CN115509765A - Super-fusion cloud computing method and system, computer equipment and storage medium - Google Patents
Super-fusion cloud computing method and system, computer equipment and storage medium Download PDFInfo
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
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- G06F9/5066—Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs
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Abstract
The invention relates to the technical field of super-fusion computing, and particularly discloses a super-fusion cloud computing method, a system, computer equipment and a storage medium, wherein the method comprises the steps of receiving a registration request containing user information input by a server, and acquiring position information, network protocol and equipment parameters of a user; calculating a negative influence factor according to the position information and a network protocol, correcting the equipment parameter according to the negative influence factor, and generating a virtual unit according to the corrected equipment parameter and the user information; connecting the virtual units and establishing a service cloud picture; receiving a service request which is input by a user end and contains position information and a work task, selecting a virtual unit in the service cloud picture according to the position information and the work task, and sending an operation task to the virtual unit; the method introduces the parameter of data transmission speed in the process of establishing the service cloud picture, greatly improves the prediction accuracy of the operation process, and meets the use experience of users in a phase-changing manner.
Description
Technical Field
The invention relates to the technical field of super-fusion computing, in particular to a super-fusion cloud computing method and system, computer equipment and a storage medium.
Background
The hyper-convergence is an improvement based on the existing cloud computing platform. The cloud data center based on the super-fusion architecture is characterized in that a large number of software-defined technologies are adopted, the computing, the storage, the network and the special hardware are decoupled, the real fusion of the IT infrastructure is realized, and the last obstacle is cleared for the implementation and the deployment of the cloud data center.
The super-fusion technology is realized by the principle that Software Defined Storage (SDS) is used for replacing SAN in the traditional fusion system, and the main components are software defined storage and (server) virtualization constructed on standard server hardware. In general, the super-fusion technology is to establish a plurality of connected virtual machines according to the existing service equipment.
In the existing super-convergence platform, the establishment process of the virtual machine mostly only pays attention to the performance of the service equipment, and the attention degree of the data transmission process is not considered.
Disclosure of Invention
The present invention is directed to a super-fusion cloud computing method, system, computer device, and storage medium, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
a method of hyper-converged cloud computing, the method comprising:
receiving a registration request containing user information input by a server, and acquiring position information, a network protocol and equipment parameters of a user;
calculating a negative influence factor according to the position information and a network protocol, correcting the equipment parameter according to the negative influence factor, and generating a virtual unit according to the corrected equipment parameter and the user information;
connecting the virtual units and establishing a service cloud picture;
receiving a service request which is input by a user end and contains position information and a work task, selecting a virtual unit in the service cloud picture according to the position information and the work task, and sending an operation task to the virtual unit.
As a further scheme of the invention: the steps of receiving the registration request containing the user information input by the server and acquiring the position information, the network protocol and the equipment parameters of the user comprise:
receiving a registration request containing user information input by a server, inputting the user information into a registered library for record, and inquiring the registered calculated amount corresponding to the user;
determining the level of the user according to the registered calculation amount; the level of the user which is registered for the first time is a default value; the level is used for distinguishing user types;
sending a preset test file to a server, and acquiring equipment parameters of a user based on the test file;
and sending an authority acquisition request to the user, and acquiring the position information and the network protocol of the user according to the acquired authority.
As a further scheme of the invention: the step of calculating a negative influence factor according to the location information and a network protocol, correcting the device parameter according to the negative influence factor, and generating a virtual unit according to the corrected device parameter and the user information includes:
calculating the transmission speed according to the position information and the network protocol;
inputting the transmission speed into a trained influence judgment model, and determining a negative influence factor; the negative influence factor is used for representing the negative influence characteristics of the transmission process on the equipment;
correcting the equipment parameters according to the negative influence factors; the equipment parameters comprise the CPU operation speed and the storage speed of a memory;
and generating a user label according to the user information, inserting the user label into the corrected equipment parameter, and generating a virtual unit.
