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

Abstract

The embodiment of the invention discloses a data product calling method and device, electronic equipment and a computer readable medium. According to one specific embodiment, the method comprises the steps that a preset scene data product information set is acquired, and preset scene data product information in the preset scene data product information set is product information formed by packaging related data in a preset scene; calling the marked data structures of the data products according to a preset sequence to obtain a data product calling sequence; and in response to determining that the data product with the calling frequency reaching the preset frequency exists in the data product calling sequence, caching the data product with the calling frequency reaching the preset frequency to a preset memory area. According to the embodiment, the data product calling period is shortened, the computing resource performance and the network resource performance of the system are improved, and the response speed of the system is increased.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technologies, and more particularly, to methods, apparatuses, electronic devices, and computer-readable media for invoking data products. Background Art

[0002] With the rapid development of the information age, there are more and more scenarios for invoking data products. Invoking a data product is a technology for invoking a data product. Currently, the commonly used method for invoking a data product is to directly invoke it after obtaining the data product.

[0003] However, when using the above method, the following technical problems often exist:

[0004] Due to the high complexity of the obtained preset scenario data product information set and the chaotic correlation relationship, it may consume too much system resources and time when invoking the preset scenario data product information, resulting in a long cycle for invoking the data product. Also, since the data product may be invoked multiple times, the performance of the system's computing resources and network resources is consumed, reducing the response speed of the system.

[0005] The above information disclosed in this background art section is only used to enhance the understanding of the background of the inventive concept, and thus, it may include information that does not constitute the prior art known to ordinary technicians in the art of this country. Summary of the Invention

[0006] The content part of the present disclosure is used to briefly introduce the inventive concepts, which will be described in detail in the following detailed implementation part. The content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose methods, apparatuses, electronic devices, and computer-readable media for invoking data products to solve one or more of the technical problems mentioned in the above background art section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for invoking a data product. The method includes: obtaining a preset scenario data product information set, where the preset scenario data product information in the preset scenario data product information set is product information formed by packaging relevant data in a preset scenario; performing a preset structure construction on the preset scenario data product information set to obtain a preset scenario data product structure, where 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 relationship between two preset scenario data product information in the preset scenario data product information set; performing a node traversal on the preset scenario 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 filtered data product nodes in the data product data structure reaches a preset numerical condition, performing a batch sequence marking on the data product data structure to obtain a marked data product data structure; invoking the marked data product data structure in a preset order to obtain a data product invocation sequence; in response to determining that there is a data product in the data product invocation sequence whose invocation times reach a preset number of times, caching the data product whose invocation times reach the preset number of times into a preset memory area.

[0009] In a second aspect, some embodiments of the present disclosure provide a data product invocation device. The device includes: an obtaining unit configured to obtain a preset scenario data product information set, where the preset scenario data product information in the preset scenario data product information set is product information formed by packaging relevant data in a preset scenario; a first construction unit configured to perform a preset structure construction on the preset scenario data product information set to obtain a preset scenario data product structure, where 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 relationship between two preset scenario data product information in the preset scenario data product information set; a traversal unit configured to perform a node traversal on the preset scenario data product structure to generate data product node graph information; a second construction 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 filtered data product nodes in the data product data structure reaches a preset numerical condition, perform a batch sequence marking on the data product data structure to obtain a marked data product data structure; an invocation unit configured to invoke the marked data product data structure in a preset order to obtain a data product invocation sequence; a caching unit configured to, in response to determining that there is a data product in the data product invocation sequence whose invocation times reach a preset number of times, cache the data product whose invocation times reach the preset number of times into a preset memory area.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device, including: one or more processors; a storage device storing 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 manner of the above 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 manner of the above first aspect.

