Data product calling method and device, electronic equipment and computer readable medium

Abstract

Embodiments of the present disclosure disclose a data product calling method and device, electronic equipment and computer readable medium. A specific embodiment of the method comprises: obtaining a preset scene data product information set, wherein the preset scene data product information in the preset scene data product information set is product information packaged by related data under a preset scene; calling the marked data product data structure in a preset order to obtain a data product calling sequence; and in response to determining that there is a data product with a calling number reaching a preset number in the data product calling sequence, caching the data product with the calling number reaching the preset number to a preset memory area. The embodiment shortens the cycle of data product calling, improves the computing resource performance and network resource performance of the system, and improves the response speed of the system.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of computer technology, and particularly, to a data product calling method and device, electronic equipment and computer readable medium. BACKGROUND

[0002] With the rapid development of the information age, the scene of data product calling is more and more. Data product calling is a technology for calling data products. At present, the commonly used way of data product calling is to directly call the data product after obtaining it.

[0003] However, when the above way is used, the following technical problems often exist:

[0004] Because the complexity of the obtained preset scene data product information set is high, and the association relationship is relatively chaotic, too much system resources and time may be consumed when calling the preset scene data product information, resulting in a long period of data product calling. In addition, the data product may be called multiple times, which consumes the computing resource performance and network resource performance of the system, and reduces the response speed of the system.

[0005] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present inventive concept, and therefore, it can include information that does not form the prior art known to those of ordinary skill in the art in the country. SUMMARY

[0006] The summary section is provided to introduce the concepts briefly in a simplified form, which will be described in detail in the specific embodiments section. The summary section is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to be used to limit the scope of the claimed technical solutions.

[0007] Some embodiments of the present disclosure provide a data product calling method, device, electronic equipment and computer readable medium to solve one or more of the technical problems mentioned in the background section.

[0008] In a first aspect, some embodiments of the present disclosure provide a data product calling method, which comprises: obtaining a preset scene data product information set, wherein the preset scene data product information in the preset scene data product information set is product information packaged by related data under a preset scene; performing preset structure construction on the preset scene data product information set to obtain a preset scene data product structure, wherein a node in the preset scene data product structure represents preset scene data product information in the preset scene data product information set, and an edge in the preset scene data product structure represents an association relationship between two preset scene data product information in the preset scene data product information set; performing node traversal on the preset scene data product structure to generate data product node graph information; constructing a data product data structure according to the data product node graph information; in response to determining that the number of screened data product nodes in the data product data structure reaches a preset numerical condition, performing batch sequence marking on the data product data structure to obtain a marked data product data structure; calling the marked data product data structure according to a preset order to obtain a data product calling sequence; and in response to determining that there is a data product with a calling number reaching a preset number in the data product calling sequence, caching the data product with the calling number reaching the preset number to a preset memory area.

[0009] In a second aspect, some embodiments of the present disclosure provide a data product calling apparatus, which comprises: an obtaining unit configured to obtain a preset scene data product information set, wherein the preset scene data product information in the preset scene data product information set is product information packaged by related data under a preset scene; a first constructing unit configured to perform preset structure construction on the preset scene data product information set to obtain a preset scene data product structure, wherein a node in the preset scene data product structure represents preset scene data product information in the preset scene data product information set, and an edge in the preset scene data product structure represents an association relationship between two preset scene data product information in the preset scene data product information set; a traversing unit configured to perform node traversal on the preset scene data product structure to generate data product node graph information; a second constructing unit configured to construct a data product data structure according to the data product node graph information; a marking unit configured to, in response to determining that the number of screened data product nodes in the data product data structure reaches a preset numerical condition, perform batch sequence marking on the data product data structure to obtain a marked data product data structure; a calling unit configured to call the marked data product data structure according to a preset order to obtain a data product calling sequence; and a caching unit configured to, in response to determining that there is a data product with a calling number reaching a preset number in the data product calling sequence, cache the data product with the calling number reaching the preset number to a preset memory area.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a memory device having stored thereon one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the method described in any implementation of the first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the method described in any implementation of the first aspect.