As a further scheme of the invention: the step of connecting the virtual units and establishing the service cloud picture comprises the following steps:
reading the position information of each virtual unit, and calculating the distance between each virtual unit according to the position information;
acquiring display parameters, and determining a reference cloud picture containing a scale according to the maximum distance and the display parameters;
inserting display points corresponding to the virtual units in the reference cloud picture based on the scale.
As a further scheme of the invention: the step of receiving a service request which is input by a user end and contains position information and a work task, selecting a virtual unit in the service cloud picture according to the position information and the work task, and sending an operation task to the virtual unit comprises the following steps:
receiving a service request input by a user end, acquiring position information of the user end, and inquiring and marking a corresponding display point in the service cloud picture according to the position information;
the method comprises the steps of obtaining a work task sent by a using end, sending the work task to a detecting end, and receiving an operation task group fed back by the detecting end; a trained recognition model is arranged in the detection end;
and selecting virtual units by taking the marked display points as centers based on the operation task group, and sending the operation tasks to the virtual units.
As a further scheme of the invention: the step of selecting a virtual unit based on the operation task group by taking a marked display point as a center and sending the operation task to each virtual unit comprises the following steps:
calculating time conditions and parameter conditions of each operation task;
sequentially inquiring equipment parameters of the virtual units by taking the marked display point as a center and a preset incremental numerical value as a radius;
comparing the equipment parameters with the parameter conditions, and acquiring the operation amount of an operation task when the equipment parameters meet the parameter conditions;
calculating the consumed time according to the operation amount and the equipment parameters, and comparing the consumed time with the time condition;
and when the consumed time meets the time condition, sending the operation task to the virtual unit.
The technical scheme of the invention also provides a super-fusion cloud computing system, which comprises:
the parameter acquisition module is used for receiving a registration request containing user information input by the server and acquiring the position information, the network protocol and the equipment parameters of the user;
a virtual unit generating module, configured to calculate a negative impact factor according to the location information and a network protocol, correct the device parameter according to the negative impact factor, and generate a virtual unit according to the corrected device parameter and the user information;
the service cloud picture generating module is used for connecting the virtual units and establishing a service cloud picture;
and the task execution module is used for receiving a service request which is input by a user end and contains position information and a work task, selecting a virtual unit in the service cloud picture according to the position information and the work task, and sending an operation task to the virtual unit.
As a further scheme of the invention: the parameter acquisition module comprises:
the information inquiry unit is used for receiving a registration request containing user information input by the server, inputting the user information into a registered library for recording, and inquiring the registered calculation amount corresponding to the user;
a level determination unit configured to determine a level of the user according to the registered calculation amount; the level of the user registered for the first time is a default value; the level is used for distinguishing user types;
the file testing unit is used for sending a preset test file to the server and acquiring the equipment parameters of the user based on the test file;
and the permission application unit is used for sending a permission acquisition request to the user and acquiring the position information and the network protocol of the user according to the acquired permission.
The technical scheme of the invention also provides computer equipment, which comprises one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and when the program code is loaded and executed by the one or more processors, the hyper-converged cloud computing method is realized.
The technical scheme of the present invention further provides a storage medium, wherein at least one program code is stored in the storage medium, and when the program code is loaded and executed by a processor, the hyper-fusion cloud computing method is implemented.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of acquiring position information, a network protocol and equipment parameters of a user in a registration link, establishing a reference virtual unit according to the equipment parameters, calculating a data transmission speed according to the position information and the network protocol, and correcting the reference virtual unit according to the data transmission speed; establishing a service cloud picture according to the corrected virtual unit, and processing based on the service cloud picture when an operation task is received; the parameter of data transmission speed is introduced in the process of establishing the service cloud picture, so that the prediction accuracy of the operation process is greatly improved, and the use experience of a user is met in a phase-changing manner.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
FIG. 1 is a flow diagram of a hyper-converged cloud computing method.
FIG. 2 is a first sub-flow block diagram of a hyper-converged cloud computing method.
FIG. 3 is a second sub-flow block diagram of a hyper-converged cloud computing method.
Fig. 4 is a third sub-flow block diagram of the super-fusion cloud computing method.
FIG. 5 is a fourth sub-flow block diagram of a hyper-converged cloud computing method.