[0012] The above embodiments of the present disclosure have the following beneficial effects: Through the data product calling method of some embodiments of the present disclosure, the cycle 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 increased. Specifically, the reasons for the long cycle of data product calling, the consumption of the computing resource performance and network resource performance of the system, and the reduction of the response speed of the system are as follows: Due to the high complexity of the preset scenario data product information set obtained and the chaotic correlation relationship, excessive system resources and time may be consumed when calling the preset scenario data product information, resulting in a long cycle of data product calling. Also, since the data product may be called multiple times, the computing resource performance and network resource performance of the system are consumed, reducing the response speed of the system. Based on this, in the data product calling method of some embodiments of the present disclosure, first, a preset scenario data product information set is obtained, where the preset scenario data product information in the above preset scenario data product information set is product information formed by packaging relevant data in a preset scenario. Thus, it can facilitate subsequent processing. Then, a preset structure is constructed for the above preset scenario data product information set to obtain a preset scenario data product structure, where the nodes in the above 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 above preset scenario data product structure represent the correlation relationship between two preset scenario data product information in the preset scenario data product information set. Thus, the complexity of the preset scenario data product information set can be reduced, avoiding excessive consumption of system resources and time when calling the preset scenario data product information, and shortening the cycle of data product calling. After that, node traversal is performed on the above preset scenario data product structure to generate data product node graph information. After that, a data product data structure is constructed according to the above data product node graph information. Then, in response to determining that the number of filtered data product nodes in the above data product data structure reaches a preset numerical condition, batch sequence marking is performed on the above data product data structure to obtain a marked data product data structure. Thus, the data product data structure can be called in batches, avoiding excessive consumption of system resources and time when calling the preset scenario data product information, and shortening the cycle of data product calling. The above 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 above data product call sequence whose call count reaches a preset number of times, the data product whose call count reaches a preset number of times is cached in a preset memory area. Thus, the data products that are called multiple times are cached, reducing the consumption of the computing resource performance and network resource performance of the system, and increasing the response speed of the system. Therefore, the cycle 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 increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and the elements and elements are not necessarily drawn 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 schematic structural diagram of some embodiments of a data product calling device according to the present disclosure;

[0016] Figure 3 is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. Specific Embodiments

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0018] In addition, it should be noted that for the sake of convenience of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0019] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependent relationships.

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

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

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

[0023] Figure 1It is the process 100 of some embodiments of the data product invocation method of the present disclosure. The data product invocation method includes the following steps:

[0024] Step 101, obtain a preset scenario data product information set.

[0025] In some embodiments, the execution subject of the data product invocation method (for example, a computing device) can obtain the preset scenario data product information set through a wired connection or a wireless connection. Among them, the preset scenario data product information in the above preset scenario data product information set is product information formed by packaging relevant data in a preset scenario.

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

[0027] It should be noted that the above wireless connection method may include, but is not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future-developed wireless connection methods.

[0028] It should be noted that the above 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 can be implemented as a single server or a single terminal device. For example, the computing device can be the above target terminal. When the computing device is embodied as software, it can be installed in the above-listed hardware devices. It can be implemented as, for example, multiple software or software modules for providing distributed services, or can be implemented as a single software or software module. No specific limitation is made here.

[0029] Step 102, perform a preset structure construction on the above preset scenario data product information set to obtain a preset scenario data product structure.

[0030] In some embodiments, the above execution subject can perform a preset structure construction on the above preset scenario data product information set to obtain a preset scenario data product structure. Among them, the nodes in the above 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 above preset scenario data product structure represent the association relationship between two preset scenario data product information in the preset scenario data product information set.

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

[0032] Optionally, the above execution entity can construct a preset structure for the above preset scenario data product information set through the following steps to obtain a preset scenario data product structure:

[0033] In the first step, determine the association relationships for the above preset scenario data product information set to obtain a data product association information group set.

[0034] Here, the above association relationships can refer to the association relationships between the preset scenario data product information in the above preset scenario data product information set and the associated data groups of the preset scenario data product information. For example, the preset scenario data product information can refer to data related to user credit. Then the associated data groups can include, but are not limited to, at least one of the following: the user's income proof data, the user's credit record data.

