Multi-stage linkage cooperative control optimization method for intelligent start-stop process of thermal power generating unit

Abstract

The invention provides a thermal power generating unit intelligent start-stop process multi-level linkage cooperative control optimization method, which relates to the field of thermal power generating unit control, and comprises the steps of collecting equipment group operation parameters, establishing a multi-level dynamic control reference, constructing a cascade response mechanism to determine a target operation interval, subintervals are divided according to the parameter coupling degree, compensation adjustment coefficients are set for hierarchical adjustment, and a linkage control link is constructed to coordinate the adjustment process of each level of equipment group. According to the method, accurate cooperation among equipment in the starting and stopping process of the thermal power generating unit is achieved, the safety and efficiency of the starting and stopping process are improved, and energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to a thermal power unit control technology, and in particular to a multi-level linkage collaborative control optimization method for an intelligent start-stop process of a thermal power unit. Background Art

[0002] During the start-up and shutdown process, thermal power units need to coordinate the operating status of multiple equipment groups. Traditional control methods mainly rely on fixed timing settings and manual experience judgment. They are difficult to adapt to the complex coupling relationship between equipment under different operating conditions, and are prone to problems such as asynchronous equipment response and parameter fluctuations.

[0003] Although existing linkage control technology has achieved the coordinated cooperation of equipment groups, due to the lack of in-depth analysis of the dynamic characteristics of the equipment and the parameter coupling relationship, the configuration scheme of the control parameters is relatively fixed, and it is difficult to adaptively adjust according to changes in working conditions, resulting in insufficient control accuracy and stability. Especially when there is a significant lag in the equipment response, it is easy to cause a chain reaction in the control link.

[0004] With the increasing requirements for flexible operation of thermal power units, the coupling relationship between equipment groups is becoming increasingly complex. Traditional hierarchical control methods are difficult to meet the collaborative control needs during rapid start-up and shutdown. Therefore, there is an urgent need for an optimization method that can adaptively configure control parameters, accurately grasp the dynamic characteristics of equipment, and realize multi-level linkage collaborative control. Summary of the Invention

[0005] The embodiment of the present invention provides a multi-level linkage coordinated control optimization method for the intelligent start-stop process of a thermal power unit, which can solve the problems in the prior art.

[0006] A first aspect of an embodiment of the present invention provides a multi-level linkage coordinated control optimization method for an intelligent start-stop process of a thermal power unit, comprising:

[0007] Collect operating parameters of thermal power plant equipment groups, extract the variation patterns and coupling relationships between parameters, generate dynamic response data, classify the dynamic response data according to the parameter fluctuation characteristics and response timing relationship, establish operating condition classification standards, and adaptively configure control parameters and sampling period combination schemes based on the operating condition classification standards to form a multi-level dynamic control benchmark;

[0008] Based on a multi-level dynamic control benchmark, the parameter changes in the dynamic response data are converted into trigger instructions between devices. A cascade response mechanism is constructed. Based on the cascade response mechanism, the coupling characteristics of upstream and downstream devices are analyzed, and a safe constraint range for device operating parameters is established. The target operating range of each device is determined based on the parameter change trend within the safe constraint range.

[0009] The target operating range is divided into multiple sub-ranges according to the parameter coupling degree, and the compensation adjustment coefficient is set according to the response characteristics of the equipment in each sub-range. The equipment operating parameters are adjusted in stages through the compensation adjustment coefficient;

[0010] The adjustment quantity generated by hierarchical adjustment is used to construct a linkage control link of the equipment group according to the physical constraint relationship between the equipment. The execution timing of the trigger instruction is set based on the inter-level transfer characteristics of the linkage control link. The adjustment process of the equipment groups at all levels is coordinated according to the execution timing to realize multi-level linkage control during the start-up and shutdown process of the thermal power unit.

[0011] In an optional embodiment,

[0012] Collect the operating parameters of the thermal power plant equipment group, extract the change patterns and coupling relationships between the parameters, and generate dynamic response data including:

[0013] Collecting operating parameters of a thermal power plant equipment group, calculating the ratio of changes between the parameters, establishing a parameter change correlation matrix, dividing the collection priority of the operating parameters according to the parameter change correlation matrix, and using corresponding sampling periods to collect data for operating parameters of different priorities to obtain an operating parameter sampling sequence;

[0014] Based on the operating parameter sampling sequence, the data processing window is determined according to the characteristic response time of the equipment. Polynomial fitting is performed on the operating parameter sampling sequence within the data processing window to extract the parameter change pattern.

[0015] Based on the parameter change law, the change rate and acceleration of the operating parameters at adjacent moments are calculated to obtain the parameter dynamic characteristics, and the information transmission amount between the operating parameters is calculated using the parameter dynamic characteristics to determine the parameter coupling relationship;

[0016] The parameter change association matrix, parameter change law and parameter coupling relationship are combined according to a time series to generate dynamic response data.

[0017] In an optional embodiment,

[0018] Dynamic response data is classified according to the relationship between parameter fluctuation characteristics and response timing, and a working condition classification standard is established. Based on the working condition classification standard, a combination of control parameters and sampling periods is adaptively configured to form a multi-level dynamic control benchmark including:

[0019] The dynamic response data is segmented according to preset time intervals, and the segmented data is subjected to wavelet transform to obtain the fluctuation components of different frequency bands. The characteristic parameters of the fluctuation components are extracted to obtain the fluctuation intensity. The parameters are graded according to the fluctuation intensity to establish parameter fluctuation characteristic data including fluctuation frequency characteristics, fluctuation amplitude characteristics and fluctuation energy characteristics;

[0020] Time window analysis is used to extract the time feature points of parameter changes. Based on the time feature points, the response time intervals between parameters are calculated, the transmission relationship of parameter changes is established, and the degree of influence between parameters is determined according to the response time interval to generate response time series data.

[0021] The parameter fluctuation characteristic data and the response time series data are combined to construct the working condition characteristic data, the characteristic distance between the characteristic data is calculated, the data with characteristic distance less than the preset distance threshold is clustered and analyzed, and the working condition classification standard is established;

[0022] The basic sampling period is calculated based on the fluctuation frequency characteristics. The sampling period combination of different frequency bands is determined based on the fluctuation intensity. The control parameter weight is calculated according to the parameter influence degree. The sampling period combination is adjusted based on the control parameter weight to obtain the final sampling period.

[0023] Establish a parameter weight mapping table under the working condition classification standard, configure the corresponding control parameter weight and sampling period for each working condition level, and generate a multi-level dynamic control benchmark.

[0024] In an optional embodiment,

[0025] Based on a multi-level dynamic control benchmark, the parameter changes in the dynamic response data are converted into trigger instructions between devices. The cascade response mechanism is constructed, including:

[0026] Collect dynamic response data from multi-level dynamic control benchmarks and construct a multi-dimensional parameter monitoring matrix;

[0027] Perform wavelet transform and singular value decomposition on the monitoring matrix to extract the frequency domain and time domain characteristics of the parameter change. Calculate the energy density distribution based on the frequency domain characteristics, extract the energy density peak and its corresponding frequency component, calculate the state transition probability based on the time domain characteristics, and extract the characteristic value and stability index of the state transition. Perform a weighted combination of the energy density peak and the state transition characteristic value to obtain a comprehensive representation value of the parameter change.

[0028] The parameter changes are divided into corresponding levels according to the numerical range of the comprehensive characterization value, and trigger instructions between devices are generated according to the parameter change level and change amplitude. The trigger instructions between devices are arranged in the order of parameter changes to form a cascade response mechanism.

[0029] In an optional embodiment,

[0030] Based on the cascade response mechanism, the coupling characteristics of upstream and downstream equipment are analyzed to establish the safety constraint range of equipment operating parameters. The target operating range of each device is determined based on the parameter change trend within the safety constraint range.

[0031] Conduct time window analysis on the cascade response mechanism, calculate the influence coefficients between devices, determine the upstream and downstream transmission relationships between devices based on the influence coefficients, and establish a cascade response transmission link;

[0032] Identify device pairs with upstream and downstream transmission relationships along the cascade response transmission link, extract the output parameters of the upstream device and the input parameters of the downstream device in each device pair, calculate the response time ratio and amplitude ratio of the input parameters and output parameters respectively, determine the delay attenuation of parameter transmission based on the response time ratio, and determine the intensity attenuation of parameter transmission based on the amplitude ratio, and use the delay attenuation and intensity attenuation as the coupling characteristics of the upstream and downstream devices;

[0033] The cumulative delay and cumulative attenuation of parameters in the cascade response transmission link are calculated based on the coupling characteristics of upstream and downstream devices. The time margin and amplitude margin are determined based on the square root of the cumulative delay and the logarithm of the cumulative attenuation. The safety constraint range of the device operating parameters is determined in combination with the physical limits of the device.

[0034] Perform segmented statistical analysis on the parameter change trends within the safety constraint range of the equipment operating parameters to determine the steady-state range and fluctuation range of the parameters, and combine the steady-state range and fluctuation range to form the target operating range of each device.

[0035] In an optional embodiment,

[0036] The target operating range is divided into multiple sub-ranges according to the parameter coupling degree. The compensation adjustment coefficient is set according to the response characteristics of the equipment in each sub-range. The equipment operating parameters are adjusted in stages using the compensation adjustment coefficient, including:

[0037] Collect time series data of equipment operating parameters within the target operating range, calculate the mutual information and time-lag correlation coefficient of parameter pairs, perform weighted combination of the mutual information and time-lag correlation coefficient to obtain the parameter coupling degree, and construct a coupling degree matrix based on the parameter coupling degree;

[0038] Spectral clustering is performed using the coupling matrix to obtain initial parameter groupings. The local density and minimum distance are calculated for each parameter grouping. The density peak point is determined based on the local density and minimum distance. The density peak point is used as the cluster center to divide the target operating range into multiple sub-ranges.