FIG. 6 is a block diagram of a constituent structure of a super-converged cloud computing system.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Example 1
Fig. 1 is a flow chart of a super-fusion cloud computing method, and in an embodiment of the present invention, the super-fusion cloud computing method includes:
step S100: receiving a registration request containing user information input by a server, and acquiring position information, a network protocol and equipment parameters of a user;
the super-fusion is an improvement based on the existing cloud computing platform. The cloud data center based on the super-fusion architecture is characterized in that a large number of software-defined technologies are adopted, the computing, the storage, the network and the special hardware are decoupled, the real fusion of the IT infrastructure is realized, and the last obstacle is cleared for the implementation and the deployment of the cloud data center. It has been a clear trend to provide cloud computing services using a super converged architecture. The software helps a user to integrate the server, the network, the virtualization and the like into an integrated system easy to manage, manual operation is reduced through automatic operation and maintenance, safety is improved, human errors are reduced, implementation and operation and maintenance risks are reduced, and operation cost is reduced.
The super-fusion technology is realized by the principle that Software Defined Storage (SDS) is used for replacing SAN in the traditional fusion system, and the main component is software defined storage and (server) virtualization constructed on standard server hardware.
In general, the super-fusion technology is to establish a plurality of connected virtual machines according to the existing service equipment; the existing service equipment is provided by a server, and the server can be a personal computer of a certain user or a server cluster of a certain enterprise; when receiving the super convergence technology platform, the location information, the network protocol and the device parameters need to be uploaded.
Step S200: calculating a negative influence factor according to the position information and a network protocol, correcting the equipment parameter according to the negative influence factor, and generating a virtual unit according to the corrected equipment parameter and the user information;
different from personal equipment, a cloud computing platform is not enough due to the fact that the light is faster and the number of cores of the CPU is more and more, and the bottleneck is that a traditional storage hard disk is too slow, most computing power of the CPU is idle or the CPU waits for the transmission of storage data. Therefore, the data transmission speed has a great influence on the task processing process, and in the technical scheme of the invention, the negative influence based on the data transmission speed is added and used for adjusting the equipment parameters of the server.
Step S300: connecting the virtual units and establishing a service cloud picture;
as can be seen from the above, the virtual unit is a virtual machine obtained by performing "subtractive configuration" on the basis of the server, and the virtual machines are connected with each other, so that a service cloud graph can be established.
Step S400: receiving a service request which is input by a user end and contains position information and a work task, selecting a virtual unit in the service cloud picture according to the position information and the work task, and sending an operation task to the virtual unit;
the user end is a port with calculation requirements, a service request containing position information and a work task is input into the user end by a user, and the service cloud picture executes an operation task according to the position information and the work task.
Fig. 2 is a first sub-flow block diagram of the hyper-convergence cloud computing method, where the step of receiving a registration request containing user information input by a server and acquiring location information, a network protocol, and device parameters of a user includes steps S101 to S104:
step S101: receiving a registration request containing user information input by a server, inputting the user information into a registered library for record, and inquiring the registered calculated amount corresponding to the user;
step S102: determining the level of the user according to the registered calculation amount; the level of the user which is registered for the first time is a default value; the level is used for distinguishing user types;
step S103: sending a preset test file to a server, and acquiring equipment parameters of a user based on the test file;
step S104: and sending an authority acquisition request to the user, and acquiring the position information and the network protocol of the user according to the acquired authority.
The registration process is specifically described in steps S101 to S104, when a registration request input by a user through a server is received, whether the user has a registration behavior and a registration calculation amount before the registration, a user level can be determined according to the registered calculation amount, the user level is used for distinguishing a user type, such as a Vip user, and in a subsequent service providing process, the priority can be appropriately adjusted or the cost can be reduced.
For the equipment parameters, the cloud platform sets the test files, the equipment parameters of the server are automatically obtained when the test files are sent to the server, and the process can be added with an authority detection process, namely, the test files can be operated only when the user allows the process.
Further, on the basis of acquiring the device parameters, the position information and the network protocol of the user are also required to be acquired for calculating the data transmission speed.
