Method, device and equipment for generating thermodynamic diagram of running vehicle and medium
By aggregating vehicle signal data and combining with road slice data tables, high-precision thermal maps of running vehicles are generated, which solves the problem of low thermal map accuracy in the prior art and achieves a more accurate and flexible thermal map display.
Patent Information
- Application Number
- CN202411742899.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-29
- Publication Date
- 2025-03-25
AI Technical Summary
In the prior art, the accuracy of running vehicles' thermal maps is low and cannot meet the needs of high-precision thermal maps.
By determining the target vehicle that meets the heatmap query conditions, its vehicle signal data is obtained, including the tag of the target road slice associated with the real-time location of the vehicle. Then, the target vehicle is aggregated and combined with the pre-constructed road slice data table, the thermal data for each target road slice is determined, and the thermal data is finally imported into the map application program interface to generate a high-precision thermal map.
The accuracy of the heat map is improved, making it more in line with business needs, and can flexibly display data with a larger time and space range to meet the needs of high-precision heat maps.
Smart Images
Figure CN119691229A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of automobile technology, and in particular to a method, device, equipment and computer-readable storage medium for generating a thermal map of a running vehicle. Background Art
[0002] Vehicle heat maps are widely used in many fields such as traffic management and vehicle monitoring. They intuitively display the distribution and activity of vehicles through the depth of color, providing strong data support for decision-making. Car companies will also judge whether the running routes of some specific models are in line with the initial design concept of the model based on the vehicle heat map, so as to optimize the performance of the model if it does not meet the initial design concept of the model. In related technologies, car companies usually control a certain number of vehicles to run in a preset operating time period, collect and store the driving trajectories of these vehicles; later, the driving trajectories of these vehicles are processed, including determining the grids corresponding to the driving trajectories of these vehicles in the map, and the total number of vehicles corresponding to each grid; finally, thermal data is generated based on the processed results, and the thermal data is imported into the grid map to generate a heat map. However, the construction of heat maps based on grids cannot meet the requirements of high-precision heat maps because the grid resolution is coarse. Summary of the invention
[0003] The present application provides a method, device, equipment and computer-readable storage medium for generating a thermal map of a running vehicle, which can solve the technical problem of low accuracy of the thermal map of a running vehicle existing in the prior art.
[0004] In a first aspect, an embodiment of the present application provides a method for generating a thermal map of a running vehicle, the method comprising:
[0005] Determine the target vehicle that meets the heat map query conditions;
[0006] Acquire vehicle signal data of the target vehicle, wherein the vehicle signal data includes a label of a target road slice associated with a real-time position of the vehicle;
[0007] Determine the label of the target road slice from the vehicle signal data of each target vehicle, and aggregate all target vehicles according to the label of each target road slice to obtain an aggregation result;
[0008] Determine the thermal data of each target road slice by combining the aggregation result and the pre-constructed road slice data table;
[0009] The thermal data of each target road slice is imported into the map application program interface to generate a thermal map of running vehicles on the map.
[0010] In combination with the first aspect, in one implementation, each road slice in the road slice data table corresponds to a label and a coordinate sequence, and combining the aggregation result and the pre-constructed road slice data table to determine the thermal data of each target road slice includes:
[0011] Querying the target coordinate sequence corresponding to the label of each target road slice in the aggregation result from the road slice data table;
[0012] The data consisting of the label of each target road slice in the aggregation result, the target coordinate sequence corresponding to the label of each target road slice in the aggregation result, and the total number of running vehicles on each target road slice in the aggregation result are used as the thermal data of each target road slice.
[0013] In combination with the first aspect, in one implementation, the acquiring the vehicle signal data of the target vehicle includes:
[0014] When the query time range in the heat map query condition is the first time period, the vehicle signal data of the target vehicle in the first time period is obtained from the time series database, wherein the vehicle signal data of several vehicles are stored in the time series database, wherein the vehicle signal data of each vehicle includes a label of a road slice associated with the real-time position of the vehicle, and the first time period is any time period of the date corresponding to the current time.
