Vehicle data right confirmation method, processor, vehicle and storage medium

Abstract

The invention discloses a vehicle data right confirmation method, a processor, a vehicle and a storage medium, the method may comprise: determining current label information of a purchase object about demand data, the demand data being used for representing vehicle driving data associated with an application scene, and the current label information being used for representing a category to which the vehicle driving data belongs; first driving data matched with the current label information is determined from a vehicle driving data set, the vehicle driving data set comprises a mapping relation between different label information and different vehicle driving data, and the first driving data is used for representing the vehicle driving data matched with the category; inputting the first driving data into a target order prediction model for prediction to obtain order information of the first driving data; and determining ownership information of the first driving data in response to the fact that the current state of the order information is the state of executing the transaction behavior. The technical problem that the utilization rate of vehicle data is low is solved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of vehicles, and in particular, to a data right method of a vehicle, a processor, a vehicle and a storage medium. BACKGROUND

[0002] At present, a vehicle generates a large amount of data and high-value data during operation. However, in the process of sharing and trading the above data, there are often data islands, privacy leaks, and unclear rights allocation, which makes it impossible to fully release the value of the above data, thereby causing the technical problem of low utilization rate of data of the vehicle.

[0003] At present, there is no effective solution to the technical problem of low utilization rate of data of the vehicle. SUMMARY

[0004] Embodiments of the present application provide a data right method of a vehicle, a processor, a vehicle and a storage medium to at least solve the technical problem of low utilization rate of data of the vehicle.

[0005] According to an aspect of an embodiment of the present application, a data right method of a vehicle is provided, comprising: determining current label information of a purchase object about demand data, wherein the demand data is used to represent vehicle driving data associated with an application scenario, and the current label information is used to represent a category to which the vehicle driving data belongs; determining first driving data matching the current label information from a vehicle driving data set, wherein the vehicle driving data set comprises a mapping relationship between different label information and different vehicle driving data, and the first driving data is used to represent vehicle driving data matching the category; inputting the first driving data into a target order prediction model for prediction to obtain order information of the first driving data, wherein the order information at least comprises a value attribute of the first driving data displayed to the purchase object; and determining ownership information of the first driving data in response to a current state of the order information being a state with an execution transaction behavior, wherein the ownership information at least comprises a profit attribute of the first driving data for a supply object, and the supply object is used to provide different vehicle driving data to the vehicle driving data set.

[0006] Further, determining the first driving data matching the current label information from the vehicle driving data set comprises: performing similarity calculation on the current label information and different label information respectively from the vehicle driving data set to obtain initial label similarities between the current label information and the different label information; determining a target label similarity from a plurality of initial label similarities, wherein the target label similarity is greater than the initial label similarity other than the target label similarity in the plurality of initial label similarities; and determining the first driving data matching the current label information based on the target label similarity.

[0007] Further, based on the target label similarity, determining the first driving data matched with the current label information comprises: determining the label information corresponding to the target label similarity as the target label information; and determining the vehicle driving data satisfying the mapping relationship with the target label information as the first driving data.

[0008] Further, in response to the current state of the order information being a state with an executed transaction behavior, determining the ownership information of the first driving data comprises: in response to the current state being the state with the executed transaction behavior, verifying the identification information of the purchase object, wherein the identification information is used to identify the purchase object; in response to the identification information being verified, performing an authorization operation on the purchase object to enable the purchase object to access the first driving data; and determining the ownership information in the process of authorizing the purchase object.

[0009] Further, in the process of authorizing the purchase object, the ownership information is determined, comprising: in the process of authorizing the purchase object, obtaining a supply degree of at least one supply object for the first driving data; determining proportion information corresponding to the supply degree, wherein the proportion information is used to represent the profit proportion of the at least one supply object for the first driving data; and dividing the order information according to the proportion information to obtain the ownership information.

[0010] Further, the method further comprises: in response to the result output by the data model of the purchase object not satisfying a preset result, collecting second driving data, wherein the collection time of the second driving data is later than the collection time of the first driving data; retraining the target order prediction model using the second driving data; and updating the target order prediction model to the retrained target order prediction model.

[0011] Further, the method further comprises: extracting demand data from demand information of the purchase object, wherein the demand information comprises the demand data; and determining current label information of the purchase object about the demand data, comprising: performing label analysis on the demand data to obtain the current label information.

[0012] Further, the method further comprises: encrypting the first driving data after authorizing the purchase object; and transmitting the encrypted first driving data to a terminal or a cloud of the purchase object for constructing a data model of the purchase object.

[0013] Further, the method further comprises: obtaining initial vehicle driving data; and performing standardization processing on the initial vehicle driving data to obtain the vehicle driving data.

[0014] According to a further aspect of the embodiments of the present application, a data right device of a vehicle is provided. The device can include a first determining unit configured to determine current label information of a purchase object with respect to demand data, wherein the demand data is configured to represent vehicle driving data associated with an application scenario, and the current label information is configured to represent a category to which the vehicle driving data belongs; a second determining unit configured to determine, from a vehicle driving data set, first driving data matching the current label information, wherein the vehicle driving data set includes a mapping relationship between different label information and different vehicle driving data, and the first driving data is configured to represent vehicle driving data matching the category; a predicting unit configured to input the first driving data into a target order predicting model for prediction to obtain order information of the first driving data, wherein the order information at least includes a value attribute of the first driving data to be displayed to the purchase object; and a third determining unit configured to determine ownership information of the first driving data in response to a current state of the order information being a state with an execution transaction behavior, wherein the ownership information at least includes a profit attribute of the first driving data for a supply object, and the supply object is configured to provide different vehicle driving data to the vehicle driving data set.

[0015] According to a further aspect of the embodiments of the present application, a vehicle is provided. The vehicle can include a memory storing an executable program; and a processor configured to execute the program, wherein the program is configured to execute the method in the embodiments of the present application when executed.

[0016] According to a further aspect of the embodiments of the present application, a computer readable storage medium is provided. The computer readable storage medium includes a stored executable program, wherein the computer readable storage medium is configured to control a device on which the computer readable storage medium is located to execute the method in the embodiments of the present application when the executable program is executed.

[0017] According to a further aspect of the embodiments of the present application, a computer program product is provided. The computer program product includes a computer program, and the computer program is configured to implement the method in the embodiments of the present application when executed by a processor.

[0018] According to a further aspect of the embodiments of the present application, a computer program product is provided. The computer program product includes a non-volatile computer readable storage medium storing a computer program, and the computer program is configured to implement the method in the embodiments of the present application when executed by a processor.

