A data processing method and device, electronic equipment and storage medium

Abstract

The application relates to the field of data processing, in particular to a data processing method and device, electronic equipment and storage medium, which are used for accurately obtaining associated data of objects in a delivery platform. The method comprises the following steps: clustering a plurality of to-be-analyzed objects based on an object category attribute and a preset number of a to-be-analyzed object, and obtaining at least one object cluster; respectively sending a corresponding acquisition request to the delivery platform for each object cluster, wherein each acquisition request is used for acquiring associated data of each to-be-analyzed object in the corresponding object cluster; receiving each feedback message returned by the delivery platform, wherein, when one feedback message representing acquisition failure is received, re-clustering the corresponding object cluster based on a comparison object number carried in the received feedback message, obtaining an updated object cluster, and sending an acquisition request to the delivery platform again based on the updated object cluster until the corresponding associated data is obtained. In this way, the associated data of the objects in the delivery platform can be accurately obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a data processing method and device, electronic equipment and storage medium. BACKGROUND

[0002] At present, with the continuous development of science and technology, more and more multimedia resources are selected to be put on various network platforms at home and abroad to better promote the multimedia resources.

[0003] However, due to the acquisition restriction of the network platform on the data generated by the put-on object on the platform, the put-on object cannot accurately acquire the complete data generated by the put-on object based on the multimedia resource on the platform.

[0004] For example, when the put-on object determines to put the multimedia resource A on the network platform 1 to put-on the put-on object set 1, the network platform 1 will autonomously select the registration object set 5 to push based on a preset algorithm for the multimedia resource A, and obtain the data corresponding to the registration object set 5 associated with the multimedia resource A, wherein the data associated with the multimedia resource A can be clicking the multimedia resource A, downloading the multimedia resource A, exposure rate, etc., and the objects in the put-on object set 1 and the registration object set 5 can be partially the same.

[0005] However, when the put-on object manually operates the electronic device to send a request for acquiring the associated data of the multimedia resource A, the network platform 1 will only return the data corresponding to the objects in the registration object set 5 when the number of objects performing the related operation on the multimedia resource is greater than a preset threshold; in this way, the put-on object needs to manually trigger the acquisition request multiple times, and cannot accurately acquire the associated data of the put-on object set 1 on the network platform 1.

[0006] In addition, although the put-on object can acquire the attribution analysis data of the multimedia resource from the third-party platform, since the third-party platform acquires the data not directly from the network platform 1, there can be problems such as data loss, so that the associated data of the put-on object set 1 on the network platform 1 cannot be accurately acquired.

[0007] In summary, the related technology has the technical problem that the associated data of the objects in the platform to which the put-on multimedia resource is put cannot be quickly and accurately acquired. SUMMARY

[0008] The present application provides a data processing method, device, electronic equipment and storage medium for quickly and accurately acquiring the associated data of the objects in the platform to which the put-on multimedia resource is put.

[0009] In one aspect, a data processing method is provided, and the method comprises:

[0010] cluster the plurality of objects to be analyzed based on the object category attribute and a preset number of objects to be analyzed, to obtain at least one object cluster, wherein each object cluster contains the preset number of objects to be analyzed;

[0011] send, respectively for each object cluster, a corresponding acquisition request to a delivery platform, wherein each acquisition request is used to acquire associated data of each object to be analyzed in the corresponding object cluster;

[0012] receive each feedback message returned by the delivery platform, wherein, upon receiving one feedback message representing a failure in acquisition, re-cluster the corresponding object cluster based on a number of comparison objects carried in the received feedback message, to obtain an updated object cluster, and send an acquisition request to the delivery platform again based on the updated object cluster, until the corresponding associated data is acquired.

[0013] In one aspect, a data processing apparatus is provided, and the apparatus comprises:

[0014] a first processing unit configured to cluster the plurality of objects to be analyzed based on the object category attribute and a preset number of objects to be analyzed, to obtain at least one object cluster, wherein each object cluster contains the preset number of objects to be analyzed;

[0015] a sending unit configured to send, respectively for each object cluster, a corresponding acquisition request to a delivery platform, wherein each acquisition request is used to acquire associated data of each object to be analyzed in the corresponding object cluster;

[0016] a second processing unit configured to receive each feedback message returned by the delivery platform, wherein, upon receiving one feedback message representing a failure in acquisition, re-cluster the corresponding object cluster based on a number of comparison objects carried in the received feedback message, to obtain an updated object cluster, and send an acquisition request to the delivery platform again based on the updated object cluster, until the corresponding associated data is acquired.

[0017] Optionally, the second processing unit is specifically configured to:

[0018] when it is determined that the number of comparison objects carried in the received feedback message is less than a current number of objects of the corresponding object cluster, determine a new number of objects based on the number of comparison objects and the current number of objects;

[0019] re-cluster the corresponding object cluster based on the new number of objects and the object category attribute, to obtain an updated object cluster.

[0020] Optionally, the second processing unit is specifically configured to:

[0021] determine, from the other object clusters, a reference object cluster based on distances between the corresponding object cluster and the other object clusters when it is determined that the number of comparison objects carried in the received feedback message is greater than the current number of objects in the corresponding object cluster;

[0022] re-cluster the reference object cluster and the corresponding object cluster to obtain an updated object cluster.

[0023] Optionally, the second processing unit is further configured to:

[0024] determine distance values between the corresponding object cluster and other object clusters respectively to obtain a distance value set, wherein a distance value is determined based on an average distance between each data point in the corresponding object cluster and each data point in another object cluster;

[0025] determine a target distance value that meets a clustering condition from the distance value set, and take an object cluster corresponding to the target distance value as the reference object cluster.

[0026] Optionally, the apparatus further comprises a third processing unit configured to:

[0027] create a local control platform, invoke a service corresponding to the delivery platform through the local control platform, send a request for obtaining associated data of each delivery object in the at least one object cluster, and receive a feedback message returned by the delivery platform.