[0012] The above various embodiments of the present disclosure have the following beneficial effects: through the data product calling method of some embodiments of the present disclosure, the period of data product calling is shortened, the computing resource performance and network resource performance of the system are improved, and the response speed of the system is improved. Specifically, the reason why the period of data product calling is relatively long, the computing resource performance and network resource performance of the system are consumed, and the response speed of the system is reduced is that: since the complexity of the obtained preset scene data product information set is high, and the association relationship is relatively chaotic, too much system resource and time may be consumed when the preset scene data product information is called, resulting in a relatively long period of data product calling. In addition, the data product may be called multiple times, so that the computing resource performance and network resource performance of the system are consumed, and the response speed of the system is reduced. Based on this, the data product calling method of some embodiments of the present disclosure first obtains a preset scene data product information set, wherein the preset scene data product information in the preset scene data product information set is product information packaged by related data under a preset scene. Thus, subsequent processing can be facilitated. Then, a preset structure is constructed for the preset scene data product information set, to obtain a preset scene data product structure, wherein the nodes in the preset scene data product structure represent the preset scene data product information in the preset scene data product information set, and the edges in the preset scene data product structure represent the association relationship between two preset scene data product information in the preset scene data product information set. Thus, the complexity of the preset scene data product information set can be reduced, and too much system resource and time can be avoided when the preset scene data product information is called, so as to shorten the period of data product calling. Then, the preset scene data product structure is traversed to generate data product node graph information. Then, according to the data product node graph information, a data product data structure is constructed. Then, in response to determining that the number of filtered data product nodes in the data product data structure reaches a preset numerical condition, the data product data structure is marked in batches in sequence to obtain a marked data product data structure. Thus, the data product data structure can be called in batches, too much system resource and time can be avoided when the preset scene data product information is called, and the period of data product calling is shortened. The marked data product data structure is called according to a preset sequence to obtain a data product calling sequence. In response to determining that there is a data product with a calling number reaching a preset number of times in the data product calling sequence, the data product with the calling number reaching the preset number of times is cached to a preset memory area. Thus, the data product called multiple times is cached, the consumption of the computing resource performance and network resource performance of the system is reduced, and the response speed of the system is improved. Therefore, the period of data product calling is shortened, the computing resource performance and network resource performance of the system are improved, and the response speed of the system is improved. BRIEF DESCRIPTION OF DRAWINGS

[0013] The above and other features, aspects and advantages of the present disclosure will become more apparent after a reading of the following detailed description together with the accompanying drawings. Throughout the drawings, similar or same reference numerals are used to denote similar or same elements. It is to be understood that the drawings are schematic, and elements and features are not necessarily to scale.

[0014] Figure 1 is a flowchart of some embodiments of a data product calling method according to the present disclosure;

[0015] Figure 2 is a structural schematic diagram of some embodiments of a data product calling apparatus according to the present disclosure;

[0016] Figure 3 is a structural schematic diagram of an electronic device suitable for use to implement some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described in more detail with reference to the drawings. Although some embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present disclosure are only for illustrative purposes and should not be construed as limiting the scope of protection of the present disclosure.

[0018] It should also be noted that, for the sake of brevity, only the parts of the drawings that are relevant to the present application are shown. The embodiments and features of the present disclosure can be combined with each other in the case of no conflict.

[0019] It should be noted that the terms “first”, “second”, and the like in the present disclosure are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the terms “one”, “multiple” in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that “one” or “multiple” should be understood as “one or more” unless otherwise explicitly stated in the context.

[0021] The names of the messages or information exchanged between the devices in the embodiments of the present disclosure are only for illustrative purposes and are not intended to limit the scope of the messages or information.

[0022] The present disclosure will be described in detail below with reference to the drawings and in conjunction with embodiments.

[0023] Figure 1is a flow 100 of some embodiments of the data product calling method of the present disclosure. The data product calling method comprises the following steps:

[0024] In step 101, a preset scene data product information set is acquired.

