Heat medium water system commissioning optimization system in million unit starting process

Abstract

The invention discloses a heat medium water system commissioning optimization system in a million unit starting process, and relates to the technical field of thermal power plant units, and the system comprises a data monitoring module which is used for obtaining real-time operation parameters of a heat medium water system at a preset position in the starting process, and converting the real-time operation parameters into a data packet in a preset format; the data analysis module is used for processing and analyzing the relationship among the parameters by using a preset data analysis model to obtain a first result, and optimizing the first result; the change information acquisition module is used for acquiring a real-time commissioning temperature rise curve and acquiring a curve obtained after the commissioning temperature rise curve is changed and a smooth coefficient of the changed curve by utilizing an optimization result; and the optimization evaluation module is used for carrying out effect evaluation on the changed curve and the smooth coefficient and feeding back to the data analysis module. According to the invention, the automatic control level of the heating medium water system can be improved, so that the operation temperature rise curve of the heating medium water system is smoother, and the safety and stability of the heating medium water system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of thermal power plant units, and in particular to a heat medium water system operation optimization system during the startup process of a million-unit unit. Background Art

[0002] At present, the heat medium water system of thermal power plants consists of two parts: a smoke cooler and a smoke heater, which transfer heat through heat medium water. The heat of the high-temperature flue gas at the outlet of the air preheater is transferred to the low-temperature flue gas at the outlet, thereby raising the chimney inlet flue gas temperature to above the flue gas dew point temperature.

[0003] During the current unit startup process, the operation of the heat transfer water system is mainly intervened by the operation personnel throughout the process, and the smoke temperature at the inlet of the smoke cooler, the smoke temperature at the outlet of the smoke heater, the heat transfer water flow rate and other parameters are manually monitored to adjust the opening of the smoke cooler water inlet valve, the heat transfer water flow rate of the smoke cooler, and the outlet temperature of the smoke heater. During the unit startup process, the unit operating conditions and equipment conditions are complex and the operation volume is large, which not only requires high operating skills of the operation personnel, but also the automatic control level of the heat transfer water system is low, and the temperature rise curve of the heat transfer water operation is not smooth. Realizing the automatic operation of the heat transfer water system has become a problem that needs to be solved urgently. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a heat medium water system operation optimization system during the startup process of a million-unit unit, which is used to reduce the amount of operation of the staff during the startup process, improve the automatic control level of the heat medium water system, and at the same time monitor the key parameters of the heat medium water system in real time and optimize the control to make the heat medium water system operation temperature rise curve smoother, thereby improving the safety and stability of the heat medium water system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a heat medium water system operation optimization system during the startup of a million-unit unit, comprising:

[0007] A data monitoring module, used to obtain real-time operating parameters of the heat medium water system at a preset position during startup, and convert the real-time operating parameters into a data packet in a preset format;

[0008] A data analysis module, which uses a preset data analysis model to process and analyze the relationship between the parameters in the data packet to obtain a first result, and uses a preset function to optimize the first result;

[0009] Changing the information acquisition module to obtain the real-time operation temperature rise curve of the heat medium water system, and using the optimization result to obtain the changed operation temperature rise curve and the smoothing coefficient of the changed curve;

[0010] The optimization evaluation module is used to evaluate the effect of the changed curve and smoothing coefficient, and generate optimization evaluation results to feed back to the data analysis module.

[0011] As a preferred solution of the heat medium water system operation optimization system during the startup of the 1,000-million-unit unit described in the present invention, the data monitoring module includes a smoke cooler monitoring unit for real-time acquisition of the first smoke temperature, the second smoke temperature and the first regulating valve opening of the heat medium water cooler in the heat medium water system;

[0012] The smoke heater monitoring unit is used to obtain the third smoke temperature, the fourth smoke temperature and the second regulating valve opening of the heat medium water in the smoke heater in the heat medium water system in real time;

[0013] The heat transfer water system monitoring unit is used to obtain the steam valve opening, bypass opening and pump port pressure of the heat transfer water system in real time;

[0014] The data generation unit is used to convert various real-time operating parameters obtained by the smoke cooler monitoring unit, the smoke heater monitoring unit and the heat medium water system monitoring unit into a data packet in a preset format and send it to the data analysis module.

[0015] As a preferred solution of the heat medium water system operation optimization system during the startup of the million-unit unit described in the present invention, the data monitoring module also includes a heat medium water system self-starting unit, which is used to obtain the water temperature of the heat medium water system, the water level of the heat medium water tank and the operating number of the heat medium water pump in real time through the heat medium water system monitoring unit, and compare the fourth smoke temperature of the smoke heater with the preset threshold condition, and determine whether the heat medium water system is started based on the comparison result.

[0016] As a preferred solution of the heat medium water system operation optimization system during the startup of the million-unit unit described in the present invention, wherein: the data analysis module includes:

[0017] A model selection unit, configured to select an adaptive data analysis model from a preset model database based on data attributes of various parameters in the data packet;

[0018] A data analysis unit, configured to analyze the relationship between various parameters in the data packet based on the selected data analysis model to obtain a first result;

[0019] The data optimization unit is used to select a first preset function from a preset function database and optimize the first result based on the first preset function.

[0020] As a preferred solution of the heat medium water system operation optimization system during the startup of the million-unit unit described in the present invention, wherein: the change information acquisition module includes a heating curve generation unit, which is used to generate an operation heating curve of the heat medium water temperature changing with time based on the real-time water temperature data in the data packet;

[0021] An optimization curve generating unit, based on the optimization result, obtains an optimization curve after optimizing the commissioning heating curve;

[0022] A node marking unit, used to mark the optimization curve according to a first node at the start and close time of the heat medium water system and a second node corresponding to the time of performing the optimization processing operation;

[0023] The coefficient generating unit is used to calculate the optimization curve based on the second preset function screened in the preset function database to obtain a smoothing coefficient, wherein the optimization curve is a changed curve.