[0035] As an example, the above execution entity can determine the association relationships for each preset scenario data product information in the above preset scenario data product information set to generate a data product association information group, and obtain a data product association information group set.

[0036] In the second step, determine the indirect association relationships for each data product association information group in the above data product association information group set to obtain a data product indirect association information set.

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

[0038] In the third step, connect each data product indirect association information in the above data product indirect association information set to obtain a connected data product indirect association information set.

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

[0040] In the fourth step, perform data product connection on the above connected data product indirect association information set to obtain a connected data product information set.

[0041] As an example, the above execution entity 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] In the fifth step, adjust the structure of the above connected data product information set to obtain an adjusted data product structure as the preset scenario data product structure.

[0043] Here, the above-mentioned structural adjustment may refer to the adjustment in the format of a forest.

[0044] Optionally, the above-mentioned execution entity may perform a preset structure construction on the above-mentioned preset scenario data product information set to obtain a preset scenario data product structure through the following steps:

[0045] In the first step, divide the path information of the above-mentioned preset scenario data product information set to obtain a divided data product path information set.

[0046] As an example, the above-mentioned execution entity may divide the path information of each preset scenario data product information in the above-mentioned preset scenario 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 above-mentioned divided data product path information set may refer to the path information of a single data product. For example, the divided data product path information in the above-mentioned divided data product path information set may refer to "personal information product - browsing record - purchase record".

[0047] In the second step, perform static analysis on the above-mentioned divided data product path information set to obtain an analyzed data product path information set.

[0048] As an example, the above-mentioned execution entity may perform symbolic execution on the above-mentioned divided data product path information set to obtain an executed data product path information set as the analyzed data product path information set.

[0049] In the third step, perform path optimization on the above-mentioned analyzed data product path information set to obtain an optimized data product path information set.

[0050] As an example, the above-mentioned execution entity may use a path planning algorithm to perform path optimization on the above-mentioned analyzed data product path information set to obtain an optimized data product path information set.

[0051] In the fourth step, according to the associated relationship matrix information corresponding to the above-mentioned optimized data product path information set, perform random data generation on the above-mentioned optimized data product path information set to obtain a data product random generation data set.

[0052] As an example, the above-mentioned execution entity may parse the associated relationship matrix information corresponding to the above-mentioned optimized data product path information set to obtain the associated relationship matrix information. Then, use the rand() function within the above-mentioned associated relationship matrix information to perform random data generation on the above-mentioned optimized data product path information set to obtain a random generation data set.

[0053] In the fifth step, perform data filtering on the above-mentioned data product random generation data set to obtain a filtered data set.

[0054] As an example, the above-mentioned execution entity can eliminate the dirty data in the above-mentioned randomly generated data set to obtain the randomly generated data set after elimination, which is used as the filtered data set.

[0055] Step 6: Perform data format conversion on the above-mentioned filtered data set to obtain the data set after conversion.

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

[0057] Step 7: Perform discretization processing on the above-mentioned data set after conversion according to the data range limit information corresponding to the above-mentioned data set after conversion to obtain the data set after discretization processing.

[0058] As an example, the above-mentioned execution entity can first determine the boundary values of the above-mentioned data set after conversion through the data range limit information corresponding to the above-mentioned data set after conversion. Then, perform equal-frequency discretization processing on the above-mentioned data set after conversion according to the boundary values to obtain the data set after discretization processing. For example, the data range limit information corresponding to the above-mentioned data set after conversion is "Item price range: (Low: 0 - 300, Medium: 300 - 700, High: 700 - 1000)".

[0059] Step 8: Perform data balancing processing on the above-mentioned data set after discretization processing to obtain the balanced data set;

[0060] Here, the above-mentioned balanced data set can refer to the data set in which the discretized data in the above-mentioned data set after discretization processing is relatively balanced.

[0061] As an example, the above-mentioned execution entity can perform class weighting on the discretized data in the above-mentioned data set after discretization processing to obtain the weighted data set, which is used as the balanced data set.

