Thermal power operation optimization control system based on multi-modal data fusion

Abstract

The invention relates to the technical field of thermal power operation optimization, and discloses a thermal power operation optimization control system based on multi-modal data fusion, which is characterized in that a data acquisition module sequentially acquires multiple groups of thermal power plant operation data of a thermal power plant based on a preset data acquisition moment, and integrates each group of thermal power plant operation data; obtaining a plurality of integrated thermal power plant operation data sets; a first calculation module analyzes the integrated thermal power plant operation data set and calculates a single-mode operation data fluctuation factor; a second calculation module performs normalization processing on all single-mode operation data fluctuation factors and calculates multi-mode operation data fluctuation factors of the thermal power plant; the optimization judgment module judges whether the thermal power plant needs operation optimization or not according to the relation between the multi-modal operation data fluctuation factors and preset multi-modal operation data fluctuation factors, and by integrating multi-modal data and quantifying data fluctuation characteristics, the judgment precision and efficiency of operation optimization of the thermal power plant are guaranteed, and the safety and stability of a thermal power generating unit are improved.

Description

Technical Field

[0001] This invention relates to the field of thermal power plant operation optimization technology, and more specifically, to a thermal power plant operation optimization control system based on multimodal data fusion. Background Technology

[0002] In recent years, with the transformation of the energy structure and increasingly stringent environmental protection requirements, thermal power generation, as an important pillar of traditional energy supply, faces an urgent need to improve operational efficiency, reduce pollutant emissions, and ensure the safe and stable operation of units. The thermal power production process involves the complex coupling of boilers, steam turbines, generators, and other auxiliary systems. Its operating status is affected by a variety of factors such as fuel characteristics, equipment conditions, and environmental parameters, exhibiting characteristics of multivariability, strong coupling, and nonlinearity.

[0003] In actual operation, thermal power units generate a large amount of multimodal operating data from different sensors, control systems, and management platforms. This data reflects the unit's operating characteristics from different dimensions. However, due to its dispersed sources, diverse structures, and different time sequences, information silos often exist, preventing the full exploitation of data value. Traditional thermal power operation optimization control systems typically rely on single-type data for analysis and control, making it difficult to grasp the overall operational fluctuations and stability of the unit, thus limiting the accuracy and real-time performance of operation optimization control. Summary of the Invention

[0004] This invention provides a thermal power plant operation optimization control system based on multimodal data fusion. By integrating multimodal data and quantifying data fluctuation characteristics, it ensures the accuracy and efficiency of judgment on thermal power plant operation optimization and improves the safety and stability of thermal power units.

[0005] To achieve the above objectives, the present invention provides a thermal power plant operation optimization control system based on multimodal data fusion, comprising:

[0006] The data acquisition module is used to sequentially collect multiple sets of thermal power plant operation data based on preset data acquisition times, and integrate each set of thermal power plant operation data to obtain multiple integrated thermal power plant operation data sets.

[0007] The first calculation module is used to analyze the integrated thermal power plant operation data set and calculate the single-mode operation data fluctuation factor of the thermal power plant based on the analysis results.

[0008] The second calculation module is used to normalize all single-mode operation data fluctuation factors and calculate the multi-mode operation data fluctuation factor of the thermal power plant based on the normalized single-mode operation data fluctuation factors.

[0009] The optimization judgment module is used to pre-set a preset multimodal operation data fluctuation factor, and determine whether the thermal power plant needs operation optimization based on the relationship between the multimodal operation data fluctuation factor and the preset multimodal operation data fluctuation factor.

[0010] Furthermore, it also includes:

[0011] The data preprocessing module is used to preprocess all thermal power plant operation data, wherein the preprocessing includes deleting duplicate thermal power plant operation data, deleting erroneous thermal power plant operation data, and deleting invalid thermal power plant operation data.

