CN117640627B - Collaborative work method and system realized through cloud computer - Google Patents
Collaborative work method and system realized through cloud computer Download PDFInfo
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Abstract
The disclosure provides a collaborative work method and a system realized by a cloud computer, which relate to the technical field of collaborative office, and the method comprises the following steps: acquiring a first interface, wherein the first interface is a working interface of a first user obtained from any one client at a first time; analyzing a first interface image time sequence of a first interface to obtain a first average change ratio; constructing a first candidate processing channel sequence by a first processing channel obtained based on a predetermined variation ratio-processing channel table matching; if the first processing channel is in an occupied condition, starting an optimal candidate processing channel in the candidate processing channels based on a preset collaborative optimization scheme to process cloud data on the first interface; and carrying out cooperative processing on the target work based on the first cloud processing result. The technical problem that the overall office efficiency is low due to unreasonable resource allocation when a user performs office work in cooperation with the cloud computer can be solved, and the overall office work efficiency of the user can be improved.
Description
Technical Field
The present disclosure relates to the field of collaborative office technology, and more particularly, to a collaborative work method and system implemented by a cloud computer.
Background
The cloud computer is an overall service scheme and comprises cloud resources, a transmission protocol and a cloud terminal. And providing resources such as desktop, application, hardware and the like for users in a service mode of on-demand service and flexible distribution through a transmission protocol by using the open cloud terminal.
At present, when a user performs collaborative office through a cloud computer, a system fails to configure proper data processing resources for the user according to the actual processing condition of overall user data, so that the overall resource configuration rationality is poor, and the overall collaborative office efficiency is affected.
The existing defects existing when the cloud computer is used for cooperative work are that: the overall collaborative office efficiency is low due to unreasonable resource allocation.
Disclosure of Invention
Therefore, in order to solve the above technical problems, the technical solution adopted in the embodiments of the present disclosure is as follows:
a collaborative work method implemented by a cloud computer, the method being applied to a collaborative work system implemented by a cloud computer, the system including a plurality of clients, and the plurality of clients being configured to perform collaborative processing on a target work, comprising the steps of: acquiring a first interface, wherein the first interface is a working interface of a first user obtained from any one of the plurality of clients at a first time; analyzing a first interface image time sequence of the first interface, which is obtained through monitoring by a CCD image sensor, to obtain a first average change ratio; constructing a first candidate processing channel sequence by a first processing channel for processing the first average variation ratio based on a predetermined variation ratio-processing channel table match, and the first candidate processing channel sequence including a plurality of candidate processing channels; if the first processing channel is in an occupied working condition, starting an optimal candidate processing channel in the candidate processing channels based on a preset collaborative optimization scheme to perform cloud data processing on the first interface, so as to obtain a first cloud processing result; and the first user performs cooperative processing on the target work based on the first cloud processing result.
A collaborative work system implemented by a cloud computer, the system comprising a plurality of clients, and the plurality of clients being configured to collaboratively process a target work, comprising: the first interface acquisition module is used for acquiring a first interface, wherein the first interface refers to a working interface of a first user obtained from any one of the plurality of clients at a first time; the first interface image time sequence analysis module is used for analyzing the first interface image time sequence of the first interface obtained through monitoring by the CCD image sensor to obtain a first average change ratio; a first candidate processing channel sequence construction module for constructing a first candidate processing channel sequence by a first processing channel for processing the first average change ratio obtained based on a predetermined change ratio-processing channel table match, and the first candidate processing channel sequence includes a plurality of candidate processing channels; the cloud data processing module is used for enabling the optimal candidate processing channel in the candidate processing channels to perform cloud data processing on the first interface based on a preset collaborative optimization scheme if the first processing channel is in an occupied working condition, so as to obtain a first cloud processing result; and the cooperative processing module is used for the first user to perform cooperative processing on the target work based on the first cloud processing result.
By adopting the technical method, compared with the prior art, the technical progress of the present disclosure has the following points:
(1) The method and the device can solve the technical problem that the overall office efficiency is low due to unreasonable resource allocation when users work cooperatively through cloud computers, and can enable the data processing resource allocation to be more reasonable and accurate by setting the cooperative optimization scheme to perform data processing for the optimal processing channel with the minimum time delay when the users match the overall response, so that the overall office efficiency of the users is improved.
(2) By carrying out interface feature contrast analysis based on a digital twin technology, the efficiency and accuracy of obtaining the interface feature deviation rate can be improved, the configuration time of data processing resources can be saved, and support is provided for accurate configuration of the resources.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are used in the description of the embodiments will be briefly described below.
Fig. 1 is a schematic flow chart of a cooperative working method implemented by a cloud computer;
fig. 2 is a schematic flow chart of core data processing of a cloud computer in a cooperative working method implemented by the cloud computer;
Fig. 3 is a schematic diagram of a response delay flow of a client-cloud computer in a cooperative working method implemented by a cloud computer;
fig. 4 is a schematic structural diagram of a cooperative working system implemented by a cloud computer.
Reference numerals illustrate: the method comprises the steps of instructing uplink time delay 1, processing time delay 2 at a server side, encoding time delay 3, downlink transmission time delay 4 and graphic decoding time delay 5.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
In the embodiment of the disclosure, the cloud computer is used for working in a new working mode, and compared with a traditional computer, the cloud computer has no hardware such as a CPU, a memory, a hard disk and the like, and all the hardware is collected in a data center of a cloud. The user can access personal desktop, data and various applications by only accessing a small terminal device to a network at any place with a network and connecting a keyboard, a mouse and a display.
