CN114693220B - Algorithm warehouse management method and system based on digital twin DaaS platform - Google Patents
Algorithm warehouse management method and system based on digital twin DaaS platform Download PDFInfo
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Abstract
The invention discloses an algorithm warehouse management method and system based on a digital twin DaaS platform, wherein the method comprises the following steps: when an algorithm generation instruction sent by target equipment is received, determining an algorithm type and functional parameters according to the algorithm generation instruction, and determining an algorithm set in an algorithm warehouse according to the algorithm type; generating a target algorithm according to the algorithm set and the function parameters through a digital twin DaaS platform, and sending the target algorithm to the target equipment; according to the invention, the corresponding algorithm is selected in the algorithm warehouse according to the algorithm type, and the target algorithm is generated according to the algorithm and the functional parameters, so that the algorithm development process does not need to be manually participated, and the algorithm development efficiency is further improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an algorithm warehouse management method and system based on a digital twin DaaS platform.
Background
At present, various intelligent devices are required to be used in various industries, and the various intelligent devices need different algorithms to process corresponding transactions, but the algorithms of the existing intelligent devices are developed manually, so that the algorithm development efficiency is low, and the cost of manpower and material resources is high. Therefore, how to improve the algorithm development efficiency and reduce the cost is an urgent problem to be solved.
Disclosure of Invention
The invention mainly aims to provide an algorithm warehouse management method and system based on a digital twin DaaS platform, and aims to solve the problems of improving algorithm development efficiency and reducing cost.
In order to achieve the above object, the present invention provides an algorithm warehouse management method based on a digital twin DaaS platform, which includes the following steps:
when an algorithm generation instruction sent by target equipment is received, determining an algorithm type and functional parameters according to the algorithm generation instruction, and determining an algorithm set in an algorithm warehouse according to the algorithm type;
and generating a target algorithm according to the algorithm set and the functional parameters through a digital twin DaaS platform, and sending the target algorithm to the target equipment.
Optionally, the step of determining an algorithm type and a functional parameter according to the algorithm generation instruction, and determining an algorithm set in an algorithm warehouse according to the algorithm type includes:
acquiring user product requirements in the algorithm generation instruction, and determining an algorithm type and functional parameters according to the user product requirements;
comparing the algorithm type with a type identifier of each algorithm in an algorithm warehouse so as to determine an algorithm set according to the algorithm with the same type identifier as the algorithm type.
Optionally, the step of generating, by the digital twin DaaS platform, a target algorithm according to the algorithm set and the functional parameters includes:
acquiring a function parameter identifier corresponding to each algorithm in the algorithm set through a digital twin DaaS platform, and calculating the similarity between the function parameter identifier corresponding to each algorithm and the function parameter;
comparing the similarity with a preset similarity threshold to obtain a comparison result;
if the comparison result is that an algorithm with the similarity larger than a preset similarity threshold exists in the algorithm set, generating a target algorithm according to the algorithm and the functional parameters;
if the comparison result is that the algorithm with the similarity larger than the preset similarity threshold value does not exist in the algorithm set, searching in an algorithm knowledge base connected with the digital twin DaaS platform through an artificial intelligence search engine according to the functional parameters to obtain an algorithm with the similarity between the functional parameter identification and the functional parameters larger than the preset similarity threshold value, and generating a target algorithm according to the algorithm and the functional parameters.
Optionally, after the step of sending the target algorithm to the target device, the method further comprises:
when an algorithm modification instruction sent by the target equipment is received, modifying the target algorithm according to the algorithm modification instruction;
and verifying the modified target algorithm, and sending the modified target algorithm passing the verification to the target equipment.
Optionally, after the step of sending the target algorithm to the target device, the method further includes:
and when an algorithm generation instruction sent by target equipment is not received within a preset time period, carrying out optimization operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform, and carrying out cleaning operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform.
Optionally, the step of performing optimization operation on the algorithm in the algorithm repository through the digital twin DaaS platform includes:
optimizing the algorithm stored in the algorithm warehouse through the digital twin DaaS platform to obtain an optimized algorithm;
and performing identification operation of algorithm types and functional parameters on the optimized algorithm, and storing the algorithm in the algorithm warehouse according to the algorithm types.
Optionally, the step of performing a cleaning operation on the algorithm in the algorithm repository through the digital twin DaaS platform includes:
obtaining the type identification and the function parameter identification of each algorithm in the algorithm warehouse through the digital twin DaaS platform, comparing the type identifications of each algorithm, and comparing the function parameter identifications of each algorithm to obtain an algorithm set with the same type identification and the same function parameter identification;
and performing simulation operation on each algorithm in the algorithm set with the same type identifier and the same functional parameter identifier, determining a performance index corresponding to each algorithm, and performing cleaning operation on the algorithms according to the performance indexes.
