Data processing method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a data processing method and device, equipment and a storage medium. The method comprises the following steps: acquiring federated order basic information data, positioning data and recorded video data; performing relevance calculation on the federated order basic information data, the positioning data and the recorded video data based on a data relevance model to obtain a comprehensive relevance degree; based on the comprehensive correlation degree, determining a data analysis result; the data analysis result is used for representing the probability that the federated order basic information data, the positioning data and the recorded video data belong to the same federated order data. By adopting the method, the accuracy of the social application program federation data can be improved.

Description

Technical Field

[0001] This application relates to the field of Internet technology, and in particular to a data processing method, apparatus, device and storage medium. Background Technology

[0002] Currently, with the development of internet technology, the frequency of use of various social applications is increasing, leading to a rise in the frequency of using these applications to record trajectory data. With the rapid development of information technology, enterprises and organizations generate massive amounts of data in their daily operations. This data originates from different systems, devices, and platforms, exhibiting diversity, heterogeneity, and complexity. Data cleaning, as a crucial step in data processing, aims to improve data quality and ensure the accuracy of data analysis and decision-making. However, the diversity of existing data sources increases the difficulty of cleaning. Traditional cleaning methods struggle to handle unstructured data. Their real-time and dynamic update capabilities are insufficient, making it difficult to meet rapidly changing business needs. Summary of the Invention

[0003] This application provides a data processing method, apparatus, device, and storage medium that can improve the accuracy of trajectory data recorded by social applications.

[0004] One embodiment of this application provides a data processing method, the method comprising:

[0005] Obtain basic information data, location data, and recorded video data for the manifest;

[0006] The correlation between the basic information data of the manifest, the location data, and the recorded video data is calculated based on the data correlation model to obtain the comprehensive correlation degree.

[0007] Based on the comprehensive correlation, the data analysis results are determined; the data analysis results are used to represent the probability that the basic information data, location data, and recorded video data of the manifest belong to the same manifest data.

[0008] One embodiment of this application provides a data processing apparatus, which includes:

[0009] The data acquisition module is used to acquire basic information data of the manifest, location data, and recorded video data;

[0010] The correlation degree acquisition module is used to perform correlation calculations on the basic information data of the manifest, location data, and recorded video data based on the data correlation model to obtain the comprehensive correlation degree.

[0011] The analysis results acquisition module is used to determine the data analysis results based on the comprehensive correlation degree; the data analysis results are used to represent the probability that the order data, location data, and recorded video data belong to the same order data.

[0012] The correlation acquisition module is also used to acquire label correlation, sample basic information data, sample location data, and sample video data; perform feature extraction processing on the sample basic information data, sample location data, and sample video data to obtain sample information features corresponding to the sample basic information data, sample location features corresponding to the sample location data, and sample video features corresponding to the sample video data, respectively; input the sample information features corresponding to the sample basic information data, sample location features corresponding to the sample location data, and sample video features corresponding to the sample video data into the initial correlation model for correlation calculation processing to obtain the sample correlation; generate a model loss value based on the sample correlation and label correlation; adjust the model parameters of the initial correlation model based on the model loss value to obtain the data correlation model.

[0013] The data acquisition module is also used to determine the collected vehicle information, time information, starting address information, and number information of the order as basic information data of the order; to obtain the route trajectory through the real-time positioning system corresponding to the social application and determine the route trajectory as positioning data; and to obtain recorded video data through the video recording device.

[0014] The analysis result acquisition module is also used to obtain the correlation threshold; if the overall correlation is greater than or equal to the correlation threshold, the correlation result is determined as the data analysis result; if the overall correlation is less than the correlation threshold, the irrelevant result is determined as the data analysis result.

[0015] The data acquisition module is also used to perform weighted processing on the vehicle information, time information, starting address information, and number information of the joint order to obtain the basic information data of the joint order.

[0016] The correlation acquisition module is also used to obtain the probability of consistency between the basic information data of the order, the location data, and the recorded video data, and to determine the probability of consistency between the order and the data as the comprehensive correlation. If the probability of consistency between the order and the data is greater than or equal to the obtained correlation probability, the correlation result is determined as the data analysis result. If the probability of consistency between the order and the data is less than the obtained correlation probability, the irrelevant result is determined as the data analysis result.

