Information processing method and device, electronic equipment and storage medium

Abstract

The invention provides an information processing method. The information processing method comprises the following steps: acquiring position information of a plurality of traffic objects; the position information at least comprises coordinate information in a first dimension and coordinate information in a second dimension; based on the coordinate information in the first dimension, dividing the plurality of traffic objects to obtain at least two first traffic object sets; on the basis of the coordinate information in the first dimension and the coordinate information in the second dimension, synchronously obtaining traffic objects of which the position information similarity is greater than a first threshold value in each first traffic object set in the at least two first traffic object sets, and obtaining one or more groups of candidate repeated traffic objects; and performing de-duplication processing based on the one or more groups of candidate repeated traffic objects to obtain an updated traffic object. The invention further provides an information processing device, electronic equipment and a storage medium.

Description

Technical Field

[0001] The present application relates to the field of intelligent connected vehicles and vehicle networking data processing technology, and in particular to an information processing method, device, electronic device and storage medium. Background Art

[0002] With the development of intelligent connected vehicles, more onboard unit (OBU) data and roadside multiple-access edge computing (MEC) perception data need to be connected to the intelligent connected vehicle platform. When the vehicle reporting OBU data is also perceived and reported by the roadside MEC, the same vehicle will be reported twice at the same time. Therefore, in the case where the same vehicle is reported twice at the same time, it is necessary to deduplicate the data received by the intelligent connected vehicle platform to obtain the fused data.

[0003] However, when traditional fusion algorithms perform deduplication processing on data, they need to perform one-to-one matching calculations on sets of data with a size of n. As the scale of data input increases, the running time for deduplication processing on large volumes of data increases, the execution efficiency is low, and the time is long, resulting in the problem that the fused data cannot meet the real-time requirements of the data. Summary of the invention

[0004] The present application provides an information processing method, device, electronic device and storage medium.

[0005] The technical solution of this application is implemented as follows:

[0006] The present application provides an information processing method, comprising:

[0007] Acquire location information of a plurality of traffic objects; the location information at least includes coordinate information in a first dimension and coordinate information in a second dimension;

[0008] Based on the coordinate information in the first dimension, the plurality of traffic objects are divided into at least two first traffic object sets;

[0009] Based on the coordinate information on the first dimension and the coordinate information on the second dimension, synchronously obtain traffic objects in each of the at least two first traffic object sets whose position information similarity is greater than a first threshold, to obtain one or more groups of candidate duplicate traffic objects;

[0010] Deduplication processing is performed based on the one or more groups of candidate duplicate traffic objects to obtain updated traffic objects.

[0011] The present application provides an information processing device, comprising:

[0012] An acquisition unit, configured to acquire location information of a plurality of traffic objects; the location information at least includes coordinate information on a first dimension and coordinate information on a second dimension;

[0013] a dividing unit, configured to divide the plurality of traffic objects into at least two first traffic object sets based on the coordinate information in the first dimension;

[0014] a determining unit, configured to synchronously acquire, based on the coordinate information on the first dimension and the coordinate information on the second dimension, traffic objects in each of the at least two first traffic object sets whose position information similarity is greater than a first threshold, to obtain one or more groups of candidate duplicate traffic objects;

[0015] The updating unit is used to perform deduplication processing based on the one or more groups of candidate duplicate traffic objects to obtain updated traffic objects.

[0016] The present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor to execute the information processing method provided above.

[0017] The present application provides a computer-readable storage medium on which a computer program is stored, and the computer program enables a computer to execute the information processing method provided above.

[0018] In the technical solution provided by the present application, the location information of multiple traffic objects is obtained, wherein the location information includes at least coordinate information on a first dimension and coordinate information on a second dimension. Based on the coordinate information on the first dimension, the multiple traffic objects are divided to obtain at least two first traffic object sets. Based on the coordinate information on the first dimension and the coordinate information on the second dimension, traffic objects whose location information similarity is greater than a first threshold value in each first traffic object set are synchronously obtained to obtain one or more groups of candidate repeated traffic objects. Deduplication is performed based on the one or more groups of candidate repeated traffic objects to obtain updated traffic objects. In this way, by dividing a large volume of traffic objects into multiple sets containing a small number of traffic objects based on the coordinate information on the first dimension in the location information, the search range is shortened, and the calculation of two traffic objects that are far apart is avoided. In addition, by synchronously searching for possible repeated traffic objects in each set, the time for searching for candidate repeated traffic objects is greatly shortened, and the time for data fusion is further shortened, the execution efficiency is improved, and the real-time nature of the data is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 An information processing method provided in the embodiment of the present application Figure 1 ;

[0020] Figure 2 An information processing method provided in the embodiment of the present application Figure 2 ;

[0021] Figure 3 An information processing method provided in the embodiment of the present application Figure 3 ;

[0022] Figure 4 A schematic diagram of dividing a first traffic object set provided in an embodiment of the present application;

[0023] Figure 5 An information processing method provided in the embodiment of the present application Figure 4 ;

[0024] Figure 6 An information processing method provided in the embodiment of the present application Figure 5 ;

[0025] Figure 7 An information processing method provided in the embodiment of the present application Figure 6 ;

[0026] Figure 8 An information processing method provided in the embodiment of the present application Figure 7 ;

[0027] Fig. 9 A schematic diagram of an OBU vehicle-mounted unit data module provided in an embodiment of the present application;

[0028] Fig.10 A schematic diagram of a MEC multi-access edge computing data module provided in an embodiment of the present application;

[0029] Fig.11 A schematic diagram of a connection structure provided in an embodiment of the present application;

[0030] Fig.12 A schematic diagram of algorithm comparison provided in an embodiment of the present application;

[0031] Fig.13 A schematic diagram of the structure of an information processing device provided in an embodiment of the present application;

[0032] Fig.14 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following will describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0034] To facilitate understanding of the technical solutions of the embodiments of the present application, the relevant technologies of the embodiments of the present application are described below. The following related technologies can be arbitrarily combined with the technical solutions of the embodiments of the present application as optional solutions, and they all belong to the protection scope of the embodiments of the present application.

[0035] In addition, in the embodiments of the present application, "first", "second", etc. are used to distinguish similar objects and are not necessarily used for a specific order or sequence.

[0036] Data fusion in the traditional transportation field focuses on solving data fusion from a single vehicle perspective or data deduplication between different sensors in the same scene. The volume of single scene data that needs to be solved by this type of fusion is not too large. For example, the volume of traffic participant data around the vehicle body that can be perceived by vehicle sensors is usually in the single digit, and the volume of traffic participant data perceived by intersection cameras is usually no more than three digits.

[0037] With the emergence of intelligent connected vehicles, intelligent connected vehicle platforms have been widely used in the field of intelligent vehicles. Intelligent connected vehicles refer to the organic combination of vehicle networks and smart cars. They are equipped with advanced on-board sensors, controllers, actuators and other devices, and integrate modern communication and network technologies to achieve intelligent information exchange and sharing between vehicles and people, roads, backgrounds, etc., and realize wireless communication and information exchange between vehicles, roads, pedestrians, business platforms, etc. through on-board information terminals.