In addition, in order to achieve the above object, the present invention further provides an algorithmic warehouse management apparatus based on a digital twin DaaS platform, including:
the receiving module is used for determining an algorithm type and functional parameters according to an algorithm generating instruction when receiving the algorithm generating instruction sent by the target equipment, and determining an algorithm set in an algorithm warehouse according to the algorithm type;
and the generating module is used for generating a target algorithm according to the algorithm set and the functional parameters through a digital twin DaaS platform and sending the target algorithm to the target equipment.
Further, the receiving module further comprises a determining module, and the determining module is configured to:
acquiring user product requirements in the algorithm generation instruction, and determining an algorithm type and functional parameters according to the user product requirements;
comparing the algorithm type with a type identifier of each algorithm in an algorithm warehouse so as to determine an algorithm set according to the algorithm with the same type identifier as the algorithm type.
Further, the generation module is further configured to:
acquiring a function parameter identifier corresponding to each algorithm in the algorithm set through a digital twin DaaS platform, and calculating the similarity between the function parameter identifier corresponding to each algorithm and the function parameter;
comparing the similarity with a preset similarity threshold to obtain a comparison result;
if the comparison result is that an algorithm with the similarity larger than a preset similarity threshold exists in the algorithm set, generating a target algorithm according to the algorithm and the functional parameters;
if the comparison result is that the algorithm with the similarity larger than the preset similarity threshold value does not exist in the algorithm set, searching in an algorithm knowledge base connected with the digital twin DaaS platform through an artificial intelligence search engine according to the functional parameters to obtain an algorithm with the similarity between the functional parameter identification and the functional parameters larger than the preset similarity threshold value, and generating a target algorithm according to the algorithm and the functional parameters.
Further, the generation module further comprises a modification verification module, the modification verification module is configured to:
when an algorithm modification instruction sent by the target equipment is received, modifying the target algorithm according to the algorithm modification instruction;
and verifying the modified target algorithm, and sending the modified target algorithm passing the verification to the target equipment.
Further, the generation module further comprises a management module, and the management module is configured to:
and when an algorithm generation instruction sent by target equipment is not received within a preset time period, carrying out optimization operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform, and carrying out cleaning operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform.
Further, the management module is further configured to:
optimizing the algorithm stored in the algorithm warehouse through the digital twin DaaS platform to obtain the optimized algorithm and store the optimized algorithm in the algorithm warehouse;
and performing identification operation of algorithm types and functional parameters on the optimized algorithm, and storing the algorithm in the algorithm warehouse according to the algorithm types.
Further, the management module is further configured to:
obtaining the type identification and the function parameter identification of each algorithm in the algorithm warehouse through the digital twin DaaS platform, comparing the type identifications of each algorithm, and comparing the function parameter identifications of each algorithm to obtain an algorithm set with the same type identification and the same function parameter identification;
and performing simulation operation on each algorithm in the algorithm set with the same type identifier and the same functional parameter identifier, determining a performance index corresponding to each algorithm, and performing cleaning operation on the algorithms according to the performance indexes.
In addition, in order to achieve the above object, the present invention further provides an algorithmic warehouse management system based on a digital twin DaaS platform, including: a memory, a processor and an algorithm warehouse manager stored on the memory and operable on the processor, the algorithm warehouse manager when executed by the processor implementing the steps of the digital twin DaaS platform based algorithm warehouse management method as described above.
In addition, to achieve the above object, the present invention further provides a computer readable storage medium, which is a computer readable storage medium, the readable storage medium having an algorithm warehouse management program stored thereon, and the algorithm warehouse management program, when executed by a processor, implements the steps of the algorithm warehouse management method based on the digital twin DaaS platform as described above.
According to the algorithm warehouse management method based on the digital twin DaaS platform, when an algorithm generation instruction sent by target equipment is received, an algorithm type and functional parameters are determined according to the algorithm generation instruction, and an algorithm set is determined in an algorithm warehouse according to the algorithm type; generating a target algorithm according to the algorithm set and the function parameters through a digital twin DaaS platform, and sending the target algorithm to the target equipment; according to the invention, the corresponding algorithm is selected in the algorithm warehouse according to the algorithm type, and the target algorithm is generated according to the algorithm and the functional parameters, so that the development process of the algorithm does not need to be manually participated, the algorithm development efficiency is further improved, and the cost is reduced.
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FIG. 1 is a schematic diagram of an apparatus architecture of a hardware operating environment according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a first embodiment of the algorithm warehouse management method based on the digital twin DaaS platform according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, fig. 1 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present invention.
The device of the embodiment of the invention can be a PC or a server device.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration of the apparatus shown in fig. 1 is not intended to be limiting of the apparatus and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, the memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and an algorithm repository management program.
The operating system is a program for managing and controlling the portable storage device and software resources, and supports the operation of a network communication module, a user interface module, an algorithm warehouse management program and other programs or software; the network communication module is used for managing and controlling the network interface 1002; the user interface module is used to manage and control the user interface 1003.
In the storage device shown in fig. 1, the storage device calls an algorithm warehouse management program stored in the memory 1005 through the processor 1001 and performs operations in the embodiments of the algorithm warehouse management method based on the digital twin DaaS platform described below.