[0012] Furthermore, the first computing module is used for:

[0013] The first calculation module is used to determine the first thermal power plant operating data corresponding to the first data acquisition time and the last thermal power plant operating data corresponding to the last data acquisition time.

[0014] The first calculation module is used to calculate the overall fluctuation metric of the thermal power plant based on the operating data of the first thermal power plant and the operating data of the last thermal power plant.

[0015] The first calculation module is used to calculate the neighborhood fluctuation metric of the thermal power plant based on the remaining thermal power plant operation data;

[0016] The first calculation module is used to perform a weighted summation of the overall fluctuation metric and the neighborhood fluctuation metric to obtain the single-mode operation data fluctuation factor of the thermal power plant.

[0017] Furthermore, the first computing module is used for:

[0018] The first calculation module is used to calculate the absolute value of the difference between the first thermal power plant operation data and the last thermal power plant operation data, as the change value of thermal power plant operation data;

[0019] The first calculation module is used to determine the data acquisition time interval between the first data acquisition time and the last data acquisition time;

[0020] The first calculation module is used to take the ratio of the change value of the thermal power plant's operating data to the data acquisition time interval as the overall fluctuation measure value of the thermal power plant.

[0021] Furthermore, the first computing module is used for:

[0022] The first calculation module is used to randomly extract one thermal power plant operation data from the remaining thermal power plant operation data as the standard thermal power plant operation data;

[0023] The first calculation module is used to pre-set a first preset separator and a second preset separator;

[0024] The first calculation module is used to determine the left-hand thermal power plant operation data of the standard thermal power plant operation data based on the first preset separator, and to determine the right-hand thermal power plant operation data of the standard thermal power plant operation data based on the second preset separator;

[0025] The first calculation module is used to construct a thermal power plant operation data set based on the standard thermal power plant operation data, the left thermal power plant operation data, and the right thermal power plant operation data;

[0026] The first calculation module is used to calculate the neighborhood fluctuation coefficient corresponding to the standard thermal power plant operation data based on the thermal power plant operation data set;

[0027] The first calculation module is used to perform curve fitting on all neighborhood fluctuation coefficients to obtain neighborhood fluctuation coefficient curves and extract the abrupt change value;

[0028] The first calculation module is used to determine the mean of all neighborhood fluctuation coefficients, and the product of the mean and the mutation degree value is used as the neighborhood fluctuation metric of the thermal power plant.

[0029] Furthermore, the first computing module is used for:

[0030] The first calculation module is used to calculate the neighborhood fluctuation coefficient corresponding to the standard thermal power plant operating data according to the following formula:

[0031]

[0032] Where d is the neighborhood fluctuation coefficient corresponding to the standard thermal power plant operating data, s is the number of thermal power plant operating data in the thermal power plant operating data set, and g j For the j-th thermal power plant operation data in the thermal power plant operation data set, g j+1 This refers to the (j+1)th thermal power plant operation data in the thermal power plant operation data set.

[0033] Furthermore, the second computing module is used for:

[0034] The second calculation module is used to preset the range of single-mode operation data fluctuation factors, and determine the data fluctuation index corresponding to each single-mode operation data fluctuation factor according to the range of single-mode operation data fluctuation factors;

[0035] The second calculation module is used to calculate the multimodal operation data fluctuation factor of the thermal power plant based on all data fluctuation indicators.

[0036] Furthermore, the second computing module is used for:

[0037] The second calculation module is used to preset the range of single-mode operation data fluctuation factors, wherein the range of single-mode operation data fluctuation factors includes a first single-mode operation data fluctuation factor and a second single-mode operation data fluctuation factor, and the first single-mode operation data fluctuation factor is smaller than the second single-mode operation data fluctuation factor.

[0038] The second calculation module is used to take the first difference between the first single-mode operation data fluctuation factor and the single-mode operation data fluctuation factor as a data fluctuation index when the single-mode operation data fluctuation factor is less than or equal to the first single-mode operation data fluctuation factor.

