CN114257640A - Cloud computing scheduling method and system - Google Patents
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Abstract
The invention relates to the technical field of resource planning, and particularly discloses a cloud computing scheduling method and a cloud computing scheduling system, wherein the method comprises the steps of receiving a computing request containing computing tasks sent by a user, and determining a service cloud deck; establishing a connection channel between a service holder and a user, acquiring uploaded data of the user in real time based on the connection channel, intercepting a data packet within a preset time range, and determining a task type of a calculation task according to the data packet; acquiring a computing protocol of a user, and determining computing resources according to the computing protocol and the task type; and monitoring the occupancy rate of the service cloud deck in real time, and establishing a connection channel between the service cloud deck and a preset auxiliary platform when the occupancy rate reaches a preset occupancy threshold value. According to the invention, the task type is determined by detecting the transmission data, and the resource scheduling mode is determined according to the task type and the computing protocol of the user and the system, so that the scheduling scheme is complete, and the orderliness of the cloud computing service process is greatly improved.
Description
Technical Field
The invention relates to the technical field of resource planning, in particular to a cloud computing scheduling method and system.
Background
In the present life, we can often hear terms of big data, cloud computing and the like, and the terms in the computer field are already spread to the public society enough to prove the degree of fire explosion of the two technologies, and also prove the prospects of the two technologies from the side, wherein the big data is easy to understand, that is, with the improvement of storage technologies, data generated by people can be stored, so that extremely large and complex data is generated, and most people want to understand the data with difficulty for the cloud computing.
There are many explanations of cloud computing, and one popular explanation is that a user terminal can obtain computing resources of other devices by means of a network, so as to obtain stronger computing power, and as for the process of obtaining computing resources of other devices by means of the network, a provider is usually provided to provide the service, but the existing cloud computing provider is often not perfect for a scheduling scheme of resource scheduling, which is also a technical problem that the technical scheme of the present invention is intended to solve.
Disclosure of Invention
The invention aims to provide a cloud computing scheduling method and a cloud computing scheduling system to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a cloud computing scheduling method, the method comprising:
receiving a calculation request containing a calculation task sent by a user, acquiring position data of the user, and determining a service holder according to the position data;
establishing a connection channel between a service holder and a user, acquiring uploaded data of the user in real time based on the connection channel, intercepting a data packet within a preset time range, and determining a task type of a calculation task according to the data packet;
acquiring the equipment authority of a user, acquiring a computing protocol of the user according to the equipment authority, and determining computing resources according to the computing protocol and the task type;
and monitoring the occupancy rate of the service cloud deck in real time, and establishing a connection channel between the service cloud deck and a preset auxiliary platform when the occupancy rate reaches a preset occupancy threshold value.
As a further scheme of the invention: the step of obtaining location data of a user comprises:
sending first short message data to a positioning server, wherein the first short message data comprises a positioning request;
receiving and analyzing second short message data, wherein the second short message data is generated by the positioning server according to satellite capturing auxiliary data;
generating pseudo-range measurement data based on the analyzed second short message data, converting the pseudo-range measurement data into third short message data, and sending the third short message data to a positioning server;
and receiving fourth short message data, wherein the fourth short message data is generated by the positioning server according to the position information of the terminal and comprises a position name.
As a further scheme of the invention: the step of intercepting the data packet within the preset time range and determining the task type of the calculation task according to the data packet comprises the following steps:
monitoring the network transmission speed in the transmission process in real time to obtain a transmission table containing a time item and a speed item;
intercepting a data packet in a preset time range, recording the uploading time and the transmission time of the data packet, and classifying the data packet according to the uploading time to obtain a data packet group; wherein, the index item of the data packet group is the uploading time, and the data packet comprises the transmission time;
reading the network transmission speed in the transmission table according to the uploading time, and calculating the data volume of different data packets in different data packet groups according to the transmission time and the network transmission speed to obtain a data table containing an uploading time item and a data volume item;
and determining the task type of the calculation task according to the data table.