[0028] Optionally, the apparatus further comprises an analysis unit configured to:

[0029] analyze associated data of each object to be analyzed in the at least one object cluster to obtain an analysis report corresponding to the associated data of each object to be analyzed in the at least one object cluster, wherein the analysis report is used to analyze resources delivered on the delivery platform and adjust a delivery strategy.

[0030] In one aspect, an electronic device is provided, which includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor performs steps of any one of the above data processing methods.

[0031] In one aspect, a computer storage medium is provided, which stores computer instructions, and when the computer instructions are run on a computer, the computer performs steps of any one of the above data processing methods.

[0032] In one aspect, an embodiment of the present application provides a computer program product, which comprises computer instructions stored in a computer readable storage medium; when a processor of an electronic device reads the computer instructions from the computer readable storage medium, the processor executes the computer instructions, so that the electronic device performs the steps of any one of the above data processing methods.

[0033] The present application has the following advantages:

[0034] In an embodiment of the present application, a data processing method, device, electronic device and storage medium are provided. First, a plurality of objects to be analyzed are clustered based on the object category attribute and the preset number of the object to be analyzed, to obtain at least one object cluster. In this way, the plurality of objects to be analyzed can be roughly grouped, and then a corresponding acquisition request is sent to the delivery platform for each object cluster. Further, each feedback message returned by the delivery platform is received. When a feedback message representing a failure to acquire is received, the corresponding object cluster is re-clustered based on the comparison object number carried in the received feedback message, to obtain an updated object cluster, and an acquisition request is sent to the delivery platform again based on the updated object cluster, until the corresponding associated data is obtained.

[0035] As can be seen, in the present application, the corresponding object cluster is continuously updated based on the feedback message returned by the delivery platform, so that an object cluster with the same preset delivery object number as the delivery platform is obtained, i.e. an updated object cluster obtained by automatic continuous clustering. A limited number of requests are sent, and the associated data of each object to be analyzed in the object cluster in the delivery platform is accurately obtained.

[0036] Other features and advantages of the present application will be described in the following description, and some will become apparent from the description, or will be learned from practice of the present application. The purpose and other advantages of the present application can be achieved and obtained by the structure specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0038] Figure 1 An optional schematic diagram for an application scenario in an embodiment of the present application;

[0039] Figure 2 Another optional schematic diagram for an application scenario in an embodiment of the present application;

[0040] Figure 3 a flowchart of a data processing method in an embodiment of the present application;

[0041] Figure 4 a schematic diagram of determining an object cluster in an embodiment of the present application;

[0042] Figure 5 a schematic diagram of determining an updated object cluster in an embodiment of the present application;

[0043] Figure 6 a schematic diagram of determining an updated object cluster in an embodiment of the present application;

[0044] Figure 7 an implementation schematic diagram of a data processing method in an embodiment of the present application;

[0045] Figure 8 an effect schematic diagram of obtaining associated data of each object to be put in an object cluster in an embodiment of the present application;

[0046] Figure 9 a schematic diagram of a data processing process in an embodiment of the present application;

[0047] Figure 10 a schematic diagram of an analysis report in an embodiment of the present application;

[0048] Figure 11 a further implementation schematic diagram of a data processing method in an embodiment of the present application;

[0049] Figure 12 a schematic diagram of a composition structure of a data processing device in an embodiment of the present application;

[0050] Figure 13 a schematic diagram of a hardware composition structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application. The embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily without conflict. Moreover, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that herein.

[0052] The terms "first" and "second" in the specification and claims of the present application and the above drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the term "comprising" and any variations thereof are intended to cover non-exclusive protection. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device. "Multiple" in the present application can mean at least two, for example, two, three or more, and the embodiments of the present application are not limited.

[0053] To facilitate understanding of the technical solutions provided by the embodiments of the present application, some part concepts related to the embodiments of the present application are explained first:

[0054] Artificial intelligence (AI) is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology of computer science, which tries to understand the essence of intelligence and produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that machines have the functions of perception, reasoning and decision-making.

[0055] Artificial intelligence technology is a comprehensive discipline, involving a wide range of fields, both hardware and software technologies. Artificial intelligence basic technologies generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0056] Machine learning (ML) is a multi-disciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc. It is a branch of computer science that studies how computers simulate or implement human learning behavior to acquire new knowledge or skills, and reorganize existing knowledge structure to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental approach to making computers intelligent, and its applications are widespread in various fields of artificial intelligence. Machine learning and deep learning usually include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and example-based learning.

[0057] With the research and progress of artificial intelligence technology, artificial intelligence technology is researched and applied in many fields, such as common smart home, smart wearable device, virtual assistant, smart speaker, smart marketing, unmanned driving, autonomous driving, unmanned aerial vehicle, robot, smart medical treatment, smart customer service, etc. It is believed that with the development of technology, artificial intelligence technology will be applied in more fields and play an increasingly important value.

[0058] Delivery platform: a network platform for delivering multimedia resources to achieve promotion of the multimedia resources. Specifically, the delivery platform includes all services of the network platform, such as search website, chat communication website, map website, email website or game website, etc.

[0059] Multimedia resource exposure (ad impression) can be understood as the number of times the multimedia resource is displayed on the corresponding delivery platform within a specified time period.

[0060] For example, the number of times multimedia resource A is displayed in delivery platform 1 from 0:00 on February 3, 2022 to 0:30 on February 3, 2022 is 500 times, and it is determined that the corresponding exposure information of multimedia resource A in delivery platform 1 from 0:00 on February 3, 2022 to 0:30 on February 3, 2022 is 500 times.

[0061] Multimedia resource exposure rate (impression rate): can be understood as the number of objects actually browsing the multimedia resource in the specified time period in the corresponding delivery platform divided by the number of objects of the specified delivery object.

[0062] Multimedia resource reach (unique impression): can be understood as the count measure of how many objects the multimedia resource reaches in the corresponding delivery platform.