Based on the above description, please refer to fig. 1, as shown in fig. 1, the disclosure provides a collaborative work method implemented by a cloud computer, where the method is applied to a collaborative work system implemented by a cloud computer, the system includes a plurality of clients, and the plurality of clients are configured to perform collaborative processing on a target work, including:
acquiring a first interface, wherein the first interface is a working interface of a first user obtained from any one of the plurality of clients at a first time;
the method provided by the disclosure is used for carrying out collaborative office through a cloud computer and improving the overall efficiency of collaborative office, wherein the method is specifically implemented in a collaborative work system realized through the cloud computer, the system comprises a plurality of clients, the clients refer to users and user work equipment which carry out collaborative office through the cloud computer, the clients comprise personal desktops, data, applications and the like of the users, and the clients are used for carrying out collaborative processing on target work.
Firstly, a first interface is acquired, wherein the first interface is a work interface for acquiring a plurality of clients at a first time, and the work interface is a display interface of a client work device, for example: the first time is any time node in a user working time period, the time node can be acquired according to actual working conditions, working interfaces of a plurality of clients are obtained, then any one client is randomly selected from the clients to serve as a first user, and the working interface of the first user is obtained to serve as a first interface.
Analyzing a first interface image time sequence of the first interface, which is obtained through monitoring by a CCD image sensor, to obtain a first average change ratio;
in one embodiment, further comprising:
sequentially acquiring a first interface image and a second interface image, wherein the first interface image and the second interface image are adjacent images in the time sequence of the first interface image;
inputting the first interface image and the second interface image into a first twin network sub-model and a second twin network sub-model in an interface twin network model respectively, and correspondingly obtaining a first interface feature set and a second interface feature set;
obtaining interface characteristic deviation rates of the first interface characteristic set and the second interface characteristic set through comparative analysis;
the CCD image sensor is used for acquiring images of the first interface to obtain a first interface image time sequence, wherein the CCD image sensor is equipment for converting optical images into digital signals, and compared with a traditional image acquisition sensor, the CCD image sensor has the advantages of being small in size, light in weight, high in resolution, high in precision and the like, the accuracy and the definition of image acquisition can be improved through image acquisition of the CCD image sensor, support is provided for accurate analysis of image data, and the first interface image time sequence is obtained through sorting acquired interface images in the first interface according to acquisition time nodes, wherein each interface image is provided with an acquisition time node mark. And then, carrying out image change analysis on the first interface image time sequence to obtain a first average change ratio.
And extracting the interface image in the first interface image time sequence, and sequentially acquiring a first interface image and a second interface image, wherein the first interface image and the second interface image are any group of adjacent images in the first interface image time sequence.
An interface twin network model is constructed, firstly, historical operation and maintenance record collection of similar equipment products is carried out on target equipment used by a first user, wherein operation and maintenance records refer to documents of equipment operation states, maintenance activities and other information, a historical operation and maintenance record data set is obtained, then, historical operation and maintenance record data are randomly selected from the historical operation and maintenance record data set to serve as a first historical operation and maintenance record, and an image feature set of a first historical operation and maintenance image is extracted according to the first historical operation and maintenance record. Based on a digital twin technology, an interface twin network model is built according to the image feature set, wherein the interface twin network model comprises a first twin network sub-model and a second twin network sub-model, and the first twin network sub-model and the second twin network sub-model are used for carrying out feature analysis on adjacent images.
Inputting the first interface image into a first twin network sub-model in the interface twin network model for image feature analysis to obtain a first interface feature set; and inputting the second interface image into a second twin network sub-model in the interface twin network model to perform image feature analysis, so as to obtain a second interface feature set. And then carrying out interface characteristic comparison analysis on the first interface characteristic set and the second interface characteristic set, and obtaining an interface characteristic deviation rate according to an interface characteristic comparison analysis result.
In one embodiment, further comprising:
comparing the first interface color features in the first interface feature set with the second interface color features in the second interface feature set through a color deviation unit to obtain an interface color feature deviation rate;
comparing the first interface texture features in the first interface feature set with the second interface texture features in the second interface feature set through a texture deviation unit to obtain an interface texture feature deviation rate;
and the interface color characteristic deviation rate and the interface texture characteristic deviation rate jointly form the interface characteristic deviation rate.
Constructing a color deviation unit, wherein the color deviation unit comprises a color deviation comparison index, and the color deviation comparison index comprises brightness differences, chroma differences and color differences; traversing and comparing the first interface color feature in the first interface feature set and the second interface color feature in the second interface feature set through a color deviation unit, wherein the color feature comparison method can be realized through the existing color feature comparison technology, and the development and the explanation are omitted, so that the interface color feature deviation rate of the first interface color feature and the second interface color feature is obtained, wherein the interface color feature deviation rate refers to the ratio of the interface color feature deviation quantity to the whole interface color comparison feature quantity.
A texture deviation unit is constructed, and the texture deviation unit is used for comparing texture features in the interface image, wherein the texture features refer to structural tissue features in the image features, such as: line features, shape features, etc. in the image. And then traversing and comparing the first interface texture features in the first interface feature set with the second interface texture features in the second interface feature set through a texture deviation unit, wherein the texture feature comparison method can be realized through the prior art, and the interface texture feature deviation rate is the ratio of the interface texture feature deviation quantity to the whole interface texture comparison feature quantity.
