A game object generation method and device based on a maximum continuous histogram

Abstract

The application provides a game object generation method and device based on a maximum continuous histogram, wherein the method converts a game area into a matrix, iterates through selectable game objects, fills the matrix, calculates the cumulative sum of each column element by using a prefix sum method to obtain a cumulative matrix, and then calculates the maximum continuous matrix area of the histogram by using a low-complexity algorithm according to the cumulative matrix, and obtains the total number of rows and the total number of columns that meet the filling or elimination rules according to the cumulative matrix. In the method, the cumulative matrix is used to convert the solution of the optimal filling problem into the solution of the maximum continuous matrix area of the histogram, the maximum area that can be filled is calculated by using a low-complexity method, and then the game block is recommended, so that the accuracy of the recommendation is ensured, the time consumption of the entire calculation process is reduced, and the speed and smoothness of the game operation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a game object generation method and device based on maximum continuous histogram, a computing device and a computer readable storage medium. BACKGROUND

[0002] In a fill-and-eliminate type game, the next game block is usually recommended so that the player can obtain a better user experience after using the recommended scheme, and does not need to frequently restart the game session. Therefore, how to recommend the optimal scheme for the player is a very important problem. In the prior art, a heuristic algorithm is usually used to make a decision. However, this scheme has some defects. For example, in some cases in the game session, the recommended optimal solution is not accurate enough, or even obviously does not conform to the current session situation, which affects the player's participation enthusiasm and the user experience is poor. At the same time, this type of calculation method also consumes too many system resources, which affects the execution efficiency of the entire game system. Therefore, there is an urgent need for a technical solution to solve the above problems. SUMMARY

[0003] Therefore, the embodiments of the present application provide a game object generation method and device based on maximum continuous histogram, a computing device and a computer readable storage medium to solve the technical defects in the prior art.

[0004] According to a first aspect of the embodiments of the present application, a game object generation method based on maximum continuous histogram is provided, comprising:

[0005] Step 202: Obtain a game area under the current game session, and convert the game area into a first matrix;

[0006] Step 204: Fill one of the selectable game block set into the blank position of the first matrix to obtain a preprocessed matrix; calculate the cumulative sum of each column element in the preprocessed matrix by using the prefix sum method to obtain a second matrix;

[0007] Step 206: Convert the second matrix into a histogram, and calculate the maximum continuous rectangle area in the histogram;

[0008] Step 208: Save the maximum continuous rectangle area, the total number of columns and the total number of rows that meet the filling rule into a recommendation result set; select the next selectable game block in the selectable game block set to continue step 204 until all the selectable game blocks are traversed and step 210 is entered;

[0009] Step 210: according to the maximum continuous rectangular area S of each selectable game block in the recommendation result set, the total number of columns and the total number of rows satisfying the filling rule, the selectable game blocks are prioritized, and the top n selectable game blocks are displayed in the game interface for user selection, where n≥1.

[0010] According to a second aspect of the embodiments of the present application, a game object generation device based on a maximum continuous histogram is provided, comprising:

[0011] A conversion module is configured to obtain a game area under a current game session and convert the game area into a first matrix.

[0012] A preprocessing module is configured to fill one of the selectable game blocks into a blank position of the first matrix to obtain a preprocessed matrix.

[0013] A first calculation module is configured to calculate the cumulative sum of each column element in the preprocessed matrix by using a prefix sum method to obtain a second matrix.

[0014] A second calculation module is configured to convert the second matrix into a histogram and calculate the maximum continuous rectangular area in the histogram.

[0015] A saving module is configured to save the maximum continuous rectangular area, the total number of columns and the total number of rows satisfying the filling rule into a recommendation result set.

[0016] A sorting module is configured to prioritize the selectable game blocks according to the maximum continuous rectangular area S of each selectable game block in the recommendation result set, the total number of columns and the total number of rows satisfying the filling rule, and display the top n selectable game blocks in the game interface for user selection, where n≥1.

[0017] According to a third aspect of the embodiments of the present application, a computing device is provided, comprising a memory, a processor, and computer instructions stored in the memory and executable on the processor, and the processor executes the instructions to implement the steps of the foregoing method.

[0018] According to a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided, which stores computer instructions, and the instructions are executed by a processor to implement the steps of the foregoing method.

