Data processing method, device, electronic device and storage medium

Abstract

The present disclosure relates to a data processing method, device, electronic device, and storage medium, and relates to the field of computer technology. The method comprises receiving an object association analysis instruction; obtaining, based on indication information of an object to be analyzed, object data corresponding to the object to be analyzed, associated object data of each of a plurality of associated objects, and inter-object association data corresponding to each of a plurality of first target object pairs; performing an association analysis on each first target object pair based on the number of first object acquisitions, the number of second object acquisitions, and the number of inter-object associations, to obtain object association index data corresponding to each first target object pair; and performing an object association analysis on the object to be analyzed based on the object association index data corresponding to each first target object pair, to obtain an object association analysis result corresponding to the object to be analyzed. The embodiments of the present disclosure can be used to comprehensively introduce a nonlinear relationship between the number of acquisitions and the number of associations to implement inter-object association analysis, thereby improving the accuracy of object association analysis.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a data processing method, device, electronic device, and storage medium. Background Art

[0002] With the rapid development of the Internet of Things, mobile internet, big data, and artificial intelligence technologies, real-time data processing and complex correlation analysis have become core requirements in fields such as financial risk management, intelligent manufacturing, online social networking, and healthcare. In traffic management and smart city development, cameras and mobile phone data collection devices deployed at key locations such as traffic checkpoints, train stations, and school gates can collect multimodal data such as mobile phone IDs, facial images, and license plates in real time, providing basic data support for inter-object correlation analysis. Combined with the results of inter-object correlation analysis, dynamic object monitoring and risk warning can be achieved. Traditional correlation analysis algorithms are mostly based on co-occurrence counts or simple similarity, resulting in low accuracy in correlation analysis of the degree of correlation between objects, which in turn leads to low accuracy in correlation analysis of inter-object correlations. Summary of the Invention

[0003] In view of the above-mentioned technical problems, the present disclosure proposes a data processing method, device, electronic device and storage medium.

[0004] According to one aspect of an embodiment of the present disclosure, a data processing method is provided, the method comprising:

[0005] receiving an object association analysis instruction; the object association analysis instruction including object to be analyzed indication information corresponding to the object to be analyzed;

[0006] Based on the indication information of the object to be analyzed, obtaining object data corresponding to the object to be analyzed, associated object data of each of a plurality of associated objects corresponding to the object to be analyzed, and inter-object association data corresponding to each of a plurality of first target object pairs; the object data to be analyzed includes a first object acquisition count, the associated object data includes a second object acquisition count, the inter-object association data includes an inter-object association count, and any first target object pair includes the object to be analyzed and any associated object among the plurality of associated objects;

[0007] performing association analysis on each first target object pair based on the first object collection count, the second object collection count corresponding to each of the plurality of associated objects, and the inter-object association count corresponding to each of the plurality of first target object pairs, to obtain object association indicator data corresponding to each first target object pair;

[0008] Based on the object association index data corresponding to each first target object pair, an object association analysis is performed on the object to be analyzed to obtain an object association analysis result corresponding to the object to be analyzed.

[0009] Optionally, performing association analysis on each first target object pair based on the first object collection count, the second object collection count corresponding to each of the multiple associated objects, and the inter-object association count corresponding to each of the multiple first target object pairs to obtain object association indicator data corresponding to each first target object pair includes:

[0010] Based on the number of inter-object associations, performing an inter-object correlation probability analysis on each first target object pair to obtain target correlation index data corresponding to each first target object pair; the target correlation index data is used to indicate the probability of a correlation between the two objects in each first target object pair;

[0011] Determining a correction coefficient corresponding to each first target object pair based on the first object acquisition number, the second object acquisition number, and the number of associations between objects; the correction coefficient is used to enhance the difference between the number of associations between objects of different object pairs when the number of associations between objects is greater than a first preset number of associations;

[0012] Based on the correction coefficient corresponding to each first target object pair, the target association index data corresponding to each first target object pair is corrected to obtain the object association index data corresponding to each first target object pair.

[0013] Optionally, determining the correction coefficient corresponding to each first target object pair based on the first object acquisition number, the second object acquisition number, and the number of associations between the objects includes:

[0014] Performing mean processing on the first object acquisition times and the second object acquisition times corresponding to the associated objects in each first target object pair to obtain an average acquisition times corresponding to each first target object pair;

[0015] Based on the average number of acquisitions corresponding to each first target object pair and the number of inter-object associations corresponding to each first target object pair, a correction coefficient corresponding to each first target object pair is determined.

[0016] Optionally, the acquiring, based on the indication information of the object to be analyzed, the object data corresponding to the object to be analyzed, the associated object data of each of the multiple associated objects corresponding to the object to be analyzed, and the inter-object association data corresponding to each of the multiple first target object pairs includes:

[0017] Based on the indication information of the object to be analyzed, acquiring the object data to be analyzed from the object data storage area;

[0018] Based on the object data to be analyzed and the associated data storage area, determining a plurality of associated objects corresponding to the object to be analyzed and the plurality of first target object pairs, and obtaining inter-object association data of each of the plurality of first target object pairs;

[0019] The related object data of each of the plurality of related objects is acquired from the object data storage area.

[0020] Optionally, the object data to be analyzed includes first object identification information corresponding to the object to be analyzed; the association data storage area stores preset association data corresponding to any preset object pair among a plurality of preset object pairs, the preset association data corresponding to any preset object pair includes first relationship indication information, second relationship indication information and the number of object associations corresponding to any preset object pair, the first relationship indication information and the second relationship indication information both include object identification information of two preset objects, the object identification information of the first preset object in any preset object pair is located at the preset object query position in the first relationship indication information, and the object identification information of the second preset object in any preset object pair is located at the preset object query position in the second relationship indication information;

[0021] The step of determining, based on the object data to be analyzed and the associated data storage area, a plurality of associated objects corresponding to the object to be analyzed and the plurality of first target object pairs, and obtaining inter-object association data of each of the plurality of first target object pairs, includes:

[0022] Matching the object identification information of the preset object query position in the two relationship indication information corresponding to each preset object pair with the first object identification information to obtain the multiple first target object pairs and the inter-object association data of the multiple first target object pairs;

[0023] Based on the plurality of first target object pairs, the plurality of associated objects are determined.

[0024] Optionally, the method further includes:

[0025] When it is detected that a target object data pair exists in multiple current object data to be detected, the associated data in the associated data storage area is updated based on the target object data pair; the target object data pair is an object data pair whose time interval between the collection time of any two corresponding object data in the multiple current object data to be detected is less than a first preset time interval.