[0024] As a preferred solution of the heat medium water system operation optimization system during the startup of the 1,000-million-unit unit described in the present invention, wherein: the heat medium water system self-starting unit includes a stage division subunit for dividing the heat medium water system startup process into a first stage, a second stage and a third stage;

[0025] A function acquisition unit, used to acquire the parsed content in the data packet, and acquire the interpolation function of the second smoke temperature and the first heat medium water valve opening according to a preset temperature-opening-function mapping table;

[0026] A first target opening acquisition subunit is used to generate a first target opening of the first regulating valve opening of the heat medium water in the first stage based on the valve pipeline characteristics and in combination with the change value of the temperature field;

[0027] A function execution subunit, configured to generate a first adjustment instruction based on the interpolation function, the first target opening, and the smoke temperature change rate in the data packet;

[0028] a preheating subunit, configured to adjust the opening of the first regulating valve of the heat medium water in combination with the first target opening and based on the first regulating instruction;

[0029] A second target opening acquisition subunit is used to acquire a second target opening of the first regulating valve of the heat medium water in the second stage when the second smoke temperature exceeds the first threshold value and in combination with the change value of the temperature field;

[0030] The bypass regulating subunit is used to obtain a second regulating instruction from a preset smoke temperature-opening-instruction comparison table based on the second smoke temperature and the second target opening, and to adjust the first regulating valve opening of the heat medium water and the bypass regulating opening based on the second regulating instruction;

[0031] A water volume stabilization subunit, used to adjust the first regulating valve opening and the bypass regulating opening in real time to maintain a constant water volume of heat medium water in the heat medium water system;

[0032] A water pressure stabilizing subunit, used to maintain a constant water pressure of the heat medium water in the heat medium water system by adjusting the heat medium water pump in combination with the water volume stabilizing subunit;

[0033] The heat medium water stable operation subunit is used to adjust the first constant corresponding to the heat medium water pump, the second constant corresponding to the heat medium water volume, the third constant corresponding to the second smoke temperature, and the fourth constant corresponding to the fourth smoke temperature respectively in combination with the equipment operation parameters and environmental parameters obtained in the third stage when the second smoke temperature reaches a preset second threshold.

[0034] As a preferred solution of the heat medium water system operation optimization system during the startup process of the million-unit unit described in the present invention, wherein: the data optimization unit includes a data extraction subunit, which is used to extract the parameter data corresponding to each preset moment in the first result, and establish a parameter real-time curve corresponding to each parameter according to the time sequence of each preset moment;

[0035] A relation model generating subunit, configured to construct a parameter relation model based on the first result and according to a mapping relationship between various parameters;

[0036] A prediction model subunit, for constructing a parameter prediction model based on the parameter real-time curve, the parameter relationship model, and combining the historical data of each parameter, and generating a parameter prediction curve;

[0037] A standard parameter acquisition subunit is used to select corresponding standard parameters in a preset parameter database through a preset attribute-parameter comparison table according to the data attributes of each parameter in the data packet and in combination with the parameter changes at the same time of the parameter change curve;

[0038] A standard curve generating subunit, configured to generate a standard curve according to the standard parameters and in combination with the time nodes in the first result;

[0039] The abnormality analysis subunit is used to compare and analyze the real-time parameter curve and the standard curve, calculate the parameter difference of the corresponding parameter at the same time, and compare it with the preset first difference to determine whether the parameter is abnormal;

[0040] A correction subunit, used for comparing and analyzing the parameter prediction curve with the parameter real-time curve, and generating a correction function based on the comparison result;

[0041] The curve optimization subunit is used to optimize the parameter real-time curve based on the correction function and the parameter real-time curve and in combination with the first preset function.

[0042] As a preferred solution of the heat medium water system operation optimization system during the startup of the million-unit unit described in the present invention, wherein: the optimization evaluation module includes:

[0043] The curve parameter extraction unit is used to extract parameter pairs from the commissioning heating curve and the optimization curve, and establish a parameter set A = {Ai, i = 1, 2, 3, ..., m}, where Ai represents the i-th parameter pair, and Ai = {a0i, a1i}, where a0 i represents the parameter value of the ith parameter pair based on the commissioning heating curve, a1 i represents the parameter value based on the optimization curve in the i-th parameter pair;

[0044] An effect evaluation unit is used for the indicator set B to evaluate the effect of the parameter set A, and establish an effect evaluation matrix X = {xij}m×n, wherein xij represents the evaluation value of the i-th parameter pair under the j-th evaluation indicator, j = 1, 2, 3, ..., n, n represents the number of evaluation indicators in the evaluation indicator set B, and the effect evaluation matrix has m rows and n columns;

[0045] A matrix normalization unit, used for performing a first normalization on each column in the effect evaluation matrix and a second normalization on each row in the effect evaluation matrix;

[0046]

[0047] Among them, y1 j represents the first normalized value of the jth column; xj represents the effect evaluation weight of the i-th element in the j-th column corresponding to the j-th evaluation indicator; i Represents the effect evaluation value of the i-th element in the j-th column; represents the minimum evaluation factor in the jth column; represents the maximum evaluation factor in the jth column;

[0048]

[0049] Among them, y2 i represents the second normalized value of the i-th row; represents the effect evaluation weight of the jth element in the jth row corresponding to the jth evaluation indicator; xi j Represents the effect evaluation value of the jth element in the i-th row; represents the minimum evaluation factor in the i-th row; represents the maximum evaluation factor in the i-th row; σ1 2 Indicates that based on all the The variance of

[0050] A first vector is constructed based on the first normalized value, and a second vector is constructed based on the second normalized value, and a final evaluation value Zp is calculated in combination with the assigned weight;

[0051]

[0052] Wherein, k represents the preset adjustment coefficient; p ij is the weight of the i-th parameter under the j-th indicator; ln is the logarithmic operator; r1 is the proportion judgment function; e01 is the preset ratio; s01 and s02 are the proportion coefficients; Δ(ph, Δp1) represents the evaluation function based on the smoothing coefficient ph and the coefficient difference Δp1 between the commissioning temperature rise curve and the optimization curve;

[0053] The final evaluation value Zp is compared with the effect evaluation table to determine the optimization evaluation factor, and the factor is fed back to the data analysis module to determine the adjustment parameters of the heat medium water system.

[0054] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of optimizing the operation of a heat medium water system during the startup of a million-unit unit are implemented.

[0055] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of optimizing the operation of a heat medium water system during the startup of a million-unit unit are implemented.