[0019] According to a further aspect of the embodiments of the present application, a computer program is provided. The computer program is configured to implement the method in the embodiments of the present application when executed by a processor.

[0020] In the embodiment of the present application, when the data of the vehicle is authorized, the current label information of the purchase object about the demand data can be determined, from the vehicle driving data set, the first driving data matched with the current label information can be determined, the matched first driving data is input into the target order prediction model for prediction, the order information of the first driving data can be obtained, and in response to the current state of the order information being in a state of executing a transaction behavior, the ownership information of the first driving data can be determined. Since in the embodiment of the present application, on the basis of determining the current label information of the purchase object about the demand data, from the vehicle driving data set, the vehicle driving data matched with the category can be determined, the matched vehicle driving data is input into the target order prediction model for prediction, the order information including the value attribute can be obtained, and in the case of judging that the current state of the order information is in a state of executing a transaction behavior, the ownership information of the first driving data can be determined, thereby achieving the purpose of fully releasing the value of the data of the vehicle, further solving the technical problem of low utilization rate of the data of the vehicle, and achieving the technical effect of improving the utilization rate of the data of the vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0021] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0022] FIG. 1(a) is a schematic diagram of an application scenario of a data authorization method of a vehicle according to an embodiment of the present application;

[0023] FIG. 1(b) is a flowchart of a data authorization method of a vehicle according to an embodiment of the present application;

[0024] FIG. 2(a) is a schematic diagram of a data element-based data element authorization system of a vehicle according to an embodiment of the present application;

[0025] FIG. 2(b) is a schematic diagram of a data element-based data element authorization method of a vehicle according to an embodiment of the present application;

[0026] Figure 3 is a schematic diagram of data interaction between a vehicle and a server according to an embodiment of the present application;

[0027] Figure 4 is a structural block diagram of a data authorization device of a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application, so that those skilled in the art can better understand the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of the present application.

[0029] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in other than the order illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to include only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0030] According to the embodiments of the present application, an embodiment of a data right method of a vehicle is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in a different order.

[0031] As an optional implementation, the above-mentioned data right method of a vehicle can be applied to, but is not limited to, the application scenario shown in FIG. 1(a). FIG. 1(a) is a schematic diagram of an application scenario of a data right method of a vehicle according to an embodiment of the present application. As shown in FIG. 1(a), in the application scenario, the terminal device 10 can communicate with the server 13 through the network 11, and the server 13 can perform operations on the database, such as writing data or reading data. The terminal device 10 can include, but is not limited to, a human-computer interaction screen, a processor and a memory. The human-computer interaction screen can be used to display a virtual machine on the mobile terminal 10, etc. The vehicle 12 can respond to the human-computer interaction operation, perform corresponding operations, or generate corresponding instructions and send the generated instructions to the server 13.

[0032] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown. Among them, the data ownership method of the vehicle in the present application can include: step S102, determining the current label information of the purchase object about the demand data; step S104, determining the first driving data matched with the current label information from the vehicle driving data set; step S106, inputting the first driving data into the target order prediction model for prediction to obtain the order information of the first driving data; and step S108, in response to the current state of the order information being a state with transaction behavior execution, determining the ownership information of the first driving data.

[0033] It should be noted that the user information (including but not limited to label information, order information, etc.) and data (including but not limited to demand data, vehicle driving data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0034] Figure 1(b) is a flowchart of a data ownership method of a vehicle according to an embodiment of the present application, as shown in Figure 1(b), the method can include the following steps:

[0035] Step S112, determining the current label information of the purchase object about the demand data.

[0036] In the technical solution provided in step S112 of the present application, the above-mentioned demand data can be used to represent vehicle driving data associated with application scenarios. Among them, the above-mentioned application scenarios can include at least one of the following scenarios: intelligent traffic management scenario, insurance risk assessment scenario, vehicle maintenance scenario, charging network optimization scenario, and new energy vehicle battery health prediction scenario, etc.

[0037] In this embodiment, the above-mentioned vehicle driving data can include at least one of the following data: vehicle trajectory data, driving behavior data and sensor display data, etc. Among them, the above-mentioned vehicle trajectory data can be used to represent the change of trajectory of the vehicle in the driving process; the above-mentioned driving behavior data (also can be called but not limited to, driving habit data) can be used to represent the operation habit of the user when controlling the vehicle to drive; the above-mentioned sensor display data can be used to represent the data displayed on the sensor, for example, the sensor can include: speed sensor, radar sensor, light sensor, pressure sensor, humidity sensor, temperature sensor and image sensor, etc. Herein, only for example, not limited.

[0038] In this embodiment, the current label information described above can be used to represent the category to which the vehicle driving data belongs. For example, if the vehicle driving data is associated with an application scenario, the vehicle driving data is associated with a prediction scenario of the battery health degree of a new energy vehicle, and the category corresponding to the current label information can include a charging frequency category, a temperature fluctuation category, and a battery use time length category, etc. Here, the scenario and the category are only exemplified and are not limited specifically.

[0039] In this embodiment, the current label information of the purchase object about the demand data is determined. Optionally, the embodiment obtains the demand data of the purchase object, and label analysis is performed on the obtained demand data, so that the current label information of the demand data can be obtained, that is, the category to which the vehicle driving data associated with the application scenario belongs can be obtained.

[0040] Step S114, from the vehicle driving data set, determine the first driving data matching the current label information.

[0041] In the technical solution provided by the above step S114 of the application, the vehicle driving data set can include a mapping relationship between different label information and different vehicle driving data. For example, the vehicle driving data set can be a feature label library, which can include different labels and a mapping relationship between different vehicle driving data encapsulated as data elements.

[0042] In this embodiment, the first driving data described above can be used to represent the vehicle driving data matching the category. For example, if the category corresponding to the current label information is the charging frequency category, the first driving data described above can be the vehicle driving data matching the charging frequency category.

[0043] In this embodiment, after determining the current label information of the purchase object about the demand data, the first driving data matching the current label information is determined from the vehicle driving data set. Optionally, based on the determination of the current label information, the initial label similarity between the current label information and different label information in the vehicle driving data set can be determined, and according to the determined initial label similarity, the first driving data matching the current label information, that is, the vehicle driving data matching the category, can be determined.

[0044] Optionally, according to the determined initial label similarity, the first driving data matched with the current label information can be determined. For example, the determined initial label similarity is sorted, and a target label similarity is selected from the sorted initial label similarity, where the target label similarity is greater than the initial label similarity except the target label similarity in the plurality of initial label similarities; and the first driving data matched with the current label information can be determined according to the label information corresponding to the target label similarity.