[0063] Target object (Target Audience): can be understood as the object that is expected to be influenced by the multimedia resource. For example, multimedia resource A is displayed in delivery platform 1 from 0:00 on February 3, 2022 to 0:30 on February 3, 2022, object 1 browses the multimedia resource A, and downloads, forwards or other associated data of the multimedia resource A, then it can be determined that object 1 is the target object.

[0064] Correlation data of the object: can be understood as data generated by the object in the related operation of the multimedia resource in the delivery platform. Among them, the correlation data is, for example, the browsing data corresponding to the object browsing the multimedia resource, the data corresponding to the object installing the promotion object corresponding to the multimedia resource, the data corresponding to the object registering the promotion object corresponding to the multimedia resource, the data corresponding to the object logging in the promotion object corresponding to the multimedia resource, the data corresponding to the object using the promotion object corresponding to the multimedia resource, and the data corresponding to the object purchasing or paying for the resource in the promotion object corresponding to the multimedia resource, and so on.

[0065] Hierarchical clustering (Hierarchical Clustering) is a kind of clustering algorithm, which creates a hierarchical nested clustering tree by calculating the similarity between different categories of data points.

[0066] The design idea of the embodiment of the present application will be briefly introduced as follows:

[0067] At present, considering that the multimedia resource corresponding to the object to be promoted can be delivered in the delivery platform to achieve vigorous promotion of the object to be promoted, how to promote the multimedia resource corresponding to the object to be promoted based on the delivery data and delivery analysis of the multimedia resource in the delivery platform has become a technical problem to be solved.

[0068] However, when performing delivery analysis on the multimedia resource, data related to the delivery effect of the multimedia resource in the delivery platform and the correlation data of the object browsing the multimedia resource are generally required. However, since the delivery platform has acquisition restrictions on the data generated by the delivered object of the multimedia resource in the platform, the delivered object of the multimedia resource cannot accurately obtain the complete data generated by the object based on the multimedia resource in the platform.

[0069] Specifically, when the delivery object sends a request for obtaining the correlation data of the multimedia resource by manually operating the electronic device, only when the number of objects performing the related operation of the multimedia resource is greater than the preset threshold, the delivery platform will return the correlation data corresponding to the object in the registered object set. That is, the algorithm logic of returning the correlation data corresponding to the object in the delivery platform and the preset threshold are completely unknown to the platform corresponding to the delivery object of the multimedia resource, so that the delivery object needs to constantly try to manually trigger the acquisition request, and still cannot obtain the correlation data of the object to be analyzed in the delivery platform.

[0070] In addition, although the delivery object can obtain the attribution analysis data and other data of the multimedia resource from the third-party platform, since the third-party platform obtains the data not directly from the delivery platform, there may be problems such as data loss, which leads to the inability to accurately obtain the correlation data of the object to be analyzed in the delivery platform.

[0071] In view of this, embodiments of this application propose a data processing method, apparatus, electronic device, and storage medium. Specifically, by first clustering multiple objects to be analyzed based on their object category attributes and a preset number, at least one object cluster is obtained. This allows for the approximate grouping of multiple objects to be analyzed. Then, a corresponding acquisition request is sent to the delivery platform for each object cluster. Further, feedback messages returned by the delivery platform are received. For each feedback message indicating acquisition failure received, the corresponding object cluster is re-clustered based on the number of comparison objects carried in the received feedback message to obtain an updated object cluster. An acquisition request is then sent to the delivery platform again based on the updated object cluster until the corresponding associated data is obtained.

[0072] As can be seen, in this application, the corresponding object clusters can be continuously updated based on the feedback messages returned by the delivery platform, thereby obtaining object clusters with the same number of preset delivery objects as the delivery platform, that is, the updated object clusters obtained by automatic and continuous clustering, and sending a limited number of requests to accurately obtain the association data of each object to be analyzed in the object cluster on the delivery platform.

[0073] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0074] like Figure 1 As shown, this is a schematic diagram of an application scenario in an embodiment of this application. The schematic diagram includes a terminal device 110 and a server 120, and the terminal device 110 and the server 120 can communicate with each other through a communication network.

[0075] In this embodiment, the terminal device 110 is an electronic device used by a user, such as a personal computer, mobile phone, tablet computer, laptop, e-book reader, smart home device, shopping mall turnstile, etc. Each terminal device 110 can communicate with the server 120 via a communication network. In one optional implementation, the communication network can be a wired network or a wireless network. Therefore, the terminal devices 110 and the server 120 can be directly or indirectly connected via wired or wireless communication. This embodiment does not impose specific limitations on this.

[0076] The server 120 can be a standalone physical server 120, an edge device 120 in the field of cloud computing, or a cloud server 120 providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, cloud functions, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0077] It should be noted that the data processing method in the embodiments of the present application can be executed by the server 120 or the terminal device 110 alone, or by the server 120 and the terminal device 110 together. When executed by the terminal device 110 and the server 120 together, for example, the terminal device 110 determines a plurality of to-be-analyzed objects, sends the plurality of to-be-analyzed objects to the server 120, the server 120 clusters the plurality of to-be-analyzed objects based on the object category attributes of the to-be-analyzed objects, obtains at least one object cluster, wherein each object cluster contains the same number of to-be-analyzed objects; for each object cluster, respectively, an acquisition request is sent to the delivery platform, wherein each acquisition request is used to acquire the associated data of each to-be-analyzed object in the corresponding object cluster; each feedback message returned by the delivery platform is received, wherein each time a feedback message representing a failure to acquire is received, the corresponding object cluster is re-clustered based on the adjustment object number carried in the received feedback message, an updated object cluster is obtained, and an acquisition request is sent to the delivery platform again based on the updated object cluster until the corresponding associated data is obtained. Further, the server 120 can feed back the associated data of each to-be-analyzed object in at least one object cluster to the terminal device 110. In the following, the server alone is taken as an example for illustration, which is not limited here.