[0039] The second calculation module is used to calculate a second difference between the single-mode operation data fluctuation factor and the first single-mode operation data fluctuation factor, and to calculate a third difference between the second single-mode operation data fluctuation factor and the single-mode operation data fluctuation factor when the single-mode operation data fluctuation factor is greater than the first single-mode operation data fluctuation factor and less than the second single-mode operation data fluctuation factor, and to use the minimum value of the second difference and the third difference as the data fluctuation index.

[0040] The second calculation module is used to take the fourth difference between the single-mode operation data fluctuation factor and the second single-mode operation data fluctuation factor as a data fluctuation index when the single-mode operation data fluctuation factor is greater than or equal to the second single-mode operation data fluctuation factor.

[0041] Furthermore, the second computing module is used for:

[0042] The second calculation module is used to calculate the multimodal operation data fluctuation factor of the thermal power plant according to the following formula:

[0043]

[0044] Where h is the multimodal operation data fluctuation factor of the thermal power plant, n is the number of data fluctuation indicators, and v r Let v be the r-th data fluctuation indicator. min v is the indicator of minimum data fluctuation. max The maximum data fluctuation indicator, (v max -v r ) max For all v max -v r The corresponding maximum value.

[0045] Furthermore, the optimization judgment module is used for:

[0046] The optimization judgment module is used to determine that the thermal power plant does not need to perform operation optimization when the multimodal operation data fluctuation factor is less than the preset multimodal operation data fluctuation factor.

[0047] The optimization judgment module is used to determine that the thermal power plant needs to be optimized when the multimodal operation data fluctuation factor is greater than or equal to the preset multimodal operation data fluctuation factor.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] This invention discloses a thermal power plant operation optimization control system based on multimodal data fusion. A data acquisition module sequentially collects multiple sets of thermal power plant operation data at preset data acquisition times, and integrates each set of data to obtain multiple integrated thermal power plant operation data sets. A first calculation module analyzes the integrated thermal power plant operation data sets and calculates the single-mode operation data fluctuation factor. A second calculation module normalizes all single-mode operation data fluctuation factors and calculates the multimodal operation data fluctuation factor of the thermal power plant. An optimization judgment module determines whether the thermal power plant needs operation optimization based on the relationship between the multimodal operation data fluctuation factor and the preset multimodal operation data fluctuation factor. By integrating multimodal data and quantifying data fluctuation characteristics, the accuracy and efficiency of thermal power plant operation optimization judgment are ensured, thereby improving the safety and stability of thermal power units. Attached Figure Description

[0050] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0051] Figure 1 A schematic diagram of the structure of a thermal power plant operation optimization control system based on multimodal data fusion in an embodiment of the present invention is shown. Detailed Implementation

[0052] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0053] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0054] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0055] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0056] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0057] like Figure 1 As shown, embodiments of the present invention disclose a thermal power plant operation optimization control system based on multimodal data fusion, comprising: a data acquisition module, a first calculation module, a second calculation module, and an optimization judgment module.

[0058] In some embodiments of this application, the data acquisition module is used to sequentially acquire multiple sets of thermal power plant operation data based on a preset data acquisition time, and integrate each set of thermal power plant operation data to obtain multiple integrated thermal power plant operation data sets.

[0059] In this embodiment, the data acquisition time is preset, preferably 12 times, including the 5th second, the 10th second, the 15th second, ..., the 60th second.

[0060] In this embodiment, the operating data of the thermal power plant includes temperature, pressure, flow rate, vibration, speed, power, etc., which are not shown one by one here.

[0061] In this embodiment, each data acquisition moment corresponds to a set of thermal power plant operation data.

[0062] In this embodiment, the operating data of the same type of thermal power plant corresponding to each data acquisition time are integrated. For example, the temperature corresponding to each data acquisition time is integrated to obtain an integrated thermal power plant operating data group that is only about temperature. An integrated thermal power plant operating data group that is only about pressure can also be obtained. Others are not shown one by one.