[0026] Optionally, the multiple current data of the objects to be detected are obtained in the following manner:

[0027] Acquire multiple current acquisition object data; the multiple current acquisition object data are object data collected by multiple preset acquisition devices respectively within the current time range;

[0028] Determining, from the plurality of current acquisition object data, a plurality of target acquisition object data corresponding to each preset acquisition device; the object feature information corresponding to the plurality of target acquisition object data is the same, and the maximum acquisition time interval corresponding to the plurality of target acquisition object data is less than a second preset time interval;

[0029] Determine first acquisition object data corresponding to each preset acquisition device and at least one second acquisition object data corresponding to each preset acquisition device, wherein the first acquisition object data is any one of the plurality of target acquisition object data, and the at least one second acquisition object data is target acquisition object data other than the first acquisition object data among the plurality of target acquisition object data;

[0030] At least one second acquisition object data corresponding to each preset acquisition device in the plurality of current acquisition object data is filtered to obtain the plurality of current to-be-detected object data.

[0031] Optionally, the method further includes:

[0032] Filtering the first target object pairs whose corresponding inter-object association times are less than a second preset association times among the plurality of first target object pairs to obtain a plurality of second target object pairs;

[0033] The performing association analysis on each first target object pair based on the first object collection count, the second object collection count corresponding to each of the plurality of associated objects, and the inter-object association count corresponding to each of the plurality of first target object pairs to obtain object association indicator data corresponding to each first target object pair includes:

[0034] Based on the first object collection times, the second object collection times and the object association times corresponding to each of the plurality of second target object pairs, association analysis is performed on each second target object pair to obtain object association index data corresponding to each second target object pair.

[0035] According to another aspect of an embodiment of the present disclosure, a data processing device is provided, the device comprising:

[0036] An instruction receiving module, configured to receive an object association analysis instruction; the object association analysis instruction includes object to be analyzed indication information corresponding to the object to be analyzed;

[0037] a first data acquisition module configured to acquire, based on the object to be analyzed indication information, object data corresponding to the object to be analyzed, associated object data of each of a plurality of associated objects corresponding to the object to be analyzed, and inter-object association data corresponding to each of a plurality of first target object pairs; the object data to be analyzed includes a first object acquisition count, the associated object data includes a second object acquisition count, the inter-object association data includes an inter-object association count, and any first target object pair includes the object to be analyzed and any associated object among the plurality of associated objects;

[0038] a first association analysis module, configured to perform an association analysis on each first target object pair based on the first object collection count, the second object collection count corresponding to each of the plurality of associated objects, and the inter-object association count corresponding to each of the plurality of first target object pairs, to obtain object association indicator data corresponding to each first target object pair;

[0039] The second association analysis module is configured to perform object association analysis on the object to be analyzed based on the object association indicator data corresponding to each first target object pair, and obtain an object association analysis result corresponding to the object to be analyzed.

[0040] According to another aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the above-mentioned data processing method.

[0041] According to another aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the above-mentioned data processing method.

[0042] According to another aspect of an embodiment of the present disclosure, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute the above data processing method.

[0043] The technical solutions provided by the embodiments of the present disclosure bring at least the following beneficial effects:

[0044] By receiving an object association analysis instruction, the object association analysis instruction includes object indication information corresponding to the object to be analyzed, and based on the object indication information, obtaining object data corresponding to the object to be analyzed, associated object data of each of multiple associated objects corresponding to the object to be analyzed, and inter-object association data corresponding to each of multiple first target object pairs, the object data to be analyzed includes the number of first object collection times, the associated object data includes the number of second object collection times, the inter-object association data includes the number of inter-object associations, any first target object pair includes the object to be analyzed and any associated object of the multiple associated objects, and relevant object data can be obtained for the object to be analyzed when the object association analysis instruction is received. According to the present invention, a method for analyzing the association data between objects is used to analyze the object association index data. The method further combines the number of first object collection times, the number of second object collection times corresponding to multiple associated objects, and the number of inter-object association times corresponding to multiple first target object pairs, and performs association analysis on each first target object pair to obtain object association index data corresponding to each first target object pair. The nonlinear relationship between the number of collection times and the number of association times can be comprehensively introduced to realize inter-object association analysis, avoid interference from high-frequency objects, and thus improve the accuracy of inter-object association analysis. Subsequently, the object association index data corresponding to each first target object pair is combined to perform object association analysis on the object to be analyzed, and obtain the object association analysis result corresponding to the object to be analyzed, which can improve the accuracy of object association analysis.

[0045] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0047] Figure 1 is a schematic diagram of an application system according to an exemplary embodiment;

[0048] Figure 2 is a flow chart showing a data processing method according to an exemplary embodiment;

[0049] Figure 3 is a block diagram of a data processing device according to an exemplary embodiment;

[0050] Figure 4 is a block diagram showing an electronic device for performing object association analysis on an object to be analyzed according to an exemplary embodiment;

[0051] Figure 5 It is a block diagram showing another electronic device for performing object association analysis on an object to be analyzed according to an exemplary embodiment. DETAILED DESCRIPTION

[0052] Various exemplary embodiments, features, and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0053] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0054] In addition, numerous specific details are provided in the detailed description below to better illustrate the present application. Those skilled in the art will appreciate that the present application can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present application.

[0055] See also Figure 1 , Figure 1 1 is a schematic diagram of an application system according to an exemplary embodiment. The application system can be used in the data processing method of the present application. The application system may include at least a server 01 and a terminal 02.

[0056] In the embodiment of the present application, server 01 can be used to perform object association analysis on the object to be analyzed. Specifically, the server 01 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0057] In the embodiment of the present application, terminal 02 can be used to generate object association analysis instructions. The terminal 02 can include physical devices such as smartphones, desktop computers, tablet computers, laptops, smart speakers, in-vehicle terminals, digital assistants, augmented reality (AR) / virtual reality (VR) devices, smart wearable devices, etc., and can also include software running on the physical devices, such as applications. The operating system running on the terminal 02 in the embodiment of the present application can include, but is not limited to, Android, GNU / Linux, Windows, etc.

[0058] In addition, it should be noted that Figure 1What is shown is only one application environment provided by the present disclosure. In actual applications, other application environments may also be included. For example, the process of performing object association analysis on the object to be analyzed may also be implemented on the terminal 02 .

[0059] In the embodiments of this specification, the terminal 02 and the server 01 may be connected directly or indirectly via wired or wireless communication, which is not limited in this application.

[0060] It should be noted that this specification provides method operation steps such as embodiments or flow charts, but more or fewer operation steps may be included based on routine or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many methods and does not represent the only execution order.

[0061] Specifically, Figure 2 FIG. 1 is a flow chart showing a data processing method according to an exemplary embodiment. Figure 2 As shown, the data processing method can be used in electronic devices such as terminals or servers, and can specifically include the following steps:

[0062] S201: Receive an object association analysis instruction.

