CN1300691C - Predicting method for system lock in pattern coordinate design - Google Patents

Abstract

The invention discloses a method for predicting system locks in pattern collaborative design. The method of the present invention helps online operation users predict their future operation area by adopting the extended locking set prediction method, and locks the area in advance, and starts a corresponding conflict resolution strategy when a conflict occurs in the locking operation. The method realizes intelligently assisting the user to lock in advance, thereby ensuring smooth operation of the design by the user and preventing possible operation conflicts.

Description

Prediction method of system lock in pattern collaborative design

technical field

The invention relates to the technical field of Internet-based distributed pattern collaborative design, in particular to a method for predicting system locks in pattern collaborative design.

Background technique

One of the outstanding achievements of mankind in the 20th century, computer technology has brought human society into the information age. With the continuous deepening of the informationization process, the integration of communication technology, computer and network technology has created a new research field - Computer Supported Cooperative Work CSCW (Computer Supported Cooperative Work).

The diversity of group collaboration methods provides rich content for CSCW research. In the CSCW system, people carry out basic activities such as Communication, Coordination, Collaboration, and Cooperation around the tasks completed together.

CSCW has a wide range of application fields and market prospects. The fields that CSCW has been applied to include: military, industry, collaborative computer-aided design, office automation and management information systems, medical care, distance education, e-commerce and commerce, trade, financial applications, E-government......

In many fields of CSCW research and application, pattern collaborative design is an important application of distributed collaborative work. Internet-based pattern collaborative design can enable collaborative designers located in different geographical locations to learn from and share the knowledge and experience of other members, to jointly operate the same task and work synchronously in real time, and to collaboratively complete the design and production of patterns, thereby greatly improving design. quality and efficiency.

Locks are often used to maintain consistency in pattern collaborative design systems, and the use of locks can greatly reduce the number of conflicting operations. When a user tries to operate an object/area, an exclusive lock on the object/area needs to be obtained. For example, if you want to move an object, you must first obtain the lock on the object, which ensures that only one user, that is, the owner of the lock, operates the object, thereby avoiding conflicts. Locks are classified according to different standards. Common classifications include mandatory locks and optional locks, non-immediate locks and immediate locks, front locks and back locks, object locks and area locks, user locks and system locks.

Front lock is to lock the object before operating on the object. Back lock is also called conflict control lock. Before operating an object, there is no need to request a lock. If a conflict occurs, the system automatically locks it.

In the system using front lock, if the locking operation is done by the user, that is, if the user tries to edit a part of the pattern, he must first perform the lock operation on the edited part, which will inevitably increase the burden on the user and reduce work efficiency . Therefore, it is necessary to have a locking strategy that can predict the user's locking intention and help the user to lock automatically. And the traditional locking method is impossible to predict the user's intention.

Contents of the invention

The object of the present invention is to provide a method for predicting system locks in pattern collaborative design.

The technical scheme that the present invention solves its technical problem adopts is as follows:

1) The original pattern is rasterized

The system divides a pattern collaborative design two-dimensional space into m grids;

2) Initialize the lock

Determine the user who owns the predictive lock of the system, and use the position of the user U _k's first click as a benchmark to determine the locking area of the user U _k , and initially lock the operation;

3) Determine the extended reference grid

Process the collected operation information of the user U _K in the n unit grids to be locked, and determine the direction of the predicted lock to be locked according to the calculation operation intensity;

The first step is to calculate the user's operation intensity I _Ri ^Uk on the n unit grids,

${\mathrm{<span\; class="notranslate">\; I</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}^{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1,2,3</span>}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; )</span>$

I _Ri ^Uk : the operation intensity of user U _K on region R _i

N _j ^{Ri is} the user's number of operations on time slot Timeslot j in area R _i , and the number of operations is represented by the number of clicks,

Time slot length n: Determined by the application system, that is, the sampled objects are the nearest n time slots, if the user does not operate within the n time slots, it will be automatically unlocked,

α _j : Indicates the weight, the closer to the current time, the greater the weight;

The second step is to sort these calculated grids according to the operation intensity. After the operation intensity is sorted from high to low, they are respectively recorded as I _1st , I _2nd , I _3rd , I _4th ..., and a new sequence is obtained, which is respectively recorded as : R _1st , R _2nd , R _3rd , R _4th …,

The third step is to extract the set of benchmark grids. The grids in this set are the basis for expansion. These benchmark grids are used to predict the grids that users will operate. The general principle of selecting benchmark grids is: select the most intensive operation one or several grids;

4) Determine the grid to be locked, and the system performs the locking operation

Determining the ForecL ^s Uk ^region of the system prediction lock to be locked is to determine the grid set W to be locked by the system. The specific method is to start from the reference grid and add the unlocked grids adjacent to the reference grid to the set W. If the grids contained by the grids that are newly added to the set W are not locked, these grids are also added to the set W to be locked, and then the elements in the set to be locked, that is, the grids, are locked.