[0025] In some embodiments, the execution subject (e.g., a computing device) of the data product calling method can acquire the preset scene data product information set through wired connection or wireless connection, wherein the preset scene data product information in the preset scene data product information set is product information packaged by relevant data in a preset scene.

[0026] Here, the preset scene can refer to a preset scene. For example, the preset scene refers to a user background information investigation scene. The product information can refer to user credit information. The relevant data can refer to data related to user credit. For example, the relevant data can include but is not limited to at least one of the following: user income proof data, user credit record data.

[0027] It should be noted that the wireless connection mode can include but is not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other now known or future developed wireless connection modes.

[0028] It should be noted that the computing device can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster composed of multiple servers or terminal devices, or as a single server or a single terminal device. For example, the computing device can be the target terminal. When the computing device is software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, or as a single software or software module. It is not limited here.

[0029] In step 102, a preset structure is constructed for the preset scene data product information set to obtain a preset scene data product structure.

[0030] In some embodiments, the execution subject can construct a preset structure for the preset scene data product information set to obtain a preset scene data product structure, wherein the nodes in the preset scene data product structure represent the preset scene data product information in the preset scene data product information set, and the edges in the preset scene data product structure represent the association relationship between two preset scene data product information in the preset scene data product information set.

[0031] Here, the preset structure can be a preset data structure. For example, the preset structure can refer to a forest structure.

[0032] Optionally, the execution subject can construct a preset structure for the preset scene data product information set by the following steps to obtain a preset scene data product structure:

[0033] Firstly, determine the association relationship of the preset scene data product information set to obtain a data product association information group set.

[0034] Here, the association relationship can refer to the association relationship between the preset scene data product information in the preset scene data product information set and the associated data group of the preset scene data product information. For example, the preset scene data product information can refer to user credit-related data. The associated data group can include but is not limited to at least one of the following: user income proof data, user credit record data.

[0035] As an example, the execution subject can determine the association relationship of each preset scene data product information in the preset scene data product information set to generate a data product association information group to obtain a data product association information group set.

[0036] Secondly, determine the indirect association relationship of each data product association information group in the data product association information group set to obtain a data product indirect association information set.

[0037] Here, the indirect association relationship can refer to the association relationship between different data products.

[0038] Thirdly, connect each data product indirect association information in the data product indirect association information set to obtain a connected data product indirect association information set.

[0039] Here, the connection can refer to the connection of edges between nodes with each data product indirect association information as a node.

[0040] Fourthly, connect the data products of the connected data product indirect association information set to obtain a connected data product information set.

[0041] As an example, the execution subject can connect the edges between nodes with each connected data product indirect association information in the connected data product indirect association information set as a node.

[0042] Fifthly, adjust the structure of the connected data product information set to obtain an adjusted data product structure as the preset scene data product structure.

[0043] Here, the structure adjustment can refer to an adjustment according to a format of the forest.

[0044] Optionally, the execution subject can perform preset structure construction on the preset scene data product information set to obtain a preset scene data product structure.

[0045] Firstly, path information division is performed on the preset scene data product information set to obtain a divided data product path information set.

[0046] For example, the execution subject can perform path information division on each preset scene data product information in the preset scene data product information set to generate divided data product path information and obtain a divided data product path information set. The divided data product path information in the divided data product path information set can refer to path information of a single data product. For example, the divided data product path information in the divided data product path information set can refer to "personal information product - browsing record - purchase record".

[0047] Secondly, static analysis is performed on the divided data product path information set to obtain an analyzed data product path information set.

[0048] For example, the execution subject can perform symbolic execution on the divided data product path information set to obtain an executed data product path information set as the analyzed data product path information set.

[0049] Thirdly, path optimization is performed on the analyzed data product path information set to obtain an optimized data product path information set.

[0050] For example, the execution subject can perform path optimization on the analyzed data product path information set by using a path planning algorithm to obtain an optimized data product path information set.

[0051] Fourthly, random data generation is performed on the optimized data product path information set according to associated relationship matrix information corresponding to the optimized data product path information set to obtain a data product random generation data set.