[0063] In a specific embodiment, an object association analysis instruction may be used to instruct an object association analysis to be performed on an object to be analyzed. The object association analysis instruction may include object indication information corresponding to the object to be analyzed. The object to be analyzed may be an object whose association with other associated objects needs to be analyzed. The object indication information may be used to indicate the object to be analyzed. Specifically, the object to be analyzed may include a living being (e.g., a person), a mobile terminal, or a license plate. Accordingly, the object indication information may include biometric image information, mobile subscriber identification information, or license plate information. Biometric image information may include facial image data or facial image feature information. License plate information may include a license plate number. Mobile subscriber identification information may be used to identify a user in a cellular network. Mobile subscriber identification information may be stored in a SIM (Subscriber Identity Module) card. It is understood that each SIM card corresponds to unique mobile subscriber identification information. If a mobile terminal includes multiple SIM cards, the mobile terminal may correspond to the mobile subscriber identification information corresponding to each of the multiple SIM cards.

[0064] In a specific embodiment, the object analysis requester can input indication information about the object to be analyzed on the target terminal, so that the target terminal obtains the indication information of the object to be analyzed. Correspondingly, based on the target terminal obtaining the indication information of the object to be analyzed, the object analysis requester can generate an instruction on the target terminal, so that the target terminal generates an object association analysis instruction that carries the indication information of the object to be analyzed. In this embodiment, taking the application of the above-mentioned data processing method to the target server as an example, the target terminal can send the above-mentioned object association analysis instruction to the target server.

[0065] S203: Based on the indication information of the object to be analyzed, obtain the object data corresponding to the object to be analyzed, the associated object data of each of the multiple associated objects corresponding to the object to be analyzed, and the inter-object association data corresponding to each of the multiple first target object pairs.

[0066] In a specific embodiment, the object data to be analyzed can be used to characterize the characteristics of the object to be analyzed. The object data to be analyzed can include the number of first object collection times. Furthermore, the object data to be analyzed can also include type indication information of the object type to be analyzed or object identification information of the object to be analyzed. The first object collection times can refer to the number of object collection times for the object to be analyzed. The object collection times can refer to the cumulative number of times the object has been collected by the target system. The target system can include multiple sets of preset collection devices, each set of preset collection devices can include multiple preset collection devices corresponding to different object types; or a set of preset collection devices can be provided at each of multiple preset collection locations. The object type can include a biometric image type, a mobile user identification type, or a license plate type. The object identification information can include biometric image information, mobile user identification information, or license plate information. For example, if the object type of the object to be analyzed is a mobile user identification type, the type indication information of the object to be analyzed can be "IMSI," the mobile user identification information of the object to be analyzed can be "46011XXXXXXX814," and the object identification information of the object to be analyzed can be "IMSI-46011XXXXXXX814." Furthermore, assuming that object A appears 2 times at preset collection location 1, 3 times at preset collection location 2, and never appears at other preset collection locations, it can be determined that the object collection count corresponding to the object A is 5.

[0067] In a specific embodiment, an associated object may refer to an object associated with the object to be analyzed. Specifically, the associated object may be an object that appears at the same location as the object to be analyzed within a first preset time interval. For example, object 1 may appear at location A at time T1, carrying a mobile terminal 1 with mobile user identification information "XXX." Object 2 may appear at location A at time T2, where location A may be a location where multiple different types of preset acquisition devices are installed. Facial image 1 of object 1 and facial image 2 of object 2 can be acquired, and mobile user identification information "XXX" can be acquired at time T3. The time interval between any two times T1, T2, and T3 is less than the first preset time interval. Accordingly, it can be determined that facial image 1 of object 1, facial image 2 of object 2, and mobile user identification information "XXX" are both associated. Assuming that the object to be analyzed is facial image 1 of object 1, it can be determined that the associated objects of the object to be analyzed include facial image 2 of object 2 and mobile user identification information "XXX." Specifically, the first preset time interval can be set according to actual application needs and is not limited by this disclosure.

[0068] In a specific embodiment, the associated object data of any associated object can be used to characterize the characteristics of any of the associated objects. The associated object data of any associated object can include the number of second object collections for any of the associated objects. The second object collection number can refer to the number of object collections for any of the associated objects. The object collection number can refer to the cumulative number of times an object has been collected by the target system.

[0069] In a specific embodiment, the associated object data of any associated object may further include type indication information of the object type of any associated object or object identification information of any associated object.

[0070] In a specific embodiment, any first target object pair may include an object to be analyzed and any associated object among the plurality of associated objects. For example, assuming that the plurality of associated objects are associated object A, associated object B, and associated object C, the plurality of first target object pairs may be determined to be [object to be analyzed, associated object A], [object to be analyzed, associated object B], and [object to be analyzed, associated object C].

[0071] In a specific embodiment, the inter-object association data corresponding to any first target object pair may include the number of inter-object associations for any first target object pair. The number of inter-object associations may refer to the cumulative number of times the corresponding two objects are collected by the target collection device within a first preset time interval. For example, assuming that the number of times object A and object B appear at preset collection location A within the first preset time interval is 3, and the number of times object A and object B appear at preset collection location B within the first preset time interval is 5, and the two objects A and B have not appeared at any other preset collection locations within the first preset time interval, it can be determined that the number of inter-object associations for the target object pair (including object A and object B) is 8.

[0072] In a specific embodiment, the above-mentioned obtaining, based on the indication information of the object to be analyzed, the object data corresponding to the object to be analyzed, the associated object data of each of the multiple associated objects corresponding to the object to be analyzed, and the inter-object association data corresponding to each of the multiple first target object pairs may include:

[0073] Based on the indication information of the object to be analyzed, obtaining the object data to be analyzed from the object data storage area;

[0074] Based on the object data to be analyzed and the associated data storage area, determining a plurality of associated objects and a plurality of first target object pairs corresponding to the object to be analyzed, and obtaining inter-object association data of each of the plurality of first target object pairs;

[0075] The related object data of each of the plurality of related objects is acquired from the object data storage area.

[0076] In a specific embodiment, the object data storage area can be used to store the object data of each of a plurality of preset objects. The object data storage area can store the object data of each of the plurality of preset objects in the form of a table.

[0077] In a specific embodiment, the data of the object to be analyzed may include first object identification information; the first object identification information may refer to object identification information of the object to be analyzed.

[0078] In a specific embodiment, the object indication information to be analyzed and the object identification information in the object data of each preset object in the object data storage area can be matched; when the object identification information in the object data of any preset object matches the above-mentioned object indication information to be analyzed, the object data of any of the above-mentioned preset objects can be used as the object data to be analyzed.