Compared with the background technology, the present invention has the beneficial effects that:

The present invention is an intelligent lock based on prediction. Its main function is that the system adopts the extended locking set prediction method to help online operation users predict their future operation area, and lock the area in advance. conflict resolution strategy. Through this lock mechanism, the system can intelligently assist the user to lock in advance to a certain extent, so as to ensure the smooth operation of the user's design and prevent possible operation conflicts.

(1) Intelligence. The system automatically predicts and locks the user, and automatically unlocks it within a certain period of time, and allows the user to freely choose whether to use the system predictive lock or not, so that the system predictive lock has a certain degree of intelligence.

(2) Practicality. The system predictive lock allows users to safely choose to lock the appropriate operating area when they start to operate, and it has been proved to be very practical through repeated trials.

Since the user knows that there is a system predictive lock to help him lock the future operation area, he can safely lock a suitable area at the beginning of the design, instead of trying to lock a large area at once. In this way, it can avoid affecting the operation of other users because a user locks a large area (mostly idle) in advance, and will not affect the fluency of his own operation because the area he wants to operate in the future is locked by other users . At the same time, it also ensures that users who join later can still start new design operations, so that new users will not have no operation area or the operation area is too small due to the wasteful "locking" of other online users.

(3) Preventive. Because the system predictive lock is a pre-lock mechanism, it can effectively prevent conflicts, and most conflicts can be resolved implicitly by the system lock, thus greatly reducing the waste of design time caused by conflicts.

Description of drawings

Figure 1 is a schematic diagram of a time slot representing the number of operations by the number of clicks;

Fig. 2 is a schematic diagram of locking the L*L grids at the bottom right of the user Uk in the implementation of the system lock based on the first click position;

Figure 3 is the case 1 of Step4 in the system lock example: first add R5 and R6;

Figure 4 is a schematic diagram of case 1 of Step4 in the system lock example: adding R7;

Figure 5 is a schematic diagram of case 2 of Step 4 in the system lock example: first add R5, R6, R7, and R8.

Figure 6 is a schematic diagram of case 2 of Step 4 in the system lock example: adding R9 and R0.

Detailed ways

The lock mechanism is widely used when implementing the internet-based distributed pattern collaborative design technology.

The method involves related symbol interpretation:

ForecL ^s Uk: The owner of the system forecast lock.

ForecL ^s Uk ^orientation : The system predicts the orientation of the lock to be locked.

ForecL ^s Uk ^region : The region where the system forecasts the lock to be locked.

ForecCoEdL ^s Uk ^region : Regions that the system predicts compete with each other for lock after a collision.

ForecEdL ^s Uk ^region : The system predicts the final locked region after conflict resolution.

L ^u Ux ^region : The region that a user has locked.

L ^u Ux ^time : the time a user has been locked.

The specific implementation process of the system predictive lock is as follows.

Step 1: Rasterize the original pattern. The system divides a pattern collaborative design two-dimensional space into m grids.

Step 2: Initialize the lock.

Determine a user lock region L ^u Uk ^region and its system predictive lock ForecL ^s Uk.

Based on the position of the first click of a certain user Uk, lock the 8 grids around it, determine the locked area L ^u Uk ^region of the user Uk, and determine the owner of the system predictive lock ForecL ^s Uk at the same time. In order to simplify the description of the algorithm, here only the L*L unit grids {R ₁ , R ₂ , R ₃ , R ₄ } on the lower right are taken as an example to perform the initial locking operation, as shown in FIG. 2 .

Step 3: Determine the ForecL ^s Uk ^orientation that the system predicts to be locked.

Process the collected operation information of the user U _K in the above L*L unit grids, and determine the predicted lock-to-be-locked orientation ForecL ^s Uk ^orientation according to the calculation operation intensity.

(1) Calculate the operation intensity I _Ri ^Uk of each grid

${\mathrm{<span\; class="notranslate">\; I</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}^{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1,2,3</span>}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; )</span>$

I _Ri ^Uk : the operation intensity of the user U _K on the region R _i .

N _j ^Ri : The number of operations performed by the user on the time slot Timeslot j in the area R _i . The number of operations is represented by the number of clicks. The time slot is shown in Figure 1.

The value of n is determined by the system, that is, the sampling object is the nearest n time slots, and if there is no operation by the user within n time slots, it will be automatically unlocked.

α _j : represents the weight, defined by α _j =2α _j+1 , and α _j decreases linearly along the reverse direction of the coordinate axis.

(2) Grid sorting

After obtaining the operation intensity of the L*L grids, sort these grids from high to low. Taking the set {R ₁ , R ₂ , R ₃ , R ₄ } as an example, the corresponding operation intensity is the set {I _R1 , I _R2 , I _R3 , I _R4 }. The operating intensity is sorted from high to low and recorded as I _1st , I _2nd , I _3rd , and I _4th . If relevant $I_{R_2}>I_{R_1}>I_{R_4}>I_{R_3}$ , then get a new sequence: R ₂ , R ₁ , R ₄ , R ₃ , denoted as: R _1st , R _2nd , R _3rd , R _4th .