Based on the hardware structure, the embodiment of the algorithm warehouse management method based on the digital twin DaaS platform is provided.
Referring to fig. 2, fig. 2 is a schematic flowchart of a first embodiment of an algorithm warehouse management method based on a digital twin DaaS platform according to the present invention, where the method includes:
step S10, when receiving an algorithm generating instruction sent by target equipment, determining an algorithm type and functional parameters according to the algorithm generating instruction, and determining an algorithm set in an algorithm warehouse according to the algorithm type;
and step S20, generating a target algorithm through the digital twin DaaS platform according to the algorithm set and the function parameters, and sending the target algorithm to the target equipment.
The algorithm warehouse management method based on the digital twin DaaS platform is applied to an algorithm warehouse management system based on the digital twin DaaS platform, and the algorithm warehouse management system based on the digital twin DaaS platform is in communication connection with an artificial intelligence Internet of things platform and the digital twin DaaS platform; for convenience of description, the algorithm warehouse management system based on the digital twin DaaS platform is simply referred to as a management system, and the management system is taken as an example for explanation; when receiving an algorithm generation instruction sent by target equipment, a management system acquires a user product requirement in the algorithm generation instruction, and determines an algorithm type and a function parameter according to the user product requirement; the management system compares the algorithm type with the type identifier of each algorithm in the algorithm warehouse so as to determine an algorithm set according to the algorithm with the same type identifier and algorithm type; the management system acquires a functional parameter identifier corresponding to each algorithm in the algorithm set through the digital twin DaaS platform, and calculates the similarity between the functional parameter identifier corresponding to each algorithm and the functional parameter; comparing the similarity with a preset similarity threshold to obtain a comparison result; if the comparison result is that an algorithm with the similarity larger than a preset similarity threshold exists in the algorithm set, generating a target algorithm according to the algorithm and the functional parameters; if the comparison result is that no algorithm with the similarity larger than the preset similarity threshold exists in the algorithm set, searching in an algorithm knowledge base connected with the digital twin DaaS platform through an artificial intelligence search engine according to the functional parameters to obtain an algorithm with the similarity between the functional parameter identification and the functional parameters larger than the preset similarity threshold, and generating a target algorithm according to the algorithm and the functional parameters. It should be noted that the digital twin DaaS platform is an AIOTDaaS digital twin platform, and includes a service console and a data console that are connected to each other; the data center station is used for acquiring, calculating, storing and processing data collected in an AIOTDAaS mode, and storing formed standard data and transmitting the standard data to the service center station; the business center is used for combining standard data transmitted based on the data center with industry application to form a model and a product aiming at the industry application, so that a user can quickly package a business product based on the business center. The artificial intelligence thing networking platform is an AIOT PaaS thing networking platform, and the artificial intelligence thing networking platform comprises an AI edge computing, a digital chip, an edge storage computing and module technology.
In the algorithm warehouse management method based on the digital twin DaaS platform in the embodiment, when an algorithm generation instruction sent by target equipment is received, an algorithm type and functional parameters are determined according to the algorithm generation instruction, and an algorithm set is determined in an algorithm warehouse according to the algorithm type; generating a target algorithm according to the algorithm set and the functional parameters through a digital twin DaaS platform, and sending the target algorithm to target equipment; according to the invention, the corresponding algorithm is selected in the algorithm warehouse according to the algorithm type, and the target algorithm is generated according to the algorithm and the functional parameters, so that the development process of the algorithm does not need to be manually participated, the algorithm development efficiency is further improved, and the cost is reduced.
The respective steps will be described in detail below:
step S10, when receiving an algorithm generating instruction sent by target equipment, determining an algorithm type and functional parameters according to the algorithm generating instruction, and determining an algorithm set in an algorithm warehouse according to the algorithm type;
in the embodiment, a management system is in communication connection with an artificial intelligence internet of things platform, when related developers need to develop algorithms for intelligent terminal equipment, the intelligent terminal equipment is in communication connection with the artificial intelligence internet of things platform, the intelligent terminal equipment serves as target equipment, algorithm generation instructions are sent to the artificial intelligence internet of things platform through the target equipment, the management system receives the algorithm generation instructions forwarded by the artificial intelligence internet of things platform, algorithm types and function parameters are determined according to the algorithm generation instructions, and algorithm sets are determined in an algorithm warehouse according to the algorithm types; it should be noted that the type of algorithm is the application field of the algorithm, such as face recognition, industrial manufacturing, medical diagnosis, automatic driving, intelligent transportation, etc.; the functional parameters comprise a business process, a process flow, an application scene and the like; the algorithm warehouse is built in the management system in advance, the algorithm warehouse comprises algorithms with different types and different functions, and each algorithm in the algorithm warehouse is identified by the algorithm type and the function parameter, so that the algorithm of the algorithm warehouse is managed conveniently.
