Intelligent operation condition optimization method and device, equipment and storage medium

Abstract

The invention relates to the technical field of thermal power generation, in particular to an intelligent operation condition optimization method, device and equipment and a storage medium, and the method comprises the steps: calculating the overall steady-state index of a unit through the operation data of multiple dimensions in the operation process of the thermal power unit, and carrying out the clustering analysis of the data of each dimension based on the overall steady-state index of the unit; the first boundary condition operation data and the second boundary condition operation data influence the operation condition of the thermal power generating unit, then the current operation condition of the thermal power generating unit is determined through a working condition recognition model to generate a working condition optimization adjustment strategy, and finally the operation data of the thermal power generating unit are controlled through the working condition optimization adjustment strategy. The operation benefit of the thermal power generating unit is improved, and the technical problem that the thermal power generation efficiency and the economic benefit are affected due to the lack of an efficient adjustment scheme for the operation of a thermal power plant in the prior art is avoided.

Description

Technical Field

[0001] The present invention relates to the field of thermal power generation technology, and in particular to an intelligent operating condition optimization method, device, equipment and storage medium. Background Art

[0002] The safe, environmentally friendly, and stable operation of thermal power plants is essential for supplying energy to society and promoting social development. However, due to the variability of loads, ambient temperatures, and various sub-operating conditions in current power plants, thermal power plants cannot achieve optimal unit performance during operation, impacting power generation efficiency and economic returns. Therefore, unit performance optimization is necessary to ensure the economic efficiency of thermal power plant operations.

[0003] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide an intelligent operating condition optimization method, device, equipment and storage medium, aiming to solve the technical problem that the existing technology lacks an efficient adjustment solution for the operation of thermal power plants, which affects the efficiency and economic benefits of thermal power generation.

[0005] To achieve the above object, the present invention provides an intelligent operating condition optimization method, which includes the following steps:

[0006] Obtain multi-dimensional operating data of thermal power units;

[0007] Calculate the steady-state index corresponding to the operating data of each dimension and the overall steady-state index of the unit;

[0008] Performing cluster analysis on the operating data of the multiple dimensions according to the overall steady-state index of the unit to obtain first boundary condition operating data and second boundary condition operating data;

[0009] Identifying the current operating condition of the thermal power unit using an operating condition identification model based on the first boundary condition operating data and the second boundary condition operating data;

[0010] A corresponding operating condition optimization adjustment strategy is generated according to the current operating condition, and the operating data of the thermal power unit is controlled based on the operating condition optimization adjustment strategy.

[0011] Optionally, the calculating of the steady-state index corresponding to the operating data of each dimension and the overall steady-state index of the unit includes:

[0012] Filtering the operating data of each dimension using a preset filtering model to obtain target operating data of each dimension, wherein the target operating data is the operating data with high-frequency parts removed;

[0013] Input the target operating data of each dimension into the least squares model to obtain the data sampling change trend of each dimension data;

[0014] Calculate the steady-state index of each dimension of data according to the data sampling change trend and the steady-state change threshold;

[0015] Logical judgment is performed based on the steady-state index of each dimension data to obtain the overall steady-state index of the unit.

[0016] Optionally, the calculating the steady-state index of each dimension of data according to the data sampling change trend and the steady-state change threshold includes:

[0017] Calculating the data change slope corresponding to the data sampling change trend;

[0018] Divide the stable state of each dimension of data according to the slope of the data change;

[0019] Divide the measurement standard deviation based on the stable state and obtain the sampling window length of each dimension data;

[0020] Calculate the steady-state judgment threshold of each dimension data according to the measurement standard deviation and the sampling window length;

[0021] The steady-state index of each dimension of data is calculated based on the data change slope and the steady-state change threshold.

[0022] Optionally, performing cluster analysis on the operating data of the multiple dimensions according to the overall steady-state index of the unit to obtain the first boundary condition operating data and the second boundary condition operating data includes:

[0023] Dividing at least two clusters and cluster parameters according to the overall steady-state index of the unit;

[0024] Calculating membership parameters between the operating data of the multiple dimensions and each cluster based on the cluster parameters;

[0025] Constructing a membership matrix according to the membership parameters;

[0026] Cluster analysis is performed on the operating data of the multiple dimensions based on the membership matrix until the operating data converges, thereby obtaining first boundary condition operating data and second boundary condition operating data.