Fig. 3 is a second sub-flow block diagram of the super-convergence cloud computing method, where the step of computing a negative impact factor according to the location information and the network protocol, correcting the device parameter according to the negative impact factor, and generating a virtual unit according to the corrected device parameter and the user information includes steps S201 to S204:
step S201: calculating the transmission speed according to the position information and the network protocol;
step S202: inputting the transmission speed into a trained influence judgment model, and determining a negative influence factor; the negative influence factor is used for representing the negative influence characteristics of the transmission process on the equipment;
step S203: correcting the equipment parameters according to the negative influence factors; the equipment parameters comprise the CPU operation speed and the storage speed of a memory;
step S204: and generating a user label according to the user information, inserting the user label into the corrected equipment parameter, and generating a virtual unit.
The transmission speed can be calculated by the position information and the network protocol, a corresponding relation exists between the transmission speed and the influence capacity, and the corresponding relation can be determined by actual conditions by means of a sampling fitting method, namely the process of generating the influence judgment model; inputting the transmission speed into an influence judgment model to obtain a negative influence factor, adjusting equipment parameters according to the negative influence factor, and generating a virtual unit according to the adjusted equipment parameters; and inserting a user tag into the virtual unit.
Fig. 4 is a third sub-flow block diagram of the super-convergence cloud computing method, where the step of connecting the virtual units and establishing the service cloud graph includes steps S301 to S303:
step S301: reading the position information of each virtual unit, and calculating the distance between each virtual unit according to the position information;
step S302: acquiring display parameters, and determining a reference cloud picture containing a scale according to the maximum distance and the display parameters;
step S303: inserting display points corresponding to the virtual units in the reference cloud picture based on the scale.
The steps from S301 to S303 define the process of establishing the service cloud map, and the core process of the process lies in the matching process, i.e. the process of determining the scale, and a reference cloud map corresponding to the actual environment is established according to the scale; and inserting a display point corresponding to the virtual unit into the reference cloud picture.
Fig. 5 is a fourth sub-flow block diagram of the super-fusion cloud computing method, where the step of receiving a service request including location information and a work task input by a user, selecting a virtual unit in the service cloud graph according to the location information and the work task, and sending an operation task to the virtual unit includes steps S401 to S403:
step S401: receiving a service request input by a user end, acquiring position information of the user end, and inquiring and marking a corresponding display point in the service cloud picture according to the position information;
step S402: the method comprises the steps of obtaining a work task sent by a using end, sending the work task to a detecting end, and receiving an operation task group fed back by the detecting end; a trained recognition model is arranged in the detection end;
step S403: and selecting virtual units by taking the marked display points as centers based on the operation task group, and sending the operation tasks to the virtual units.
The user end can be one of the service ends or an external user end, and the virtual unit with the shorter distance can be selected from the service cloud picture according to the position information of the user end; when a work task sent by a user end is received, the work task is sent to a detection end, the detection end interacts with a detection party, a trained recognition model is arranged in the detection end, and the segmentation efficiency and the segmentation accuracy of the work task are guaranteed by adopting a mode of manually matching the recognition model.
Further, the step of selecting a virtual unit based on the operation task group with the marked display point as a center and sending the operation task to each virtual unit includes:
calculating time conditions and parameter conditions of each operation task;
sequentially inquiring the equipment parameters of the virtual units by taking the marked display point as a center and a preset incremental numerical value as a radius;
comparing the equipment parameters with the parameter conditions, and acquiring the computation amount of the computation task when the equipment parameters meet the parameter conditions;
calculating the consumption time according to the operation amount and the equipment parameters, and comparing the consumption time with the time condition;
and when the consumed time meets the time condition, sending the operation task to the virtual unit.
The selection process of the virtual units is described, for the work tasks input by the user, the work tasks are firstly divided into a plurality of operation tasks, each operation task has a time condition and a parameter condition, the selection process of the virtual units is carried out on each operation task, the equipment parameters of the surrounding virtual units and the two conditions are sequentially compared by taking a display point as a center, and then the virtual units meeting the conditions can be determined.
