A method and system for intelligently controlling generator power generation

Abstract

The present application provides a method and system for intelligently controlling the power generation of a generator. When processing the equipment operation monitoring data, the characteristics of the reference equipment operation monitoring data to be processed and the processed sample equipment operation monitoring data in several operation states are obtained, and the characteristics of the reference equipment operation monitoring data and the sample equipment operation monitoring data in several operation states are integrated and processed to obtain the splicing characteristics. Based on the splicing characteristics, it is determined whether the reference equipment operation monitoring data is the same or similar to the sample equipment operation monitoring data, so as to process the reference equipment operation monitoring data. By integrating the characteristics of different operation states, the interference of the characteristics of different operation states on the processing results can be fully considered, so that the operation status of the equipment can be accurately determined, and the generator can be accurately controlled according to the operation status of the equipment. In this way, the working status of the equipment can be effectively guaranteed and the work efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for intelligently controlling power generation of a generator. Background Art

[0002] With the continuous development of society, a large number of devices need to work 24 hours a day to meet the needs of people or society. In this way, it is necessary to continuously monitor the equipment to avoid the equipment stopping working due to power outages. At present, if the power generation is cut off, the equipment still cannot continue to work. Therefore, a technical solution is urgently needed to solve the above-mentioned timely problem. Summary of the invention

[0003] In order to improve the technical problems existing in the related technologies, the present application provides a method and system for intelligently controlling the power generation of a generator.

[0004] In a first aspect, a method for intelligently controlling power generation of a generator is provided, the method at least comprising: obtaining characteristics of reference device operation monitoring data to be processed and processed sample device operation monitoring data in several operation states one by one; wherein the characteristics of the several operation states include at least: a first device state and a second device state, wherein the first device state is used to indicate a stop state of a device in the device operation monitoring data, and the second device state is used to indicate an insufficient energy consumption state of a device in the device operation monitoring data; integrating the characteristics of the reference device operation monitoring data in several operation states and the characteristics of the sample device operation monitoring data in several operation states to obtain a splicing feature characterizing a matching degree vector of a device in the reference device operation monitoring data and a device in the sample device operation monitoring data; determining whether the reference device operation monitoring data is identical or similar to the sample device operation monitoring data in combination with the splicing feature, wherein identical or similar device operation monitoring data is device operation monitoring data of the same device; if the reference device operation monitoring data is identical or similar to the sample device operation monitoring data, the generator is intelligently controlled to perform power generation operation.

[0005] In an independently implemented embodiment, the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states are integrated to obtain a splicing feature that characterizes the matching vector of the devices in the reference device operation monitoring data and the devices in the sample device operation monitoring data, including: for any of the several operation states, combining the features of the reference device operation monitoring data in the operation state and the features of the sample device operation monitoring data in the operation state to determine the matching feature of the operation state, the matching feature being used to represent the matching degree between the features of the reference device operation monitoring data in the operation state and the features of the sample device operation monitoring data in the operation state; integrating the matching features of the several operation states to determine the splicing feature.

[0006] In an independently implemented embodiment, the integration of the matching features of several operating states to determine the splicing features includes: integrating the matching features of several operating states to obtain an integration result; and identifying key content of the integration result to determine the splicing features.

[0007] In an independently implemented embodiment, the features of the reference device operation monitoring data in several operating states and the features of the sample device operation monitoring data in several operating states are integrated to obtain a spliced ​​feature that characterizes the matching degree vector of the devices in the reference device operation monitoring data and the devices in the sample device operation monitoring data, including: integrating the features of the reference device operation monitoring data in several operating states to obtain a first integrated feature; integrating the features of the sample device operation monitoring data in several operating states to obtain a second integrated feature; and combining the first integrated feature with the second integrated feature to determine a feature used to represent the matching degree between the first integrated feature and the second integrated feature as the spliced ​​feature.

[0008] In an independently implemented embodiment, before the features of the reference device operation monitoring data in several operating states and the features of the sample device operation monitoring data in several operating states are integrated to obtain the splicing features that characterize the matching degree vectors of the devices in the reference device operation monitoring data and the sample device operation monitoring data, the method further includes: loading the features of the reference device operation monitoring data in several operating states and the features of the sample device operation monitoring data in several operating states into the same feature area one by one and simplifying them; the integrating the features of the reference device operation monitoring data in several operating states and the features of the sample device operation monitoring data in several operating states to determine the splicing features includes: integrating the features of the reference device operation monitoring data in several operating states after simplification and the features of the sample device operation monitoring data in several operating states after simplification to determine the splicing features.