As a further scheme of the invention: the step of determining the task type of the calculation task according to the data table comprises the following steps:
determining coordinates by taking the uploading time as an independent variable and the data amount as a dependent variable, and fitting the coordinates to generate a fluctuation curve;
inserting the fluctuation curve into a preset background image according to a preset color value to obtain a fluctuation image;
carrying out contour recognition on the fluctuating image, and determining and marking an image inflection point according to the contour recognition result;
and determining a characteristic dot matrix according to the image inflection point, traversing a preset reference database according to the characteristic dot matrix, and determining the task type of the calculation task.
As a further scheme of the invention: the step of performing contour recognition on the fluctuating image, and determining and marking an image inflection point according to the contour recognition result comprises the following steps of:
marking pixel points in the fluctuating image according to a preset color value to obtain a curve profile;
sequentially traversing pixel points in the curve contour according to a preset identification direction;
intercepting the curve contour within a preset identification range by taking pixel points in the curve contour as circle centers, and marking the intercepted points;
generating a fitting circle according to the intercept point, calculating the contact ratio of the fitting circle and the curve contour, and acquiring the radius of the fitting circle when the contact ratio is greater than a preset contact threshold value;
and when the contact ratio is smaller than a preset contact threshold or the radius of the fitting circle is smaller than a preset radius threshold, marking the pixel point as an image inflection point.
As a further scheme of the invention: the step of determining a characteristic lattice according to the image inflection point comprises the following steps:
reading coordinates of the image inflection points, and sequencing the coordinates according to the abscissa;
and screening the sorted coordinates according to a preset interception step length to obtain a characteristic dot matrix.
As a further scheme of the invention: the method further comprises the following steps:
when a calculation request sent by a user is received, generating an access report, wherein the access report comprises a position name item and an access frequency item;
traversing an access report based on the position name, and reading the access times corresponding to the position name when the access report contains the position name;
when the position name is not contained in the access report, inserting the position name into the access report, and assigning the corresponding access times as one;
and determining a risk level according to the access times corresponding to the position name, and reading a preset verification scheme according to the risk level.
The technical scheme of the invention also provides a cloud computing scheduling system, which comprises:
the service cloud platform determining module is used for receiving a calculation request containing a calculation task sent by a user, acquiring position data of the user and determining a service cloud platform according to the position data;
the task identification module is used for establishing a connecting channel between the service holder and the user, acquiring uploaded data of the user in real time based on the connecting channel, intercepting a data packet within a preset time range, and determining a task type of a calculation task according to the data packet;
the resource allocation module is used for acquiring the equipment authority of the user, acquiring the computing protocol of the user according to the equipment authority, and determining computing resources according to the computing protocol and the task type;
and the auxiliary operation module is used for monitoring the occupancy rate of the service cloud deck in real time, and when the occupancy rate reaches a preset occupancy threshold value, a connecting channel between the service cloud deck and a preset auxiliary platform is established.
As a further scheme of the invention: the task identification module comprises:
the transmission table generating unit is used for monitoring the network transmission speed in the transmission process in real time to obtain a transmission table containing a time item and a speed item;
the data packet group generating unit is used for intercepting data packets within a preset time range, recording the uploading time and the transmission time of the data packets, and classifying the data packets according to the uploading time to obtain a data packet group; wherein, the index item of the data packet group is the uploading time, and the data packet comprises the transmission time;
the data table generating unit is used for reading the network transmission speed in the transmission table according to the uploading time, calculating the data volume of different data packets in different data packet groups according to the transmission time and the network transmission speed, and obtaining a data table containing an uploading time item and a data volume item;
and the processing execution unit is used for determining the task type of the calculation task according to the data table.
