CN112650528A - Personalized algorithm generation method and device, electronic equipment and computer readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses a personalized algorithm generation method, a personalized algorithm generation device, electronic equipment and a computer readable medium. One embodiment of the method comprises: classifying the equipment related information in the acquired equipment related information set to obtain an equipment related information class; adding label codes to the device related information in the device related information class; identifying the label code of each piece of equipment-related information in the equipment-related information class to obtain an identification result so as to form an identification result set; and generating a personalized algorithm of the relevant information of each device based on the identification result set. According to the embodiment, the machine is used for identifying the type of the equipment, the personalized algorithm suitable for the equipment is generated, the personalized algorithm is automatically matched with the equipment, and the labor cost is saved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a personalized algorithm generation method, a personalized algorithm generation device, electronic equipment and a computer readable medium.
Background
At present, a specific personalized algorithm written by an expert team is suitable for specific instantiated equipment and configuration of a project, the specific instantiated equipment and configuration are identified and allocated manually, and before allocation, the expert and technical support team is required to consult a front end/sell specific equipment types and configurations one by one, so that time and labor are wasted. Thus, there is a need for a method that can match algorithms for devices.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose personalized algorithm generation methods, apparatuses, electronic devices, and computer readable media to solve the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a method for generating a personalized algorithm, the method including: classifying the equipment related information in the acquired equipment related information set to obtain an equipment related information class; adding label codes to the device related information in the device related information class; identifying the label code of each piece of equipment-related information in the equipment-related information class to obtain an identification result so as to form an identification result set; and generating a personalized algorithm of the relevant information of each device based on the identification result set.
In a second aspect, some embodiments of the present disclosure provide a personalization algorithm generation apparatus, comprising: the classification unit is configured to classify the acquired device-related information in the device-related information set to obtain a device-related information class; an adding unit configured to add a tag code to the device-related information in the device-related information class; the identification unit is configured to identify the label code of each piece of equipment-related information in the equipment-related information class to obtain an identification result so as to form an identification result set;
a generating unit configured to generate a personalized algorithm of the information related to each device based on the recognition result set.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement the method as described in the first aspect.
In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in the first aspect.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: by classifying the acquired device-related information and adding label codes, the device-related information can be classified into categories and system structures. And then, generating a personalized algorithm of the relevant information of the equipment by identifying the label code of the relevant information of the equipment. The machine is used for identifying the type of the equipment, and the personalized algorithm suitable for the equipment is generated, so that the personalized algorithm is automatically matched with the equipment, and the labor cost is saved.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic illustration of one application scenario of a personalization algorithm generation method, in accordance with some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of a personalization algorithm generation method in accordance with the present disclosure;
FIG. 3 is a schematic block diagram of some embodiments of a personalization algorithm generation apparatus in accordance with the present disclosure;
FIG. 4 is a schematic block diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of one application scenario of a personalization algorithm generation method according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may classify the acquired device-related information in the device-related information set 102 to obtain a device-related information class 103. The computing device 101 may then tag the device-related information in the device-related information class 103, as indicated by reference numeral 104. Thereafter, the computing device 101 may identify the tag code of each piece of device-related information in the device-related information class 103 to obtain an identification result, so as to form an identification result set 105. Finally, the computing device 101 may generate a personalization algorithm 106 for each device-related information based on the set of recognition results 105.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to fig. 2, a flow 200 of some embodiments of a personalization algorithm generation method in accordance with the present disclosure is shown. The method may be performed by the computing device 101 of fig. 1. The personalized algorithm generation method comprises the following steps:
In some embodiments, the device-related information includes device name information, device subsystem configuration information, and device configuration information. An executing body (e.g., the computing device 101 shown in fig. 1) of the personalized algorithm generating method may classify the device-related information in the device-related information set based on the device subsystem configuration information to obtain a device-related information class.
As an example, for the water-cooled chiller, the subsystem configuration information may be "number of refrigeration circuits and compressors", and the execution main body may classify the water-cooled chiller according to the number of refrigeration circuits and compressors, and may be classified into the following categories, "single refrigeration circuit dual head", "dual refrigeration circuit dual head", and "single refrigeration circuit single head".