Specifically, the step of determining an algorithm type and a functional parameter according to the algorithm generation instruction, and determining an algorithm set in an algorithm warehouse according to the algorithm type includes:
step a, acquiring user product requirements in the algorithm generation instruction, and determining an algorithm type and functional parameters according to the user product requirements;
in the step, when a related developer sends an algorithm generation instruction through a target device, the algorithm type and the function parameter needed by the target device are determined according to the requirement of the related developer, the corresponding user product requirement is generated according to the algorithm type and the function parameter, the algorithm generation instruction is generated, the algorithm generation instruction is sent to an artificial intelligence internet of things platform connected with a management system through the target device, the management system obtains the algorithm generation instruction in the artificial intelligence internet of things platform, and the algorithm type and the function parameter needed by the related developer are determined according to the user product requirement in the algorithm generation instruction.
And b, comparing the algorithm type with the type identifier of each algorithm in the algorithm warehouse so as to determine an algorithm set according to the algorithm with the type identifier same as the algorithm type.
In the step, after determining the algorithm type, the management system compares the algorithm type with the type identifier of each algorithm in the algorithm warehouse in sequence to obtain a comparison result, and the obtained comparison result is an algorithm determination algorithm set with the type identifier being the same as the algorithm type; it can be understood that a large number of algorithms of different types are stored in the algorithm warehouse, and the management system selects the algorithm with the type identifier same as the type of the algorithm required by the relevant developer from all types of algorithms, so that the selection efficiency can be improved, and the algorithm development efficiency can be further improved.
And step S20, generating a target algorithm through the digital twin DaaS platform according to the algorithm set and the function parameters, and sending the target algorithm to the target equipment.
In this embodiment, after determining the algorithm set, the management system obtains, through the digital twinborn DaaS platform, the function parameter identifier corresponding to each algorithm in the algorithm set, compares the function identifier corresponding to each algorithm with the function parameter to obtain a comparison result, generates a target algorithm by combining the comparison result and the function parameter, and sends the target algorithm to the target device.
Specifically, step S20 includes:
step c, acquiring a functional parameter identifier corresponding to each algorithm in the algorithm set through a digital twin DaaS platform, and calculating the similarity between the functional parameter identifier corresponding to each algorithm and the functional parameter;
in this step, the management system obtains a functional parameter identifier corresponding to each algorithm in the algorithm set through the digital twin DaaS platform, and calculates the similarity between the functional parameter identifier corresponding to each algorithm and the functional parameter required by the relevant developer.
Further, in a possible embodiment, the management system obtains the similarity of 100%, that is, the functional parameter identifier in the algorithm repository is identical to the functional parameter requested by the relevant developer, and at this time, the management system may directly use the algorithm as the target algorithm and directly send the target algorithm to the target device.
Step d, comparing the similarity with a preset similarity threshold to obtain a comparison result;
in this step, after determining the similarity between the functional parameter identifier and the functional parameter corresponding to each algorithm, the management system compares the similarity between the functional parameter identifier and the functional parameter corresponding to each algorithm with a preset similarity threshold, respectively, to obtain a comparison result.
Step e, if the comparison result is that an algorithm with the similarity larger than a preset similarity threshold exists in the algorithm set, generating a target algorithm according to the algorithm and the functional parameters;
in the step, if the comparison result obtained by the management system is that an algorithm with the similarity greater than a preset similarity threshold exists in the algorithm set, generating a target algorithm according to the algorithm and the functional parameters; it can be understood that there may be one or more algorithms with similarity greater than the preset similarity threshold, and the management system combines all the algorithms with similarity greater than the preset similarity threshold with the functional parameters to generate the target algorithm.
And f, if the comparison result shows that the algorithm with the similarity larger than the preset similarity threshold does not exist in the algorithm set, searching in an algorithm knowledge base connected with the digital twin DaaS platform through an artificial intelligence search engine according to the function parameters to obtain an algorithm with the similarity between the function parameter identification and the function parameters larger than the preset similarity threshold, and generating a target algorithm according to the algorithm and the function parameters.
In the step, if the comparison result is obtained, if an algorithm with the similarity larger than a preset similarity threshold value does not exist in the algorithm set, the management system calls an artificial intelligence search engine in the algorithm set through the digital twin DaaS platform, searches in an algorithm knowledge base connected with the digital twin DaaS platform according to the function parameters to obtain an algorithm with the similarity between the function parameter identification and the function parameters larger than the preset similarity threshold value, and generates a target algorithm according to the algorithm and the function parameters; it should be noted that the algorithm knowledge base exists on the internet, when the management system cannot search the corresponding algorithm in the algorithm warehouse of the management system, the algorithm knowledge base on the internet is searched through an artificial intelligence search engine to obtain an algorithm of which the similarity between the functional parameter identifier and the functional parameter is greater than a preset similarity threshold, and a target algorithm is generated according to the algorithm and the functional parameter; it can be understood that the functional parameters include a business process, a process flow, an application scenario, etc., and the functional parameter identifier of the algorithm determined by the management system has a very high similarity to the functional parameters required by the relevant developers, so that the management system modifies and adjusts the functional parameters of the algorithm according to the functional parameters required by the relevant developers, and can quickly obtain the target algorithm.