And forming the interface characteristic deviation rate according to the interface color characteristic deviation rate and the interface texture characteristic deviation rate.
In one embodiment, further comprising:
obtaining interface loss data, wherein the interface loss data are loss data obtained by carrying out loss analysis on the interface characteristic deviation rate based on a preset loss function, and the preset loss function is expressed as follows:
；
wherein L (x) 1 ,x 2 ) Refers to the predetermined loss function, M refers to the interface color feature deviation rate, N refers to the interface texture feature deviation rate, M (x) 1 ,x 2 ) Refers to the actual interface color feature deviation ratio, m' (x), of the first interface color feature and the second interface color feature 1 ,x 2 ) Means that the first interface color feature and the second interface color feature are specific to predetermined interface colorsDeviation rate of sign, n (x) 1 ,x 2 ) Refers to the actual interface texture feature deviation ratio, n' (x), of the first interface texture feature and the second interface texture feature 1 ,x 2 ) The deviation rate of the predetermined interface texture features of the first interface texture feature and the second interface texture feature is defined, alpha and beta are coefficients of the deviation rate of the interface color feature and the deviation rate of the interface texture feature, and alpha+beta=1;
and adjusting the interface characteristic deviation rate based on the interface loss data.
Obtaining a predetermined loss function expression, wherein the predetermined loss function expression is:；
in the predetermined loss function expression, L (x 1 ,x 2 ) Means loss data of the predetermined loss function; m is the interface color characteristic deviation rate, N is the interface texture characteristic deviation rate, and M (x) 1 ,x 2 ) Refers to the actual interface color feature deviation ratio, m' (x), of the first interface color feature and the second interface color feature 1 ,x 2 ) The preset interface color characteristic deviation rate refers to a preset fixed interface color characteristic deviation index, and can be set by a person skilled in the art according to actual conditions.
n(x 1 ,x 2 ) Refers to the actual interface texture feature deviation ratio, n' (x), of the first interface texture feature and the second interface texture feature 1 ,x 2 ) The deviation rate of the first interface texture feature and the second interface texture feature refers to a preset fixed interface texture feature deviation index, and the deviation rate of the first interface texture feature and the second interface texture feature can be set according to actual conditions.
Alpha and beta are coefficients of the interface color characteristic deviation rate and the interface texture characteristic deviation rate respectively, and alpha+beta=1; the method comprises the steps of setting the influence degree of alpha and beta on interface loss data according to the interface color characteristic deviation rate and the interface texture characteristic deviation rate, wherein the larger the influence degree on the interface loss data is, the larger the coefficient setting is, and the coefficient setting can be carried out through the existing coefficient of variation method, wherein the coefficient of variation method is a method for calculating the change degree of each index of a system according to a statistical method, each index is weighted according to the change degree of the current value and the target value of each evaluation index, and if the numerical value difference of a certain index is larger, each evaluated object can be clearly distinguished, the resolution information of the index is rich, and therefore the index is given a larger weight; conversely, if the difference in the numerical value of each object to be evaluated is small in a certain index, the index is weak in the ability to distinguish each object to be evaluated, and thus the index should be given a small weight. The coefficient of variation method is a common weighting means for those skilled in the art, and will not be described here.
And carrying out loss calculation on the interface characteristic deviation rate according to the preset loss function to obtain loss data, taking the loss data as interface loss data, and then adjusting the interface characteristic deviation rate according to the interface loss data. By constructing the predetermined loss function, the accuracy of obtaining the interface loss data can be improved, and then the interface characteristic deviation rate is adjusted according to the interface loss data, so that the accuracy of obtaining the interface characteristic deviation rate can be further improved, and the accuracy of the first average change ratio is improved.
And determining the first average change ratio according to the interface characteristic deviation rate.
And sequentially calculating the interface characteristic deviation rate according to the first interface image time sequence to obtain a plurality of interface characteristic deviation rates, then calculating the average value of the interface characteristic deviation rates, taking the average value calculation result of the interface characteristic deviation rates as a first average change rate, and providing support for matching of a next step data processing channel by obtaining the first average change rate.
Constructing a first candidate processing channel sequence by a first processing channel for processing the first average variation ratio based on a predetermined variation ratio-processing channel table match, and the first candidate processing channel sequence including a plurality of candidate processing channels;
In one embodiment, further comprising:
determining a predetermined data processing resource constraint, wherein the predetermined data processing resource constraint is determined by analyzing the target cloud computer performance of the target work, and the target cloud computer performance comprises a plurality of board hardware index parameters;
setting a cloud data processing channel through the preset data processing resource constraint, wherein the cloud data processing channel comprises a graph mode channel and a stream mode channel, the graph mode channel comprises a first channel and a second channel, and the stream mode channel comprises a third channel and a fourth channel;
the predetermined change ratio-processing channel table is constructed, the predetermined change ratio-processing channel table includes the first channel, the second channel, the third channel, and the fourth channel, and the first channel corresponds to a first ratio range, the second channel corresponds to a second ratio range, the third channel corresponds to a third ratio range, and the fourth channel corresponds to a fourth ratio range.