[0019] In the embodiment of the present application, firstly, the game area is converted into a matrix, the selectable game objects are traversed, the matrix is filled, the cumulative sum of each column element is calculated by using the prefix sum method, the cumulative matrix is obtained, and the problem is converted to finding the maximum continuous matrix area of the histogram; then, according to the cumulative matrix, the maximum continuous matrix area of the histogram is calculated by using a low-complexity algorithm, and the total number of rows and the total number of columns that meet the filling or elimination rules are obtained according to the cumulative matrix. In this method, the cumulative matrix is used to convert the solving of the optimal filling problem into the solving of the maximum continuous matrix area of the histogram, according to the objective phenomenon that each data item of the histogram is always continuous, it is ensured that the output matrix is the maximum continuous sub-matrix, then the maximum continuous rectangle area in the histogram is calculated by using a low-complexity method, that is, the maximum area that can be filled, the game block is recommended, the accuracy of the recommendation is ensured, the time consumption of the entire calculation process is reduced, and the speed and smoothness of the game operation are improved. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a structural block diagram of a computing device provided by the embodiment of the present application;

[0021] Figure 2 is a flowchart of a game object generation method based on the maximum continuous histogram provided by the embodiment of the present application;

[0022] Figure 3a is a schematic diagram of a game area of a game match in the embodiment of the present application;

[0023] Figure 3b is a schematic diagram of converting the game area into a first matrix in the embodiment of the present application;

[0024] Figure 4 is a schematic diagram of a set of selectable game blocks in the embodiment of the present application;

[0025] Figure 5 is a schematic diagram of the maximum continuous matrix area obtained after the second matrix is converted into a histogram in the embodiment of the present application;

[0026] Figure 6 is a structural diagram of a game object generation device based on the maximum continuous histogram provided by the embodiment of the present application. DETAILED DESCRIPTION

[0027] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in a variety of ways beyond the specific embodiments described herein without departing from the scope of the present application, and it is understood that similar implementations can be used in the place of or in combination with those described herein without departing from the scope of the present application. Accordingly, the present application is not limited to the embodiments described hereinafter.

[0028] The terminology used in this disclosure of one or more embodiments is for the purpose of describing particular embodiments only and is not intended to be limiting of one or more embodiments. As used in this disclosure and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0029] It is to be understood that, although the terms first, second, etc. can be used herein to describe various information, these terms are not intended to denote

[0030] In the present application, a game object generation method and device based on maximum continuous histogram, a computing device and a computer readable storage medium are provided, which are described in detail one by one in the following embodiments.

[0031] Figure 1 A structural block diagram of a computing device 100 according to an embodiment of the present application is shown. The components of the computing device 100 include, but are not limited to, a memory 110 and a processor 120. The processor 120 is connected with the memory 110 through a bus 130, and a database 150 is used to save data.

[0032] The computing device 100 also includes an access device 140, which enables the computing device 100 to communicate via one or more networks 160. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 140 can include one or more of any type of network interface (e.g., network interface card (NIC)) such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a worldwide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, etc., either wired or wireless.

[0033] In an embodiment of the present application, the above-mentioned components of the computing device 100 and other components not shown in the above-mentioned components of the computing device 100 can also be connected with each other, for example, through a bus. It should be understood that Figure 1 Figure 1 ​The illustrated computing device structural block diagram is merely for the purpose of example, and is not a limitation on the scope of the present application. Other components can be added or replaced as needed by those skilled in the art.

[0034] The computing device 100 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or PC. The computing device 100 can also be a mobile or stationary server.

[0035] Figure 2 A flowchart of a game object generation method based on a maximum continuous histogram is shown, including steps 202 to 206.

[0036] Step 202: Obtain a game area under a current game session, and convert the game area into a first matrix.

[0037] In this step, the game area under the current game session state is obtained, and the game area is converted into a matrix according to the minimum unit of the game object, such as Figure 3a An 8X8 matrix.

[0038] Further, as shown in Figure 3b According to the positions occupied by the game objects in the game area under the current state, the elements of the matrix are set to 0 and 1, wherein the positions filled by the game objects are set to 1, and vice versa, thereby obtaining the first matrix.

[0039] Step 204: Fill one of the selectable game block set into the blank position of the first matrix to obtain a preprocessed matrix; and calculate the cumulative sum of each column element in the preprocessed matrix using the prefix sum method to obtain a second matrix.