[0079] In a specific embodiment, the associated data storage area may be used to store preset associated data corresponding to each of a plurality of preset object pairs. Specifically, the associated data storage area stores the preset associated data corresponding to any of the plurality of preset object pairs. The associated data storage area may store the preset associated data corresponding to each preset object pair in a table format.

[0080] In a specific embodiment, the preset association data corresponding to any preset object pair may include first relationship indication information, second relationship indication information, and the number of object associations corresponding to any preset object pair. The first relationship indication information and the second relationship indication information may each include object identification information of the two preset objects. The object identification information of the first preset object in any preset object pair may be located at the preset object query position in the first relationship indication information. The object identification information of the second preset object in any preset object pair may be located at the preset object query position in the second relationship indication information. The preset object query position may be the left prefix position in the relationship indication information. For example, assuming that the preset object pair includes preset object A (i.e., the corresponding object identification information is "A") and preset object B (i.e., the corresponding object identification information is "B"), accordingly, the first relationship indication information may be "AB" and the second relationship indication information may be "BA", wherein the preset object query position is the left prefix position in the relationship indication information.

[0081] In a specific embodiment, the above-mentioned determining, based on the object data to be analyzed and the associated data storage area, multiple associated objects corresponding to the object to be analyzed and multiple first target object pairs, and obtaining the inter-object association data of each of the multiple first target object pairs may include:

[0082] Matching the object identification information of the preset object query position in the two relationship indication information corresponding to each preset object pair with the first object identification information to obtain a plurality of first target object pairs and respective inter-object association data of the plurality of first target object pairs;

[0083] Based on the plurality of first target object pairs, a plurality of associated objects are determined.

[0084] In a specific embodiment, based on each preset thread among multiple preset threads, the object identification information and the first object identification information of the preset object query position in the two relationship indication information corresponding to each preset object pair can be matched; when the object identification information of the preset object query position in the two relationship indication information of any preset object pair is matched, the above-mentioned any preset object pair can be used as the first target object pair, and the preset association data of the above-mentioned any preset object pair can be used as the inter-object association data of the above-mentioned first target object pair. Accordingly, multiple first target object pairs and the inter-object association data of each of the multiple first target object pairs can be obtained.

[0085] In a specific embodiment, the objects other than the object to be analyzed in the first target object pair may be used as the multiple associated objects.

[0086] In the above embodiment, by storing two association indication information in the association data storage area, and by matching the object identification information of the preset object query position and the first object identification information in the two relationship indication information corresponding to each preset object pair, multiple first target object pairs and the object association data of each of the multiple first target object pairs are obtained, which can avoid global scanning matching, improve matching efficiency, and thus improve object association analysis efficiency.

[0087] In a specific embodiment, the object data of each associated object may be searched in the object data storage area, and accordingly, the object data of the multiple associated objects found may be used as the associated object data of each of the multiple associated objects.

[0088] In the above embodiment, by storing object data and associated data in the object data storage area and the associated data storage area respectively, obtaining the object data to be analyzed from the object data storage area based on the indication information of the object to be analyzed, determining multiple associated objects and multiple first target object pairs corresponding to the object to be analyzed based on the object data to be analyzed and the associated data storage area, and obtaining the inter-object association data of each of the multiple first target object pairs, and obtaining the associated object data of each of the multiple associated objects from the object data storage area, it is possible to take into account both write throughput and query efficiency, support efficient updating of data collected in real time, improve the accuracy of inter-object association analysis by timely updating dynamic information, and thereby improve the efficiency and accuracy of object association analysis.

[0089] In a specific embodiment, when multiple current object data to be detected are detected, the object data in the object data storage area can be updated based on each current object data to be detected. Specifically, if the object data storage area stores object data for a preset object corresponding to any current object data to be detected, the object collection count in the object data can be updated. If the object data storage area does not store object data for a preset object corresponding to any current object data to be detected, the current object data to be detected can be added, and the collection count in the object data can be set to 1. Furthermore, if the object data storage area stores object data for a preset object corresponding to any current object data to be detected, a collection count update instruction for the preset object corresponding to any current object data to be detected can be sent to the target server, so that upon receiving the collection count update instruction, the target server increases the object collection count in the object data for the preset object corresponding to any current object data to be detected by 1 based on the current object collection count. Alternatively, in the case where the object data of the preset objects corresponding to the multiple current object data to be detected need to be updated, a batch number update instruction for the preset objects corresponding to the above multiple current object data to be detected can be sent to the target server, so that when the target server receives the above batch number update instruction, the current object collection times in the object data of the preset objects corresponding to the above multiple current object data to be detected are all increased by 1.

[0090] In the above embodiment, by sending a collection frequency update instruction to the target server and automatically updating the object collection frequency, the number of data interactions can be reduced, the efficiency of data update and maintenance can be improved, and the dynamic real-time nature of object data can be achieved. Furthermore, by improving the update timeliness of object data, the accuracy of object association analysis can be improved.

[0091] In a specific embodiment, the above method may further include:

[0092] When it is detected that a target object data pair exists in a plurality of current object data to be detected, the associated data in the associated data storage area is updated based on the target object data pair.

[0093] In a specific embodiment, the target object data pair may be an object data pair in which the time interval between the collection times of any two corresponding object data in the plurality of currently detected object data is less than a first preset time interval. The first preset time interval can be set based on actual application needs and is not limited in this disclosure. For example, the first preset time interval may be 5 minutes.

[0094] In a specific embodiment, a sliding window of a corresponding time length can be set based on a first preset time interval; accordingly, multiple current object data to be detected can be detected based on the sliding window; when it is detected that there is a target object data pair in the multiple current object data to be detected that belongs to the sliding window, the associated data in the associated data storage area can be updated based on the target object data pair.

[0095] In a specific embodiment, when a target object data pair is detected in multiple pieces of currently detected object data, a search may be performed in an associated data storage area to determine whether associated data corresponding to the target object data pair exists. If the associated data does not exist in the associated data storage area, associated data corresponding to the target object data pair may be generated. If the associated data does exist in the associated data storage area, the number of inter-object associations in the associated data corresponding to the target object data pair may be increased by 1.

[0096] In a specific embodiment, an association count update instruction or a batch update instruction for the association count between objects in the association data can be sent to the target server, causing the target server to automatically increment the association count between the target object data pairs within the association data storage area. Specifically, the specific process for automatically incrementing the association count between objects can refer to the specific process for automatically incrementing the object collection count described above, and will not be further described in this disclosure.