[0039] A compensation adjustment coefficient is constructed based on the dynamic response characteristics of the equipment in the sub-interval, and the compensation adjustment coefficient is dynamically updated according to the real-time change rate of the parameter deviation in the sub-interval;

[0040] The updated compensation adjustment coefficient is combined with the parameter deviation and its integral term to obtain the parameter adjustment amount. The constraint threshold of the adjustment amount is set according to the parameter coupling degree corresponding to the sub-interval. The adjustment amount is compared with the constraint threshold and the adjustment instruction is output;

[0041] The parameter coupling degree difference between adjacent sub-intervals is calculated to determine the range of the transition area. Within the transition area, the weight coefficient is set based on the parameter coupling degree, and the adjustment instructions are smoothly switched. An adjustment priority sequence is established based on the parameter coupling degree sorted from large to small. The adjustment instructions are executed in sequence according to the adjustment priority sequence to achieve hierarchical adjustment of equipment operating parameters.

[0042] In an optional embodiment,

[0043] The regulation variables generated by hierarchical regulation are used to construct linkage control links for equipment groups according to the physical constraints between the equipment. The execution sequence of trigger instructions is set based on the inter-level transfer characteristics of the linkage control links. The regulation process of equipment groups at all levels is coordinated according to the execution sequence to achieve multi-level linkage control during the start-up and shutdown of thermal power units. This includes:

[0044] Extract the amplitude and change direction of the adjustment amount generated by the hierarchical adjustment, generate the adjustment amount control instruction, collect the operating status data of the device group based on the adjustment amount control instruction, and perform a weighted combination of the adjustment amount control instruction and the operating status data of the device group to build a linkage control link;

[0045] Analyze the input-output response relationship between device groups based on the linkage control link, calculate the dynamic characteristic indicators between the device groups, extract the delay time and hysteresis interval of the device group response based on the dynamic characteristic indicators, and use the delay time and hysteresis interval as constraints to determine the inter-stage transfer characteristics;

[0046] Based on the inter-stage transfer characteristics, the benchmark execution interval of the trigger instruction is set to form the initial execution sequence. The response status of the equipment groups at each level under the initial execution sequence is monitored, the execution deviation is calculated, and the execution sequence parameters are dynamically adjusted according to the execution deviation to generate the optimal execution sequence.

[0047] The optimal execution sequence is combined with the linkage control link to generate the final adjustment instructions, coordinate the adjustment process of equipment groups at all levels, and realize multi-level linkage control during the start-up and shutdown process of thermal power units through feedback compensation and dynamic switching.

[0048] According to a second aspect of an embodiment of the present invention, an electronic device is provided, including:

[0049] processor;

[0050] a memory for storing processor-executable instructions;

[0051] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0052] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0053] In this embodiment, by establishing a multi-level dynamic control benchmark, adaptive parameter adjustment is achieved during the startup and shutdown of thermal power units, improving control accuracy and response speed, and reducing safety risks caused by parameter fluctuations. A cascade response mechanism and linkage control link based on device coupling characteristics are constructed, enabling each level of equipment to operate collaboratively according to an optimized execution sequence. This effectively addresses the issues of poor inter-device coordination and delayed response in traditional control methods, and improves overall system stability. A method combining hierarchical parameter adjustment with a compensation adjustment coefficient refines the control process, adapting to the changing needs of different operating conditions, reducing energy consumption, extending equipment life, and reducing emissions during startup and shutdown, resulting in significant economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Schematic diagram of the process of multi-level linkage coordinated control optimization method for the intelligent start-stop process of a thermal power unit according to an embodiment of the present invention;

[0055] Figure 2 This is a flow chart of hierarchical adjustment of parameter coupling degree according to an embodiment of the present invention;

[0056] Figure 3 Schematic diagram showing a comparison of multi-stage linkage control efficiency during the start-up and shutdown process of a thermal power unit according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0058] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0059] Figure 1 FIG. 1 is a flow chart of a multi-level coordinated control optimization method for the intelligent start-stop process of a thermal power unit according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0060] Collect operating parameters of thermal power plant equipment groups, extract the variation patterns and coupling relationships between parameters, generate dynamic response data, classify the dynamic response data according to the parameter fluctuation characteristics and response timing relationship, establish operating condition classification standards, and adaptively configure control parameters and sampling period combination schemes based on the operating condition classification standards to form a multi-level dynamic control benchmark;

[0061] Based on a multi-level dynamic control benchmark, the parameter changes in the dynamic response data are converted into trigger instructions between devices. A cascade response mechanism is constructed. Based on the cascade response mechanism, the coupling characteristics of upstream and downstream devices are analyzed, and a safe constraint range for device operating parameters is established. The target operating range of each device is determined based on the parameter change trend within the safe constraint range.

[0062] The target operating range is divided into multiple sub-ranges according to the parameter coupling degree, and the compensation adjustment coefficient is set according to the response characteristics of the equipment in each sub-range. The equipment operating parameters are adjusted in stages through the compensation adjustment coefficient;

[0063] The adjustment quantity generated by hierarchical adjustment is used to construct a linkage control link of the equipment group according to the physical constraint relationship between the equipment. The execution timing of the trigger instruction is set based on the inter-level transfer characteristics of the linkage control link. The adjustment process of the equipment groups at all levels is coordinated according to the execution timing to realize multi-level linkage control during the start-up and shutdown process of the thermal power unit.

[0064] In an optional embodiment, collecting operating parameters of a thermal power plant group, extracting the variation patterns and coupling relationships between the parameters, and generating dynamic response data includes:

[0065] Collecting operating parameters of a thermal power plant equipment group, calculating the ratio of changes between the parameters, establishing a parameter change correlation matrix, dividing the collection priority of the operating parameters according to the parameter change correlation matrix, and using corresponding sampling periods to collect data for operating parameters of different priorities to obtain an operating parameter sampling sequence;

[0066] Based on the operating parameter sampling sequence, the data processing window is determined according to the characteristic response time of the equipment. Polynomial fitting is performed on the operating parameter sampling sequence within the data processing window to extract the parameter change pattern.

[0067] Based on the parameter change law, the change rate and acceleration of the operating parameters at adjacent moments are calculated to obtain the parameter dynamic characteristics, and the information transmission amount between the operating parameters is calculated using the parameter dynamic characteristics to determine the parameter coupling relationship;

[0068] The parameter change association matrix, parameter change law and parameter coupling relationship are combined according to a time series to generate dynamic response data.

[0069] During the operation of a thermal power unit, a plurality of equipment parameters are collected in real time, including but not limited to boiler temperature, pressure, main steam flow, feedwater flow, turbine speed, generator power, etc. For the collected parameters, the change ratio between adjacent time points is calculated. Taking temperature parameter T1 and pressure parameter P1 as examples, between time points t1 and t2, the temperature change ΔT1 = T1(t2) - T1(t1) and the pressure change ΔP1 = P1(t2) - P1(t1) are calculated, and then the change ratio ΔT1 / ΔP1 is calculated. Similarly, the change ratios between all parameters are calculated to form a parameter change correlation matrix. For example, for six parameters of temperature T1, T2, pressure P1, P2, flow F1, F2, a 6x6 correlation matrix is constructed, and the element (i, j) in the matrix represents the change ratio between parameter i and parameter j.

[0070] According to the parameter change correlation matrix, the parameter collection priority is divided. For the parameter pair with a larger change ratio, a slight change in one parameter may lead to a significant change in the other parameter, and such parameters are assigned a high priority. For example, if the value of ΔT1 / ΔP1 is large, T1 or P1 will be divided into a high-priority parameter. According to the priority, different sampling periods are set: a high-priority parameter uses a 100-millisecond sampling period, a medium-priority parameter uses a 500-millisecond sampling period, and a low-priority parameter uses a 1-second sampling period. Through this differentiated sampling strategy, a running parameter sampling sequence containing timestamps and parameter values is obtained.

[0071] The data processing window is determined according to the equipment characteristic response time. For example, for the turbine speed parameter, the characteristic response time is 10 seconds, and the data processing window is set to 30 seconds; for the boiler temperature parameter, the characteristic response time is 2 minutes, and the data processing window is set to 6 minutes. Within the determined data processing window, the sampling sequence is polynomial fitted. Taking the boiler temperature parameter as an example, within the 6-minute data processing window, about 360 sampling points are collected (assuming a 1-second sampling period), and a cubic polynomial is used for fitting to obtain a function expression of the parameter change over time. Through analysis of the fitted function, the parameter change rule is extracted, such as rising, falling, fluctuating or stable states.

[0072] Based on the extracted parameter variation patterns, the rate of change and acceleration of the operating parameters at adjacent moments are calculated to obtain the parameter dynamic characteristics. Taking boiler temperature T as an example, assuming the temperature values ​​at times t1, t2, and t3 are T1, T2, and T3, respectively, the rate of change of temperature between t1 and t2 is (T2-T1) / (t2-t1), and the rate of change between t2 and t3 is (T3-T2) / (t3-t2). The acceleration of temperature can be expressed as the rate of change of the rate of change, i.e., [(T3-T2) / (t3-t2)-(T2-T1) / (t2-t1)] / (t3-t1). By calculating the rate of change and acceleration of all parameters, a parameter dynamic characteristic set is formed.