As a further scheme of the invention: the process execution unit includes:
the fitting subunit is used for determining coordinates by taking the uploading time as an independent variable and the data amount as a dependent variable, and fitting the coordinates to generate a fluctuation curve;
the image generation subunit is used for inserting the fluctuation curve into a preset background image according to a preset color value to obtain a fluctuation image;
the inflection point marking subunit is used for carrying out contour identification on the fluctuating image, and determining and marking an image inflection point according to the contour identification result;
and the traversal identification subunit determines a characteristic dot matrix according to the image inflection point, and traverses a preset reference database according to the characteristic dot matrix to determine the task type of the calculation task.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the task type is determined by detecting the transmission data, and the resource scheduling mode is determined according to the task type and the computing protocol of the user and the system, so that the scheduling scheme is complete, and the orderliness of the cloud computing service process is greatly improved.
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 shows a flow diagram of a cloud computing scheduling method.
Fig. 2 shows a first sub-flow block diagram of a cloud computing scheduling method.
Fig. 3 shows a second sub-flow block diagram of a cloud computing scheduling method.
Fig. 4 shows a third sub-flow block diagram of a cloud computing scheduling method.
Fig. 5 shows a block diagram of a component structure of the cloud computing scheduling system.
Fig. 6 is a block diagram illustrating a structure of a task identification module in the cloud computing scheduling system.
Fig. 7 is a block diagram showing a constitutional structure of a processing execution unit in the task recognition module.
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 are not intended to limit the invention.
Example 1
Fig. 1 shows a flow chart of a cloud computing scheduling method, and in an embodiment of the present invention, a cloud computing scheduling method includes steps S100 to S400:
step S100: receiving a calculation request containing a calculation task sent by a user, acquiring position data of the user, and determining a service holder according to the position data;
the purpose of step S100 is to select the most suitable service cloud deck according to the user location, and in the cloud computing process, the computing end and the user end are often spatially separated, which relates to the network transmission process, and although the current network transmission speed is very fast and the spatial factor can be ignored, when the data amount reaches a certain degree, the spatial factor is amplified, the transmission time is one aspect, and the interference in the transmission process is one aspect.
Step S200: establishing a connection channel between a service holder and a user, acquiring uploaded data of the user in real time based on the connection channel, intercepting a data packet within a preset time range, and determining a task type of a calculation task according to the data packet;
the function of step S200 is a core function of the technical solution of the present invention, and is to identify the uploaded data of the user, and of course, in this identification process, a label may be added to the uploaded data, and then the service cradle head determines the task type of the uploaded data according to the label. The process is convenient, labels are data related to user information, but the mode is suitable for packaged data, the technical scheme of the invention is in the field of cloud computing, tasks are uploaded continuously, background continuous operation is performed, then the data interaction process is not stopped, the process is a real-time process, the task is not uploaded by a user, and the packaged result is acquired after two days. Therefore, the above-mentioned seemingly simple "label" approach, which is a complex procedure, is not without limitation, and the process of receiving the label and identifying the label must be a separate process, for example, identification of the label is performed first, and then the task is calculated, which is performed separately in the background, and the combination of these processes affects the accuracy of label identification.
Step S300: acquiring the equipment authority of a user, acquiring a computing protocol of the user according to the equipment authority, and determining computing resources according to the computing protocol and the task type;
step S300 is a specific scheduling step, and for the calculation protocol, for example, for the Vip user and the general user, it is certainly different, and the reason why this part needs the device authority is that the user information needs to be read according to the device authority, and then the calculation protocol is determined according to the user information.
Step S400: and monitoring the occupancy rate of the service cloud deck in real time, and establishing a connection channel between the service cloud deck and a preset auxiliary platform when the occupancy rate reaches a preset occupancy threshold value.
Step S400 is an auxiliary step, and the purpose of the step is to meet the calculation requirement of the customer as much as possible, and therefore, some auxiliary platforms are provided, and the auxiliary platforms are put into use under the condition that the pressure of the service platform is large.
As a preferred embodiment of the technical solution of the present invention, the step of acquiring the location data of the user includes:
sending first short message data to a positioning server, wherein the first short message data comprises a positioning request;
receiving and analyzing second short message data, wherein the second short message data is generated by the positioning server according to satellite capturing auxiliary data;
generating pseudo-range measurement data based on the analyzed second short message data, converting the pseudo-range measurement data into third short message data, and sending the third short message data to a positioning server;
and receiving fourth short message data, wherein the fourth short message data is generated by the positioning server according to the position information of the terminal and comprises a position name.