[0052] For example, the execution subject can parse the associated relationship matrix information corresponding to the optimized data product path information set to obtain the associated relationship matrix information. Then, the rand() function is used in the associated relationship matrix information to perform random data generation on the optimized data product path information set to obtain a random generation data set.

[0053] Fifthly, data filtering is performed on the data product random generation data set to obtain a filtered data set.

[0054] As an example, the execution subject can eliminate the dirty data in the random generated data set to obtain an eliminated random generated data set as the filtered data set.

[0055] Step 6, performing data format conversion on the filtered data set to obtain a converted data set.

[0056] As an example, the execution subject can use the pandas library in Python to perform data format conversion on the filtered data set to obtain a converted data set. The converted data set can refer to a data set whose format is converted to JSON format. For example, the data format of age is (years old).

[0057] Step 7, performing discretization processing on the converted data set according to the data range limit information corresponding to the converted data set to obtain a discretized processed data set.

[0058] As an example, the execution subject can first determine the boundary value of the converted data set through the data range limit information corresponding to the converted data set. Then, the converted data set is discretized according to the boundary value to obtain a discretized processed data set. For example, the data range limit information corresponding to the converted data set is “item price range: (low: 0-300, medium: 300-700, high: 700-1000)”.

[0059] Step 8, performing data balancing processing on the discretized processed data set to obtain a balanced data set.

[0060] Here, the balanced data set can refer to a data set in which each discretized processed data in the discretized processed data set is relatively balanced.

[0061] As an example, the execution subject can perform class weighting on the discretized processed data in the discretized processed data set to obtain a weighted data set as the balanced data set.

[0062] Step 9, in response to determining that the path coverage rate corresponding to the balanced data set is not equal to the preset coverage rate, performing the following processing steps:

[0063] First sub-step, determining the difference between the path coverage rate corresponding to the balanced data set and the preset coverage rate as coverage rate difference information.

[0064] Here, the preset coverage rate can refer to 60%.

[0065] As an example, the execution subject can subtract the preset coverage rate from the path coverage corresponding to the balanced data set to obtain coverage difference information.

[0066] In a second sub-step, the balanced data set is adjusted by data sampling according to the coverage difference information to obtain an adjusted data set.

[0067] As an example, the execution subject can, in response to determining that the coverage difference information is greater than a preset difference threshold, undersample the balanced data set to obtain an undersampled data set as the adjusted data set. In response to determining that the coverage difference information is less than or equal to the preset difference threshold, oversample the balanced data set to obtain an oversampled data set as the adjusted data set.

[0068] In a third sub-step, the adjusted data set is subjected to a significance test to obtain a tested data set.

[0069] In a fourth sub-step, coverage information corresponding to the tested data set is compared with coverage information corresponding to the adjusted data set to obtain compared coverage information.

[0070] As an example, the execution subject can compare a difference rate between the coverage information corresponding to the tested data set and the coverage information corresponding to the adjusted data set to obtain compared difference rate information as the compared coverage information.

[0071] In a tenth step, in response to determining that coverage corresponding to the compared coverage information is greater than or equal to a preset value, a directed edge is added to a data product node corresponding to the tested data set to obtain a preset scenario data product structure.