[0017] This application provides a computer device, including: a processor, a memory, and a network interface; the processor is connected to the memory and the network interface, wherein the network interface is used to provide data communication functions, the memory is used to store computer programs, and the processor is used to call the computer programs so that the computer device executes the methods in the embodiments of this application.

[0018] One aspect of this application provides a computer-readable storage medium storing a computer program adapted for loading by a processor and executing the methods described in this application.

[0019] One aspect of this application provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method described in this application.

[0020] This application's embodiments acquire basic information data of a manifest, location data, and recorded video data; perform correlation calculations on the basic information data of the manifest, location data, and recorded video data based on a data correlation model to obtain a comprehensive correlation degree; and determine the data analysis results based on the comprehensive correlation degree. The data analysis results are used to represent the probability that the basic information data of the manifest, location data, and recorded video data belong to the same manifest data. This application uses a data correlation model to perform correlation calculations on the basic information data of the manifest, location data, and recorded video data to obtain a comprehensive correlation degree, thus determining the correlation between the basic information data of the manifest, location data, and recorded video data. Using this application can improve the accuracy of determining whether the basic information data of the manifest, location data, and recorded video data belong to the same manifest data in social applications. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of a network architecture provided in an embodiment of this application;

[0023] Figure 2 This is a schematic diagram illustrating a scenario for obtaining data analysis results, provided in an embodiment of this application.

[0024] Figure 3 This is a flowchart illustrating a data processing method provided in an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0026] Figure 5This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0028] It is understood that in the specific implementation of this application, data related to objects or users (such as basic information data of manifests, location data, and video data) are involved. When the following embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant regions.

[0029] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0030] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0031] This application provides a video rendering scheme involving video encoding and decoding technology, and the specific process is illustrated in the following embodiments.

[0032] Please see Figure 1 , Figure 1This is a schematic diagram of a network architecture provided in an embodiment of this application. The network interaction architecture may include a server 100 and a terminal cluster. The terminal cluster may include terminal devices 200a, 200b, 200c, ..., 200n. Communication connections may exist between terminal devices in the cluster; for example, there is a communication connection between terminal devices 200a and 200b, and between terminal devices 200a and 200c. Simultaneously, any terminal device in the terminal cluster may have a communication connection with the server 100; for example, there is a communication connection between terminal device 200a and server 100. The communication connection method is not limited; it can be established directly or indirectly through wired communication, wireless communication, or other methods. This application does not impose any restrictions on this method.

[0033] It is understood that the methods provided in this application embodiment can be executed by computer devices, including but not limited to the aforementioned network nodes (which can be terminal devices or servers in this application embodiment). In this application embodiment, the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud databases, cloud services, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal devices mentioned above can be electronic devices, including but not limited to mobile phones, tablets, desktop computers, laptops, PDAs, in-vehicle devices, augmented reality / virtual reality (AR / VR) devices, head-mounted displays, smart TVs, wearable devices, smart speakers, digital cameras, webcams, and other mobile internet devices (MIDs) with network access capabilities, or terminal devices in scenarios such as trains, ships, and flights. In this application embodiment, the terminal device may include a video client.

[0034] It is understood that the embodiments of this application can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, and smart transportation. For example, the video rendering scheme of this application can be used to render video data about driving behavior and road trajectory sent by the vehicle terminal (simply put, to obtain high-definition video data) to obtain clearer video data.

[0035] The computer equipment mentioned in this application may be a server or terminal device (such as a video playback client), or a system composed of a server and a terminal device.

[0036] It is understood that, in a specific embodiment, for example, when the user of an application (such as a social application) needs to determine the association data of a multi-part order within the application (such as a social application), the association analysis of the multi-part order association data can be performed using the multi-part order association data determination method of this application. Specifically, the multi-part order association data determination method of this application involves the following steps: Specifically, a computer device can acquire basic information data of the multi-part order, location data, and recorded video data; based on a data association model, the association calculation of the basic information data of the multi-part order, location data, and recorded video data is performed to obtain a comprehensive association degree; based on the comprehensive association degree, the data analysis result is determined; the data analysis result is used to represent the probability that the multi-part order data, location data, and recorded video data belong to the same multi-part order data.