[0038] With the development of intelligent connected vehicles, more on-board OBU data and roadside MEC perception data need to be connected to the intelligent connected vehicle platform. When the vehicle reporting OBU data is also perceived and reported by the roadside MEC, the same vehicle will be reported twice at the same time. In the case where the same vehicle is reported twice at the same time, the intelligent connected vehicle platform needs to process the data to obtain fused data. However, when the traditional brute force algorithm fuses the data, it is necessary to match the set of data with a volume of n one by one. The time complexity of the calculation can be expressed as O(n2). As the scale of data input increases, the running time increases exponentially, resulting in low execution efficiency and long time consumption. In actual projects, it cannot meet the real-time requirements.

[0039] Based on the above-mentioned related issues, an embodiment of the present application provides an information processing method for obtaining location information of multiple traffic objects, wherein the location information includes at least coordinate information on a first dimension and coordinate information on a second dimension, and according to the coordinate information on the first dimension, the multiple traffic objects are divided into at least two first traffic object sets, and according to the coordinate information on the first dimension and the coordinate information on the second dimension, the traffic objects whose location information similarity is greater than a first threshold value in each first traffic object set are synchronously obtained to obtain one or more groups of candidate repeated traffic objects, and deduplication processing is performed according to the one or more groups of candidate repeated traffic objects to obtain updated traffic objects. In this way, by dividing a large volume of traffic objects into multiple sets containing a small number of traffic objects according to the coordinate information on the first dimension in the location information, the search range is shortened, and the calculation of two traffic objects that are far apart is avoided at the same time, and by synchronously searching for possible repeated traffic objects in each set, the time for searching candidate repeated traffic objects is greatly shortened, and the time for data fusion is further shortened, the execution efficiency is improved, and the real-time nature of the data is ensured.

[0040] The intelligent connected vehicle platform information processing method provided in this application can realize any intelligent vehicle function, including but not limited to car navigation, vehicle-to-vehicle communication, vehicle-to-human communication, and the devices that can be used include but are not limited to cars, trucks, large vehicles, ships and other means of transportation with networking functions, and this application does not impose any restrictions.

[0041] In the embodiments of the present application, Figure 1 The information processing method provided in the embodiment of the present application Figure 1 ,like Figure 1 As shown, the information processing includes:

[0042] Step S110: Acquire location information of a plurality of traffic objects; the location information at least includes coordinate information in a first dimension and coordinate information in a second dimension.

[0043] In an embodiment of the present application, the position information of multiple traffic objects is collected through a position information collection device, wherein the position information collection device not only collects the position information of the traffic objects, but also can collect other attribute information of the traffic objects, such as size information, category and other information of the traffic objects. Therefore, in addition to the position information of the traffic objects, other attribute information is also included. The position information collection device can include an OBU on the vehicle and an MEC on the roadside. The OBU and MEC can be used to collect the position information of the traffic objects at the same time. The embodiment of the present application does not limit the number of collection devices.

[0044] In the embodiment of the present application, when at least two position information acquisition devices are used to collect the position information of traffic objects in real time, it is necessary to perform time synchronization processing on the position information acquisition devices to ensure the time synchronization of the collected information. For example, when the position acquisition devices are OBU and MEC, first, calibrate the time of all acquisition devices, intelligent vehicle network connection platform and other used equipment to China standard time, which is 8 hours earlier than Greenwich Mean Time (GMT). The OBU time is based on the time reported by each vehicle-mounted unit, and the MEC time is based on the time detected by the sensing device. The nearest neighbor data frame of OBU data and MEC data is found by finding the adjacent timestamp method. However, if the two timestamps differ greatly, a large synchronization error will eventually be obtained. In this case, data fusion is not considered. When the reporting frequencies of OBU and MEC are inconsistent, the interpolation and extrapolation method is used, mainly using the time tags of the two acquisition devices to calculate the time difference, and the data of each target in the frame is calculated. The position of each target in the new frame is calculated, and a new frame is established between the original two frames according to the time frame to obtain time-synchronized position information, and subsequent information processing is performed based on the time-synchronized position information. Of course, time synchronization processing can also be performed in other ways, such as time synchronization of each sensor. The embodiment of the present application does not specifically limit the method of time synchronization.

[0045] In the embodiment of the present application, traffic objects refer to traffic participants, where traffic participants include vehicles and non-vehicles, vehicles include motor vehicles and non-motor vehicles, and non-vehicles include humans or other living organisms.

[0046] In the embodiments of the present application, location information refers to coordinate information that can characterize the location of a vehicle. Generally, the X-axis, Y-axis, and Z-axis are used in a plane coordinate system to represent the location of an object. The Global Positioning System (GPS) is an altitude radio navigation positioning system based on artificial satellites. Longitude, latitude, and altitude are used to represent the location of an object. The embodiments of the present application do not impose specific restrictions on the method of representing location information.

[0047] In an embodiment of the present application, the coordinate information on the first dimension can be the coordinate value on the X-axis, the coordinate axis of the Y-axis, the coordinate on the longitude, or the coordinate on the dimension. The coordinate information on the second dimension can be the coordinate value on the X-axis, the coordinate axis of the Y-axis, the coordinate on the longitude, or the coordinate on the dimension. It should be understood that the first dimension and the second dimension represent different dimensions.

[0048] In the embodiment of the present application, in order to accurately describe the position of the traffic object, the acquired position information of the traffic object can be converted into longitude and latitude in the WGS84 coordinate system. It should be understood that it is generally difficult to obtain the altitude of the location of the traffic object. Therefore, the longitude and latitude are mostly used to represent the position of the traffic object. Of course, this is only one of the implementation methods in the embodiment of the present application. If the altitude is added to the position of the traffic object, the information processing method provided in the present application is still applicable, and the embodiment of the present application does not limit this.

[0049] Step S120: Based on the coordinate information in the first dimension, the multiple traffic objects are divided into at least two first traffic object sets.

[0050] In the embodiment of the present application, the first traffic set includes one or more traffic objects, and the difference between the largest coordinate information on the first dimension and the smallest coordinate information on the first dimension in each traffic object set is less than a preset threshold value, which can be understood as the difference between the coordinate information on the first dimension of any two traffic objects is less than the preset threshold value. The preset threshold value can be set according to an empirical value, and the embodiment of the present application does not specifically limit the preset threshold value.

[0051] In the embodiment of the present application, a plurality of traffic objects are divided into at least two first traffic object sets according to the coordinate information in the first dimension.

[0052] Step S130: Based on the coordinate information on the first dimension and the coordinate information on the second dimension, synchronously obtain traffic objects in each of the at least two first traffic object sets whose position information similarity is greater than a first threshold, to obtain one or more groups of candidate duplicate traffic objects.

[0053] In the embodiment of the present application, synchronization refers to performing the same processing on each first traffic object set at the same time. It should be understood that when processing the first traffic object set, synchronous processing saves a lot of time compared to sequential processing.

[0054] In the embodiment of the present application, the similarity is generally expressed by the distance between two traffic objects, wherein the greater the distance between the two traffic objects, the smaller the similarity between the two traffic objects, and the closer the distance between the two traffic objects, the greater the similarity between the two traffic objects.

