Wind power generation equipment health state assessment method and device and storage medium

Abstract

The invention belongs to the field of information monitoring, and discloses a wind power generation equipment health state assessment method and device and a storage medium. The method comprises the following steps: acquiring a real-time frequent item number of real-time operation data of the wind power generation equipment; the health state of the wind power generation equipment is evaluated according to the real-time frequent item number and a frequent item reference value, and the frequent item reference value is determined based on the historical frequent item number of historical operation data of the wind power generation equipment in the health state; the data type of the historical operation data corresponds to the data type of the real-time operation data. Through the scheme, the health state of the wind power generation equipment can be accurately evaluated in real time.

Description

Technical Field

[0001] The present application relates to the field of information monitoring, and in particular to a method, device and storage medium for evaluating the health status of wind power generation equipment. Background Art

[0002] Wind power generation refers to converting the kinetic energy of wind into electrical energy. Wind energy is a clean, pollution-free renewable energy source. Wind power grid connection is an important way to reduce air pollution and improve energy structure. Therefore, wind power generation systems are increasingly valued by countries around the world.

[0003] In the application scenarios of wind power generation equipment, efficient and reliable health status diagnosis becomes the key to optimizing operation and maintenance and ensuring energy output. Traditional methods are limited by the inefficiency of manual inspection and the delayed discovery of hidden faults, which affects the overall efficiency. Summary of the invention

[0004] The embodiments of the present application provide a method, device and storage medium for evaluating the health status of wind power generation equipment, which can accurately evaluate the health status of wind power generation equipment in real time.

[0005] In a first aspect, a method for evaluating the health status of a wind power generation device is provided, the method comprising:

[0006] Acquire the number of real-time frequent items of the real-time operation data of the wind power generation equipment;

[0007] The health status of the wind power generation equipment is evaluated according to the number of real-time frequent items and a frequent item reference value, wherein the frequent item reference value is determined based on the number of historical frequent items of historical operating data of the wind power generation equipment in a healthy state, and a data type of the historical operating data corresponds to a data type of the real-time operating data.

[0008] In combination with the first aspect, in certain implementations of the first aspect, evaluating the health status of the wind power generation equipment according to the real-time frequent item number and the frequent item reference value includes:

[0009] Establishing a probability density function of the historical operation data according to the frequent item reference value;

[0010] The health status of the wind power generation equipment is evaluated according to the distribution of the number of real-time frequent items in the probability density function.

[0011] In combination with the first aspect, in some implementations of the first aspect, evaluating the health status of the wind power generation equipment according to the distribution of the number of real-time frequent items in the probability density function includes:

[0012] Dividing the probability density function into a plurality of parts, each part corresponding to a different level of the health status of the wind power generation equipment;

[0013] The health status of the wind power generation equipment is evaluated according to the part where the number of real-time frequent items is located in the probability density function.

[0014] In combination with the first aspect, in certain implementations of the first aspect, the levels of the health status of the wind power generation equipment include at least the following three levels: normal state, abnormal state, and alarm state.

[0015] In combination with the first aspect, in certain implementations of the first aspect, before obtaining the number of real-time frequent items of the real-time operation data of the wind power generation equipment, the method further includes:

[0016] Acquiring the historical operation data;

[0017] Performing a first preprocessing operation on the historical operation data to obtain first processed data, wherein the first preprocessing operation includes a first cleaning process and a first discretization process;

[0018] Frequent item mining is performed on the first processed data to obtain the number of historical frequent items.

[0019] In combination with the first aspect, in some implementations of the first aspect, the acquiring the number of real-time frequent items of the real-time operation data of the wind power generation equipment includes:

[0020] Acquiring the real-time operation data;

[0021] Performing a second preprocessing operation on the real-time operation data to obtain second processed data, wherein the second preprocessing operation includes data smoothing processing, a second cleaning processing, and a second discretization processing, and the second discretization processing is determined based on the first discretization processing;

[0022] Frequent item mining is performed on the second processed data to obtain the number of real-time frequent items.

[0023] In combination with the first aspect, in certain implementations of the first aspect, the real-time operating data includes one or more of the following: bearing temperature and vibration.