[0037] In one specific embodiment, the application field of this application may include the field of construction waste disposal. Specifically, the computer device can analyze and determine the correlation data of GPS trajectories of construction waste vehicles during the construction waste disposal process. The computer device can obtain data related to the GPS trajectories of construction waste vehicles during the disposal process from social applications, such as basic manifest information data, location data, and recorded video data. Further, the computer device can use the manifest correlation data determination method of this application to perform correlation analysis of the manifest correlation data and obtain data analysis results. Then, based on the data analysis results, it can determine whether the manifest basic information data, location data, and recorded video data belong to the same manifest.

[0038] Further, please see Figure 2 , Figure 2 This is a schematic diagram illustrating a scenario for obtaining data analysis results, provided in an embodiment of this application. For example... Figure 2 As shown, this application embodiment provides a data processing method, which is executed by server 100 and involves terminal device 200a. The server 100 may refer to the aforementioned... Figure 1 The server mentioned above, the terminal device 200a, may refer to the one mentioned above. Figure 1The terminal device in the process. Specifically, server 100 can obtain basic information data 21D of the manifest 21D, location data 22D, and recorded video data 23D through terminal device 200a. Further, server 100 can perform correlation calculations on the basic information data 21D of the manifest 21D, location data 22D, and recorded video data 23D based on data correlation model 24M to obtain a comprehensive correlation degree 25X. Further, server 100 can determine the data analysis result 26X based on the comprehensive correlation degree 25X. The data analysis result 26X represents the probability that the basic information data 21D of the manifest 21D, location data 22D, and recorded video data 23D belong to the same manifest data.

[0039] Further, please see Figure 3 , Figure 3 This is a flowchart illustrating a data processing method provided in an embodiment of this application. Figure 3 As shown, this method can be executed by a server, which can be configured to perform the above-mentioned actions. Figure 1 The server 100 shown is not limited here. For ease of understanding, this application embodiment uses the method executed by the server as an example for illustration. This data processing method may include at least the following steps S101-S104:

[0040] Step S101: Obtain basic information data of the manifest, location data, and recorded video data.

[0041] Specifically, server 100 can determine the collected vehicle information, time information, starting address information, and order number information of the combined order as basic order information data. Furthermore, server 100 can obtain the route trajectory through the real-time location system corresponding to the social application and determine the route trajectory as location data. Furthermore, server 100 can obtain recorded video data through a video recording device.

[0042] Optionally, the server 100 can perform weighted processing on the vehicle information, time information, starting address information, and number information of the manifest to obtain basic manifest information data.

[0043] Specifically, the server can acquire basic manifest information, location data, and recorded video data via wired or wireless networks. Specifically, the server can acquire these data via a wired communication network. This communication network can include a communication system consisting of a gateway device, a router, and transmission cables. Correspondingly, the server can acquire these data via Bluetooth in wireless transmission.

[0044] Step S102: Based on the data correlation model, perform correlation calculation on the basic information data of the manifest, the location data, and the recorded video data to obtain the comprehensive correlation degree.

[0045] Specifically, the process of training the data correlation model on server 100 can be as follows:

[0046] Server 100 can acquire tag correlation, sample basic information data, sample location data, and sample video data; perform feature extraction processing on the sample basic information data, sample location data, and sample video data to obtain sample information features corresponding to the sample basic information data, sample location features corresponding to the sample location data, and sample video features corresponding to the sample video data, respectively; input the sample information features corresponding to the sample basic information data, sample location features corresponding to the sample location data, and sample video features corresponding to the sample video data into an initial correlation model for correlation calculation processing to obtain sample correlation; generate a model loss value based on the sample correlation and the tag correlation; adjust the model parameters of the initial correlation model based on the model loss value to obtain a data correlation model.