[0072] The above-mentioned first step to the tenth step is related to the disclosure as one of the application points, which solves the technical problem that "calling preset scene data product information may consume too much system resources and time, and the period of data product calling is longer". The factors that cause the period of data product calling to be longer are often as follows: the correlation between the preset scene data product information set is not fully considered, resulting in the structure of the obtained preset scene data product being not accurate enough, thereby causing too much system resources and time to be consumed when calling the preset scene data product information, resulting in the period of data product calling being longer. If the above factors are solved, the performance of the test case application program can be improved, and the security of the test case application program can be improved. In order to achieve this effect, first, the above-mentioned preset scene data product information set is divided into path information, and a divided data product path information set is obtained. Second, the above-mentioned divided data product path information set is statically analyzed, and an analyzed data product path information set is obtained. Third, the above-mentioned analyzed data product path information set is path optimized, and an optimized data product path information set is obtained. Fourth, according to the correlation matrix information corresponding to the above-mentioned optimized data product path information set, the above-mentioned optimized data product path information set is randomly generated, and a data product random generation data set is obtained. Fifth, the above-mentioned data product random generation data set is filtered, and a filtered data set is obtained. Sixth, the above-mentioned filtered data set is converted into data format, and a converted data set is obtained. Seventh, according to the data range limit information corresponding to the above-mentioned converted data set, the above-mentioned converted data set is discretized, and a discretized data set is obtained. Eighth, the above-mentioned discretized data set is balanced, and a balanced data set is obtained. Ninth, in response to determining that the path coverage rate corresponding to the above-mentioned balanced data set is not equal to the preset coverage rate, the following processing steps are performed: first substep, the difference between the path coverage rate corresponding to the above-mentioned balanced data set and the above-mentioned preset coverage rate is determined as coverage rate difference information. Second substep, according to the above-mentioned coverage rate difference information, the above-mentioned balanced data set is sampled and adjusted, and an adjusted data set is obtained. Third substep, the above-mentioned adjusted data set is subjected to significance test, and a tested data set is obtained. Fourth substep, the coverage rate information corresponding to the above-mentioned tested data set is compared with the coverage rate information corresponding to the above-mentioned adjusted data set, and compared coverage rate information is obtained. Tenth step, in response to determining that the coverage rate corresponding to the above-mentioned compared coverage rate information is greater than or equal to the preset value, the data product node corresponding to the above-mentioned tested data set is added with a directed edge, and a preset scene data product structure is obtained. Therefore, when calling the preset scene data product information, too much system resources and time are reduced, and the period of data product calling is shortened.

[0073] Step 103, node traversal is performed on the above-mentioned preset scene data product structure to generate data product node graph information.

[0074] In some embodiments, the execution subject can perform node traversal on the preset scenario data product structure to generate data product node graph information.

[0075] Optionally, the execution subject can perform node traversal on the preset scenario data product structure to generate data product node graph information by the following steps:

[0076] Firstly, the execution subject accesses root nodes of the preset scenario data product structure to obtain a set of data product structure root nodes.

[0077] Here, the data product structure root node in the set of data product structure root nodes can refer to a data product node without a parent node.

[0078] Secondly, the execution subject accesses at least one adjacent node of each data product structure root node in the set of data product structure root nodes to generate a set of data product structure adjacent node groups, and obtain a set of data product structure adjacent node groups.

[0079] Here, the data product structure adjacent node group in the set of data product structure adjacent node groups can refer to a combination of left and right child nodes of a root node.

[0080] Thirdly, the execution subject performs backtracking on each data product structure adjacent node group in the set of data product structure adjacent node groups, and accesses adjacent nodes of the backtracked nodes to obtain a set of final data product structure adjacent node groups.

[0081] Fourthly, the execution subject integrates information of the set of final data product structure adjacent node groups to obtain data product node graph information.

[0082] In step 104, the execution subject constructs a data product data structure according to the data product node graph information.

[0083] In some embodiments, the execution subject can construct a data product data structure according to the data product node graph information.

[0084] Optionally, the execution subject can perform the following steps:

[0085] Firstly, the execution subject merges the same nodes in the data product node graph information to obtain merged data product node graph information.

[0086] Here, the merging can refer to combination.

[0087] Secondly, the execution subject filters the merged data product node graph information to generate a set of filtered data product nodes.

[0088] In a third step, each filtered data product node in the filtered data product node set is stored in a preset empty data structure in sequence to obtain a data product data structure.

[0089] Here, the preset empty data structure can be a preset empty forest structure.

[0090] In a fifth step, in response to determining that the number of filtered data product nodes in the data product data structure reaches a preset numerical condition, the data product data structure is marked in batch sequence to obtain a marked data product data structure.

[0091] In some embodiments, the execution subject can mark the data product data structure in batch sequence to obtain a marked data product data structure in response to determining that the number of filtered data product nodes in the data product data structure reaches a preset numerical condition.