[0078] Referring to Figure 2 Fig. 1 shows a schematic diagram of an optional application scenario provided by the embodiments of the present application. In the figure, Figure 2 The electronic device 201 interacts with the device 202 deployed with the delivery platform to realize the acquisition of the associated data of the to-be-delivered object in the delivery platform.

[0079] Specifically, the electronic device 201 includes a delivery module 2011 configured to determine a plurality of to-be-promoted multimedia resources and a plurality of to-be-analyzed objects; a processing module 2012 configured to cluster the plurality of to-be-analyzed objects to obtain at least one object cluster; a sending module 2013 configured to send the to-be-promoted multimedia resources to the device 202 deployed with the delivery platform, and send an acquisition request for acquiring the associated data of each to-be-analyzed object in the object cluster; and a receiving module 2014 configured to receive the feedback message sent by the delivery platform.

[0080] Specifically, the device 202 deployed with the delivery platform includes a promotion module 2021 configured to send multimedia resources to be promoted to objects registered or using the delivery platform; a calculation module 2022 configured to calculate association data corresponding to the objects registered or using the delivery platform and the multimedia resources to be promoted; and a feedback module 2023 configured to respond to the received acquisition request and return the corresponding feedback message to the electronic device 201.

[0081] In the embodiments of the present application, the electronic device 201 can interact with the device 202 deployed with the delivery platform. Specifically, the electronic device 201 can interact with one or more devices 202 deployed with the delivery platform, which is not limited in the present application. That is, the electronic device 201 can determine the association data of the objects to be analyzed in one or more delivery platforms based on the scheme provided in the present application.

[0082] It should be noted that, within the scope of collecting, storing or using the object information provided by the objects to be analyzed and the association data of the objects to be analyzed in the above embodiments, it should be understood that the use of such information should comply with all applicable laws related to information protection. In addition, the collection, storage and use of the association data of the objects to be analyzed need the consent of the objects to such activities, for example, by prompting the object to agree to the relevant agreement of XX platform, and the like. In addition, the storage and use of the association data of the objects to be analyzed can reflect the information in a proper security manner, for example, by various encryption and anonymization techniques for particularly sensitive information, processing the association data of the objects to be analyzed, and using the processed data.

[0083] The data processing method provided by the exemplary embodiments of the present application will be described below in combination with the application scenarios described above and with reference to the accompanying drawings. It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited in this respect.

[0084] Referring to Figure 3 As shown in FIG. 8, an implementation flowchart of a data processing method provided by the embodiments of the present application is shown, which is introduced here taking a server as an example of an execution subject. The specific implementation flowchart of the method is as follows:

[0085] Step 301: clustering a plurality of objects to be analyzed based on the object category attributes of the objects to be analyzed and a preset number, to obtain at least one object cluster, wherein each object cluster contains a preset number of objects to be analyzed.

[0086] In the embodiments of the present application, the electronic device can take all objects delivered by the multimedia resources in one promotion as objects to be analyzed. For example, assuming that the delivery object determines 1000 objects as the objects of the multimedia resources in one promotion, the 1000 objects can be taken as the objects to be analyzed.

[0087] In the embodiment of the present application, the electronic device can cluster the plurality of objects to be analyzed based on the object category attribute of the objects to be analyzed and the preset number of objects in each cluster, and obtain at least one object cluster. In the embodiment of the present application, the object category attribute of the objects to be analyzed is determined based on the object to be promoted, which is not limited herein. For example, the object to be promoted is a virtual game, and the multimedia resource is a multimedia resource related to the virtual game, so that the object category attribute of the objects to be analyzed can be a favorite game, a deep game hobby, etc.

[0088] For example, assuming that the number of objects to be analyzed is 1000 and the preset number of objects in each cluster is 50, the 1000 objects can be clustered based on the object category attribute of the objects to be analyzed and the preset number of objects in each cluster, so that 20 object clusters can be obtained.

[0089] For example, assuming that the number of objects to be analyzed is 1000 and the preset number of objects in each cluster is 50, the 1000 objects can be clustered based on the object category attribute of the objects to be analyzed and the preset number of objects in each cluster, so that 20 object clusters can be obtained.

[0090] Specifically, the electronic device can select K clustering centers based on the object category attribute of the objects to be analyzed and the preset number of objects in each cluster, and then calculate the Euclidean distance between each remaining point and the K points, such as the Euclidean distance corresponding to point A and the K points, select the point that meets the condition, for example, K1, and then A point can be divided into K1 group, until each point is divided into a group, so that at least one object cluster is obtained. That is, each group corresponds to an object cluster.

[0091] For example, referring to FIG. 1, Figure 4 , Figure 4 is a schematic diagram for determining an object cluster provided by the embodiment of the present application. In the embodiment of the present application, Figure 4 , the number of objects to be analyzed is 300, and the preset number of objects in each cluster is 100, so that the 300 objects can be clustered based on the object category attribute of the objects to be analyzed and the preset number of objects in each cluster, so that 3 object clusters can be obtained, which are Figure 4 the first object cluster, the second object cluster and the third object cluster in FIG. 1.

[0092] It should be noted that in the embodiment of the present application, the preset number of objects in each object cluster is set based on actual experience or randomly, which is not limited herein.

[0093] Step 302: sending, respectively for each object cluster, a corresponding acquisition request to the delivery platform, where each acquisition request is used to acquire associated data of each to-be-analyzed object in the corresponding object cluster.

[0094] In the embodiment of the present application, the electronic device can create a local control platform, call the service of the delivery platform through the local control platform, send an acquisition request of the associated data of each delivery object in at least one object cluster, and receive the feedback message returned by the delivery platform. In this way, the present application does not need manual operation to establish communication between the electronic device and the delivery platform, thereby realizing automatic sending of the acquisition request and receiving of the feedback message returned by the delivery platform, and improving the efficiency of data processing.