In some embodiments, the execution body may add tag codes to the device-related information in the device-related information class. As an example, the device-related information class of the device-related information may be a "single refrigeration circuit and single head" class, and the execution body may add a code "ScircuitScpr" to the device-related information.
Preferably, when the method is applied to the Internet of things delivery platform, strong association can be performed on the equipment corresponding to the equipment related information and the subsystem corresponding to the subsystem configuration information.
In some embodiments, the execution subject may read a tag code of each piece of device-related information in the device-related information class, and obtain a read result. The fir tree execution subject can then identify the read result. Here, the recognition may be a method of identifying a correspondence between the code and the classification of the device-related information. As an example, the execution body may read the tag code, resulting in the reading result "ScircuitScpr". The execution main body can identify the reading result and determine that the related information of the equipment belongs to the category of 'single refrigeration loop and single machine head'.
Preferably, the executing body can control the algorithm/application platform to read and identify the tag code.
And 204, generating a personalized algorithm of the relevant information of each device based on the identification result set.
In some embodiments, the executing entity may generate a personalized algorithm of the information related to each device based on the recognition result set by: firstly, the execution main body can extract the individual information of each identification result to obtain an individual information set; and secondly, the execution main body can generate a personalized algorithm of each piece of personalized information based on the personalized information set and a preset algorithm set. Here, the personal information may be an internet of things parameter different from other device-related information corresponding to the device-related information. The execution main body can obtain the technical requirements and characteristics corresponding to the personalized information, and a preset algorithm is called to generate the personalized algorithm.
In some optional implementations of some embodiments, the executing the subject method further includes: and adding an algorithm identifier for each personalized algorithm in the personalized algorithm set to obtain an added personalized algorithm set. As an example, the tag code of the device-related information may be "ScircuitScpr", and the executing entity may add an algorithm identifier "ScircuitScpr" to the personalized algorithm corresponding to the device-related information.
In some optional implementations of some embodiments, the executing the subject method further includes: storing the device related information in the device related information set to a target application platform; in response to determining that the adding is completed, storing the personalized algorithm in the added personalized algorithm set to the target application platform (e.g., SaaS platform); and controlling the target application platform to display the equipment-related information set and the added personalized algorithm set. Here, the display may be device name information, tag coding, and algorithm identification of a personalization algorithm that controls display of device-related information.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: by classifying the acquired device-related information and adding label codes, the device-related information can be classified into categories and system structures. And then, generating a personalized algorithm of the relevant information of the equipment by identifying the label code of the relevant information of the equipment. The machine is used for identifying the type of the equipment, and the personalized algorithm suitable for the equipment is generated, so that the personalized algorithm is automatically matched with the equipment, and the labor cost is saved. In addition, algorithm identification is added for the personalized algorithm, and convenience is provided for algorithm identification and algorithm utilization.
With further reference to fig. 3, as an implementation of the above method for the above figures, the present disclosure provides some embodiments of a personalized algorithm generating apparatus, which correspond to those of the method embodiments described above in fig. 2, and which may be applied in various electronic devices.
As shown in fig. 3, the personalization algorithm generation apparatus 300 of some embodiments includes: a classification unit 301, an addition unit 302, a recognition unit 303, and a generation unit 304. The classification unit 301 is configured to classify the device related information in the obtained device related information set to obtain a device related information class; an adding unit 302 configured to add a tag code to the device-related information in the device-related information class; an identifying unit 303 configured to identify the tag code of each piece of device-related information in the device-related information class to obtain an identifying result, so as to form an identifying result set; a generating unit 304 configured to generate a personalized algorithm of the information related to each device based on the recognition result set.
In some optional implementations of some embodiments, the device-related information includes device name information and device subsystem configuration information.
In some optional implementations of some embodiments, the classification unit 301 of the personalization algorithm generating apparatus 300 is further configured to: and classifying the equipment related information in the equipment related information set based on the equipment subsystem configuration information to obtain an equipment related information class.