[0027] Optionally, identifying the current operating condition of the thermal power unit by using an operating condition identification model based on the first boundary condition operating data and the second boundary condition operating data includes:

[0028] determining target operating data among the first boundary condition operating data and the second boundary condition operating data;

[0029] Calculating the operating performance index of the thermal power unit according to the target operating data;

[0030] The current operating condition of the thermal power unit is identified through an operating condition identification model according to the operating performance index.

[0031] Optionally, generating a corresponding operating condition optimization adjustment strategy according to the current operating condition, and controlling the operating data of the thermal power unit based on the operating condition optimization adjustment strategy includes:

[0032] Calculating the similarity between the current operating condition and the historical operating condition;

[0033] Determine the target historical operating condition and the thermal power unit operating data corresponding to the target historical operating condition in the historical operating data according to the approximation;

[0034] The operating data of the thermal power unit is adjusted according to the operating data of the thermal power unit.

[0035] In addition, to achieve the above-mentioned purpose, the present invention further proposes an intelligent operating condition optimization device, which includes:

[0036] An acquisition module is used to obtain operating data of multiple dimensions of thermal power units;

[0037] The calculation module is used to calculate the steady-state index corresponding to the operating data of each dimension and the overall steady-state index of the unit;

[0038] a clustering module, configured to perform cluster analysis on the operating data of the multiple dimensions according to the overall steady-state index of the unit to obtain first boundary condition operating data and second boundary condition operating data;

[0039] an identification module, configured to identify a current operating condition of the thermal power unit by using an operating condition identification model based on the first boundary condition operating data and the second boundary condition operating data;

[0040] The control module is used to generate a corresponding operating condition optimization adjustment strategy according to the current operating condition, and control the operating data of the thermal power unit based on the operating condition optimization adjustment strategy.

[0041] In addition, to achieve the above-mentioned purpose, the present invention also proposes an intelligent operating condition optimization device, which includes: a memory, a processor, and an intelligent operating condition optimization program stored in the memory and executable on the processor, and the intelligent operating condition optimization program is configured to implement the steps of the intelligent operating condition optimization method described above.

[0042] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which an intelligent operating condition optimization program is stored. When the intelligent operating condition optimization program is executed by a processor, the steps of the intelligent operating condition optimization method described above are implemented.

[0043] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the intelligent operating condition optimization method as described above.

[0044] The present invention obtains the operation data of multiple dimensions of the thermal power unit; calculates the steady-state index corresponding to the operation data of each dimension and the overall steady-state index of the unit; performs cluster analysis on the operation data of the multiple dimensions according to the overall steady-state index of the unit to obtain the first boundary condition operation data and the second boundary condition operation data; uses the operation condition identification model based on the first boundary condition operation data and the second boundary condition operation data to identify the current operation condition of the thermal power unit; generates a corresponding operation condition optimization adjustment strategy according to the current operation condition, and controls the operation data of the thermal power unit based on the operation condition optimization adjustment strategy, and obtains the first boundary condition operation data and the second boundary condition operation data through the operation condition identification model. The overall steady-state index of the thermal power unit is calculated based on the operating data of multiple dimensions during the operation process, and cluster analysis is performed on the data of each dimension based on the overall steady-state index of the unit, so as to divide the first boundary condition operating data and the second boundary condition operating data that affect the operating condition of the thermal power unit, and then determine the current operating condition of the thermal power unit through the operating condition identification model to generate an operating condition optimization adjustment strategy, and finally control the operating data of the thermal power unit with the operating condition optimization adjustment strategy to improve the operating efficiency of the thermal power unit, thereby avoiding the technical problem of lack of efficient adjustment scheme for the operation of thermal power plants in the prior art, which affects the efficiency of thermal power generation and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 This is a flow chart of the first embodiment of the intelligent operating condition optimization method of the present invention;

[0048] Figure 2 This is a flow chart of a second embodiment of the intelligent operating condition optimization method of the present invention;

[0049] Figure 3 This is a structural block diagram of the first embodiment of the intelligent operating condition optimization device of the present invention;

[0050] Figure 4 It is a structural diagram of an intelligent operating condition optimization device for a hardware operating environment involved in an embodiment of the present invention.