[0062] Step 9: 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, perform the following processing steps:

[0063] The first sub-step: Determine the difference between the path coverage rate corresponding to the above-mentioned balanced data set and the above-mentioned preset coverage rate as the coverage rate difference information.

[0064] Here, the above-mentioned preset coverage rate can be 60%.

[0065] As an example, the above-mentioned execution entity may subtract the above-mentioned preset coverage rate from the path coverage rate corresponding to the above-mentioned balanced data set to obtain coverage difference information.

[0066] The second sub-step is to adjust the data sampling of the above-mentioned balanced data set according to the above-mentioned coverage difference information to obtain an adjusted data set.

[0067] As an example, the above-mentioned execution entity may, in response to determining that the above-mentioned coverage difference information is greater than a preset difference threshold, perform undersampling on the above-mentioned balanced data set to obtain an undersampled data set as the adjusted data set. In response to determining that the above-mentioned coverage difference information is less than or equal to the preset difference threshold, perform oversampling on the above-mentioned balanced data set to obtain an oversampled data set as the adjusted data set.

[0068] The third sub-step is to perform a significance test on the above-mentioned adjusted data set to obtain a tested data set.

[0069] The fourth sub-step is to compare the coverage information corresponding to the above-mentioned tested data set with the coverage information corresponding to the above-mentioned adjusted data set to obtain the compared coverage information.

[0070] As an example, the above-mentioned execution entity may compare the difference rate between the coverage information corresponding to the above-mentioned tested data set and the coverage information corresponding to the above-mentioned adjusted data set to obtain the compared difference rate information as the compared coverage information.

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

[0072] The relevant content in the above first step to tenth step is an inventive point of the present disclosure, which solves the following technical problem: "When calling the preset scenario data product information, it may consume too much system resources and time, and the cycle of data product call is relatively long." The factors that lead to a relatively long cycle of data product call are often as follows: The correlation relationship between the preset scenario data product information sets cannot be fully considered, resulting in an inaccurate preset scenario data product structure, thus causing excessive consumption of system resources and time when calling the preset scenario data product information, and leading to a relatively long cycle of data product call. 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 enhanced. To achieve this effect, first, divide the path information of the above preset scenario data product information set to obtain the divided data product path information set. Second, perform static analysis on the above divided data product path information set to obtain the analyzed data product path information set. Third, optimize the path of the above analyzed data product path information set to obtain the optimized data product path information set. Fourth, according to the correlation matrix information corresponding to the above optimized data product path information set, generate random data for the above optimized data product path information set to obtain a data product random generation data set. Fifth, filter the above data product random generation data set to obtain a filtered data set. Sixth, convert the data format of the above filtered data set to obtain a converted data set. Seventh, perform discretization processing on the above converted data set according to the data range limit information corresponding to the above converted data set to obtain a discretized data set. Eighth, perform data balancing processing on the above discretized data set to obtain a balanced data set. Ninth, in response to determining that the path coverage rate corresponding to the above balanced data set is not equal to the preset coverage rate, execute the following processing steps: The first sub-step is to determine the difference between the path coverage rate corresponding to the above balanced data set and the above preset coverage rate as the coverage rate difference information. The second sub-step is to adjust the data sampling of the above balanced data set according to the above coverage rate difference information to obtain an adjusted data set. The third sub-step is to perform a significance test on the above adjusted data set to obtain a tested data set. The fourth sub-step is to compare the coverage rate information corresponding to the above tested data set with the coverage rate information corresponding to the above adjusted data set to obtain a compared coverage rate information. Tenth, in response to determining that the coverage rate corresponding to the above compared coverage rate information is greater than or equal to the preset value, add a directed edge to the data product node corresponding to the above tested data set to obtain a preset scenario data product structure. Therefore, when calling the preset scenario data product information, excessive system resources and time are reduced, and the cycle of data product call is shortened.