Specifically, the management system can be applied to industries such as industrial internet, smart medical treatment, smart supply chain, smart finance, smart agriculture, smart community, smart park, smart traffic and the like, for example, when applied to industrial internet and relevant developers of the industrial internet need to develop an algorithm for a certain production device in the industrial internet, the relevant developers connect the production device to an artificial intelligent internet of things platform in the management system, send an algorithm generation instruction to the artificial intelligent internet of things platform through the production device, the management system obtains a process flow corresponding to the production device from the algorithm generation instruction, searches a corresponding algorithm in an algorithm warehouse through a digital generation DaaS platform according to the process flow, calculates the similarity between the process flow corresponding to the algorithm and the process flow of the production device, and when the similarity between the process flow corresponding to the algorithm and the process flow of the production device is 100%, directly using the algorithm as the algorithm of the production equipment, sending the algorithm to an artificial intelligence Internet of things platform through a digital twin DaaS platform, and then sending the algorithm to the production equipment through the artificial intelligence Internet of things platform, wherein the production equipment runs according to the algorithm and executes a corresponding process flow to produce a corresponding product; when the similarity between the process flow corresponding to the algorithm and the process flow of the production equipment does not exist in the algorithm warehouse and is 100%, the algorithm with the highest similarity is selected, other similar algorithms are searched in the algorithm knowledge base through an artificial intelligence search engine of the digital twin DaaS platform, the algorithm set is fused and derived to obtain the algorithm which is in line with the process flow of the production equipment, the algorithm is sent to the artificial intelligence Internet of things platform through the digital twin DaaS platform and then sent to the production equipment through the artificial intelligence Internet of things platform, and the production equipment runs according to the algorithm to execute the corresponding process flow to produce the corresponding product.
Another example is: when the method is applied to intelligent traffic and relevant developers need to develop algorithms for a certain automatic driving automobile in the intelligent traffic, the corresponding automatic driving algorithms are generated through the management system and are sent to the automatic driving automobile, so that the automatic driving automobile can be automatically driven according to the generated automatic driving algorithms.
The management system of the embodiment acquires user product requirements in the algorithm generation instruction when receiving the algorithm generation instruction sent by the target device, and determines the algorithm type and the function parameters according to the user product requirements; the management system compares the algorithm type with the type identifier of each algorithm in the algorithm warehouse so as to determine an algorithm set according to the algorithm with the same type identifier and algorithm type; the management system acquires a functional parameter identifier corresponding to each algorithm in the algorithm set through the digital twin DaaS platform, and calculates the similarity between the functional parameter identifier corresponding to each algorithm and the functional parameter; comparing the similarity with a preset similarity threshold to obtain a comparison result; if the comparison result is that an algorithm with the similarity larger than a preset similarity threshold exists in the algorithm set, generating a target algorithm according to the algorithm and the functional parameters; if the comparison result is that no algorithm with the similarity larger than the preset similarity threshold exists in the algorithm set, searching in an algorithm knowledge base connected with the digital twin DaaS platform according to the functional parameters through an artificial intelligence search engine to obtain an algorithm with the similarity between the functional parameter identification and the functional parameters larger than the preset similarity threshold, and generating a target algorithm according to the algorithm and the functional parameters without manually participating in the development process of the algorithm, so that the algorithm development efficiency is improved, and the cost is reduced.
Further, based on the first embodiment of the algorithm warehouse management method based on the digital twin DaaS platform, the second embodiment of the algorithm warehouse management method based on the digital twin DaaS platform is provided.
The second embodiment of the digital twin DaaS platform-based algorithm warehouse management method is different from the first embodiment of the digital twin DaaS platform-based algorithm warehouse management method in that, after step S20, the digital twin DaaS platform-based algorithm warehouse management method includes:
step g, when an algorithm modification instruction sent by the target equipment is received, modifying the target algorithm according to the algorithm modification instruction;
and h, verifying the modified target algorithm, and sending the verified modified target algorithm to the target equipment.
In this embodiment, after the management system sends the target algorithm to the target device, the target device performs simulation operation on the target algorithm to verify whether the function and syntax of the target algorithm meet the requirements, in general, the target algorithm can meet the requirements of the target device, in some special cases, the target device determines that the target algorithm does not meet the requirements, then, according to the simulation operation result, an algorithm modification instruction is generated and sent to an artificial intelligent internet of things platform in communication connection with the management system, the algorithm modification instruction includes a modification requirement, the modification requirement includes a part and a reason of the target device detecting the part and the reason of the target algorithm that do not meet the requirements, the management system obtains the modification requirement in the algorithm modification instruction, selects an algorithm meeting the modification requirement in an algorithm warehouse, and fuses the algorithm and the target algorithm, the target algorithm corresponding to the target equipment is modified, the modified target algorithm is verified, and the modified target algorithm which passes the verification is sent to the target equipment. It can be understood that, in the process of fusing the algorithm satisfying the modification requirement with the target algorithm, generally, only the part of the algorithm satisfying the modification requirement, which satisfies the modification requirement, needs to be replaced into the part of the target algorithm, which does not satisfy the requirement of the target device.