[0056] Beneficial effects of the present invention: The present invention can obtain various key operating parameters of the heat medium water system during the startup process in real time through the data monitoring module, without the need for manual monitoring of the operating parameters, which not only greatly saves human resources, but also improves the monitoring level and safety of the system; at the same time, through the data analysis module and the information change module, each operating parameter can be optimized, and the smoothing coefficient of the heating curve of the heat medium water system can be continuously improved, thereby improving the stability of the system; and through the effect evaluation module, the optimization effect after each optimization is judged, the system performance is continuously optimized, the flue gas waste heat recovery efficiency is improved, and the system operation cost is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0058] Figure 1A schematic diagram of a system flow chart for optimizing the operation of a heat medium water system during the startup of a MW unit provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0062] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0063] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0064] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0065] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a heat medium water system operation optimization system during the startup of a million-unit unit, comprising:

[0066] A data monitoring module is used to obtain the real-time operating parameters of the heat medium water system at a preset position during the startup process, and convert the real-time operating parameters into a data packet in a preset format;

[0067] The data analysis module processes and analyzes the relationship between the parameters in the data packet using a preset data analysis model to obtain a first result, and optimizes the first result using a preset function;

[0068] The information acquisition module is changed to obtain the real-time operation temperature rise curve of the heat medium water system, and the operation temperature rise curve is changed by using the optimization result and the smoothing coefficient of the changed curve is obtained;

[0069] The optimization evaluation module is used to evaluate the effect of the changed curve and smoothing coefficient, and generate optimization evaluation results to feed back to the data analysis module.

[0070] In this embodiment, the preset position is: the installation position of the equipment used to monitor various important operating parameters in the heat medium water system, such as the smoke inlet and outlet of the smoke cooler, the smoke inlet and outlet of the smoke heater, the heat medium water inlet and outlet of the smoke cooler, etc.;

[0071] In this embodiment, real-time operating parameters: real-time data of various important operating parameters during the operation of the heat medium water system, such as the smoke temperature at the inlet and outlet of the smoke cooler, the smoke temperature at the inlet and outlet of the smoke heater, the opening of the heat medium water regulating valve of the smoke cooler, and the flow rate and water pressure of the heat medium water inlet and outlet of the smoke cooler;

[0072] In this embodiment, the preset format: a format used to facilitate subsequent data processing;

[0073] In this embodiment, data packet: a data unit generated by converting the format of various real-time operating parameters acquired by the data monitoring module;

[0074] In this embodiment, the preset data analysis model: a model used to process and analyze various parameters in a data packet is pre-trained using big data;

[0075] In this embodiment, the first result: the result obtained after the data packet is analyzed and processed by a preset data analysis model;

[0076] In this embodiment, the preset function: a function used to optimize the first result, which is preset;

[0077] In this embodiment, the operation temperature rise curve is the temperature change curve of the heat medium water when the heat medium water system is put into operation during the system startup process;

[0078] In this embodiment, the optimization result is a result generated after the first result is optimized by a preset function;

[0079] In this embodiment, smoothing coefficient: a coefficient used to describe the smoothness of a curve. The larger the smoothing coefficient, the higher the smoothness of the curve.

[0080] In this embodiment, effect evaluation: an operation of evaluating the effect of the change of the changed curve and the corresponding smoothing coefficient;

[0081] In this embodiment, the optimization evaluation result is an evaluation result generated after evaluating the effect of the changed curve and the smoothing coefficient.

[0082] The working principle and beneficial effects of the above technical solution are as follows: the present invention can obtain various key operating parameters of the heat medium water system during the startup process in real time through the data monitoring module, without the need for manual monitoring of the operating parameters, which not only greatly saves human resources, but also improves the monitoring level and safety of the system; at the same time, through the data analysis module and the information change module, each operating parameter can be optimized, and the smoothing coefficient of the heating curve of the heat medium water system can be continuously improved, thereby improving the stability of the system; and the effect evaluation module can be used to judge the optimization effect after each optimization, continuously optimize the system performance, improve the flue gas waste heat recovery efficiency, and reduce the system operation cost.

[0083] The embodiment of the present invention provides a heat medium water system operation optimization system during the startup of a million-unit unit, and a data monitoring module, including:

[0084] A smoke cooler monitoring unit is used to obtain the first smoke temperature, the second smoke temperature and the first regulating valve opening of the heat medium water in the smoke cooler in the heat medium water system in real time;

[0085] The smoke heater monitoring unit is used to obtain the third smoke temperature, the fourth smoke temperature and the second regulating valve opening of the heat medium water in the smoke heater in the heat medium water system in real time;

[0086] The heat transfer water system monitoring unit is used to obtain the steam valve opening, bypass opening and pump port pressure of the heat transfer water system in real time;

[0087] The data generation unit is used to convert various real-time operating parameters obtained by the smoke cooler monitoring unit, the smoke heater monitoring unit and the heat medium water system monitoring unit into a data packet in a preset format and send it to the data analysis module.

[0088] In this embodiment, the first smoke temperature is the inlet smoke temperature of the smoke cooler;

[0089] In this embodiment, the second smoke temperature: that is, the outlet smoke temperature of the smoke cooler;

[0090] In this embodiment, the first regulating valve opening of the heat medium water is: that is, the regulating valve opening of the heat medium water at the inlet of the smoke cooler;

[0091] In this embodiment, the third smoke temperature: that is, the inlet smoke temperature of the smoke heater;

[0092] In this embodiment, the fourth smoke temperature: that is, the outlet smoke temperature of the smoke heater;

[0093] In this embodiment, the steam valve opening: that is, the valve opening of the steam heater in the heat medium water system;

[0094] In this embodiment, the bypass opening is the valve opening corresponding to each branch or loop in the heat medium water system, which is used to adjust the flow rate of the heat medium water;

[0095] In this embodiment, the pump outlet pressure refers to the outlet pressure of the heat medium water pump;

[0096] The working principle and beneficial effects of the above technical solution are as follows: the present invention can obtain the inlet and outlet smoke temperatures of the smoke cooler, the inlet and outlet smoke temperatures of the smoke heater and the valve openings of various parts of the heat medium water system in real time through the smoke cooler monitoring unit, the smoke heater monitoring unit and the heat medium water system monitoring unit respectively, so as to accurately monitor the real-time operating parameters of the heat medium water system, greatly improving the monitoring accuracy of the heat medium water system, and at the same time, by converting various real-time operating parameters into data packets in a preset format, it can facilitate subsequent processing and analysis of the operating parameters, thereby improving the efficiency of data processing.