Firstly, acquiring a plurality of board hardware index parameters of a target cloud computer, wherein the board hardware index comprises a plurality of indexes such as a main control index, a memory index, a storage index, a display interface, a video format, a picture format and the like, and the board hardware index parameters refer to specific parameters of the board hardware index, for example: 8GB of memory, etc. And then determining the performance of the target cloud computer according to the plurality of board card hardware index parameters, and determining the preset data processing resource constraint according to the performance analysis result of the target cloud computer, wherein the data processing resource constraint refers to the constraint condition when the target cloud computer performs data processing.
Setting a cloud data processing channel according to the preset data processing resource constraint, wherein the cloud data processing channel comprises a graph mode channel and a stream mode channel, and the graph mode channel comprises a first channel and a second channel as shown in fig. 2; the flow mode channels include a third channel and a fourth channel; the method comprises the steps that a graph mode is a mode used when the image change frequency of a cloud computer is low, the whole cloud desktop is 1 large bitmap, data of a change area are only transmitted to a client in an RGBA format when the image is changed, and then the client correspondingly updates the data of the change area in the large graph; the streaming mode is a mode used when the image change frequency in the cloud desktop is high, in the mode, the whole desktop image is encoded by h264 protocol and then transmitted to the client, and then the media codec used by the client is decoded and rendered on the Surface.
A predetermined change ratio-processing channel table is constructed based on the first channel, the second channel, the third channel, and the fourth channel, wherein the predetermined change ratio-processing channel table includes a first channel, a second channel, a third channel, and a fourth channel, and the first channel corresponds to a first ratio range, the second channel corresponds to a second ratio range, the third channel corresponds to a third ratio range, and the fourth channel corresponds to a fourth ratio range.
And inputting the first average change ratio into the preset change ratio-processing channel table to perform processing channel matching, and obtaining a first processing channel matched with the first average change ratio, wherein the first processing channel is a channel for performing first interface data processing. And then a first candidate processing channel sequence is constructed according to the first processing channel, wherein the first candidate processing channel sequence comprises all channels with processing performance superior to that of the first processing channel. By constructing the first candidate processing channel sequence, the data of the first interface can be processed in time under the condition that the first processing channel is occupied, so that the data processing efficiency is improved.
If the first processing channel is in an occupied working condition, starting an optimal candidate processing channel in the candidate processing channels based on a preset collaborative optimization scheme to perform cloud data processing on the first interface, so as to obtain a first cloud processing result;
in one embodiment, further comprising:
collecting a first historical time delay record, wherein the first historical time delay record refers to a historical time delay record of cloud data processing through a first candidate processing channel in the plurality of candidate processing channels;
The first historical cloud processing time delay information is extracted and calculated from the first historical time delay record and is used as first prediction time delay of the first user, wherein the first historical cloud processing time delay information comprises first historical instruction uplink time delay, first historical server processing time delay, first historical coding time delay, first historical downlink transmission time delay and first historical graphic decoding time delay;
when the first processing channel is in an occupied working condition, enabling an optimal candidate processing channel in the candidate processing channels to process cloud data on the first interface according to a preset collaborative optimization scheme, wherein the preset collaborative optimization scheme is that when multiple users work cooperatively through a cloud computer, the cloud computer is in a multi-thread parallel working state, the optimal cloud data processing channel is matched for the users according to user working characteristics and cloud computer data processing actual conditions, and the optimal cloud data processing channel is the processing channel with the minimum overall response time delay of all the users.
Firstly, cloud data processing record extraction is carried out according to a first candidate processing channel in the candidate processing channels, wherein the first candidate processing channel is any one candidate processing channel in the candidate processing channels, a historical time delay record of cloud data processing of the first candidate processing channel is obtained, and the historical time delay record is used as a first historical time delay record.
As shown in fig. 3, when the data processing channel performs cloud data processing, the generated delay information includes an instruction uplink delay 1, a server side processing delay 2, an encoding delay 3, a downlink transmission delay 4 and a graphic decoding delay 5; the instruction uplink time delay refers to time delay in the process that the client sends the acquired data to the cloud computer to receive the acquired data; the processing time delay of the server side refers to time delay in the processes of input processing of acquired data, logic operation and graphic rendering by a cloud computer; the coding time delay refers to time delay in the process of carrying out graph grabbing by a cloud computer; the downlink transmission delay refers to delay in the process that the cloud computer sends the captured graph to the client; the graphic decoding time delay refers to time delay in the graphic encoding process of the client.
Then processing time delay calculation is carried out according to the first historical time delay record, and first historical cloud processing time delay information is obtained, wherein the first historical cloud processing time delay information comprises first historical instruction uplink time delay, first historical server processing time delay, first historical coding time delay, first historical downlink transmission time delay and first historical graphic decoding time delay; and taking the first historical cloud processing delay information as a first predicted delay of the first user.
In one embodiment, further comprising:
the first prediction time delay is subjected to weight adjustment based on first historical non-cloud processing time delay information obtained through extraction and calculation from the first historical time delay record, and the weight adjustment is expressed as follows:
T=T cpro +ɛ*T opro ；
wherein T is the first predicted time delay after weight adjustment, T cpro Means that the first predicted time delay is not weighted and T opro The first historical non-cloud processing time delay information comprises first historical client processing time delay, first historical client rendering time delay and first historical display time delay, and ɛ is a non-cloud processing time delay feedback adjustment coefficient.