[0047] Step S103: Based on the comprehensive correlation, determine the data analysis results; the data analysis results are used to represent the probability that the order data, location data, and recorded video data belong to the same order data.

[0048] Specifically, server 100 can obtain a correlation threshold. If the overall correlation is greater than or equal to the correlation threshold, the correlation result is determined as a data analysis result. Conversely, if the overall correlation is less than the correlation threshold, an irrelevant result is determined as a data analysis result.

[0049] Optionally, server 100 can obtain the consistency probability of the basic information data of the manifest, the location data, and the recorded video data, and determine the consistency probability as the comprehensive correlation degree. Further, if the consistency probability is greater than or equal to the obtained correlation probability, the correlation result is determined as the data analysis result. Correspondingly, if the consistency probability is less than the obtained correlation probability, the irrelevant result is determined as the data analysis result.

[0050] It is understood that, in the specific embodiments of this application, data such as basic information data of manifests, location data, and recorded video data are involved. When the above and below embodiments of this application are applied to specific products or technologies, the collection and processing of related data should strictly comply with the requirements of relevant regional laws and regulations, obtain the informed consent or separate consent of the personal information subject (or have a legal basis), and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject. When facial (or other biometric) recognition technology is involved, the relevant data collection, use, and processing processes should comply with the requirements of relevant regional laws and regulations. Before collecting facial information, the information processing rules should be informed and the separate consent of the target object should be obtained (or have a legal basis). Facial information should be processed in strict accordance with the requirements of laws and regulations and personal information processing rules, and technical measures should be taken to ensure the security of related data.

[0051] This application's embodiments acquire basic information data of a manifest, location data, and recorded video data; perform correlation calculations on the basic information data of the manifest, location data, and recorded video data based on a data correlation model to obtain a comprehensive correlation degree; and determine the data analysis results based on the comprehensive correlation degree. The data analysis results are used to represent the probability that the basic information data of the manifest, location data, and recorded video data belong to the same manifest data. This application uses a data correlation model to perform correlation calculations on the basic information data of the manifest, location data, and recorded video data to obtain a comprehensive correlation degree, thus determining the correlation between the basic information data of the manifest, location data, and recorded video data. Using this application can improve the accuracy of determining whether the basic information data of the manifest, location data, and recorded video data belong to the same manifest data in social applications.

[0052] Further, please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a data processing apparatus provided in an embodiment of this application. The aforementioned data processing apparatus can be a computer program (including program code) running on a computer device; for example, the data processing apparatus is application software. This apparatus can be used to execute corresponding steps in the methods provided in the embodiments of this application. Figure 4 As shown, the data processing device 1 may include: a data acquisition module 11, a correlation acquisition module 12, and an analysis result acquisition module 13.

[0053] Data acquisition module 11 is used to acquire basic information data of the manifest, location data and recorded video data;

[0054] The correlation degree acquisition module 12 is used to perform correlation calculations on the basic information data of the manifest, the location data, and the recorded video data based on the data correlation model to obtain the comprehensive correlation degree.

[0055] The analysis result acquisition module 13 is used to determine the data analysis results based on the comprehensive correlation degree; the data analysis results are used to represent the probability that the order data, location data and recorded video data belong to the same order data.

[0056] The specific implementation methods of the data acquisition module 11, the correlation acquisition module 12, and the analysis result acquisition module 13 can be found in the above description. Figure 3 Steps S101-S103 in the corresponding embodiment will not be described again here.

[0057] Please see again Figure 4 The correlation acquisition module 12 is further used to acquire label correlation, sample basic information data, sample location data, and sample video data; perform feature extraction processing on the sample basic information data, sample location data, and sample video data to obtain sample information features corresponding to the sample basic information data, sample location features corresponding to the sample location data, and sample video features corresponding to the sample video data, respectively; input the sample information features corresponding to the sample basic information data, sample location features corresponding to the sample location data, and sample video features corresponding to the sample video data into the initial correlation model for correlation calculation processing to obtain the sample correlation; generate model loss values ​​based on the sample correlation and label correlation; adjust the model parameters of the initial correlation model based on the model loss values ​​to obtain the data correlation model.