The positioning request is carried out in a short message mode, and the signal transmission mode is more stable and more stable than a wireless network transmission signal; the first short message data includes a positioning request, and the purpose of the first short message data is to send a request, and certainly, other information, such as user identity information and the like, may be assisted, which is not described in detail, and only needs to achieve the purpose of sending the request.
After receiving the first short message data, the positioning server sends second short message data to the terminal, wherein the second short message data is generated by the positioning server according to satellite acquisition auxiliary data, the step is a feedback signal sent by the positioning server and a request signal, the positioning server sends a request to the terminal, and the request content is pseudo-range measurement data acquisition, so that the positioning process is more accurate, and the step is a common step in positioning service; and finally, the terminal receives the second short message data and analyzes the second short message data, namely, analyzes the request sent by the positioning server and confirms the data required by the positioning obedient.
The second short message data is analyzed to obtain various data required by the positioning server, and pseudo-range measurement data is generated based on the data, the process belongs to a conventional positioning service process, and step S23 is actually a signal acquisition process to acquire data required by the positioning server to make accurate positioning.
The positioning server receives the third short message data, generates fourth short message data based on the third short message data and sends the fourth short message data to the terminal equipment; the fourth short message data is generated by the positioning server according to the position information of the terminal, the fourth short message data comprises a position name, the position name is necessary data, and other data are not indispensable data of the invention.
Fig. 2 shows a first sub-flow block diagram of a cloud computing scheduling method, where the step of intercepting a data packet within a preset time range and determining a task type of a computing task according to the data packet includes steps S201 to S204:
step S201: monitoring the network transmission speed in the transmission process in real time to obtain a transmission table containing a time item and a speed item;
step S202: intercepting a data packet in a preset time range, recording the uploading time and the transmission time of the data packet, and classifying the data packet according to the uploading time to obtain a data packet group; wherein, the index item of the data packet group is the uploading time, and the data packet comprises the transmission time;
step S203: reading the network transmission speed in the transmission table according to the uploading time, and calculating the data volume of different data packets in different data packet groups according to the transmission time and the network transmission speed to obtain a data table containing an uploading time item and a data volume item;
step S204: and determining the task type of the calculation task according to the data table.
The determination process of the task type is specifically refined in steps S201 to S204, data is transmitted in the form of data packets in the transmission process, the data packets are relatively small, the transmission sequence of the data packets is likely to be random, and the calculation process of the data amount of the data packets is calculated through the network transmission speed and the transmission time, and the network transmission speed and the transmission time are related, so that the data packets uploaded at different times are classified, and then the corresponding network transmission speed is read, and the data amount of the uploading time can be calculated.
It should be noted that the upload time is preset discrete data, and may be, for example, 7: 00. 7: 05. 7: the uploading time of 10 types can be 7: 00. 7: 01. 7: 02, it is contemplated that the shorter the time interval, the more data table elements and the more accurate.
Fig. 3 shows a second sub-flow block diagram of the cloud computing scheduling method, and the step of determining the task type of the computing task according to the data table includes steps S2041 to S2044:
step S2041: determining coordinates by taking the uploading time as an independent variable and the data amount as a dependent variable, and fitting the coordinates to generate a fluctuation curve;
step S2042: inserting the fluctuation curve into a preset background image according to a preset color value to obtain a fluctuation image;
step S2043: carrying out contour recognition on the fluctuating image, and determining and marking an image inflection point according to the contour recognition result;
step S2044: and determining a characteristic dot matrix according to the image inflection point, traversing a preset reference database according to the characteristic dot matrix, and determining the task type of the calculation task.
Step S2041 to step S2044 further describe the process of determining the task type of the calculation task according to the data table, and the principle is to convert the table into visual data, determine information points through an image processing technique, and then compare the information points with reference information points, thereby determining the task type. The wave images generated by each task type are different and represent the characteristics of the task type.