[0040] In the prior art, the prefix sum is an important preprocessing that can greatly reduce the time complexity of the query. For a one-dimensional array, for example, a = [1, 2, 3, 4], the calculation method of its prefix sum array is:

[0041] sum[0] = a[0] = 1

[0042] sum[1] = a[0] + a[1] = 3

[0043] sum[2] = a[0] + a[1] + a[2] = 6

[0044] sum[3] = a[0] + a[1] + a[2] + a[3] = 10

[0045] i.e. the prefix sum array of the array a a = [1, 3, 6, 10].

[0046] In step 204, one game object in the set of optional game blocks is selected and filled into the blank position of the first matrix to obtain a preprocessed matrix; for each column in the preprocessed matrix, the prefix sum array of the column is calculated, and then the cumulative sum matrix of the preprocessed matrix, i.e. the second matrix, is obtained.

[0047] For example, after the optional game block a in Figure 4 is filled into the blank position of the first matrix, the matrix element after filling changes from 0 to 1, and the following preprocessed matrix is obtained:

[0048]

[0049] Then, the cumulative sum of each column of the preprocessed matrix is calculated to obtain the second matrix:

[0050]

[0051] Those skilled in the art should know that in each of the above loops, the optional game block can be filled into different blank positions of the first matrix to obtain multiple preprocessed matrices; and the above matrix types are only examples, and those skilled in the art can adjust the size and type of the matrix according to actual needs, which will not be described here.

[0052] Further, the total number of columns satisfying the filling rule is obtained.

[0053] In a possible implementation, the value of the last element of each column in the second matrix is obtained, and if the value of the last element of the current column is equal to the length of the filling rule, such as the length of the first matrix, it indicates that the column has been completely filled with the optional game block and satisfies the filling rule; and then all columns satisfying the filling rule are counted to obtain the total number C of columns satisfying the filling rule.

[0054] At the same time, the total number of rows satisfying the filling rule is obtained.

[0055] In a possible implementation, the elements of each row of the preprocessed matrix are traversed, and if the elements of the current row satisfy the filling rule, such as each element of the current row being 1, it indicates that the row has been completely filled with the optional game block and satisfies the filling rule. Then, all rows satisfying the filling rule are counted to obtain the total number L of rows satisfying the filling rule.

[0056] Step 206: converting the second matrix into a histogram and calculating the maximum continuous rectangular area in the histogram.

[0057] In one possible implementation, the second matrix is converted into a histogram as shown in Figure 5 wherein the value of each item in the histogram is the same as the last element of each column of the second matrix.

[0058] Further, the maximum continuous rectangular area in the histogram is calculated as shown by the shaded area in Figure 5 In the prior art, there are various algorithms to calculate the maximum continuous rectangular area in a histogram, and a monotonic stack is usually used to reduce the complexity of the algorithm. The elements in the stack must be monotonous in a certain order. If the new element violates the monotonicity, the elements in the stack are popped until the monotonicity is satisfied. It can be easily obtained that the first element greater than or less than a certain number on the left or right side, and the total time complexity is O(n). The method of calculating the maximum continuous rectangular area in the histogram by using the monotonic stack is well known to those skilled in the art, and will not be described here.

[0059] In the embodiments of the present application, in order to solve the problem of high complexity and long calculation time caused by calculating the maximum subsequence sum of each column of the matrix when solving similar problems in the prior art, the second matrix calculated by using the prefix sum is converted into a histogram, i.e., the optimal filling problem is converted into a problem of calculating the maximum continuous rectangular area in the histogram by using the cumulative matrix, and the output matrix is ensured to be a continuous maximum submatrix according to the objective phenomenon that the data items in the histogram are always continuous.

[0060] Step 208: save the maximum continuous rectangular area S, the total number of columns and the total number of rows satisfying the filling rule to the recommended result set, and select the next selectable game block to continue step 204 until all the selectable game blocks are traversed and enter step 210.

[0061] In this step, after saving the maximum continuous rectangular area S, the total number of columns and the total number of rows satisfying the filling rule calculated according to the current selectable game block as a whole object to the recommended result set, the next selectable game block in the selectable game block set is selected to continue step 204 until all the selectable game blocks are traversed and the final recommended result set is obtained.

[0062] Step 210: prioritize the selectable game blocks according to the maximum continuous rectangular area S, the total number of columns and the total number of rows satisfying the filling rule of each selectable game block in the recommended result set.