[0073] The amount of information transfer between parameters is calculated using their dynamic characteristics. For example, analyzing the dynamic characteristics of boiler temperature T and main steam pressure P, if changes in T often precede those in P, and if the trends are similar, then significant information transfer from T to P is considered. By calculating the amount of information transfer between all pairs of parameters within a specific time window, the parameter coupling relationship is determined. For example, a strong coupling relationship is determined between boiler temperature T and main steam pressure P, while a weaker coupling relationship is determined between boiler temperature T and generator voltage V.

[0074] The parameter change association matrix, parameter change pattern, and parameter coupling relationship are combined in a time series to generate dynamic response data. At each time point, the current parameter value, parameter change association matrix, parameter change pattern, and parameter coupling relationship are recorded. For example, at time t = 10:00:00, the boiler temperature T = 540°C and the main steam pressure P = 16.7 MPa are recorded, along with information such as the ratio of the change in T to P, the rising pattern of T, and the strong coupling relationship between T and P. This information constitutes the dynamic response data at that moment. Over time, the dynamic response data is continuously updated to form a complete time series dataset.

[0075] Based on the above technical solution, dynamic feature extraction and efficient data modeling of the operating status of thermal power plant equipment groups can be achieved, thereby improving the accuracy and timeliness of operating parameter acquisition. By constructing a parameter change correlation matrix and setting acquisition priorities, adaptive sampling of key parameters is achieved, reducing data redundancy and computational burden. Polynomial fitting is used to extract parameter change patterns, and combined with dynamic feature analysis to analyze the amount of information transferred, the coupling relationship between operating parameters can be accurately identified, improving the understanding of equipment collaborative behavior. The resulting dynamic response data provides a precise and time-consistent input data foundation for subsequent control benchmark construction and equipment linkage control, enhancing the system's responsiveness and safety during startup and shutdown processes.

[0076] In an optional embodiment, dynamic response data is classified according to the relationship between parameter fluctuation characteristics and response timing, and a working condition classification standard is established. The control parameter and sampling period combination scheme is adaptively configured according to the working condition classification standard to form a multi-level dynamic control benchmark including:

[0077] The dynamic response data is segmented according to preset time intervals, and the segmented data is subjected to wavelet transform to obtain the fluctuation components of different frequency bands. The characteristic parameters of the fluctuation components are extracted to obtain the fluctuation intensity. The parameters are graded according to the fluctuation intensity to establish parameter fluctuation characteristic data including fluctuation frequency characteristics, fluctuation amplitude characteristics and fluctuation energy characteristics;

[0078] Time window analysis is used to extract the time feature points of parameter changes. Based on the time feature points, the response time intervals between parameters are calculated, the transmission relationship of parameter changes is established, and the degree of influence between parameters is determined according to the response time interval to generate response time series data.

[0079] The parameter fluctuation characteristic data and the response time series data are combined to construct the working condition characteristic data, the characteristic distance between the characteristic data is calculated, the data with characteristic distance less than the preset distance threshold is clustered and analyzed, and the working condition classification standard is established;

[0080] The basic sampling period is calculated based on the fluctuation frequency characteristics. The sampling period combination of different frequency bands is determined based on the fluctuation intensity. The control parameter weight is calculated according to the parameter influence degree. The sampling period combination is adjusted based on the control parameter weight to obtain the final sampling period.

[0081] Establish a parameter weight mapping table under the working condition classification standard, configure the corresponding control parameter weight and sampling period for each working condition level, and generate a multi-level dynamic control benchmark.

[0082] In this embodiment, the dynamic response data is segmented according to a preset time interval, where the preset time interval can be set to 5 minutes. For example, for dynamic response data such as temperature, pressure, flow, etc. collected from the equipment, it is divided into a data segment every 5 minutes. The segmented data is subjected to wavelet transform processing, and the db4 wavelet basis function can be selected for 3-layer decomposition to obtain the fluctuation components of the three frequency bands of high frequency, medium frequency and low frequency. The characteristic parameters of the extracted fluctuation components include mean, standard deviation, maximum value, minimum value and peak-to-peak value. The fluctuation intensity is calculated based on these characteristic parameters, such as defining the ratio of the standard deviation to the mean as a fluctuation intensity index. When the fluctuation intensity is greater than 0.2, it is defined as a strong fluctuation, the fluctuation intensity between 0.1 and 0.2 is defined as a medium fluctuation, and the fluctuation intensity less than 0.1 is defined as a weak fluctuation. In this way, the parameters are graded to establish parameter fluctuation characteristic data containing fluctuation frequency characteristics, fluctuation amplitude characteristics and fluctuation energy characteristics.

[0083] When analyzing the time characteristic points of parameter changes, a 60-second sliding time window is used with a window overlap rate of 50%. Within each time window, when the parameter change rate exceeds the set threshold (such as 10%), the moment is marked as a time characteristic point. By recording the time characteristic points of multiple parameters such as temperature, pressure, and flow, the response time intervals between different parameters are calculated. For example, when the flow changes, the pressure parameter changes significantly after 15 seconds, and the response time interval between flow and pressure is 15 seconds. The degree of influence between parameters is determined based on the response time interval. The shorter the response time interval, the greater the degree of influence. It can be set that when the response time interval is less than 10 seconds, the degree of influence is high; when the response time interval is between 10 seconds and 30 seconds, the degree of influence is medium; when the response time interval is greater than 30 seconds, the degree of influence is low. In this way, response time series data is generated.

[0084] The parameter fluctuation characteristic data and the response time series data are combined to construct the working condition characteristic data. An example of working condition characteristic data includes: temperature parameters (fluctuation frequency: 0.05Hz, fluctuation amplitude: 2.5℃, fluctuation energy: 6.3), pressure parameters (fluctuation frequency: 0.08Hz, fluctuation amplitude: 0.6MPa, fluctuation energy: 4.2) and the response time interval between them (18 seconds). To calculate the characteristic distance between different working condition characteristic data, the Euclidean distance calculation method can be used. When the characteristic distance is less than the preset distance threshold (such as set to 5.0), these data are classified into the same category. By performing cluster analysis on a large amount of working condition characteristic data, three levels of stable working conditions, fluctuating working conditions and violently fluctuating working conditions are finally summarized, and a working condition classification standard is established.

[0085] The basic sampling period is calculated based on the fluctuation frequency characteristics and can be set to 1 / 10 of the inverse of the fluctuation frequency. For example, when the temperature parameter's fluctuation frequency is 0.05 Hz, the basic sampling period is 2 seconds. The sampling period combinations for different frequency bands are determined based on the fluctuation intensity. For strongly fluctuating parameters, the high-frequency sampling period is 0.5 times the basic sampling period, the medium-frequency band is 1 times the basic sampling period, and the low-frequency band is 2 times the basic sampling period. For medium-fluctuating parameters, the high-frequency sampling period is 1 times the basic sampling period, the medium-frequency band is 2 times the basic sampling period, and the low-frequency band is 4 times the basic sampling period. For weakly fluctuating parameters, the high-frequency sampling period is 2 times the basic sampling period, the medium-frequency band is 4 times the basic sampling period, and the low-frequency band is 8 times the basic sampling period. Control parameter weights are calculated based on the parameter's influence: high-impact parameters have a weight of 0.6, medium-impact parameters have a weight of 0.3, and low-impact parameters have a weight of 0.1. The sampling period combination is adjusted based on the control parameter weights. The adjusted sampling period is equal to the weighted average of the sampling period of each frequency band multiplied by the corresponding weight.

[0086] A parameter weight mapping table based on the operating condition classification standard is established, and corresponding control parameter weights and sampling periods are configured for each operating condition level. Under stable operating conditions, the temperature parameter weight is 0.2, and the sampling period is 10 seconds; the pressure parameter weight is 0.3, and the sampling period is 8 seconds; and the flow parameter weight is 0.5, and the sampling period is 5 seconds. Under fluctuating operating conditions, the temperature parameter weight is 0.3, and the sampling period is 5 seconds; the pressure parameter weight is 0.4, and the sampling period is 3 seconds; and the flow parameter weight is 0.3, and the sampling period is 2 seconds. Under violently fluctuating operating conditions, the temperature parameter weight is 0.4, and the sampling period is 2 seconds; the pressure parameter weight is 0.4, and the sampling period is 1 second; and the flow parameter weight is 0.2, and the sampling period is 1 second. In this way, a multi-level dynamic control benchmark is generated, enabling adaptive adjustment of control parameters and sampling periods under different operating conditions, improving system control efficiency and stability.

[0087] In this embodiment, by segmenting and wavelet transforming the dynamic response data, extracting multi-dimensional fluctuation characteristics and combining them with the response time series relationship, the accuracy of identifying the changing trend of the operating status is improved; the operating condition classification standard constructed based on cluster analysis can effectively reflect the fluctuation coupling characteristics between parameters under different operating conditions; through the dynamic matching of parameter weights and sampling periods, the control strategy can be adjusted according to the real-time operating conditions, the control system's response capability and robustness to sudden conditions are enhanced, and the stability and energy efficiency level of the overall start-stop process are improved.

[0088] In an optional embodiment, based on a multi-level dynamic control benchmark, the parameter changes in the dynamic response data are converted into trigger instructions between devices, and the cascade response mechanism is constructed, including:

[0089] Collect dynamic response data from multi-level dynamic control benchmarks and construct a multi-dimensional parameter monitoring matrix;

[0090] Perform wavelet transform and singular value decomposition on the monitoring matrix to extract the frequency domain and time domain characteristics of the parameter change. Calculate the energy density distribution based on the frequency domain characteristics, extract the energy density peak and its corresponding frequency component, calculate the state transition probability based on the time domain characteristics, and extract the characteristic value and stability index of the state transition. Perform a weighted combination of the energy density peak and the state transition characteristic value to obtain a comprehensive representation value of the parameter change.