[0009] In an independently implemented embodiment, the features of the reference device operation monitoring data in several operating states and the features of the sample device operation monitoring data in several operating states are integrated to obtain splicing features that characterize the device matching vectors of the reference device operation monitoring data and the sample device operation monitoring data, and the step of determining whether the reference device operation monitoring data is the same or similar to the sample device operation monitoring data based on the splicing features is executed by a previously configured artificial intelligence thread.

[0010] In an independently implemented embodiment, the artificial intelligence thread is configured based on the following method: obtaining a first example monitoring data pair and a second example monitoring data pair, the matching degree of the first state of the device of the two device operation monitoring data in the first example monitoring data pair and the second example monitoring data pair both exceeds a specified matching degree reference value, the devices in the two device operation monitoring data of the first example monitoring data pair are the same device, and the devices in the two device operation monitoring data of the second example monitoring data pair are different devices; configuring the original thread specified by the first example monitoring data pair and the second example monitoring data pair to determine the artificial intelligence thread.

[0011] In an independently implemented embodiment, the sample device operation monitoring data is determined based on the following method: determining the degree of match between the first device state of the reference device operation monitoring data and the first device state of various processed device operation monitoring data; and taking the first X types of device operation monitoring data with the largest match as the sample device operation monitoring data, where X is an integer greater than or equal to 1.

[0012] In an independently implemented embodiment, the combining of the splicing features to determine whether the reference device operation monitoring data is the same or similar to the sample device operation monitoring data includes: for each type of sample device operation monitoring data, combining the splicing features one by one to determine the confidence that the reference device operation monitoring data is the same or similar to each type of sample device operation monitoring data; on the basis that the best value in the confidence exceeds a specified confidence, determining that the reference device operation monitoring data is the same or similar to the sample device operation monitoring data corresponding to the best value; or, on the basis that the best value in the confidence is less than the specified confidence, configuring a new quantization result and adding the reference device operation monitoring data to the new quantization result.

[0013] In a second aspect, a system for intelligently controlling power generation of a generator is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the above method.

[0014] The method and system for intelligently controlling the power generation of a generator provided in the embodiment of the present application can obtain the characteristics of the reference device operation monitoring data to be processed and the processed sample device operation monitoring data in several operation states, such as the first state of the device and the second state of the device, and then integrate the characteristics of the reference device operation monitoring data and the sample device operation monitoring data in several operation states to obtain the splicing characteristics of the matching degree vectors representing the devices in the reference device operation monitoring data and the devices in the sample device operation monitoring data, and then further determine whether the reference device operation monitoring data is the same or similar to the sample device operation monitoring data based on the splicing characteristics, so as to process the reference device operation monitoring data. By integrating the characteristics of different operation states, the interference of the characteristics of different operation states on the processing results can be fully considered, so that the operation status of the equipment can be accurately determined, and the generator can be accurately controlled according to the operation status of the equipment. In this way, the working status of the equipment can be effectively guaranteed and the work efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a method for intelligently controlling generator power generation provided in an embodiment of the present application.

[0017] Figure 2 A block diagram of a device for intelligently controlling generator power generation provided in an embodiment of the present application.

[0018] Figure 3 An architectural diagram of a system for intelligently controlling generator power generation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0020] See also Figure 1 , shows a method for intelligently controlling generator power generation, which may include the technical solutions described in the following steps 100 to 300.

[0021] Step 100, obtaining the characteristics of the reference device operation monitoring data to be processed and the processed sample device operation monitoring data in several operation states one by one; wherein the characteristics of the several operation states include at least: a first state of the device and a second state of the device, the first state of the device is used to indicate the stop state of the device in the device operation monitoring data, and the second state of the device is used to indicate the insufficient energy consumption state of the device in the device operation monitoring data.

[0022] Step 200, integrating the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states, to obtain a splicing feature characterizing the matching degree vector of the device in the reference device operation monitoring data and the device in the sample device operation monitoring data.