In some optional implementations of some embodiments, the identifying unit 303 of the personalization algorithm generating device 300 is further configured to: reading the label code of each piece of equipment related information in the equipment related information class to obtain a reading result so as to form a reading result set; and identifying each reading result in the reading result set to obtain an identification result set.
In some optional implementations of some embodiments, the generation unit 304 of the personalization algorithm generation apparatus 300 is further configured to: extracting the individual information of each identification result in the identification result set to obtain an individual information set; and generating an individualized algorithm of each piece of individualized information in the individualized information set based on the individualized information set and a preset algorithm set to obtain an individualized algorithm set.
In some optional implementations of some embodiments, the personalization algorithm generating means 300 is further configured to: and adding an algorithm identifier for each personalized algorithm in the personalized algorithm set to obtain an added personalized algorithm set.
In some optional implementations of some embodiments, the personalization algorithm generating means 300 is further configured to: storing the device-related information in the device-related information set to a target application platform; in response to determining that the adding is complete, storing the personalized algorithms in the added set of personalized algorithms to the target application platform; and controlling the target application platform to display the equipment related information set and the added personalized algorithm set.
It will be understood that the units described in the apparatus 300 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 300 and the units included therein, and are not described herein again.
Referring now to FIG. 4, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1)400 suitable for use in implementing some embodiments of the present disclosure is shown. The server shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 4 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 409, or from the storage device 408, or from the ROM 402. The computer program, when executed by the processing apparatus 401, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: classifying the equipment related information in the acquired equipment related information set to obtain an equipment related information class; adding label codes to the device related information in the device related information class; identifying the label code of each piece of equipment-related information in the equipment-related information class to obtain an identification result so as to form an identification result set; and generating a personalized algorithm of the relevant information of each device based on the identification result set.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes a classification unit, an addition unit, a recognition unit, and a generation unit. The names of these units do not form a limitation on the units themselves in some cases, for example, the classification unit may also be described as a unit that classifies the device-related information in the acquired device-related information set to obtain the device-related information class.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A method for generating a personalized algorithm, comprising:
classifying the equipment related information in the acquired equipment related information set to obtain an equipment related information class;
adding label codes to the device related information in the device related information class;
identifying the label code of each piece of equipment-related information in the equipment-related information class to obtain an identification result so as to form an identification result set;
and generating a personalized algorithm of the relevant information of each device based on the identification result set.
2. The method of claim 1, wherein the device-related information comprises device name information and device subsystem configuration information.
3. The method according to claim 2, wherein the classifying the device-related information in the obtained device-related information set to obtain a device-related information class comprises:
and classifying the equipment related information in the equipment related information set based on the equipment subsystem configuration information to obtain an equipment related information class.
4. The method of claim 2, wherein the identifying the tag code of each piece of equipment-related information in the equipment-related information class to obtain an identification result to form an identification result set comprises:
reading the label code of each piece of equipment related information in the equipment related information class to obtain a reading result so as to form a reading result set;
and identifying each reading result in the reading result set to obtain an identification result set.
5. The method according to any one of claims 1 to 4, wherein the algorithm for generating the per-device related information based on the recognition result set comprises:
extracting the individual information of each identification result in the identification result set to obtain an individual information set;
and generating an individualized algorithm of each piece of individualized information in the individualized information set based on the individualized information set and a preset algorithm set to obtain an individualized algorithm set.
6. The method of claim 5, further comprising:
and adding an algorithm identifier for each personalized algorithm in the personalized algorithm set to obtain an added personalized algorithm set.
7. The method of claim 6, further comprising:
storing the device-related information in the device-related information set to a target application platform;
in response to determining that the adding is complete, storing the personalized algorithms in the added set of personalized algorithms to the target application platform;
and controlling the target application platform to display the equipment related information set and the added personalized algorithm set.
8. A personalization algorithm generation apparatus, comprising:
the classification unit is configured to classify the acquired device-related information in the device-related information set to obtain a device-related information class;
an adding unit configured to add a tag code to the device-related information in the device-related information class;
the identification unit is configured to identify the label code of each piece of equipment-related information in the equipment-related information class to obtain an identification result so as to form an identification result set;
a generating unit configured to generate a personalized algorithm of the information related to each device based on the recognition result set.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.
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