[0055] In the embodiment of the present application, the first threshold value may be a value obtained through multiple tests, or may be a value randomly set by a staff member. The embodiment of the present application does not impose any restrictions on the first threshold value.

[0056] In the embodiments of the present application, methods for calculating distance include Euclidean distance, cosine distance, Hamming distance, etc. The present application does not impose any restrictions on the methods for calculating distance.

[0057] Exemplarily, the location information of traffic object A is (a1, b1), where a1 is the coordinate information of traffic object A in the first dimension, b1 is the coordinate information of traffic object A in the second dimension, and the location information of traffic object B is (a2, b2), where a2 is the coordinate information of traffic object B in the first dimension, and b2 is the coordinate information of traffic object B in the second dimension. The similarity is calculated using the Euclidean distance as follows: Among them, the smaller d is, the greater the similarity is.

[0058] In an embodiment of the present application, candidate repeated traffic objects refer to traffic objects that may be repeated, and each group of candidate repeated traffic objects includes at least two traffic objects. At least two traffic objects in each group of candidate repeated traffic objects may be repeated traffic objects, or may not be repeated traffic objects. For example, when the similarity is greater than a first threshold, it means that the probability that the two traffic objects are traffic objects is relatively high, and they are candidate repeated traffic objects. When the similarity is less than the first threshold, it means that the two traffic objects are not repeated traffic objects. It should be understood that there may be one or more groups of possibly repeated traffic objects in a large volume of data, and there are two or more traffic objects in each group of candidate repeated traffic objects.

[0059] Step S140: performing deduplication processing based on the one or more groups of candidate duplicate traffic objects to obtain updated traffic objects.

[0060] In the embodiment of the present application, the updated traffic object refers to the traffic object after duplicate traffic objects are removed from multiple traffic objects, and there are no duplicate traffic objects among the remaining traffic objects after the update.

[0061] In an embodiment of the present application, location information of multiple traffic objects is obtained, wherein the location information includes at least coordinate information on a first dimension and coordinate information on a second dimension. Based on the coordinate information on the first dimension, the multiple traffic objects are divided to obtain at least two first traffic object sets. Based on the coordinate information on the first dimension and the coordinate information on the second dimension, traffic objects with location information similarity greater than a first threshold value in each first traffic object set are synchronously obtained to obtain one or more groups of candidate duplicate traffic objects. Deduplication is performed based on the one or more groups of candidate duplicate traffic objects to obtain updated traffic objects. In this way, by dividing a large volume of traffic objects into multiple sets containing a small number of traffic objects based on the coordinate information on the first dimension in the location information, the search range is shortened, and calculation of two traffic objects that are far apart is avoided. In addition, by synchronously searching for possible duplicate traffic objects in each set, the time for searching for candidate duplicate traffic objects is greatly shortened, and the time for data fusion is further shortened, the execution efficiency is improved, and the real-time nature of the data is ensured.

[0062] In the embodiments of the present application, Figure 2 The information processing method provided in the embodiment of the present application Figure 2 ,like Figure 2 As shown, step S140, performing deduplication processing based on the one or more groups of candidate duplicate traffic objects to obtain updated traffic objects, includes:

[0063] Step S141: determining repeated traffic objects in the one or more groups of candidate repeated traffic objects based on the first category and / or the second category; the first category is the category of a collection device for collecting position information of the candidate repeated traffic objects, and the second category is the category of the candidate repeated traffic objects; the repeated traffic objects include at least two repeated traffic objects;

[0064] In the embodiment of the present application, the first category refers to the category of the device for collecting candidate repeated traffic object location information, which can be understood as different sources of traffic objects, such as OBU and MEC, and may also include other location collection devices, which is not limited by the embodiment of the present application.

[0065] In an embodiment of the present application, the second category refers to the category of candidate repeated traffic objects, and the category of candidate repeated traffic objects includes transportation tools and non-transportation tools. Transportation tools include motor vehicles and non-motor vehicles, such as cars, motorcycles, electric bicycles and bicycles. Non-transportation tools include organisms with life characteristics, such as humans and animals.

[0066] In the embodiment of the present application, the repeated traffic object refers to a certain repeated traffic object determined among at least two traffic objects among the candidate repeated traffic objects, and the repeated traffic object includes at least two repeated traffic objects.

[0067] Step S142: retain any one of the repeated traffic objects in the multiple traffic objects to obtain the updated traffic object.

[0068] It should be understood that repeated traffic objects include two or more repeated traffic objects. After the repeated traffic objects are determined among the candidate repeated traffic objects, any one of the repeated traffic objects is retained among the multiple traffic objects, and the other traffic objects among the repeated traffic objects are deleted. The remaining non-duplicate traffic objects are updated traffic objects.

[0069] In an embodiment of the present application, after determining one or more groups of candidate repeated traffic objects, a certain number of repeated traffic objects in one or more groups of candidate repeated traffic objects are determined according to the category of the device for collecting the position information of the candidate repeated traffic objects and / or the category of the candidate repeated traffic objects, and any one of the repeated traffic objects is retained among the multiple traffic objects, and the remaining non-repeated traffic objects are updated traffic objects, and the updated traffic objects are displayed on the large screen of the intelligent networked vehicle platform. In this way, the repeated traffic objects are searched for in the candidate repeated traffic objects according to the first category and the second category, the search scope is narrowed, a lot of time is saved, and the accuracy of finding repeated traffic objects is improved due to the increase of further search in the candidate repeated traffic objects, and the accuracy of the updated traffic objects is further improved.

[0070] In the embodiment of the present application, the determining of the repeated traffic objects in the one or more groups of candidate repeated traffic objects based on the first category and / or the second category includes:

[0071] If the first category and the second category of each candidate duplicate traffic object in each group of candidate duplicate traffic objects are the same, then each group of candidate duplicate traffic objects is determined as a duplicate traffic object;

[0072] If the first categories of each candidate duplicate traffic object in each group of candidate duplicate traffic objects are different, and the second categories are all vehicles, then each group of candidate duplicate traffic objects is determined as a duplicate traffic object;

[0073] If the first category of each candidate repeated traffic object in each group of candidate repeated traffic objects is the same, and the second category includes vehicles and non-vehicles, then each group of candidate repeated traffic objects is determined as a non-repeated traffic object;

[0074] If the first categories of each candidate duplicate traffic object in each group of candidate duplicate traffic objects are different, and the second categories are all non-transportation tools, then each group of candidate duplicate traffic objects is determined as a non-duplicate traffic object.