[0097] The embodiment of the present invention provides a heat medium water system operation optimization system during the startup process of a million-unit unit, and a data monitoring module, which also includes:

[0098] The heat medium water system self-start unit is used to obtain the water temperature of the heat medium water system, the water level of the heat medium water tank and the number of operation of the heat medium water pump in real time through the heat medium water system monitoring unit, and compare it with the preset threshold conditions in combination with the fourth smoke temperature of the smoke heater, and determine whether the heat medium water system is started based on the comparison result.

[0099] In this embodiment, water temperature: refers to the water temperature of the heat medium water in the heat medium water system;

[0100] In this embodiment, water level refers to the water level of the heat medium water in the water tank in the heat medium water system;

[0101] In this embodiment, the operating quantity refers to the number of heat medium water pumps currently in operation;

[0102] In this embodiment, the preset threshold condition: the threshold condition used to determine whether the heat medium water system is put into operation based on parameter comparison is preset;

[0103] In this embodiment, the comparison result is a result obtained by comparing and analyzing the water temperature of the heat medium water system, the water level of the heat medium water tank, the number of operations of the heat medium water pump, and the fourth smoke temperature with the preset threshold conditions.

[0104] The working principle and beneficial effect of the above technical solution are: the present invention also uses the heat medium water system monitoring unit to accurately monitor the water temperature in the heat medium water system, the water level of the heat medium water in the heat medium water tank and the number of heat medium water pumps put into operation, and compares and analyzes the outlet smoke temperature of the smoke heater with the preset threshold conditions to determine whether various parameters meet the threshold for the heat medium water system to be put into operation. This not only reduces the monitoring error of the heat medium water temperature, but also can accurately control the timing of the heat medium water system to be put into operation, thereby improving the smoothness of the commissioning temperature rise curve, and then improving the stability of the system.

[0105] The embodiment of the present invention provides a heat medium water system operation optimization system during the startup of a million-unit unit, and a data analysis module, including:

[0106] A model selection unit, used to select an adaptive data analysis model from a preset model database based on data attributes of various parameters in a data packet;

[0107] A data analysis unit, configured to analyze the relationship between various parameters in the data packet based on the selected data analysis model to obtain a first result;

[0108] The data optimization unit is used to select a first preset function from a preset function database and optimize the first result based on the first preset function.

[0109] In this embodiment, data attributes: that is, the categories of various parameters in the data packet, for example, temperature parameters, pressure parameters, time parameters, etc.;

[0110] In this embodiment, the preset model database: a database containing a large number of models used for data analysis and trained with big data, is preset;

[0111] In this embodiment, the data analysis model: a model obtained by screening from a preset model database and used for analyzing and processing a currently input data packet;

[0112] In this embodiment, the first result: the analysis result obtained by analyzing the relationship between the various parameters in the data analysis package by the data analysis unit, which facilitates the subsequent construction of the association relationship between the various parameters;

[0113] In this embodiment, the preset function database: a database containing a large number of functions for data processing and analysis, which is preset;

[0114] In this embodiment, the first preset function is a function selected from a preset function database and used to optimize the first result. For example, a constant value analysis function is used to optimize the relationship between various parameters to provide the best performance and a smoother commissioning temperature rise curve for the heat medium water system.

[0115] The working principle and beneficial effects of the above technical solution are as follows: the present invention can select a data analysis model that is compatible with the data attributes of the parameters to be processed in the data packet from a preset model database through a model selection unit, thereby improving the matching degree between the parameters in the data packet and the corresponding model, reducing the occurrence of incompatibility or mismatch between the data and the model, and thus improving the data processing efficiency; at the same time, the correlation between the parameters can be analyzed and optimized through the data analysis unit and the data optimization unit, providing the best performance for the heat medium water system and optimizing the heating curve generated next.

[0116] The embodiment of the present invention provides a system for optimizing the operation of a heat medium water system during the startup of a 1,000-million-unit unit, and changes an information acquisition module, including:

[0117] A heating curve generating unit, used to generate a heating curve of the heat medium water temperature changing with time based on the real-time water temperature data in the data packet;

[0118] An optimization curve generating unit, based on the optimization result, obtains an optimization curve after optimizing the commissioning heating curve;

[0119] A node marking unit, used to mark the optimization curve according to a first node at the start and close time of the heat medium water system and a second node corresponding to the time of performing the optimization processing operation;

[0120] The coefficient generating unit is used to calculate the optimization curve based on the second preset function screened in the preset function database to obtain a smoothing coefficient, wherein the optimization curve is the changed curve.

[0121] In this embodiment, the real-time water temperature data refers to the real-time data of the water temperature of the heat medium water in the heat medium water system;

[0122] In this embodiment, the operation temperature rise curve can be a temperature rise curve generated according to real-time water temperature data, or it can be a temperature rise curve of the most recent few times in the historical records, which is used as a reference;

[0123] In this embodiment, the optimization curve is: the commissioning temperature rise curve obtained by optimizing the relationship between the parameters through a preset function, which corresponds to the commissioning temperature rise curve before optimization;

[0124] In this embodiment, the first node is the time node when the heat medium water system starts to be put into operation and ends to be put into operation;

[0125] In this embodiment, the second node: the time node corresponding to the execution of the optimization operation is between the time nodes corresponding to the start and stop times of the heat medium water system. For example, the start time of the heat medium water system is t1, and the end time of the heat medium water system is t2, then the second node is between t1 and t2, that is, t2>t3>t1;

[0126] In this embodiment, marking: marking the first node and the second node to the time corresponding to the optimization curve, so as to facilitate the subsequent analysis of the optimization curve;

[0127] In this embodiment, the second preset function is a function obtained by screening in the preset function database and used to calculate the smoothing coefficient of the optimization curve, which is preset;

[0128] In this embodiment, the smoothing coefficient is a coefficient used to describe the smoothness of the optimization curve. The larger the smoothing coefficient is, the smoother the optimization curve is; the smaller the smoothing coefficient is, the greater the volatility of the optimization curve is; the smoothing coefficient will affect the response speed of the system and its sensitivity to fluctuations.