[0095] In the specific implementation process, after obtaining at least one object cluster, the electronic device can process each object cluster to obtain the identity of each to-be-analyzed object in the object cluster. The identity can be an information tag of the to-be-analyzed object, or other identities that can represent the identity of each delivery object, which are not limited in the present application.

[0096] Specifically, the electronic device can control the local control platform to call the service of the delivery platform, upload the identity of each to-be-analyzed object carried in the corresponding acquisition request to the delivery platform, and then the delivery platform determines the feedback message based on the identity of each to-be-analyzed object and the intersection of the identities of the objects to which the platform delivers multimedia resources.

[0097] In the embodiment of the present application, when the number of objects in the intersection of the identity of each to-be-analyzed object in the corresponding object cluster and the identity of the object to which the delivery platform delivers multimedia resources is less than or greater than the threshold number, it can be determined as a feedback message representing failure, and the feedback message carries the number of objects in the intersection. The threshold number can be the acquisition limit threshold corresponding to the delivery platform.

[0098] Specifically, the electronic device can determine the number of objects in the intersection based on the identity of the object in the data fed back by the delivery platform and the identity of each to-be-analyzed object in the corresponding object cluster.

[0099] In the embodiment of the present application, when the number of objects in the intersection of the identity of each to-be-analyzed object in the corresponding object cluster and the identity of the object to which the delivery platform delivers multimedia resources is equal to the threshold number, it can be determined as a feedback message representing success, so that the associated data of each to-be-analyzed object in the corresponding object cluster can be acquired.

[0100] Step 303: receiving each feedback message returned by the delivery platform, wherein, when receiving a feedback message representing a failure of acquisition, re-clustering the corresponding object cluster based on the number of compared objects carried in the received feedback message, obtaining an updated object cluster, and sending an acquisition request to the delivery platform again based on the updated object cluster until the corresponding associated data is obtained.

[0101] In the embodiment of the present application, the electronic device can receive each feedback message returned by the delivery platform. Specifically, when the electronic device receives a feedback message representing success, the associated data of each delivery object of the corresponding object cluster can be determined based on the associated data of the object carried in the feedback message.

[0102] In the embodiment of the present application, when the electronic device receives a feedback message representing a failure of acquisition, the corresponding object cluster is re-clustered based on the number of compared objects carried in the received feedback message, an updated object cluster is obtained, and an acquisition request is sent to the delivery platform again based on the updated object cluster until the corresponding associated data is obtained. The number of compared objects can be understood as the intersection of the number of objects determined based on the data fed back by the delivery platform and the identification of each object to be analyzed in the corresponding object cluster.

[0103] In a possible implementation, when the electronic device determines that the number of compared objects carried in the received feedback message is less than the current number of objects in the corresponding object cluster, a new number of objects is determined based on the number of compared objects and the current number of objects; the corresponding object cluster is re-clustered based on the new number of objects and the object category attribute to obtain an updated object cluster.

[0104] In the embodiment of the present application, when the electronic device determines that the number of compared objects carried in the received feedback message is less than the current number of objects in the corresponding object cluster, that is, the number of objects in the current object cluster is inconsistent with the number of objects in the acquisition limit in the delivery platform, and therefore the updated object cluster needs to be re-clustered and determined.

[0105] Specifically, since the number of compared objects carried in the received feedback message is less than the current number of objects in the corresponding object cluster, it is determined that the number of objects in the initially determined object cluster is large, and therefore a new number of objects can be determined based on the number of compared objects and the current number of objects. The new number of objects can be the average of the sum of the number of compared objects and the current number of objects, or other ways, which are not limited in the present application.

[0106] Further, after determining the new object number, that is, after determining the preset object number of each cluster, the Kmeans clustering can be used again to divide more object clusters, so as to obtain the updated object cluster, that is, the object number in the updated object cluster is less than the object number in the updated object cluster.

[0107] For example, refer to Figure 5 as shown in Figure 5 Fig. 1 is a schematic diagram of an updated object cluster in the present application. It is assumed that the object number in the updated object cluster is 10, and the object number in the updated object cluster is determined to be 7. That is, the object number in the updated object cluster is less than the object number in the updated object cluster.

[0108] In a possible implementation, when the electronic device determines that the comparison object number carried in the received feedback message is greater than the current object number of the corresponding object cluster, the reference object cluster is determined from other object clusters based on the distance between the corresponding object cluster and other object clusters; and the reference object cluster and the corresponding object cluster are re-clustered to obtain the updated object cluster.

[0109] In the embodiment of the present application, when the comparison object number carried in the feedback message received by the electronic device is greater than the current object number of the corresponding object cluster, and the comparison object number is less than the acquisition limit threshold, it can be determined that the object number in the initially determined object cluster is small, and therefore the corresponding object cluster can need to be hierarchically clustered to determine the updated object cluster.

[0110] In the embodiment of the present application, the electronic device can determine the reference object cluster from other object clusters based on the distance between the corresponding object cluster and other object clusters, and then re-cluster the reference object cluster and the corresponding object cluster to obtain the updated object cluster.

[0111] Specifically, the reference object cluster can be determined by the following steps, but is not limited to the following steps:

[0112] Step A: respectively determine the distance values between the corresponding object cluster and other object clusters to obtain a distance value set; wherein the distance value is determined based on the average distance between each data point in the corresponding object cluster and each data point in another object cluster.

[0113] In the embodiment of the present application, the electronic device can calculate the distance between each data point in two combined data points (there are multiple points in an object cluster) and all other data points, and take the average of all distances as the distance between the two combined data points.

[0114] For example, taking an object cluster (A, F) and another object cluster (B, C) as an example, the distance value D between the two object clusters is:

[0115]

[0116] In this embodiment of the application, the electronic device can determine the distance values ​​between the corresponding object cluster and other object clusters based on the aforementioned method of determining distance values, thereby obtaining a set of distance values.