[0063] In some embodiments of this application, it also includes:

[0064] The data preprocessing module is used to preprocess all thermal power plant operation data, wherein the preprocessing includes deleting duplicate thermal power plant operation data, deleting erroneous thermal power plant operation data, and deleting invalid thermal power plant operation data.

[0065] The beneficial effects of the above technical solution are: by preprocessing the operating data of thermal power plants through the data preprocessing module, duplicate, erroneous and invalid data can be effectively removed, ensuring that the data used in subsequent analysis is accurate and reliable, and avoiding deviations in calculation results due to interference from bad data.

[0066] In some embodiments of this application, a first calculation module is used to analyze the integrated thermal power plant operation data set and calculate the single-mode operation data fluctuation factor of the thermal power plant based on the analysis results.

[0067] In some embodiments of this application, the first computing module is used for:

[0068] The first calculation module is used to determine the first thermal power plant operating data corresponding to the first data acquisition time and the last thermal power plant operating data corresponding to the last data acquisition time.

[0069] The first calculation module is used to calculate the overall fluctuation metric of the thermal power plant based on the operating data of the first thermal power plant and the operating data of the last thermal power plant.

[0070] The first calculation module is used to calculate the neighborhood fluctuation metric of the thermal power plant based on the remaining thermal power plant operation data;

[0071] The first calculation module is used to perform a weighted summation of the overall fluctuation metric and the neighborhood fluctuation metric to obtain the single-mode operation data fluctuation factor of the thermal power plant.

[0072] In this embodiment, the first data acquisition time is the 5th second mentioned above, and the last data acquisition time is the 60th second mentioned above.

[0073] In this embodiment, the weight of the overall volatility metric is preferably 0.4, and the weight of the neighborhood volatility metric is preferably 0.6.

[0074] The beneficial effects of the above technical solution are as follows: by determining the thermal power plant operation data corresponding to the first and last data collection times to calculate the overall fluctuation metric, and using the remaining data to calculate the neighborhood fluctuation metric, the single-mode operation data fluctuation factor is finally obtained by weighted summation. This calculation method can comprehensively and accurately reflect the fluctuation of thermal power plant operation data at different levels, providing a reliable basis for accurately judging whether thermal power plants need operation optimization in the future.

[0075] In some embodiments of this application, the first computing module is used for:

[0076] The first calculation module is used to calculate the absolute value of the difference between the first thermal power plant operation data and the last thermal power plant operation data, as the change value of thermal power plant operation data;

[0077] The first calculation module is used to determine the data acquisition time interval between the first data acquisition time and the last data acquisition time;

[0078] The first calculation module is used to take the ratio of the change value of the thermal power plant's operating data to the data acquisition time interval as the overall fluctuation measure value of the thermal power plant.

[0079] The beneficial effects of the above technical solution are: by calculating the absolute value of the difference between the first and last thermal power plant operation data and the data collection time interval, and taking their ratio as the overall fluctuation measure, the overall operation fluctuation of the thermal power plant during the entire data collection period can be measured intuitively and accurately, providing basic data support for subsequent comprehensive evaluation of the thermal power plant operation status.

[0080] In some embodiments of this application, the first computing module is used for:

[0081] The first calculation module is used to randomly extract one thermal power plant operation data from the remaining thermal power plant operation data as the standard thermal power plant operation data;

[0082] The first calculation module is used to pre-set a first preset separator and a second preset separator;

[0083] The first calculation module is used to determine the left-hand thermal power plant operation data of the standard thermal power plant operation data based on the first preset separator, and to determine the right-hand thermal power plant operation data of the standard thermal power plant operation data based on the second preset separator;

[0084] The first calculation module is used to construct a thermal power plant operation data set based on the standard thermal power plant operation data, the left thermal power plant operation data, and the right thermal power plant operation data;

[0085] The first calculation module is used to calculate the neighborhood fluctuation coefficient corresponding to the standard thermal power plant operation data based on the thermal power plant operation data set;

[0086] The first calculation module is used to perform curve fitting on all neighborhood fluctuation coefficients to obtain neighborhood fluctuation coefficient curves and extract the abrupt change value;

[0087] The first calculation module is used to determine the mean of all neighborhood fluctuation coefficients, and the product of the mean and the mutation degree value is used as the neighborhood fluctuation metric of the thermal power plant.