[0091] The parameter changes are divided into corresponding levels according to the numerical range of the comprehensive characterization value, and trigger instructions between devices are generated according to the parameter change level and change amplitude. The trigger instructions between devices are arranged in the order of parameter changes to form a cascade response mechanism.

[0092] In the embodiment, the multi-level dynamic control benchmark refers to a set of standard operating parameters of devices at different levels in an industrial automation system, including control parameters of multiple levels such as bottom sensors, middle controllers, and top management systems. Dynamic response data in the multi-level dynamic control benchmark is collected to construct a multi-dimensional parameter monitoring matrix. In actual applications, key parameters of multiple systems involved in the start-stop process of a thermal power unit, such as a steam turbine system, a boiler system, and an electrical system, are collected in real time, including but not limited to main steam pressure, main steam temperature, feedwater flow, turbine speed, generator power, and other parameters. Taking a 600 MW unit as an example, in the cold start process, data is collected every 5 seconds, a total of 120 key parameter points are collected, and a 120-dimensional parameter vector is constructed. In the 4-hour start-up process, a total of 2880 time point data is collected, forming a 2880x120 multi-dimensional parameter monitoring matrix M.

[0093] The monitoring matrix is subjected to wavelet transform and singular value decomposition to extract frequency domain features and time domain features of parameter changes. In the wavelet transform process, a wavelet basis function suitable for the dynamic characteristics of the thermal power unit, such as Daubechies wavelet (db4), is selected, and each parameter sequence is decomposed into 3 layers to obtain wavelet coefficients at each frequency band. Taking the main steam pressure parameter as an example, low-frequency coefficients a3 and high-frequency coefficients d1, d2, and d3 are obtained after wavelet transform, which reflect the change characteristics of the parameter at different time scales. For singular value decomposition, the monitoring matrix M is decomposed into the form of UxSxV, where S is a singular value diagonal matrix. By analyzing the first 10 singular values and their corresponding eigenvectors, the main mode of parameter change is extracted.

[0094] Based on the frequency domain features, the energy density distribution is calculated, and the energy density peak value and its corresponding frequency component are extracted. The energy density of each frequency band coefficient obtained by wavelet transform is calculated, for example, for the main steam pressure parameter, the d1, d2, and d3 coefficients are squared and summed, and then divided by the bandwidth of the corresponding frequency band to obtain the energy density values of different frequency bands. In actual applications, it is found that the energy density of the main steam pressure reaches a peak value of 0.78 near 0.05 Hz, indicating that this frequency component has a significant impact on the system dynamic characteristics. Similarly, the energy density peak value of the turbine speed at 0.08 Hz is 0.65, and the energy density peak value of the generator power at 0.03 Hz is 0.82.

[0095] The state transition probability is calculated based on the time domain characteristics, and the characteristic value and stability index of state transition are extracted. The parameter variation is divided into different state intervals, such as the main steam pressure variation is divided into low pressure (0-3 MPa), medium pressure (3-6 MPa) and high pressure (6-10 MPa) three states. By statistical analysis of the state transition of adjacent time points, the state transition probability matrix is constructed. For the main steam pressure, the transition probability from low pressure to medium pressure is 0.15, the transition probability from medium pressure to high pressure is 0.08, and the probability of falling back from high pressure to medium pressure is only 0.02. The characteristic value of the state transition matrix is extracted, and the maximum characteristic value is 0.95, indicating that the system has good stability. At the same time, the average duration of state maintenance is calculated, and the average duration of main steam pressure in medium pressure state is 35 minutes. This time index is an important reference for state stability.

[0096] The energy density peak value and the state transition characteristic value are combined by weighting to obtain the comprehensive representation value of the parameter variation. For each key parameter, the weight of the energy density peak value is set to 0.6, and the weight of the state transition characteristic value is set to 0.4. The comprehensive representation value is obtained by weighted summation. Taking the main steam pressure as an example, the energy density peak value is 0.78, the state transition characteristic value is 0.95, and the comprehensive representation value after weighted combination is 0.85. Similarly, the comprehensive representation value of the turbine speed is 0.76, and the comprehensive representation value of the generator power is 0.87. These comprehensive representation values comprehensively reflect the variation characteristics of the parameters in the frequency domain and the time domain.

[0097] According to the numerical interval of the comprehensive representation value, the parameter variation is divided into corresponding levels. The comprehensive representation value is divided into low impact area (0-0.3), medium impact area (0.3-0.7) and high impact area (0.7-1.0) three levels. For the main steam pressure, the comprehensive representation value 0.85 belongs to the high impact area, indicating that the parameter variation has a significant impact on the system operation; the comprehensive representation value of the turbine speed 0.76 also belongs to the high impact area; and the comprehensive representation value of the feed water flow 0.45 belongs to the medium impact area, indicating that its influence degree is relatively small.

[0098] Trigger instructions are generated between devices based on the level and amplitude of parameter changes. For parameters in the high-impact zone, when the amplitude of the change exceeds the set threshold, a corresponding device trigger instruction is immediately generated. For example, when the main steam pressure rises by more than 2MPa within 10 minutes, a "reduce the main steam valve opening" instruction is generated and transmitted to the turbine control system, and a "adjust the feedwater flow" instruction is simultaneously generated and transmitted to the feedwater system. For parameters in the medium-impact zone, when the change persists for a certain period of time and the cumulative change reaches a threshold, a corresponding device trigger instruction is generated. For example, when the feedwater flow deviates from the set value by more than 5% for 30 consecutive minutes, an "adjust the feedwater pump speed" instruction is generated. For parameters in the low-impact zone, trigger instructions are only generated when multiple low-impact parameters change simultaneously and meet a specific pattern.

[0099] A cascade response mechanism is formed by arranging the triggering commands between devices according to the order of parameter changes. During the startup process of a thermal power unit, parameter changes typically follow a certain temporal relationship, such as boiler parameter changes occurring before turbine parameter changes, which in turn occur before generator parameter changes. Based on this temporal relationship, the triggering commands are arranged into a cascade response chain. For example, during a cold start, the "Start induced draft fan and forced draft fan" command is triggered first. When the boiler pressure reaches 0.5 MPa, the "Open steam trap" command is triggered. When the main steam temperature reaches 350°C, the "Turbine crank" command is triggered. When the turbine speed reaches 300 rpm, the "Synchronize generator" command is triggered. This cascade response mechanism enables coordinated control of various devices during the startup and shutdown of the thermal power unit.

[0100] Prior art methods often rely on fixed thresholds or manual empirical rules to trigger device responses during the startup and shutdown process of thermal power units. This makes it difficult to adapt to the interconnected relationships between device operating parameters under variable operating conditions, resulting in response delays, insufficient control accuracy, and even device conflicts or system instability. This application constructs a parameter monitoring matrix based on multidimensional feature extraction, combines wavelet transform and singular value decomposition, extracts the energy density and state transition characteristics of parameter changes from both the frequency and time domains, and performs a weighted fusion of the two to obtain a comprehensive parameter change value with greater holistic characterization capabilities. This comprehensive value is then used to classify parameter changes into levels, and combined with the change amplitude, trigger instructions between devices are generated, achieving dynamic adaptation and precise push notification of trigger logic. Compared to existing methods that rely on single thresholds or static logic judgments, this solution can more accurately capture the coupling changes between devices under complex operating conditions, establish a time-series cascade response mechanism, thereby improving the accuracy and timeliness of inter-device response coordination, and enhancing the stability and intelligent control capabilities of the system during startup and shutdown.

[0101] In an optional embodiment, the coupling characteristics of upstream and downstream devices are analyzed based on the cascade response mechanism, and a safety constraint range of device operating parameters is established. The target operating range of each device is determined based on the parameter change trend within the safety constraint range, including:

[0102] Conduct time window analysis on the cascade response mechanism, calculate the influence coefficients between devices, determine the upstream and downstream transmission relationships between devices based on the influence coefficients, and establish a cascade response transmission link;

[0103] Identify device pairs with upstream and downstream transmission relationships along the cascade response transmission link, extract the output parameters of the upstream device and the input parameters of the downstream device in each device pair, calculate the response time ratio and amplitude ratio of the input parameters and output parameters respectively, determine the delay attenuation of parameter transmission based on the response time ratio, and determine the intensity attenuation of parameter transmission based on the amplitude ratio, and use the delay attenuation and intensity attenuation as the coupling characteristics of the upstream and downstream devices;

[0104] The cumulative delay and cumulative attenuation of parameters in the cascade response transmission link are calculated based on the coupling characteristics of upstream and downstream devices. The time margin and amplitude margin are determined based on the square root of the cumulative delay and the logarithm of the cumulative attenuation. The safety constraint range of the device operating parameters is determined in combination with the physical limits of the device.

[0105] Perform segmented statistical analysis on the parameter change trends within the safety constraint range of the equipment operating parameters to determine the steady-state range and fluctuation range of the parameters, and combine the steady-state range and fluctuation range to form the target operating range of each device.