[0051] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0052] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0053] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0054] Based on this, the embodiment of the present invention provides an intelligent operating condition optimization method, referring to Figure 1 , Figure 1 This is a flow chart of a first embodiment of an intelligent operating condition optimization method of the present invention.

[0055] In this embodiment, the intelligent operating condition optimization method includes:

[0056] Step S10: Acquire operating data of multiple dimensions of the thermal power unit.

[0057] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device, control computer, etc. that can realize the above functions. The following takes the control computer as an example to illustrate this embodiment and the following embodiments.

[0058] The existing unit operating condition optimization method is to establish a model based on data-driven. This method has certain limitations in guiding and analyzing the optimal operating conditions of thermal power plant units. For example, when it comes to specific systems, the calculations in the optimization process will be too complicated. Most importantly, when faced with large amounts of operating data, it is difficult to separate the data that affects the operating status of the thermal power unit. When adjusting the unit operating conditions, the real-time performance and adjustment effect are poor.

[0059] In this embodiment and subsequent embodiments, the operating data of multiple dimensions of the thermal power unit include at least: unit load, main steam temperature, main steam pressure, reheat steam pressure and feed water flow, etc. This embodiment does not impose specific restrictions on this.

[0060] Step S20: Calculate the steady-state index corresponding to the operating data of each dimension and the overall steady-state index of the unit.

[0061] In this embodiment, the steady-state index corresponding to each dimension of operating data is used to characterize the degree of influence of each dimension of operating data on the thermal power unit. In this embodiment, the minimum steady-state parameter of each parameter is 0, indicating a greater degree of influence on the operation of the thermal power unit. Conversely, the minimum steady-state parameter is 1, indicating a smaller degree of influence on the operation of the thermal power unit. However, since the data related to the operation process of the thermal power unit in this embodiment comes from multiple dimensions, in order to accurately determine the current operating condition of the thermal power unit, adjust the operating state, and improve the operating efficiency of the thermal power unit, this embodiment can calculate the overall steady-state index of the thermal power unit based on the steady-state index corresponding to each dimension of operating data, thereby classifying the operating data. This selects the data that has a greater impact on the operating state of the thermal power unit, thereby improving the efficiency of unit optimization.

[0062] Step S30: performing cluster analysis on the operating data of the multiple dimensions according to the overall steady-state index of the unit to obtain first boundary condition operating data and second boundary condition operating data.

[0063] It should be noted that the first boundary condition operating data refers to the operating data in which the change of parameters has a greater impact on the steady state of the thermal power unit; the second boundary condition operating data refers to the operating data in which the change of parameters has a smaller impact on the steady state of the thermal power unit.

[0064] Furthermore, performing cluster analysis on the operating data of the multiple dimensions according to the overall steady-state index of the unit to obtain the first boundary condition operating data and the second boundary condition operating data includes:

[0065] Dividing at least two clusters and cluster parameters according to the overall steady-state index of the unit;

[0066] Calculating membership parameters between the operating data of the multiple dimensions and each cluster based on the cluster parameters;

[0067] Constructing a membership matrix according to the membership parameters;

[0068] Cluster analysis is performed on the operating data of the multiple dimensions based on the membership matrix until the operating data converges, thereby obtaining first boundary condition operating data and second boundary condition operating data.

[0069] In the specific implementation, the specific calculation formula for calculating the membership parameters between the running data of multiple dimensions and each cluster based on the cluster parameters is:

[0070]

[0071] Among them, k is the number of clusters, vi is the running data of multiple dimensions, and cj is the data of the center of the cluster.

[0072] In order to improve the clustering effect of this embodiment, in this embodiment, a membership matrix is constructed by using membership parameters, and the center data of the cluster is updated based on the membership matrix, thereby controlling the convergence of data in the cluster. Specifically, the formula for updating the center of the cluster is:

[0073]

[0074] Among them, ω ij is the membership matrix, p is the cluster parameter, and cj is the data of the center of the cluster.