Fig. 4 shows a third sub-flow block diagram of the cloud computing scheduling method, where the step of performing contour recognition on the fluctuating image, and determining and marking an image inflection point according to the contour recognition result includes steps S20431 to S20435:
step S20431: marking pixel points in the fluctuating image according to a preset color value to obtain a curve profile;
step S20432: sequentially traversing pixel points in the curve contour according to a preset identification direction;
step S20433: intercepting the curve contour within a preset identification range by taking pixel points in the curve contour as circle centers, and marking the intercepted points;
step S20434: generating a fitting circle according to the intercept point, calculating the contact ratio of the fitting circle and the curve contour, and acquiring the radius of the fitting circle when the contact ratio is greater than a preset contact threshold value;
step S20435: and when the contact ratio is smaller than a preset contact threshold or the radius of the fitting circle is smaller than a preset radius threshold, marking the pixel point as an image inflection point.
The principle of the technical scheme is that a pixel point is taken as a center, one section of a curve contour is intercepted, the number of the intercepted points is two, a circle center is added, a circle can be determined, the intercepted points and the circle center are all points on a fitting circle, if the contact ratio between the fitting circle and the intercepted curve contour is high, the section is free of the inflection point, and if the contact ratio is low, or the fitting circle is small, the section is free of the inflection point.
It is worth mentioning that the calculation process of the contact ratio can sequentially calculate the difference between the fitting circle and the curve profile based on the abscissa, and determine the contact ratio according to the difference.
Specifically, the step of determining the characteristic lattice according to the image inflection point includes:
reading coordinates of the image inflection points, and sequencing the coordinates according to the abscissa;
and screening the sorted coordinates according to a preset interception step length to obtain a characteristic dot matrix.
And (4) screening the coordinates of the image inflection points to obtain a coordinate set, namely the characteristic dot matrix.
As a preferred embodiment of the technical solution of the present invention, the method further comprises:
when a calculation request sent by a user is received, generating an access report, wherein the access report comprises a position name item and an access frequency item;
traversing an access report based on the position name, and reading the access times corresponding to the position name when the access report contains the position name;
when the position name is not contained in the access report, inserting the position name into the access report, and assigning the corresponding access times as one;
and determining a risk level according to the access times corresponding to the position name, and reading a preset verification scheme according to the risk level.
The above is an authentication for a user, specifically, according to the access location of the user, if the user accesses the same location for multiple times, the location is a safe location, and if the location changes, it is likely that the user account is lost, and therefore, further authentication is required. The verification scheme may be face recognition or other verification means.
Example 2
Fig. 5 is a block diagram illustrating a configuration of a cloud computing scheduling system, and in an embodiment of the present invention, a cloud computing scheduling system 10 includes:
the service cloud platform determining module 11 is configured to receive a calculation request containing a calculation task sent by a user, acquire position data of the user, and determine a service cloud platform according to the position data;
the task identification module 12 is used for establishing a connection channel between the service holder and the user, acquiring uploaded data of the user in real time based on the connection channel, intercepting a data packet within a preset time range, and determining a task type of a calculation task according to the data packet;
the resource allocation module 13 is configured to obtain an equipment right of a user, obtain a computing protocol of the user according to the equipment right, and determine computing resources according to the computing protocol and the task type;
and the auxiliary operation module 14 is configured to monitor the occupancy rate of the service cloud deck in real time, and when the occupancy rate reaches a preset occupancy threshold, establish a connection channel between the service cloud deck and a preset auxiliary platform.
Fig. 6 is a block diagram illustrating a structure of a task identification module in a cloud computing scheduling system, where the task identification module 12 includes:
a transmission table generating unit 121, configured to monitor a network transmission speed in a transmission process in real time to obtain a transmission table containing a time item and a speed item;
a data packet group generating unit 122, configured to intercept a data packet within a preset time range, record upload time and transmission time of the data packet, and classify the data packet according to the upload time to obtain a data packet group; wherein, the index item of the data packet group is the uploading time, and the data packet comprises the transmission time;
a data table generating unit 123, configured to read a network transmission speed in the transmission table according to the upload time, and calculate data volumes of different data packets in different data packet groups according to the transmission time and the network transmission speed, so as to obtain a data table containing an upload time item and a data volume item;
and the processing execution unit 124 is used for determining the task type of the calculation task according to the data table.
