Water turbine state evaluation method, system, equipment and medium under operation load disturbance

Abstract

The invention provides a method, a system, equipment and a medium for evaluating the state of a water turbine under operation load disturbance, and provides digital twin evaluation simulation of the state of the water turbine under the operation load disturbance, and real-time data synchronization and virtual-real interaction with a physical water turbine are realized by establishing a virtual interaction system of the water turbine; in a digital twin environment, through constructing normal and load disturbance samples of the water turbine and combining real-time data monitoring deviation degree analysis, accurate scoring and evaluation of the state of the water turbine are realized.

Description

Technical Field

[0001] The invention relates to the technical field of hydraulic turbine condition maintenance, and in particular to a method, system, equipment and medium for evaluating hydraulic turbine condition under operating load disturbance. Background Art

[0002] As an important component of hydropower generation equipment, the stability and safety of the turbine's operating state are directly related to the reliability and efficiency of the entire power grid. During the operation of the turbine, due to the influence of various factors such as the environment, load, and material aging, its state will continue to change. Therefore, it is necessary to conduct a state assessment of the turbine. In the face of complex and changeable operating load disturbances, how to accurately and real-time evaluate the operating state of the turbine has become an important issue that needs to be solved urgently.

[0003] At present, the condition of turbines is usually assessed by online sensor monitoring data or maintenance records of operation and maintenance personnel. However, the evaluation results of these methods have problems such as poor timeliness and insufficient accuracy. Therefore, an effective method for assessing the condition of turbines under operating load disturbance is urgently needed to solve the above problems. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a method, system, device and medium for evaluating the state of a hydraulic turbine under operating load disturbance, which overcomes the above problems or at least partially solves the above problems.

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention provides a method for evaluating the state of a hydraulic turbine under operating load disturbance, which is applied to a hydraulic turbine virtual-real interaction system. The hydraulic turbine virtual-real interaction system includes a physical hydraulic turbine unit and a corresponding hydraulic turbine virtual model. The method includes:

[0006] Collecting stable operation data of the physical water turbine unit under various normal states to construct a normal sample set of the physical water turbine unit, and collecting load operation data of the physical water turbine unit under various load states to construct a load disturbance sample set of the physical water turbine unit;

[0007] According to the normal sample set and the load disturbance sample set, and using a hydraulic turbine virtual model to simulate different load disturbance scenarios, a normal reference value and a threshold value of a characteristic indicator under normal conditions and under load disturbance are extracted and calculated;

[0008] real-time acquisition and processing of current monitoring data of the physical water turbine unit, and identification of the current load state of the physical water turbine unit, so as to determine a real-time measured value of a characteristic index of the physical water turbine unit, and calculate a deviation of the characteristic index by using the normal reference value, the threshold value and the real-time measured value of the characteristic index;

[0009] Based on the deviation of the characteristic index, the physical turbine unit is comprehensively evaluated and calculated in combination with the hierarchical analysis method, and the state level is divided according to the comprehensive index evaluation result to determine the final evaluation result of the physical turbine unit under the current load state.

[0010] Optionally, before the step of collecting stable operation data of the physical water turbine unit in each normal state to construct a normal sample set of the physical water turbine unit, the step further includes:

[0011] Determine the mathematical model of the turbine under rigid water hammer, and collect historical operating data of the physical turbine unit under different working conditions; wherein the historical operating data includes power, flow, guide vane opening and water level difference;

[0012] Based on a least squares parameter identification algorithm, the historical operating data is used to determine unknown parameters in the hydraulic turbine mathematical model, and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model;

[0013] Based on the digital twin technology, the turbine mathematical model, the unknown parameters and the input-output relationship between the turbine mathematical model and the turbine virtual model of the physical turbine unit and its virtual-to-real mapping relationship are constructed, and a virtual-to-real interactive system of the turbine with virtual-to-real synchronization is built to achieve real-time data synchronization between the physical turbine unit and the turbine virtual model.

[0014] Optionally, the least squares parameter identification algorithm is based on using the historical operation data to determine unknown parameters in the hydraulic turbine mathematical model, and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model, including:

[0015] Preprocessing the historical operation data to obtain preprocessed historical operation data;

[0016] The preprocessed historical operation data is substituted into the hydraulic turbine mathematical model, and a matrix form formula of the hydraulic turbine mathematical model is constructed, and an iterative calculation is performed using a least squares parameter identification algorithm to determine unknown parameters in the hydraulic turbine mathematical model, as well as an input-output relationship between the unknown parameters and the hydraulic turbine mathematical model.

[0017] Optionally, the digital twin technology is used to construct a virtual model of the hydraulic turbine of the physical hydraulic turbine unit by using the hydraulic turbine mathematical model, the unknown parameters and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model, including:

[0018] Collecting intermediate parameters generated during the identification process of the hydraulic turbine mathematical model, and integrating the intermediate parameters and the model structure of the hydraulic turbine mathematical model into a standardized data format to obtain standardized intermediate parameters and a standardized hydraulic turbine mathematical model;

[0019] Using the standardized intermediate parameters and the standardized hydraulic turbine mathematical model, constructing an initial virtual model of the hydraulic turbine;

[0020] The initial virtual model of the hydraulic turbine is updated and optimized by using the unknown parameters and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model to obtain the hydraulic turbine virtual model of the physical hydraulic turbine unit.

[0021] Optionally, according to the normal sample set and the load disturbance sample set, different load disturbance scenarios are simulated using a hydraulic turbine virtual model to extract and calculate normal reference values ​​and thresholds of characteristic indicators under normal conditions and load disturbances, including:

[0022] Extracting characteristic indicators from the normal sample set and the load disturbance sample set;

[0023] The load variation range is set, and the characteristic indicators of different load disturbance scenarios are simulated using the virtual model of the turbine. The threshold of the parameters is set using the normal distribution theory, and the normal baseline value of the normal sample set and the threshold under load disturbance are calculated.