[0075] Step S40: identifying the current operating condition of the thermal power unit through an operating condition identification model based on the first boundary condition operating data and the second boundary condition operating data.

[0076] The operating condition identification model is used to match the first boundary condition operating data and the second boundary condition operating data with the historical operating data, thereby determining the current operating condition based on the operating performance indicators of the historical thermal power units in the historical operating data, and improving the optimization efficiency of the operating conditions of subsequent thermal power units.

[0077] Furthermore, the identifying the current operating condition of the thermal power unit by using an operating condition identification model based on the first boundary condition operating data and the second boundary condition operating data includes:

[0078] determining target operating data among the first boundary condition operating data and the second boundary condition operating data;

[0079] Calculating the operating performance index of the thermal power unit according to the target operating data;

[0080] The current operating condition of the thermal power unit is identified through an operating condition identification model according to the operating performance index.

[0081] In a specific implementation, the factors affecting the power generation efficiency of a thermal power unit mainly include the heat rate operating performance index. In this embodiment, the calculation formula of the heat rate operating performance index is:

[0082]

[0083] Among them, D0, D zr 、D fw , D gi , D zj are the main steam flow, reheat steam flow, feed water flow, superheated desuperheated water flow and reheated desuperheated water flow respectively; h0, h fw , h gi, h zj are the main steam enthalpy, feed water enthalpy, superheated desuperheated water enthalpy and reheated desuperheated water enthalpy respectively; △h zr is the enthalpy rise of reheat steam; Pel is the unit load.

[0084] Step S50: generating a corresponding operating condition optimization adjustment strategy according to the current operating condition, and controlling the operating data of the thermal power unit based on the operating condition optimization adjustment strategy.

[0085] Under different operating conditions, the control strategies of the corresponding thermal power units are different. For example, when the heat rate operating performance index is low, at least one of the main steam flow, reheat steam flow, feed water flow, superheated desuperheating water flow and reheated desuperheating water flow can be selectively controlled to increase, thereby improving the heat rate operating performance index and improving the power generation efficiency of the thermal power unit.

[0086] Furthermore, generating a corresponding operating condition optimization adjustment strategy according to the current operating condition, and controlling the operating data of the thermal power unit based on the operating condition optimization adjustment strategy, includes:

[0087] Calculating the similarity between the current operating condition and the historical operating condition;

[0088] Determine the target historical operating condition and the thermal power unit operating data corresponding to the target historical operating condition in the historical operating data according to the approximation;

[0089] The operating data of the thermal power unit is adjusted according to the operating data of the thermal power unit.

[0090] In the specific implementation, the formula for calculating the approximation between the current working condition and the historical working condition is as follows:

[0091]

[0092] Among them, r is the approximation, Vi is the historical working condition data, vi is the current working condition data, and n is the amount of data involved in the calculation.

[0093] This embodiment calculates the overall steady-state index of the thermal power unit through the operating data of multiple dimensions during the operation of the thermal power unit, and performs cluster analysis on the data of each dimension based on the overall steady-state index of the unit, thereby dividing the first boundary condition operating data and the second boundary condition operating data that affect the operating condition of the thermal power unit, and then determining the current operating condition of the thermal power unit through the operating condition identification model to generate an operating condition optimization adjustment strategy, and finally controlling the operating data of the thermal power unit with the operating condition optimization adjustment strategy to improve the operating efficiency of the thermal power unit, thereby avoiding the technical problem in the prior art of lacking an efficient adjustment solution for the operation of thermal power plants, which affects the efficiency of thermal power generation and economic benefits.

[0094] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 Step S20 includes:

[0095] Step 201: filtering the operating data of each dimension using a preset filtering model to obtain target operating data of each dimension, wherein the target operating data is the operating data with high-frequency parts removed.

[0096] Step 202: Input the target operating data of each dimension into the least squares model to obtain the data sampling change trend of the data of each dimension.

[0097] Step 203: Calculate the steady-state index of each dimension of data according to the data sampling change trend and the steady-state change threshold.

[0098] Step 204: Perform logical judgment based on the steady-state index of each dimension data to obtain the overall steady-state index of the unit.