[0129] The working principle and beneficial effects of the above technical solution are as follows: the present invention obtains the heating curve before optimization and the optimization curve after optimization through the heating curve generation unit and the optimization curve generation unit respectively, so as to facilitate the evaluation of the optimization effect of the optimization curve, and at the same time, the node marking unit can accurately mark the time corresponding to each operation in the commissioning heating curve, so as to facilitate the subsequent analysis of the optimization curve, improve the readability of the curve change information and reduce the complexity of subsequent data analysis.

[0130] An embodiment of the present invention provides a heat medium water system operation optimization system during the startup of a million-unit unit, and a heat medium water system self-starting unit, comprising:

[0131] The stage division subunit is used to divide the start-up process of the heat medium water system into the first stage, the second stage and the third stage;

[0132] A function acquisition unit is used to acquire the parsed content in the data packet, and acquire the interpolation function of the second smoke temperature and the first regulating valve opening of the heat medium water according to a preset temperature-opening-function mapping table;

[0133] A first target opening acquisition subunit is used to generate a first target opening of the first regulating valve opening of the heat medium water in the first stage based on the valve pipeline characteristics and in combination with the change value of the temperature field;

[0134] A function execution subunit, for generating a first adjustment instruction based on the interpolation function, the first target opening, and the smoke temperature change rate in the data packet;

[0135] The preheating subunit is used to adjust the opening of the first regulating valve of the heat medium water in combination with the first target opening and based on the first adjustment instruction;

[0136] A second target opening acquisition subunit is used to acquire a second target opening of the first regulating valve of the heat medium water in the second stage when the second smoke temperature exceeds the first threshold value and in combination with the change value of the temperature field;

[0137] The bypass regulating subunit is used to obtain a second regulating instruction from a preset smoke temperature-opening-instruction comparison table based on the second smoke temperature and the second target opening, and to adjust the first regulating valve opening of the heat medium water and the bypass regulating opening based on the second regulating instruction;

[0138] The water volume stabilization subunit is used to adjust the opening of the first regulating valve and the bypass regulating opening in real time to maintain a constant water volume of the heat medium water in the heat medium water system;

[0139] The water pressure stabilizing subunit is used to maintain a constant water pressure of the heat medium water in the heat medium water system by adjusting the heat medium water pump in combination with the water volume stabilizing subunit;

[0140] The heat medium water stable operation subunit is used to adjust the first constant corresponding to the heat medium water pump, the second constant corresponding to the heat medium water volume, the third constant corresponding to the second smoke temperature, and the fourth constant corresponding to the fourth smoke temperature respectively, in combination with the equipment operation parameters and environmental parameters obtained in the third stage when the second smoke temperature reaches a preset second threshold.

[0141] In this embodiment, the first stage is the initial stage when the heat medium water system starts to operate. In this stage, the temperature of the heat medium water is relatively low, and the main function is to heat the heat medium water to a certain temperature to meet the operation requirements of the system.

[0142] In this embodiment, the second stage: i.e., the adjustment stage, is a process in which the system automatically adjusts the opening of each valve to maintain a constant amount of heat medium water and water pressure as the flue gas temperature at the flue gas cooler outlet increases;

[0143] In this embodiment, the third stage is the stable stage, during which the various parameters of the heat medium water system are stable within a certain range, and the operating parameters of the boiler remain stable without large fluctuations;

[0144] In this embodiment, parsing content: that is, obtaining various data in the data packet by parsing the data in the data packet;

[0145] In this embodiment, the preset temperature-opening-function mapping table: a mapping table including the mapping relationship between the second smoke temperature, the first heat medium water valve opening and the interpolation function, is pre-set and used to obtain the corresponding interpolation function according to the input second smoke temperature and the first heat medium water valve opening;

[0146] In this embodiment, the interpolation function is: a function constructed to fill in missing values ​​between data and make predictions under given discrete data of the second smoke temperature and the first regulating valve opening of the heat medium water, which can increase the smoothness of the data curve;

[0147] In this embodiment, valve-pipe characteristics: that is, the characteristics of energy loss and reduced efficiency caused by the resistance and pressure loss of the pipeline and valve to the flow of fluid;

[0148] In this embodiment, the temperature field refers to the temperature distribution of the heat medium water in the system space;

[0149] In this embodiment, the first target opening is the target opening value of the first regulating valve opening of the heat medium water in the first stage. When the first regulating valve opening of the heat medium water reaches the first target opening in the first stage, the first regulating valve opening of the heat medium water is stabilized at the first target opening and is no longer adjusted.

[0150] In this embodiment, the smoke temperature change rate: that is, the rate of change of the smoke temperature over time;

[0151] In this embodiment, the first adjustment instruction is an instruction obtained according to the interpolation function, the first target opening and the smoke temperature change rate, and is used to adjust the opening of each regulating valve in the heat medium water system;

[0152] In this embodiment, the first threshold value is a threshold value used to determine whether the heat medium water system enters the second stage from the first stage;

[0153] In this embodiment, the second target opening is the target opening value of the first regulating valve opening of the heat medium water in the second stage. When the heat medium water system enters the second stage, the first regulating valve opening of the heat medium water is controlled to gradually approach the second target opening, corresponding to the first target opening.

[0154] In this embodiment, a preset smoke temperature-opening-instruction comparison table: a comparison table including a mapping relationship between the second smoke temperature, the second target opening and the second adjustment instruction, is preset and used to obtain the corresponding second adjustment instruction according to the input second smoke temperature and the second target opening;

[0155] In this embodiment, the second adjustment instruction: in the second stage, an instruction for adjusting the opening of the first regulating valve of the heat medium water and the bypass adjustment opening;

[0156] In this embodiment, constant water volume: by adjusting the opening of the first regulating valve of the heat medium water and the bypass regulating opening, the amount of heat medium water in the system is kept constant, so as to improve the stability, heat transfer efficiency and energy saving effect of the heat medium water system;

[0157] In this embodiment, the heat medium water pump: a device for controlling the water pressure in the heat medium water system;

[0158] In this embodiment, constant water pressure: the water pressure of the heat medium water in the heat medium water system is adjusted and maintained constant by the heat medium water pump, which can improve the operating stability of the system and the flow performance of the heat medium water, and at the same time prevent gas from entering the heat medium water system, thereby improving the heat transfer efficiency of the heat medium water system;

[0159] In this embodiment, the second threshold value: a threshold value used to determine whether the heat medium water system enters the third stage from the second stage;

[0160] In this embodiment, the equipment operating parameters: namely, operating parameters such as boiler load and thermal efficiency;

[0161] In this embodiment, environmental parameters: namely, environmental data around the heat medium water system, such as ambient temperature, humidity, etc.;

[0162] In this embodiment, the first constant value is the pressure constant value of the outlet main pipe of the heat medium water pump when the heat medium water system is in the third stage;

[0163] In this embodiment, the second constant value is the constant value of the heat medium water volume at the outlet of the smoke cooler when the heat medium water system is in the third stage;

[0164] In this embodiment, the third constant value is the constant value of the flue gas temperature at the flue gas cooler outlet when the heat medium water system is in the third stage;

[0165] In this embodiment, the fourth constant is the constant value of the outlet flue gas temperature of the flue gas heater when the heat medium water system is in the third stage.