[0058] The specific implementation of the correlation acquisition module 12 can be found in the above description. Figure 3 Step S102 in the corresponding embodiment will not be described again here.

[0059] Please see again Figure 4 The data acquisition module 11 is also used to determine the collected vehicle information, time information, starting address information, and number information of the order as basic information data of the order; to obtain the route trajectory through the real-time positioning system corresponding to the social application and determine the route trajectory as positioning data; and to obtain recorded video data through the video recording device.

[0060] The specific implementation of the data acquisition module 11 can be found in the above description. Figure 3 Step S101 in the corresponding embodiment will not be described again here.

[0061] Please see again Figure 4 The analysis result acquisition module 13 is also used to acquire the correlation threshold; if the overall correlation is greater than or equal to the correlation threshold, the correlation result is determined as the data analysis result; if the overall correlation is less than the correlation threshold, the irrelevant result is determined as the data analysis result.

[0062] The specific implementation of the analysis result acquisition module 13 can be found in the above description. Figure 3 Step S103 in the corresponding embodiment will not be described again here.

[0063] Please see again Figure 4 The data acquisition module 11 is also used to perform weighted processing on the vehicle information, time information, starting address information and number information of the joint order to obtain the basic information data of the joint order.

[0064] The specific implementation of the data acquisition module 11 can be found in the above description. Figure 3 Step S101 in the corresponding embodiment will not be described again here.

[0065] Please see again Figure 4 The correlation acquisition module 12 is also used to acquire the probability of consistency of the basic information data of the order, the location data and the recorded video data, and to determine the probability of consistency of the order as the comprehensive correlation. If the probability of consistency of the order is greater than or equal to the acquired correlation probability, the correlation result is determined as the data analysis result. If the probability of consistency of the order is less than the acquired correlation probability, the irrelevant result is determined as the data analysis result.

[0066] The specific implementation of the correlation acquisition module 12 can be found in the above description. Figure 3 Step S103 in the corresponding embodiment will not be described again here.

[0067] Further, please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 5 As shown, the computer device 1000 may include: at least one processor 1001, such as a CPU; at least one network interface 1004; a user interface 1003; a memory 1005; and at least one communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and a keyboard. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as at least one disk drive. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 5 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a device control application program.

[0068] exist Figure 5 In the computer device 1000 shown, the network interface 1004 provides network communication functionality; the user interface 1003 is mainly used to provide an input interface for the user; and the processor 1001 can be used to call the device control application stored in the memory 1005 to achieve:

[0069] Acquire basic information data, location data, and recorded video data of the manifest; perform correlation calculation on the basic information data, location data, and recorded video data based on a data correlation model to obtain a comprehensive correlation degree; determine the data analysis result based on the comprehensive correlation degree; the data analysis result is used to represent the probability that the basic information data, location data, and recorded video data belong to the same manifest data.

[0070] It should be understood that the computer device 1000 described in the embodiments of this application can execute the foregoing text. Figure 2 as well as Figure 3 The description of the data processing method in the corresponding embodiments can also be performed as described above. Figure 4 The description of the data processing device 1 in the corresponding embodiments will not be repeated here. Furthermore, the beneficial effects of using the same method will also not be repeated here.

[0071] This application also provides a computer-readable storage medium storing a computer program, the computer program including program instructions that are executed by a processor. Figure 2 as well as Figure 3 For details on the data processing methods provided in each step, please refer to the above. Figure 2 as well as Figure 3 The implementation methods provided for each step will not be elaborated here. Furthermore, the beneficial effects of using the same method will also not be described in detail.

[0072] The aforementioned computer-readable storage medium can be an internal storage unit of the data processing apparatus or computer device provided in any of the foregoing embodiments, such as a hard disk or memory of the computer device. The computer-readable storage medium can also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the computer device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0073] This application also provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the aforementioned... Figure 2 as well as Figure 3 The description of the data processing method in the corresponding embodiments will not be repeated here. Furthermore, the beneficial effects of using the same method will also not be repeated.