When receiving an algorithm modification instruction sent by target equipment, the management system in the embodiment modifies the target algorithm according to the algorithm modification instruction; and carrying out verification operation on the modified target algorithm, and sending the verified modified target algorithm to the target equipment. And modifying the target algorithm according to the algorithm modification instructions of the algorithm warehouse and the target equipment, so that the algorithm modification process does not need manual intervention, the algorithm development efficiency can be further improved, and the cost is reduced.
Further, based on the first embodiment and the second embodiment of the algorithm warehouse management method based on the digital twin DaaS platform of the present invention, a third embodiment of the algorithm warehouse management method based on the digital twin DaaS platform of the present invention is provided.
The third embodiment of the digital twin DaaS platform-based algorithmic warehouse management method differs from the first and second embodiments of the digital twin DaaS platform-based algorithmic warehouse management method in that, after step S20, the digital twin DaaS platform-based algorithmic warehouse management method further comprises:
and i, when an algorithm generation instruction sent by target equipment is not received within a preset time period, carrying out optimization operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform, and carrying out cleaning operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform.
In this embodiment, when the management system does not receive an algorithm generation instruction sent by the target device within a preset time period, the algorithm in the algorithm warehouse is optimized through the digital twin DaaS platform, and the algorithm in the algorithm warehouse is cleaned through the digital twin DaaS platform; it can be understood that when related developers need to develop algorithms for the intelligent terminal device, the intelligent terminal device is in communication connection with the artificial intelligence internet of things platform, the intelligent terminal device serves as a target device, the target device sends algorithm generation instructions to the artificial intelligence internet of things platform, and the management system does not receive the algorithm generation instructions sent by the target device within a preset time period, namely no target device is connected to the artificial intelligence internet of things platform within the preset time period.
Specifically, the step of performing optimization operation on the algorithm in the algorithm repository through the digital twin DaaS platform includes:
step i1, optimizing the algorithm stored in the algorithm warehouse through the digital twin DaaS platform to obtain an optimized algorithm;
in the step, when the management system does not receive the algorithm generation instruction sent by the target device within the preset time period, optimizing the algorithm stored in the algorithm warehouse through a digital twin DaaS platform to obtain the optimized algorithm, it can be understood that, when the management system optimizes the algorithms stored in the algorithm warehouse through the digital twin DaaS platform, the method can combine a certain algorithm in the algorithm warehouse with a certain application scene to derive a new algorithm, can fuse certain algorithms in the algorithm warehouse to derive the new algorithm, can search different algorithm libraries, third party platforms and the like which are in butt joint with the digital twin DaaS platform through an artificial intelligence search engine to search algorithms which are not stored in the algorithm warehouse, and can fuse certain algorithms in the algorithm warehouse with algorithms obtained by the artificial intelligence search engine to derive the new algorithm.
And step i2, performing identification operation of algorithm types and functional parameters on the optimized algorithm, and storing the algorithm in the algorithm warehouse according to the algorithm types.
In this step, the management system performs an identification operation of an algorithm type and a function parameter on the optimized algorithm, and stores the algorithm in an algorithm repository according to the algorithm type, such as: the algorithm type of the optimized algorithm is identified as face identification, the face identification is induced and stored in an algorithm set of a face identification type, and the optimized algorithm is subjected to functional parameter identification, so that an algorithm warehouse or a digital twin DaaS platform can quickly determine that the functional parameters of each algorithm are a business process, a process flow or an application scene.
Specifically, the step of performing a cleaning operation on the algorithm in the algorithm repository through the digital twin DaaS platform includes:
step i3, obtaining the type identifier and the function parameter identifier of each algorithm in the algorithm warehouse through the digital twin DaaS platform, comparing the type identifiers of each algorithm, and comparing the function parameter identifiers of each algorithm to obtain an algorithm set with the same type identifier and the same function parameter identifier;
and i4, performing simulation operation on each algorithm in the algorithm set with the same type identification and the same function parameter identification, determining a performance index corresponding to each algorithm, and performing cleaning operation on the algorithms according to the performance indexes.
In steps i3 to i4, when the management system does not receive an algorithm generation instruction sent by the target device within a preset time period, acquiring a type identifier and a function parameter identifier of each algorithm in an algorithm warehouse through a digital twin DaaS platform, comparing the type identifiers of each algorithm, and comparing the function parameter identifiers of each algorithm to obtain an algorithm set with the same type identifier and the same function parameter identifier; the management system carries out simulation operation on each algorithm in the algorithm set with the same type identification and the same function parameter identification, determines the performance index corresponding to each algorithm, reserves the algorithm with the optimal performance index according to the performance index, and carries out cleaning operation on other algorithms so as to ensure the simplicity of an algorithm warehouse, so that the management system can rapidly determine the target algorithm according to the algorithm in the algorithm warehouse, and the development efficiency of the target algorithm is improved.