[0024] Optionally, the comprehensive evaluation calculation of the physical water turbine unit based on the characteristic index deviation and combined with the hierarchical analysis method includes:

[0025] According to the virtual model of the hydraulic turbine of the physical hydraulic turbine unit and its virtual-real mapping relationship, an evaluation hierarchy system for hydraulic turbine status evaluation is determined; wherein the evaluation hierarchy system includes an indicator layer, a component layer and a system layer;

[0026] A scoring standard is established according to the deviation, and a weight coefficient of each indicator layer, each component layer and each system layer is allocated in combination with the hierarchical analysis method. The status scores of the indicator layer, the component layer and the system layer are calculated layer by layer to obtain the overall status score of the physical turbine unit.

[0027] Optionally, the method further comprises:

[0028] The operating status of the physical turbine unit is simulated under various load scenarios using a virtual turbine model. The operating status data of the physical turbine unit and the virtual turbine model under various load disturbances are collected at the same time. The error rate between the physical turbine unit and the virtual turbine model is calculated using the operating status data.

[0029] In a second aspect, the present invention further provides a system for evaluating the state of a hydraulic turbine under operating load disturbance, which is applied to a hydraulic turbine virtual-real interaction system, wherein the hydraulic turbine virtual-real interaction system includes a physical hydraulic turbine unit and a corresponding hydraulic turbine virtual model, and the system includes:

[0030] A construction module is used to collect stable operation data of the physical water turbine unit under various normal states to construct a normal sample set of the physical water turbine unit, and to collect load operation data of the physical water turbine unit under various load states to construct a load disturbance sample set of the physical water turbine unit;

[0031] An extraction module, for simulating different load disturbance scenarios based on the normal sample set and the load disturbance sample set using a virtual model of a hydraulic turbine, extracting and calculating normal reference values ​​and threshold values ​​of characteristic indicators under normal conditions and under load disturbance;

[0032] A calculation module, used for real-time acquisition and processing of current monitoring data of the physical water turbine unit, and identifying the current load state of the physical water turbine unit, so as to determine the real-time measured value of the characteristic index of the physical water turbine unit, and calculate the deviation of the characteristic index by using the normal reference value, the threshold value and the real-time measured value of the characteristic index;

[0033] A determination module is used to perform a comprehensive evaluation calculation on the physical turbine unit based on the deviation of the characteristic index and in combination with the hierarchical analysis method, and to divide the state level according to the comprehensive index evaluation result to determine the final evaluation result of the physical turbine unit under the current load state.

[0034] In a third aspect, the present invention provides an electronic device, comprising: a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the electronic device performs the steps of the method for evaluating the state of a turbine under operating load disturbance as described above.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the steps of the method for evaluating the state of a turbine under operating load disturbance as described above.

[0036] The above one or more technical solutions provided by the present invention may have the following advantages or at least achieve the following technical effects:

[0037] The present invention provides a method, system, device and medium for evaluating the state of a turbine under operating load disturbance. The present invention proposes a digital twin evaluation simulation of the state of a turbine under operating load disturbance, and realizes real-time data synchronization and virtual-reality interaction with a physical turbine by establishing a virtual interactive system for the turbine. In a digital twin environment, normal and load disturbance samples of the turbine are constructed, combined with real-time data monitoring deviation analysis, to achieve accurate scoring and evaluation of the turbine state. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Shown is a flow chart of a method for evaluating a hydraulic turbine state under operating load disturbance in one embodiment of the present invention;

[0039] Figure 2 Shown is a schematic diagram of a flow chart of a turbine state assessment in one embodiment of the present invention;

[0040] Figure 3 Shown is a schematic diagram of a virtual environment of a hydraulic turbine virtual interactive system according to an embodiment of the present invention;

[0041] Figure 4 Shown is a structural schematic diagram of a hydraulic turbine virtual-real interaction system in one embodiment of the present invention;

[0042] Figure 5 It is a schematic diagram of the functional modules of a hydraulic turbine state assessment system under operating load disturbance in one embodiment of the present invention;

[0043] Figure 6 Shown is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0045] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0046] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0047] The terms "first", "second", etc. in the specification and claims of the disclosed embodiments and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the disclosed embodiments described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0048] Unless otherwise stated, the term "plurality" means two or more.

[0049] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0050] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0051] The technical solutions in the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0052] See also Figure 1 An embodiment of the present invention provides a method for evaluating a turbine state under operating load disturbance, which is applied to a turbine virtual-real interaction system. The turbine virtual-real interaction system includes a physical turbine unit and a corresponding turbine virtual model. The method may include the following steps S10 to S40:

[0053] Step S10, collecting stable operation data of the physical water turbine unit under various normal states to construct a normal sample set of the physical water turbine unit, and collecting load operation data of the physical water turbine unit under various load states to construct a load disturbance sample set of the physical water turbine unit.

[0054] Among them, physical turbine units can include new units and old units;

[0055] New units: The initial stable operation data can be used, combined with the virtual-real mapping relationship in the virtual-real interactive system of the turbine, to simulate the stable operation state through time series analysis and construct a normal sample.

[0056] Old units: Based on the load change records in the historical data and combined with the monitoring data after overhaul, statistical methods (such as mean and standard deviation) are used to construct normal samples and load disturbance samples.

[0057] In the specific implementation, the stable operation data of the physical turbine units (including new units and old units) under various normal states can be collected, and the normal sample set of the physical turbine units can be constructed in combination with the time series analysis method; the load operation data of the physical turbine units (including old units) under various load states can be collected, and the load disturbance sample set of the physical turbine units can be constructed in combination with the statistical method.