[0075] In an embodiment of the present application, logical judgment can be performed on candidate repeated traffic objects according to the first category and / or the second category to determine traffic objects in one or more groups of candidate repeated traffic objects. Specifically, it includes: if the first category of each candidate repeated traffic object in each group of candidate repeated traffic objects is the same and the second category is the same, then each group of candidate repeated traffic objects is determined as a repeated traffic object. For example, taking each group of candidate repeated traffic objects as an example, if the two candidate repeated traffic objects are both from OBU, and the OBU collects the location information of the vehicle, then they are determined as repeated traffic objects, and one is retained after merging and removing duplicates; or if the two candidate repeated traffic objects are both from MEC, and the two candidate repeated traffic objects are both motor vehicles or non-motor vehicles or people, then they are determined as repeated traffic objects, and one is retained after merging and removing duplicates. If the first category of each candidate repeated traffic object in each group of candidate repeated traffic objects is different, and the second category is all transportation tools, then each group of candidate repeated traffic objects is determined as a repeated traffic object. For example, if the two candidate repeated traffic objects are from OBU and MEC respectively, and the traffic object category is motor vehicle or non-motor vehicle, then they are determined as repeated traffic objects, and one is retained after merging and removing duplicates. If the first category of each candidate duplicate traffic object in each group of candidate duplicate traffic objects is the same, and the second category includes vehicles and non-vehicles, then each group of candidate duplicate traffic objects is determined as a non-duplicate traffic object. For example, if two candidate duplicate traffic objects come from MEC respectively, and the categories of traffic objects are both non-motor vehicles or motor vehicles and people, then they are determined as non-duplicate traffic objects and both are retained. If the first category of each candidate duplicate traffic object in each group of candidate duplicate traffic objects is different, and the second category is both non-vehicles, then each group of candidate duplicate traffic objects is determined as a non-duplicate traffic object. For example, if two candidate duplicate traffic objects come from OBU and MEC respectively, and the categories of traffic objects are both people, then they are determined as non-duplicate traffic objects and both are retained.

[0076] In an embodiment of the present application, duplicate traffic objects are determined among candidate duplicate traffic objects according to the first category and / or the second category, and duplicate traffic objects among multiple traffic objects are accurately determined through the first category and / or the second category. Since further searches are performed among candidate duplicate traffic objects, the accuracy of searching for duplicate traffic objects is improved, and the accuracy of updated traffic objects is further improved.

[0077] In the embodiments of the present application, Figure 3 The information processing method provided in the embodiment of the present application Figure 3 ,like Figure 3 As shown, step S120, dividing the plurality of traffic objects into at least two first traffic object sets based on the coordinate information in the first dimension, includes:

[0078] Step S121: Arrange the plurality of traffic objects based on the coordinate information in the first dimension to obtain an arrangement order of the plurality of traffic objects.

[0079] Step S122: Based on the arrangement order, the multiple traffic objects are divided to obtain the at least two first traffic object sets.

[0080] In the embodiment of the present application, arrangement refers to sorting multiple traffic objects according to a certain arrangement rule, and the arrangement rule may include sorting in the order of the coordinate information on the first dimension from small to large, or in the order of the coordinate information on the first dimension from large to small, or in the order of gradually increasing and then gradually decreasing the coordinate information on the second dimension. The embodiment of the present application does not make specific restrictions on this.

[0081] In an embodiment of the present application, after sorting multiple traffic objects according to a certain arrangement rule, an arrangement order of the multiple traffic objects is obtained, and the multiple traffic objects are divided according to the arrangement order to obtain at least two first traffic object sets.

[0082] For example, Figure 4 As shown, Figure 4 A schematic diagram of dividing a first traffic object set provided in an embodiment of the present application, wherein the x-axis represents latitude, the y-axis represents longitude, and the location information of multiple traffic objects is sorted into Where N is the total number of traffic objects reported by OBU and MEC. Here, we assume that N is 10. Figure 4 The 10 five-pointed stars in the figure represent 10 traffic objects. The numbers 1-10 are based on the longitude value of the y-axis. Lon represents the longitude value. Figure 4 In the y-axis, lat represents the latitude value. Figure 4 In the x-axis, alt represents the height value, and the longitude value is the dimension pair set O i Here, the latitude and longitude are mainly used, and the height value alt can be ignored. The coordinate information of the first dimension of the 10 traffic objects is sorted in descending order to obtain the arrangement order of the 10 traffic objects, such as Figure 4 The longitudes of the 10 five-pointed stars in the image decrease from top to bottom, and the 10 traffic objects are divided. The division method includes dividing from the middle of the 10 traffic objects to obtain two first sets S1 and S2, corresponding to Figure 4 The traffic objects in the S1 and S2 areas are expressed as formula (1):

[0083]

[0084] If N is an even number, then If N is an odd number, then x is the lat value in the latitude coordinate set O, y is the lon value in the longitude coordinate set O, and N is the total amount of all data. Of course, this is just one of the division methods provided in the embodiment of the present application. It can also be divided at 1 / 3 and 2 / 3 after multiple traffic objects are sorted to obtain three first traffic object sets. The embodiment of the present application does not limit the division method.

[0085] In the embodiment of the present application, the sorting and division can be performed based on the longitude value as the dimension, or the sorting and division can be performed based on the latitude value as the dimension, and the embodiment of the present application does not limit this.

[0086] In an embodiment of the present application, multiple traffic objects are sorted according to a certain rule based on the coordinate information on the first dimension, and the multiple traffic objects are divided based on the sorted arrangement order to obtain at least two first traffic object sets. In this way, traffic objects with similar coordinate information on the first dimension among the multiple traffic objects are divided into one set through sorting, which narrows the search range, reduces the amount of calculation for two traffic objects that are far apart, greatly saves time, improves the efficiency of information processing, and saves computing resources.

[0087] In the embodiments of the present application, Figure 5 The information processing method provided in the embodiment of the present application Figure 4 ,like Figure 5 As shown, step S122, dividing the plurality of traffic objects based on the arrangement order to obtain the at least two first traffic object sets, includes:

[0088] Step S1221: Based on the arrangement order, the multiple traffic objects are divided to obtain at least two second traffic object sets.

[0089] Step S1222: forming a third traffic object set by combining some traffic objects in each of two second traffic object sets that are adjacent in arrangement order, to obtain at least one third traffic object set; wherein the distance between the coordinates of some traffic objects in the first second traffic object set and some traffic objects in the second second traffic object set of the two adjacent second traffic object sets in the first dimension is less than a second threshold; and the first traffic object set includes the at least two second traffic object sets and the at least one third traffic object set.

[0090] In the embodiment of the present application, the second traffic set is a set obtained by dividing a plurality of traffic objects according to an arrangement order, and the second traffic object set includes one or more traffic objects.

[0091] In an embodiment of the present application, the division method can be, after sorting, dividing at 1 / 2 of the multiple traffic objects to obtain two second traffic object sets, or dividing at 1 / 3 and 2 / 3 of the multiple traffic objects to obtain three second traffic object sets, or dividing at 1 / 4, 2 / 4 and 3 / 4 of the multiple traffic objects to obtain four second traffic object sets. The embodiment of the present application does not impose any restrictions on this.

[0092] In an embodiment of the present application, the third traffic object set is composed of some traffic objects in each of two second traffic object sets that are adjacent in arrangement order. Here, the number of the third traffic object sets is determined based on the number of the second traffic object sets. If the number of the second traffic object sets is 2, the number of the third traffic object sets is 1; if the number of the second traffic object sets is 3, the number of the third traffic object sets is 2. The embodiment of the present application does not limit the number of the third traffic object sets.