[0106] During implementation, the cascade response mechanism was first analyzed within a time window, the influence coefficients between devices were calculated, and the upstream and downstream transmission relationships between devices were determined based on the influence coefficients, thus establishing a cascade response transmission link. During the startup and shutdown of a thermal power unit, three different time windows (5 minutes, 10 minutes, and 30 minutes) were selected to analyze the changes in device parameters. For a 600MW unit, during the cold start process, the parameter data for key devices—the boiler, turbine, and generator—were segmented by time window, and the influence coefficients between devices were calculated using time series analysis. Specifically, key parameters such as boiler main steam pressure, turbine inlet pressure, turbine speed, and generator active power were selected, and the correlation coefficients between these parameters were calculated within each time window. When the boiler main steam pressure changed, the correlation coefficient for turbine inlet pressure was 0.92, indicating a high correlation between the two. When the turbine inlet pressure changed, the correlation coefficient for turbine speed was 0.85. And when the turbine speed changed, the correlation coefficient for generator active power was 0.89. Based on these influence coefficients, the upstream and downstream transmission relationship between the equipment is determined as: boiler → turbine → generator, and a cascade response transmission link is constructed.

[0107] Along the cascade response transfer link, device pairs with upstream and downstream transfer relationships were identified. The output parameters of the upstream device and the input parameters of the downstream device in each device pair were extracted. The response time ratio and amplitude ratio of the input and output parameters were calculated, respectively. The delay decay of parameter transfer was determined based on the response time ratio, and the intensity decay of parameter transfer was determined based on the amplitude ratio. The delay decay and intensity decay were used as coupling characteristics of the upstream and downstream devices. Based on the established transfer link, two key device pairs were identified: the boiler-turbine and the turbine-generator. For the boiler-turbine pair, the output parameter of the upstream boiler is the main steam pressure, and the input parameter of the downstream turbine is the intake pressure. Analysis of parameter changes during startup revealed that when the boiler main steam pressure increases from 2 MPa to 4 MPa, the turbine intake pressure increases from 1.8 MPa to 3.6 MPa, with a response time of 65 seconds. The boiler main steam pressure change takes 55 seconds, resulting in a calculated response time ratio of 1.18. At the same time, the turbine inlet pressure changes by 1.8 MPa, while the boiler main steam pressure changes by 2 MPa, with an amplitude ratio of 0.9. Based on a response time ratio of 1.18, the delay decay of parameter transfer is determined to be 0.18, indicating an 18% delay in the turbine's response to the boiler pressure change. Based on an amplitude ratio of 0.9, the intensity decay of parameter transfer is determined to be 0.1, indicating a 10% reduction in the turbine inlet pressure change compared to the boiler main steam pressure. Similarly, for the turbine-generator pair, the calculated response time ratio is 1.25, the amplitude ratio is 0.85, the delay decay is 0.25, and the intensity decay is 0.15. These delay decay and intensity decay together constitute the coupling characteristics of the upstream and downstream equipment.

[0108] Based on the coupling characteristics of upstream and downstream equipment, the cumulative delay and cumulative attenuation of parameters in the cascade response transmission link are calculated. The time margin and amplitude margin are determined based on the square root of the cumulative delay and the logarithm of the cumulative attenuation. The safety constraints of the equipment operating parameters are determined in conjunction with the physical limitations of the equipment. During the startup and shutdown of a thermal power unit, parameters are transmitted from the boiler to the generator along the cascade response transmission link, accumulating the delay and attenuation effects of multiple stages of equipment. For the complete boiler-to-generator transmission link, the boiler-turbine delay attenuation is 0.18, the turbine-generator delay attenuation is 0.25, and the cumulative delay is 0.43. The intensity attenuation is 0.1 and 0.15, respectively, with a cumulative attenuation of 0.25. Based on actual operating experience, the square root of the cumulative delay, 0.66, is used as the time margin factor, and the logarithm of the cumulative attenuation, -0.6, is used as the amplitude margin factor. The safety constraints of the equipment operating parameters are determined in conjunction with the physical limitations of the equipment, such as the maximum boiler pressure of 16.8 MPa, the maximum turbine speed of 3600 rpm, and the maximum generator power of 660 MW. For example, for boiler main steam pressure, considering a time margin factor of 0.66, the pressure rise rate during startup should not exceed 0.5 MPa / min. Considering an amplitude margin factor of -0.6, the upper limit of operating pressure is set at 16 MPa, which is lower than the physical limit. For turbine speed, the increase rate should not exceed 120 rpm / min, and the maximum speed is limited to 3500 rpm. For generator power, the load increase rate should not exceed 15 MW / min, and the maximum power is limited to 630 MW. These safety constraints ensure safe and reliable operation of the equipment during startup and shutdown.

[0109] A segmented statistical analysis of parameter variation trends within the safety constraints of equipment operating parameters is performed to determine the steady-state and fluctuation ranges of the parameters. These are then combined to form the target operating range for each device. Within the defined safety constraints, parameter variation trends are meticulously analyzed to identify the steady-state and fluctuation characteristics of parameter variation. Taking main steam pressure as an example, analysis of historical startup data revealed that within the 0-4 MPa range, the pressure rise rate is rapid and the fluctuations are large, with a standard deviation of 0.2 MPa. Within the 4-12 MPa range, the pressure changes smoothly, with a standard deviation of only 0.05 MPa. Within the 12-16 MPa range, the pressure changes slowly and fluctuates slightly, with a standard deviation of 0.1 MPa. Based on this, the main steam pressure is divided into three ranges: 0-4 MPa as the fluctuation range, 4-12 MPa as the steady-state range, and 12-16 MPa as the transition range. Similarly, turbine speed is analyzed, with 0-1800 rpm identified as the fluctuation range, 1800-3000 rpm as the steady-state range, and 3000-3500 rpm as the transition range. Generator power is analyzed, with 0-200 MW identified as the fluctuation range, 200-500 MW as the steady-state range, and 500-630 MW as the transition range. By comprehensively considering the steady-state and fluctuation ranges of each parameter, the target operating range for each device is determined. For example, the target operating range for a boiler is a main steam pressure of 4-12 MPa and a main steam temperature of 450-540°C; the target operating range for a turbine is a speed of 1800-3000 rpm and an inlet pressure of 3.6-10.8 MPa; and the target operating range for a generator is an active power of 200-500 MW and a power factor of 0.85-0.95. During the actual start-up and shutdown process, the control strategy will guide the parameters of each device to operate within the steady-state range first, and only briefly pass through the fluctuation range when necessary, thereby improving the stability and safety of equipment operation.

[0110] In this embodiment, by constructing a cascade response transmission link, analyzing the influence coefficients between devices, systematically identifying the response time ratio and amplitude ratio between the upstream output parameters and the downstream input parameters, quantifying the delay and attenuation in the transmission process, and then extracting the real coupling characteristics. Based on the cumulative effect of the coupling characteristics, combined with the physical boundary conditions of the equipment, a safety constraint range that reflects the actual dynamic behavior of the system is constructed, and through statistical analysis of the parameter change trend within this range, the steady-state interval and the fluctuation interval are identified, thereby formulating a more adaptive target operating range for each device. Compared with the existing method that relies on static limits or experience intervals, this solution dynamically constructs the parameter operating boundaries based on the actual coupling relationship, so that the control strategy is more in line with the actual system operation, and improves the control accuracy, safety and system collaborative stability during the start-stop process.

[0111] In an alternative embodiment, the target operation interval is divided into a plurality of sub-intervals according to the parameter coupling degree, the compensation adjustment coefficient is set according to the response characteristics of the equipment in each sub-interval, and the hierarchical adjustment of the equipment operation parameters is performed through the compensation adjustment coefficient, including:

[0112] The time series data of the equipment operation parameters in the target operation interval is collected, the mutual information and the time delay correlation coefficient of the parameter pair are calculated, the parameter coupling degree is obtained by weighting and combining the mutual information and the time delay correlation coefficient, and the coupling degree matrix is constructed based on the parameter coupling degree;

[0113] The initial grouping of parameters is obtained by spectral clustering using the coupling degree matrix, the local density and the minimum distance are calculated for each parameter group, the density peak point is determined based on the local density and the minimum distance, and the target operation interval is divided into a plurality of sub-intervals with the density peak point as the clustering center;

[0114] The compensation adjustment coefficient is constructed based on the dynamic response characteristics of the equipment in the sub-interval, and the compensation adjustment coefficient is dynamically updated according to the real-time change rate of the parameter deviation in the sub-interval;

[0115] The updated compensation adjustment coefficient is combined with the parameter deviation and its integral term to obtain the parameter adjustment amount, the constraint threshold of the adjustment amount is set according to the parameter coupling degree corresponding to the sub-interval, the adjustment amount is compared with the constraint threshold, and the adjustment instruction is outputted;

[0116] The parameter coupling degree difference of adjacent sub-intervals is calculated to determine the range of the transition region, the weight coefficient is set based on the parameter coupling degree in the transition region, the adjustment instruction is smoothly switched, the adjustment priority sequence is established according to the parameter coupling degree from large to small, the adjustment instruction is executed in sequence according to the adjustment priority sequence, and the hierarchical adjustment of the equipment operation parameters is realized.