[0023] Step 300 determines whether the reference device operation monitoring data is identical or similar to the sample device operation monitoring data in combination with the splicing features, wherein the identical or similar device operation monitoring data is device operation monitoring data of the same device; if the reference device operation monitoring data is identical or similar to the sample device operation monitoring data, the generator is intelligently controlled to perform power generation operations.

[0024] It can be understood that when executing the contents described in the above steps 100 to 300, when processing the equipment operation monitoring data, the characteristics of the reference equipment operation monitoring data to be processed and the processed sample equipment operation monitoring data in several operation states can be obtained, such as the first state of the equipment and the second state of the equipment, and then the characteristics of the reference equipment operation monitoring data and the sample equipment operation monitoring data in several operation states are integrated and processed to obtain the splicing characteristics of the matching degree vectors characterizing the equipment in the reference equipment operation monitoring data and the equipment in the sample equipment operation monitoring data, and then further determine whether the reference equipment operation monitoring data is the same or similar to the sample equipment operation monitoring data based on the splicing characteristics, so as to process the reference equipment operation monitoring data. By integrating the characteristics of different operation states, the interference of the characteristics of different operation states on the processing results can be fully considered, so that the operation status of the equipment can be accurately determined, and the generator can be accurately controlled according to the operation status of the equipment, so that the working status of the equipment can be effectively guaranteed and the work efficiency can be improved.

[0025] In a possible implementation, the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states are integrated to obtain a splicing feature that characterizes the matching vector of the devices in the reference device operation monitoring data and the devices in the sample device operation monitoring data, including: for any of the several operation states, combining the features of the reference device operation monitoring data in the operation state and the features of the sample device operation monitoring data in the operation state to determine the matching feature of the operation state, the matching feature being used to represent the matching degree between the features of the reference device operation monitoring data in the operation state and the features of the sample device operation monitoring data in the operation state; integrating the matching features of the several operation states to determine the splicing feature.

[0026] In a possible implementation, the integrating the matching features of several operating states to determine the splicing features includes: integrating the matching features of several operating states to obtain an integration result; and identifying key content of the integration result to determine the splicing features.

[0027] In a possible implementation, the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states are integrated to obtain a spliced ​​feature that characterizes a matching degree vector of devices in the reference device operation monitoring data and devices in the sample device operation monitoring data, including: integrating the features of the reference device operation monitoring data in several operation states to obtain a first integrated feature; integrating the features of the sample device operation monitoring data in several operation states to obtain a second integrated feature; and combining the first integrated feature with the second integrated feature to determine a feature used to represent the matching degree between the first integrated feature and the second integrated feature as the spliced ​​feature.

[0028] In a possible implementation example, before the integration processing of the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states to obtain the splicing features characterizing the matching degree vectors of the devices in the reference device operation monitoring data and the sample device operation monitoring data, it also includes: loading the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states into the same feature area one by one and simplifying them; the integration processing of the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states to determine the splicing features includes: integrating the features of the reference device operation monitoring data in several operation states after simplification processing and the features of the sample device operation monitoring data in several operation states after simplification processing to determine the splicing features.

[0029] In one possible implementation, the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states are integrated to obtain a splicing feature that characterizes a matching degree vector of a device in the reference device operation monitoring data and a device in the sample device operation monitoring data, and the step of determining whether the reference device operation monitoring data is identical or similar to the sample device operation monitoring data in combination with the splicing feature is performed by a previously configured artificial intelligence thread.

[0030] In one possible implementation embodiment, the artificial intelligence thread is configured based on the following method: obtaining a first example monitoring data pair and a second example monitoring data pair, wherein the matching degree of the first state of the device in the two device operation monitoring data in the first example monitoring data pair and the second example monitoring data pair exceeds a specified matching degree reference value, the devices in the two device operation monitoring data in the first example monitoring data pair are the same device, and the devices in the two device operation monitoring data in the second example monitoring data pair are different devices; configuring the original thread specified by the first example monitoring data pair and the second example monitoring data pair to determine the artificial intelligence thread.

[0031] In one possible implementation, the sample device operation monitoring data is determined based on the following method: determining the degree of match between the first device state of the reference device operation monitoring data and the first device state of various processed device operation monitoring data; and taking the first X types of device operation monitoring data with the largest match as the sample device operation monitoring data, where X is an integer greater than or equal to 1.