[0074] The term "comprising," and any variations thereof, in the specification, claims, and drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the steps or modules listed, but may optionally include steps or modules not listed, or may optionally include other steps or units inherent to such processes, methods, apparatus, products, or devices.

[0075] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0076] The methods and related apparatus provided in this application are described with reference to the method flowcharts and / or structural diagrams provided in this application. Specifically, each block of the method flowchart and / or structural diagram, as well as combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to create a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 A process or multiple processes and / or structures illustrate the steps of the functions specified in one or more boxes.

[0077] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A data processing method, characterized by, The method comprises the following steps: obtaining basic information data, positioning data and recorded video data of a joint order; based on the data correlation model, the basic information data, the positioning data and the recorded video data of the joint order are correlated to obtain a comprehensive correlation degree; based on the comprehensive correlation degree, a data analysis result is determined; the data analysis result is used to represent the probability that the basic information data, the positioning data and the recorded video data belong to one joint order data.

2. The data processing method according to claim 1, characterized in that, Further comprising: obtaining label correlation degree, sample basic information data, sample positioning data and sample video data; performing feature extraction processing on the sample basic information data, the sample positioning data and the sample video data to obtain sample information features corresponding to the sample basic information data, sample positioning features corresponding to the sample positioning data and sample video features corresponding to the sample video data; inputting the sample information features, the sample positioning features and the sample video features into an initial correlation model for correlation calculation processing to obtain a sample correlation degree; generating a model loss value according to the sample correlation degree and the label correlation degree, adjusting the model parameters of the initial correlation model according to the model loss value to obtain a data correlation model.

3. The data processing method of claim 1, wherein, The method comprises the following steps: determining the collected joint order vehicle information, joint order time information, joint order starting address information and joint order number information as the basic information data of the joint order; obtaining route trajectory through the real-time positioning system corresponding to the social application, and determining the route trajectory as the positioning data; obtaining recorded video data through a video recording device.

4. The data processing method of claim 1, wherein, The method comprises the following steps: obtaining a correlation degree threshold value; if the comprehensive correlation degree is greater than or equal to the correlation degree threshold value, determining the correlation result as the data analysis result; if the comprehensive correlation degree is less than the correlation degree threshold value, determining the unrelated result as the data analysis result.

5. The data processing method according to claim 3, characterized in that, The method comprises the following steps: performing weighted processing on the joint order vehicle information, the joint order time information, the joint order starting address information and the joint order number information to obtain the basic information data of the joint order.

6. The data processing method of claim 1, wherein, The method comprises the following steps: obtaining the homologous probability of the basic information data, the positioning data and the recorded video data of the joint order, and determining the homologous probability as the comprehensive correlation degree; The method comprises the following steps: if the homologous probability is greater than or equal to the obtained correlation probability, determining the correlation result as the data analysis result; if the homologous probability is less than the obtained correlation probability, determining the unrelated result as the data analysis result.

7. A data processing apparatus, characterized by, The data processing device comprises: The data acquisition module is configured to acquire basic information data of a joint form, positioning data, and recorded video data. The correlation degree acquisition module is configured to perform correlation calculation on the basic information data of the joint form, the positioning data, and the recorded video data based on a data correlation model to obtain a comprehensive correlation degree. The analysis result acquisition module is configured to determine a data analysis result based on the comprehensive correlation degree, wherein the data analysis result is used to represent a probability that the basic information data of the joint form, the positioning data, and the recorded video data belong to one joint form data.

8. A computer device, comprising: The computer device comprises: a processor, a memory, and a network interface; the network interface is configured to provide a data communication function, the memory is configured to store a computer program, and the processor is configured to call the computer program to enable the computer device to perform the method in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer program is stored in the computer readable storage medium and is adapted to be loaded and executed by the processor to enable the computer device having the processor to perform the method in any one of claims 1-6.

10. A computer program product, characterised in that, The computer program product comprises the computer program stored in the computer readable storage medium, and the computer program is adapted to be read and executed by the processor to enable the computer device having the processor to perform the steps of the data processing method in any one of claims 1-6.

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