[0099] It should be noted that the load and operating parameters of thermal power units are often in a state of flux due to internal and external factors such as the grid's peak-shaving demands and the unit's inherent disturbances. Due to factors such as unit heat storage and lag in automatic control system adjustments, the unit's operating parameters during this transition cannot represent the unit's current operating state. Accordingly, the more dramatic the parameter changes, the greater the deviation in the unit's economic indicators calculated based on the changing parameters. Therefore, before analyzing and processing the data, filtering the operating data in each dimension can be performed to improve the data's credibility and enhance the accuracy of operating condition judgment and data calculations.

[0100] In this embodiment, the preset filtering model refers to a network model or algorithm model with a low-pass filtering function, and may also be other driving devices that can achieve the same or similar functions. This embodiment does not impose any specific restrictions on this.

[0101] It should be understood that in the traditional field, when processing data signals, the processed data signals can be converted into the form of digital signals. In this embodiment, in order to determine the stable state of each dimensional data so as to facilitate the classification of each dimensional data, the first boundary condition operating data and the second boundary condition operating data, which refer to the second boundary condition operating data for which the parameter change has a greater impact on the steady state of the thermal power unit, are determined, thereby improving the accuracy of the operating condition division.

[0102] In this embodiment, the operating data of each dimension is converted into a second-order digital signal, namely:

[0103] x(t)=p+p1t

[0104] Among them, p is the signal average value, p1 is the time-based data sampling change trend of each dimension data, and t is time.

[0105] Furthermore, the step of calculating the steady-state index of each dimension of data according to the data sampling change trend and the steady-state change threshold includes:

[0106] Calculating the data change slope corresponding to the data sampling change trend;

[0107] Divide the stable state of each dimension of data according to the slope of the data change;

[0108] Divide the measurement standard deviation based on the stable state and obtain the sampling window length of each dimension data;

[0109] Calculate the steady-state judgment threshold of each dimension data according to the measurement standard deviation and the sampling window length;

[0110] The steady-state index of each dimension of data is calculated based on the data change slope and the steady-state change threshold.

[0111] The calculation formula for the steady-state index of each dimension data is:

[0112]

[0113] Among them, β i It refers to the steady-state index of each dimension of data, p1 is the time-based data sampling change trend of each dimension of data, H is the sampling window length, and δ is the measurement standard deviation.

[0114] This embodiment obtains target operating data of each dimension by filtering the operating data of each dimension through a preset filtering model, wherein the target operating data is the operating data with the high-frequency part removed; the target operating data of each dimension is input into a least squares model to obtain the data sampling change trend of the data of each dimension; the steady-state index of the data of each dimension is calculated based on the data sampling change trend and the steady-state change threshold; logical judgment is performed based on the steady-state index of the data of each dimension to obtain the overall steady-state index of the unit. By filtering the operating data of each dimension, the reliability of data sampling is improved, thereby improving the accuracy of subsequent operating condition judgment.

[0115] This application also provides an intelligent operating condition optimization device, please refer to Figure 3 , the intelligent operating condition optimization device includes:

[0116] The acquisition module 10 is used to obtain operating data of multiple dimensions of the thermal power unit.

[0117] The calculation module 20 is used to calculate the steady-state index corresponding to the operating data of each dimension and the overall steady-state index of the unit.

[0118] The clustering module 30 is configured to perform cluster analysis on the operating data of the multiple dimensions according to the overall steady-state index of the unit to obtain first boundary condition operating data and second boundary condition operating data.

[0119] The identification module 40 is configured to identify the current operating condition of the thermal power unit through an operating condition identification model based on the first boundary condition operating data and the second boundary condition operating data.

[0120] The control module 50 is configured to generate a corresponding operating condition optimization adjustment strategy according to the current operating condition, and control the operating data of the thermal power unit based on the operating condition optimization adjustment strategy.

[0121] In one embodiment, the calculation module 20 is further used to filter the operating data of each dimension through a preset filtering model to obtain target operating data of each dimension, where the target operating data is the operating data with the high-frequency part removed; input the target operating data of each dimension into a least squares model to obtain the data sampling change trend of the data of each dimension; calculate the steady-state index of the data of each dimension based on the data sampling change trend and the steady-state change threshold; perform logical judgment based on the steady-state index of the data of each dimension to obtain the overall steady-state index of the unit.