[0097] In a specific embodiment, the above-mentioned multiple current to-be-detected object data may be obtained in the following manner:

[0098] Get multiple current collection object data;

[0099] Determining, from the plurality of current acquisition object data, a plurality of target acquisition object data corresponding to each preset acquisition device;

[0100] Determine first acquisition object data corresponding to each preset acquisition device and at least one second acquisition object data corresponding to each preset acquisition device;

[0101] At least one second acquisition object data corresponding to each preset acquisition device in the plurality of current acquisition object data is filtered to obtain a plurality of current to-be-detected object data.

[0102] In a specific embodiment, the multiple currently collected object data may be object data collected by multiple preset collection devices within a current time range, wherein the duration corresponding to the current time range may be greater than the duration corresponding to the first preset time interval.

[0103] In a specific embodiment, a group of preset collection devices may be set at each preset collection position, and object data of different object types may be collected by using a plurality of preset collection devices of different object types in each group of preset collection devices.

[0104] In a specific embodiment, the object feature information corresponding to the multiple target acquisition object data is the same, and the maximum acquisition time interval corresponding to the multiple target acquisition object data is less than a second preset time interval. Specifically, the second preset time interval may be less than or equal to the first preset time interval. The second preset time interval can be set according to actual application needs and is not limited by this disclosure. For example, the second preset time interval may be 1 minute.

[0105] In a specific embodiment, the multiple target acquisition object data corresponding to any preset acquisition device may refer to multiple current acquisition object data with the same object feature information acquired by any of the above preset acquisition devices.

[0106] In a specific embodiment, the first acquisition object data may be any one of a plurality of target acquisition object data, and the at least one second acquisition object data may be target acquisition object data other than the first acquisition object data among the plurality of target acquisition object data.

[0107] In the above embodiment, by acquiring a plurality of current acquisition object data, determining a plurality of target acquisition object data corresponding to each preset acquisition device from the plurality of current acquisition object data, determining the first acquisition object data corresponding to each preset acquisition device and at least one second acquisition object data corresponding to each preset acquisition device, filtering the at least one second acquisition object data corresponding to each preset acquisition device in the plurality of current acquisition object data, and obtaining a plurality of current object data to be detected, deduplication of the current acquisition object data collected by each preset acquisition device can be achieved, and the inaccurate acquisition times or association times caused by the high-frequency acquisition device collecting the same object multiple times due to the different acquisition frequencies of different acquisition devices can be avoided, thereby improving the accuracy of the data stored in the object data storage area and the associated data storage area, and further improving the accuracy of subsequent object association analysis.

[0108] S205: Based on the first object collection times, the second object collection times corresponding to each of the multiple associated objects, and the inter-object association times corresponding to each of the multiple first target object pairs, perform association analysis on each first target object pair to obtain object association indicator data corresponding to each first target object pair.

[0109] In a specific embodiment, the object association index data corresponding to any first target object pair can be used to represent the degree of association between the two objects in any first target object pair.

[0110] In a specific embodiment, the association analysis is performed on each first target object pair based on the number of first object collections, the number of second object collections corresponding to each of the multiple associated objects, and the number of object associations corresponding to each of the multiple first target object pairs to obtain the object association indicator data corresponding to each first target object pair, which may include:

[0111] Based on the number of inter-object associations, performing inter-object correlation probability analysis on each first target object pair to obtain target association index data corresponding to each first target object pair;

[0112] Determining a correction coefficient corresponding to each first target object pair based on the first object acquisition number, the second object acquisition number, and the number of associations between the objects;

[0113] Based on the correction coefficient corresponding to each first target object pair, the target association index data corresponding to each first target object pair is corrected to obtain the object association index data corresponding to each first target object pair.

[0114] In a specific embodiment, the target association indicator data may be used to indicate the probability of a correlation between the two objects in each first target object pair.

[0115] In a specific embodiment, the target association index data corresponding to any of the first target objects can be obtained by the following formula:

[0116]

[0117] Wherein, P is the target association index data corresponding to any first target object pair; cc is the number of object associations corresponding to any first target object pair.

[0118] It can be understood that by assuming that the prior probability of any two objects meeting is P0=0.2 and the probability of no correlation is 1-P0=0.8, the calculation formula for the above target association index data can be determined as follows:

[0119] .

[0120] In a specific embodiment, the correction coefficient may be used to enhance the difference between the numbers of associations between objects of different pairs of objects when the number of associations between objects is greater than a first preset number of associations.

[0121] In a specific embodiment, determining the correction coefficient corresponding to each first target object pair based on the first object acquisition number, the second object acquisition number, and the number of associations between objects may include:

[0122] Performing mean processing on the first object acquisition times and the second object acquisition times corresponding to the associated objects in each first target object pair to obtain an average acquisition times corresponding to each first target object pair;

[0123] Based on the average number of acquisitions corresponding to each first target object pair and the number of inter-object associations corresponding to each first target object pair, a correction coefficient corresponding to each first target object pair is determined.

[0124] In a specific embodiment, the average acquisition times corresponding to any first target object pair can be used to characterize the average trend of the object acquisition times of the two objects in any first target object pair.

[0125] In a specific embodiment, the average number of acquisitions corresponding to each first target object pair may be obtained by performing geometric mean processing on the number of first object acquisitions and the number of second object acquisitions corresponding to the associated object in each first target object pair.

[0126] In a specific embodiment, the average number of acquisitions corresponding to each first target object pair may be obtained by performing arithmetic averaging on the number of first object acquisitions and the number of second object acquisitions corresponding to the associated object in each first target object pair.

[0127] In a specific embodiment, the correction coefficient corresponding to any of the first target objects can be obtained by the following formula:

[0128]

[0129] Wherein, K is the correction coefficient corresponding to any first target object pair; cc is the number of object associations corresponding to any first target object pair; SC1 and SC2 are the number of object acquisitions corresponding to each of the two objects in any first target object pair; α is a preset parameter, and illustratively, α can be 0.2.

[0130] In a specific embodiment, the object association index data corresponding to any first target object pair can be obtained by the following formula:

[0131]

[0132] in, prob is the object association index data corresponding to any first target object pair; K is the correction coefficient corresponding to any of the above-mentioned first target object pairs; and P is the target association index data corresponding to any of the first target object pairs.

[0133] In the above embodiment, by combining the number of associations between objects, an object correlation probability analysis is performed on each first target object pair to obtain the target correlation index data corresponding to each first target object pair, so as to realize the correlation probability prediction of each first target object pair. Then, the correction coefficient corresponding to each first target object pair is determined by combining the number of first object collections, the number of second object collections and the number of associations between objects. Based on the correction coefficient corresponding to each first target object pair, the target correlation index data corresponding to each first target object pair is corrected to obtain the object correlation index data corresponding to each first target object pair. This can effectively suppress the excessive increase in the degree of correlation caused by high collection times, suppress the divergence of the number of associations at a high level, and enhance the difference in the degree of correlation at a high number of associations between objects through exponential smoothing, so that the object correlation index data is more in line with the real scene and the accuracy of the object correlation index data is improved.