[0092] Here, the preset numerical condition can be a preset numerical condition. For example, the preset numerical condition can be a multiple of 10.

[0093] In a sixth step, the marked data product data structure is called in a preset order to obtain a data product calling sequence.

[0094] In some embodiments, the execution subject can call the marked data product data structure in a preset order to obtain a data product calling sequence.

[0095] Here, the preset order can be a preset order. For example, the preset order can be a reverse order of the batch sequence.

[0096] In a seventh step, in response to determining that there is a data product with a calling number reaching a preset number of times in the data product calling sequence, the data product with the calling number reaching the preset number of times is cached to a preset memory area.

[0097] In some embodiments, the execution subject can cache the data product with the calling number reaching the preset number of times to a preset memory area in response to determining that there is a data product with a calling number reaching a preset number of times in the data product calling sequence.

[0098] Here, the preset memory area can be a preset memory area. The preset number of times can be a preset number of times. For example, the preset number of times can be 6 times.

[0099] Optionally, after the seventh step, the method further includes:

[0100] In response to determining that the called data product exists in the above-mentioned preset memory area, reading from the above-mentioned preset memory area.

[0101] The above various embodiments of the present disclosure have the following beneficial effects: through the data product calling method of some embodiments of the present disclosure, the period of data product calling is shortened, the computing resource performance and network resource performance of the system are improved, and the response speed of the system is improved. Specifically, the reason why the period of data product calling is relatively long, the computing resource performance and network resource performance of the system are consumed, and the response speed of the system is reduced is that: since the complexity of the obtained preset scene data product information set is high, and the association relationship is relatively chaotic, too much system resource and time may be consumed when the preset scene data product information is called, resulting in a relatively long period of data product calling. In addition, the data product may be called multiple times, so that the computing resource performance and network resource performance of the system are consumed, and the response speed of the system is reduced. Based on this, the data product calling method of some embodiments of the present disclosure first obtains a preset scene data product information set, wherein the preset scene data product information in the preset scene data product information set is product information packaged by related data under a preset scene. Thus, subsequent processing can be facilitated. Then, a preset structure is constructed for the preset scene data product information set, to obtain a preset scene data product structure, wherein the nodes in the preset scene data product structure represent the preset scene data product information in the preset scene data product information set, and the edges in the preset scene data product structure represent the association relationship between two preset scene data product information in the preset scene data product information set. Thus, the complexity of the preset scene data product information set can be reduced, and too much system resource and time can be avoided when the preset scene data product information is called, so as to shorten the period of data product calling. Then, the preset scene data product structure is traversed to generate data product node graph information. Then, according to the data product node graph information, a data product data structure is constructed. Then, in response to determining that the number of filtered data product nodes in the data product data structure reaches a preset numerical condition, the data product data structure is marked in batches in sequence to obtain a marked data product data structure. Thus, the data product data structure can be called in batches, too much system resource and time can be avoided when the preset scene data product information is called, and the period of data product calling is shortened. The marked data product data structure is called according to a preset sequence to obtain a data product calling sequence. In response to determining that there is a data product with a calling number reaching a preset number of times in the data product calling sequence, the data product with the calling number reaching the preset number of times is cached to a preset memory area. Thus, the data product called multiple times is cached, the consumption of the computing resource performance and network resource performance of the system is reduced, and the response speed of the system is improved. Therefore, the period of data product calling is shortened, the computing resource performance and network resource performance of the system are improved, and the response speed of the system is improved.