When the management system of this embodiment does not receive the algorithm generation instruction sent by the target device within the preset time period, the algorithm in the algorithm warehouse is optimized through the digital twin DaaS platform, and the algorithm in the algorithm warehouse is cleaned through the digital twin DaaS platform. Unnecessary storage in the algorithm warehouse is reduced, so that the algorithm warehouse is simple, the management system can quickly determine the target algorithm according to the algorithm in the algorithm warehouse, and the development efficiency of the algorithm is improved.
The invention also provides an algorithm warehouse management device based on the digital twin DaaS platform. The invention discloses an algorithm warehouse management device based on a digital twin DaaS platform, which comprises:
the receiving module is used for determining an algorithm type and functional parameters according to an algorithm generating instruction when receiving the algorithm generating instruction sent by the target equipment, and determining an algorithm set in an algorithm warehouse according to the algorithm type;
and the generating module is used for generating a target algorithm according to the algorithm set and the functional parameters through a digital twin DaaS platform and sending the target algorithm to the target equipment.
Further, the receiving module further comprises a determining module, and the determining module is configured to:
acquiring user product requirements in the algorithm generation instruction, and determining an algorithm type and functional parameters according to the user product requirements;
comparing the algorithm type with a type identifier of each algorithm in an algorithm warehouse so as to determine an algorithm set according to the algorithm with the same type identifier as the algorithm type.
Further, the generation module is further configured to:
acquiring a function parameter identifier corresponding to each algorithm in the algorithm set through a digital twin DaaS platform, and calculating the similarity between the function parameter identifier corresponding to each algorithm and the function parameter;
comparing the similarity with a preset similarity threshold to obtain a comparison result;
if the comparison result is that an algorithm with the similarity larger than a preset similarity threshold exists in the algorithm set, generating a target algorithm according to the algorithm and the functional parameters;
if the comparison result is that the algorithm with the similarity larger than the preset similarity threshold value does not exist in the algorithm set, searching in an algorithm knowledge base connected with the digital twin DaaS platform through an artificial intelligence search engine according to the functional parameters to obtain an algorithm with the similarity between the functional parameter identification and the functional parameters larger than the preset similarity threshold value, and generating a target algorithm according to the algorithm and the functional parameters.
Further, the generation module further comprises a modification verification module, the modification verification module is configured to:
when an algorithm modification instruction sent by the target equipment is received, modifying the target algorithm according to the algorithm modification instruction;
and verifying the modified target algorithm, and sending the modified target algorithm passing the verification to the target equipment.
Further, the generation module further comprises a management module, and the management module is configured to:
and when an algorithm generation instruction sent by target equipment is not received within a preset time period, carrying out optimization operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform, and carrying out cleaning operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform.
Further, the management module is further configured to:
optimizing the algorithm stored in the algorithm warehouse through the digital twin DaaS platform to obtain an optimized algorithm;
and performing identification operation of algorithm types and functional parameters on the optimized algorithm, and storing the algorithm in the algorithm warehouse according to the algorithm types.
Further, the management module is further configured to:
obtaining the type identification and the function parameter identification of each algorithm in the algorithm warehouse through the digital twin DaaS platform, comparing the type identifications of each algorithm, and comparing the function parameter identifications of each algorithm to obtain an algorithm set with the same type identification and the same function parameter identification;
and performing simulation operation on each algorithm in the algorithm set with the same type identifier and the same function parameter identifier, determining a performance index corresponding to each algorithm, and performing cleaning operation on the algorithms according to the performance indexes.
The invention also provides an algorithm warehouse management system based on the digital twin DaaS platform.
The algorithm warehouse management system based on the digital twin DaaS platform comprises: a memory, a processor and an algorithm warehouse manager stored on the memory and operable on the processor, the algorithm warehouse manager when executed by the processor implementing the steps of the digital twin DaaS platform based algorithm warehouse management method as described above.
The method implemented when the algorithm warehouse management program running on the processor is executed may refer to each embodiment of the algorithm warehouse management method based on the digital twin DaaS platform of the present invention, and details thereof are not described herein.
The invention also provides a computer readable storage medium.
The computer readable storage medium has stored thereon an algorithm warehouse management program which, when executed by the processor, implements the steps of the digital twin DaaS platform based algorithm warehouse management method as described above.
The method implemented when the algorithm warehouse management program running on the processor is executed may refer to each embodiment of the algorithm warehouse management method based on the digital twin DaaS platform of the present invention, and details thereof are not described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention essentially or contributing to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. An algorithm warehouse management method based on a digital twin DaaS platform is characterized by comprising the following steps:
when an algorithm generation instruction sent by target equipment is received, determining an algorithm type and functional parameters according to the algorithm generation instruction, and determining an algorithm set in an algorithm warehouse according to the algorithm type;
generating a target algorithm according to the algorithm set and the function parameters through a digital twin DaaS platform, and sending the target algorithm to the target equipment;
wherein, after the step of sending the target algorithm to the target device, the method further comprises:
when an algorithm generation instruction sent by target equipment is not received within a preset time period, carrying out optimization operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform, and carrying out cleaning operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform;
the step of performing optimization operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform comprises the following steps:
and deriving a new algorithm from the algorithm in the algorithm warehouse according to an application scene through the digital twin DaaS platform, or deriving a new algorithm by fusing the algorithms in the algorithm warehouse, or searching the algorithm which is not stored in the algorithm warehouse through an artificial intelligence search engine in a different algorithm library and a third party platform which are in butt joint with the digital twin DaaS platform, and fusing the algorithm in the algorithm warehouse by the algorithm which is not stored to derive a new algorithm so as to complete optimization operation.