Example 2
Fig. 6 is a block diagram of a composition structure of a super-fusion cloud computing system, in an embodiment of the present invention, a super-fusion cloud computing system includes:
the parameter acquiring module 11 is configured to receive a registration request containing user information input by a server, and acquire location information, a network protocol, and device parameters of a user;
a virtual unit generating module 12, configured to calculate a negative impact factor according to the location information and a network protocol, correct the device parameter according to the negative impact factor, and generate a virtual unit according to the corrected device parameter and the user information;
a service cloud map generating module 13, configured to connect the virtual unit and establish a service cloud map;
and the task execution module 14 is configured to receive a service request including location information and a work task input by a user, select a virtual unit in the service cloud graph according to the location information and the work task, and send an operation task to the virtual unit.
Wherein, the parameter obtaining module 11 includes:
the information inquiry unit is used for receiving a registration request containing user information input by the server, inputting the user information into a registered library for recording and inquiring the registered calculated amount corresponding to the user;
a level determination unit configured to determine a level of the user based on the registered calculation amount; the level of the user registered for the first time is a default value; the level is used for distinguishing user types;
the file testing unit is used for sending a preset test file to the server and acquiring the equipment parameters of the user based on the test file;
and the permission application unit is used for sending a permission acquisition request to the user and acquiring the position information and the network protocol of the user according to the acquired permission.
The functions that can be implemented by the hyper-converged cloud computing method are all performed by a computer device comprising one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and loaded and executed by the one or more processors to implement the functions of the hyper-converged cloud computing method.
The processor fetches instructions and analyzes the instructions one by one from the memory, then completes corresponding operations according to the instruction requirements, generates a series of control commands, enables all parts of the computer to automatically, continuously and coordinately act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
Illustratively, a computer program can be partitioned into one or more modules, which are stored in memory and executed by a processor to implement the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, the instruction segments being used to describe the execution of the computer program in the terminal device.
It will be appreciated by those skilled in the art that the above description of the serving device is merely an example and does not constitute a limitation of the terminal device, and may include more or less components than those described above, or some of the components may be combined, or different components may include, for example, input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory mainly comprises a storage program area and a storage data area, wherein the storage program area can store an operating system, application programs (such as an information acquisition template display function, a product information publishing function and the like) required by at least one function and the like; the storage data area may store data created according to the use of the berth status display system (such as product information acquisition templates corresponding to different product categories, product information that needs to be issued by different product providers, and the like). In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the modules/units in the system according to the above embodiment may also be implemented by instructing relevant hardware by a computer program, and the computer program may be stored in a computer-readable storage medium, and when executed by a processor, the computer program may implement the functions of the above embodiments of the system. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
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 apparatus 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 apparatus. Without further limitation, an element defined by the phrases "comprising a component of' 8230; \8230;" does not exclude the presence of another like element in a process, method, article, or apparatus that comprises the element.
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 (10)
1. A hyper-converged cloud computing method, the method comprising:
receiving a registration request containing user information input by a server, and acquiring position information, a network protocol and equipment parameters of a user;
calculating a negative influence factor according to the position information and a network protocol, correcting the equipment parameter according to the negative influence factor, and generating a virtual unit according to the corrected equipment parameter and the user information;
connecting the virtual units and establishing a service cloud picture;
receiving a service request which is input by a user end and contains position information and a work task, selecting a virtual unit in the service cloud picture according to the position information and the work task, and sending an operation task to the virtual unit.
2. The super-convergence cloud computing method according to claim 1, wherein the step of receiving a registration request containing user information input by a server and acquiring location information, network protocols and device parameters of a user comprises:
receiving a registration request containing user information input by a server, inputting the user information into a registered library for record, and inquiring the registered calculated amount corresponding to the user;
determining the level of the user according to the registered calculation amount; the level of the user registered for the first time is a default value; the level is used for distinguishing user types;
sending a preset test file to a server, and acquiring equipment parameters of a user based on the test file;
and sending an authority acquisition request to the user, and acquiring the position information and the network protocol of the user according to the acquired authority.