[0166] The working principle and beneficial effects of the above technical solution are as follows: the present invention divides the process of putting the heat medium water system into operation into three stages through the stage division sub-unit, and uses the first threshold and the second threshold corresponding to the outlet smoke temperature of the smoke cooler as the basis for dividing the three stages, so that the specific stage the system is currently in can be accurately identified, and the throttle opening and other parameters in each stage are automatically adjusted respectively. Under the premise of ensuring the stable operation of the system, the smoothness of the heat medium water commissioning temperature rise curve is increased, thereby improving the heat transfer efficiency of the system.

[0167] The embodiment of the present invention provides a heat medium water system operation optimization system during the startup of a million-unit unit, and a data optimization unit, including:

[0168] A data extraction subunit, used to extract various parameter data corresponding to each preset moment in the first result, and to establish a parameter real-time curve corresponding to each parameter according to the time sequence of each preset moment;

[0169] A relation model generating subunit, configured to construct a parameter relation model based on the first result and according to the mapping relationship between various parameters;

[0170] The prediction model subunit is used to construct a parameter prediction model based on the parameter real-time curve, the parameter relationship model, and the historical data of each parameter, and generate a parameter prediction curve;

[0171] The standard parameter acquisition subunit is used to select the corresponding standard parameter in the preset parameter database through the preset attribute-parameter comparison table according to the data attributes of each parameter in the data packet and the parameter changes at the same time of the parameter change curve;

[0172] A standard curve generating subunit, used to generate a standard curve according to the standard parameters and in combination with the time nodes in the first result;

[0173] The abnormality analysis subunit is used to compare and analyze the real-time parameter curve with the standard curve, calculate the parameter difference of the corresponding parameter at the same time, and compare it with the preset first difference to determine whether the parameter is abnormal;

[0174] A correction subunit is used to compare and analyze the parameter prediction curve with the parameter real-time curve, and generate a correction function based on the comparison result;

[0175] The curve optimization subunit is used to optimize the parameter real-time curve based on the correction function and the parameter real-time curve and in combination with the first preset function.

[0176] In this embodiment, the preset time is a preset data extraction time node, which is generally an equally spaced time point;

[0177] In this embodiment, the parameter real-time curve is a curve of parameter changes over time formed according to the time sequence of preset moments and the parameters at corresponding moments;

[0178] In this embodiment, parameter relationship model: a digital model constructed based on the mapping relationship between various parameters, for example, a model describing the relationship between water pressure, water volume and heat transfer efficiency in a heat medium water system;

[0179] In this embodiment, historical data refers to the historical data values ​​of various parameters stored in the database;

[0180] In this embodiment, the parameter prediction model: a model for predicting the trend of the current parameter generated based on the parameter real-time curve, the parameter relationship model and the parameter historical data, is pre-trained with big data;

[0181] In this embodiment, parameter prediction curve: a prediction curve of various parameters obtained by a parameter prediction model;

[0182] In this embodiment, the preset attribute-parameter comparison table: a comparison table containing the association relationship between the data attributes of each parameter and the standard parameters, which is preset and used to obtain the corresponding standard parameters according to the data attributes of the input parameters;

[0183] In this embodiment, the time node is a time that corresponds to each preset time in the first result;

[0184] In this embodiment, the standard curve: a curve of changes in the standard parameters generated based on the acquired standard parameters and the time nodes corresponding one-to-one to the preset moments in the first result;

[0185] In this embodiment, parameter difference refers to the parameter difference between the same parameter in the real-time parameter curve and the standard parameter curve at the same time;

[0186] In this embodiment, the first difference is preset: a threshold value for comparing with the parameter difference and determining whether the parameter is abnormal based on the comparison result;

[0187] In this embodiment, abnormality: for example, leakage of flue gas or leakage of heat medium water in the system caused by human factors or pipeline damage;

[0188] In this embodiment, the correction function is a function obtained by analyzing the comparison result and used to correct and adjust the real-time parameter curve and continuously approach the standard parameter curve.

[0189] The working principle and beneficial effects of the above technical solution are as follows: the present invention generates a real-time parameter curve by extracting various parameter data from the first result, and at the same time, establishes a relationship model according to the relationship between various parameters, and predicts the future trend of various parameters in combination with the historical data of various parameters, thereby constructing a prediction model, so that the commissioning temperature rise curve can be optimized by pre-adjusting the operating parameters of each device, thereby improving the smoothing coefficient of the commissioning temperature rise curve and the stability of the system; at the same time, the abnormal data in the system can be accurately identified through the abnormal analysis subunit, so that the system can be maintained in time, reducing the probability of safety accidents caused by improper operation or equipment damage, thereby greatly improving the safety of the system.