[0058] Step S20, based on the normal sample set and the load disturbance sample set, and using a turbine virtual model to simulate different load disturbance scenarios, extract and calculate normal reference values ​​and thresholds of characteristic indicators under normal conditions and load disturbances.

[0059] Among them, characteristic indicators can refer to the turbine status assessment in the digital twin environment, which are key parameters or variables used to measure the turbine status; they can reflect the operating status of the turbine under different working conditions, including but not limited to power, flow, speed, temperature, vibration, etc.

[0060] It should be noted that by comparing the actual monitoring data with the characteristic indicators in the normal sample or the load disturbance sample, it is possible to evaluate whether the operating status of the turbine is normal and whether there are any abnormalities or faults.

[0061] Among them, load disturbance simulation:

[0062] Set the load variation range and use the virtual model of the turbine corresponding to the physical turbine unit to simulate the data under different load disturbance scenarios. Apply the normal distribution theory (3σ criterion) to set the threshold of the parameter and consider the impact of load disturbance on the threshold. The adjustment formula is:

[0063]

[0064] Where: D rt is the new threshold under load disturbance; and σ are the normal sample baseline value and standard deviation respectively; k is a coefficient determined according to the 3σ criterion or other statistical criteria, usually taking a value of 3; α is an adjustment factor used to control the degree of influence of load disturbance on the threshold; f is the load disturbance influence function.

[0065] In the specific implementation, the load variation range is set, and the data under different load disturbance scenarios (that is, normal sample set and load disturbance sample set) are simulated using the virtual model of the turbine. The threshold of the parameter is set by applying the normal distribution theory, and the influence of the load disturbance on the threshold is considered. The normal reference value of the characteristic index under normal and load disturbance is extracted and calculated. and threshold (D rt ).

[0066] Step S30, real-time collection and processing of the current monitoring data of the physical water turbine unit, and identification of the current load state of the physical water turbine unit, so as to determine the real-time measured value of the characteristic index of the physical water turbine unit, and calculate the deviation of the characteristic index by using the normal reference value, the threshold value and the real-time measured value of the characteristic index.

[0067] Among them, the current monitoring data may be the actual monitored operating data of the physical turbine unit under the current load state; it may include but is not limited to power, flow, water level difference between upstream and downstream, rotation speed, relative value of main relay increment, etc.

[0068] Deviation (R D ), is calculated by comparing the difference between the actual monitored characteristic index value and the benchmark value in the normal sample, and then dividing it by the threshold value under load disturbance; its calculation formula can be expressed as:

[0069]

[0070] If the actual monitoring value of a characteristic indicator is higher than the normal reference value and exceeds the threshold value under load disturbance, the deviation of the indicator (R D ) will be higher, indicating that the turbine may have an abnormality or failure in this indicator.

[0071] As an example: assuming that power is one of the characteristic indicators, the actually monitored power value is P_actual, the power reference value in the normal sample is P_base, and the power threshold under load disturbance is P_threshold, then the deviation RD_power can be calculated as (P_actual-P_base) / P_threshold.

[0072] In the specific implementation, the current monitoring data of the physical turbine unit is collected and processed in real time, and the current load state of the physical turbine unit is identified at the same time, so that the real-time measured value of the characteristic index under the current load state can be determined; then, the normal reference value in the normal sample, the threshold value under load disturbance and the real-time measured value of the characteristic index can be used to calculate the deviation degree (R D ).

[0073] Step S40, based on the deviation of the characteristic index and in combination with the hierarchical analysis method, a comprehensive evaluation calculation is performed on the physical turbine unit, and the state level is divided according to the comprehensive index evaluation result to determine the final evaluation result of the physical turbine unit under the current load state.

[0074] Among them, the scoring standard is set according to the deviation of the characteristic index, which can be specifically expressed as:

[0075]

[0076] See also Figure 2 , Figure 2 The display is a schematic diagram of the turbine status assessment process; based on the established virtual model of the physical turbine unit and its virtual-real mapping relationship, the unit hierarchical analysis system (index layer-component layer-system layer) is determined. The scoring standard is established based on the deviation, and the status scores of the indicator layer, component layer and system layer are calculated layer by layer in combination with the hierarchical analysis method comprehensive characteristic index scores and weights:

[0077]

[0078] In the formula, ω i and W i are the weights and scores of each state quantity of the turbine components respectively. The overall state score of the turbine is obtained, and the calculation method is:

[0079]

[0080] Where: i and W i They are the weights and scores of various components of the turbine.

[0081] The status level can be divided into five levels: good (100-80), normal (80-60), attention (60-40), abnormal (40-20), and major abnormal (below 20) based on the normal sample conditions under various working conditions. Each level corresponds to a clear score range.

[0082] In the specific implementation, the unit hierarchical analysis system (index layer-component layer-system layer) can be determined based on the established virtual model of the turbine of the physical turbine unit and its virtual-real mapping relationship; then a scoring standard is established based on the deviation degree, and the state scores of the indicator layer, component layer and system layer are calculated layer by layer in combination with the hierarchical analysis method comprehensive characteristic index scores and weights to obtain the overall state score of the physical turbine unit (that is, the comprehensive state evaluation result); the state level is divided according to the overall state score, and the final evaluation result of the physical turbine unit under the current load state can be determined; and the evaluation results can also be output, including the state level and possible improvement measures (such as automatic warning in case of abnormality), thereby improving system safety and efficiency.

[0083] Furthermore, in one embodiment, step S20 may include the following sub-steps S201 to S202:

[0084] Sub-step S201, extracting characteristic indicators from the normal sample set and the load disturbance sample set;

[0085] Sub-step S202, sets the load variation range, and uses the turbine virtual model to simulate the characteristic indicators of different load disturbance scenarios, and applies the normal distribution theory to set the parameter thresholds, and calculates the normal baseline value of the normal sample set and the threshold value under load disturbance.