[0045] Optionally, the first driving data matched with the current label information can be determined according to the label information corresponding to the target label similarity. For example, the vehicle driving data matched with the corresponding label information is determined as the first driving data on the basis of determining the label information corresponding to the target label similarity, for example, the vehicle driving data satisfying the mapping relationship with the corresponding label information is determined as the first driving data on the basis of determining the label information corresponding to the target label similarity.

[0046] In step S116, the first driving data is input into the target order prediction model for prediction to obtain order information of the first driving data.

[0047] In the technical solution provided by the above step S116 of the present application, the order information can at least include a value attribute of the first driving data displayed to a purchase object, permission information of the first driving data, and packaging information of the first driving data. The value attribute can be used to reflect the optimal offer of the first driving data, the permission information can be used to reflect access permission, use permission, distribution permission, and modification permission of the first driving data, and the packaging information can be used to reflect the version, packaging time, and digital signature of the first driving data.

[0048] In this embodiment, the target order prediction model can be an order prediction model constructed based on a three-dimensional pricing model, where the three-dimensional pricing model can be a pricing model considering data quality dimension, use intensity dimension, and scene value dimension.

[0049] In this embodiment, after determining the first driving data matched with the current label information from the vehicle driving data set, the first driving data is input into the target order prediction model for prediction to obtain order information of the first driving data. Optionally, on the basis of determining the first driving data matched with the current label information, the determined first driving data is input into the prediction layer of the target order prediction model for prediction to obtain the order information of the first driving data, and the prediction obtained order information is output by the output layer of the target order prediction model.

[0050] It should be noted that the method of inputting the first driving data into the target order prediction model for prediction to obtain the order information of the first driving data is only for example and is not specifically limited here. As long as the process and method of inputting the first driving data into the target order prediction model for prediction to obtain the order information based on the determination of the first driving data matching the current tag information, they are within the protection scope of the embodiments of the present application, and will not be illustrated one by one here.

[0051] In step S118, the ownership information of the first driving data is determined in response to the current state of the order information being the state of performing the transaction behavior.

[0052] In the technical solution provided by the above step S118 of the present application, the ownership information can at least include the profit attribute of the supply object for the first driving data and the association relationship between the supply objects. The supply object can be used to provide different vehicle driving data for the vehicle driving data set.

[0053] In this embodiment, the profit attribute can be the share profit of the contribution party of the vehicle driving data for the first driving data. For example, the contribution party can include but is not limited to a data source developer and a model developer.

[0054] In this embodiment, after inputting the first driving data into the target order prediction model for prediction to obtain the order information of the first driving data, the ownership information of the first driving data is determined in response to the current state of the order information being the state of performing the transaction behavior. Alternatively, the current state of the order information is detected based on the prediction of the order information of the first driving data. If it is detected that the current state of the order information is the state of performing the transaction behavior, the ownership information of the first driving data can be determined, that is, the profit attribute of the supply object for the first driving data and the association relationship between the supply objects can be determined.

[0055] Alternatively, if it is detected that the current state of the order information is not the state of performing the transaction behavior, the current state of the order information is continuously detected until it is detected that the current state of the order information is the state of performing the transaction behavior, and then the ownership information of the first driving data is determined.

[0056] In the steps S112 to S118, when the data of the vehicle is authenticated, the current label information of the purchase object about the demand data can be determined, the first driving data matching the current label information can be determined from the vehicle driving data set, the matched first driving data can be input into the target order prediction model for prediction, the order information of the first driving data can be obtained, and the ownership information of the first driving data can be determined in response to the current state of the order information being in a state of executing a transaction behavior. In the embodiment, based on the current label information of the purchase object about the demand data being determined, the vehicle driving data matching the category can be determined from the vehicle driving data set, the matched vehicle driving data can be input into the target order prediction model for prediction, the order information including the value attribute can be obtained, and the ownership information of the first driving data can be determined in the case where the current state of the order information is in a state of executing a transaction behavior. Therefore, the value of the data of the vehicle can be fully released, the technical problem of low utilization rate of the data of the vehicle is solved, and the technical effect of improving the utilization rate of the data of the vehicle is achieved.

[0057] The above method of the embodiment will be further described below.

[0058] As an optional embodiment, in the step S114, the first driving data matching the current label information is determined from the vehicle driving data set, including: performing similarity calculation on the current label information and different label information respectively to obtain initial label similarities between the current label information and the different label information; determining a target label similarity from the plurality of initial label similarities; and determining the first driving data matching the current label information based on the target label similarity.

[0059] In the embodiment, the similarity calculation can be implemented based on any one of the following algorithms: cosine similarity algorithm, random forest algorithm, support vector machine algorithm, etc., which are only used as examples and are not limited in a specific manner.

[0060] In the embodiment, after the current label information of the purchase object about the demand data is determined, similarity calculation is performed on the current label information and different label information respectively to obtain initial label similarities between the current label information and the different label information. Alternatively, based on the current label information being determined, similarity calculation is performed on the current label information and different label information respectively to obtain initial label similarities between the current label information and the different label information, thereby achieving the purpose of determining the label similarities.

[0061] In this embodiment, the target label similarity can be greater than the initial label similarities other than the target label similarity.

[0062] In this embodiment, after the similarity calculation between the current label information and different label information is performed based on the vehicle driving data set, the initial label similarities between the current label information and different label information are obtained, and then the target label similarity is determined from the plurality of initial label similarities. Optionally, the initial label similarities determined are sorted based on the initial label similarities obtained, and the target label similarity can be selected from the sorted initial label similarities, thereby achieving the purpose of determining the target label similarity.

[0063] Optionally, the initial label similarities determined are sorted in ascending order, and the target label similarity can be selected from the initial label similarities sorted in ascending order. Alternatively, the initial label similarities determined are sorted in descending order, and the target label similarity can be selected from the initial label similarities sorted in descending order.

[0064] In this embodiment, after the target label similarity is determined from the plurality of initial label similarities, the first driving data matched with the current label information is determined based on the target label similarity. Optionally, the vehicle driving data matched with the corresponding label information is determined as the first driving data based on the target label similarity determined, for example, the label information corresponding to the target label similarity is determined, and the vehicle driving data satisfying the mapping relationship with the corresponding label information is determined as the first driving data, thereby achieving the purpose of determining the first driving data, and further achieving the technical effect of improving the matching accuracy of the driving data.

[0065] The method of determining the first driving data matched with the current label information based on the target label similarity in this embodiment will be further described below.

[0066] As an optional embodiment, determining the first driving data matched with the current label information based on the target label similarity includes: determining the label information corresponding to the target label similarity as the target label information; and determining the vehicle driving data satisfying the mapping relationship with the target label information as the first driving data.