[0015] In combination with the first aspect, in one implementation, the process of constructing data in the time series database includes:
[0016] Acquire real-time vehicle signal data and perform encoding and encryption to obtain the encoded and encrypted vehicle signal data;
[0017] Parsing and formatting the encoded and encrypted vehicle signal data to obtain formatted vehicle signal data;
[0018] Cleaning the formatted vehicle signal data to obtain cleaned vehicle signal data;
[0019] Matching a target road slice from the road slice data table based on the position information in the cleaned vehicle signal data;
[0020] After adding the label corresponding to the target road slice to the cleaned vehicle signal data, updated vehicle signal data is obtained, and the updated vehicle signal data is stored in a time series database.
[0021] In combination with the first aspect, in one implementation, matching the target road slice from the road slice data table based on the position information in the cleaned vehicle signal data includes:
[0022] Based on the coordinate points corresponding to the position information in the cleaned vehicle signal data, a target coordinate sequence with the shortest distance to the coordinate points is selected from the road slice data table;
[0023] The road slice corresponding to the target coordinate sequence is used as the target road slice.
[0024] In combination with the first aspect, in one implementation, after the step of storing the updated vehicle signal data in a time series database, the method further includes:
[0025] At every preset time interval, the data in the time series database is aggregated using preset scene tags, and the aggregated data is stored in the distributed database.
[0026] In combination with the first aspect, in one implementation, the acquiring the vehicle signal data of the target vehicle includes:
[0027] When the query time range in the heat map query condition is a second time period, vehicle signal data of the target vehicle in the second time period is obtained from the distributed database, and the second time period is any time period of a date not corresponding to the current time.
[0028] In a second aspect, an embodiment of the present application provides a device for generating a thermal map of a running vehicle, the device comprising:
[0029] A first determination module is used to determine a target vehicle that meets the heat map query condition;
[0030] An acquisition module, used to acquire vehicle signal data of the target vehicle, wherein the vehicle signal data includes a label of a target road slice associated with a real-time position of the vehicle;
[0031] An aggregation module is used to determine the label of the target road slice from the vehicle signal data of each target vehicle, and aggregate all target vehicles according to the label of each target road slice to obtain an aggregation result;
[0032] A second determination module is used to determine the thermal data of each target road slice by combining the aggregation result and a pre-constructed road slice data table;
[0033] The import module is used to import the thermal data of each target road slice into the map application program interface to generate a thermal map of running vehicles on the map.
[0034] In a third aspect, an embodiment of the present application provides a device for generating a thermal map of a running vehicle, the device comprising a processor, a memory, and a thermal map generation program for a running vehicle stored in the memory and executable by the processor, wherein when the thermal map generation program for a running vehicle is executed by the processor, the steps of the method for generating a thermal map of a running vehicle as described in any one of the first aspects are implemented.
[0035] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a running vehicle thermal map generation program is stored. When the running vehicle thermal map generation program is executed by a processor, the steps of the running vehicle thermal map generation method as described in any one of the first aspects are implemented.
[0036] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:
[0037] By determining the target vehicle that meets the heat map query conditions; then obtaining the vehicle signal data of the target vehicle, the vehicle signal data including the label of the target road slice associated with the real-time position of the vehicle; further determining the label of the target road slice from the vehicle signal data of each target vehicle, and aggregating all target vehicles according to the label of each target road slice to obtain an aggregation result; then combining the aggregation result and a pre-constructed road slice data table to determine the thermal data of each target road slice; finally, importing the thermal data of each target road slice into the map application program interface for generating a running vehicle heat map on the map. This application associates the real-time position of the vehicle with the label of the road slice and uses road slicing to construct a running vehicle heat map. The display result is more in line with business needs, and data in a larger time and space range can be more flexibly displayed according to business needs, which can improve the accuracy of the heat map. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] Figure 1 A schematic diagram of the process of running the first embodiment of the vehicle thermal map generation method of the present application;
[0039] Figure 2 A schematic diagram of functional modules of an embodiment of a device for generating a thermal map of a running vehicle of the present application;
[0040] Figure 3 This is a schematic diagram of the hardware structure of a vehicle thermal map generating device involved in the embodiment of the present application. DETAILED DESCRIPTION
[0041] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only 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.