Fig. 7 is a block diagram illustrating a structure of a process execution unit in the task identification module, where the process execution unit 124 includes:
a fitting subunit 1241, configured to determine a coordinate with the upload time as an independent variable and the data amount as a dependent variable, and fit the coordinate to generate a fluctuation curve;
an image generation subunit 1242, configured to insert the fluctuation curve into a preset background image according to a preset color value, to obtain a fluctuation image;
a corner marking subunit 1243, configured to perform contour identification on the fluctuating image, and determine and mark an image corner according to the contour identification result;
and traversing the identifying subunit 1244, determining a feature lattice according to the image inflection point, and traversing a preset reference database according to the feature lattice to determine the task type of the calculation task.
The functions that can be realized by the cloud computing scheduling method are all completed by a computer device, the computer device comprises one or more processors and one or more memories, and at least one program code is stored in the one or more memories and is loaded and executed by the one or more processors to realize the functions of the cloud computing scheduling 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, which are used to describe the execution of the computer program in the terminal device.
Those skilled in the art will appreciate that the above description of the service device is merely exemplary and not limiting of the terminal device, and may include more or less components than those described, or combine certain components, or different components, such as may include 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-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), 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 be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the functions of the 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 phrase "comprising an … …" does not exclude the presence of other like elements 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 cloud computing scheduling method, the method comprising:
receiving a calculation request containing a calculation task sent by a user, acquiring position data of the user, and determining a service holder according to the position data;
establishing a connection channel between a service holder and a user, acquiring uploaded data of the user in real time based on the connection channel, intercepting a data packet within a preset time range, and determining a task type of a calculation task according to the data packet;
acquiring the equipment authority of a user, acquiring a computing protocol of the user according to the equipment authority, and determining computing resources according to the computing protocol and the task type;
and monitoring the occupancy rate of the service cloud deck in real time, and establishing a connection channel between the service cloud deck and a preset auxiliary platform when the occupancy rate reaches a preset occupancy threshold value.
2. The cloud computing scheduling method of claim 1, wherein the step of obtaining location data of a user comprises:
sending first short message data to a positioning server, wherein the first short message data comprises a positioning request;
receiving and analyzing second short message data, wherein the second short message data is generated by the positioning server according to satellite capturing auxiliary data;
generating pseudo-range measurement data based on the analyzed second short message data, converting the pseudo-range measurement data into third short message data, and sending the third short message data to a positioning server;
and receiving fourth short message data, wherein the fourth short message data is generated by the positioning server according to the position information of the terminal and comprises a position name.
3. The cloud computing scheduling method according to claim 1, wherein the step of intercepting a data packet within a preset time range and determining a task type of a computing task according to the data packet comprises:
monitoring the network transmission speed in the transmission process in real time to obtain a transmission table containing a time item and a speed item;
intercepting a data packet in a preset time range, recording the uploading time and the transmission time of the data packet, and classifying the data packet according to the uploading time to obtain a data packet group; wherein, the index item of the data packet group is the uploading time, and the data packet comprises the transmission time;
reading the network transmission speed in the transmission table according to the uploading time, and calculating the data volume of different data packets in different data packet groups according to the transmission time and the network transmission speed to obtain a data table containing an uploading time item and a data volume item;
and determining the task type of the calculation task according to the data table.
4. The cloud computing scheduling method of claim 3, wherein the step of determining the task type of the computing task from the data table comprises:
determining coordinates by taking the uploading time as an independent variable and the data amount as a dependent variable, and fitting the coordinates to generate a fluctuation curve;
inserting the fluctuation curve into a preset background image according to a preset color value to obtain a fluctuation image;
carrying out contour recognition on the fluctuating image, and determining and marking an image inflection point according to the contour recognition result;
and determining a characteristic dot matrix according to the image inflection point, traversing a preset reference database according to the characteristic dot matrix, and determining the task type of the calculation task.