[0086] In the specific implementation, characteristic indicators from the normal sample set and the load disturbance sample set can be extracted, and the load variation range can be set according to the normal sample set and the load disturbance sample set; then the turbine virtual model can be used to simulate the data (i.e., characteristic indicators) under different load disturbance scenarios, the normal distribution theory can be applied to set the threshold of the parameter, and the influence of the load disturbance on the threshold can be considered, and then the normal baseline value of the characteristic indicator under normal and load disturbance conditions can be calculated. and threshold (D rt ); thus, in the offline stage, the normal samples and load samples constructed by historical monitoring data can be used to calculate the normal baseline values ​​and thresholds of the characteristic indicators under normal and load disturbance conditions.

[0087] Furthermore, in one embodiment, step S30 may include the following sub-steps S301 to S302:

[0088] Sub-step S301, determining an evaluation hierarchy system for turbine status evaluation based on the turbine virtual model of the physical turbine unit and its virtual-real mapping relationship; wherein the evaluation hierarchy system includes an indicator layer, a component layer, and a system layer;

[0089] Sub-step S302, establishes a scoring standard based on the deviation, and allocates weight coefficients of each indicator layer, each component layer and each system layer in combination with the hierarchical analysis method, calculates the status scores of the indicator layer, the component layer and the system layer layer by layer, and obtains the overall status score of the physical turbine unit.

[0090] In the specific implementation, the unit hierarchical analysis system (index layer-component layer-system layer) can be determined based on the established virtual model of the turbine of the physical turbine unit and its virtual-real mapping relationship; then the scoring standard is established based on the deviation, and the weight coefficients of each index layer, each component layer and each system layer are allocated in combination with the hierarchical analysis method, and the state scores of the index layer, component layer and system layer are calculated layer by layer to obtain the overall state score of the physical turbine unit (that is, the comprehensive state assessment result). Therefore, the weights of each level are allocated by the hierarchical analysis method, and the influence of load disturbance on weight allocation is especially considered, so that the accurate scoring and assessment of the turbine state can be achieved.

[0091] Furthermore, in one embodiment, the method may further include step C10:

[0092] Step C10, using the turbine virtual model to simulate the operating status of the physical turbine unit under various load scenarios, and at the same time collecting the operating status data of the physical turbine unit and the turbine virtual model under various load disturbances, and using the operating status data to calculate the error rate between the physical turbine unit and the turbine virtual model.

[0093] The operating status data may be key parameters describing the operating status of the physical turbine unit, which may include but are not limited to: speed, flow, etc. For example, the measured values ​​of the physical turbine unit (such as speed, flow), and the predicted values ​​of the turbine virtual model (such as speed, flow).

[0094] As an example, see the operating status data under various load disturbances recorded in Table 1 below;

[0095]

[0096]

[0097] Table 1 Operation status data of physical turbine unit and turbine virtual model under various load disturbances

[0098] In the specific implementation, the turbine virtual model can be used to simulate the operating status of the physical turbine unit under various load scenarios (such as sudden increase in load, sudden decrease in load, periodic load, etc.); at the same time, the measured values ​​of the physical turbine unit under various load disturbances and the predicted values ​​of the turbine virtual model are recorded; then the predicted values ​​and measured values ​​can be used to calculate the error rate between the physical turbine unit and the turbine virtual model; thereby verifying the accuracy, timeliness and ability to capture dynamic changes of the turbine virtual model.

[0099] In this embodiment, a digital twin evaluation simulation of the turbine state under operating load disturbance is proposed, and a virtual interactive system of the turbine is established to achieve real-time data synchronization and virtual-reality interaction with the physical turbine. In the digital twin environment, normal and load disturbance samples of the turbine are constructed, combined with real-time data monitoring deviation analysis, to achieve accurate scoring and evaluation of the turbine state.

[0100] Based on the above embodiments, a second embodiment of the method for evaluating the state of a hydraulic turbine under operating load disturbance of the present invention is proposed. In this embodiment, before step S10, the method may further include the following sub-steps A10 to A30:

[0101] Step A10, determining a mathematical model of a hydraulic turbine under rigid water hammer, and collecting historical operating data of the physical hydraulic turbine unit under different operating conditions; wherein the historical operating data includes power, flow, guide vane opening, and water level difference.

[0102] The historical operation data may include, but are not limited to, key parameters such as power, flow, guide vane opening, water level difference or pressure difference; these data should include the input variables and output variables of the turbine. Input variables usually include guide vane opening, water level difference, water flow velocity, etc.; output variables are mainly flow, speed, output power, etc. of the turbine.

[0103] It should be noted that in order to ensure that the collected data is representative, it covers the operation of the physical turbine unit under different working conditions, such as different load levels (e.g., sudden load increase, sudden load decrease, periodic load changes, etc.), different guide vane opening adjustment ranges, etc.

[0104] In the specific implementation, firstly, a mathematical model suitable for power system analysis and capable of reflecting the dynamic behavior of the turbine under load disturbance is selected. The mathematical model can reflect the linear relationship between the turbine power and flow and the main input (such as the guide vane opening); to meet the needs of digital twin evaluation, the mathematical model can be simplified as necessary to reduce the computational complexity while maintaining the accuracy and reliability of the mathematical model; then, rigid water hammer can be taken into account, and the simplified mathematical model can be corrected using the rigid water hammer, so that the mathematical model of the turbine under rigid water hammer can be determined. It is also possible to collect historical operating data of multiple physical turbines under different working conditions through a variety of sensors (flow sensors, pressure sensors, guide vane opening sensors, and tachometers, etc.) installed in the physical turbine unit. The historical operating data may include but is not limited to key parameters such as power, flow, guide vane opening, water level difference or pressure difference.