[0042] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.
[0043] In a first aspect, an embodiment of the present application provides a method for generating a thermal map of a running vehicle.
[0044] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle thermal map generation method for this application. Figure 1 As shown, the method for generating a thermal map of a running vehicle includes:
[0045] Step 110, determining a target vehicle that meets the heat map query condition;
[0046] In the specific implementation, the car company or major customer enters the vehicle model, vehicle use label (such as freight, express delivery, etc.), time range and other heat map query conditions to be queried in the heat map display module of the Internet of Vehicles platform service end. Then, the heat map display module searches the vehicle data table for the ID of the target vehicle that meets the heat map query conditions according to the vehicle model and vehicle use label. Among them, the heat map display module can adopt the browser / server architecture (BS architecture) or the client / server architecture (CS architecture), and this application does not make specific limitations on this.
[0047] Step 120: Acquire vehicle signal data of the target vehicle, wherein the vehicle signal data includes a label of a target road slice associated with a real-time position of the vehicle;
[0048] The present application pre-slices the roads in the map according to a preset length (e.g., 30 meters) to form road slice data, wherein each road slice corresponds to a label id and a coordinate sequence, the coordinate sequence is the horizontal coordinate and vertical coordinate corresponding to the road slice in the map, and each road slice is represented by a triple list, which includes the road slice id, horizontal coordinate, and vertical coordinate.
[0049] The target road slice is the road slice closest to the real-time position of the vehicle. The vehicle signal data stored in the database includes the vehicle speed, real-time position, driving angle and the tag ID of the target road slice associated with the real-time position of the vehicle.
[0050] Step 130: determine the label of the target road slice from the vehicle signal data of each target vehicle, and aggregate all target vehicles according to the label of each target road slice to obtain an aggregation result;
[0051] Exemplarily, target vehicle 1 corresponds to label a of the target road slice, target vehicle 2 corresponds to label a of the target road slice, target vehicle 3 corresponds to label b of the target road slice, target vehicle 4 corresponds to label c of the target road slice, and target vehicle 5 corresponds to label b of the target road slice.
[0052] First, all target vehicles are aggregated according to the label a of the target road slice, and aggregated into target vehicle 1 and target vehicle 2; then all target vehicles are aggregated according to the label b of the target road slice, and aggregated into target vehicle 3 and target vehicle 5; then all target vehicles are aggregated according to the label c of the target road slice, and aggregated into target vehicle 4, so as to obtain the final aggregation result, that is, the total number of running vehicles corresponding to each target road slice label.
[0053] Step 140: Determine the thermal data of each target road slice by combining the aggregation result and the pre-constructed road slice data table;
[0054] Among them, each road slice in the road slice data table corresponds to a label and a coordinate sequence.
[0055] In specific implementation, the target coordinate sequence corresponding to the label of each target road slice in the aggregation result is queried from the road slice data table; the thermal data of each target road slice is determined by combining the target coordinate sequence corresponding to the label of each target road slice in the aggregation result and the aggregation result.
[0056] Step 150: Import the thermal data of each target road slice into a map application program interface to generate a thermal map of running vehicles on the map.
[0057] During specific implementation, the thermal data of each target road slice is returned to the thermal map display module of the Internet of Vehicles platform server. The thermal map display module of the Internet of Vehicles platform server imports the thermal data of each target road slice into the map application program interface API, thereby generating a thermal map of running vehicles on the map to present the running vehicle conditions.