Constructing a weight adjustment expression, wherein the weight adjustment expression is as follows:
T=T cpro +ɛ*T opro the method comprises the steps of carrying out a first treatment on the surface of the In the weight adjustment expression, T refers to the first predicted time delay after weight adjustment, T cpro Means that the first predicted time delay is not weighted and T opro The first historical non-cloud processing time delay information comprises first historical client processing time delay, first historical client rendering time delay and first historical display time delay, and ɛ is a non-cloud processing time delay feedback adjustment coefficient; the first historical client rendering delay comprises waiting of decoded graphics in a client display memory, color processing, and the like, Delay of links such as layer superposition; the non-cloud processing time delay feedback adjustment coefficient is related to client equipment such as a keyboard, a mouse, a display and a network of the first user, and can be obtained by calculation according to the actual situation of the client equipment of the first user.
And carrying out weight adjustment on the first predicted time delay according to the weight adjustment expression to obtain a first predicted time delay weight adjustment result, and updating the first predicted time delay according to the first predicted time delay weight adjustment result. By carrying out weighted adjustment on the first prediction time delay according to the first historical non-cloud processing time delay information, the time delay information generated in the non-cloud processing process can be considered, so that the accuracy of the first prediction time delay is higher, and the accuracy of obtaining the optimal candidate channel can be improved.
Obtaining a target prediction delay according to the first prediction delay summation, wherein the target prediction delay comprises the prediction delays of all collaborative work users participating in the target work;
and determining the optimal candidate processing channel by taking the minimum target prediction delay as a constraint.
And obtaining the prediction delays of a plurality of clients through calculation, adding the prediction delays of the plurality of clients, and taking the sum of the prediction delays as a target prediction delay, wherein the target prediction delay comprises the prediction delays of all collaborative work users participating in the target work, namely the overall response delays of all users. And then taking the processing channel corresponding to the target prediction time delay with the minimum as the optimal candidate processing channel, namely the processing channel under the condition of the minimum overall response time delay of the user. And then, carrying out cloud data processing on the first interface according to the optimal candidate processing channel to obtain a first cloud processing result. By obtaining the optimal candidate processing channel, the data processing of the first user can be performed under the condition that the overall response time delay is minimum, so that the working efficiency of the overall user is improved.
And the first user performs cooperative processing on the target work based on the first cloud processing result.
And finally, finishing cooperative processing of the first user on the target work according to the first cloud processing result. By the method, the technical problem that the overall office efficiency is low due to unreasonable resource allocation when a user performs office work through the cloud computer is solved, and the overall office work efficiency of the user can be improved.
In one embodiment, as shown in fig. 4, there is provided a collaborative work system implemented by a cloud computer, the system including a plurality of clients, and the plurality of clients being configured to perform collaborative processing on a target work, including: the system comprises a first interface acquisition module, a first interface image time sequence analysis module, a first candidate processing channel sequence building module, a cloud data processing module and a cooperative processing module, wherein:
the first interface acquisition module is used for acquiring a first interface, wherein the first interface refers to a working interface of a first user obtained from any one of the plurality of clients at a first time;
the first interface image time sequence analysis module is used for analyzing the first interface image time sequence of the first interface obtained through monitoring by the CCD image sensor to obtain a first average change ratio;
A first candidate processing channel sequence construction module for constructing a first candidate processing channel sequence by a first processing channel for processing the first average change ratio obtained based on a predetermined change ratio-processing channel table match, and the first candidate processing channel sequence includes a plurality of candidate processing channels;
the cloud data processing module is used for enabling the optimal candidate processing channel in the candidate processing channels to perform cloud data processing on the first interface based on a preset collaborative optimization scheme if the first processing channel is in an occupied working condition, so as to obtain a first cloud processing result;
and the cooperative processing module is used for the first user to perform cooperative processing on the target work based on the first cloud processing result.
In one embodiment, the system further comprises:
the interface image acquisition module is used for sequentially acquiring a first interface image and a second interface image, wherein the first interface image and the second interface image are adjacent images in the time sequence of the first interface image;
The interface feature set obtaining module is used for inputting the first interface image and the second interface image into a first twin network sub-model and a second twin network sub-model in an interface twin network model respectively, and correspondingly obtaining a first interface feature set and a second interface feature set;
the interface characteristic deviation rate obtaining module is used for obtaining the interface characteristic deviation rate of the first interface characteristic set and the second interface characteristic set through comparison analysis;
and the first average change ratio determining module is used for determining the first average change ratio according to the interface characteristic deviation rate.
In one embodiment, the system further comprises:
the interface color feature comparison module is used for comparing the first interface color features in the first interface feature set with the second interface color features in the second interface feature set through the color deviation unit to obtain an interface color feature deviation rate;
the interface texture feature comparison module is used for comparing the first interface texture features in the first interface feature set with the second interface texture features in the second interface feature set through the texture deviation unit to obtain an interface texture feature deviation rate;
The interface characteristic deviation rate obtaining module is used for jointly forming the interface characteristic deviation rate by the interface color characteristic deviation rate and the interface texture characteristic deviation rate.