[0067] In this embodiment, after determining the target label similarity from the plurality of initial label similarities, the label information corresponding to the target label similarity is determined as the target label information. Optionally, this embodiment determines the label information corresponding to the target label similarity as the target label information on the basis of the determination of the target label similarity. For example, if the category corresponding to the label information is the temperature fluctuation category, the category corresponding to the target label information is the temperature fluctuation category, thereby achieving the purpose of determining the target label information.

[0068] In this embodiment, after determining the target label information corresponding to the target label similarity, the vehicle driving data satisfying the mapping relationship with the target label information is determined as the first driving data. Optionally, this embodiment determines the vehicle driving data satisfying the mapping relationship with the target label information as the first driving data on the basis of the determination of the target label information. For example, if the category corresponding to the target label information is the battery use time category, the vehicle driving data satisfying the mapping relationship with the target label information representing the battery use time category is determined as the first driving data, thereby achieving the purpose of determining the first driving data, and further achieving the technical effect of improving the matching accuracy of the driving data.

[0069] The method for determining the ownership information of the first driving data in response to the current state of the order information being in the state of having the transaction behavior performed will be further described below.

[0070] As an optional embodiment, step S118, in response to the current state of the order information being in the state of having the transaction behavior performed, determining the ownership information of the first driving data, includes: in response to the current state being in the state of having the transaction behavior performed, verifying the identification information of the purchase object; in response to the identification information being verified, performing an authorization operation on the purchase object to enable the purchase object to access the first driving data; and determining the ownership information in the process of authorizing the purchase object.

[0071] In this embodiment, the identification information can be used to identify the purchase object. For example, the identification information can be a transaction two-dimensional code of the purchase object for the first driving data, or a transaction order number of the purchase object for the first driving data, etc., which are only used as examples and are not limited in a specific manner.

[0072] In this embodiment, after the first driving data is input into the target order prediction model to obtain the order information of the first driving data, the identification information of the purchase object is verified in response to the current state being the state of having the transaction behavior performed. Optionally, the embodiment detects the current state of the order information on the basis of the prediction of the order information of the first driving data, and if the current state of the order information is detected as the state of having the transaction behavior performed, the identification information of the purchase object is verified, so as to achieve the purpose of determining whether the purchase object completes the transaction of the first driving data.

[0073] In this embodiment, the authorization operation can be implemented on the basis of dynamic access authorization. The dynamic access authorization is an attribute encryption technology, and the purchase object can only decrypt and obtain the first driving data when the preset condition is met. Meanwhile, the permission information of the purchase object on the first driving data can be embedded in the first driving data, so that the authorization function can be automatically performed for the purchase object that passes the verification.

[0074] In this embodiment, after the identification information of the purchase object is verified in response to the current state being the state of having the transaction behavior performed, the purchase object is authorized to access the first driving data in response to the identification information passing the verification. Optionally, the embodiment authorizes the purchase object to access the first driving data on the basis of the verification of the identification information of the purchase object, so as to achieve the purpose of enabling the purchase object that passes the verification to access the first driving data.

[0075] Optionally, if the identification information of the purchase object does not pass the verification, the verification of the identification information of the purchase object is continued until the identification information of the purchase object passes the verification, and then the purchase object is authorized.

[0076] In this embodiment, after the purchase object is authorized to access the first driving data in response to the identification information passing the verification, the ownership information is determined in the process of authorizing the purchase object. Optionally, the embodiment can obtain the supply degree of at least one supply object to the first driving data in the process of authorizing the purchase object, and the ownership information of the first driving data can be determined according to the supply degree, that is, the profit attribute of the supply object to the first driving data and the correlation among the supply objects can be determined, so as to achieve the purpose of fully releasing the value of the data of the vehicle, and further achieve the technical effect of improving the utilization rate of the data of the vehicle.

[0077] The method for determining the ownership information in the process of authorizing the purchase object is described in further detail as follows.

[0078] As an optional embodiment, the method for determining the ownership information in the process of authorizing the purchase object comprises: obtaining a supply degree of at least one supply object for the first driving data in the process of authorizing the purchase object; determining proportional information corresponding to the supply degree; and dividing the order information according to the proportional information to obtain the ownership information.

[0079] In this embodiment, the supply degree can be a contribution degree of at least one contributor for the first driving data.

[0080] In this embodiment, the supply degree of at least one supply object for the first driving data is obtained in the process of authorizing the purchase object after the purchase object is authorized to access the first driving data in response to the identification information being verified. Alternatively, the supply degree of at least one supply object for the first driving data can be obtained in the process of authorizing the purchase object, for example, the contribution degree of at least one contributor for the first driving data can be obtained, so as to achieve the purpose of fully releasing the value of the data of the vehicle.

[0081] For example, if the contributors of the first driving data include a first contributor and a second contributor, and the driving data contributed by the first contributor in the first driving data is different from the driving data contributed by the second contributor in the first driving data, the contribution degree of the first contributor for the first driving data and the contribution degree of the second contributor for the first driving data can be obtained in the process of authorizing the purchase object, which is only an example and is not limited in particular.

[0082] In this embodiment, the proportional information can be used to represent the profit proportion of at least one supply object for the first driving data. For example, the proportional information can be the share proportion of the profit of at least one contributor for the first driving data.

[0083] In the embodiment, in the process of authorizing the purchase object, after obtaining the supply degree of at least one supply object for the first driving data, the proportion information corresponding to the supply degree is determined; and the order information is divided according to the proportion information to obtain the ownership information. Optionally, on the basis of obtaining the supply degree, the embodiment can also obtain the association relationship between the supply objects, and according to the obtained supply degree, the proportion information corresponding to the supply degree can be determined; the value corresponding to the value attribute in the order information is determined, and the difference between the value corresponding to the value attribute and the cost of the first driving data is calculated to obtain the difference value between the value and the cost; and the obtained difference value is divided according to the determined proportion information to obtain the profit attribute of the supply object for the first driving data, thereby achieving the purpose of fully releasing the value of the data of the vehicle, and further achieving the technical effect of improving the utilization rate of the data of the vehicle.

[0084] The updating method of the target order prediction model in the embodiment will be further described below.

[0085] As an optional embodiment, the method further includes: in response to the result output by the data model of the purchase object not satisfying the preset result, collecting second driving data; retraining the target order prediction model by using the second driving data; and updating the target order prediction model to the retrained target order prediction model.

[0086] In the embodiment, the collection time of the second driving data can be later than the collection time of the first driving data.