[0058] In this embodiment, a target vehicle that meets the heat map query conditions is determined; then the vehicle signal data of the target vehicle is obtained, the vehicle signal data including the label of the target road slice associated with the real-time position of the vehicle; the label of the target road slice is further determined from the vehicle signal data of each target vehicle, and all target vehicles are aggregated according to the label of each target road slice to obtain an aggregation result; then the thermal data of each target road slice is determined in combination with the aggregation result and a pre-constructed road slice data table; finally, the thermal data of each target road slice is imported into the map application program interface for generating a running vehicle heat map on the map. This application associates the real-time position of the vehicle with the label of the road slice and uses road slicing to construct a running vehicle heat map. The display result is more in line with business needs, and data in a larger time and space range can be more flexibly displayed according to business needs, which can improve the accuracy of the heat map.
[0059] Furthermore, in one embodiment, each road slice in the road slice data table corresponds to a label and a coordinate sequence, and the combining of the aggregation result and the pre-constructed road slice data table to determine the thermal data of each target road slice includes:
[0060] Step 210: query the target coordinate sequence corresponding to the label of each target road slice in the aggregation result from the road slice data table;
[0061] Step 220: Use the label of each target road slice in the aggregation result, the target coordinate sequence corresponding to the label of each target road slice in the aggregation result, and the total number of running vehicles on each target road slice in the aggregation result as the thermal data of each target road slice.
[0062] It can be understood that each road slice in the road slice data table corresponds to a label and a coordinate sequence, that is, the label can correspond to a coordinate sequence.
[0063] In specific implementation, according to the label slice_id of each target road slice in the aggregation result, the target coordinate sequence coordinates_sequence corresponding to the label slice_id of each target road slice in the aggregation result is queried from the road slice data table; the data consisting of the label slice_id of each target road slice in the aggregation result, the target coordinate sequence coordinates_sequence corresponding to the label of each target road slice in the aggregation result, and the total number of running vehicles total_count on each target road slice in the aggregation result are used as the thermal data heatmap_data of each target road slice, and the implementation code is as follows: heatmap_data = {(slice_id, coordinates_sequence, total_count)}.
[0064] In this embodiment, it is shown how to combine the aggregation results and the pre-constructed road slice data table to determine the thermal data of each target road slice, and the thermal data is generated by road slicing, which can improve the accuracy of generating a thermal map.
[0065] Furthermore, in one embodiment, the acquiring of the vehicle signal data of the target vehicle includes:
[0066] When the query time range in the heat map query condition is the first time period, the vehicle signal data of the target vehicle in the first time period is obtained from the time series database, wherein the vehicle signal data of several vehicles are stored in the time series database, wherein the vehicle signal data of each vehicle includes a label of a road slice associated with the real-time position of the vehicle, and the first time period is any time period of the date corresponding to the current time.
[0067] In specific implementation, when the query time range in the heat map query condition is any time period of the date corresponding to the current time, that is, on the same day, the heat map display module searches the vehicle data table for the id of the target vehicle that matches the heat map query condition according to the vehicle model and vehicle use label in the heat map query condition. Then, the vehicle signal data of the target vehicle in the first time period is obtained from the time series database, and the process returns to step 130, and steps 130 to 150 are executed in sequence, and finally a heat map of running vehicles is generated on the map. Among them, the time series database includes but is not limited to tdengine and IoTDB, and other spatiotemporal database components can also be used, which is not specifically limited in this application.
[0068] In this embodiment, when the query time range in the heat map query condition is the first time period, the dynamic distribution of the vehicle can be reflected in real time by acquiring the vehicle signal data of the target vehicle in the first time period from the time series database.
[0069] Furthermore, in one embodiment, the process of constructing the data in the time series database includes:
[0070] Step 310: Acquire real-time vehicle signal data and perform encoding and encryption to obtain the encoded and encrypted vehicle signal data;
[0071] During the specific implementation, the on-board TBOX terminal collects vehicle signal data such as vehicle speed, real-time position, and driving angle through the CAN bus, encodes and encrypts the collected vehicle signal data, and then transmits the encoded and encrypted vehicle signal data to the Internet of Vehicles platform.