[0063] In this step, each selectable game block is sorted according to the maximum continuous rectangular area S, the total number of columns and the total number of rows satisfying the filling rule, for example, the selectable game blocks are sorted according to the size of the maximum continuous rectangular area S, or sorted according to the sum of the total number of columns and the total number of rows satisfying the filling rule.

[0064] Further, the top n selectable game blocks are displayed in the game interface for the user to select.

[0065] In the above embodiments of the present application, in order to improve the accuracy of recommended game objects in the elimination type game and reduce the consumption of device resources, first, the game area is converted into a matrix, the selectable game objects are traversed, and after the matrix is filled, the cumulative sum of each column element is calculated by using the prefix sum method to obtain a cumulative matrix, and the problem is converted to finding the maximum continuous matrix area of the histogram; further, according to the cumulative matrix, the maximum continuous matrix area of the histogram is calculated by using the monotone stack algorithm, and the total number of rows and the total number of columns that meet the filling or elimination rules are obtained according to the cumulative matrix. The above area, total number of rows and total number of columns are stored as result objects in a result set, and the above steps are repeated until all the selectable game objects are traversed, and finally the elements of the result set are sorted to filter out a plurality of recommended game objects displayed in the game interface. In this method, the second matrix obtained by using the prefix sum calculation is converted into a histogram, that is, the optimal filling problem is converted into solving the maximum continuous matrix area of the histogram by using the cumulative matrix, and according to the objective phenomenon that each data item of the histogram is always continuous, it is ensured that the output matrix is a continuous maximum sub-matrix, and then a method with lower complexity is used to calculate the maximum continuous rectangle area in the histogram, that is, the maximum area that can be filled, and then the game block is recommended, which not only ensures the accuracy of the recommendation, but also reduces the time consumption of the entire calculation process and improves the speed and smoothness of the game running.

[0066] Corresponding to the above method embodiments, the present application also provides an embodiment of a game object generation device based on the maximum continuous histogram, as shown in Figure 6 The device comprises:

[0067] A conversion module is configured to obtain a game area under a current game session and convert the game area into a first matrix.

[0068] A preprocessing module is configured to fill one of the selectable game blocks in the set of selectable game blocks into a blank position of the first matrix to obtain a preprocessed matrix.

[0069] A first calculation module is configured to calculate the cumulative sum of each column element in the preprocessed matrix by using the prefix sum method to obtain a second matrix.

[0070] A second calculation module is configured to convert the second matrix into a histogram and calculate the maximum continuous rectangle area in the histogram.

[0071] A saving module is configured to save the maximum continuous rectangle area, the total number of columns and the total number of rows that meet the filling rule into a recommended result set.

[0072] The sorting module is configured to sort the selectable game blocks according to the maximum continuous rectangular area S of each selectable game block in the set of recommendation results, the total number of columns and the total number of rows that meet the filling rule, and display the top n selectable game blocks in the game interface for the user to select, where n>1.

[0073] The above describes a schematic solution of the game object generation device based on the maximum continuous histogram. It should be noted that the technical solution of the device belongs to the same concept as the technical solution of the game object generation method based on the maximum continuous histogram, and the details of the technical solution of the device not described in detail can be referred to the description of the technical solution of the game object generation method based on the maximum continuous histogram.

[0074] In an embodiment of the present application, a computing device is also provided, which includes a memory, a processor, and computer instructions stored in the memory and executable on the processor, and the processor executes the instructions to implement the steps of the game object generation method based on the maximum continuous histogram.

[0075] The above describes a schematic solution of the computing device. It should be noted that the technical solution of the computing device belongs to the same concept as the technical solution of the game object generation method based on the maximum continuous histogram, and the details of the technical solution of the computing device not described in detail can be referred to the description of the technical solution of the game object generation method based on the maximum continuous histogram.

[0076] In an embodiment of the present application, a computer readable storage medium is also provided, which stores computer instructions, and the instructions are executed by a processor to implement the steps of the game object generation method based on the maximum continuous histogram.

[0077] The above describes a schematic solution of the computer readable storage medium. It should be noted that the technical solution of the storage medium belongs to the same concept as the technical solution of the game object generation method based on the maximum continuous histogram, and the details of the technical solution of the storage medium not described in detail can be referred to the description of the technical solution of the game object generation method based on the maximum continuous histogram.