[0088] In this embodiment, the first preset separator is preferably 2, and the second preset separator is preferably 3. It should be noted that if the number of data is less than the first preset separator or the second preset separator, the actual number shall prevail.

[0089] In this embodiment, the neighborhood fluctuation coefficient corresponding to the operating data of each thermal power plant can be calculated according to the above steps.

[0090] In this embodiment, the mutation degree value is the maximum slope of the neighborhood fluctuation coefficient curve.

[0091] The beneficial effects of the above technical solution are as follows: by randomly selecting standard data from the remaining thermal power plant operation data and using a preset separator to determine the data on the left and right sides to construct a data set, the neighborhood fluctuation coefficient is calculated. Then, curve fitting is performed on all neighborhood fluctuation coefficients to extract the degree of mutation value. Finally, the neighborhood fluctuation measurement value is obtained by combining the mean and the degree of mutation value. This calculation method can meticulously and accurately depict the fluctuation characteristics of thermal power plant operation data in a local range. It complements the overall fluctuation measurement value and together provides strong support for accurately calculating the fluctuation factor of single-mode operation data, which helps to more comprehensively evaluate the operation status of thermal power plants.

[0092] In some embodiments of this application, the first computing module is used for:

[0093] The first calculation module is used to calculate the neighborhood fluctuation coefficient corresponding to the standard thermal power plant operating data according to the following formula:

[0094]

[0095] Where d is the neighborhood fluctuation coefficient corresponding to the standard thermal power plant operating data, s is the number of thermal power plant operating data in the thermal power plant operating data set, and g j For the j-th thermal power plant operation data in the thermal power plant operation data set, g j+1 This refers to the (j+1)th thermal power plant operation data in the thermal power plant operation data set.

[0096] In some embodiments of this application, the second calculation module is used to normalize all single-mode operation data fluctuation factors and calculate the multi-mode operation data fluctuation factor of the thermal power plant based on the normalized single-mode operation data fluctuation factors.

[0097] In this embodiment, the fluctuation factors of all single-mode operation data are normalized to the [0,1] interval. The specific normalization method will not be described in detail here.

[0098] In some embodiments of this application, the second computing module is used for:

[0099] The second calculation module is used to preset the range of single-mode operation data fluctuation factors, and determine the data fluctuation index corresponding to each single-mode operation data fluctuation factor according to the range of single-mode operation data fluctuation factors;

[0100] The second calculation module is used to calculate the multimodal operation data fluctuation factor of the thermal power plant based on all data fluctuation indicators.

[0101] In some embodiments of this application, the second computing module is used for:

[0102] The second calculation module is used to preset the range of single-mode operation data fluctuation factors, wherein the range of single-mode operation data fluctuation factors includes a first single-mode operation data fluctuation factor and a second single-mode operation data fluctuation factor, and the first single-mode operation data fluctuation factor is smaller than the second single-mode operation data fluctuation factor.

[0103] The second calculation module is used to take the first difference between the first single-mode operation data fluctuation factor and the single-mode operation data fluctuation factor as a data fluctuation index when the single-mode operation data fluctuation factor is less than or equal to the first single-mode operation data fluctuation factor.

[0104] The second calculation module is used to calculate a second difference between the single-mode operation data fluctuation factor and the first single-mode operation data fluctuation factor, and to calculate a third difference between the second single-mode operation data fluctuation factor and the single-mode operation data fluctuation factor when the single-mode operation data fluctuation factor is greater than the first single-mode operation data fluctuation factor and less than the second single-mode operation data fluctuation factor, and to use the minimum value of the second difference and the third difference as the data fluctuation index.