[0102] Further referenceFigure 2 As an implementation of the method shown in the above figures, the disclosure provides some embodiments of a data product calling method, which device embodiments correspond to those method embodiments, and the device can be specifically applied to various electronic devices. Figure 1

[0103] As shown in Figure 2 , the data product calling device 200 of some embodiments includes an acquisition unit 201, a first construction unit 202, a traversal unit 203, a second construction unit 204, a marking unit 205, a calling unit 206, and a caching unit 207. The acquisition unit 201 is configured to acquire a preset scene data product information set, wherein the preset scene data product information in the preset scene data product information set is product information packaged by related data under a preset scene; the first construction unit 202 is configured to perform preset structure construction on the preset scene data product information set to obtain a preset scene data product structure, wherein the nodes in the preset scene data product structure represent the preset scene data product information in the preset scene data product information set, and the edges in the preset scene data product structure represent the association relationship between two preset scene data product information in the preset scene data product information set; the traversal unit 203 is configured to perform node traversal on the preset scene data product structure to generate data product node graph information; the second construction unit 204 is configured to construct a data product data structure according to the data product node graph information; the marking unit 205 is configured to, in response to determining that the number of screened data product nodes in the data product data structure reaches a preset numerical condition, mark the data product data structure in batch order to obtain a marked data product data structure; the calling unit 206 is configured to call the marked data product data structure according to a preset order to obtain a data product calling sequence; and the caching unit 207 is configured to, in response to determining that there is a data product with a calling number reaching a preset number of times in the data product calling sequence, cache the data product with the calling number reaching the preset number of times to a preset memory area.

[0104] It can be understood that the units recorded in the device 200 correspond to the respective steps in the method described with reference to Figure 1 . Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, which will not be described here.

[0105] Reference is made below to Figure 3 , which shows a structural schematic diagram of an electronic device (such as a computing device) 300 suitable for implementing some embodiments of the disclosure. Figure 3 ​The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0106] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 304. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 304 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0107] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0108] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0109] Note that the computer-readable medium in some embodiments of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example and without limitation, be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, the computer-readable signal medium can include a data signal propagated in baseband or propagated as a carrier wave in a propagated data signal, in which the computer-readable program code is contained. Such a propagated data signal can take a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium that is not a storage medium and that can communicate, propagate, or transport program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained in the computer-readable medium can be transmitted using any suitable medium, including but not limited to wire, cable, RF, etc., or any suitable combination of the foregoing.

[0110] In some embodiments, the client, server, or both can communicate using any current known or future developed network protocol, such as HTTP (Hyper Text Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet, and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any current known or future developed networks.

[0111] The computer readable medium can be included in the electronic device, or exist separately from the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: obtain a preset scene data product information set, wherein preset scene data product information in the preset scene data product information set is product information packaged by related data under a preset scene; perform preset structure construction on the preset scene data product information set to obtain a preset scene data product structure, wherein a node in the preset scene data product structure represents preset scene data product information in the preset scene data product information set, and an edge in the preset scene data product structure represents an association relationship between two pieces of preset scene data product information in the preset scene data product information set; perform node traversal on the preset scene data product structure to generate data product node graph information; construct a data product data structure according to the data product node graph information; in response to determining that a number of screened data product nodes in the data product data structure reaches a preset number condition, perform batch order marking on the data product data structure to obtain a marked data product data structure; call the marked data product data structure according to a preset order to obtain a data product calling sequence; and in response to determining that there is a data product with a calling number reaching a preset number in the data product calling sequence, cache the data product with the calling number reaching the preset number to a preset memory area.

[0112] Computer program code for carrying out operations of some embodiments of the disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0113] The flow and block diagrams in the drawings represent possible architectural, functional, and operational architectures of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow and block diagrams can represent a module, a segment, or a portion of code that comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0114] The units described in some embodiments of the present disclosure can be implemented by means of software, or can be implemented by hardware. The described units can also be arranged in a processor, for example, can be described as: a processor comprising an acquisition unit, a first construction unit, a traversal unit, a second construction unit, a marking unit, a calling unit and a caching unit. Among them, the names of these units do not constitute a limitation to the units themselves in some cases, for example, the traversal unit can also be described as "a unit for traversing the nodes of the preset scene data product structure to generate data product node graph information".

[0115] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, example types of hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), etc.

[0116] The above description is merely some of the preferred embodiments of the present disclosure and a description of the technical principles applied thereto. Those skilled in the art should understand that the scope of the application involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combinations of the above technical features or equivalent features thereof without departing from the above inventive concept. For example, the above technical features can be replaced with technical features disclosed in the embodiments of the present disclosure (but not limited to) having similar functions to form technical solutions.