[0087] In the embodiment, the preset result can be a preset threshold.

[0088] In the embodiment, in response to the result output by the data model of the purchase object not satisfying the preset result, the second driving data is collected. Optionally, the embodiment determines the relationship between the output result and the preset result on the basis of determining the output result of the data model of the purchase object, and if it is determined that the output result does not satisfy the preset result, the second driving data is collected, thereby achieving the purpose of automatically triggering incremental data collection.

[0089] In this embodiment, in response to the result output by the data model of the purchase object not satisfying the preset result, the second driving data is collected, the target order prediction model is retrained by using the second driving data, and the target order prediction model is updated to the retrained target order prediction model. Optionally, based on the collection of the second driving data, the target order prediction model is retrained by using the collected second driving data, and the target order prediction model is updated to the retrained target order prediction model, so as to achieve the purpose of updating the target order prediction model, and further achieve the technical effect of improving the accuracy of the data model.

[0090] The method for determining the current label information of the purchase object about the demand data in this embodiment will be further described below.

[0091] As an optional embodiment, the method further includes: extracting the demand data from the demand information of the purchase object; and determining the current label information of the purchase object about the demand data includes: performing label analysis on the demand data to obtain the current label information.

[0092] In this embodiment, the demand information can include the demand data. For example, the demand information can be displayed in the form of a demand form, and the demand form can record the application scenario of the purchase object for the vehicle driving data, the budget range of the purchase object for the purchase of the vehicle driving data, and the quality requirement of the purchase object for the vehicle driving data, and the like, which are only illustrative and not limited.

[0093] In this embodiment, the demand data is extracted from the demand information of the purchase object. Optionally, based on the acquisition of the demand information of the purchase object, the demand data can be extracted by performing keyword extraction on the acquired demand information, so as to achieve the purpose of determining the demand of the purchase object for the vehicle driving data.

[0094] In this embodiment, after the demand data is extracted from the demand information of the purchase object, the demand data is analyzed by label analysis to obtain the current label information. Optionally, based on the extraction of the demand data, the current label information can be obtained by performing label analysis on the extracted demand data, that is, the category to which the vehicle driving data associated with the application scenario belongs can be obtained, so as to achieve the purpose of determining the current label information, and further achieve the technical effect of improving the accuracy of determining the demand of the purchase object.

[0095] The transmission method of the first driving data in this embodiment will be further described below.

[0096] As an optional embodiment, the method further comprises: encrypting the first driving data after the purchase object is authorized; and transmitting the encrypted first driving data to a terminal or a cloud of the purchase object for constructing a data model of the purchase object.

[0097] In this embodiment, the encryption can be, but is not limited to, homomorphic encryption.

[0098] In this embodiment, the first driving data is encrypted after the purchase object is authorized, and the encrypted first driving data is transmitted to a terminal or a cloud of the purchase object for constructing a data model of the purchase object. Optionally, the embodiment encrypts the first driving data obtained after the purchase object is authorized, and transmits the encrypted first driving data to a terminal or a cloud of the purchase object. If the user needs to use the first driving data, the first driving data is downloaded from the terminal or the cloud, the downloaded first driving data is decrypted, and the decrypted first driving data is used to construct a data model, thereby achieving the purpose of allowing the purchase object to obtain the first driving data, and further achieving the technical effect of improving the security of data transmission.

[0099] The determination method of the vehicle driving data of this embodiment is further described below.

[0100] As an optional embodiment, the method further comprises: obtaining initial vehicle driving data; and performing standardization processing on the initial vehicle driving data to obtain the vehicle driving data.

[0101] In this embodiment, the standardization processing can include data preprocessing and desensitization, and standardized packaging. The data preprocessing and desensitization can include format unification processing, desensitization processing (also referred to as privacy enhancement processing), feature extraction, etc., and the standardized packaging can be structured packaging, that is, different data is packaged in a unified standard. The packaged data can include: version of the data, packaging time, feature field (such as numerical or label type, etc.), and digital signature, etc.

[0102] In the embodiment, initial vehicle driving data is acquired, and the initial vehicle driving data is standardized to obtain vehicle driving data. Optionally, the embodiment first acquires initial vehicle driving data from different vehicles, performs format unification processing on the acquired initial vehicle driving data, performs desensitization processing on the initial vehicle driving data after the format unification processing, performs feature extraction on the initial vehicle driving data after the desensitization processing, and performs structured encapsulation on the initial vehicle driving data after the feature extraction, so as to obtain the vehicle driving data, thereby achieving the purpose of acquiring different vehicle driving data, and further achieving the technical effect of improving the richness of the vehicle driving data.

[0103] In the embodiment, when the data of the vehicle is authenticated, the current label information of the purchase object about the demand data can be determined, the first driving data matched with the current label information can be determined from the vehicle driving data set, the matched first driving data is input into the target order prediction model for prediction, the order information of the first driving data can be obtained, and the current state of the order information is in a state of executing a transaction behavior, and the ownership information of the first driving data can be determined. Since in the embodiment, on the basis of determining the current label information of the purchase object about the demand data, the vehicle driving data matched with the category can be determined from the vehicle driving data set, the matched vehicle driving data is input into the target order prediction model for prediction, the order information including the value attribute can be obtained, and in the case where it is judged that the current state of the order information is in a state of executing a transaction behavior, the ownership information of the first driving data can be determined, thereby achieving the purpose of fully releasing the value of the data of the vehicle, further solving the technical problem of low utilization rate of the data of the vehicle, and achieving the technical effect of improving the utilization rate of the data of the vehicle.

[0104] The technical solutions of the embodiments of the application will be described below in conjunction with preferred embodiments.

[0105] At present, a large amount of high-value data is generated during the operation of a vehicle. However, in the process of sharing and trading the data, there are often data islands, privacy leakage, and unclear rights allocation, which makes it impossible to fully release the value of the data, thereby causing the technical problem of low utilization rate of the data of the vehicle.

[0106] However, the embodiment of the present application proposes a data right method of a vehicle, based on the determination of the current label information of the demand data of the purchase object, the vehicle driving data set can be determined, the matching vehicle driving data is input into the target order prediction model for prediction, the order information including the value attribute can be obtained, and in the case that the current state of the order information is determined to be the state of executing the transaction behavior, the ownership information of the first driving data can be determined, thereby achieving the purpose of fully releasing the value of the data of the vehicle, and further solving the technical problem of low utilization rate of the data of the vehicle, thereby realizing the technical effect of improving the utilization rate of the data of the vehicle.