[0072] Step 320: parsing and formatting the encoded and encrypted vehicle signal data to obtain formatted vehicle signal data;
[0073] The Internet of Vehicles platform receives the encoded and encrypted vehicle signal data reported by the on-board TBOX terminal, parses the corresponding transmission protocol, and then formats the transmission protocol uniformly into the target format (such as json format) to obtain the formatted vehicle signal data.
[0074] Step 330: Cleaning the formatted vehicle signal data to obtain cleaned vehicle signal data;
[0075] In specific implementation, the formatted vehicle signal data is cleaned including but not limited to using flink real-time streaming analysis and processing. The cleaning steps are as follows:
[0076] First, determine the abnormal vehicle speed from the formatted vehicle signal data, such as a vehicle speed greater than 130 km / h and less than 0 km / h, and then delete the entire data corresponding to the abnormal vehicle speed; after deleting the entire data corresponding to the abnormal vehicle speed, obtain the vehicle signal data after the initial deletion; then calculate the theoretical driving distance and spatial distance between the real-time positions of the vehicles corresponding to two adjacent data in the vehicle signal data after the initial deletion; wherein, the theoretical driving distance D = v*(t1-t2) is calculated by the interval time (t1-t2) between the two adjacent data and the vehicle speed v, and the spatial distance d = ST_Distance(g1, g2) is calculated by the spatial positions g1 and g2 between the two adjacent data, and when the theoretical driving distance D is greater than the product of the spatial distance d and a preset coefficient (such as: 1.2), delete the second data of the two adjacent data, and finally obtain the cleaned vehicle signal data.
[0077] Step 340: Matching a target road slice from the road slice data table based on the position information in the cleaned vehicle signal data;
[0078] In a specific implementation, based on the position information in the cleaned vehicle signal data, a road slice closest to the position information is matched from the road slice data table, and the road slice closest to the position information is used as the target road slice.
[0079] Step 350: After adding the label corresponding to the target road slice to the cleaned vehicle signal data, updated vehicle signal data is obtained, and the updated vehicle signal data is stored in a time series database.
[0080] After adding the label corresponding to the target road slice to the cleaned vehicle signal data, updated vehicle signal data is obtained, and the updated vehicle signal data is stored in a time series database to associate the real-time position of the vehicle with the label of the road slice.
[0081] In this embodiment, the data construction process in the time series database is described to support efficient time series data query.
[0082] Further, in one embodiment, matching the target road slice from the road slice data table based on the position information in the cleaned vehicle signal data includes:
[0083] Step 410: based on the coordinate points corresponding to the position information in the cleaned vehicle signal data, select a target coordinate sequence with the shortest distance to the coordinate points from the road slice data table;
[0084] Step 420: Use the road slice corresponding to the target coordinate sequence as the target road slice.
[0085] Since each road slice in the road slice data table corresponds to a label and a coordinate sequence, wherein the coordinate sequence is the horizontal coordinate and the vertical coordinate of the road slice in the map.
[0086] In a specific implementation, based on the position information in the cleaned vehicle signal data, that is, the coordinate point corresponding to the real-time position of the vehicle, the coordinate sequence corresponding to each road slice in the road slice data table is traversed, and the Euclidean distance between the coordinate point corresponding to the real-time position of the vehicle and the coordinate sequence corresponding to each road slice is calculated, and the target coordinate sequence with the smallest distance to the coordinate point is selected, and the road slice corresponding to the target coordinate sequence is used as the target road slice, that is, the target road slice represents the road slice closest to the real-time position of the vehicle.
[0087] In this embodiment, it is shown how to match the target road slice from the road slice data table based on the position information in the cleaned vehicle signal data, laying the foundation for associating the label corresponding to the target road slice with the real-time position of the vehicle.
[0088] Furthermore, in one embodiment, after the step of storing the updated vehicle signal data in a time series database, the method further includes:
[0089] At every preset time interval, the data in the time series database is aggregated using preset scene tags, and the aggregated data is stored in the distributed database.