[0117] Step B: From the set of distance values, determine the target distance values ​​that meet the clustering conditions, and use the object clusters corresponding to the target distance values ​​as reference object clusters.

[0118] In this embodiment, target distance values ​​that meet clustering conditions can be determined from a set of distance values. The clustering conditions can be selecting the smallest distance value in the set, or selecting distance values ​​within a preset range; this embodiment does not limit the selection criteria.

[0119] For example, please see Figure 6 As shown, Figure 6 This is a schematic diagram of another updated object cluster shown in an embodiment of this application. Assuming that the number of objects in the object cluster before the update is 10, the number of objects in the updated object cluster is 20.

[0120] In the embodiments of this application, after the electronic device obtains the reference object cluster based on the aforementioned method, it can cluster the corresponding object cluster and the reference object cluster to obtain the updated object cluster.

[0121] Furthermore, once the electronic device obtains the updated object cluster, it can send another request to the delivery platform based on the updated object cluster until it obtains the corresponding associated data.

[0122] Please see Figure 7 As shown, Figure 7 This diagram illustrates the determination of association data for each object to be analyzed within at least one object cluster in an embodiment of this application. As can be seen, in this embodiment, for objects in a multimedia resource delivery, under the premise that the number of objects in an object cluster meets the delivery platform's acquisition limit, multiple objects to be analyzed are clustered into as many clusters as possible, so that the number of people in each cluster exactly meets the limit. At this point, the electronic device can acquire the most and most accurate association data for the objects.

[0123] Please see Figure 8 , Figure 8The diagram shows the number of request and the amount of associated data of each to-be-analyzed object in the obtained object cluster. The number of ADH request can be understood as the number of request. Obviously, when the electronic device sends about 15 request, the associated data of multiple to-be-analyzed objects can be obtained, thereby greatly improving the efficiency of obtaining the associated data of multiple to-be-analyzed objects, and the associated data of multiple to-be-analyzed objects can be accurately obtained.

[0124] Referring to Figure 9 , Figure 9 The diagram shows the division of multiple to-be-analyzed object clusters. The cluster in 9 can be understood as an object cluster, and the "0-9" can be understood as the label of the object cluster, that is, nine object clusters. The "size" can be understood as the size of each object cluster, that is, the number of samples included in each object cluster. The "imp size" can be understood as the intersection data, and the "imp rate" can be understood as one of the associated data, such as exposure rate. Obviously, when the number of intersection is equal to the threshold number, the associated data of multiple to-be-analyzed objects can be accurately obtained.

[0125] In a possible implementation, after successfully obtaining the associated data of each to-be-analyzed object in at least one object cluster, the electronic device can further analyze and process the associated data of each to-be-analyzed object in at least one object cluster, and obtain an analysis report corresponding to the associated data of each to-be-analyzed object in at least one object cluster. The analysis report is used for analyzing the resource put on the delivery platform and adjusting the delivery strategy.

[0126] For example, referring to Figure 10 , Figure 10 The diagram shows one analysis report in the embodiment of the present application. It should be noted that in the actual implementation process, the analysis report can be a curve diagram as shown in Figure 10 , or a tree diagram or other presentation mode, which is not limited in the embodiment of the present application.

[0127] In the specific implementation process, the delivery object can analyze the delivery effect of the multimedia resource based on the analysis report determined by the electronic device, so as to adjust the delivery strategy in the delivery platform, for example, adjust the delivery time period of the multimedia resource, adjust the delivery object of the multimedia resource, and of course, the delivery platform can also be adjusted, for example, the delivery platform of the multimedia resource is replaced, and the multimedia resource can also be adjusted, and so on, thereby realizing the promotion of the to-be-promoted object.

[0128] Based on the above embodiment, the data processing method in the embodiment of the present application will be described in detail by taking a specific example. Referring to Figure 11As shown, it is an embodiment schematic diagram of the data processing method in the present application.

[0129] Specifically, the electronic device can determine a plurality of to-be-analyzed objects, for example, 800 to-be-analyzed objects, for the sake of description, the 800 to-be-analyzed objects are referred to as an intervention group. Then, the electronic device can use the kmeans clustering method to cluster the 800 to-be-analyzed objects to obtain 20 object clusters, and each object cluster includes 40 to-be-analyzed objects, so that the complexity of subsequent data processing can be reduced.

[0130] Further, the electronic device can perform the following operations on each object cluster: the electronic device can send a request for obtaining the associated data of each to-be-analyzed object in the object cluster, and upload the identifier of each to-be-analyzed object in the object cluster to the delivery platform through the created local control platform. Then, the delivery platform determines the data intersected with the objects of the multimedia resource delivered by the delivery platform, and returns the intersected data to the electronic device in the feedback message.

[0131] Specifically, assuming that the preset number of the delivery platform acquisition limit is 50, when the electronic device determines that the number of comparison objects carried in the feedback message is 35, it is determined that the number of comparison objects 35 is less than the preset number 50, that is, the received feedback message represents a failure. Then, when the electronic device determines that the number of comparison objects 35 is less than the number of objects in the object cluster 40, it can determine the reference object cluster corresponding to the object cluster, and cluster the reference object cluster with the object cluster to obtain an updated object cluster, until the number of objects in the updated object cluster is equal to 50, then the associated data of each to-be-analyzed object in the object cluster can be obtained.

[0132] It can be seen that the present application provides an effective way to combine the associated data of the objects in the delivery platform with the associated data of the to-be-promoted objects corresponding to the electronic device. Specifically, on the one hand, the associated data of the objects corresponding to the to-be-promoted objects corresponding to the electronic device is used to determine the identifier of the object, and similar objects are aggregated by clustering method, on the one hand, the acquisition limit of the delivery platform is obeyed, the associated data of the object in the delivery platform can be accurately obtained, so as to obtain the delivery effect of the to-be-promoted object. Moreover, in the present application, the associated data of the objects of the first-party delivery platform is directly used, so that the associated data of each to-be-analyzed object and the to-be-promoted object can be accurately obtained, and the risk of being unable to obtain the associated data of the to-be-analyzed object in the delivery platform or obtaining inaccurate data is reduced. In addition, the scheme provided by the present application is a relatively general technical solution, which can establish contact with each delivery platform with acquisition limit, so as to obtain the associated data of the to-be-analyzed object in various delivery platforms.