[0073] Step 103: Traverse the nodes of the above preset scenario data product structure to generate data product node graph information.

[0074] In some embodiments, the above-mentioned execution entity may perform a node traversal on the above-mentioned preset scenario data product structure to generate data product node graph information.

[0075] Optionally, the above-mentioned execution entity may perform a node traversal on the above-mentioned preset scenario data product structure through the following steps to generate data product node graph information:

[0076] First step, access the root nodes of the above-mentioned preset scenario data product structure to obtain a set of data product structure root nodes.

[0077] Here, the data product structure root nodes in the above-mentioned set of data product structure root nodes may refer to data product nodes without parent nodes.

[0078] Second step, access at least one adjacent node of the data product structure root nodes in the above-mentioned set of data product structure root nodes to generate a group of data product structure adjacent nodes, and obtain a set of data product structure adjacent node groups.

[0079] Here, the data product structure adjacent node groups in the above-mentioned set of data product structure adjacent node groups may refer to the combination of left and right child nodes under the root node.

[0080] Third step, perform a backtracking process on the data product structure adjacent nodes in the above-mentioned set of data product structure adjacent node groups, and access the adjacent nodes of the nodes after backtracking to obtain a final set of data product structure adjacent node groups.

[0081] Fourth step, integrate the information in the above-mentioned final set of data product structure adjacent node groups to obtain data product node graph information.

[0082] Step 104, construct a data product data structure according to the above-mentioned data product node graph information.

[0083] In some embodiments, the above-mentioned execution entity may construct a data product data structure according to the above-mentioned data product node graph information.

[0084] Optionally, the above-mentioned execution entity may perform the following steps:

[0085] First step, merge the same nodes in the above-mentioned data product node graph information to obtain the merged data product node graph information.

[0086] Here, the above-mentioned merge may refer to a combination.

[0087] Second step, perform node screening on the above-mentioned merged data product node graph information to generate a set of screened data product nodes.

[0088] Step 3: Store each of the filtered data product nodes in the above-mentioned filtered data product node set into a preset empty data structure in sequence to obtain a data product data structure.

[0089] Here, the above-mentioned preset empty data structure may refer to a preset empty forest structure.

[0090] Step 105: In response to determining that the number of filtered data product nodes in the above-mentioned data product data structure reaches a preset numerical condition, perform batch sequence marking on the above-mentioned data product data structure to obtain a marked data product data structure.

[0091] In some embodiments, the above-mentioned execution entity may, in response to determining that the number of filtered data product nodes in the above-mentioned data product data structure reaches a preset numerical condition, perform batch sequence marking on the above-mentioned data product data structure to obtain a marked data product data structure.

[0092] Here, the above-mentioned preset numerical condition may refer to a condition of a preset value. For example, the above-mentioned preset numerical condition may refer to a multiple of 10.

[0093] Step 106: Call the above-mentioned marked data product data structure in a preset order to obtain a data product call sequence.

[0094] In some embodiments, the above-mentioned execution entity may call the above-mentioned marked data product data structure in a preset order to obtain a data product call sequence.

[0095] Here, the above-mentioned preset order may refer to a preset order. For example, the above-mentioned preset order may refer to the reverse order of the batch sequence.

[0096] Step 107: In response to determining that there is a data product in the above-mentioned data product call sequence whose call count reaches a preset number of times, cache the data product whose call count reaches the preset number of times into a preset memory area.

[0097] In some embodiments, the above-mentioned execution entity may, in response to determining that there is a data product in the above-mentioned data product call sequence whose call count reaches a preset number of times, cache the data product whose call count reaches the preset number of times into a preset memory area.

[0098] Here, the above-mentioned preset memory area may refer to a preset memory area. The above-mentioned preset number of times may refer to a preset number of times. For example, the above-mentioned preset number of times may refer to 6 times.