[0093] In an embodiment of the present application, the distance between the coordinates of some traffic objects in the first second traffic object set and some traffic objects in the second second traffic object set in two adjacent second traffic object sets on the first dimension is less than a second threshold value. It should be understood that when multiple traffic objects are divided, two adjacent traffic objects will be divided into two different second traffic object sets respectively, and these two adjacent traffic objects may be repeated traffic objects. Therefore, in order to reduce errors, it is necessary to extract some traffic objects from each second traffic object set in the two adjacent second traffic object sets to form a third traffic object set. Among them, the coordinate information of some traffic objects in each second traffic object set is arranged sequentially on the first dimension. The embodiment of the present application does not limit the number of extracted partial traffic objects.

[0094] For example, Figure 4 As shown, Figure 4 The traffic objects within the S3 area are the third traffic set in this application, and the third traffic object set includes the fifth traffic object and the sixth traffic object, which can be expressed as formula (2):

[0095]

[0096] Wherein, i is the range of the traffic object in the third traffic object set among the multiple traffic objects, and [0.5N, 0.6N] represents the position of the third traffic object set in the third traffic object set at 0.5-0.6 of the sorted multiple traffic objects. Of course, this is only one implementation method in the embodiment of the present application, and there is no restriction on the value range of the third traffic object set.

[0097] In an embodiment of the present application, the first traffic object set includes at least two second traffic object sets and at least one third traffic object set. After obtaining the second traffic object set and the third traffic object set, the first traffic object set is obtained. The third traffic object set is formed by extracting some traffic objects in each of two adjacent second traffic object sets. This avoids the error caused by dividing two adjacent critical values ​​that may be duplicate traffic objects into two different second traffic object sets when dividing the second traffic object set, thereby further improving the accuracy of deduplication.

[0098] In the embodiments of the present application, Figure 6 The information processing method provided in the embodiment of the present application Figure 5 ,like Figure 6 As shown, step S130, based on the coordinate information on the first dimension and the coordinate information on the second dimension, synchronously acquiring traffic objects in each of the at least two first traffic object sets whose position information similarity is greater than a first threshold, to obtain one or more groups of candidate repeated traffic objects, includes:

[0099] Step S131a: Based on the coordinate information on the first dimension and the coordinate information on the second dimension, synchronously calculate the similarity between the position information of every two traffic objects in each first traffic object set.

[0100] Step S132a: Determine the two traffic objects corresponding to the similarity greater than the first threshold as a group of candidate repeated traffic objects, to obtain the one or more groups of candidate repeated traffic objects.

[0101] In the embodiment of the present application, the similarity between the positions represents the possibility that the two traffic objects are duplicate traffic objects. The greater the similarity, the greater the possibility that the two traffic objects are duplicate traffic objects. The smaller the similarity, the smaller the possibility that the two traffic objects are duplicates.

[0102] In the embodiment of the present application, the method of calculating the similarity is described in detail above, and the similarity will not be described again here.

[0103] In the embodiment of the present application, based on the coordinate information on the first dimension and the coordinate information on the second dimension, the similarity between the position information of each two traffic objects in each first traffic object set is synchronously calculated, and the two traffic objects corresponding to the similarity greater than the first threshold are determined as a group of candidate repeated traffic objects, and multiple groups of candidate repeated traffic objects are obtained. In this way, the candidate repeated traffic objects are quickly determined based on the similarity, and the time performance guarantee of the fusion algorithm processing of the positions of a large number of traffic objects after connecting to the intelligent connected vehicle platform is achieved.

[0104] In the embodiments of the present application, Figure 7 The information processing method provided in the embodiment of the present application Figure 6 ,like Figure 7 As shown, step S130, based on the coordinate information on the first dimension and the coordinate information on the second dimension, synchronously acquiring traffic objects in each of the at least two first traffic object sets whose position information similarity is greater than a first threshold, to obtain one or more groups of candidate repeated traffic objects, includes:

[0105] Step S131b: sorting the multiple traffic objects in each first traffic object set based on the coordinate information in the second dimension to obtain a sorted first traffic object set.

[0106] Step S132b: Based on the coordinate information on the first dimension and the coordinate information on the second dimension, synchronously calculate the similarity between the position information of the i-th and i+1-th, i+2-th, ... i+n-th traffic objects in each sorted first traffic object set; if the similarity between the position information of the i-th traffic object and the i+1-th traffic object is less than the first threshold, calculate the similarity between the position information of the i+1-th traffic object and the i+2-th traffic object; if the similarity between the position information of the i+1-th traffic object and the i+2-th traffic object is less than the first threshold, calculate the similarity between the position information of the i+2-th traffic object and the i+3-th traffic object, until the similarity between the position information of the i+n-1-th traffic object and the i+n-th traffic object is calculated; wherein i is an integer greater than or equal to 1.

[0107] Step S133b: Determine the two traffic objects corresponding to the similarity greater than the first threshold as a group of candidate repeated traffic objects, and obtain multiple groups of candidate repeated traffic objects.

[0108] In an embodiment of the present application, sorting refers to sorting multiple traffic objects in each first set according to certain rules. The certain rules may include sorting multiple traffic objects in the order of coordinate information on the second dimension from small to large, or may include sorting multiple traffic objects in the order of coordinate information on the second dimension from large to small, or may be arranged in the order of gradually increasing and then gradually decreasing coordinate information on the second dimension. The embodiment of the present application does not limit the arrangement order.

[0109] In an embodiment of the present application, after sorting multiple traffic objects in each first traffic object set according to the coordinate information on the second dimension, the similarity between the position information of the i-th and i+1-th, i+2-th, ... i+n-th traffic objects in each first traffic set is synchronously calculated according to the coordinate information on the first dimension and the coordinate information on the second dimension. If the similarity between the position information of the i-th traffic object and the i+1-th traffic object is less than the first threshold, the similarity between the position information of the i+1-th traffic object and the i+2-th traffic object is calculated. If the similarity between the position information of the i+1-th traffic object and the i+2-th traffic object is less than the first threshold, the similarity between the position information of the i+2-th traffic object and the i+3-th traffic object is calculated, until the similarity between the position information of the i+n-1-th traffic object and the i+n-th traffic object is calculated; wherein i is an integer greater than or equal to 1, and the two traffic objects corresponding to the similarity greater than the first threshold are both determined as a group of candidate repeated traffic objects, to obtain multiple groups of candidate repeated traffic objects.

[0110] Exemplarily, multiple traffic objects in each first traffic object set are sorted in ascending order. For example, the first traffic object set includes 4 traffic objects, each traffic object is represented by h, and the sorted first traffic object set includes: P = {h1, h2, h3, h4}, and the Euclidean distance d between the position information of h1 and h2 is calculated. 12 , if d 12 is less than the first threshold, then the Euclidean distance d between the position information of h1 and h3 is calculated 13 , if d 13 is greater than the first threshold, then the Euclidean distance d between the position information of h2 and h3 is calculated 23 , if d 23 is greater than the first threshold, then the Euclidean distance d between the position information of h1 and h4 is calculated 14 , two traffic objects corresponding to the Euclidean distance greater than the first threshold are determined as candidate repeated traffic objects, and all candidate repeated traffic objects in the first set are stored in the set R. Of course, this is only one of the implementation methods in the embodiment of the present application and is not a limitation to this.