[0122] In one embodiment, the calculation module 20 is also used to calculate the data change slope corresponding to the data sampling change trend; divide the stable state of each dimensional data according to the data change slope; divide the measurement standard deviation based on the stable state, and obtain the sampling window length of each dimensional data; calculate the steady-state judgment threshold of each dimensional data according to the measurement standard deviation and the sampling window length; calculate the steady-state index of each dimensional data based on the data change slope and the steady-state change threshold.

[0123] In one embodiment, the clustering module 30 is further used to divide the unit into at least two clusters and cluster parameters according to the overall steady-state index; calculate the membership parameters between the operating data of the multiple dimensions and each cluster based on the cluster parameters; construct a membership matrix according to the membership parameters; and perform cluster analysis on the operating data of the multiple dimensions based on the membership matrix until the operating data converges to obtain first boundary condition operating data and second boundary condition operating data.

[0124] In one embodiment, the identification module 40 is also used to determine the target operating data in the first boundary condition operating data and the second boundary condition operating data; calculate the operating performance index of the thermal power unit based on the target operating data; and identify the current operating condition of the thermal power unit through the operating condition identification model based on the operating performance index.

[0125] In one embodiment, the control module 50 is further used to calculate the approximation between the current operating condition and the historical operating condition; determine the target historical operating condition and the thermal power unit operating data corresponding to the target historical operating condition in the historical operating data based on the approximation; and adjust the operating data of the thermal power unit based on the thermal power unit operating data.

[0126] The present application provides an intelligent operating condition optimization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intelligent operating condition optimization method in the above-mentioned embodiment one.

[0127] Reference below Figure 4 , which shows a schematic diagram of the structure of an intelligent operating condition optimization device suitable for implementing the embodiments of the present application. The intelligent operating condition optimization device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The intelligent operating condition optimization device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0128] like Figure 4As shown, the intelligent operating condition optimization device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the intelligent operating condition optimization device. Processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the intelligent operating condition optimization device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an intelligent operating condition optimization device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have instead.

[0129] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0130] The intelligent operating condition optimization device provided in this application utilizes the intelligent operating condition optimization method described in the aforementioned embodiment to solve the technical problems associated with intelligent operating condition optimization. Compared to the prior art, the beneficial effects of the intelligent operating condition optimization device provided in this application are the same as those of the intelligent operating condition optimization method described in the aforementioned embodiment. Other technical features of the intelligent operating condition optimization device are the same as those disclosed in the aforementioned embodiment and are not further elaborated upon here.

[0131] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0132] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0133] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, and the computer-readable program instructions are used to execute the intelligent operating condition optimization method in the above-mentioned embodiment.

[0134] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0135] The computer-readable storage medium may be included in the intelligent operating condition optimization device; or may exist independently without being assembled into the intelligent operating condition optimization device.

[0136] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the intelligent operating condition optimization device, the intelligent operating condition optimization device performs intelligent operating condition optimization.

[0137] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0138] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0139] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0140] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned intelligent operating condition optimization method, thereby resolving the technical problem of intelligent operating condition optimization. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the intelligent operating condition optimization method provided in the aforementioned embodiments, and are not further elaborated here.

[0141] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned intelligent operating condition optimization method when executed by a processor.

[0142] The computer program product provided in this application can solve the technical problem of intelligent operating condition optimization. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the intelligent operating condition optimization method provided in the above embodiment, and will not be repeated here.

[0143] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An intelligent operating condition optimization method, characterized in that: The intelligent operating condition optimization method includes: Obtain multi-dimensional operating data of thermal power units; Calculate the steady-state index corresponding to the operating data of each dimension and the overall steady-state index of the unit; Performing cluster analysis on the operating data of the multiple dimensions according to the overall steady-state index of the unit to obtain first boundary condition operating data and second boundary condition operating data; Identifying the current operating condition of the thermal power unit using an operating condition identification model based on the first boundary condition operating data and the second boundary condition operating data; A corresponding operating condition optimization adjustment strategy is generated according to the current operating condition, and the operating data of the thermal power unit is controlled based on the operating condition optimization adjustment strategy.