2. The algorithm warehouse management method based on the digital twin DaaS platform according to claim 1, wherein the step of determining an algorithm type and functional parameters according to the algorithm generation instruction and determining an algorithm set in an algorithm warehouse according to the algorithm type comprises:
acquiring user product requirements in the algorithm generation instruction, and determining an algorithm type and functional parameters according to the user product requirements;
comparing the algorithm type with a type identifier of each algorithm in an algorithm warehouse so as to determine an algorithm set according to the algorithm with the same type identifier as the algorithm type.
3. The algorithm warehouse management method based on the digital twin DaaS platform as claimed in claim 1, wherein the generating of the target algorithm by the digital twin DaaS platform according to the algorithm set and the function parameters comprises:
acquiring a function parameter identifier corresponding to each algorithm in the algorithm set through a digital twin DaaS platform, and calculating the similarity between the function parameter identifier corresponding to each algorithm and the function parameter;
comparing the similarity with a preset similarity threshold to obtain a comparison result;
if the comparison result is that an algorithm with the similarity larger than a preset similarity threshold exists in the algorithm set, generating a target algorithm according to the algorithm and the functional parameters;
if the comparison result is that the algorithm with the similarity larger than the preset similarity threshold value does not exist in the algorithm set, searching in an algorithm knowledge base connected with the digital twin DaaS platform through an artificial intelligence search engine according to the functional parameters to obtain an algorithm with the similarity between the functional parameter identification and the functional parameters larger than the preset similarity threshold value, and generating a target algorithm according to the algorithm and the functional parameters.
4. The digital twin DaaS platform based algorithm warehouse management method of claim 1, wherein the step of sending the target algorithm to the target device is followed by:
when an algorithm modification instruction sent by the target equipment is received, modifying the target algorithm according to the algorithm modification instruction;
and carrying out verification operation on the modified target algorithm, and sending the verified modified target algorithm to the target equipment.
5. The method for algorithm warehouse management based on a digital twin DaaS platform as claimed in claim 1, wherein the step of performing optimization operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform comprises:
optimizing the algorithm stored in the algorithm warehouse through the digital twin DaaS platform to obtain an optimized algorithm;
and performing identification operation of algorithm types and functional parameters on the optimized algorithm, and storing the algorithm in the algorithm warehouse according to the algorithm types.
6. The algorithm warehouse management method based on the digital twin DaaS platform as claimed in claim 1, wherein the step of performing a cleaning operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform comprises:
obtaining the type identification and the function parameter identification of each algorithm in the algorithm warehouse through the digital twin DaaS platform, comparing the type identifications of each algorithm, and comparing the function parameter identifications of each algorithm to obtain an algorithm set with the same type identification and the same function parameter identification;
and performing simulation operation on each algorithm in the algorithm set with the same type identifier and the same function parameter identifier, determining a performance index corresponding to each algorithm, and performing cleaning operation on the algorithms according to the performance indexes.
7. An algorithmic warehouse management device based on a digital twin DaaS platform, comprising:
the receiving module is used for determining an algorithm type and functional parameters according to an algorithm generating instruction when receiving the algorithm generating instruction sent by the target equipment, and determining an algorithm set in an algorithm warehouse according to the algorithm type;
the generating module is used for generating a target algorithm according to the algorithm set and the functional parameters through a digital twin DaaS platform and sending the target algorithm to the target equipment;
the generation module is further configured to perform optimization operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform and perform cleaning operation on the algorithm in the algorithm warehouse through the digital twin DaaS platform when an algorithm generation instruction sent by a target device is not received within a preset time period;
the generating module is further configured to derive a new algorithm from the algorithm in the algorithm repository through the digital twin DaaS platform according to an application scenario, or to fuse the algorithms in the algorithm repository to derive a new algorithm, or to search for an algorithm not stored in the algorithm repository through an artificial intelligence search engine on a different algorithm library and a third-party platform that are docked with the digital twin DaaS platform, and to fuse the algorithm in the algorithm repository with the algorithm not stored to derive a new algorithm, so as to complete optimization operation.
8. An algorithmic warehouse management system based on a digital twin DaaS platform, comprising: a memory, a processor and an algorithm warehouse management program stored on the memory and executable on the processor, the algorithm warehouse management program when executed by the processor implementing the steps of the digital twin DaaS platform based algorithm warehouse management method according to any one of claims 1 to 6.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon an algorithmic warehouse management program, which when executed by a processor, implements the steps of the digital twin DaaS platform based algorithmic warehouse management method according to any of the claims 1 to 6.
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