[0099] Optionally, after the above-mentioned "Step 107", the above method further includes:

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

[0101] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the data product invocation method of some embodiments of the present disclosure, the cycle of data product invocation is shortened, the computing resource performance and network resource performance of the system are improved, and the response speed of the system is increased. Specifically, the reasons for the long cycle of data product invocation, the consumption of the computing resource performance and network resource performance of the system, and the reduction of the response speed of the system are as follows: Due to the high complexity of the preset scenario data product information set obtained and the relatively chaotic association relationship, excessive system resources and time may be consumed when invoking the preset scenario data product information, resulting in a long cycle of data product invocation. Also, since the data product may be invoked multiple times, the computing resource performance and network resource performance of the system are consumed, reducing the response speed of the system. Based on this, in the data product invocation method of some embodiments of the present disclosure, first, a preset scenario data product information set is obtained, where the preset scenario data product information in the above-mentioned preset scenario data product information set is product information formed by packaging relevant data in a preset scenario. Thus, it can facilitate subsequent processing. Then, a preset structure is constructed for the above-mentioned preset scenario data product information set to obtain a preset scenario data product structure, where the nodes in the above-mentioned 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 above-mentioned preset scenario data product structure represent the association relationship between two preset scenario data product information in the preset scenario data product information set. Thus, the complexity of the preset scenario data product information set can be reduced, avoiding excessive consumption of system resources and time when invoking the preset scenario data product information, and shortening the cycle of data product invocation. After that, node traversal is performed on the above-mentioned preset scenario data product structure to generate data product node graph information. After that, a data product data structure is constructed according to the above-mentioned data product node graph information. Then, in response to determining that the number of filtered data product nodes in the above-mentioned data product data structure reaches a preset numerical condition, batch sequence marking is performed on the above-mentioned data product data structure to obtain a marked data product data structure. Thus, the data product data structure can be batch-invoked, avoiding excessive consumption of system resources and time when invoking the preset scenario data product information, and shortening the cycle of data product invocation. The above-mentioned marked data product data structure is invoked in a preset order to obtain a data product invocation sequence. In response to determining that there is a data product in the above-mentioned data product invocation sequence whose invocation times reach a preset number of times, the data product whose invocation times reach the preset number of times is cached in a preset memory area. Thus, the data products that are invoked multiple times are cached, reducing the consumption of the computing resource performance and network resource performance of the system, and increasing the response speed of the system. Therefore, the cycle of data product invocation is shortened, the computing resource performance and network resource performance of the system are improved, and the response speed of the system is increased.

[0102] Further referenceFigure 2 , as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a method for calling data products. These device embodiments correspond to Figure 1 the method embodiments shown, and the device can be specifically applied to various electronic devices.

[0103] As Figure 2 shown, the data product calling device 200 in 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. Among them, the acquisition unit 201 is configured to acquire a preset scenario data product information set, where the preset scenario data product information in the preset scenario data product information set is product information formed by packaging relevant data in a preset scenario; the first construction unit 202 is configured to perform a preset structure construction on the preset scenario data product information set to obtain a preset scenario data product structure, where 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 relationship between two preset scenario data product information in the preset scenario data product information set; the traversal unit 203 is configured to perform node traversal on the preset scenario 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 filtered 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; the calling unit 206 is configured to call the marked data product data structure in a preset order to obtain a data product call sequence; the caching unit 207 is configured to, in response to determining that there is a data product in the data product call sequence whose call count reaches a preset number of times, cache the data product whose call count reaches a preset number of times to a preset memory area.

[0104] It can be understood that the units described 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 included therein, and will not be repeated here.

[0105] Next, refer to Figure 3 , which shows a schematic structural diagram of an electronic device (such as a computing device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3The electronic device shown is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

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

[0107] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or wirelessly to exchange data. Although Figure 3 the electronic device 300 with various devices is shown, it should be understood that it is not required to implement or include all the shown devices. More or fewer devices may be alternatively implemented or included. Figure 3 Each block shown in

[0108] particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the method shown in the flowchart. In such some embodiments, the computer program may be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the functions defined in the method of some embodiments of the present disclosure are executed.