[0090] The distributed database includes but is not limited to the MPP database, the Doris database and the clickHouse database, and other spatiotemporal database components may also be used, which is not specifically limited in this application. The preset scene tags include but are not limited to the vehicle model, the vehicle use tag and the like.
[0091] Exemplarily, taking the preset duration of 1 day as an example, every day all vehicle signal data of the previous day in the time series database are aggregated according to preset scene labels such as vehicle model and vehicle use label, and the aggregated data are synchronously stored in the distributed database.
[0092] In this embodiment, the data in the time series database is aggregated using preset scenario tags at preset time intervals, and the aggregated data is stored in a distributed database, supporting more complex query scenarios with larger time spans.
[0093] Furthermore, in one embodiment, the acquiring of the vehicle signal data of the target vehicle includes:
[0094] When the query time range in the heat map query condition is a second time period, vehicle signal data of the target vehicle in the second time period is obtained from the distributed database, and the second time period is any time period of a date not corresponding to the current time.
[0095] In specific implementation, when the query time range in the heat map query condition is any period of the date corresponding to the non-current time, that is, the time range span is greater than 1 day, the heat map display module searches the vehicle data table for the id of the target vehicle that meets the heat map query condition according to the vehicle model and vehicle use label in the heat map query condition. Then, the vehicle signal data of the target vehicle in the second period is obtained from the distributed database, and the process returns to step 130, and steps 130 to 150 are executed in sequence, and finally a heat map of running vehicles is generated on the map.
[0096] In this embodiment, when the query time range in the heat map query condition is the second time period, the vehicle signal data of the target vehicle in the second time period is obtained from the distributed database. Because the data in the distributed database is obtained by aggregating the data in the time series database according to the preset scene labels, the vehicle signal data of the target vehicle in the second time period is directly obtained from the distributed database, which can improve the efficiency of generating the heat map of the running vehicle.
[0097] In a second aspect, an embodiment of the present application also provides a device for generating a thermal map of a running vehicle.
[0098] In one embodiment, referring to Figure 2 , Figure 2 This is a functional module diagram of an embodiment of a vehicle thermal map generation device for running the present application. Figure 2 As shown, the running vehicle thermal map generating device 200 includes:
[0099] A first determination module 201 is used to determine a target vehicle that meets the heat map query condition;
[0100] An acquisition module 202 is used to acquire vehicle signal data of the target vehicle, wherein the vehicle signal data includes a label of a target road slice associated with a real-time position of the vehicle;
[0101] Aggregation module 203, used to determine the label of the target road slice from the vehicle signal data of each target vehicle, and aggregate all target vehicles according to the label of each target road slice to obtain an aggregation result;
[0102] A second determination module 204 is used to determine the thermal data of each target road slice by combining the aggregation result and a pre-constructed road slice data table;
[0103] The import module 205 is used to import the thermal data of each target road slice into the map application program interface to generate a thermal map of running vehicles on the map.
[0104] Furthermore, in one embodiment, each road slice in the road slice data table corresponds to a label and a coordinate sequence, and the second determination module is specifically used to:
[0105] Querying the target coordinate sequence corresponding to the label of each target road slice in the aggregation result from the road slice data table;
[0106] The data consisting of the label of each target road slice in the aggregation result, the target coordinate sequence corresponding to the label of each target road slice in the aggregation result, and the total number of running vehicles on each target road slice in the aggregation result are used as the thermal data of each target road slice.
[0107] Furthermore, in one embodiment, the acquisition module is specifically used for:
[0108] When the query time range in the heat map query condition is the first time period, the vehicle signal data of the target vehicle in the first time period is obtained from the time series database, wherein the vehicle signal data of several vehicles are stored in the time series database, wherein the vehicle signal data of each vehicle includes a label of a road slice associated with the real-time position of the vehicle, and the first time period is any time period of the date corresponding to the current time.