In one embodiment, the system further comprises:
the interface loss data obtaining module is used for obtaining interface loss data, wherein the interface loss data is obtained by carrying out loss analysis on the interface characteristic deviation rate based on a preset loss function, and the preset loss function is expressed as follows:
；
a predetermined loss function parameter module, wherein L (x 1 ,x 2 ) Refers to the predetermined loss function, M refers to the interface color feature deviation rate, N refers to the interface texture feature deviation rate, M (x) 1 ,x 2 ) Refers to the actual interface color feature deviation ratio, m' (x), of the first interface color feature and the second interface color feature 1 ,x 2 ) Refers to a predetermined interface color feature deviation ratio, n (x) 1 ,x 2 ) Refers to the actual interface texture feature deviation ratio, n' (x), of the first interface texture feature and the second interface texture feature 1 ,x 2 ) The deviation rate of the predetermined interface texture features of the first interface texture feature and the second interface texture feature is defined, alpha and beta are coefficients of the deviation rate of the interface color feature and the deviation rate of the interface texture feature, and alpha+beta=1;
and the interface characteristic deviation rate adjusting module is used for adjusting the interface characteristic deviation rate based on the interface loss data.
In one embodiment, the system further comprises:
the system comprises a predetermined data processing resource constraint determining module, a target cloud computer performance determining module and a data processing resource processing module, wherein the predetermined data processing resource constraint determining module is used for determining predetermined data processing resource constraint, the predetermined data processing resource constraint is determined by analyzing the target cloud computer performance of the target work, and the target cloud computer performance comprises a plurality of board card hardware index parameters;
the cloud data processing channel setting module is used for setting a cloud data processing channel through the preset data processing resource constraint, the cloud data processing channel comprises a graph mode channel and a stream mode channel, the graph mode channel comprises a first channel and a second channel, and the stream mode channel comprises a third channel and a fourth channel;
A predetermined change ratio-processing channel table construction module for constructing the predetermined change ratio-processing channel table, the predetermined change ratio-processing channel table including the first channel, the second channel, the third channel, and the fourth channel, and the first channel corresponding to a first ratio range, the second channel corresponding to a second ratio range, the third channel corresponding to a third ratio range, and the fourth channel corresponding to a fourth ratio range.
In one embodiment, the system further comprises:
the first historical time delay record acquisition module is used for acquiring a first historical time delay record, wherein the first historical time delay record refers to a historical time delay record of cloud data processing through a first candidate processing channel in the plurality of candidate processing channels;
the first prediction delay obtaining module is used for extracting and calculating first history cloud processing delay information from the first history delay record to serve as first prediction delay of the first user, wherein the first history cloud processing delay information comprises first history instruction uplink delay, first history server processing delay, first history coding delay, first history downlink transmission delay and first history graph decoding delay;
The target prediction time delay obtaining module is used for obtaining target prediction time delay according to the first prediction time delay summation, and the target prediction time delay comprises the prediction time delays of all collaborative work users participating in the target work;
and the optimal candidate processing channel determining module is used for determining the optimal candidate processing channel by taking the minimum target prediction delay as a constraint.
In one embodiment, the system further comprises:
the weighting adjustment module is used for carrying out weighting adjustment on the first predicted time delay through extracting and calculating first historical non-cloud processing time delay information from the first historical time delay record, and the weighting adjustment is expressed as follows:
T=T cpro +ɛ*T opro ；
the weighting adjustment expression parameter module refers to a weighting adjustment expression parameter module, wherein T refers to the first prediction time delay after weighting adjustment, and T cpro Means that the first predicted time delay is not weighted and T opro The first historical non-cloud processing time delay information comprises first historical client processing time delay, first historical client rendering time delay and first historical display time delay, and ɛ is a non-cloud processing time delay feedback adjustment coefficient.
In summary, compared with the prior art, the embodiments of the present disclosure have the following technical effects:
1. by setting the collaborative optimization scheme, the data processing is performed for the optimal processing channel with the smallest response time delay when the user is matched with the whole response time delay, so that the data processing resource allocation is more reasonable and accurate, and the efficiency of the whole collaborative office of the user is improved.
2. By constructing the predetermined loss function, the accuracy of obtaining the interface loss data can be improved, and then the interface characteristic deviation rate is adjusted according to the interface loss data, so that the accuracy of obtaining the interface characteristic deviation rate can be further improved, and the accuracy of the first average change ratio is improved.
3. By carrying out weighted adjustment on the first prediction time delay according to the first historical non-cloud processing time delay information, the time delay information generated in the non-cloud processing process can be considered, so that the accuracy of the first prediction time delay is higher, and the accuracy of obtaining the optimal candidate channel can be improved.
The above examples merely represent a few embodiments of the present disclosure and are not to be construed as limiting the scope of the invention. Accordingly, various alterations, modifications and variations may be made by those having ordinary skill in the art without departing from the scope of the disclosed concept as defined by the following claims and all such alterations, modifications and variations are intended to be included within the scope of the present disclosure.