Claims

1. A method for invoking a data product, comprising: Obtain a preset scenario data product information set, wherein the preset scenario data product information in the preset scenario data product information set is product information packaged from relevant data under the preset scenario; A preset structure is constructed on the preset scenario data product information set to obtain a preset scenario data product structure. The nodes in the preset scenario data product structure represent the preset scenario data product information in the preset scenario data product information set, and the edges in the preset scenario data product structure represent the association between two preset scenario data product information in the preset scenario data product information set. The preset scenario data product structure is traversed node by node to generate data product node graph information; Based on the data product node graph information, construct the data product data structure; In response to determining that the number of filtered data product nodes in the data product data structure reaches a preset value condition, the data product data structure is batch-ordered and marked to obtain the marked data product data structure. The marked data product data structure is called in a preset order to obtain a data product call sequence; In response to determining that there is a data product in the data product call sequence that has been called a preset number of times, the data product that has been called a preset number of times is cached in a preset memory area.

2. The method according to claim 1, wherein, The method further includes: In response to the determination that the data product to be invoked exists in the preset memory area, it is read from the preset memory area.

3. The method according to claim 1, wherein, The step of constructing the data product data structure based on the data product node graph information includes: Merge identical nodes in the data product node graph information to obtain merged data product node graph information; The merged data product node graph information is filtered to generate a filtered data product node set; Each filtered data product node in the filtered data product node set is sequentially stored into a preset empty data structure to obtain the data product data structure.

4. The method according to claim 1, wherein, The step of traversing the nodes of the preset scenario data product structure to generate data product node graph information includes: The root node of the data product structure in the preset scenario is accessed to obtain the root node set of the data product structure. At least one adjacent node is accessed for the root node of the data product structure in the root node set of the data product structure to generate a data product structure adjacent node group, thus obtaining a data product structure adjacent node group set. The data product structure adjacent node set is backtracked, and the adjacent node access is performed on the backtracked nodes to obtain the final data product structure adjacent node set. Information is integrated from the set of adjacent nodes of the final data product structure to obtain data product node graph information.

5. The method according to claim 1, wherein, The step of constructing a preset structure for the preset scenario data product information set to obtain a preset scenario data product structure includes: The association relationships of the preset scenario data product information set are determined to obtain a data product association information set; Indirect relationships are determined among the data product association information groups in the data product association information group set to obtain the data product indirect association information set; Connect the indirect association information of each data product in the indirect association information set of the data products to obtain the connected indirect association information set of the data products; The data products are connected to the indirect association information set of the connected data products to obtain the connected data product information set. The structure of the connected data product information set is adjusted to obtain the adjusted data product structure, which serves as the preset scenario data product structure.

6. A data product retrieval device, comprising: The acquisition unit is configured to acquire a preset scenario data product information set, wherein the preset scenario data product information in the preset scenario data product information set is product information packaged from relevant data under the preset scenario; The first construction unit is configured to construct a preset structure for the preset scenario data product information set to obtain a preset scenario data product structure, wherein the nodes in the preset scenario data product structure represent preset scenario data product information in the preset scenario data product information set, and the edges in the preset scenario data product structure represent the association relationship between two preset scenario data product information in the preset scenario data product information set. The traversal unit is configured to perform node traversal on the preset scenario data product structure to generate data product node graph information. The second building unit is configured to build a data product data structure based on the data product node graph information; The marking unit is configured to perform batch sequential marking on the data product data structure in response to determining that the number of filtered data product nodes in the data product data structure reaches a preset numerical condition, thereby obtaining the marked data product data structure. The calling unit is configured to call the marked data product data structure in a preset order to obtain a data product calling sequence; The caching unit is configured to cache the data product that has been called a preset number of times in a data product call sequence to a preset memory area in response to determining that there is a data product that has been called a preset number of times.

7. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 5.

8. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 5.

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