[0133] Based on the same inventive concept, the present application also provides a data processing device. As shown inFigure 12 As shown in FIG. 12, which is a structural schematic diagram of the data processing apparatus 1200, the data processing apparatus 1200 can include the following components:

[0134] a first processing unit 1201, configured to cluster a plurality of to-be-analyzed objects based on an object category attribute of the to-be-analyzed objects and a preset number, to obtain at least one object cluster, wherein each object cluster contains the preset number of to-be-analyzed objects;

[0135] a sending unit 1202, configured to send, respectively for each object cluster, a corresponding acquisition request to a delivery platform, wherein each acquisition request is used to acquire associated data of each to-be-analyzed object in the corresponding object cluster;

[0136] a second processing unit 1203, configured to receive each feedback message returned by the delivery platform, wherein when a feedback message representing a failure of acquisition is received, the corresponding object cluster is re-clustered based on a number of comparison objects carried in the received feedback message, to obtain an updated object cluster, and the delivery platform is sent an acquisition request again based on the updated object cluster, until the corresponding associated data is obtained.

[0137] Optionally, the second processing unit 1203 is specifically configured to:

[0138] when it is determined that the number of comparison objects carried in the received feedback message is less than a current number of objects of the corresponding object cluster, determine a new number of objects based on the number of comparison objects and the current number of objects;

[0139] re-cluster the corresponding object cluster based on the new number of objects and the object category attribute, to obtain the updated object cluster.

[0140] Optionally, the second processing unit 1203 is specifically configured to:

[0141] when it is determined that the number of comparison objects carried in the received feedback message is greater than the current number of objects of the corresponding object cluster, determine a reference object cluster from other object clusters based on distances between the corresponding object cluster and the other object clusters respectively;

[0142] re-cluster the reference object cluster and the corresponding object cluster, to obtain the updated object cluster.

[0143] Optionally, the second processing unit 1203 is further configured to:

[0144] determine distance values between the corresponding object cluster and other object clusters respectively, to obtain a distance value set, wherein the distance value is determined based on an average distance between each data point in the corresponding object cluster and each data point in another object cluster;

[0145] From the distance value set, a target distance value meeting a clustering condition is determined, and an object cluster corresponding to the target distance value is taken as a reference object cluster.

[0146] Optionally, the data processing apparatus further comprises a third processing unit, configured to:

[0147] The local control platform is created, the service corresponding to the delivery platform is invoked through the local control platform, the acquisition request of the associated data of each delivery object in the at least one object cluster is sent, and the feedback message returned by the delivery platform is received.

[0148] Optionally, the data processing apparatus further comprises an analysis unit, configured to:

[0149] The associated data of each analysis object in the at least one object cluster is analyzed and processed, and an analysis report corresponding to the associated data of each analysis object in the at least one object cluster is obtained; the analysis report is used for analyzing the resources delivered on the delivery platform and adjusting the delivery strategy.

[0150] Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method or a program product. Therefore, each aspect of the present application can be specifically implemented as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software, which can be collectively referred to as "circuitry", "module" or "system".

[0151] In some possible implementation, the data processing apparatus according to the present application can at least include a processor and a memory. Wherein, the memory stores program code, when the program code is executed by the processor, the processor executes the steps in the data processing method according to various exemplary embodiments of the present application described in the specification. For example, the processor can execute the steps as shown in Figure 3 .

[0152] Based on the same inventive concept as the above method embodiments, the present embodiment also provides an electronic device. In an embodiment, the electronic device can be a server, such as the server 120 as shown in Figure 1 . In this embodiment, the structure of the electronic device can be as shown in Figure 13 , including a memory 1301, a communication module 1303 and one or more processors 1302.

[0153] The memory 1301 is used to store the computer program executed by the processor 1302. The memory 1301 can mainly include a program storage area and a data storage area, wherein the program storage area can store the operating system, programs required for running the instant messaging function and the like; the data storage area can store various instant messaging information and operation instruction sets and the like.

[0154] The memory 1301 can be a volatile memory, such as a random-access memory (RAM); the memory 1301 can also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); or the memory 1301 can be any other medium that can be used to carry or store desired program codes in the form of instructions or data structures and that can be accessed by a computer, but is not limited to this. The memory 1301 can be a combination of the above-mentioned memories.

[0155] The processor 1302 can include one or more central processing units (CPUs) or digital processing units, etc. The processor 1302 is used to implement the above-mentioned data processing method when calling the computer program stored in the memory 1301.

[0156] The communication module 1303 is used to communicate with terminal devices and other servers.

[0157] The specific connection medium between the above-mentioned memory 1301, communication module 1303 and processor 1302 is not limited in the embodiments of the present application. In the embodiments of the present application, the memory 1301 and the processor 1302 are connected through the bus 1304, and the bus 1304 is described as a thick line in the embodiments of the present application. The connection mode between other components is only schematically described, and is not limited. The bus 1304 can be divided into an address bus, a data bus, a control bus, etc. For the convenience of description, only one thick line is described in the embodiments of the present application, but it is not described that there is only one bus or only one type of bus. Figure 13 Figure 13 Figure 13

[0158] The memory 1301 stores a computer storage medium, and the computer storage medium stores computer executable instructions. The computer executable instructions are used to implement the data processing method of the embodiments of the present application. The processor 1302 is used to execute the above-mentioned data processing method, as shown in the above-mentioned data processing method. Figure 3

[0159] In some possible implementation manners, each aspect of the data processing method provided by the present application can also be implemented in the form of a program product, which includes program codes. When the program product runs on a computer device, the program codes are used to make the computer device execute the steps in the data processing method according to various exemplary embodiments of the present application described in the specification, for example, the computer device can execute the steps in the data processing method according to various exemplary embodiments of the present application described in the specification.​​​​Figure 3 the steps shown in the flowchart of FIG. 6.