[0107] In this embodiment, the data element-based vehicle data element right system is used, in the case that the current state of the order information is determined to be the state of executing the transaction behavior, the profit attribute of the supply object for the first driving data and the association relationship between each supply object can be determined. For example, FIG. 2(a) is a schematic diagram of a data element-based vehicle data element right system according to an embodiment of the present application, as shown in FIG. 2(a), the system 200 can include a data element generation module 201, a transaction matching and pricing module 202 and a trusted transmission and right module 203.

[0108] In this embodiment, the data element generation module 201 can be used for preprocessing, desensitization and feature modeling of multi-source raw data, forming standardized and high-value-density data units, thereby realizing the decoupling of data and application scenarios. The multi-source raw data can include data collected by vehicle-mounted sensors and user behavior logs, etc. The preprocessing of the data can include the collection of multi-source raw data, such as the integration of heterogeneous data sources such as data collected by vehicle-mounted sensors, user behavior logs and charging piles, and the format unification of the integrated heterogeneous data sources through data extraction, transformation and loading (ETL) tools. The desensitization of the data can include privacy enhancement processing of the data, such as generalization of direct identifiers (e.g. license plate number) using anonymous algorithms, and protection of trajectory data using differential privacy. The feature modeling of the data can include extracting high-value feature fields through machine learning algorithms, such as driving behavior risk score and battery health prediction value.

[0109] It should be noted that the working principle of the data element generation module 201 is to connect the data supply side and the demand side, that is, to keep the value density of the data element and avoid the risk of privacy leakage. For example, in the smart city scenario, after the original data generated in the traffic environment is desensitized and feature extracted, standardized data elements (such as, but not limited to, congestion index model and carbon emission feature label) can be generated, which can be directly used for cross-departmental collaborative decision-making without exposing the details of the original data.

[0110] In this embodiment, the transaction matching and pricing module 202 can be used to support intelligent pricing and efficient circulation of data elements through a number of fields (such as a supply and demand matching platform) and a number of networking (such as a number of networking based on cross-domain data protocol), thereby realizing the release of the economic value of data elements. The transaction matching and pricing module 202 can be used to realize accurate docking of supply and demand through a multi-modal algorithm engine. For example, the transaction matching and pricing module 202 can be used to analyze the buyer demand label (such as a “new energy vehicle battery health prediction label”), extract keywords (such as “charging frequency” and “temperature fluctuation characteristics”), and map the extracted keywords to the feature label library of the data element, and use the cosine similarity algorithm to calculate the matching degree of the buyer demand label and the data element label based on the deep semantic matching model trained by the historical transaction data, and preferentially recommend elements with high similarity. In addition, the transaction matching and pricing module 202 can introduce a reinforcement learning mechanism, so as to dynamically optimize the recommendation strategy according to the buyer feedback (such as clicking, purchasing, and evaluating), and improve the success rate of recommendation to the buyer.

[0111] For example, first, the published demand is obtained, for example, the buyer submits a demand form, which can include at least one of the following information: application scenario, budget range, and data quality requirement, etc.; then, the candidate set is screened, for example, the platform filters out compliant elements based on a rule engine; then, bidding and bargaining: supporting open bidding or one-on-one negotiation, and the intelligent contract automatically generates the optimal bid; finally, real-time transaction: the transaction is completed through the memory matching technology, and the blockchain records the hash evidence.

[0112] In this embodiment, the intelligent pricing of the data element is calculated by using a pricing model, wherein the pricing model is constructed based on the standardized feature field of the data element, and the pricing model considers the following dimensions: a data quality dimension, which dynamically adjusts the price of each data element according to the integrity, accuracy and timeliness of the feature field, and also introduces data entropy pricing, that is, by using an information entropy algorithm, the information density of the data element can be calculated, the higher the entropy value of the data element, the higher the information density, and the corresponding pricing is higher; a usage intensity dimension: a step-by-step pricing strategy is set, for example, an application programming interface (API) for logistics path optimization is charged according to the number of calls, which encourages high-frequency use, and a subscription mode is adopted, for example, the buyer pays a basic access fee and a percentage of the effect; a scenario value dimension: based on a game theory model, the expected difference between the supply and demand parties in terms of data value is analyzed, and the balanced price is automatically negotiated by the smart contract; and the reference market method is used to compare similar data transaction cases, and the premium rate of the data element is adjusted in combination with the supply and demand heat.

[0113] It should be noted that if the accuracy of the model fed back after receiving the data element purchased by the buyer is lower than the preset threshold, the incremental data collection and feature retraining are automatically triggered, the data element is updated, and the updated data element is used to update the pricing model.

[0114] In this embodiment, the trusted transmission and right confirmation module 203 can be used to complete the secure transmission and ownership confirmation of the data element in the trusted data space by relying on the blockchain and privacy computing, thereby realizing the protection of the "available but invisible" and "controllable and measurable" of the data element.

[0115] Optionally, the secure transmission of the data element can include identity trust and permission control, which can be implemented by: decentralized identity verification: in combination with the blockchain, a unique identity is assigned to each party of the data transaction (for example, an automobile enterprise, an insurance company and a platform), and "controllable anonymous" verification is performed, and dynamic access authorization: based on attribute encryption technology, if it is judged that the buyer needs to meet the preset conditions, the data element is decrypted for the buyer, wherein the permission tag can be embedded in the data element, and the smart contract automatically executes the authorization logic for the buyer who meets the preset conditions.

[0116] Optionally, the secure transmission of the data element can include data secure transmission and storage. The data secure transmission and storage can be implemented by: encrypted transmission: using homomorphic encryption to implement ciphertext transmission of the characteristics of the data element, supporting calculation in the ciphertext state, and ensuring that the data is available but not visible; blockchain storage: real-time on-chain storage of key information such as transaction hash, element characteristic fingerprint, and timestamp, with small storage timestamp error and supporting full-process traceability; and trusted data space technology: building a distributed “data sandbox”, allowing the buyer to access the data element only through a virtual view, and the original data not leaving the supplier environment, thereby preventing the spread of horizontal permissions of the data element.

[0117] Optionally, the ownership confirmation of the data element can be implemented by the following steps: by separating information and data, the ownership of the data resource can be determined; by using the two-separation principle, the ownership of the data element converges to the model developer and the operating unit, and both parties share the processing and use rights of the data element; the buyer processes the data element into terminal products such as risk control models, and the operating right belongs to a single subject, realizing the simplification of “N→2→1” ownership; when the data element is traded, the ownership certificate is automatically generated, the contribution ratio of the contributors (for example, data source developers and model developers) is recorded, and the effect-based sharing is supported; and by connecting the private chain of the vehicle enterprise and the public transaction chain through the cross-chain protocol, the ownership information is synchronously updated.