[0111] In an embodiment of the present application, multiple traffic objects in each first traffic object set are first sorted, and then candidate repeated traffic objects are determined based on the sorted first traffic object set. In this way, after determining that the similarity between the position information of the ith traffic object and the i+1th traffic object is less than a first threshold, it can be directly determined that the similarity between the position information of the ith traffic object and the i+2th traffic object and traffic objects after the i+2th is less than the first threshold, thereby reducing the amount of calculation, saving a lot of time, and improving the efficiency of information processing, thereby meeting the real-time requirements for traffic objects.

[0112] The information processing method provided in the embodiment of the present application is described in detail below in conjunction with specific applications.

[0113] The information processing method in this application scenario is mainly used in the intelligent connected vehicle platform. The intelligent connected vehicle platform performs information processing and matches and removes duplicates of the location information reported by MEC and the location information reported by OBU based on the fusion algorithm; the OBU on-board unit data and the MEC multi-access edge computing data are respectively connected to the vehicle-road-cloud integrated fusion control system to output the data results after the fusion algorithm; the data of the duplicate point set is logically processed, and the data source and attributes of each two points are used to determine whether they are duplicate points and remove duplicates. The problem of fusion and deduplication of the vehicle location information reported by OBU and the traffic participant information reported by MEC in large-volume data is solved, and the algorithm duration and performance of large-volume data deduplication in the field of intelligent connected vehicles is mainly improved. Under the premise of ensuring the integrity of the data fusion function, the processing time of data deduplication is optimized, the unnecessary calculation amount is optimized, and the time complexity of the algorithm is reduced to the O(n) level, which has an excellent improvement in the processing performance of the fusion algorithm for large-volume data. It should be noted that in this application scenario, the location information of the traffic object in this application may be the data of the traffic object in this application scenario, the information processing method in this application may be the fusion algorithm in this application scenario, and the intelligent connected vehicle platform is the vehicle-road-cloud integrated fusion control system in this application scenario. Figure 8 An information processing method provided in the embodiment of the present application Figure 7 ,like Figure 8 As shown, the specific implementation steps of the information processing method are as follows:

[0114] S801: The intelligent connected vehicle platform accesses the real-time reporting data of OBU and MEC.

[0115] It should be understood that OBU and MEC are location information collection devices in this application. After OBU and MEC collect, they will report to the intelligent connected vehicle platform. Fig. 9 As shown, Fig. 9A schematic diagram of an OBU vehicle-mounted unit data module provided in an embodiment of the present application, wherein the OBU vehicle-mounted unit data module 901 obtains vehicle information, wherein the vehicle information reported by the OBU includes: vehicle location information, license plate information, and size information (body length, body width, body height), such as Fig.10 As shown, Fig.10 A schematic diagram of a MEC multi-access edge computing data module provided for an embodiment of the present application, a MEC multi-access edge computing data module 1010, a camera 1011 and a lidar 1012 deployed on the roadside report perceived structured data, and the reported traffic participant information includes traffic participant categories (people, motor vehicles, non-motor vehicles) and size information (length, width, height of the participants), etc.

[0116] S802: The intelligent connected vehicle platform synchronizes the time of OBU access data and MEC access data.

[0117] It should be understood that when determining whether a traffic object is a duplicate traffic object, a comparison is made based on the location information collected at the same time. Therefore, in order to ensure that the location information collected by each location information collection device is synchronized and facilitate subsequent information processing, it is necessary to time synchronize the OBU access data and the MEC access data.

[0118] The time synchronization processing method in this application scenario includes: calibrating the time of all devices and software platforms to China Standard Time, which is 8 hours earlier than Greenwich Mean Time (GMT); the OBU data time is based on the time reported by each on-board unit, and the MEC data time is based on the time detected by the sensing device; the nearest neighbor data frames of OBU data and MEC data are found by finding adjacent timestamps, but if the two timestamps differ greatly, a large synchronization error will eventually be obtained, and data fusion is not considered in this case; when the reporting frequencies of OBU data and MEC data are inconsistent, the interpolation and extrapolation method is used, mainly using the time tags of the two access sources to calculate the time difference, and the data of each target in the frame is extrapolated to deduce the position of each target in the new frame, and a new frame is established between the original two frames to obtain time-synchronized position information.

[0119] S803: The intelligent connected vehicle platform matches and removes duplicates of the location information reported by MEC and the location information reported by OBU based on a fusion algorithm.

[0120] It should be understood that the fusion algorithm here can be the information processing method in this application, including determining the possible repeated traffic objects among multiple traffic objects, namely the candidate repeated traffic objects in this application, and the specific implementation method includes: arranging the location information of all traffic objects into a set Where N is the total number of traffic participants and vehicles reported by OBU and MEC; lon represents the longitude value, lat represents the latitude value, and alt represents the altitude value. i Perform a sorting algorithm, ignore the altitude value alt, generate three new sets S1, S2, S3, determine the possible duplicate traffic objects in S1, S2, S3 respectively, first perform a sorting algorithm according to the latitude value, and generate a set Where M is the total amount of data in the set. The Euclidean distance between each point in the set P and the adjacent points is calculated by traversing. The longitude and latitude are in the WGS84 coordinate system. A distance threshold is set. This threshold is used to filter the parameters of two traffic participants in the distance dimension. When the distance between two traffic participants is less than the threshold, it is determined that the two data may be the detection data of the same traffic participant from different devices. This value can be set and adjusted manually. Calculate the distance between point i and adjacent points i+1, i+2, i+3... When the calculated distance between point i+n and point i on the x-axis is greater than the threshold, jump to the next point i+1 and calculate its distance to i+2, i+3, i+4...

[0121] Perform the above operation on S1, S2, and S3, and save all two points whose distance is less than the distance threshold in the set R. R is a set that stores all possible duplicate points.

[0122] S804: The intelligent connected vehicle platform outputs the data results after the fusion algorithm.

[0123] It should be understood that the data result after the fusion algorithm here refers to the set R that stores all possible duplicate points. Fig.11 As shown, Fig.11 A connection structure schematic diagram is provided for an embodiment of the present application, in which the data of traffic objects collected by the OBU on-board unit data module 1110 and the MEC multi-access edge computing data module 1120 are reported to the intelligent connected vehicle platform 1130, and the intelligent connected vehicle platform performs time synchronization processing on the collected OBU data and MEC data, and performs a fusion algorithm on the data after time synchronization processing. The fusion algorithm includes sorting the traffic objects based on the latitude value as the dimension, dividing the multiple traffic objects into three sets according to the sorted order, and determining the possible repeated traffic objects in the three sets respectively, to obtain all possible repeated sets in the end.

[0124] S805: The intelligent connected vehicle platform performs logical processing on the data in set R, determines whether two points are duplicate points based on the data source and attributes, and removes duplicates.

[0125] It should be understood that the data of set R are logically processed, and the specific implementation method includes: if both points are OBU data, merge the data to remove duplicates and retain one. If both points are MEC data, and the attributes of the traffic participants are motor vehicles or non-motor vehicles or people, merge the data to remove duplicates and retain one. If both points are MEC data, and the attributes of the traffic participants are: non-motor vehicles and motor vehicles, merge the data to remove duplicates and retain one. If the two points are: OBU data and MEC data, and the attributes of the traffic participants are motor vehicles or non-motor vehicles, merge the data to remove duplicates. If both points are MEC data, and the attributes of the traffic participants are: non-motor vehicles or motor vehicles and people, retain the data of both. If the two points are: OBU data and MEC data, and the attributes of the traffic participants are people, retain the data of both.