[0105] The second calculation module is used to take the fourth difference between the single-mode operation data fluctuation factor and the second single-mode operation data fluctuation factor as a data fluctuation index when the single-mode operation data fluctuation factor is greater than or equal to the second single-mode operation data fluctuation factor.

[0106] In this embodiment, the preferred range of the single-mode operation data fluctuation factor is (0.4, 0.7).

[0107] The beneficial effects of the above technical solution are: by presetting the range of single-mode operation data fluctuation factors and using different calculation methods for different intervals to determine the data fluctuation index, it is possible to more accurately reflect the relative position and influence of each single-mode operation data fluctuation factor in the whole, thereby providing a reasonable basis for accurately calculating the multi-mode operation data fluctuation factors, so that the final multi-mode operation data fluctuation factors can more comprehensively and accurately reflect the overall fluctuation of thermal power plant operation data.

[0108] In some embodiments of this application, the second computing module is used for:

[0109] The second calculation module is used to calculate the multimodal operation data fluctuation factor of the thermal power plant according to the following formula:

[0110]

[0111] Where h is the multimodal operation data fluctuation factor of the thermal power plant, n is the number of data fluctuation indicators, and v r Let v be the r-th data fluctuation indicator. min v is the indicator of minimum data fluctuation. max The maximum data fluctuation indicator, (v nax -v r ) nax For all v max -v r The corresponding maximum value.

[0112] In this embodiment, if v appears max -v r If it is 0, then let The overall value is 0 to avoid affecting the calculation.

[0113] The beneficial effects of the above technical solution are: it comprehensively considers the relative magnitude of various data fluctuation indicators and their differences from the maximum and minimum data fluctuation indicators, integrates the information of all single-mode operation data fluctuation factors, and thus more accurately determines the degree of fluctuation of multi-mode operation data of thermal power plants, providing a key and accurate reference for subsequent judgment on whether thermal power plants need operation optimization and the formulation of optimization strategies.

[0114] In some embodiments of this application, the optimization judgment module is used to pre-set a preset multimodal operation data fluctuation factor, and determine whether the thermal power plant needs operation optimization based on the relationship between the multimodal operation data fluctuation factor and the preset multimodal operation data fluctuation factor.

[0115] In some embodiments of this application, the optimization judgment module is used for:

[0116] The optimization judgment module is used to determine that the thermal power plant does not need to perform operation optimization when the multimodal operation data fluctuation factor is less than the preset multimodal operation data fluctuation factor.

[0117] The optimization judgment module is used to determine that the thermal power plant needs to be optimized when the multimodal operation data fluctuation factor is greater than or equal to the preset multimodal operation data fluctuation factor.

[0118] In this embodiment, the preset multimodal operation data fluctuation factor is preferably 4, but it can be adjusted adaptively according to actual needs.

[0119] In this embodiment, when the multimodal operation data fluctuation factor is less than the preset multimodal operation data fluctuation factor, there is no operation abnormality. When the multimodal operation data fluctuation factor is greater than or equal to the preset multimodal operation data fluctuation factor, there is an operation abnormality.