[0117] For example, time series data of equipment operating parameters within the target operating range are first collected. The mutual information and time-lag correlation coefficients of parameter pairs are calculated. The mutual information and time-lag correlation coefficients are weighted and combined to obtain the parameter coupling degree. A coupling degree matrix is ​​then constructed based on the parameter coupling degree. For a 600MW thermal power unit operating within the target operating range, data is collected once per second for four hours. Time series data for 20 key parameters, including boiler main steam pressure, main steam temperature, feedwater flow, turbine speed, and generator power, are obtained. Mutual information is calculated for each parameter pair. For example, the mutual information between main steam pressure and turbine speed is 0.82, and the mutual information between main steam temperature and generator power is 0.75. Time-lag correlation coefficients are also calculated. The correlation coefficient between main steam pressure and turbine speed with a 20-second time lag is 0.88, and the correlation coefficient between main steam temperature and generator power with a 35-second time lag is 0.79. The mutual information and time-lag correlation coefficient are weighted together using weights of 0.6 and 0.4 to obtain the coupling degree of each parameter pair. For example, the coupling degree between main steam pressure and turbine speed is 0.846, and the coupling degree between main steam temperature and generator power is 0.766. Similarly, the coupling degrees of all parameter pairs are calculated, constructing a 20 × 20 coupling degree matrix.

[0118] Spectral clustering was performed using the coupling matrix to obtain initial parameter groupings. For each parameter group, the local density and minimum distance were calculated. Density peaks were identified based on these local density and minimum distances, and the target operating range was divided into multiple subranges using these density peaks as cluster centers. Spectral clustering analysis was performed on the constructed coupling matrix, initially classifying the 20 parameters into four groups: boiler parameters, turbine parameters, generator parameters, and auxiliary system parameters. Local density was calculated for each parameter group. For example, in the boiler parameter group, the local density for main steam pressure was 8.5, for main steam temperature was 7.2, and for feedwater flow was 6.8. The minimum distance from each parameter to a high-density point was also calculated: 0.92 for main steam pressure, 0.75 for main steam temperature, and 0.63 for feedwater flow. The density peak point is determined by the product of local density and minimum distance. The density peak point of the boiler parameter group is the main steam pressure, the density peak point of the turbine parameter group is the turbine speed, the density peak point of the generator parameter group is the generator power, and the density peak point of the auxiliary system parameter group is the cooling water flow. With these four density peak points as cluster centers, the target operating range is divided into four sub-ranges: boiler sub-range (4-12MPa main steam pressure, 450-540℃ main steam temperature), turbine sub-range (1800-3000rpm speed, 3.6-10.8MPa intake pressure), generator sub-range (200-500MW power, 0.85-0.95 power factor), auxiliary system sub-range (8000-12000m 3 / h cooling water flow, 70-85% feed water pump load).

[0119] Compensation adjustment coefficients are constructed based on the dynamic response characteristics of the equipment within the subintervals. These compensation adjustment coefficients are dynamically updated based on the real-time rate of change of the parameter deviation within the subintervals. For each subinterval, the dynamic response characteristics of the equipment, including response time, overshoot, and steady-state error, are analyzed. Taking the boiler subinterval as an example, step response testing revealed a response time of 90 seconds, an overshoot of 8%, and a steady-state error of 2% for the main steam pressure. Based on these dynamic characteristics, initial compensation adjustment coefficients are constructed, with a proportional coefficient of 0.8, an integral coefficient of 0.05, and a differential coefficient of 0.2 for the main steam pressure. During actual operation, the real-time rate of change of the parameter deviation is monitored. When the rate of change of the main steam pressure deviation exceeds 0.2 MPa / min, the proportional coefficient is adjusted to 0.7, the integral coefficient to 0.04, and the differential coefficient to 0.25 to enhance the system's anti-disturbance capability. When the rate of change of the deviation is less than 0.05 MPa / min, the proportional coefficient is adjusted to 0.9, the integral coefficient to 0.06, and the differential coefficient to 0.15 to improve control accuracy. Similarly, corresponding compensation adjustment coefficients are set for parameters in other sub-intervals and dynamically updated according to the real-time change rate of the parameter deviation.

[0120] The updated compensation adjustment coefficient is combined with the parameter deviation and its integral term to obtain the parameter adjustment value. A constraint threshold for the adjustment value is set based on the parameter coupling degree corresponding to the subinterval. The adjustment value is compared with the constraint threshold and a control instruction is output. For each parameter within a subinterval, the updated compensation adjustment coefficient is combined with the parameter deviation and its integral term. For the main steam pressure, for example, the current actual value is 8.5 MPa, the target value is 9 MPa, the deviation is -0.5 MPa, the integral term of the deviation is -15 MPa·s, and the rate of change of the deviation is -0.1 MPa / min. The proportional coefficient of 0.8, the integral coefficient of 0.05, and the differential coefficient of 0.2 are multiplied by the deviation, the integral term, and the rate of change, respectively, and the sum is calculated to obtain the parameter adjustment value of -0.4 MPa. The constraint threshold for the adjustment value is set based on the parameter coupling degree of the boiler subinterval. The constraint threshold for the main steam pressure adjustment value is ±0.5 MPa. Since the calculated adjustment value of -0.4 MPa is within the constraint threshold, the control instruction "increase the coal feed by 0.4 t / h" is directly output. Similarly, the turbine speed regulation is calculated as +50 rpm, the constraint threshold is ±100 rpm, and the output regulation instruction is "Increase the regulating stage opening by 5%." The generator power regulation is calculated as +20 MW, the constraint threshold is ±30 MW, and the output regulation instruction is "Increase the excitation current by 50 A."

[0121] The transition region is determined by calculating the difference in parameter coupling between adjacent sub-intervals. Within this transition region, weight coefficients are set based on the parameter coupling, enabling smooth switching of control commands. A control priority sequence is established based on descending parameter coupling, and control commands are executed sequentially according to the priority sequence, achieving hierarchical control of equipment operating parameters. The difference in parameter coupling between the boiler and turbine sub-intervals is calculated to be 0.12, resulting in a transition region with a main steam pressure of 11-12 MPa and a turbine speed of 1800-1900 rpm. Within this transition region, weight coefficients are set based on the parameter coupling: a weight of 0.6 for the boiler parameter group and a weight of 0.4 for the turbine parameter group. When the unit transitions from low to high load, control commands are smoothly switched. For example, the strength of the "increase coal feed" command is reduced by a weight of 0.6, and the strength of the "increase regulating stage opening" command is reduced by a weight of 0.4, to avoid system oscillations caused by sudden parameter changes. By sorting the parameters by coupling degree from highest to lowest, a priority adjustment sequence is established: main steam pressure (0.846) → turbine speed (0.823) → generator power (0.766) → feedwater flow (0.742) → cooling water flow (0.685). Following this priority sequence, the main steam pressure adjustment command is executed first. Once the pressure stabilizes, the turbine speed adjustment command is executed, and so on. The adjustment commands for subsequent parameters are executed in sequence, achieving hierarchical adjustment of equipment operating parameters.

[0122] like Figure 2 As shown, the parameter coupling degree hierarchical adjustment process of this embodiment is demonstrated.

[0123] In this embodiment, the mutual information and time-lag correlation coefficients of the operating parameters are calculated to comprehensively construct a parameter coupling matrix, and spectral clustering combined with density peak recognition methods are used to subdivide the target operating range into multiple sub-ranges with consistent coupling characteristics. The compensation adjustment coefficient is set and updated in real time based on the dynamic response characteristics of the equipment in different sub-ranges to achieve accurate response of the adjustment amount to the parameter deviation; at the same time, the transition area is identified based on the coupling difference between the sub-ranges, and the smooth transition of the adjustment instructions is achieved through weight control to avoid adjustment shock; the adjustment priority sequence established according to the coupling degree ensures the orderliness of collaborative control among multiple devices. The hierarchical and dynamic parameter adjustment is achieved from a data-driven perspective, effectively improving the system response sensitivity, control accuracy and collaborative robustness.

[0124] In an optional embodiment, the adjustment amount generated by the hierarchical adjustment is used to construct a linkage control link for the device group according to the physical constraint relationship between the devices. The execution sequence of the trigger instruction is set based on the inter-level transfer characteristics of the linkage control link. The adjustment process of the device groups at each level is coordinated according to the execution sequence to achieve multi-level linkage control during the start-up and shutdown process of the thermal power unit, including:

[0125] Extract the amplitude and change direction of the adjustment amount generated by the hierarchical adjustment, generate the adjustment amount control instruction, collect the operating status data of the device group based on the adjustment amount control instruction, and perform a weighted combination of the adjustment amount control instruction and the operating status data of the device group to build a linkage control link;

[0126] Analyze the input-output response relationship between device groups based on the linkage control link, calculate the dynamic characteristic indicators between the device groups, extract the delay time and hysteresis interval of the device group response based on the dynamic characteristic indicators, and use the delay time and hysteresis interval as constraints to determine the inter-stage transfer characteristics;

[0127] Based on the inter-stage transfer characteristics, the benchmark execution interval of the trigger instruction is set to form the initial execution sequence. The response status of the equipment groups at each level under the initial execution sequence is monitored, the execution deviation is calculated, and the execution sequence parameters are dynamically adjusted according to the execution deviation to generate the optimal execution sequence.

[0128] The optimal execution sequence is combined with the linkage control link to generate the final adjustment instructions, coordinate the adjustment process of equipment groups at all levels, and realize multi-level linkage control during the start-up and shutdown process of thermal power units through feedback compensation and dynamic switching.