[0105] The linear relationship between turbine power and flow and main inputs (such as guide vane opening) can be expressed as:

[0106]

[0107] Where: Δa t , Δa are the relative increments of turbine power and flow rate; Δh is the water level difference or pressure difference between the turbine inlet (upstream) and outlet (downstream); Δx is the speed change; Δy is the relative increment of the main relay; a ij is the transfer coefficient.

[0108] The deviation of turbine speed Δx can be ignored when the unit is connected to the grid and connected to the large power grid, and the above formula can be simplified to:

[0109]

[0110] In the formula, G h (s) is the transfer function from flow change to head change. Considering the rigid water hammer G h (s)=-Tw (s), then the mathematical model of the turbine at this time (that is, the mathematical model of the turbine under rigid water hammer) is expressed as:

[0111]

[0112] Step A20, based on the least squares parameter identification algorithm, using the historical operation data to determine the unknown parameters in the hydraulic turbine mathematical model, and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model.

[0113] Among them, the unknown parameters are used to characterize the coefficients or constants that are difficult to directly measure or obtain in the turbine mathematical model determined by the least squares parameter identification algorithm.

[0114] The input-output relationship between the unknown parameters and the hydraulic turbine mathematical model is used to characterize the relationship between the unknown parameters and the actual input values ​​and actual output values ​​of the hydraulic turbine mathematical model; it can be constructed in a specific formula form (eg, matrix form).

[0115] In a specific implementation, the historical operating data can be subjected to preprocessing operations such as data cleaning, outlier removal, and data normalization, and then the preprocessed historical operating data (including input-output variables) can be substituted into the turbine mathematical model; it can then be constructed into a formula in a specific matrix form, and the matrix form formula can be solved using a least squares parameter identification algorithm, thereby determining the unknown parameters in the turbine mathematical model, as well as the input-output relationship between the unknown parameters and the turbine mathematical model.

[0116] Step A30, based on the digital twin technology, using the turbine mathematical model, the unknown parameters and their input-output relationship with the turbine mathematical model, construct a turbine virtual model of the physical turbine unit and its virtual-to-real mapping relationship, and build a virtual-to-real synchronized turbine virtual-to-real interaction system to synchronize real-time data between the physical turbine unit and the turbine virtual model.

[0117] Among them, the virtual model of the turbine is a virtual model built based on the physical entity (physical turbine unit), integrating its geometry, materials, operating data and physical laws. It can achieve highly realistic simulation and prediction through virtual data analysis, support evaluation decision optimization, design improvement and maintenance management, thereby improving operational efficiency and reliability.

[0118] The virtual-real mapping relationship can characterize the state quantity mapping relationship between the physical turbine unit and the virtual model of the turbine; it can be expressed as follows:

[0119]

[0120] Where, FU is the digital twin turbine; TU is the actual turbine; is a mapping relationship; IP is a digital twin turbine geometric model set; YH is a physical attribute set; RT is an operation logic model set; MK is an operation action behavior set; is a natural connection; WE is a parameter that reflects the status of the turbine in real time; when the PO state mode matches the BN operation behavior, the turbine status is mapped to the virtual model to achieve real-time status synchronization.

[0121] The turbine virtual-reality interactive system mainly consists of four parts: physical turbine unit, turbine virtual model, turbine twin data (such as historical operation data of the physical turbine unit under different working conditions), and turbine service system.

[0122] See also Figure 3 , Figure 3 The virtual environment diagram of the turbine interactive system is shown; the virtual environment mainly includes the physical turbine unit, the turbine virtual model, the turbine twin data, and the turbine service system. In the digital twin system, the turbine virtual model is one of the core components. The identified model parameters (i.e., unknown parameters) and relationships (i.e., the input-output relationship between the unknown parameters and the turbine mathematical model) are directly applied to the standardized turbine mathematical model. The model is updated to obtain the turbine virtual model of the physical turbine unit. In this way, the turbine virtual model can more accurately simulate the dynamic behavior of the physical turbine under load disturbance.

[0123] See also Figure 4 , Figure 4 The diagram shows the structure of the turbine interaction system. The system is divided into three layers: the equipment layer collects and processes real-time data of physical turbines; the twin layer combines models with big data to build accurate models; the service layer intuitively displays results and optimizes the operation of physical machines. These three layers work together to ensure information security and data circulation, and achieve efficient virtual-real interaction.

[0124] After building a virtual-reality interactive system for a hydraulic turbine that is synchronized with the real and the virtual, real-time data synchronization and interaction are also required.

[0125] Real-time data synchronization and virtual-real interaction:

[0126] (1) Data synchronization: that is, establishing a real-time synchronization mechanism between the physical and virtual turbines to ensure accurate data transmission and drive accurate simulation of the virtual model;

[0127] (2) Virtual-real optimization: Use virtual models to predict the state of the physical machine, adjust strategies in advance to deal with load disturbances, and reduce fluctuation failures. Monitor the physical machine in real time, automatically warn when an abnormality occurs, and improve system safety and efficiency. Virtual-real interaction adapts to changing environments and ensures safe and efficient operation of the turbine.

[0128] In the specific implementation, the digital twin technology is used to construct a virtual model of the physical turbine unit and its virtual-to-real mapping relationship based on the turbine mathematical model, unknown parameters, and the input-output relationship between the unknown parameters and the turbine mathematical model; then, the physical turbine unit, the turbine virtual model, and the virtual-to-real mapping relationship can be used to build a virtual-to-real interactive system for the turbine that synchronizes virtual and real things, so as to synchronize real-time data between the physical turbine unit and the turbine virtual model.