[0024] In a second aspect, a device for evaluating the health status of a wind power generation device is provided, characterized in that it comprises:

[0025] A mining module, used for obtaining the number of real-time frequent items of the real-time operation data of the wind power generation equipment;

[0026] an evaluation module, configured to evaluate the health status of the wind power generation equipment according to the number of real-time frequent items and a frequent item reference value, wherein the frequent item reference value is determined based on the number of historical frequent items of historical operating data of the wind power generation equipment in a healthy state, and a data type of the historical operating data corresponds to a data type of the real-time operating data.

[0027] In a third aspect, an embodiment of the present application provides a device for assessing the health status of wind power generation equipment, characterized in that it comprises: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method steps as claimed in any one of claims 1 to 7.

[0028] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, and the computer program is executed by a processor to implement any of the methods described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of a flow chart of a method for evaluating the health status of a wind power generation equipment provided in an embodiment of the present application is shown;

[0030] Figure 2 A schematic diagram of a technical route of an association rule model provided by an embodiment of the present application is shown;

[0031] Figure 3A and Figure 3B A schematic diagram of data before and after cleaning provided in an embodiment of the present application;

[0032] Figure 4A and Figure 4B Two probability density distribution diagrams provided for the embodiments of the present application;

[0033] Figure 5 A schematic diagram of frequent item changes in real-time operation data provided by an embodiment of the present application;

[0034] Figure 6 A structural schematic diagram of an electronic device provided in an embodiment of the present application is shown;

[0035] Figure 7 A structural schematic diagram of another electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0037] The terms "first", "second", "third", "fourth" and various other terminology labels (if any) in the specification and claims of the present application 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 data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "plurality" or "multiple" means two or more than two.

[0039] The specific operation method in the method embodiment of the present application can also be applied to the device embodiment or the system embodiment.

[0040] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0041] In the various embodiments of the present application, unless otherwise specified or provided for in any logical conflict, the terms and / or descriptions between the different embodiments are consistent and may be referenced to each other, and the technical features in the different embodiments may be combined to form new embodiments according to their inherent logical relationships.

[0042] It is understood that the various numbers involved in this application are only used for the convenience of description and are not used to limit the scope of this application. The order of execution of each process should be determined by its function and internal logic.

[0043] As described in the background technology section, in the wind power generation technology scenario, efficient and reliable health status diagnosis is the key to optimizing operation and maintenance and ensuring energy output. Traditional health status detection methods are limited by the inefficiency of manual detection and the subsequent discovery of hidden faults, which will affect the overall efficiency. In view of this, the present application provides a method for evaluating the health status of wind power generation equipment based on association rules. In this method, the equipment status evaluation model based on association rules is based on the structural characteristics, environmental conditions and related historical data of the unit, and determines the frequent item reference value of the wind power generation equipment in the health state through the association rule algorithm, and then evaluates the real-time health status of the wind power generation equipment based on the reference value. Through this scheme, the health status of wind power generation equipment can be evaluated in real time and accurately, providing guarantee for the efficient operation of the equipment. The following is combined with Figure 1 This method is described in detail.

[0044] S110: Acquire the number of real-time frequent items of the real-time operation data of the wind power generation equipment.

[0045] For example, a wind power generation equipment is usually composed of a tower, a nacelle frame, a hub, blades, etc. The nacelle frame is equipped with a main shaft, a coupling, a speed increaser, a yaw bearing, a yaw control system, a main control system, a double-fed motor, etc., while the direct drive type does not have a speed increaser. The hub is equipped with a pitch controller, a pitch motor, a pitch bearing, etc. In order to achieve the corresponding functions, various types of sensors are usually arranged on the key components of the wind power generation equipment. These sensors are used to collect various types of signals when the wind power generation equipment is working. However, due to various factors such as maintenance shutdown and signal disturbance, a considerable part of the signals collected by the sensors are invalid data, which needs to be ignored in predicting faults.