5. The cloud computing scheduling method according to claim 4, wherein the step of performing contour recognition on the fluctuating image, and determining and marking an image inflection point according to the contour recognition result comprises:
marking pixel points in the fluctuating image according to a preset color value to obtain a curve profile;
sequentially traversing pixel points in the curve contour according to a preset identification direction;
intercepting the curve contour within a preset identification range by taking pixel points in the curve contour as circle centers, and marking the intercepted points;
generating a fitting circle according to the intercept point, calculating the contact ratio of the fitting circle and the curve contour, and acquiring the radius of the fitting circle when the contact ratio is greater than a preset contact threshold value;
and when the contact ratio is smaller than a preset contact threshold or the radius of the fitting circle is smaller than a preset radius threshold, marking the pixel point as an image inflection point.
6. The cloud computing scheduling method of claim 4, wherein the step of determining a feature lattice from the image inflection point comprises:
reading coordinates of the image inflection points, and sequencing the coordinates according to the abscissa;
and screening the sorted coordinates according to a preset interception step length to obtain a characteristic dot matrix.
7. The cloud computing scheduling method of claim 6, wherein the method further comprises:
when a calculation request sent by a user is received, generating an access report, wherein the access report comprises a position name item and an access frequency item;
traversing an access report based on the position name, and reading the access times corresponding to the position name when the access report contains the position name;
when the position name is not contained in the access report, inserting the position name into the access report, and assigning the corresponding access times as one;
and determining a risk level according to the access times corresponding to the position name, and reading a preset verification scheme according to the risk level.
8. A cloud computing scheduling system, the system comprising:
the service cloud platform determining module is used for receiving a calculation request containing a calculation task sent by a user, acquiring position data of the user and determining a service cloud platform according to the position data;
the task identification module is used for establishing a connecting channel between the service holder and the user, acquiring uploaded data of the user in real time based on the connecting channel, intercepting a data packet within a preset time range, and determining a task type of a calculation task according to the data packet;
the resource allocation module is used for acquiring the equipment authority of the user, acquiring the computing protocol of the user according to the equipment authority, and determining computing resources according to the computing protocol and the task type;
and the auxiliary operation module is used for monitoring the occupancy rate of the service cloud deck in real time, and when the occupancy rate reaches a preset occupancy threshold value, a connecting channel between the service cloud deck and a preset auxiliary platform is established.
9. The cloud computing scheduling system of claim 8, wherein the task identification module comprises:
the transmission table generating unit is used for monitoring the network transmission speed in the transmission process in real time to obtain a transmission table containing a time item and a speed item;
the data packet group generating unit is used for intercepting data packets within a preset time range, recording the uploading time and the transmission time of the data packets, and classifying the data packets according to the uploading time to obtain a data packet group; wherein, the index item of the data packet group is the uploading time, and the data packet comprises the transmission time;
the data table generating unit is used for reading the network transmission speed in the transmission table according to the uploading time, calculating the data volume of different data packets in different data packet groups according to the transmission time and the network transmission speed, and obtaining a data table containing an uploading time item and a data volume item;
and the processing execution unit is used for determining the task type of the calculation task according to the data table.
10. The cloud computing scheduling system of claim 9, wherein the processing execution unit comprises:
the fitting subunit is used for determining coordinates by taking the uploading time as an independent variable and the data amount as a dependent variable, and fitting the coordinates to generate a fluctuation curve;
the image generation subunit is used for inserting the fluctuation curve into a preset background image according to a preset color value to obtain a fluctuation image;
the inflection point marking subunit is used for carrying out contour identification on the fluctuating image, and determining and marking an image inflection point according to the contour identification result;
and the traversal identification subunit determines a characteristic dot matrix according to the image inflection point, and traverses a preset reference database according to the characteristic dot matrix to determine the task type of the calculation task.
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