[0129] In this embodiment, by establishing a mathematical model of the turbine under rigid water hammer and using the least squares parameter identification method, the input-output relationship under different working conditions is understood, providing accurate parameters and model structure for the digital twin technology; secondly, based on these parameters and model structure, a digital twin turbine virtual model is established, which can achieve real-time data synchronization and virtual-reality interaction with the physical turbine, and can evaluate the turbine status under operating load disturbance in real time.

[0130] Based on the above embodiments, a third embodiment of the method for evaluating the state of a hydraulic turbine under operating load disturbance is proposed. In this embodiment, step A20 may include the following sub-steps A201 to A202:

[0131] Sub-step A201, pre-processing the historical operation data to obtain pre-processed historical operation data.

[0132] The preprocessed historical operation data may be data obtained after preprocessing operations such as cleaning, removing outliers, and normalizing the historical operation data.

[0133] In the specific implementation, the historical operation data can be cleaned, outliers can be removed (such as data points and erroneous data caused by sensor failure or transient interference), and normalized (the data range of different physical quantities can be adjusted to a suitable interval for subsequent calculations and model building) and other preprocessing operations can be performed to obtain the preprocessed historical operation data.

[0134] Sub-step A202, substitutes the preprocessed historical operation data into the hydraulic turbine mathematical model, constructs a matrix form formula of the hydraulic turbine mathematical model, and uses a least squares parameter identification algorithm to perform iterative calculations to determine the unknown parameters in the hydraulic turbine mathematical model, as well as the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model.

[0135] Among them, the structure is as follows: y(k) = φ T The formula for (k)θ+ε(k) is:

[0136]

[0137] In the formula, θ is the object parameter; u(k) and y(k) are the actual input and output values ​​of the system; φ T (k) is the input and output matrix value of the system k at the current moment; ε(k) is the error term.

[0138] As an example, for a value of the form y(k)=φ T The formula of (k)θ+ε(k) may include, but is not limited to:

[0139] The water level difference between upstream and downstream (ΔH) can be expressed as the water level difference or pressure difference between the turbine inlet (upstream) and outlet (downstream);

[0140] Speed ​​(ω), which can represent the speed of the turbine;

[0141] The relative value of the main servomotor increment (Δμ) can represent the increment of the main servomotor, that is, the signal for controlling the opening of the turbine guide vanes.

[0142] The output parameters may include, but are not limited to: the flow rate (Q) of the turbine and the output power (P) of the turbine.

[0143] In the parameter identification process, a formula of the form is constructed based on these input and output parameters, and through iterative calculation, the optimal parameter estimate of the object parameter θ is gradually approached. The goal is to predict the output There is a minimum error between the actual output y(k), and the specific formula is:

[0144]

[0145] In the formula, e(k) is the prediction error. Finally, the parameter estimation value is obtained at this time as the final parameter identification result.

[0146] In the specific implementation, the preprocessed historical operation data can be substituted into the turbine mathematical model, and a formula such as y(k)=φ can be constructed. T The matrix form formula of (k)θ+ε(k) is used, and the least squares parameter identification algorithm is used for iterative calculation to gradually approach the optimal parameter estimation value of the object parameter until the error between the predicted data and the actual data is minimized (the iterative calculation is completed at this time). The obtained parameter estimation value is the final parameter identification result (that is, the unknown parameter). These parameter representation results reflect the input-output relationship of the turbine mathematical model under specific working conditions.

[0147] In this embodiment, the historical operation data is preprocessed to obtain the preprocessed historical operation data; the preprocessed historical operation data is substituted into the hydraulic turbine mathematical model, and a matrix formula of the hydraulic turbine mathematical model is constructed, and an iterative calculation is performed using a least squares parameter identification algorithm to determine the unknown parameters in the hydraulic turbine mathematical model and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model. Thus, through the model identification technology, the model parameters are estimated and optimized using the preprocessed data, so that the model can more accurately reflect the actual operating characteristics of the hydraulic turbine.

[0148] Based on the above embodiments, a fourth embodiment of the method for evaluating the state of a hydraulic turbine under operating load disturbance of the present invention is proposed. In this embodiment, step A30 may include the following sub-steps A301 to A303:

[0149] Sub-step A301, collecting the intermediate parameters generated during the identification process of the hydraulic turbine mathematical model, and integrating the intermediate parameters and the model structure of the hydraulic turbine mathematical model into a standardized data format to obtain standardized intermediate parameters and a standardized hydraulic turbine mathematical model.

[0150] Among them, the intermediate parameters can be the data generated during the identification of the hydraulic turbine mathematical model, which may include but are not limited to: transfer coefficient, water level difference, speed change, relative value of main relay increment, etc. These data may come from different experimental conditions or working conditions and need to be uniformly sorted and classified.

[0151] In the specific implementation, all the intermediate parameters generated during the identification of the turbine mathematical model are collected, and the collected intermediate parameters are cleaned and invalid or abnormal data points are removed to ensure the accuracy and reliability of the data; at the same time, the data format is verified to ensure that it meets the data input requirements of the data twin system. Then, the cleaned and verified intermediate parameters can be converted into a standardized data format; this includes defining a unified data type, data range, data accuracy, etc. to ensure the consistency and comparability of the data in the digital twin system. Furthermore, according to the needs of the data twin system, the standardized data needs to be encoded and mapped (for example, encoding the intermediate parameters into specific identifiers, mapping the model structure into a data structure that can be recognized by the system, etc.); finally, the standardized intermediate parameters are stored in the digital twin system, and a corresponding data management mechanism is established to ensure the accessibility, security and sustainability of the data; through the above steps (classification, cleaning, standardization, encoding, storage), the intermediate parameters and model structure are integrated into a standardized data format, providing a reliable data foundation for subsequent digital twin modeling.