[0046] Based on this, the solution provided in the embodiment of the present application evaluates the health status of wind power generation equipment through association rules. In order to facilitate understanding of the solution in the present application, the definitions of some terms involved in the association rule design of the present application are first exemplified:

[0047] Definition 1: Let the data set D = {w1,w2,w3,…w n}, where w k ={e1,e2,e3,…e p},w k It is called a transaction, m It is called a project.

[0048] Definition 2: Let E = {e1, e2, e3, …e m} is the set of all items in D. Any subset A of E is called the item set in D. Let w k and A are transactions and itemsets in D respectively, if X∈wk , called transaction w k Contains item set A. For the convenience of calculation, each transaction is assigned a unique identifier, recorded as ID.

[0049] Definition 3: The number of times item set A appears in data set D is called the support number of item set A, denoted by σ a The support of item set A is denoted as support(A).

[0050] Where |D| is the total number of transactions in the data set D. If the support support(A) of the item set A is greater than or equal to the minimum support (minsupport), and the minimum support is set by the user, it is called a frequent set.

[0051] Definition 4: If support(A=>B)≥minsupport, and confidence(A=>B)≥minconfidence, then the association rule A=>B is called a strong rule, otherwise it is called a weak rule. The purpose of mining association rules is to mine all strong rules in the data set D. The item set (AUB) used to generate the strong rule A=>B must be a frequent set. The confidence of the association rule A=>B generated by the frequent set (AUB) can be calculated through the support of the frequent set A and (AUB).

[0052] Based on the above definition, the process of mining association rules can be summarized into the following two steps:

[0053] Step 1: Mining all frequent sets in the data set D according to the minimum support set by the user. The specific relationship can be expressed by the following formula:

[0054]

[0055] Within the range of the mined frequency set, association rules are generated based on the minimum confidence level. The specific relationship can be expressed by the following formula:

[0056]

[0057] In the solution provided in the embodiment of the present application, real-time operating data of the wind power generation equipment is obtained. The real-time operating data here may refer to the real-time operating parameters of key components of the wind power generation equipment, such as yaw bearing temperature, coupling vibration, etc.

[0058] In a possible implementation, after acquiring the real-time operation data, a second preprocessing operation is performed on the real-time operation data to obtain second processed data, and the second preprocessing operation includes data smoothing processing, second cleaning processing, and second discretization processing. After acquiring the second processed data, frequent item mining is performed on the second processed data to obtain the number of real-time frequent items. The specific frequent item mining process can refer to the above definition of association rules, which will not be described in detail here.

[0059] S120: Evaluate the health status of the wind power generation equipment according to the real-time frequent item number and the frequent item reference value.

[0060] Illustratively, in an embodiment of the present application, a fault diagnosis model based on association rule mining is established to address the situation where historical measurement point data of wind power generation equipment is unbalanced. That is, association rules between various data are mined from the historical operation data of the wind power generation equipment to generate association rules. Then, based on the association rules and the number of real-time frequent items mentioned above, the health status of the wind power generation equipment is evaluated.

[0061] It is understandable that the frequent item sets and association rule base formed by the historical operation data in the embodiment of the present application can also be updated in real time, so as to continuously improve the reliability and accuracy of the model.

[0062] Combine the following Figure 2 The generation and application process of a model architecture is illustrated.

[0063] The original data set is the input variable of the model, mainly the operating parameters such as yaw bearing temperature and coupling vibration. When building the model, the historical operating data is used as the input variable, and the historical operating data corresponds to the health status of the wind power generation equipment. Since the acquired parameters are not continuous values, in order to ensure the lightness and effectiveness of the model, in the data preprocessing stage, the input data is cleaned and discretized according to the equipment maintenance log.

[0064] For example, the historical operation data is obtained, and then the historical operation data is subjected to a first preprocessing operation to obtain first processed data, wherein the first preprocessing operation includes a first cleaning operation and a first discretization operation. After the first processed data is obtained, frequent item mining is performed on the first processed data to obtain the number of historical frequent items.

[0065] It can be understood that the above model building process is performed before step S110, and the second discretization process should be determined based on the first discretization process.