Claims (2)
1. The method is applied to a collaborative work system realized by a cloud computer, the system comprises a plurality of clients, and the clients are used for collaborative processing of target works, and the method comprises the following steps:
acquiring a first interface, wherein the first interface is a working interface of a first user obtained from any one of the plurality of clients at a first time;
analyzing a first interface image time sequence of the first interface, which is obtained through monitoring by a CCD image sensor, to obtain a first average change ratio;
constructing a first candidate processing channel sequence by a first processing channel for processing the first average variation ratio based on a predetermined variation ratio-processing channel table match, and the first candidate processing channel sequence including a plurality of candidate processing channels;
if the first processing channel is in an occupied working condition, starting an optimal candidate processing channel in the candidate processing channels based on a preset collaborative optimization scheme to perform cloud data processing on the first interface, so as to obtain a first cloud processing result;
the first user performs cooperative processing on the target work based on the first cloud processing result;
The obtaining a first average change ratio includes:
sequentially acquiring a first interface image and a second interface image, wherein the first interface image and the second interface image are adjacent images in the time sequence of the first interface image;
inputting the first interface image and the second interface image into a first twin network sub-model and a second twin network sub-model in an interface twin network model respectively, and correspondingly obtaining a first interface feature set and a second interface feature set;
obtaining interface characteristic deviation rates of the first interface characteristic set and the second interface characteristic set through comparative analysis;
determining the first average change ratio according to the interface characteristic deviation rate;
the interface characteristic deviation rate of the first interface characteristic set and the second interface characteristic set is obtained through comparative analysis, and the method comprises the following steps:
comparing the first interface color features in the first interface feature set with the second interface color features in the second interface feature set through a color deviation unit to obtain an interface color feature deviation rate;
comparing the first interface texture features in the first interface feature set with the second interface texture features in the second interface feature set through a texture deviation unit to obtain an interface texture feature deviation rate;
The interface color characteristic deviation rate and the interface texture characteristic deviation rate jointly form the interface characteristic deviation rate;
after the interface color feature deviation rate and the interface texture feature deviation rate jointly form the interface feature deviation rate, the method further comprises the following steps:
obtaining interface loss data, wherein the interface loss data are loss data obtained by carrying out loss analysis on the interface characteristic deviation rate based on a preset loss function, and the preset loss function is expressed as follows:
；
wherein L (x) 1 ,x 2 ) Is the predetermined loss function, M is the interface color characteristic deviation rate, N is the interface texture characteristic deviation rate，m(x 1 ,x 2 ) Refers to the actual interface color feature deviation ratio, m' (x), of the first interface color feature and the second interface color feature 1 ,x 2 ) Refers to a predetermined interface color feature deviation ratio, n (x) 1 ,x 2 ) Refers to the actual interface texture feature deviation ratio, n' (x), of the first interface texture feature and the second interface texture feature 1 ,x 2 ) The deviation rate of the predetermined interface texture features of the first interface texture feature and the second interface texture feature is defined, alpha and beta are coefficients of the deviation rate of the interface color feature and the deviation rate of the interface texture feature, and alpha+beta=1;
Adjusting the interface characteristic deviation rate based on the interface loss data;
the first processing channel for processing the first average change ratio obtained by matching based on a predetermined change ratio-processing channel table constitutes a first candidate processing channel sequence, comprising:
determining a predetermined data processing resource constraint, wherein the predetermined data processing resource constraint is determined by analyzing the target cloud computer performance of the target work, and the target cloud computer performance comprises a plurality of board hardware index parameters;
setting a cloud data processing channel through the preset data processing resource constraint, wherein the cloud data processing channel comprises a graph mode channel and a stream mode channel, the graph mode channel comprises a first channel and a second channel, and the stream mode channel comprises a third channel and a fourth channel;
constructing the predetermined change ratio-processing channel table, wherein the predetermined change ratio-processing channel table comprises the first channel, the second channel, the third channel and the fourth channel, the first channel corresponds to a first ratio range, the second channel corresponds to a second ratio range, the third channel corresponds to a third ratio range, and the fourth channel corresponds to a fourth ratio range;
Enabling the optimal candidate processing channel in the plurality of candidate processing channels to perform cloud data processing on the first interface based on a preset collaborative optimization scheme, including:
collecting a first historical time delay record, wherein the first historical time delay record refers to a historical time delay record of cloud data processing through a first candidate processing channel in the plurality of candidate processing channels;
the first historical cloud processing time delay information is extracted and calculated from the first historical time delay record and is used as first prediction time delay of the first user, wherein the first historical cloud processing time delay information comprises first historical instruction uplink time delay, first historical server processing time delay, first historical coding time delay, first historical downlink transmission time delay and first historical graphic decoding time delay;
obtaining a target prediction delay according to the first prediction delay summation, wherein the target prediction delay comprises the prediction delays of all collaborative work users participating in the target work;
determining the optimal candidate processing channel by taking the minimum target prediction delay as a constraint;
after the first historical cloud processing time delay information is extracted and calculated from the first historical time delay record and is used as the first predicted time delay of the first user, the method further comprises the following steps:
The first prediction time delay is subjected to weight adjustment based on first historical non-cloud processing time delay information obtained through extraction and calculation from the first historical time delay record, and the weight adjustment is expressed as follows:
T=T cpro +ε*T opro ；
wherein T is the first predicted time delay after weight adjustment, T cpro Means that the first predicted time delay is not weighted and T opro The cloud processing time delay information comprises first historical client processing time delay, first historical client rendering time delay and first historical display time delay, and epsilon is a non-cloud processing time delay feedback adjustment coefficient.