[0152] Sub-step A302, constructing an initial virtual model of the turbine using the standardized intermediate parameters and the standardized mathematical model of the turbine.

[0153] Among them, the initial virtual model of the turbine can be a virtual turbine model constructed using standardized intermediate parameters and a standardized turbine mathematical model; it is a virtual model that can accurately simulate the dynamic behavior of a physical turbine unit.

[0154] In a specific implementation, the standardized intermediate parameters and the standardized mathematical model of the turbine can be used to construct an initial virtual model of the turbine.

[0155] Sub-step A303, using the unknown parameters and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model, the hydraulic turbine initial virtual model is updated and optimized to obtain a hydraulic turbine virtual model of the physical hydraulic turbine unit.

[0156] In a specific implementation, the unknown parameters obtained by identification and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model can be directly applied to the hydraulic turbine initial virtual model to update and optimize the hydraulic turbine initial virtual model, thereby obtaining the hydraulic turbine virtual model of the physical hydraulic turbine unit (that is, Figure 4 The precise model constructed by the twin layer in .

[0157] In this embodiment, by integrating the model parameters and model structure obtained in the model identification process into a standardized data format, it can be ensured that these parameters and model structure can be directly read and used by the virtual-reality synchronized turbine virtual-reality interaction system, providing data support for the subsequent construction of the turbine virtual model and analysis.

[0158] Based on the same inventive concept, the fifth embodiment of the present invention also provides a system for evaluating the state of a turbine under operating load disturbance corresponding to the method for evaluating the state of a turbine under operating load disturbance in the previous embodiment. Since the principle of solving the problem by the system in the fifth embodiment of the present invention is similar to the method for evaluating the state of a turbine under operating load disturbance in the previous embodiment of the present invention, the implementation of the system can refer to the implementation of the method, and the repeated parts will not be repeated. Figure 5 The hydraulic turbine state assessment system under load disturbance of the present invention may include:

[0159] A construction module 10 is used to collect stable operation data of the physical water turbine unit under various normal states to construct a normal sample set of the physical water turbine unit, and to collect load operation data of the physical water turbine unit under various load states to construct a load disturbance sample set of the physical water turbine unit;

[0160] An extraction module 20 is used to extract and calculate normal reference values ​​and thresholds of characteristic indicators under normal conditions and under load disturbances based on the normal sample set and the load disturbance sample set and by simulating different load disturbance scenarios using a hydraulic turbine virtual model;

[0161] The calculation module 30 is used to collect and process the current monitoring data of the physical water turbine unit in real time, and identify the current load state of the physical water turbine unit to determine the real-time measured value of the characteristic index of the physical water turbine unit, and calculate the deviation of the characteristic index by using the normal reference value, the threshold value and the real-time measured value of the characteristic index;

[0162] The determination module 40 is used to perform a comprehensive evaluation calculation on the physical turbine unit based on the deviation of the characteristic index and in combination with the hierarchical analysis method, and divide the state level according to the comprehensive index evaluation result to determine the final evaluation result of the physical turbine unit under the current load state.

[0163] In addition, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for evaluating the state of a hydraulic turbine under operating load disturbance is implemented.

[0164] Figure 6 is a schematic block diagram of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device includes: at least one processor 401, a memory 402, at least one network interface 403 and a user interface 405. The various components in the electronic device are coupled together through a bus system 404. It can be understood that the bus system 404 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 404 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 6 In the specification, various buses are labeled as bus systems.

[0165] The user interface 405 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0166] It is understood that the memory 402 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable categories of memory.

[0167] The memory 402 in the embodiment of the present invention is used to store various categories of data to support the operation of the electronic device 400. Examples of these data include: any executable program for operating on the electronic device 400, such as an operating system 4021 and an application 4022; the operating system 4021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 4022 can include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The method for evaluating the state of a turbine under operating load disturbance provided in the embodiment of the present invention can be included in the application 4022.

[0168] The method disclosed in the above embodiment of the present invention can be applied to the processor 401, or implemented by the processor 401. The processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 401 or the instruction in the form of software. The above processor 401 can be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 401 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general processor 401 can be a microprocessor or any conventional processor, etc. In combination with the steps of the method for evaluating the state of a turbine under operating load disturbance provided in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0169] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0170] In summary, the present invention proposes a digital twin evaluation simulation of the turbine state under operating load disturbance, and realizes real-time data synchronization and virtual-reality interaction with the physical turbine by establishing a virtual interactive system of the turbine; in a digital twin environment, by constructing normal and load disturbance samples of the turbine, combined with real-time data monitoring deviation analysis, accurate scoring and evaluation of the turbine state can be achieved.

[0171] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for evaluating the state of a hydraulic turbine under operating load disturbance, characterized in that: Applied to a hydraulic turbine virtual-reality interaction system, the hydraulic turbine virtual-reality interaction system includes a physical hydraulic turbine unit and a corresponding hydraulic turbine virtual model, the method includes: Collecting stable operation data of the physical water turbine unit under various normal states to construct a normal sample set of the physical water turbine unit, and collecting load operation data of the physical water turbine unit under various load states to construct a load disturbance sample set of the physical water turbine unit; According to the normal sample set and the load disturbance sample set, and using a hydraulic turbine virtual model to simulate different load disturbance scenarios, a normal reference value and a threshold value of a characteristic indicator under normal conditions and under load disturbance are extracted and calculated; real-time acquisition and processing of current monitoring data of the physical water turbine unit, and identification of the current load state of the physical water turbine unit, so as to determine a real-time measured value of a characteristic index of the physical water turbine unit, and calculate a deviation of the characteristic index by using the normal reference value, the threshold value and the real-time measured value of the characteristic index; Based on the deviation of the characteristic index, the physical turbine unit is comprehensively evaluated and calculated in combination with the hierarchical analysis method, and the state level is divided according to the comprehensive index evaluation result to determine the final evaluation result of the physical turbine unit under the current load state.