[0066] Assumptions Figure 3A The data shown is the original historical operation data of a device (such as the vibration of a coupling in a certain direction). After data cleaning, the data obtained is as follows: Figure 3B shown.

[0067] The first processed data is data obtained by performing discrete processing on the cleaned data, so the first processed data may be a discrete table composed of discrete data, which may be specifically shown in Table 1.

[0068] Table 1

[0069] 0 1 2 3 4 5 6 7 8 9 0 a7 b7 c5 d2 e2 f3 g2 h4 i5 j4 1 a7 b7 c5 d2 e2 f3 g2 h4 i5 j4 2 a7 b7 c5 d2 e2 f3 g2 h4 i5 j4 3 a6 b7 c5 d2 e2 f3 g2 h4 i5 j4 4 a8 b8 c5 d2 e2 f3 g2 h4 i5 j4 5 a7 b7 c5 d2 e2 f3 g2 h4 i5 j4 6 a3 b4 c5 d2 e2 f3 g2 h4 i5 j4 7 a3 b4 c5 d2 e2 f3 g2 h4 i5 j4 8 a7 b7 c5 d2 e2 f3 g2 h4 i5 j4 9 a5 b5 c5 d2 e2 f3 g2 h4 i5 j4

[0070] Furthermore, according to the discrete table of historical operation data of wind power generation equipment in healthy state, the total frequent item set of equipment normal state is mined, and the reference value of the frequent item of normal state is determined according to the historical frequent item change trend of different equipment.

[0071] After the real-time frequent item number is obtained, the health state of the wind power generation equipment is evaluated according to the real-time frequent item number and a frequent item reference value, where the frequent item reference value is determined based on the number of historical frequent items of historical operating data of the wind power generation equipment in a healthy state, and the data type of the historical operating data corresponds to the data type of the real-time operating data.

[0072] The following is an introduction to a method for assessing health status.

[0073] A probability density function of the historical operation data is established according to the frequent item reference value, and a health state of the wind power generation equipment is evaluated according to the distribution of the number of real-time frequent items in the probability density function.

[0074] In a possible implementation, the probability density function is divided into a plurality of parts, each part corresponds to a different level of the health status of the wind power generation equipment, and the level of the health status of the wind power generation equipment includes at least the following three levels: normal state, abnormal state, and alarm state. The health status of the wind power generation equipment is evaluated according to the part where the number of real-time frequent items is located in the probability density function.

[0075] That is to say, in the scheme provided by the embodiment of the present application, the current operating data of the unit is first obtained to obtain the real-time values ​​of state parameters such as vibration, and the real-time values ​​are discretized according to the historical data discrete table, and then frequent items are mined, and the health status of the equipment is analyzed and judged based on the comparison of the number of real-time frequent items with the normal state reference value. Then, using the historical data of parameters such as vibration temperature, separate state probability density functions are established for them respectively, and then the health degree of the function distribution is divided to achieve quantification, and finally the current health degree of the parameter is calculated based on the distribution of its real-time value.

[0076] The above solution is explained below with an example: the association rule algorithm is used to mine the discretization results of historical data to obtain the total frequent item set of historical health status (normal). For each measurement point parameter, a separate state probability density function is established based on historical data, such as Figure 4A and 4B Shown are the historical data state probability density distribution diagrams of the yaw bearing temperature and the coupling X-axis vibration respectively.

[0077] Assumptions Figure 5 The frequent item change trend diagram of some data of real-time operation data of wind power generation equipment is given, where the ordinate is the number of frequent items, the gray curve is the real-time frequent item value, and the black curve is the smoothed result. The results are evaluated according to the number of real-time frequent items, and are divided into three levels in total (0: normal, 1: abnormal, 2: alarm).

[0078] In summary, the equipment status assessment model based on association rules provided by this application is based on the structural characteristics, environmental conditions and related historical data of the unit, and establishes an assessment model for the health status of the equipment through an association rule algorithm; then, the real-time health status of the equipment is evaluated by comparing the current status parameters of the equipment with the historical health status parameters. This model can not only reflect the overall operating status of the equipment, but also provide effective real-time monitoring of the status of key parameters and related components, providing a certain reference basis for the operation, maintenance and transformation of on-site equipment.