[0134] In a specific embodiment, the above method may further include:

[0135] Filtering the first target object pairs whose corresponding inter-object association times are less than a second preset association times among the plurality of first target object pairs to obtain a plurality of second target object pairs;

[0136] Accordingly, the object association indicator data corresponding to each first target object pair is obtained by performing association analysis on each first target object pair based on the number of first object collections, the number of second object collections corresponding to each of the multiple associated objects, and the number of object associations corresponding to each of the multiple first target object pairs, and may include:

[0137] Based on the first object collection times, the second object collection times and the object association times corresponding to each of the plurality of second target object pairs, association analysis is performed on each second target object pair to obtain object association index data corresponding to each second target object pair.

[0138] In a specific embodiment, the second preset number of associations can be set according to actual application needs, which is not limited in this disclosure.

[0139] In a specific embodiment, based on the number of first object collections, the number of second object collections, and the number of associations between objects, association analysis can be performed on the filtered target object pairs to obtain object association indicator data corresponding to each of the filtered target object pairs; accordingly, based on the object association indicator data corresponding to each of the filtered target object pairs, object association analysis can be performed on the objects to be analyzed to obtain object association analysis results corresponding to the objects to be analyzed.

[0140] In the above embodiment, when the number of associations between objects in any first target object pair is less than the second preset number of associations, it can be determined that the degree of association between the two objects in any first target object pair is low. In the subsequent object association analysis of the objects to be analyzed, it is necessary to analyze the specific association relationship types between the objects. For the first target object pairs with a low degree of association, they may not belong to any association relationship type. By filtering the first target object pairs with a low degree of association, the system calculation amount can be reduced and the overall object association analysis efficiency of the system can be improved.

[0141] In a specific embodiment, through multiple preset threads, combined with the number of first object collection times, the number of second object collection times corresponding to multiple associated objects, and the number of object associations corresponding to multiple first target object pairs, association analysis can be performed on each first target object pair to obtain object association indicator data corresponding to each first target object pair, thereby improving the efficiency of association analysis of multiple first target object pairs.

[0142] In the above embodiment, by sinking the association analysis process of each first target object pair to the application layer, the query layer is only responsible for data query, which can reduce the computing burden of the database and improve the scalability and stability of the system.

[0143] S207: performing object association analysis on the object to be analyzed based on the object association indicator data corresponding to each first target object pair, and obtaining an object association analysis result corresponding to the object to be analyzed.

[0144] In a specific embodiment, the object association analysis result corresponding to the object to be analyzed can be used to indicate the association relationship between the object to be analyzed and multiple associated objects. Specifically, the object association analysis result corresponding to the object to be analyzed can include object identification information of associated objects that have an association relationship with the object to be analyzed, and association relationship indication information for each first target object pair.

[0145] In a specific embodiment, association relationship indication information for each first target object pair can be generated based on the object association indicator data corresponding to each first target object pair. The association relationship indication information for any first target object pair can be used to indicate the association relationship between the two objects in any first target object pair. The association relationship indication information can include relationship type indication information corresponding to any preset association relationship type. Specifically, the preset association relationship type can include an accompanying relationship type, a same-device relationship type, or an belonging relationship type. The accompanying relationship type can be used to indicate that the two corresponding objects are two people who may have a relationship (such as relatives, friends, or colleagues). The same-device relationship type can be used to indicate that the two corresponding objects are two SIM cards that may belong to the same mobile terminal. The belonging relationship type can be used to indicate that the two corresponding objects belong to the same person or that one of the objects (such as a license plate or a SIM card) belongs to another object (for example, a person).

[0146] In a specific embodiment, multiple different object association indicator ranges can be preset; the object association indicator data corresponding to each first target object pair can be matched with each of the above-preset object association indicator ranges to obtain a range matching result; accordingly, the range matching result corresponding to any first target object pair and the object types of the two objects in any first target object pair can be combined to determine the target association relationship type corresponding to any first target object pair; accordingly, the object association analysis result corresponding to the object to be analyzed can be generated based on the association relationship indication information corresponding to the target association relationship type.

[0147] In a specific embodiment, the object association analysis result corresponding to the object to be analyzed may include association relationship indication information of multiple third target object pairs. The multiple third target object pairs may refer to a preset number of first target object pairs whose corresponding object association indicator data are ranked first in descending order among the multiple first target object pairs.

[0148] In the above embodiment, by receiving an object association analysis instruction, the object association analysis instruction includes the object to be analyzed indication information corresponding to the object to be analyzed, and based on the object indication information, the object to be analyzed data corresponding to the object to be analyzed, the associated object data of each of the multiple associated objects corresponding to the object to be analyzed, and the inter-object association data corresponding to each of the multiple first target object pairs are obtained, the object to be analyzed data includes the first object collection number, the associated object data includes the second object collection number, the inter-object association data includes the inter-object association number, any first target object pair includes the object to be analyzed and any associated object of the multiple associated objects, and the corresponding object to be analyzed can be obtained for the object to be analyzed when the object association analysis instruction is received. The object data and inter-object association data related to the first object are combined with the number of first object collections, the number of second object collections corresponding to each of the multiple associated objects, and the number of inter-object associations corresponding to each of the multiple first target object pairs, and an association analysis is performed on each first target object pair to obtain the object association index data corresponding to each first target object pair. The nonlinear relationship between the number of collections and the number of associations can be comprehensively introduced to realize the inter-object association analysis, avoid the interference of high-frequency objects, and improve the accuracy of the inter-object association analysis. Then, in combination with the object association index data corresponding to each first target object pair, an object association analysis is performed on the object to be analyzed to obtain the object association analysis result corresponding to the object to be analyzed, which can improve the accuracy of the object association analysis.

[0149] Figure 3 FIG. 1 is a block diagram of a data processing device according to an exemplary embodiment. Specifically, Figure 3 As shown, the device may include:

[0150] The instruction receiving module 310 may be configured to receive an object association analysis instruction; the object association analysis instruction includes object indication information corresponding to the object to be analyzed;

[0151] The first data acquisition module 320 may be configured to acquire, based on the indication information of the object to be analyzed, object data corresponding to the object to be analyzed, associated object data of each of multiple associated objects corresponding to the object to be analyzed, and inter-object association data corresponding to each of multiple first target object pairs; the object data to be analyzed includes the number of times the first object is collected, the associated object data includes the number of times the second object is collected, and the inter-object association data includes the number of times the objects are associated with each other; any first target object pair includes the object to be analyzed and any associated object among the multiple associated objects;

[0152] The first association analysis module 330 may be configured to perform association analysis on each first target object pair based on the number of first object collections, the number of second object collections corresponding to each of the plurality of associated objects, and the number of object associations corresponding to each of the plurality of first target object pairs, to obtain object association indicator data corresponding to each first target object pair;

[0153] The second association analysis module 340 may be configured to perform object association analysis on the object to be analyzed based on the object association index data corresponding to each first target object, and obtain an object association analysis result corresponding to the object to be analyzed.