[0109] Furthermore, in one embodiment, the running vehicle thermal map generating device further includes a construction module, and the construction module is specifically used to construct data in a time series database. The construction process of the data in the time series database includes:
[0110] Acquire real-time vehicle signal data and perform encoding and encryption to obtain the encoded and encrypted vehicle signal data;
[0111] Parsing and formatting the encoded and encrypted vehicle signal data to obtain formatted vehicle signal data;
[0112] Cleaning the formatted vehicle signal data to obtain cleaned vehicle signal data;
[0113] Matching a target road slice from the road slice data table based on the position information in the cleaned vehicle signal data;
[0114] After adding the label corresponding to the target road slice to the cleaned vehicle signal data, updated vehicle signal data is obtained, and the updated vehicle signal data is stored in a time series database.
[0115] Furthermore, in one embodiment, the building block is specifically used for:
[0116] Based on the coordinate points corresponding to the position information in the cleaned vehicle signal data, a target coordinate sequence with the shortest distance to the coordinate points is selected from the road slice data table;
[0117] The road slice corresponding to the target coordinate sequence is used as the target road slice.
[0118] Furthermore, in one embodiment, the building block is also used for:
[0119] At every preset time interval, the data in the time series database is aggregated using preset scene tags, and the aggregated data is stored in the distributed database.
[0120] Furthermore, in one embodiment, the acquisition module is further used for:
[0121] When the query time range in the heat map query condition is a second time period, vehicle signal data of the target vehicle in the second time period is obtained from the distributed database, and the second time period is any time period of a date not corresponding to the current time.
[0122] Among them, the functional implementation of each module in the above-mentioned running vehicle thermal map generation device corresponds to the various steps in the above-mentioned running vehicle thermal map generation method embodiment, and its functions and implementation processes will not be repeated here one by one.
[0123] In a third aspect, an embodiment of the present application provides a device for generating a thermal map of a running vehicle. The device for generating a thermal map of a running vehicle may be a personal computer (PC), a laptop computer, a server, or other device having a data processing function.
[0124] Reference Figure 3 , Figure 3 Schematic diagram of the hardware structure of the running vehicle heat map generation device involved in the embodiment of the present application. In the embodiment of the present application, the running vehicle heat map generation device may include a processor, a memory, a communication interface and a communication bus.
[0125] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.
[0126] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces for interconnecting devices within the vehicle thermal map generation device, and interfaces for interconnecting the vehicle thermal map generation device with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.
[0127] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.
[0128] The processor may be a general-purpose processor, and the general-purpose processor may call the running vehicle thermal map generation program stored in the memory, and execute the running vehicle thermal map generation method provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). Among them, the method executed when the running vehicle thermal map generation program is called can refer to the various embodiments of the running vehicle thermal map generation method of the present application, which will not be repeated here.
[0129] Those skilled in the art will understand that Figure 3 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.
[0130] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.
[0131] The computer-readable storage medium of the present application stores a running vehicle thermal map generation program, wherein when the running vehicle thermal map generation program is executed by a processor, the steps of the running vehicle thermal map generation method as described above are implemented.
[0132] Among them, the method implemented when the vehicle thermal map generation program is executed can refer to the various embodiments of the vehicle thermal map generation method of the present application, and will not be repeated here.
[0133] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.
[0134] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.
[0135] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.
[0136] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.
[0137] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.
[0138] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.
[0139] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.
Claims
1. A method for generating a thermal map of a running vehicle, characterized in that: The method for generating a thermal map of a running vehicle comprises: Determine the target vehicle that meets the heat map query conditions; Acquire vehicle signal data of the target vehicle, wherein the vehicle signal data includes a label of a target road slice associated with a real-time position of the vehicle; Determine the label of the target road slice from the vehicle signal data of each target vehicle, and aggregate all target vehicles according to the label of each target road slice to obtain an aggregation result; Determine the thermal data of each target road slice by combining the aggregation result and the pre-constructed road slice data table; The thermal data of each target road slice is imported into the map application program interface to generate a thermal map of running vehicles on the map.