[0079] Figure 6 600 according to an embodiment of the present application. For the sake of convenience, only the parts related to the embodiment of the present application are shown. Figure 6 , the device may specifically include the following modules:

[0080] A mining module 610, configured to obtain the number of real-time frequent items of the real-time operation data of the wind power generation equipment;

[0081] An evaluation module 620 is used to evaluate the health status of the wind power generation equipment according to the number of real-time frequent items and a frequent item reference value, wherein the frequent item reference value is determined based on the number of historical frequent items of historical operating data of the wind power generation equipment in a healthy state, and the data type of the historical operating data corresponds to the data type of the real-time operating data.

[0082] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method and system embodiment section and will not be repeated here.

[0083] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0084] like Figure 7 As shown, an embodiment of the present application also provides a device 700, which includes: at least one processor 710, a memory 720, and a computer program 521 stored in the memory and executable on at least one processor, and when the processor executes the computer program, the steps in any of the above-mentioned method embodiments are implemented.

[0085] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0086] An embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, a mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0087] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0088] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0090] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for evaluating the health status of a wind power generation equipment, characterized in that: include: Acquire the number of real-time frequent items of the real-time operation data of the wind power generation equipment; The health status of the wind power generation equipment is evaluated according to the number of real-time frequent items and a frequent item reference value, wherein the frequent item reference value is determined based on the number of historical frequent items of historical operating data of the wind power generation equipment in a healthy state, and a data type of the historical operating data corresponds to a data type of the real-time operating data.

2. The method according to claim 1, characterized in that The step of evaluating the health status of the wind power generation equipment according to the real-time frequent item number and the frequent item reference value includes: Establishing a probability density function of the historical operation data according to the frequent item reference value; The health status of the wind power generation equipment is evaluated according to the distribution of the number of real-time frequent items in the probability density function.

3. The method according to claim 2, characterized in that The step of evaluating the health status of the wind power generation equipment according to the distribution of the number of real-time frequent items in the probability density function comprises: Dividing the probability density function into a plurality of parts, each part corresponding to a different level of the health status of the wind power generation equipment; The health status of the wind power generation equipment is evaluated according to the part where the number of real-time frequent items is located in the probability density function.

4. The method according to claim 3, characterized in that: The health status levels of the wind power generation equipment include at least the following three levels: normal state, abnormal state, and alarm state.

5. The method according to any one of claims 1 to 4, characterized in that Before acquiring the number of real-time frequent items of the real-time operation data of the wind power generation equipment, the method further includes: Acquiring the historical operation data; Performing a first preprocessing operation on the historical operation data to obtain first processed data, wherein the first preprocessing operation includes a first cleaning process and a first discretization process; Frequent item mining is performed on the first processed data to obtain the number of historical frequent items.

6. The method according to claim 5, characterized in that The step of obtaining the number of real-time frequent items of the real-time operation data of the wind power generation equipment includes: Acquiring the real-time operation data; Performing a second preprocessing operation on the real-time operation data to obtain second processed data, wherein the second preprocessing operation includes data smoothing processing, a second cleaning processing, and a second discretization processing, and the second discretization processing is determined based on the first discretization processing; Frequent item mining is performed on the second processed data to obtain the number of real-time frequent items.

7. The method according to any one of claims 1 to 4, characterized in that The real-time operation data includes one or more of the following: yaw bearing temperature, coupling vibration.

8. A wind power equipment health status assessment device, characterized in that: include: A mining module, used for obtaining the number of real-time frequent items of the real-time operation data of the wind power generation equipment; an evaluation module, configured to evaluate the health status of the wind power generation equipment according to the number of real-time frequent items and a frequent item reference value, wherein the frequent item reference value is determined based on the number of historical frequent items of historical operating data of the wind power generation equipment in a healthy state, and a data type of the historical operating data corresponds to a data type of the real-time operating data.

9. A wind power equipment health status assessment device, characterized in that: include: A processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method steps according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the speed measurement method according to any one of claims 1 to 7 are implemented.

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