[0126] S806: The intelligent connected vehicle platform will display the updated data on the large screen of the intelligent connected vehicle platform.

[0127] It should be understood that the updated data is the remaining duplicate traffic objects after removing them. After the data is fused, the updated data is obtained and displayed on the large screen of the intelligent connected vehicle platform.

[0128] The fusion algorithm in this application scenario and the traditional fusion algorithm are tested and analyzed. Fig.12 A schematic diagram of algorithm comparison provided in an embodiment of the present application is shown in FIG. Fig.12 As shown in Figure 1, it is a time complexity comparison chart of the traditional brute force algorithm and the fusion algorithm in this application scenario. The horizontal axis is the number of traffic participants, and the vertical axis is the number of fusion processing times. A is the fusion algorithm in this application scenario, and B is the traditional fusion algorithm. It can be seen that the time complexity of the fusion algorithm in this application scenario for data fusion is compared with the time complexity of the traditional fusion algorithm, and the fusion algorithm processing performance of large-volume data has been significantly improved. Moreover, as shown in Table 1, under the same number of traffic parameters, the processing time of the fusion algorithm in this application scenario is much shorter than that of the traditional fusion algorithm. Therefore, after the fusion algorithm in this application scenario is applied to the access of large-volume traffic participant data to the intelligent connected vehicle platform, the time performance of the fusion algorithm processing is greatly improved compared with the traditional algorithm.

[0129]

[0130] Table 1

[0131] The fusion algorithm in this application scenario includes: the intelligent networked vehicle platform accesses the real-time reported data of OBU and MEC; time synchronization of OBU access data and MEC access data; matching and deduplication of MEC reported location information and OBU reported location information based on the fusion algorithm; OBU on-board unit data and MEC multi-access edge computing data are respectively connected to the vehicle-road-cloud integrated fusion control system, and the data results after the fusion algorithm are output; logical processing of the data of the set of duplicate points, judging whether it is a duplicate point and deduplication according to the data source and attributes of each two points; updating data to the intelligent networked vehicle platform. The algorithm processing time performance problem of large-volume data deduplication is solved, and the processing time of data deduplication and unnecessary calculation amount are optimized while ensuring the integrity of the data fusion function.

[0132] Based on the total number of traffic participants and vehicles reported by OBU and MEC, longitude, latitude, and altitude, all location information is sorted into the first set; the first set is sorted by the longitude value to generate three sets, of which the closest points are the third set; the three sets are deduplicated by the deduplication algorithm, and all two points that meet the distance threshold less than the distance threshold, that is, all possible duplicate points, are saved. This method is suitable for improving the deduplication performance of large-volume data in the field of intelligent connected vehicles, and can also be extracted separately and standardized in the processing of other similar large-volume real-time data.

[0133] The present application also provides an information processing device 1300, Fig.13 A schematic diagram of the structure of an information processing device provided in an embodiment of the present application is shown in FIG. Fig.13 As shown, the information processing device 1300 includes:

[0134] An acquisition unit 1301 is used to acquire location information of a plurality of traffic objects; the location information at least includes coordinate information in a first dimension and coordinate information in a second dimension;

[0135] A division unit 1302 is used to divide the multiple traffic objects into at least two first traffic object sets based on the coordinate information in the first dimension;

[0136] A determining unit 1303 is configured to synchronously acquire, based on the coordinate information on the first dimension and the coordinate information on the second dimension, traffic objects in each of the at least two first traffic object sets whose position information similarity is greater than a first threshold, to obtain one or more groups of candidate duplicate traffic objects;

[0137] The updating unit 1304 is configured to perform deduplication processing based on the one or more groups of candidate duplicate traffic objects to obtain updated traffic objects.

[0138] In some embodiments of the present application, the updating unit 1304 is further used to determine the repeated traffic objects in the one or more groups of candidate repeated traffic objects based on the first category and / or the second category; the first category is the category of the collection device that collects the position information of the candidate repeated traffic objects, and the second category is the category of the candidate repeated traffic objects; the repeated traffic objects include at least two repeated traffic objects; and any one of the repeated traffic objects is retained among the multiple traffic objects to obtain the updated traffic object.

[0139] In some embodiments of the present application, the updating unit 1304 is further used to determine each group of candidate duplicate traffic objects as duplicate traffic objects if the first category of each candidate duplicate traffic object in each group of candidate duplicate traffic objects is the same and / or the second category is the same; if the first category of each candidate duplicate traffic object in each group of candidate duplicate traffic objects is different and the second category is all vehicles, then each group of candidate duplicate traffic objects is determined as duplicate traffic objects; if the first category of each candidate duplicate traffic object in each group of candidate duplicate traffic objects is different and the second category is all non-vehicles, then each group of candidate duplicate traffic objects is determined as non-duplicate traffic objects; if the first category of each candidate duplicate traffic object in each group of candidate duplicate traffic objects is different and the second category includes vehicles and non-vehicles, then each group of candidate duplicate traffic objects is determined as non-duplicate traffic objects.

[0140] In some embodiments of the present application, the division unit 1302 is further used to arrange the multiple traffic objects based on the coordinate information on the first dimension to obtain an arrangement order of the multiple traffic objects; and divide the multiple traffic objects based on the arrangement order to obtain the at least two first traffic object sets.

[0141] In some embodiments of the present application, the division unit 1302 is further used to divide the multiple traffic objects based on the arrangement order to obtain at least two second traffic object sets; to form a third traffic object set with some traffic objects in each of two second traffic object sets that are adjacent in arrangement order, to obtain at least one third traffic object set; wherein the distance between the coordinates of some traffic objects in the first second traffic object set and some traffic objects in the second second traffic object set in the two adjacent second traffic object sets in the first dimension is less than a second threshold; and the first traffic object set includes the at least two second traffic object sets and the at least one third traffic object set.

[0142] In some embodiments of the present application, the determination unit 1303 is also used to synchronously calculate the similarity between the position information of every two traffic objects in each first traffic object set based on the coordinate information on the first dimension and the coordinate information on the second dimension; and determine the two traffic objects corresponding to the similarity greater than the first threshold as a group of candidate repeated traffic objects, to obtain multiple groups of candidate repeated traffic objects.

[0143] In some embodiments of the present application, the determination unit 1303 is further used to synchronously sort the multiple traffic objects in each of the first traffic object sets based on the coordinate information on the second dimension to obtain a sorted first traffic object set; synchronously calculate the similarity between the position information of the i-th and i+1-th, i+2-th, ... i+n-th traffic objects in each sorted first traffic object set based on the coordinate information on the first dimension and the coordinate information on the second dimension, if the similarity between the position information of the i-th traffic object and the i+1-th traffic object is less than the first threshold, then calculate the similarity between the position information of the i+1-th traffic object and the i+2-th traffic object. The similarity between the position information of the i+1th traffic object and the i+2th traffic object is calculated. If the similarity between the position information of the i+1th traffic object and the i+2th traffic object is less than the first threshold, the similarity between the position information of the i+2th traffic object and the i+3th traffic object is calculated, until the similarity between the position information of the i+n-1th traffic object and the i+nth traffic object is calculated; wherein i is an integer greater than or equal to 1; the two traffic objects corresponding to the similarity greater than the first threshold are both determined as a group of candidate repeated traffic objects, and multiple groups of candidate repeated traffic objects are obtained.