[0129] This implementation first extracts the amplitude and direction of change of the regulation variable generated by the hierarchical regulation, generates a regulation variable control instruction, collects the operating status data of the equipment group based on the regulation variable control instruction, and performs a weighted combination of the regulation variable control instruction and the equipment group operating status data to construct a linkage control link. During the start-up and shutdown process of a 600MW thermal power unit, the regulation variable generated by the hierarchical regulation is extracted and analyzed to identify its amplitude and direction of change. For example, during the cold start phase, the regulation variable of the boiler coal feed is +2.5t / h, with an increasing direction; the regulation variable of the feedwater flow is +150t / h, with an increasing direction; the regulation variable of the turbine control valve opening is +8%, with an increasing direction; and the regulation variable of the generator excitation current is +300A, with an increasing direction. Based on these control variables, corresponding control instructions are generated: the boiler control system receives the instruction to "increase coal feed by 2.5 t / h," the boiler feedwater system receives the instruction to "increase feedwater flow by 150 t / h," the turbine control system receives the instruction to "increase regulating valve opening by 8%," and the generator excitation system receives the instruction to "increase excitation current by 300 A." After executing these instructions, operating status data for each device group is collected, including an increase in boiler steam pressure from 5.5 MPa to 6.8 MPa, steam temperature from 480°C to 510°C, turbine speed from 1500 rpm to 1800 rpm, and generator power from 150 MW to 200 MW. The control instructions and operating status data are weighted and combined, with the boiler system's weight being 0.4, the turbine system's weight being 0.35, and the generator system's weight being 0.25. This constructs a linkage control chain: boiler coal feeding system → boiler heating surface → boiler feed water system → turbine regulating system → turbine rotor system → generator excitation system → generator stator system.

[0130] According to the input-output response relationship of the linkage control link, the dynamic characteristic index between the equipment groups is calculated, the delay time and the lag interval of the equipment group response are extracted based on the dynamic characteristic index, and the delay time and the lag interval are taken as constraints to determine the inter-stage transfer characteristic. The input-output response relationship of adjacent equipment groups in the linkage control link is analyzed, such as the output of the boiler coal feeding system is the input of the boiler heating surface, and the output of the boiler heating surface is the input of the turbine regulating system. Through historical data analysis, the dynamic characteristic index between the equipment groups is calculated, including the response time, the rise time, the overshoot and the stabilization time, etc. Taking the response of the boiler coal feeding system to the boiler heating surface as an example, when the coal feeding amount increases by 2.5t / h, the delay time of the boiler steam pressure to start responding is 45 seconds, the rise time of the pressure from 5.5MPa to 6.8MPa is 180 seconds, the overshoot is 0.2MPa, and the stabilization time is 360 seconds. Similarly, the response delay time of the boiler heating surface to the turbine regulating system is 30 seconds, the response delay time of the turbine regulating system to the turbine rotor system is 15 seconds, and the response delay time of the turbine rotor system to the generator excitation system is 10 seconds. Based on these dynamic characteristic indexes, the delay time of each equipment group response is extracted to form a delay time sequence: 45 seconds→30 seconds→15 seconds→10 seconds. At the same time, the lag interval is determined, that is, the time interval from the start of the adjustment of the first-stage equipment group to the obvious response of the next-stage equipment group, and a lag interval sequence is formed: 0-45 seconds→45-75 seconds→75-90 seconds→90-100 seconds. The delay time and the lag interval are taken as constraints to determine the inter-stage transfer characteristic of the linkage control link.

[0131] Based on the inter-stage transfer characteristic, the reference execution interval of the trigger instruction is set, the initial execution time sequence is formed, the response state of each stage equipment group under the initial execution time sequence is monitored, the execution deviation is calculated, the execution time sequence parameters are dynamically adjusted according to the execution deviation, and the optimal execution time sequence is generated. According to the inter-stage transfer characteristic obtained by the foregoing analysis, the reference execution interval of the trigger instruction is set. Considering that the delay time of the boiler coal feeding system to the boiler heating surface is 45 seconds, the delay time of the boiler heating surface to the turbine regulating system is 30 seconds, the delay time of the turbine regulating system to the turbine rotor system is 15 seconds, and the delay time of the turbine rotor system to the generator excitation system is 10 seconds, the reference execution interval is set as: boiler coal feeding system instruction→45 seconds later→boiler feedwater system instruction→30 seconds later→turbine regulating system instruction→15 seconds later→generator excitation system instruction. Based on this reference execution interval, the initial execution time sequence is formed.

[0132] During actual operation, the response status of each level of equipment group under the initial execution sequence is monitored. For example, after the boiler coal feeding system executes the command to increase the coal feed rate, the boiler pressure begins to rise significantly after 50 seconds, 5 seconds later than the expected 45 seconds. After the turbine control system executes the command to increase the valve opening, the turbine speed begins to rise significantly after 12 seconds, 3 seconds earlier than the expected 15 seconds. Calculate the execution deviation: the execution deviation from the boiler coal feeding system to the boiler heating surface is +5 seconds, the execution deviation from the boiler heating surface to the turbine control system is -2 seconds, the execution deviation from the turbine control system to the turbine rotor system is -3 seconds, and the execution deviation from the turbine rotor system to the generator excitation system is +1 second. Based on these execution deviations, we dynamically adjusted the execution sequence parameters: the execution interval from the boiler coal feed system to the boiler feed water system was adjusted from 45 seconds to 50 seconds, the execution interval from the boiler feed water system to the turbine control system was adjusted from 30 seconds to 28 seconds, the execution interval from the turbine control system to the turbine rotor system was adjusted from 15 seconds to 12 seconds, and the execution interval from the turbine rotor system to the generator excitation system was adjusted from 10 seconds to 11 seconds. Through this dynamic adjustment, we generated the optimal execution sequence.

[0133] The optimal execution time sequence is combined with the linkage control link to generate a final adjustment instruction, coordinates the adjustment process of each level of equipment group, and realizes the multi-level linkage control in the start-stop process of the thermal power generating unit through feedback compensation and dynamic switching. The optimal execution time sequence is combined with the linkage control link to generate a final adjustment instruction sequence. In the cold start process of the thermal power generating unit, the adjustment instructions are issued in turn according to the optimal execution time sequence: at t=0, the boiler coal feeding system receives the instruction of 'increasing the coal feeding amount by 2.5t / h'; at t=50s, the boiler water feeding system receives the instruction of 'increasing the water feeding flow by 150t / h'; at t=78s, the steam turbine adjustment system receives the instruction of 'increasing the adjustment valve opening by 8%'; at t=90s, the steam turbine rotor system receives the instruction of 'increasing the rotating speed by 200rpm'; at t=101s, the generator excitation system receives the instruction of 'increasing the excitation current by 300A'. In the execution process, the response state of each equipment group is continuously monitored, and when it is found that there is a deviation between the actual response and the expectation, the feedback compensation mechanism is started. For example, when the rising rate of the boiler steam pressure is lower than the expectation, the adjustment range of the coal feeding amount is automatically increased from 2.5t / h to 3.0t / h; when the rotating speed of the steam turbine rises too fast, the adjustment range of the adjustment valve opening is automatically reduced from 8% to 6%. At the same time, according to the change of the unit state, the control strategy is dynamically switched. In the low load section (0-30% of the rated load), the steam pressure coordination control strategy is adopted, and the boiler is the leading one; in the medium load section (30%-70% of the rated load), the steam turbine following control strategy is adopted, and the generator load is the leading one; in the high load section (70%-100% of the rated load), the boiler following control strategy is adopted, and the grid demand is the leading one. Through the feedback compensation and dynamic switching, the multi-level linkage control in the start-stop process of the thermal power generating unit is realized.

[0134] In the embodiment, the adjustment amount generated by the hierarchical adjustment is weighted and fused with the operation state data of the equipment group, the linkage control link reflecting the real physical constraint relationship is constructed, then the inter-stage transmission characteristics between the equipments are extracted through the response delay and the hysteresis interval, the reference execution time sequence is set and is dynamically optimized to the optimal execution time sequence in combination with the actual response state. The optimal execution time sequence further guides the hierarchical equipment group to coordinately adjust the process as required, and realizes the start-stop control of the multi-level equipment in cooperation, order and stability in combination with the feedback compensation and dynamic switching mechanism. The response reality and the adaptation ability of the control link are improved from the data driving and the physical constraint, so that the start-stop process has higher stability, coordination and control efficiency.

[0135] Figure 3 The multi-level linkage control efficiency comparison schematic diagram of the start-stop process of the thermal power generating unit in the embodiment is as shown in Figure 3As shown in the figure, the figure shows the efficiency comparison of three thermal power unit start-stop control strategies. The multi-linkage control based on physical constraints (gray grid column) performs best in four key indicators, especially the system coordination reaches 94.5%. Compared with the traditional single-loop control (white column) and the conventional sequence control (gray diagonal column), the scheme extracts the amplitude and change direction of the regulating quantity generated by the hierarchical regulation, constructs the linkage control link according to the physical constraint relationship between the devices, and sets the execution time sequence of the trigger command according to the inter-stage transmission characteristics, and realizes a more efficient start-stop process. The data shows that the cold start response time is improved by 23.9 percentage points, and the hot start stability is improved by 13.9 percentage points, effectively verifying the advantages of the technical path based on device group physical constraints and dynamic optimization execution time, making the thermal power unit start-stop process more coordinated, stable and efficient.

[0136] In a second aspect, an electronic device is provided, comprising:

[0137] a processor;

[0138] a memory for storing processor-executable instructions;

[0139] wherein the processor is configured to invoke the instructions stored by the memory to perform the method described above.

[0140] In a third aspect, a computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0141] The present application can be a method, device, system and / or computer program product. The computer program product can include a computer-readable storage medium having stored thereon computer-readable program instructions that, when executed by a computer, cause the computer to carry out various aspects of the present application.