[0118] It should be noted that the data element right confirmation system of the vehicle based on the data element can be implemented by the following settings: setting one, configuring secure hardware, supporting homomorphic encryption and federated learning, and single-field encryption with low time consumption; setting two, using a fault-tolerant (Practical Byzantine Fault Tolerance, PBFT for short) algorithm to achieve low consensus delay and support thousands of node scales; setting three, based on a flow-batch integrated architecture, realizing dynamic desensitization and access control of the data element; setting four, a closed-loop verification mechanism, for example, verifying the integrity of the data element through a hash algorithm, and if the characteristic fingerprint does not match the on-chain record, an arbitration process is automatically triggered; setting five, resolving ownership disputes, for example, establishing an on-chain arbitration pool, and a third-party institution quickly resolves disputes based on storage records.

[0119] In this embodiment, the data element right confirmation method of the vehicle based on the data element is executed. In the case where it is judged that the current state of the order information is a state with a transaction behavior, the profit attribute of the supply object to the first driving data and the correlation between each supply object can be determined. For example, FIG. 2(b) is a schematic diagram of a data element right confirmation method of a vehicle based on a data element according to an embodiment of the present application. As shown in FIG. 2(b), the method can include the following steps:

[0120] Step S211, generating a data element.

[0121] After the data element is generated, step S212 is entered to determine the data element matching the current label information and generate order information of the matching data element.

[0122] After the order information of the matching data element is generated, step S213 is entered to perform trusted transmission on the data element and determine the ownership information of the matching data element.

[0123] Figure 3 is a schematic diagram of data interaction between a vehicle and a server according to an embodiment of the present application, as Figure 3 shown, the vehicle 300 can upload initial vehicle driving data to the server 301, the server 301 can perform standardization processing on the initial vehicle driving data to obtain vehicle driving data, and the vehicle driving data is downloaded to the vehicle 300.

[0124] In this embodiment, when the data of the vehicle is authenticated, the current label information of the purchase object about the demand data can be determined, from the vehicle driving data set, the first driving data matching the above current label information can be determined, the matching first driving data is input into the target order prediction model for prediction, the order information of the first driving data can be obtained, and in response to the current state of the above order information being in a state of executing a transaction behavior, the ownership information of the first driving data can be determined. Since in the present application, on the basis of determining the current label information of the purchase object about the demand data, from the vehicle driving data set, the vehicle driving data matching the category can be determined, the matching vehicle driving data is input into the target order prediction model for prediction, the order information including the value attribute can be obtained, and in the case where it is judged that the current state of the order information is in a state of executing a transaction behavior, the ownership information of the first driving data can be determined, thereby achieving the purpose of fully releasing the value of the data of the vehicle, and further solving the technical problem of low utilization rate of the data of the vehicle, thereby realizing the technical effect of improving the utilization rate of the data of the vehicle.

[0125] According to another aspect of the embodiment of the present application, corresponding to the above-mentioned embodiment of the data authentication method of the vehicle, the present application further provides a data authentication device of a vehicle. Figure 4 is a structural block diagram of a data authentication device of a vehicle according to an embodiment of the present application, as Figure 4 shown, the data authentication device 400 of the vehicle can include a first determination unit 402, a second determination unit 404, a prediction unit 406 and a third determination unit 408.

[0126] The first determining unit 402 is configured to determine current label information of the purchase object with respect to demand data, wherein the demand data is used to represent vehicle driving data associated with an application scenario, and the current label information is used to represent a category to which the vehicle driving data belongs.

[0127] The second determining unit 404 is configured to determine, from a vehicle driving data set, first driving data matching the current label information, wherein the vehicle driving data set comprises a mapping relationship between different label information and different vehicle driving data, and the first driving data is used to represent vehicle driving data matching the category.

[0128] The prediction unit 406 is configured to input the first driving data into a target order prediction model for prediction to obtain order information of the first driving data, wherein the order information at least comprises a value attribute of the first driving data to be displayed to the purchase object.

[0129] The third determining unit 408 is configured to determine ownership information of the first driving data in response to a current state of the order information being a state with a transaction behavior performed, wherein the ownership information at least comprises a profit attribute of the first driving data for a supply object, and the supply object is used to provide different vehicle driving data to the vehicle driving data set.

[0130] Optionally, the second determining unit 404 can comprise: a calculation module configured to perform similarity calculation on the current label information and different label information respectively from the vehicle driving data set to obtain initial label similarities between the current label information and the different label information; a first determination module configured to determine a target label similarity from a plurality of initial label similarities, wherein the target label similarity is greater than initial label similarities other than the target label similarity in the plurality of initial label similarities; and a second determination module configured to determine the first driving data matching the current label information based on the target label similarity.

[0131] Optionally, the second determination module can comprise: a first determination submodule configured to determine label information corresponding to the target label similarity as a target label information; and a second determination submodule configured to determine, as the first driving data, vehicle driving data satisfying the mapping relationship with the target label information.

[0132] Optionally, the third determining unit 408 can comprise: a verification module configured to verify identification information of the purchase object in response to the current state being the state with the transaction behavior performed, wherein the identification information is used to identify the purchase object; an authorization module configured to perform an authorization operation on the purchase object to enable the purchase object to access the first driving data in response to the identification information being verified; and a third determination module configured to determine the ownership information in the process of authorizing the purchase object.

[0133] Optionally, the third determining module can comprise: an acquisition submodule, configured to acquire, in the process of authorizing the purchase object, a supply degree of the at least one supply object for the first driving data; a third determining submodule, configured to determine proportion information corresponding to the supply degree, wherein the proportion information is used to represent a profit proportion of the at least one supply object for the first driving data; and a division submodule, configured to divide the order information according to the proportion information to obtain the ownership information.

[0134] Optionally, the data right confirmation apparatus 400 of the vehicle can further comprise: an acquisition unit, configured to acquire second driving data in response to the result output by the data model of the purchase object not satisfying a preset result, wherein the acquisition time of the second driving data is later than the acquisition time of the first driving data; a retraining unit, configured to retrain the target order prediction model by using the second driving data; and an updating unit, configured to update the target order prediction model to the retrained target order prediction model.

[0135] Optionally, the data right confirmation apparatus 400 of the vehicle can further comprise: an extraction unit, configured to extract demand data from demand information of the purchase object, wherein the demand information comprises the demand data; and the first determining unit can comprise: a fourth determining module, configured to perform label analysis on the demand data to obtain current label information.