[0109] It should be noted that the computer-readable media described in some embodiments of the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

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

[0111] The above computer-readable medium may be included in the above electronic device; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device is caused to: obtain a preset scenario data product information set, where the preset scenario data product information in the preset scenario data product information set is product information formed by packaging relevant data in a preset scenario; perform a preset structure construction on the preset scenario data product information set to obtain a preset scenario data product structure, where 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 relationship between two preset scenario data product information in the preset scenario data product information set; perform a node traversal on the preset scenario 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 the number of filtered data product nodes in the data product data structure reaches a preset numerical condition, perform a batch sequence marking on the data product data structure to obtain a marked data product data structure; call the marked data product data structure 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 whose call count reaches a preset number of times, cache the data product whose call count reaches the preset number of times to a preset memory area.

[0112] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The above programming languages include object-oriented programming languages - such as Java, Smalltalk, C++; and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0114] The units described in some embodiments of the present disclosure can be implemented in software or in hardware. The described units can also be provided in a processor. For example, it can be described as: a processor includes: an acquisition unit, a first construction unit, a traversal unit, a second construction unit, a marking unit, a call unit, and a cache unit. Among them, the names of these units do not constitute a limitation to the unit itself in some cases. For example, the traversal unit can also be described as "a unit that traverses the node of the above-mentioned preset scenario data product structure to generate data product node diagram information".

[0115] The functions described above can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary 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), systems on a chip (SOCs), complex programmable logic devices (CPLDs), and so on.

[0116] The above description is only some preferred embodiments of the present disclosure and an explanation of the technical principles applied above. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A data product calling method, comprising: 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 from relevant data under the preset scene; Constructing a preset structure for 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 value condition, batch-sequentially marking the data product data structure to obtain a marked data product data structure; Calling the marked data product data structure in a preset order to obtain a data product calling sequence; In response to determining that there is a data product whose call times reach a preset number in the data product call sequence, the data product whose call times reach the preset number is cached in a preset memory area.

2. The method according to claim 1, wherein: The method further comprises: In response to determining that the called data product exists in the preset memory area, reading is performed from the preset memory area.

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

4. The method according to claim 1, wherein: The node traversal of the preset scene data product structure to generate data product node graph information includes: Access the root node of the preset scenario data product structure to obtain a data product structure root node set; Performing at least one adjacent node access on the data product structure root node in the data product structure root node set to generate a data product structure adjacent node group, and obtaining a data product structure adjacent node group set; Performing backtracking processing on the data product structure adjacent nodes in the data product structure adjacent node set, and performing adjacent node access on the backtracked nodes to obtain a final data product structure adjacent node set; The information of the adjacent node group set of the final data product structure is integrated 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 scene data product information set to obtain a preset scene data product structure includes: Determining the association relationship of the preset scenario data product information set to obtain a data product association information group set; Determining an indirect association relationship for each data product association information group in the data product association information group set to obtain a data product indirect association information set; Connecting each piece of indirect association information of data products in the data product indirect association information set to obtain a connected data product indirect association information set; Performing data product connection on the connected data product indirectly associated information set to obtain a connected data product information set; The structure of the connected data product information set is adjusted to obtain an adjusted data product structure as a preset scenario data product structure.

6. A data product calling device, comprising: An acquisition unit 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 from relevant data under a preset scene; A first construction unit is configured to perform a 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 traversal unit is configured to perform node traversal on the preset scene data product structure to generate data product node graph information; A second construction unit is 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, mark the data product data structure in batch order to obtain a marked data product data structure; A calling unit, configured to call the marked data product data structure in a preset order to obtain a data product calling sequence; The cache unit is configured to cache the data product with the preset number of calls in a preset memory area in response to determining that there is a data product with the preset number of calls in the data product call sequence.

7. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to 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 a processor, the method according to any one of claims 1 to 5 is implemented.

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