[0032] In a possible implementation, the determining whether the reference device operation monitoring data is identical or similar to the sample device operation monitoring data in combination with the splicing feature includes: for each type of sample device operation monitoring data, determining the confidence level that the reference device operation monitoring data is identical or similar to each type of sample device operation monitoring data in combination with the splicing feature one by one; determining, on the basis that the best value in the confidence level exceeds a specified confidence level, that the reference device operation monitoring data is identical or similar to the sample device operation monitoring data corresponding to the best value; or, on the basis that the best value in the confidence level is less than the specified confidence level, configuring a new quantization result and adding the reference device operation monitoring data to the new quantization result.

[0033] Based on the above, please refer to Figure 2 , a device 200 for intelligently controlling the power generation of a generator is provided, which is applied to a system for intelligently controlling the power generation of a generator, and the device comprises:

[0034] The feature acquisition module 210 is used to obtain the features of the reference device operation monitoring data to be processed and the processed sample device operation monitoring data in a plurality of operation states one by one; wherein the features of the plurality of operation states at least include: a first device state and a second device state, wherein the first device state is used to indicate the stop state of the device in the device operation monitoring data, and the second device state is used to indicate the insufficient energy consumption state of the device in the device operation monitoring data;

[0035] A feature splicing module 220 is used to integrate the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states to obtain a splicing feature representing a matching degree vector of a device in the reference device operation monitoring data and a device in the sample device operation monitoring data;

[0036] The power generation trigger module 230 is used to determine whether the reference device operation monitoring data is the same or similar to the sample device operation monitoring data in combination with the splicing features, wherein the same or similar device operation monitoring data is the device operation monitoring data of the same device; if the reference device operation monitoring data is the same or similar to the sample device operation monitoring data, the generator is intelligently controlled to perform power generation operations.

[0037] Based on the above, please refer to Figure 3 , shows a system 300 for intelligently controlling power generation of a generator, including a processor 310 and a memory 320 that communicate with each other, and the processor 310 is used to read and execute a computer program from the memory 320 to implement the above method.

[0038] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0039] In summary, based on the above scheme, when processing the equipment operation monitoring data, the characteristics of the reference equipment operation monitoring data to be processed and the processed sample equipment operation monitoring data in several operating states can be obtained, such as the first state of the equipment and the second state of the equipment, and then the characteristics of the reference equipment operation monitoring data and the sample equipment operation monitoring data in several operating states are integrated and processed to obtain the splicing characteristics of the matching degree vectors of the equipment in the reference equipment operation monitoring data and the equipment in the sample equipment operation monitoring data, and then further determine whether the reference equipment operation monitoring data is the same or similar to the sample equipment operation monitoring data based on the splicing characteristics, so as to process the reference equipment operation monitoring data. By integrating the characteristics of different operating states, the interference of the characteristics of different operating states on the processing results can be fully considered, so that the operating status of the equipment can be accurately determined, and the generator can be accurately controlled according to the operating status of the equipment. In this way, the working status of the equipment can be effectively guaranteed and the work efficiency can be improved.

[0040] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0041] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other beneficial effects that may be obtained.