[0136] Optionally, the data right confirmation apparatus 400 of the vehicle can further comprise: an encryption unit, configured to encrypt the first driving data after authorizing the purchase object; and a transmission unit, configured to transmit the encrypted first driving data to a terminal or a cloud of the purchase object, so as to construct a data model of the purchase object.

[0137] Optionally, the data right confirmation apparatus 400 of the vehicle can further comprise: an acquisition unit, configured to acquire initial vehicle driving data; and a processing unit, configured to perform standardization processing on the initial vehicle driving data to obtain the vehicle driving data.

[0138] In the embodiment, in the data right device of the vehicle, the following units are arranged: a first determination unit configured to determine current label information of a purchase object about demand data, wherein the demand data is used to represent vehicle driving data associated with an application scenario, and the current label information is used to represent a category to which the vehicle driving data belongs; a second determination unit configured to determine first driving data matching the current label information from a vehicle driving data set, wherein the vehicle driving data set includes a mapping relationship between different label information and different vehicle driving data, and the first driving data is used to represent vehicle driving data matching the category; a prediction unit configured to input the first driving data into a target order prediction model for prediction to obtain order information of the first driving data, wherein the order information at least includes a value attribute of the first driving data to be displayed to the purchase object; and a third determination unit configured to determine ownership information of the first driving data in response to a current state of the order information being a state with an execution transaction behavior, wherein the ownership information at least includes a profit attribute of the first driving data for a supply object, and the supply object is used to provide different vehicle driving data to the vehicle driving data set, so that the value of the data of the vehicle can be fully released, thereby solving the technical problem of low utilization rate of the data of the vehicle, and achieving the technical effect of improving the utilization rate of the data of the vehicle.

[0139] The embodiment of the present application further provides a vehicle, comprising: a memory storing an executable program; and a processor configured to execute the program, wherein the program is executed to perform the method in the various embodiments of the present application.

[0140] The embodiment of the present application further provides a computer readable storage medium, which comprises a stored executable program, wherein the executable program is executed to control a device where the computer readable storage medium is located to perform the method in the various embodiments of the present application.

[0141] The embodiment of the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the method in the various embodiments of the present application.

[0142] The embodiment of the present application further provides a computer program product, which comprises a non-volatile computer readable storage medium, and the non-volatile computer readable storage medium is used to store a computer program, and the computer program is executed by a processor to implement the method in the various embodiments of the present application.

[0143] The embodiment of the present application further provides a computer program, and the computer program is executed by a processor to implement the method in the various embodiments of the present application.

[0144] In the above-described embodiments of the present application, the description of each embodiment focuses on different aspects, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0145] In several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. Among them, the above-mentioned device embodiments are only schematic, for example, the division of the units can be a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or modules shown or discussed can be indirect coupling or communication connection through some interface, unit or module, and can be electrical or other forms.

[0146] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0147] In addition, each functional unit in each embodiment of the present application can be integrated in a processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0148] The integrated unit, if realized in the form of software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that makes a contribution or the whole or part of the technical solutions can be embodied in the form of software product, which is stored in a storage medium and includes a plurality of instructions for making a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: U disk, read-only memory (ROM, for short), random access memory (RAM, for short), mobile hard disk, magnetic disk or optical disk and various program code storage media.

[0149] The above merely describes the preferred embodiments of the present application, and it should be pointed out that, for those skilled in the art, some improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A vehicle data ownership confirmation method, characterized in that: include: Determining current tag information of the purchase object regarding demand data, wherein the demand data is used to represent vehicle driving data associated with an application scenario, and the current tag information is used to represent a category to which the vehicle driving data belongs; Determining, from a vehicle driving data set, first driving data that matches the current label information, wherein the vehicle driving data set includes a mapping relationship between different label information and different vehicle driving data, and the first driving data is used to represent the vehicle driving data that matches the category; Inputting the first driving data into a target order prediction model for prediction, thereby obtaining order information for the first driving data, wherein the order information at least includes a value attribute of the first driving data displayed to the purchase object; In response to the current status of the order information being a status of executing a transaction behavior, the ownership information of the first driving data is determined, wherein the ownership information at least includes the profit attribute of the supply object for the first driving data, and the supply object is used to provide different vehicle driving data to the vehicle driving data set.

2. The method according to claim 1, characterized in that Determining first driving data matching the current tag information from the vehicle driving data set includes: From the vehicle travel data set, performing similarity calculations on the current label information and different label information to obtain initial label similarities between the current label information and the different label information; Determining a target tag similarity from the multiple initial tag similarities, wherein the target tag similarity is greater than the initial tag similarities other than the target tag similarity among the multiple initial tag similarities; Based on the target tag similarity, the first driving data matching the current tag information is determined.

3. The method according to claim 2, characterized in that Determining the first driving data matching the current tag information based on the target tag similarity includes: Determine the tag information corresponding to the target tag similarity as the target tag information; The vehicle driving data that satisfies the mapping relationship with the target tag information is determined as the first driving data.

4. The method according to claim 1, wherein In response to the current state of the order information being a state in which a transaction behavior is executed, determining ownership information of the first driving data includes: In response to the current state being the state of executing a transaction, verifying identification information of the purchase object, wherein the identification information is used to identify the purchase object; In response to the identification information being verified, performing an authorization operation on the purchase object to enable the purchase object to access the first driving data; In the process of authorizing the purchase object, the ownership information is determined.

5. The method according to claim 4, characterized in that In the process of authorizing the purchase object, determining the ownership information includes: In the process of authorizing the purchase object, obtaining a supply level of at least one of the supply objects with respect to the first driving data; determining ratio information corresponding to the supply degree, wherein the ratio information is used to represent a profit ratio of at least one of the supply objects with respect to the first driving data; The order information is divided according to the ratio information to obtain the ownership information.

6. The method according to claim 1, characterized in that The method further comprises: In response to a result output by the data model of the purchase object not satisfying a preset result, collecting second driving data, wherein the second driving data is collected later than the first driving data; retraining the target order prediction model using the second driving data; The target order prediction model is updated to the retrained target order prediction model.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: extracting the demand data from the demand information of the purchase object, wherein the demand information includes the demand data; Determining current tag information of the purchase object regarding demand data includes: performing tag parsing on the demand data to obtain the current tag information.

8. A processor, characterized in that: The processor is used to run a program, wherein the program, when run by the processor, executes the vehicle data confirmation method according to any one of claims 1 to 7.

9. A vehicle, characterized in that: include: a memory storing an executable program; A processor for running the program, wherein the program, when running, executes the vehicle data confirmation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein, when the executable program is running, the device where the storage medium is located is controlled to execute the vehicle data confirmation method according to any one of claims 1 to 7.

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