[0160] The program product of the embodiments of the present application can employ any combination of one or more computer-readable media. The computer-readable media can be a computer- readable signal medium, or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable disc, 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.

[0161] The program product of the embodiments of the present application can employ a compact disc read-only memory (CD-ROM) and include a program code, and can be run on a computing device. However, the program product of the present application is not limited thereto, and in the present document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with a command execution system, apparatus, or device.

[0162] The computer-readable signal medium can include a data signal that is propagated in baseband or that is propagated as a carrier wave. Such propagated data signals can take a wide variety of forms, including but not limited to electro-magnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium that is not a storage medium, that can communicate, propagate, or transport programming for use by or in connection with a command execution system, apparatus, or device.

[0163] The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber cable, RF, and the like, or any suitable combination of the foregoing.

[0164] The program code may, for example, be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device 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 computing device, for example, through the Internet using an Internet Service Provider.

[0165] It should be noted that, although the above detailed description refers to several units or sub-units of the apparatus, such a division is merely exemplary and not mandatory. Indeed, according to an embodiment of the application, the features and functionalities of two or more units described above can be embodied in one unit. Conversely, the features and functionalities of one unit described above can be further divided into units embodied by several units.

[0166] Moreover, although the operations of the method(s) herein can be described in a particular, sequential order, this order is not meant to be a limitation and is not intended to imply that

[0167] Those of skill in the art would understand that information and signals can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0168] While the preferred embodiments of the application have been described, additional variations and modifications can be employed, as will be appreciated by those of ordinary skill in the art once advised of the essential inventive concepts. Therefore, the scope of the application should be determined not with reference to the preferred embodiments, but with reference to the appended claims construed in accordance with their full scope along with the legal equivalents.

[0169] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A data processing method, characterized by, The method comprises: Clustering a plurality of objects to be analyzed based on an object category attribute of the objects to be analyzed and a preset number, to obtain at least one object cluster, wherein each object cluster contains the preset number of objects to be analyzed; For each object cluster, a corresponding acquisition request is sent to a delivery platform, wherein each acquisition request is used to acquire associated data of each object to be analyzed in the corresponding object cluster; Upon receiving each feedback message returned by the delivery platform, if a feedback message representing a failure to acquire is received, the corresponding object cluster is re-clustered based on a comparison object number carried in the received feedback message, to obtain an updated object cluster, and an acquisition request is sent to the delivery platform again based on the updated object cluster, until the corresponding associated data is obtained.

2. The method of claim 1, wherein, Re-clustering the corresponding object cluster based on the comparison object number carried in the received feedback message to obtain an updated object cluster comprises: When it is determined that the comparison object number carried in the received feedback message is less than a current object number of the corresponding object cluster, a new object number is determined based on the comparison object number and the current object number; Re-clustering the corresponding object cluster based on the new object number and the object category attribute to obtain an updated object cluster.

3. The method of claim 1 or 2, wherein, Re-clustering the corresponding object cluster based on the comparison object number carried in the received feedback message to obtain an updated object cluster comprises: When it is determined that the comparison object number carried in the received feedback message is greater than a current object number of the corresponding object cluster, a reference object cluster is determined from other object clusters based on distances between the corresponding object cluster and the other object clusters. Re-clustering the reference object cluster and the corresponding object cluster to obtain an updated object cluster.

4. The method of claim 3, wherein, Determining a reference object cluster from other object clusters based on distances between the corresponding object cluster and the other object clusters comprises: Distance values between the corresponding object cluster and other object clusters are respectively determined to obtain a distance value set, wherein a distance value is determined based on an average distance between each data point in the corresponding object cluster and each data point in another object cluster; A target distance value meeting a clustering condition is determined from the distance value set, and an object cluster corresponding to the target distance value is taken as the reference object cluster.

5. The method of claim 1 or 2, wherein, After obtaining at least one object cluster, the method further comprises: A local control platform is created, the local control platform is used to call a service corresponding to the delivery platform, send an acquisition request of associated data of each delivery object in the at least one object cluster to the delivery platform, and receive a feedback message returned by the delivery platform.

6. The method of claim 1 or 2, wherein, After successfully obtaining associated data of each object to be analyzed in the at least one object cluster, the method further comprises: The associated data of each object to be analyzed in the at least one object cluster is analyzed and processed to obtain an analysis report corresponding to the associated data of each object to be analyzed in the at least one object cluster, wherein the analysis report is used to analyze resources delivered on the delivery platform and adjust a delivery strategy.

7. A data processing apparatus, characterized by The device comprises: The first processing unit is configured to cluster a plurality of objects to be analyzed based on an object category attribute of the objects to be analyzed and a preset number, to obtain at least one object cluster, wherein each object cluster contains the preset number of objects to be analyzed; The sending unit is configured to send, for each object cluster, a corresponding acquisition request to a delivery platform, wherein each acquisition request is used to acquire associated data of each object to be analyzed in the corresponding object cluster; The second processing unit is configured to receive each feedback message returned by the delivery platform, wherein, upon receiving one feedback message representing a failure in acquisition, the corresponding object cluster is re-clustered based on a number of comparison objects carried in the received feedback message, to obtain an updated object cluster, and the delivery platform is sent an acquisition request again based on the updated object cluster, until the corresponding associated data is obtained.

8. An electronic device, comprising: The computer program product comprises a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the method in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer program product comprises program code, and when the storage medium is executed on an electronic device, the program code is used to make the electronic device execute the steps of the method in any one of claims 1-6.

10. A computer program product, characterised in that, The computer program product comprises computer instructions, and when the computer instructions are executed by a processor, the steps of the method in any one of claims 1-6 are implemented.

Images