[0154] In a specific embodiment, the first association analysis module 330 may include:

[0155] The correlation probability analysis module can be used to perform inter-object correlation probability analysis on each first target object pair based on the number of inter-object correlations, and obtain target correlation index data corresponding to each first target object pair; the target correlation index data is used to indicate the probability of correlation between the two objects in each first target object pair;

[0156] A correction coefficient determination module may be configured to determine a correction coefficient corresponding to each first target object pair based on the first object acquisition number, the second object acquisition number, and the number of inter-object associations; the correction coefficient is configured to enhance the difference between the number of inter-object associations of different object pairs when the number of inter-object associations is greater than a first preset number of associations;

[0157] The correction processing module can be used to perform correction processing on the target association index data corresponding to each first target object pair based on the correction coefficient corresponding to each first target object pair, so as to obtain the object association index data corresponding to each first target object pair.

[0158] In a specific embodiment, the correction coefficient determination module may include:

[0159] A mean processing module may be used to perform mean processing on the first object acquisition times and the second object acquisition times corresponding to the associated objects in each first target object pair to obtain an average acquisition times corresponding to each first target object pair;

[0160] The correction coefficient generation module can be used to determine the correction coefficient corresponding to each first target object pair based on the average number of acquisition times corresponding to each first target object pair and the number of object associations corresponding to each first target object pair.

[0161] In a specific embodiment, the first data acquisition module 320 may include:

[0162] The second data acquisition module may be configured to acquire the data of the object to be analyzed from the object data storage area based on the indication information of the object to be analyzed;

[0163] The third data acquisition module may be used to determine a plurality of associated objects and a plurality of first target object pairs corresponding to the object to be analyzed based on the object data to be analyzed and the associated data storage area, and to obtain the inter-object association data of each of the plurality of first target object pairs;

[0164] The fourth data acquisition module may be configured to acquire associated object data of each of a plurality of associated objects from the object data storage area.

[0165] In a specific embodiment, the third data acquisition module may include:

[0166] a matching processing module, which can be used to match the object identification information of the preset object query position and the first object identification information in the two relationship indication information corresponding to each preset object pair, to obtain a plurality of first target object pairs and the inter-object association data of the plurality of first target object pairs;

[0167] The associated object determination module may be configured to determine a plurality of associated objects based on a plurality of first target object pairs.

[0168] In a specific embodiment, the above device may further include:

[0169] The data update module can be used to update the associated data in the associated data storage area based on the target object data pair when it is detected that there is a target object data pair in multiple current object data to be detected; the target object data pair is an object data pair in the multiple current object data to be detected, and the time interval between the collection time of any two corresponding object data of each of them is less than the first preset time interval.

[0170] In a specific embodiment, the above device may further include:

[0171] The fifth data acquisition module can be used to acquire multiple current acquisition object data; the multiple current acquisition object data are object data collected by multiple preset acquisition devices within the current time range;

[0172] The first data determination module may be configured to determine, from the plurality of current acquisition object data, a plurality of target acquisition object data corresponding to each preset acquisition device; the plurality of target acquisition object data corresponding to the same object feature information, and the maximum acquisition time interval corresponding to the plurality of target acquisition object data is less than a second preset time interval;

[0173] The second data determination module may be used to determine first acquisition object data corresponding to each preset acquisition device and at least one second acquisition object data corresponding to each preset acquisition device, wherein the first acquisition object data is any one of a plurality of target acquisition object data, and the at least one second acquisition object data is a target acquisition object data other than the first acquisition object data among the plurality of target acquisition object data;

[0174] The data filtering module can be used to filter at least one second acquisition object data corresponding to each preset acquisition device in the multiple current acquisition object data to obtain multiple current object data to be detected.

[0175] In a specific embodiment, the above device may further include:

[0176] An object pair filtering module may be used to filter out first target object pairs whose corresponding inter-object association times are less than a second preset association times from among the plurality of first target object pairs, to obtain a plurality of second target object pairs;

[0177] Accordingly, the first association analysis module 330 may include:

[0178] The third association analysis module can be used to perform association analysis on each second target object pair based on the first object collection number, the second object collection number and the number of object associations corresponding to each of the multiple second target object pairs, to obtain object association indicator data corresponding to each second target object pair.

[0179] Regarding the apparatus in the above embodiment, the specific manner in which each module and unit performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0180] Figure 4 This is a block diagram of an electronic device for performing object association analysis on an object to be analyzed according to an exemplary embodiment. The electronic device may be a server, and its internal structure diagram may be as shown in FIG. Figure 4 As shown. The electronic device includes a processor, a memory, and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a data processing method is implemented.

[0181] Figure 5 This is a block diagram of another electronic device for performing object association analysis on an object to be analyzed according to an exemplary embodiment. The electronic device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The electronic device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a data processing method is implemented. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the electronic device, or an external keyboard, touchpad or mouse, etc.

[0182] Those skilled in the art will understand that Figure 4 or Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present disclosure, and does not constitute a limitation on the electronic device to which the scheme of the present disclosure is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0183] In an exemplary embodiment, an electronic device is further provided, including: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the data processing method in the embodiment of the present disclosure.

[0184] In an exemplary embodiment, a computer-readable storage medium is further provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the data processing method in the embodiment of the present disclosure.

[0185] In an exemplary embodiment, a computer program product containing instructions is also provided. When the computer program product is run on a computer, the computer is caused to execute the data processing method in the embodiment of the present disclosure.