(3) Extract the reference grid set

The grids in this collection are the basis of the expansion. These reference grids are used to predict the area (grid) that the user will operate. The general principle of selecting the reference grid is: select one or several grids with the greatest operation intensity. For example:

Once the set of reference grids is determined, the system predictive locking orientation ForecL ^s Uk ^orientation is determined.

Step 4: Determine the ForecL ^s Uk ^region of the system predictive lock to be locked.

Determining the ForecL ^s Uk ^region to be locked by the system is to determine the grid set W to be locked by the system. The specific method is to start from the reference grid and add the unlocked grids adjacent to the reference grid to the set W. If the set {R ₁ , R ₂ , R ₃ , R ₄ } is locked, then:

1: If the reference grid set is {R _1st }, that is, {R ₂ }, then first add the unlocked {R ₅ , R ₆ } adjacent to R ₂ to the set W, as shown in Figure 3 .

Secondly, if the grids contained by the grids newly added to the set W are not locked, these grids are also added to the set W to be locked. In Fig. 4, {R ₇ } is an unlocked grid clamped by {R ₅ , R ₆ }, then add {R ₇ } to the set W.

The final grid set W to be locked is {R ₅ , R ₆ , R ₇ }, and the set W is locked. If the lock is successful, join the locked set {R ₁ , R ₂ , R ₃ , R ₄ } to obtain the set {R ₁ , R ₂ , R ₃ , R ₄ , R ₅ , R ₆ , R ₇ }.

2: If the reference grid set is {R _1st , R _2nd }, that is, {R ₂ , R ₁ }, then first set the unlocked {R ₅ , R ₆ , adjacent to {R ₂ , R ₁ } R ₇ , R ₈ } join the set W, as shown in Figure 5.

Secondly, if the grids sandwiched by the grids newly added to the set W to be locked are not locked, these grids are also added to the set W to be locked. In Figure 6, {R ₉ } is the unlocked grid clamped by {R ₅ , R ₆ }, then add {R ₉ } to the set W, and {R ₀ } is the grid clamped by {R ₇ , R ₈ } If there is no locked grid, add {R ₀ } to the set W.

The final raster set W to be locked is {R ₅ , R ₆ , R ₇ , R ₈ , R ₉ , R ₀ }, and the set W is locked. If the lock is successful, join the locked set {R ₁ , R ₂ , R ₃ , R ₄ }, to obtain a locked set {R ₁ , R ₂ , R ₃ , R ₄ , R ₅ , R ₆ , R ₇ , R ₈ , R ₉ , R ₀ }.

Therefore, this method saves the step of the user locking the target, improves the user's work efficiency and greatly reduces the waste of design time caused by the user's conflict.

Claims (1)

1. A method for predicting system locks in pattern collaborative design, characterized in that: 1) The original pattern is rasterized The system divides a pattern collaborative design two-dimensional space into m grids; 2) Initialize the lock Determine the user who owns the predictive lock of the system, and use the position of the user U _k's first click as a benchmark to determine the locking area of the user U _k , and initially lock the operation; 3) Determine the extended reference grid Process the collected operation information of the user U _K in the n unit grids to be locked, and determine the direction of the predicted lock to be locked according to the calculation operation intensity; The first step is to calculate the user's operation intensity I _Ri ^Uk on the n unit grids, ${\mathrm{<span\; class="notranslate">\; I</span>}}_{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}^{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1,2,3</span>}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; )</span>$ I _Ri ^Uk : the operation intensity of user U _K on region R _i N _j ^Ri : the number of operations performed by the user on time slot Timeslot j in area R _i , the number of operations is represented by the number of clicks, Time slot length n: Determined by the application system, that is, the sampled objects are the nearest n time slots, if the user does not operate within the n time slots, it will be automatically unlocked, α _j : Indicates the weight, the closer to the current time, the greater the weight; The second step is to sort these calculated grids according to the operation intensity. After the operation intensity is sorted from high to low, they are respectively recorded as I _1st , I _2nd , I _3rd , I _4th ..., and a new sequence is obtained, which is respectively recorded as : R _1st , R _2nd , R _3rd , R _4th …, The third step is to extract the set of benchmark grids. The grids in this set are the basis for expansion. These benchmark grids are used to predict the grids that users will operate. The general principle of selecting benchmark grids is: select the most intensive operation one or several grids; 4) Determine the grid to be locked, and the system performs the locking operation Determining the ForecL ^S Uk ^region of the system prediction lock to be locked is to determine the grid set W to be locked by the system. The specific method is to start from the reference grid and add the unlocked grids adjacent to the reference grid to the set W. If the grids contained by the grids that are newly added to the set W are not locked, these grids are also added to the set W to be locked, and then the elements in the set to be locked, that is, the grids, are locked.

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