2. The collaborative work system realized by a cloud computer is characterized in that the system comprises a plurality of clients, the clients are used for collaborative processing of target works, and the system comprises:
the first interface acquisition module is used for acquiring a first interface, wherein the first interface refers to a working interface of a first user obtained from any one of the plurality of clients at a first time;
the first interface image time sequence analysis module is used for analyzing the first interface image time sequence of the first interface obtained through monitoring by the CCD image sensor to obtain a first average change ratio;
A first candidate processing channel sequence construction module for constructing a first candidate processing channel sequence by a first processing channel for processing the first average change ratio obtained based on a predetermined change ratio-processing channel table match, and the first candidate processing channel sequence includes a plurality of candidate processing channels;
the cloud data processing module is used for enabling the optimal candidate processing channel in the candidate processing channels to perform cloud data processing on the first interface based on a preset collaborative optimization scheme if the first processing channel is in an occupied working condition, so as to obtain a first cloud processing result;
the cooperative processing module is used for the first user to perform cooperative processing on the target work based on the first cloud processing result;
the system further comprises:
the interface image acquisition module is used for sequentially acquiring a first interface image and a second interface image, wherein the first interface image and the second interface image are adjacent images in the time sequence of the first interface image;
the interface feature set obtaining module is used for inputting the first interface image and the second interface image into a first twin network sub-model and a second twin network sub-model in an interface twin network model respectively, and correspondingly obtaining a first interface feature set and a second interface feature set;
The interface characteristic deviation rate obtaining module is used for obtaining the interface characteristic deviation rate of the first interface characteristic set and the second interface characteristic set through comparison analysis;
a first average change ratio determination module configured to determine the first average change ratio according to the interface characteristic deviation rate;
the interface color feature comparison module is used for comparing the first interface color features in the first interface feature set with the second interface color features in the second interface feature set through the color deviation unit to obtain an interface color feature deviation rate;
the interface texture feature comparison module is used for comparing the first interface texture features in the first interface feature set with the second interface texture features in the second interface feature set through the texture deviation unit to obtain an interface texture feature deviation rate;
the interface characteristic deviation rate obtaining module is used for jointly forming the interface characteristic deviation rate by the interface color characteristic deviation rate and the interface texture characteristic deviation rate;
The interface loss data obtaining module is used for obtaining interface loss data, wherein the interface loss data is obtained by carrying out loss analysis on the interface characteristic deviation rate based on a preset loss function, and the preset loss function is expressed as follows:
；
a predetermined loss function parameter module, wherein L (x 1 ,x 2 ) Is the predetermined loss function, M is the interface color characteristic deviation rate, N isThe interface texture feature deviation ratio, m (x 1 ,x 2 ) Refers to the actual interface color feature deviation ratio, m' (x), of the first interface color feature and the second interface color feature 1 ,x 2 ) Refers to a predetermined interface color feature deviation ratio, n (x) 1 ,x 2 ) Refers to the actual interface texture feature deviation ratio, n' (x), of the first interface texture feature and the second interface texture feature 1 ,x 2 ) The deviation rate of the predetermined interface texture features of the first interface texture feature and the second interface texture feature is defined, alpha and beta are coefficients of the deviation rate of the interface color feature and the deviation rate of the interface texture feature, and alpha+beta=1;
The interface characteristic deviation rate adjusting module is used for adjusting the interface characteristic deviation rate based on the interface loss data;
the system comprises a predetermined data processing resource constraint determining module, a target cloud computer performance determining module and a data processing resource processing module, wherein the predetermined data processing resource constraint determining module is used for determining predetermined data processing resource constraint, the predetermined data processing resource constraint is determined by analyzing the target cloud computer performance of the target work, and the target cloud computer performance comprises a plurality of board card hardware index parameters;
the cloud data processing channel setting module is used for setting a cloud data processing channel through the preset data processing resource constraint, the cloud data processing channel comprises a graph mode channel and a stream mode channel, the graph mode channel comprises a first channel and a second channel, and the stream mode channel comprises a third channel and a fourth channel;
a predetermined change ratio-processing channel table construction module for constructing the predetermined change ratio-processing channel table, the predetermined change ratio-processing channel table including the first channel, the second channel, the third channel, and the fourth channel, the first channel corresponding to a first ratio range, the second channel corresponding to a second ratio range, the third channel corresponding to a third ratio range, and the fourth channel corresponding to a fourth ratio range;
The first historical time delay record acquisition module is used for acquiring a first historical time delay record, wherein the first historical time delay record refers to a historical time delay record of cloud data processing through a first candidate processing channel in the plurality of candidate processing channels;
the first prediction delay obtaining module is used for extracting and calculating first history cloud processing delay information from the first history delay record to serve as first prediction delay of the first user, wherein the first history cloud processing delay information comprises first history instruction uplink delay, first history server processing delay, first history coding delay, first history downlink transmission delay and first history graph decoding delay;
the target prediction time delay obtaining module is used for obtaining target prediction time delay according to the first prediction time delay summation, and the target prediction time delay comprises the prediction time delays of all collaborative work users participating in the target work;
the optimal candidate processing channel determining module is used for determining the optimal candidate processing channel by taking the minimum target prediction delay as a constraint;
The weighting adjustment module is used for carrying out weighting adjustment on the first predicted time delay through extracting and calculating first historical non-cloud processing time delay information from the first historical time delay record, and the weighting adjustment is expressed as follows:
T=T cpro +ε*T opro ；
the weighting adjustment expression parameter module refers to a weighting adjustment expression parameter module, wherein T refers to the first prediction time delay after weighting adjustment, and T cpro Means that the first predicted time delay is not weighted and T opro The first historical non-cloud processing time delay information comprises a first historical client processing time delay,And the rendering time delay of the first historical client and the display time delay of the first historical display are respectively epsilon, and epsilon is a non-cloud processing time delay feedback adjustment coefficient.
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