[0186] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, which can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0187] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0188] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data processing method, characterized in that: The method comprises: receiving an object association analysis instruction; the object association analysis instruction including object to be analyzed indication information corresponding to the object to be analyzed; Based on the indication information of the object to be analyzed, obtaining object data corresponding to the object to be analyzed, associated object data of each of a plurality of associated objects corresponding to the object to be analyzed, and inter-object association data corresponding to each of a plurality of first target object pairs; the object data to be analyzed includes a first object acquisition count, the associated object data includes a second object acquisition count, the inter-object association data includes an inter-object association count, and any first target object pair includes the object to be analyzed and any associated object among the plurality of associated objects; performing association analysis on each first target object pair based on the first object collection count, the second object collection count corresponding to each of the plurality of associated objects, and the inter-object association count corresponding to each of the plurality of first target object pairs, to obtain object association indicator data corresponding to each first target object pair; performing object association analysis on the object to be analyzed based on the object association indicator data corresponding to each first target object pair to obtain an object association analysis result corresponding to the object to be analyzed; The acquiring, based on the indication information of the object to be analyzed, the object data corresponding to the object to be analyzed, the associated object data of each of the multiple associated objects corresponding to the object to be analyzed, and the inter-object association data corresponding to each of the multiple first target object pairs includes: Based on the indication information of the object to be analyzed, acquiring the object data to be analyzed from the object data storage area; the object data to be analyzed includes first object identification information corresponding to the object to be analyzed; Based on the object data to be analyzed and the associated data storage area, a plurality of associated objects corresponding to the object to be analyzed and the plurality of first target object pairs are determined, and inter-object association data of each of the plurality of first target object pairs is obtained; the associated data storage area stores preset association data corresponding to any preset object pair among a plurality of preset object pairs, the preset association data corresponding to any preset object pair including first relationship indication information, second relationship indication information, and the number of inter-object associations corresponding to any preset object pair, the first relationship indication information and the second relationship indication information both including object identification information of two preset objects, the object identification information of the first preset object in any preset object pair is located at a preset object query position in the first relationship indication information, and the object identification information of the second preset object in any preset object pair is located at a preset object query position in the second relationship indication information; acquiring, from the object data storage area, associated object data of each of the plurality of associated objects; The step of determining, based on the object data to be analyzed and the associated data storage area, a plurality of associated objects corresponding to the object to be analyzed and the plurality of first target object pairs, and obtaining inter-object association data of each of the plurality of first target object pairs, includes: Matching the object identification information of the preset object query position in the two relationship indication information corresponding to each preset object pair with the first object identification information to obtain the multiple first target object pairs and the inter-object association data of the multiple first target object pairs; Based on the plurality of first target object pairs, the plurality of associated objects are determined.

2. The method according to claim 1, characterized in that The performing association analysis on each first target object pair based on the first object collection count, the second object collection count corresponding to each of the plurality of associated objects, and the inter-object association count corresponding to each of the plurality of first target object pairs to obtain object association indicator data corresponding to each first target object pair includes: Based on the number of inter-object associations, performing an inter-object correlation probability analysis on each first target object pair to obtain target correlation index data corresponding to each first target object pair; the target correlation index data is used to indicate the probability of a correlation between the two objects in each first target object pair; Determining a correction coefficient corresponding to each first target object pair based on the first object acquisition number, the second object acquisition number, and the number of associations between objects; the correction coefficient is used to enhance the difference between the number of associations between objects of different object pairs when the number of associations between objects is greater than a first preset number of associations; Based on the correction coefficient corresponding to each first target object pair, the target association index data corresponding to each first target object pair is corrected to obtain the object association index data corresponding to each first target object pair.

3. The method according to claim 2, characterized in that The determining of the correction coefficient corresponding to each first target object pair based on the first object acquisition number, the second object acquisition number, and the number of associations between the objects includes: Performing mean processing on the first object acquisition times and the second object acquisition times corresponding to the associated objects in each first target object pair to obtain an average acquisition times corresponding to each first target object pair; Based on the average number of acquisitions corresponding to each first target object pair and the number of inter-object associations corresponding to each first target object pair, a correction coefficient corresponding to each first target object pair is determined.

4. The method according to claim 1, wherein The method further comprises: When it is detected that a target object data pair exists in multiple current object data to be detected, the associated data in the associated data storage area is updated based on the target object data pair; the target object data pair is an object data pair whose time interval between the collection time of any two corresponding object data in the multiple current object data to be detected is less than a first preset time interval.

5. The method according to claim 4, characterized in that The multiple current data of the objects to be detected are obtained in the following ways: Acquire multiple current acquisition object data; the multiple current acquisition object data are object data collected by multiple preset acquisition devices respectively within the current time range; Determining, from the plurality of current acquisition object data, a plurality of target acquisition object data corresponding to each preset acquisition device; the object feature information corresponding to the plurality of target acquisition object data is the same, and the maximum acquisition time interval corresponding to the plurality of target acquisition object data is less than a second preset time interval; Determine first acquisition object data corresponding to each preset acquisition device and at least one second acquisition object data corresponding to each preset acquisition device, wherein the first acquisition object data is any one of the plurality of target acquisition object data, and the at least one second acquisition object data is target acquisition object data other than the first acquisition object data among the plurality of target acquisition object data; At least one second acquisition object data corresponding to each preset acquisition device in the plurality of current acquisition object data is filtered to obtain the plurality of current to-be-detected object data.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Filtering the first target object pairs whose corresponding inter-object association times are less than a second preset association times among the plurality of first target object pairs to obtain a plurality of second target object pairs; The performing association analysis on each first target object pair based on the first object collection count, the second object collection count corresponding to each of the plurality of associated objects, and the inter-object association count corresponding to each of the plurality of first target object pairs to obtain object association indicator data corresponding to each first target object pair includes: Based on the first object collection times, the second object collection times and the object association times corresponding to each of the plurality of second target object pairs, association analysis is performed on each second target object pair to obtain object association index data corresponding to each second target object pair.

7. A data processing device, characterized in that: The device is used to implement the data processing method according to any one of claims 1 to 6, and the device includes: An instruction receiving module, configured to receive an object association analysis instruction; the object association analysis instruction includes object to be analyzed indication information corresponding to the object to be analyzed; a first data acquisition module configured to acquire, based on the object to be analyzed indication information, object data corresponding to the object to be analyzed, associated object data of each of a plurality of associated objects corresponding to the object to be analyzed, and inter-object association data corresponding to each of a plurality of first target object pairs; the object data to be analyzed includes a first object acquisition count, the associated object data includes a second object acquisition count, the inter-object association data includes an inter-object association count, and any first target object pair includes the object to be analyzed and any associated object among the plurality of associated objects; a first association analysis module, configured to perform an association analysis on each first target object pair based on the first object collection count, the second object collection count corresponding to each of the plurality of associated objects, and the inter-object association count corresponding to each of the plurality of first target object pairs, to obtain object association indicator data corresponding to each first target object pair; The second association analysis module is configured to perform object association analysis on the object to be analyzed based on the object association indicator data corresponding to each first target object pair, and obtain an object association analysis result corresponding to the object to be analyzed.

8. An electronic device, characterized in that: include: processor; memory for storing computer programs; The processor is configured to execute the computer program to implement the data processing method according to any one of claims 1 to 6.

Images