conditions. The scoring optimization module is used to calculate the deviation between the current real-time value and the current target value. This deviation is used as the cumulative score within the scoring calculation period, and the optimization strategy for power plant operation is determined based on the cumulative score.
[0062] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another device, or some features may be ignored or not executed. The modules described as separate components may or may not be physically separated. The components shown as modules may be one or more physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs.
[0063] Furthermore, in the various embodiments of the present invention, the modules can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.
[0064] An electronic device provided in this application includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of a method for calculating the target value of a power generation company indicator as described in any of the above embodiments.
[0065] Another electronic device provided in this application embodiment may further include: an input port connected to a processor for transmitting multimodal data collected by an external acquisition device to the processor; a display unit connected to the processor for displaying the processor's processing results to the outside world; and a communication module connected to the processor for enabling communication between the electronic device and the outside world. The display unit may be a display panel, a laser scanning display, etc.; the communication method adopted by the communication module includes, but is not limited to, Mobile High Definition Link (HML), Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), and wireless connection (including Wi-Fi, Bluetooth, Bluetooth Low Energy, and IEEE 802.11s-based communication technology).
[0066] This application provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps of a method for calculating the target value of a power generation company's indicators as described in any of the above embodiments.
[0067] For descriptions of relevant parts of the calculation system, electronic device, and computer-readable storage medium for target values of power generation companies provided in this application, please refer to the detailed description of the corresponding parts in the calculation method for target values of power generation companies provided in this application, which will not be repeated here. Furthermore, parts of the technical solutions provided in this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.
[0068] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.
Claims
1. A method for calculating target values for power generation companies, characterized in that, Includes the following steps: Step 1: Collect working conditions according to the configured working condition collection task's condition logic within the set period to obtain the working condition time period that meets the set conditions. Step 2: Set the characteristic indicators of the working condition model. Based on the working condition model, output the statistical values of the characteristic indicators for the collected working condition time periods to obtain a list of working condition time periods. Each working condition in the list of working condition time periods includes multiple characteristic indicators. Step 3: Use feature indicators as filtering conditions to filter the working conditions in the working condition time period list to obtain the optimal working condition list. Step 4: Select two characteristic indicators in the optimal working condition, and use them as the independent and dependent variables of the target curve, respectively. Construct the target curve under the optimal working condition based on the statistical values of the characteristic indicators of all optimal working conditions. Step 5: Determine the target value based on the target curve and real-time values of characteristic indicators under optimal operating conditions; Step 6: Calculate the deviation between the current real-time value and the current target value, use the deviation as the cumulative score within the scoring calculation period, and determine the optimization strategy for power plant operation based on the cumulative score.
2. The method for calculating the target value of a power generation company's indicators according to claim 1, characterized in that, The configuration method for the operating condition data acquisition task is as follows: Set the baseline and dead zone conditions for operating condition data acquisition. Use the baseline and dead zone conditions as the condition logic for operating condition data acquisition. Execute the operating condition data acquisition task according to the sampling parameters to obtain the operating condition time period that meets the set conditions.
3. The method for calculating the target value of a power generation company's indicators according to claim 1, characterized in that, The characteristic indicators of the operating condition include at least two of the following: unit load, main steam temperature, flue gas humidity, flue gas oxygen content, and average load.
4. The method for calculating the target value of a power generation company's indicators according to claim 1, characterized in that, The process of filtering the operating conditions in the operating condition time period list to obtain the optimal operating condition list includes: Analyze multiple characteristic indicators to identify those strongly correlated with the operating conditions. A strongly correlated characteristic indicator can be one or more indicators. Use these strongly correlated characteristic indicators as screening criteria to select the optimal operating conditions list.
5. The method for calculating the target value of a power generation company's indicators according to claim 1, characterized in that, Step 4 also includes dividing the target curve under the optimal working condition into intervals according to the characteristic index values to obtain multiple interval target curves.
6. The method for calculating the target value of a power generation company's indicators according to claim 1, characterized in that, The deviation value is the absolute value of the difference between the real-time target value and the target value.
7. The method for calculating the target value of a power generation company's indicators according to claim 1, characterized in that, The cumulative score is either the sum of scores for each shift or the average score for each shift. The cumulative score for each shift is obtained by summing the scores of all cycles within a given shift. Calculate the average score of all cycles within a given shift, and use this average score as the shift average score.
8. A system for calculating target values for power generation companies, characterized in that, include: The configuration module is used to collect working conditions according to the conditional logic of the configured working condition collection task within a set period, and obtain the working condition time period that meets the set conditions. The data acquisition module is used to set the characteristic indicators of the working condition model, and output the statistical values of the characteristic indicators for the collected working condition time periods based on the working condition model, thereby obtaining a list of working condition time periods. Each working condition in the list of working condition time periods includes multiple characteristic indicators. The filtering module is used to filter the working conditions in the working condition time period list using feature indicators as filtering conditions, so as to obtain the optimal working condition list. The target curve module is used to select two characteristic indicators in the optimal working condition, which are used as the independent and dependent variables of the target curve, respectively. The target curve under the optimal working condition is constructed based on the statistical values of the characteristic indicator values of all optimal working conditions. The target value module is used to determine the target value based on the target curve and real-time values of characteristic indicators under optimal working conditions. The scoring optimization module is used to calculate the deviation between the current real-time value and the current target value. This deviation is used as the cumulative score within the scoring calculation period, and the optimization strategy for power plant operation is determined based on the cumulative score.
9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for calculating the target value of a power generation company indicator as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for calculating the target value of a power generation company's indicators as described in any one of claims 1-7.