2. The method according to claim 1, characterized in that Before the step of collecting stable operation data of the physical water turbine unit in each normal state to construct a normal sample set of the physical water turbine unit, the step further includes: Determine the mathematical model of the turbine under rigid water hammer, and collect historical operating data of the physical turbine unit under different working conditions; wherein the historical operating data includes power, flow, guide vane opening and water level difference; Based on a least squares parameter identification algorithm, the historical operating data is used to determine unknown parameters in the hydraulic turbine mathematical model, and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model; Based on the digital twin technology, the turbine mathematical model, the unknown parameters and the input-output relationship between the turbine mathematical model and the turbine virtual model of the physical turbine unit and its virtual-to-real mapping relationship are constructed, and a virtual-to-real interactive system of the turbine with virtual-to-real synchronization is built to achieve real-time data synchronization between the physical turbine unit and the turbine virtual model.

3. The method according to claim 2, characterized in that The least squares parameter identification algorithm is based on using the historical operation data to determine the unknown parameters in the hydraulic turbine mathematical model, and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model, including: Preprocessing the historical operation data to obtain preprocessed historical operation data; The preprocessed historical operation data is substituted into the hydraulic turbine mathematical model, and a matrix form formula of the hydraulic turbine mathematical model is constructed, and an iterative calculation is performed using a least squares parameter identification algorithm to determine unknown parameters in the hydraulic turbine mathematical model, as well as an input-output relationship between the unknown parameters and the hydraulic turbine mathematical model.

4. The method according to claim 2 or 3, characterized in that: The method based on the digital twin technology, using the hydraulic turbine mathematical model, the unknown parameters and the input-output relationship between the hydraulic turbine mathematical model and the unknown parameters, constructs a hydraulic turbine virtual model of the physical hydraulic turbine unit, including: Collecting intermediate parameters generated during the identification process of the hydraulic turbine mathematical model, and integrating the intermediate parameters and the model structure of the hydraulic turbine mathematical model into a standardized data format to obtain standardized intermediate parameters and a standardized hydraulic turbine mathematical model; Using the standardized intermediate parameters and the standardized hydraulic turbine mathematical model, constructing an initial virtual model of the hydraulic turbine; The initial virtual model of the hydraulic turbine is updated and optimized by using the unknown parameters and the input-output relationship between the unknown parameters and the hydraulic turbine mathematical model to obtain the hydraulic turbine virtual model of the physical hydraulic turbine unit.

5. The method according to claim 1, characterized in that According to the normal sample set and the load disturbance sample set, and using the turbine virtual model to simulate different load disturbance scenarios, extracting and calculating the normal reference value and threshold value of the characteristic index under normal and load disturbance conditions, including: Extracting characteristic indicators from the normal sample set and the load disturbance sample set; The load variation range is set, and the characteristic indicators of different load disturbance scenarios are simulated using the virtual model of the turbine. The threshold of the parameters is set using the normal distribution theory, and the normal baseline value of the normal sample set and the threshold under load disturbance are calculated.

6. The method according to claim 1, characterized in that The comprehensive evaluation calculation of the physical turbine unit based on the characteristic index deviation and combined with the hierarchical analysis method includes: According to the virtual model of the hydraulic turbine of the physical hydraulic turbine unit and its virtual-real mapping relationship, an evaluation hierarchy system for hydraulic turbine status evaluation is determined; wherein the evaluation hierarchy system includes an indicator layer, a component layer and a system layer; A scoring standard is established according to the deviation, and a weight coefficient of each indicator layer, each component layer and each system layer is allocated in combination with the hierarchical analysis method. The status scores of the indicator layer, the component layer and the system layer are calculated layer by layer to obtain the overall status score of the physical turbine unit.

7. The method according to claim 1, characterized in that The method further comprises: The operating status of the physical turbine unit is simulated under various load scenarios using a virtual turbine model. The operating status data of the physical turbine unit and the virtual turbine model under various load disturbances are collected at the same time. The error rate between the physical turbine unit and the virtual turbine model is calculated using the operating status data.

8. A system for evaluating the state of a hydraulic turbine under load disturbance, characterized in that: Applied to a hydraulic turbine virtual-reality interaction system, the hydraulic turbine virtual-reality interaction system includes a physical hydraulic turbine unit and its corresponding hydraulic turbine virtual model, the system includes: A construction module is used to collect stable operation data of the physical water turbine unit under various normal states to construct a normal sample set of the physical water turbine unit, and to collect load operation data of the physical water turbine unit under various load states to construct a load disturbance sample set of the physical water turbine unit; An extraction module, for simulating different load disturbance scenarios based on the normal sample set and the load disturbance sample set using a virtual model of a hydraulic turbine, extracting and calculating normal reference values ​​and threshold values ​​of characteristic indicators under normal conditions and under load disturbance; A calculation module, used for real-time acquisition and processing of current monitoring data of the physical water turbine unit, and identifying the current load state of the physical water turbine unit, so as to determine the real-time measured value of the characteristic index of the physical water turbine unit, and calculate the deviation of the characteristic index by using the normal reference value, the threshold value and the real-time measured value of the characteristic index; A determination module is used to perform a comprehensive evaluation calculation on the physical turbine unit based on the deviation of the characteristic index and in combination with the hierarchical analysis method, and to divide the state level according to the comprehensive index evaluation result to determine the final evaluation result of the physical turbine unit under the current load state.

9. An electronic device, characterized in that: The electronic device comprises: a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the steps of the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and when the program is run, it is used to implement the steps of the method described in any one of claims 1 to 7.

Images