[0144] Fig.14 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Fig.14 As shown, in actual application, the various components in the electronic device are coupled together through the bus system 1402. It can be understood that the bus system 1402 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 1402 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Fig.14 The various buses are collectively labeled bus system 1402 .

[0145] It can be understood that the memory in this embodiment can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memories described in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0146] The method disclosed in the above embodiment of the present application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, a DSP, or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0147] The present application also provides a computer storage medium, specifically a computer-readable storage medium, on which computer instructions are stored. As a first implementation, when the computer storage medium is located in an electronic device, the computer instructions are executed by a processor to implement any step of the above-mentioned information processing method in the present application.

[0148] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0149] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0150] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately configured as a unit, or at least two units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0151] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, disks or optical disks.

[0152] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0153] It should be noted that the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0154] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An information processing method, characterized in that: The method comprises: Acquire location information of a plurality of traffic objects; the location information at least includes coordinate information in a first dimension and coordinate information in a second dimension; Based on the coordinate information in the first dimension, the plurality of traffic objects are divided into at least two first traffic object sets; Based on the coordinate information on the first dimension and the coordinate information on the second dimension, synchronously obtain traffic objects in each of the at least two first traffic object sets whose position information similarity is greater than a first threshold, to obtain one or more groups of candidate duplicate traffic objects; Deduplication processing is performed based on the one or more groups of candidate duplicate traffic objects to obtain updated traffic objects.

2. The processing method according to claim 1, characterized in that: The performing deduplication processing based on the one or more groups of candidate duplicate traffic objects to obtain updated traffic objects includes: Based on the first category and / or the second category, determining the repeated traffic objects in the one or more groups of candidate repeated traffic objects; the first category is the category of the collecting device for collecting the position information of the candidate repeated traffic objects, and the second category is the category of the candidate repeated traffic objects; the repeated traffic objects include at least two repeated traffic objects; Any one of the repeated traffic objects is retained in the multiple traffic objects to obtain the updated traffic object.

3. The processing method according to claim 2, characterized in that: The determining of the repeated traffic objects in the one or more groups of candidate repeated traffic objects based on the first category and / or the second category includes: If the first category and the second category of each candidate duplicate traffic object in each group of candidate duplicate traffic objects are the same, then each group of candidate duplicate traffic objects is determined as a duplicate traffic object; If the first categories of each candidate duplicate traffic object in each group of candidate duplicate traffic objects are different, and the second categories are all vehicles, then each group of candidate duplicate traffic objects is determined as a duplicate traffic object; If the first category of each candidate repeated traffic object in each group of candidate repeated traffic objects is the same, and the second category includes vehicles and non-vehicles, then each group of candidate repeated traffic objects is determined as a non-repeated traffic object; If the first categories of each candidate repeated traffic object in each group of candidate repeated traffic objects are different, and the second categories are all non-transportation tools, then each group of candidate repeated traffic objects is determined as a non-repeated traffic object.

4. The processing method according to any one of claims 1 to 3, characterized in that: The dividing the plurality of traffic objects into at least two first traffic object sets based on the coordinate information in the first dimension includes: Arranging the plurality of traffic objects based on the coordinate information in the first dimension to obtain an arrangement order of the plurality of traffic objects; Based on the arrangement order, the multiple traffic objects are divided to obtain the at least two first traffic object sets.

5. The processing method according to claim 4, characterized in that: The dividing the plurality of traffic objects based on the arrangement order to obtain the at least two first traffic object sets includes: Based on the arrangement order, the plurality of traffic objects are divided to obtain at least two second traffic object sets; Part of the traffic objects in each of two second traffic object sets that are adjacent in arrangement order are combined into a third traffic object set to obtain at least one third traffic object set; wherein the distance between the coordinates of the part of the traffic objects in the first second traffic object set and the part of the traffic objects in the second second traffic object set in the two adjacent second traffic object sets in the first dimension is less than a second threshold; and the first traffic object set includes the at least two second traffic object sets and the at least one third traffic object set.

6. The processing method according to any one of claims 1 to 3, characterized in that: The method of synchronously acquiring, based on the coordinate information on the first dimension and the coordinate information on the second dimension, traffic objects in each of the at least two first traffic object sets whose position information similarity is greater than a first threshold, to obtain one or more groups of candidate repeated traffic objects comprises: Based on the coordinate information on the first dimension and the coordinate information on the second dimension, synchronously calculating the similarity between the position information of every two traffic objects in each first traffic object set; The two traffic objects corresponding to the similarity being greater than the first threshold are both determined as a group of candidate repeated traffic objects, to obtain the one or more groups of candidate repeated traffic objects.

7. The processing method according to any one of claims 1 to 3, characterized in that: The method of synchronously acquiring, based on the coordinate information on the first dimension and the coordinate information on the second dimension, traffic objects in each of the at least two first traffic object sets whose position information similarity is greater than a first threshold, to obtain one or more groups of candidate repeated traffic objects comprises: Based on the coordinate information in the second dimension, synchronously sorting the multiple traffic objects in each of the first traffic object sets to obtain a sorted first traffic object set; Based on the coordinate information on the first dimension and the coordinate information on the second dimension, the similarity between the position information of the i-th traffic object and the i+1-th, i+2-th, ..., i+n-th traffic object in each sorted first traffic object set is synchronously calculated; if the similarity between the position information of the i-th traffic object and the i+1-th traffic object is less than the first threshold, the similarity between the position information of the i+1-th traffic object and the i+2-th traffic object is calculated; if the similarity between the position information of the i+1-th traffic object and the i+2-th traffic object is less than the first threshold, the similarity between the position information of the i+2-th traffic object and the i+3-th traffic object is calculated, until the similarity between the position information of the i+n-1-th traffic object and the i+n-th traffic object is calculated; wherein i and n are both integers greater than or equal to 1; The two traffic objects corresponding to the similarity being greater than the first threshold are both determined as a group of candidate repeated traffic objects, to obtain multiple groups of candidate repeated traffic objects.

8. An information processing device, characterized in that: The device comprises: An acquisition unit, configured to acquire location information of a plurality of traffic objects; the location information at least includes coordinate information on a first dimension and coordinate information on a second dimension; a dividing unit, configured to divide the plurality of traffic objects into at least two first traffic object sets based on the coordinate information in the first dimension; a determining unit, configured to synchronously acquire, based on the coordinate information on the first dimension and the coordinate information on the second dimension, traffic objects in each of the at least two first traffic object sets whose position information similarity is greater than a first threshold, to obtain one or more groups of candidate duplicate traffic objects; The updating unit is used to perform deduplication processing based on the one or more groups of candidate duplicate traffic objects to obtain updated traffic objects.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the information processing method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the information processing method according to any one of claims 1 to 7 is implemented.

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