[0120] The beneficial effects of the above technical solution are as follows: By pre-setting a multimodal operating data fluctuation factor and comparing it with the actual calculated multimodal operating data fluctuation factor, it is possible to accurately determine whether a thermal power plant needs operational optimization. This provides a basis for judgment on the stable operation and timely adjustment of the thermal power plant, helping to ensure the operating efficiency and safety of the plant. Furthermore, when it is determined that the thermal power plant needs operational optimization, a specific optimization strategy is formulated based on the multimodal operating data fluctuation factor and a pre-set optimization strategy library. The optimization strategy formulation combines the actual operating parameters and equipment status of the thermal power plant, fine-tuning the optimization strategy to obtain the final specific optimization strategy. The pre-set optimization strategy library contains multiple optimization strategy schemes corresponding to different ranges. Each scheme details the specific measures to be taken for different operating data fluctuations, such as adjusting fuel supply, changing fan speed, and optimizing boiler combustion parameters. Actual operating parameters include, but are not limited to, current power generation, fuel consumption rate, steam temperature and pressure, etc. Equipment status includes equipment operating time, maintenance records, and the presence of potential faults. It can formulate more practical, targeted and operable optimization strategies, effectively improve the operating efficiency and stability of thermal power plants, and reduce operating costs and failure rates.

[0121] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0122] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0123] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A thermal power plant operation optimization control system based on multimodal data fusion, characterized in that, include: The data acquisition module is used to sequentially collect multiple sets of thermal power plant operation data based on preset data acquisition times, and integrate each set of thermal power plant operation data to obtain multiple integrated thermal power plant operation data sets. The first calculation module is used to analyze the integrated thermal power plant operation data set and calculate the single-mode operation data fluctuation factor of the thermal power plant based on the analysis results. The second calculation module is used to normalize all single-mode operation data fluctuation factors and calculate the multi-mode operation data fluctuation factors of the thermal power plant based on the normalized single-mode operation data fluctuation factors. The optimization judgment module is used to pre-set a preset multimodal operation data fluctuation factor, and determine whether the thermal power plant needs operation optimization based on the relationship between the multimodal operation data fluctuation factor and the preset multimodal operation data fluctuation factor.

2. The thermal power plant operation optimization control system based on multimodal data fusion according to claim 1, characterized in that, Also includes: The data preprocessing module is used to preprocess all thermal power plant operation data, wherein the preprocessing includes deleting duplicate thermal power plant operation data, deleting erroneous thermal power plant operation data, and deleting invalid thermal power plant operation data.

3. The thermal power plant operation optimization control system based on multimodal data fusion according to claim 1, characterized in that, The first calculation module is used for: The first calculation module is used to determine the first thermal power plant operating data corresponding to the first data acquisition time and the last thermal power plant operating data corresponding to the last data acquisition time. The first calculation module is used to calculate the overall fluctuation metric of the thermal power plant based on the operating data of the first thermal power plant and the operating data of the last thermal power plant. The first calculation module is used to calculate the neighborhood fluctuation metric of the thermal power plant based on the remaining thermal power plant operation data; The first calculation module is used to perform a weighted summation of the overall fluctuation metric and the neighborhood fluctuation metric to obtain the single-mode operation data fluctuation factor of the thermal power plant.

4. The thermal power plant operation optimization control system based on multimodal data fusion according to claim 3, characterized in that, The first calculation module is used for: The first calculation module is used to calculate the absolute value of the difference between the first thermal power plant operation data and the last thermal power plant operation data, as the change value of thermal power plant operation data; The first calculation module is used to determine the data acquisition time interval between the first data acquisition time and the last data acquisition time; The first calculation module is used to take the ratio of the change value of the thermal power plant's operating data to the data acquisition time interval as the overall fluctuation measure value of the thermal power plant.

5. The thermal power plant operation optimization control system based on multimodal data fusion according to claim 3, characterized in that, The first calculation module is used for: The first calculation module is used to randomly extract one thermal power plant operation data from the remaining thermal power plant operation data as the standard thermal power plant operation data; The first calculation module is used to pre-set a first preset separator and a second preset separator; The first calculation module is used to determine the left-hand thermal power plant operation data of the standard thermal power plant operation data based on the first preset separator, and to determine the right-hand thermal power plant operation data of the standard thermal power plant operation data based on the second preset separator; The first calculation module is used to construct a thermal power plant operation data set based on the standard thermal power plant operation data, the left thermal power plant operation data, and the right thermal power plant operation data; The first calculation module is used to calculate the neighborhood fluctuation coefficient corresponding to the standard thermal power plant operation data based on the thermal power plant operation data set; The first calculation module is used to perform curve fitting on all neighborhood fluctuation coefficients to obtain neighborhood fluctuation coefficient curves and extract the abrupt change value; The first calculation module is used to determine the mean of all neighborhood fluctuation coefficients, and the product of the mean and the mutation degree value is used as the neighborhood fluctuation metric of the thermal power plant.