[0190] The embodiment of the present invention provides a heat medium water system operation optimization system during the startup of a million-unit unit, and an optimization evaluation module, including:

[0191] The curve parameter extraction unit is used to extract parameter pairs from the commissioning heating curve and the optimization curve, and establish a parameter set A = {A i ,i=1,2,3,...,m}, where A i represents the i-th parameter pair, and A i ={a0 i ,a1i}, where a0 i represents the parameter value of the ith parameter pair based on the commissioning heating curve, a1 i represents the parameter value based on the optimization curve in the i-th parameter pair;

[0192] The effect evaluation unit is used to evaluate the effect of the indicator set B on the parameter set A and establish the effect evaluation matrix X = {x ij} m×n , where x ij represents the evaluation value of the i-th parameter under the j-th evaluation index, j = 1, 2, 3, ..., n, n represents the number of evaluation indicators in the evaluation index set B, and the effect evaluation matrix has m rows and n columns;

[0193] A matrix normalization unit, used for performing a first normalization on each column in the effect evaluation matrix and a second normalization on each row in the effect evaluation matrix;

[0194]

[0195] Among them, y1 j represents the first normalized value of the jth column; xj represents the effect evaluation weight of the i-th element in the j-th column corresponding to the j-th evaluation indicator; i Represents the effect evaluation value of the i-th element in the j-th column; represents the minimum evaluation factor in the jth column; represents the maximum evaluation factor in the jth column;

[0196]

[0197] Among them, y2 i represents the second normalized value of the i-th row; represents the effect evaluation weight of the jth element in the jth row corresponding to the jth evaluation indicator; xi j Represents the effect evaluation value of the jth element in the i-th row; represents the minimum evaluation factor in the i-th row; represents the maximum evaluation factor in the i-th row; σ1 2 Indicates that based on all the The variance of

[0198] A first vector is constructed based on the first normalized value, and a second vector is constructed based on the second normalized value, and a final evaluation value Zp is calculated in combination with the assigned weight;

[0199]

[0200] Wherein, k represents the preset adjustment coefficient; p ijis the weight of the i-th parameter under the j-th indicator; ln is the logarithmic operator; r1 is the proportion judgment function; e01 is the preset ratio; s01 and s02 are the proportion coefficients; Δ(ph, Δp1) represents the evaluation function based on the smoothing coefficient ph and the coefficient difference Δp1 between the commissioning temperature rise curve and the optimization curve;

[0201] The final evaluation value Zp is compared with the effect evaluation table to determine the optimization evaluation factor, and then fed back to the data analysis module to determine the adjustment parameters of the heat medium water system.

[0202] In this embodiment, the parameter pair: includes a data pair of parameter values ​​based on the commissioning temperature rise curve and the optimization curve;

[0203] In this embodiment, normalization is used to unify the value ranges of different parameters, eliminate the comparison deviation caused by different value ranges or different attributes of the parameters, and facilitate the subsequent comparison and analysis of the relationship between the parameters;

[0204] In this embodiment, the first normalized value is a value obtained by performing the first normalization on each column in the effect evaluation matrix;

[0205] In this embodiment, the second normalized value is a value obtained by performing the second normalization on each row in the effect evaluation matrix;

[0206] In this embodiment, the effect evaluation table: a data table used to determine the optimization evaluation factor according to the final evaluation value, is preset.

[0207] The working principle and beneficial effects of the above technical solution are as follows: the present invention establishes an effect evaluation matrix and normalizes the matrix, transforms the attribute values ​​according to the formula, and converts the data into quantitative values ​​under the same scale while retaining the size relationship between the numerical values, thereby reducing the negative impact on the effect evaluation results caused by the difference in attribute types and excessive numerical differences of the parameters in the matrix, and improving the accuracy of data processing; at the same time, by assigning corresponding weights to indicators corresponding to different parameters, the optimization effect of the commissioning heating curve is comprehensively evaluated, reducing the situation where the effect evaluation is inaccurate due to data errors, thereby improving the accuracy of the effect evaluation and facilitating the subsequent continuous optimization of the commissioning heating curve.

[0208] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0209] Example 2

[0210] The second embodiment of the present invention is different from the previous embodiment in that:

[0211] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0212] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0213] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0214] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0215] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0216] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A heat medium water system operation optimization system during the startup of a million-unit unit, characterized by: include, A data monitoring module, used to obtain real-time operating parameters of the heat medium water system at a preset position during startup, and convert the real-time operating parameters into a data packet in a preset format; A data analysis module, which uses a preset data analysis model to process and analyze the relationship between the parameters in the data packet to obtain a first result, and uses a preset function to optimize the first result; Changing the information acquisition module to obtain the real-time operation temperature rise curve of the heat medium water system, and using the optimization result to obtain the changed operation temperature rise curve and the smoothing coefficient of the changed curve; The optimization evaluation module is used to evaluate the effect of the changed curve and smoothing coefficient, and generate optimization evaluation results to feed back to the data analysis module.

2. The heat medium water system operation optimization system during the startup of a million-unit unit as claimed in claim 1, characterized in that: The data monitoring module includes a smoke cooler monitoring unit for obtaining the first smoke temperature, the second smoke temperature and the first regulating valve opening of the heat medium water cooler in the heat medium water system in real time; The smoke heater monitoring unit is used to obtain the third smoke temperature, the fourth smoke temperature and the second regulating valve opening of the heat medium water in the smoke heater in the heat medium water system in real time; The heat transfer water system monitoring unit is used to obtain the steam valve opening, bypass opening and pump port pressure of the heat transfer water system in real time; The data generation unit is used to convert various real-time operating parameters obtained by the smoke cooler monitoring unit, the smoke heater monitoring unit and the heat medium water system monitoring unit into a data packet in a preset format and send it to the data analysis module.

3. The heat medium water system operation optimization system during the startup of a million-unit unit as claimed in claim 2, characterized in that: The data monitoring module also includes a heat medium water system self-start unit, which is used to obtain the water temperature of the heat medium water system, the water level of the heat medium water tank and the number of operations of the heat medium water pump in real time through the heat medium water system monitoring unit, and compare the fourth smoke temperature of the smoke heater with the preset threshold condition, and determine whether the heat medium water system is started based on the comparison result.

4. The heat medium water system operation optimization system during the startup of a million-unit unit as claimed in claim 3, characterized in that: The data analysis module includes: A model selection unit, configured to select an adaptive data analysis model from a preset model database based on data attributes of various parameters in the data packet; A data analysis unit, configured to analyze the relationship between various parameters in the data packet based on the selected data analysis model to obtain a first result; The data optimization unit is used to select a first preset function from a preset function database and optimize the first result based on the first preset function.