[0142] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-level linkage coordinated control optimization method for the intelligent start-stop process of a thermal power unit, characterized in that: include: Collect operating parameters of thermal power plant equipment groups, extract the variation patterns and coupling relationships between parameters, generate dynamic response data, classify the dynamic response data according to the parameter fluctuation characteristics and response timing relationship, establish operating condition classification standards, and adaptively configure control parameters and sampling period combination schemes based on the operating condition classification standards to form a multi-level dynamic control benchmark; Based on a multi-level dynamic control benchmark, the parameter changes in the dynamic response data are converted into trigger instructions between devices. A cascade response mechanism is constructed. Based on the cascade response mechanism, the coupling characteristics of upstream and downstream devices are analyzed, and a safe constraint range for device operating parameters is established. The target operating range of each device is determined based on the parameter change trend within the safe constraint range. The target operating range is divided into multiple sub-ranges according to the parameter coupling degree, and the compensation adjustment coefficient is set according to the response characteristics of the equipment in each sub-range. The equipment operating parameters are adjusted in stages through the compensation adjustment coefficient; The adjustment quantity generated by hierarchical adjustment is used to construct a linkage control link of the equipment group according to the physical constraint relationship between the equipment. The execution timing of the trigger instruction is set based on the inter-level transfer characteristics of the linkage control link. The adjustment process of the equipment groups at all levels is coordinated according to the execution timing to realize multi-level linkage control during the start-up and shutdown process of the thermal power unit.

2. The method according to claim 1, characterized in that Collect the operating parameters of the thermal power plant equipment group, extract the change patterns and coupling relationships between the parameters, and generate dynamic response data including: Collecting operating parameters of a thermal power plant equipment group, calculating the ratio of changes between the parameters, establishing a parameter change correlation matrix, dividing the collection priority of the operating parameters according to the parameter change correlation matrix, and using corresponding sampling periods to collect data for operating parameters of different priorities to obtain an operating parameter sampling sequence; Based on the operating parameter sampling sequence, the data processing window is determined according to the characteristic response time of the equipment. Polynomial fitting is performed on the operating parameter sampling sequence within the data processing window to extract the parameter change pattern. Based on the parameter change law, the change rate and acceleration of the operating parameters at adjacent moments are calculated to obtain the parameter dynamic characteristics, and the information transmission amount between the operating parameters is calculated using the parameter dynamic characteristics to determine the parameter coupling relationship; The parameter change association matrix, parameter change law and parameter coupling relationship are combined according to a time series to generate dynamic response data.

3. The method according to claim 1, characterized in that Dynamic response data is classified according to the relationship between parameter fluctuation characteristics and response timing, and a working condition classification standard is established. Based on the working condition classification standard, a combination of control parameters and sampling periods is adaptively configured to form a multi-level dynamic control benchmark including: The dynamic response data is segmented according to preset time intervals, and the segmented data is subjected to wavelet transform to obtain the fluctuation components of different frequency bands. The characteristic parameters of the fluctuation components are extracted to obtain the fluctuation intensity. The parameters are graded according to the fluctuation intensity to establish parameter fluctuation characteristic data including fluctuation frequency characteristics, fluctuation amplitude characteristics and fluctuation energy characteristics; Time window analysis is used to extract the time feature points of parameter changes. Based on the time feature points, the response time intervals between parameters are calculated, the transmission relationship of parameter changes is established, and the degree of influence between parameters is determined according to the response time interval to generate response time series data. Combine parameter fluctuation characteristic data with response time series data to construct working condition characteristic data, calculate characteristic distances between characteristic data, perform cluster analysis on data with characteristic distances less than a preset distance threshold, and establish working condition classification standards; The basic sampling period is calculated based on the fluctuation frequency characteristics. The sampling period combination of different frequency bands is determined based on the fluctuation intensity. The control parameter weight is calculated according to the parameter influence degree. The sampling period combination is adjusted based on the control parameter weight to obtain the final sampling period. Establish a parameter weight mapping table under the working condition classification standard, configure the corresponding control parameter weight and sampling period for each working condition level, and generate a multi-level dynamic control benchmark.

4. The method according to claim 1, wherein Based on a multi-level dynamic control benchmark, the parameter changes in the dynamic response data are converted into trigger instructions between devices. The cascade response mechanism is constructed, including: Collect dynamic response data from multi-level dynamic control benchmarks and construct a multi-dimensional parameter monitoring matrix; Perform wavelet transform and singular value decomposition on the monitoring matrix to extract the frequency domain and time domain characteristics of the parameter change. Calculate the energy density distribution based on the frequency domain characteristics, extract the energy density peak and its corresponding frequency component, calculate the state transition probability based on the time domain characteristics, and extract the characteristic value and stability index of the state transition. Perform a weighted combination of the energy density peak and the state transition characteristic value to obtain a comprehensive representation value of the parameter change. The parameter changes are divided into corresponding levels according to the numerical range of the comprehensive characterization value, and trigger instructions between devices are generated according to the parameter change level and change amplitude. The trigger instructions between devices are arranged in the order of parameter changes to form a cascade response mechanism.

5. The method according to claim 1, wherein Based on the cascade response mechanism, the coupling characteristics of upstream and downstream equipment are analyzed to establish the safety constraint range of equipment operating parameters. The target operating range of each device is determined based on the parameter change trend within the safety constraint range. Conduct time window analysis on the cascade response mechanism, calculate the influence coefficients between devices, determine the upstream and downstream transmission relationships between devices based on the influence coefficients, and establish a cascade response transmission link; Identify device pairs with upstream and downstream transmission relationships along the cascade response transmission link, extract the output parameters of the upstream device and the input parameters of the downstream device in each device pair, calculate the response time ratio and amplitude ratio of the input parameters and output parameters respectively, determine the delay attenuation of parameter transmission based on the response time ratio, and determine the intensity attenuation of parameter transmission based on the amplitude ratio, and use the delay attenuation and intensity attenuation as the coupling characteristics of the upstream and downstream devices; The cumulative delay and cumulative attenuation of parameters in the cascade response transmission link are calculated based on the coupling characteristics of upstream and downstream devices. The time margin and amplitude margin are determined based on the square root of the cumulative delay and the logarithm of the cumulative attenuation. The safety constraint range of the device operating parameters is determined in combination with the physical limits of the device. Perform segmented statistical analysis on the parameter change trends within the safety constraint range of the equipment operating parameters to determine the steady-state range and fluctuation range of the parameters, and combine the steady-state range and fluctuation range to form the target operating range of each device.

6. The method according to claim 1, characterized in that The target operating range is divided into multiple sub-ranges according to the parameter coupling degree. The compensation adjustment coefficient is set according to the response characteristics of the equipment in each sub-range. The equipment operating parameters are adjusted in stages using the compensation adjustment coefficient, including: Collect time series data of equipment operating parameters within the target operating range, calculate the mutual information and time-lag correlation coefficient of parameter pairs, perform weighted combination of the mutual information and time-lag correlation coefficient to obtain the parameter coupling degree, and construct a coupling degree matrix based on the parameter coupling degree; Spectral clustering is performed using the coupling matrix to obtain initial parameter groupings. The local density and minimum distance are calculated for each parameter grouping. The density peak point is determined based on the local density and minimum distance. The density peak point is used as the cluster center to divide the target operating range into multiple sub-ranges. A compensation adjustment coefficient is constructed based on the dynamic response characteristics of the equipment in the sub-interval, and the compensation adjustment coefficient is dynamically updated according to the real-time change rate of the parameter deviation in the sub-interval; The updated compensation adjustment coefficient is combined with the parameter deviation and its integral term to obtain the parameter adjustment amount. The constraint threshold of the adjustment amount is set according to the parameter coupling degree corresponding to the sub-interval. The adjustment amount is compared with the constraint threshold and the adjustment instruction is output; The parameter coupling degree difference between adjacent sub-intervals is calculated to determine the range of the transition area. Within the transition area, the weight coefficient is set based on the parameter coupling degree, and the adjustment instructions are smoothly switched. An adjustment priority sequence is established based on the parameter coupling degree sorted from large to small. The adjustment instructions are executed in sequence according to the adjustment priority sequence to achieve hierarchical adjustment of equipment operating parameters.

7. The method according to claim 1, characterized in that The regulation variables generated by hierarchical regulation are used to construct linkage control links for equipment groups according to the physical constraints between the equipment. The execution sequence of trigger instructions is set based on the inter-level transfer characteristics of the linkage control links. The regulation process of equipment groups at all levels is coordinated according to the execution sequence to achieve multi-level linkage control during the start-up and shutdown of thermal power units. This includes: Extract the amplitude and change direction of the adjustment amount generated by the hierarchical adjustment, generate the adjustment amount control instruction, collect the operating status data of the device group based on the adjustment amount control instruction, and perform a weighted combination of the adjustment amount control instruction and the operating status data of the device group to build a linkage control link; Analyze the input-output response relationship between device groups based on the linkage control link, calculate the dynamic characteristic indicators between the device groups, extract the delay time and hysteresis interval of the device group response based on the dynamic characteristic indicators, and use the delay time and hysteresis interval as constraints to determine the inter-stage transfer characteristics; Based on the inter-stage transfer characteristics, the benchmark execution interval of the trigger instruction is set to form the initial execution sequence. The response status of the equipment groups at each level under the initial execution sequence is monitored, the execution deviation is calculated, and the execution sequence parameters are dynamically adjusted according to the execution deviation to generate the optimal execution sequence. The optimal execution sequence is combined with the linkage control link to generate the final adjustment instructions, coordinate the adjustment process of equipment groups at all levels, and realize multi-level linkage control during the start-up and shutdown process of thermal power units through feedback compensation and dynamic switching.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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