[0078] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than the order in which they are recited and still achieve the desired results. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0079] The computer readable medium can include any entity or system capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, software distribution medium, etc. It should be noted that the computer readable medium can include appropriate additions or subtractions according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0080] It should be noted that for the foregoing method embodiments, in order to facilitate description, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0081] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0082] The preferred embodiments of the present application disclosed above are only used to help explain the present application. The alternative embodiments do not describe all the details and do not limit the application to the specific embodiments described. Obviously, according to the content of the present application, many modifications and changes can be made. The present application selects and describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application. The present application is limited by the claims and their entire scope and equivalents.

Claims

1. A method for generating a game object based on a maximum continuous histogram, characterized by, The method comprises the following steps: Step 202: obtaining a game area under a current game session, and converting the game area into a first matrix; wherein the conversion of the game area into the first matrix comprises: converting the game area into a matrix according to the minimum constituent unit of a game object, and setting the elements of the matrix as 0 and 1 according to the occupation condition of the game area, wherein a position filled by the game object is set as 1, and vice versa is set as 0, thereby obtaining the first matrix; Step 204: filling one of the selectable game block set into the blank position of the first matrix to obtain a preprocessed matrix; and calculating the cumulative sum of each column element in the preprocessed matrix by using the prefix sum method, thereby obtaining a second matrix; Step 206: converting the second matrix into a histogram, and calculating the maximum continuous rectangular area in the histogram; Step 208: saving the maximum continuous rectangular area, the total number of columns and the total number of rows satisfying the filling rule into a recommendation result set; selecting the next selectable game block in the selectable game block set to continue the step 204, until all the selectable game blocks are traversed, and then entering the step 210; Step 210: performing priority sorting on the selectable game blocks according to the maximum continuous rectangular area S of each selectable game block in the recommendation result set, the total number of columns and the total number of rows satisfying the filling rule, and displaying the top n selectable game blocks in the game interface for the user to select, wherein n≥1.

2. The method of claim 1, wherein, The filling of one of the selectable game block set into the blank position of the first matrix to obtain the preprocessed matrix comprises: After filling the selectable game block into the blank position of the first matrix, the element of the filled matrix is changed from 0 to 1.

3. The method of claim 1, wherein, The conversion of the second matrix into a histogram comprises: The last element of each column of the second matrix is converted into a histogram.

4. The method of claim 1, wherein, The calculation of the maximum continuous rectangular area in the histogram comprises but is not limited to: The maximum continuous rectangular area in the histogram is calculated by using a monotonic stack.

5. The method of claim 1, wherein, The acquisition of the total number of columns and the total number of rows satisfying the filling rule comprises: The value of the last element of each column in the second matrix is acquired, the value of the last element is compared with the filling rule, all the columns satisfying the filling rule are counted, and the total number of columns satisfying the filling rule is obtained; The elements of each row of the preprocessed matrix are traversed, the value of each row of the elements is compared with the filling rule, all the rows satisfying the filling rule are counted, and the total number of rows satisfying the filling rule is obtained.

6. A maximum continuous histogram-based game object generation apparatus, characterized by comprising: The method comprises the following steps: A conversion module is configured to obtain a game area under a current game session, and convert the game area into a first matrix; wherein the conversion of the game area into the first matrix comprises: converting the game area into a matrix according to the minimum constituent unit of a game object, and setting the elements of the matrix as 0 and 1 according to the occupation condition of the game area, wherein a position filled by the game object is set as 1, and vice versa is set as 0, thereby obtaining the first matrix; A preprocessing module is configured to fill one of the selectable game block set into the blank position of the first matrix to obtain a preprocessed matrix; The first calculation module is configured to calculate cumulative sums of elements in each column of the preprocessing matrix by using a prefix sum method, and obtain a second matrix; The second calculation module is configured to convert the second matrix into a histogram, and calculate a maximum continuous rectangle area in the histogram; The saving module is configured to save the maximum continuous rectangle area, the total number of columns and the total number of rows satisfying the filling rule into a recommended result set; The sorting module is configured to sort the selectable game blocks in priority according to the maximum continuous rectangle area S, the total number of columns and the total number of rows satisfying the filling rule of each selectable game block in the recommended result set, and display the top n selectable game blocks in the game interface for the user to select, where n≥1.

7. A computing device comprising a memory, a processor, and computer instructions stored on the memory and executable on the processor, wherein, The processor executes the instructions to implement the steps of the method in any one of claims 1-5.

8. A computer-readable storage medium storing computer instructions, wherein, The instructions are executed by the processor to implement the steps of the method in any one of claims 1-5.

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