2. The method for generating a thermal map of a running vehicle according to claim 1, characterized in that: Each road slice in the road slice data table corresponds to a label and a coordinate sequence, and the combining of the aggregation result and the pre-constructed road slice data table to determine the thermal data of each target road slice includes: Querying the target coordinate sequence corresponding to the label of each target road slice in the aggregation result from the road slice data table; The data consisting of the label of each target road slice in the aggregation result, the target coordinate sequence corresponding to the label of each target road slice in the aggregation result, and the total number of running vehicles on each target road slice in the aggregation result are used as the thermal data of each target road slice.
3. The method for generating a thermal map of a running vehicle according to claim 1, characterized in that: The obtaining of the vehicle signal data of the target vehicle comprises: When the query time range in the heat map query condition is the first time period, the vehicle signal data of the target vehicle in the first time period is obtained from the time series database, wherein the vehicle signal data of several vehicles are stored in the time series database, wherein the vehicle signal data of each vehicle includes a label of a road slice associated with the real-time position of the vehicle, and the first time period is any time period of the date corresponding to the current time.
4. The method for generating a thermal map of a running vehicle according to claim 3, characterized in that: The process of constructing data in the time series database includes: Acquire real-time vehicle signal data and perform encoding and encryption to obtain the encoded and encrypted vehicle signal data; Parsing and formatting the encoded and encrypted vehicle signal data to obtain formatted vehicle signal data; Cleaning the formatted vehicle signal data to obtain cleaned vehicle signal data; Matching a target road slice from the road slice data table based on the position information in the cleaned vehicle signal data; After adding the label corresponding to the target road slice to the cleaned vehicle signal data, updated vehicle signal data is obtained, and the updated vehicle signal data is stored in a time series database.
5. The method for generating a thermal map of a running vehicle according to claim 4, characterized in that: The matching of the target road slice from the road slice data table based on the position information in the cleaned vehicle signal data includes: Based on the coordinate points corresponding to the position information in the cleaned vehicle signal data, a target coordinate sequence with the shortest distance to the coordinate points is selected from the road slice data table; The road slice corresponding to the target coordinate sequence is used as the target road slice.
6. The method for generating a thermal map of a running vehicle according to claim 4, characterized in that: After the step of storing the updated vehicle signal data in a time series database, the method further comprises: At every preset time interval, the data in the time series database is aggregated using preset scene tags, and the aggregated data is stored in the distributed database.
7. The method for generating a thermal map of a running vehicle according to claim 6, characterized in that: The obtaining of the vehicle signal data of the target vehicle comprises: When the query time range in the heat map query condition is a second time period, vehicle signal data of the target vehicle in the second time period is obtained from the distributed database, and the second time period is any time period of a date not corresponding to the current time.
8. A device for generating a thermal map of a running vehicle, characterized in that: The running vehicle thermal map generating device comprises: A first determination module is used to determine a target vehicle that meets the heat map query condition; An acquisition module, used to acquire vehicle signal data of the target vehicle, wherein the vehicle signal data includes a label of a target road slice associated with a real-time position of the vehicle; An aggregation module is used to determine the label of the target road slice from the vehicle signal data of each target vehicle, and aggregate all target vehicles according to the label of each target road slice to obtain an aggregation result; A second determination module is used to determine the thermal data of each target road slice by combining the aggregation result and a pre-constructed road slice data table; The import module is used to import the thermal data of each target road slice into the map application program interface to generate a thermal map of running vehicles on the map.
9. A device for generating a thermal map of a running vehicle, characterized in that: The running vehicle thermal map generating device includes a processor, a memory, and a running vehicle thermal map generating program stored in the memory and executable by the processor, wherein when the running vehicle thermal map generating program is executed by the processor, the steps of the running vehicle thermal map generating method as described in any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a running vehicle thermal map generation program, wherein when the running vehicle thermal map generation program is executed by a processor, the steps of the running vehicle thermal map generation method according to any one of claims 1 to 7 are implemented.