6. The thermal power plant operation optimization control system based on multimodal data fusion according to claim 5, characterized in that, The first calculation module is used for: The first calculation module is used to calculate the neighborhood fluctuation coefficient corresponding to the standard thermal power plant operating data according to the following formula: Where d is the neighborhood fluctuation coefficient corresponding to the standard thermal power plant operating data, s is the number of thermal power plant operating data in the thermal power plant operating data set, and g j For the j-th thermal power plant operation data in the thermal power plant operation data set, g j+1 This refers to the (j+1)th thermal power plant operation data in the thermal power plant operation data set.

7. The thermal power plant operation optimization control system based on multimodal data fusion according to claim 1, characterized in that, The second calculation module is used for: The second calculation module is used to preset the range of single-mode operation data fluctuation factors, and determine the data fluctuation index corresponding to each single-mode operation data fluctuation factor according to the range of single-mode operation data fluctuation factors; The second calculation module is used to calculate the multimodal operation data fluctuation factor of the thermal power plant based on all data fluctuation indicators.

8. The thermal power plant operation optimization control system based on multimodal data fusion according to claim 7, characterized in that, The second calculation module is used for: The second calculation module is used to preset the range of single-mode operation data fluctuation factors, wherein the range of single-mode operation data fluctuation factors includes a first single-mode operation data fluctuation factor and a second single-mode operation data fluctuation factor, and the first single-mode operation data fluctuation factor is smaller than the second single-mode operation data fluctuation factor. The second calculation module is used to take the first difference between the first single-mode operation data fluctuation factor and the single-mode operation data fluctuation factor as a data fluctuation index when the single-mode operation data fluctuation factor is less than or equal to the first single-mode operation data fluctuation factor. The second calculation module is used to calculate a second difference between the single-mode operation data fluctuation factor and the first single-mode operation data fluctuation factor, and to calculate a third difference between the second single-mode operation data fluctuation factor and the single-mode operation data fluctuation factor when the single-mode operation data fluctuation factor is greater than the first single-mode operation data fluctuation factor and less than the second single-mode operation data fluctuation factor, and to use the minimum value of the second difference and the third difference as the data fluctuation index. The second calculation module is used to take the fourth difference between the single-mode operation data fluctuation factor and the second single-mode operation data fluctuation factor as a data fluctuation index when the single-mode operation data fluctuation factor is greater than or equal to the second single-mode operation data fluctuation factor.

9. The thermal power plant operation optimization control system based on multimodal data fusion according to claim 7, characterized in that, The second calculation module is used for: The second calculation module is used to calculate the multimodal operation data fluctuation factor of the thermal power plant according to the following formula: Where h is the multimodal operation data fluctuation factor of the thermal power plant, n is the number of data fluctuation indicators, and v r Let v be the r-th data fluctuation indicator. min v is the indicator of minimum data fluctuation. max The maximum data fluctuation indicator, (v max -v r ) max For all v max -v r The corresponding maximum value.

10. The thermal power plant operation optimization control system based on multimodal data fusion according to claim 1, characterized in that, The optimization judgment module is used for: The optimization judgment module is used to determine that the thermal power plant does not need to perform operation optimization when the multimodal operation data fluctuation factor is less than the preset multimodal operation data fluctuation factor. The optimization judgment module is used to determine that the thermal power plant needs to be optimized when the multimodal operation data fluctuation factor is greater than or equal to the preset multimodal operation data fluctuation factor.

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