3. The super-convergence cloud computing method of claim 1, wherein the steps of computing a negative influence factor according to the location information and a network protocol, correcting the device parameter according to the negative influence factor, and generating a virtual unit according to the corrected device parameter and the user information comprise:
calculating the transmission speed according to the position information and the network protocol;
inputting the transmission speed into a trained influence judgment model, and determining a negative influence factor; the negative influence factor is used for representing the negative influence characteristics of the transmission process on the equipment;
correcting the equipment parameters according to the negative influence factors; the equipment parameters comprise the CPU operation speed and the storage speed of a memory;
and generating a user label according to the user information, inserting the user label into the corrected equipment parameter, and generating a virtual unit.
4. The super-converged cloud computing method according to claim 1, wherein the step of connecting the virtual units and establishing a service cloud graph comprises:
reading the position information of each virtual unit, and calculating the distance between each virtual unit according to the position information;
acquiring display parameters, and determining a reference cloud picture containing a scale according to the maximum distance and the display parameters;
inserting display points corresponding to the virtual units in the reference cloud picture based on the scale.
5. The super-fusion cloud computing method according to claim 1, wherein the step of receiving a service request containing position information and a work task input by a user, selecting a virtual unit in the service cloud image according to the position information and the work task, and sending an operation task to the virtual unit comprises:
receiving a service request input by a user end, acquiring position information of the user end, and inquiring and marking a corresponding display point in the service cloud picture according to the position information;
the method comprises the steps of obtaining a work task sent by a using end, sending the work task to a detecting end, and receiving an operation task group fed back by the detecting end; a trained recognition model is arranged in the detection end;
and selecting virtual units by taking the marked display points as centers based on the operation task group, and sending the operation tasks to the virtual units.
6. The super-fusion cloud computing method of claim 5, wherein the step of selecting virtual units based on the operation task group with marked display points as centers and sending the operation tasks to the virtual units comprises:
calculating time conditions and parameter conditions of each operation task;
sequentially inquiring the equipment parameters of the virtual units by taking the marked display point as a center and a preset incremental numerical value as a radius;
comparing the equipment parameters with the parameter conditions, and acquiring the operation amount of an operation task when the equipment parameters meet the parameter conditions;
calculating the consumed time according to the operation amount and the equipment parameters, and comparing the consumed time with the time condition;
and when the consumed time meets the time condition, sending the operation task to the virtual unit.
7. A hyper-converged cloud computing system, the system comprising:
the parameter acquisition module is used for receiving a registration request containing user information input by the server and acquiring the position information, the network protocol and the equipment parameters of the user;
a virtual unit generating module, configured to calculate a negative impact factor according to the location information and a network protocol, correct the device parameter according to the negative impact factor, and generate a virtual unit according to the corrected device parameter and the user information;
the service cloud picture generating module is used for connecting the virtual units and establishing a service cloud picture;
and the task execution module is used for receiving a service request which is input by a user end and contains position information and a work task, selecting a virtual unit in the service cloud picture according to the position information and the work task, and sending an operation task to the virtual unit.
8. The super-fusion cloud computing system of claim 7, wherein the parameter acquisition module comprises:
the information inquiry unit is used for receiving a registration request containing user information input by the server, inputting the user information into a registered library for recording, and inquiring the registered calculation amount corresponding to the user;
a level determination unit configured to determine a level of the user according to the registered calculation amount; the level of the user registered for the first time is a default value; the level is used for distinguishing user types;
the file testing unit is used for sending a preset test file to the server and acquiring the equipment parameters of the user based on the test file;
and the permission application unit is used for sending a permission acquisition request to the user and acquiring the position information and the network protocol of the user according to the acquired permission.
9. A computer device comprising one or more processors and one or more memories having at least one program code stored therein, the program code when loaded and executed by the one or more processors implementing the hyper-converged cloud computing method of any one of claims 1-6.
10. A storage medium having at least one program code stored therein, the program code when loaded and executed by a processor implementing the hyper-converged cloud computing method of any one of claims 1-6.
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