[0042] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0043] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0044] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0045] A computer storage medium may include a propagated data signal containing computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0046] The computer program codes required for the operation of each part of the present application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0047] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0048] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0049] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0050] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0051] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, as an example and not a limitation, the alternative configurations of the embodiments of the present application may be considered to be consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0052] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for intelligently controlling the power generation of a generator, It is characterized in that The method at least comprises: Obtaining characteristics of the reference device operation monitoring data to be processed and the processed sample device operation monitoring data in a plurality of operation states one by one; wherein the characteristics of the plurality of operation states at least include: a first device state and a second device state, wherein the first device state is used to indicate a stopped state of the device in the device operation monitoring data, and the second device state is used to indicate an insufficient energy consumption state of the device in the device operation monitoring data; Integrate the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states to obtain a splicing feature representing a matching degree vector of a device in the reference device operation monitoring data and a device in the sample device operation monitoring data; Determine whether the reference device operation monitoring data is identical or similar to the sample device operation monitoring data in combination with the splicing feature, wherein the identical or similar device operation monitoring data is device operation monitoring data of the same device; if the reference device operation monitoring data is identical or similar to the sample device operation monitoring data, intelligently control the generator to perform power generation operation; The integration of the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states to obtain the splicing features representing the matching degree vectors of the devices in the reference device operation monitoring data and the devices in the sample device operation monitoring data includes: Integrate the features of the reference equipment operation monitoring data in a plurality of operation states to obtain a first integrated feature; Integrate the features of the sample equipment operation monitoring data in a plurality of operation states to obtain a second integrated feature; Determine, by combining the first integration feature and the second integration feature, a feature used to indicate a matching degree between the first integration feature and the second integration feature as the splicing feature; Wherein, before the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states are integrated and processed to obtain the splicing features characterizing the matching degree vectors of the devices in the reference device operation monitoring data and the devices in the sample device operation monitoring data, the method further includes: loading the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states into the same feature area one by one and performing simplification processing; The integrating and processing the characteristics of the reference device operation monitoring data in several operation states and the characteristics of the sample device operation monitoring data in several operation states to determine the splicing characteristics includes: integrating and processing the characteristics of the reference device operation monitoring data in several operation states after simplification processing and the characteristics of the sample device operation monitoring data in several operation states after simplification processing to determine the splicing characteristics.

2. The method according to claim 1, It is characterized in that The integrating process of the features of the reference device operation monitoring data in a plurality of operation states and the features of the sample device operation monitoring data in a plurality of operation states to obtain a splicing feature characterizing a matching degree vector of a device in the reference device operation monitoring data and a device in the sample device operation monitoring data includes: For any operating state of the plurality of operating states, determining a matching feature of the operating state by combining the features of the reference device operating monitoring data in the operating state and the features of the sample device operating monitoring data in the operating state, wherein the matching feature is used to indicate a matching degree between the features of the reference device operating monitoring data in the operating state and the features of the sample device operating monitoring data in the operating state; The matching degree features of the plurality of operating states are integrated to determine the splicing feature.

3. The method according to claim 2, It is characterized in that The integrating and processing the matching degree features of the plurality of operating states to determine the splicing features includes: Integrating the matching degree features of several operating states to obtain an integration result; Key content is identified on the integration result to determine the splicing features.

4. The method according to claim 1, It is characterized in that The steps of integrating the features of the reference device operation monitoring data in several operation states and the features of the sample device operation monitoring data in several operation states to obtain the splicing features that characterize the device matching vectors of the reference device operation monitoring data and the sample device operation monitoring data, and determining whether the reference device operation monitoring data is the same or similar to the sample device operation monitoring data based on the splicing features are executed by the previously configured artificial intelligence thread.

5. The method according to claim 4, It is characterized in that The artificial intelligence thread is configured based on the following method: Obtaining a first example monitoring data pair and a second example monitoring data pair, wherein the matching degrees of the first state of the device of the two types of device operation monitoring data in the first example monitoring data pair and the second example monitoring data pair both exceed a specified matching degree reference value, the devices in the two types of device operation monitoring data in the first example monitoring data pair are the same device, and the devices in the two types of device operation monitoring data in the second example monitoring data pair are different devices; The artificial intelligence thread is determined by configuring the original thread specified by the first example monitoring data pair and the second example monitoring data pair.

6. The method according to claim 5, It is characterized in that The sample equipment operation monitoring data is determined based on the following method: Determine the matching degree between the first device state of the reference device operation monitoring data and the first device state of the processed device operation monitoring data of various devices; The device operation monitoring data of the first X types of devices with the largest matching degree are used as the sample device operation monitoring data, where X is an integer greater than or equal to 1.

7. The method according to claim 6, It is characterized in that The determining whether the reference device operation monitoring data is identical or similar to the sample device operation monitoring data in combination with the splicing feature includes: For each type of sample device operation monitoring data, determine the confidence level that the reference device operation monitoring data is the same or similar to each type of sample device operation monitoring data by combining the splicing features one by one; On the basis that the best value in the confidence level exceeds a specified confidence level, determining that the reference device operation monitoring data is identical or similar to the sample device operation monitoring data corresponding to the best value; Alternatively, based on the best value in the confidence level being smaller than the specified confidence level, a new quantification result is configured, and the reference device operation monitoring data is added to the new quantification result.

8. A system for intelligently controlling the power generation of a generator, It is characterized in that The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 7.

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