2. The intelligent operating condition optimization method according to claim 1, characterized in that: The calculation of the steady-state index corresponding to the operating data of each dimension and the overall steady-state index of the unit includes: Filtering the operating data of each dimension using a preset filtering model to obtain target operating data of each dimension, wherein the target operating data is the operating data with high-frequency parts removed; Input the target operating data of each dimension into the least squares model to obtain the data sampling change trend of each dimension data; Calculate the steady-state index of each dimension of data according to the data sampling change trend and the steady-state change threshold; Logical judgment is performed based on the steady-state index of each dimension data to obtain the overall steady-state index of the unit.

3. The intelligent operating condition optimization method according to claim 2, characterized in that: The calculating of the steady-state index of each dimension of data according to the data sampling change trend and the steady-state change threshold includes: Calculating the data change slope corresponding to the data sampling change trend; Divide the stable state of each dimension of data according to the slope of the data change; Divide the measurement standard deviation based on the stable state and obtain the sampling window length of each dimension data; Calculate the steady-state judgment threshold of each dimension data according to the measurement standard deviation and the sampling window length; The steady-state index of each dimension of data is calculated based on the data change slope and the steady-state change threshold.

4. The intelligent operating condition optimization method according to claim 1, characterized in that: The cluster analysis of the operating data of the multiple dimensions according to the overall steady-state index of the unit to obtain the first boundary condition operating data and the second boundary condition operating data includes: Dividing at least two clusters and cluster parameters according to the overall steady-state index of the unit; Calculating membership parameters between the operating data of the multiple dimensions and each cluster based on the cluster parameters; Constructing a membership matrix according to the membership parameters; Cluster analysis is performed on the operating data of the multiple dimensions based on the membership matrix until the operating data converges, thereby obtaining first boundary condition operating data and second boundary condition operating data.

5. The intelligent operating condition optimization method according to claim 1, characterized in that: The identifying the current operating condition of the thermal power unit by using an operating condition identification model based on the first boundary condition operating data and the second boundary condition operating data includes: determining target operating data among the first boundary condition operating data and the second boundary condition operating data; Calculating the operating performance index of the thermal power unit according to the target operating data; The current operating condition of the thermal power unit is identified through an operating condition identification model according to the operating performance index.

6. The intelligent operating condition optimization method according to claim 1, characterized in that: Generating a corresponding operating condition optimization adjustment strategy according to the current operating condition, and controlling the operating data of the thermal power unit based on the operating condition optimization adjustment strategy, includes: Calculating the similarity between the current operating condition and the historical operating condition; Determine the target historical operating condition and the thermal power unit operating data corresponding to the target historical operating condition in the historical operating data according to the approximation; The operating data of the thermal power unit is adjusted according to the operating data of the thermal power unit.

7. An intelligent operating condition optimization device, characterized in that: The intelligent operating condition optimization device includes: An acquisition module is used to obtain operating data of multiple dimensions of thermal power units; The calculation module is used to calculate the steady-state index corresponding to the operating data of each dimension and the overall steady-state index of the unit; a clustering module, configured to perform cluster analysis on the operating data of the multiple dimensions according to the overall steady-state index of the unit to obtain first boundary condition operating data and second boundary condition operating data; an identification module, configured to identify a current operating condition of the thermal power unit by using an operating condition identification model based on the first boundary condition operating data and the second boundary condition operating data; The control module is used to generate a corresponding operating condition optimization adjustment strategy according to the current operating condition, and control the operating data of the thermal power unit based on the operating condition optimization adjustment strategy.

8. An intelligent operating condition optimization device, characterized in that: The intelligent operating condition optimization device includes: a memory, a processor, and an intelligent operating condition optimization program stored in the memory and executable on the processor. The intelligent operating condition optimization program is configured to implement the intelligent operating condition optimization method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: An intelligent operating condition optimization program is stored on the storage medium, and when the intelligent operating condition optimization program is executed by the processor, the intelligent operating condition optimization method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the intelligent operating condition optimization method according to any one of claims 1 to 6 are implemented.

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