5. The heat medium water system operation optimization system during the startup of a 1,000-million-unit unit as claimed in claim 4, characterized in that: The change information acquisition module includes a temperature rise curve generation unit, which is used to generate a commissioning temperature rise curve of the heat medium water temperature changing with time based on the real-time water temperature data in the data packet; An optimization curve generating unit, based on the optimization result, obtains an optimization curve after optimizing the commissioning heating curve; A node marking unit, used to mark the optimization curve according to a first node at the start and close time of the heat medium water system and a second node corresponding to the time of performing the optimization processing operation; The coefficient generating unit is used to calculate the optimization curve based on the second preset function screened in the preset function database to obtain a smoothing coefficient, wherein the optimization curve is a changed curve.

6. The heat medium water system operation optimization system during the startup of a MW unit as claimed in claim 5, characterized in that: The heat medium water system self-starting unit includes a stage division subunit, which is used to divide the heat medium water system startup process into a first stage, a second stage and a third stage; A function acquisition unit, used to acquire the parsed content in the data packet, and acquire the interpolation function of the second smoke temperature and the first heat medium water valve opening according to a preset temperature-opening-function mapping table; A first target opening acquisition subunit is used to generate a first target opening of the first regulating valve opening of the heat medium water in the first stage based on the valve pipeline characteristics and in combination with the change value of the temperature field; A function execution subunit, configured to generate a first adjustment instruction based on the interpolation function, the first target opening, and the smoke temperature change rate in the data packet; a preheating subunit, configured to adjust the opening of the first regulating valve of the heat medium water in combination with the first target opening and based on the first regulating instruction; A second target opening acquisition subunit is used to acquire a second target opening of the first regulating valve of the heat medium water in the second stage when the second smoke temperature exceeds the first threshold value and in combination with the change value of the temperature field; The bypass regulating subunit is used to obtain a second regulating instruction from a preset smoke temperature-opening-instruction comparison table based on the second smoke temperature and the second target opening, and to adjust the first regulating valve opening of the heat medium water and the bypass regulating opening based on the second regulating instruction; A water volume stabilization subunit, used to adjust the first regulating valve opening and the bypass regulating opening in real time to maintain a constant water volume of heat medium water in the heat medium water system; A water pressure stabilizing subunit, used to maintain a constant water pressure of the heat medium water in the heat medium water system by adjusting the heat medium water pump in combination with the water volume stabilizing subunit; The heat medium water stable operation subunit is used to adjust the first constant corresponding to the heat medium water pump, the second constant corresponding to the heat medium water volume, the third constant corresponding to the second smoke temperature, and the fourth constant corresponding to the fourth smoke temperature respectively in combination with the equipment operation parameters and environmental parameters obtained in the third stage when the second smoke temperature reaches a preset second threshold.

7. The heat medium water system operation optimization system during the startup of a million-unit unit as claimed in claim 6, characterized in that: The data optimization unit includes a data extraction subunit, which is used to extract various parameter data corresponding to each preset moment in the first result, and establish a parameter real-time curve corresponding to each parameter according to the time sequence of each preset moment; A relation model generating subunit, configured to construct a parameter relation model based on the first result and according to a mapping relationship between various parameters; A prediction model subunit, for constructing a parameter prediction model based on the parameter real-time curve, the parameter relationship model, and combining the historical data of each parameter, and generating a parameter prediction curve; A standard parameter acquisition subunit is used to select corresponding standard parameters in a preset parameter database through a preset attribute-parameter comparison table according to the data attributes of each parameter in the data packet and in combination with the parameter changes at the same time of the parameter change curve; A standard curve generating subunit, configured to generate a standard curve according to the standard parameters and in combination with the time nodes in the first result; The abnormality analysis subunit is used to compare and analyze the real-time parameter curve and the standard curve, calculate the parameter difference of the corresponding parameter at the same time, and compare it with the preset first difference to determine whether the parameter is abnormal; A correction subunit, used for comparing and analyzing the parameter prediction curve with the parameter real-time curve, and generating a correction function based on the comparison result; The curve optimization subunit is used to optimize the parameter real-time curve based on the correction function and the parameter real-time curve and in combination with the first preset function.

8. The heat medium water system operation optimization system during the startup of a MW unit as claimed in claim 7, characterized in that: The optimization evaluation module includes: The curve parameter extraction unit is used to extract parameter pairs from the operation heating curve and the optimization curve, and establish a parameter set A = {A i , i=1, 2, 3,..., m}, where, A i represents the i-th parameter pair, and A i ={a0 i , a1 i }, where a0 i represents the parameter value of the ith parameter pair based on the commissioning heating curve, a1 i represents the parameter value based on the optimization curve in the i-th parameter pair; The effect evaluation unit is used to evaluate the effect of the indicator set B on the parameter set A and establish the effect evaluation matrix X = {x ij } m×n , where x ij represents the evaluation value of the i-th parameter under the j-th evaluation index, j=1, 2, 3, ..., n, n represents the number of evaluation indicators in the evaluation indicator set B, and the effect evaluation matrix has m rows and n columns; A matrix normalization unit, used for performing a first normalization on each column in the effect evaluation matrix and a second normalization on each row in the effect evaluation matrix; Among them, y1 j represents the first normalized value of the jth column; xj represents the effect evaluation weight of the i-th element in the j-th column corresponding to the j-th evaluation indicator; i Represents the effect evaluation value of the i-th element in the j-th column; represents the minimum evaluation factor in the jth column; represents the maximum evaluation factor in the jth column; Among them, y2 i represents the second normalized value of the i-th row; represents the effect evaluation weight of the jth element in the jth row corresponding to the jth evaluation indicator; xi j Represents the effect evaluation value of the jth element in the i-th row; represents the minimum evaluation factor in the i-th row; represents the maximum evaluation factor in the i-th row; σ1 2 Indicates that based on all the The variance of A first vector is constructed based on the first normalized value, and a second vector is constructed based on the second normalized value, and a final evaluation value vp is calculated in combination with the assigned weight; Wherein, k represents the preset adjustment coefficient; p ij is the weight of the i-th parameter under the j-th indicator; ln is the logarithmic operator; r1 is the proportion judgment function; e01 is the preset ratio; s01 and s02 are the proportion coefficients; Δ(ph, Δp1) represents the evaluation function based on the smoothing coefficient ph and the coefficient difference Δp1 between the commissioning temperature rise curve and the optimization curve; The final evaluation value Zp is compared with the effect evaluation table to determine the optimization evaluation factor, and the factor is fed back to the data analysis module to determine the adjustment parameters of the heat medium water system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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