1. An updating method, characterized in that the method comprises:

acquiring at least one crowdsourcing road data acquired by at least one information acquisition terminal to a target road;

associating the crowdsourcing road data according to a preset strategy to obtain an association relation between the crowdsourcing road data;

and merging the crowdsourcing road data according to the incidence relation to obtain the updated data of the target road.

2. The updating method according to claim 1, wherein before the associating each of the crowd-sourced road data according to a preset policy to obtain an association relationship between the crowd-sourced road data, the method further comprises:

detecting whether the crowdsourcing road data meets preset requirements or not; and

deleting the crowdsourcing road data which does not meet preset requirements.

3. The updating method according to claim 2, wherein the detecting whether the crowd-sourced road data meets a preset requirement comprises:

and detecting whether the track type corresponding to each crowdsourcing road data is consistent with the track type of the target road.

4. The updating method according to claim 1, wherein the associating the crowdsourced road data according to a preset strategy to obtain an association relationship between the crowdsourced road data comprises:

optimizing each crowdsourcing road data to obtain optimized crowdsourcing road data;

and performing feature extraction and comparison on each optimized crowdsourcing road data, and determining an incidence relation between the crowdsourcing road data based on an obtained feature comparison result.

5. The updating method according to claim 4, wherein the optimizing each crowdsourced road data to obtain each optimized crowdsourced road data includes:

determining target crowdsourcing road data from each of the crowdsourcing road data, wherein the sum of deviations between a trajectory corresponding to the target crowdsourcing road data and trajectories corresponding to other crowdsourcing road data in each of the crowdsourcing road data is equal to or greater than a preset threshold;

optimizing the target crowd-sourced road data based on the other crowd-sourced road data to obtain the optimized target crowd-sourced road data.

6. The updating method according to claim 1, wherein the merging the crowd-sourced road data according to the association relationship comprises:

grouping the crowdsourcing road data according to the incidence relation; the crowdsourcing road data of the same group comprises data corresponding to the same road section;

and carrying out road characteristic alignment on the crowdsourcing road data in each group, and merging the crowdsourcing road data after alignment among the groups.

7. The updating method according to claim 1, further comprising:

and verifying the updated data of the target road according to a preset verification mode.

8. The updating method according to claim 1, further comprising:

and after the verification of the update data of the target road is passed, updating the target road in the map according to the update data of the target road.

9. An updating apparatus, comprising: a processor and a memory storing a computer program which, when executed by the processor, implements the updating method of any one of claims 1 to 8.

10. A computer storage medium, characterized in that a computer program is stored which, when executed by a processor, implements the updating method of any one of claims 1 to 8.

Background

Compared with the traditional map, the high-precision map is an electronic map with higher precision and richer data dimensionality, and can provide powerful support for the landing realization of a high-level automatic (auxiliary) driving system. The traditional high-precision map is mainly generated by adopting a single high-precision acquisition vehicle to acquire road surface information, but the problems of high implementation cost and slow updating period exist. If the road data collected by the common vehicle is directly adopted to generate the map, the problems of large data error and inaccurate information exist. Therefore, how to accurately update roads in a map at low cost and quickly is always under study.

Disclosure of Invention

In view of the above technical problems, the present application provides an updating method, an updating apparatus, and a computer storage medium, which can accurately update roads in a map in a low-cost and fast manner, thereby improving user experience.

In order to solve the above technical problem, the present application provides an updating method, including the following steps:

acquiring at least one crowdsourcing road data acquired by at least one information acquisition terminal to a target road;

associating the crowdsourcing road data according to a preset strategy to obtain an association relation between the crowdsourcing road data;

and merging the crowdsourcing road data according to the incidence relation to obtain the updated data of the target road.

Optionally, before the associating the crowd-sourced road data according to a preset policy and obtaining an association relationship between the crowd-sourced road data, the method further includes:

detecting whether the crowdsourcing road data meets preset requirements or not; and

deleting the crowdsourcing road data which does not meet preset requirements.

Optionally, the crowd-sourced road data includes trajectory data, and the detecting whether each crowd-sourced road data meets a preset requirement includes:

and detecting whether the track type corresponding to the track data of each crowdsourcing road data is consistent with the track type of the target road.

Optionally, the associating the crowd-sourced road data according to a preset policy to obtain an association relationship between the crowd-sourced road data includes:

optimizing each crowdsourcing road data to obtain optimized crowdsourcing road data;

and performing feature extraction and comparison on each optimized crowdsourcing road data, and determining an incidence relation between the crowdsourcing road data based on an obtained feature comparison result.

Optionally, the optimizing each piece of crowd-sourced road data to obtain each optimized crowd-sourced road data includes:

determining target crowdsourcing road data from each of the crowdsourcing road data, wherein the sum of deviations between a trajectory corresponding to the target crowdsourcing road data and trajectories corresponding to other crowdsourcing road data in each of the crowdsourcing road data is equal to or greater than a preset threshold;

optimizing the target crowd-sourced road data based on the other crowd-sourced road data to obtain the optimized target crowd-sourced road data.

Optionally, the merging the crowd-sourced road data according to the association relationship includes:

grouping the crowdsourcing road data according to the incidence relation; wherein the crowd-sourced road data of the same group comprises data of the same road segment;

and carrying out road characteristic alignment on the crowdsourcing road data in each group, and merging the crowdsourcing road data after alignment among the groups.

Optionally, the method further comprises:

and verifying the updated data of the target road according to a preset verification mode.

Optionally, the method further comprises:

and after the verification of the update data of the target road is passed, updating the target road in the map according to the update data of the target road.

Accordingly, the present application provides an updating apparatus for executing the method, including: a processor and a memory storing a computer program, the steps of the above described updating method being implemented when the computer program is run by the processor.

Accordingly, the present application provides a computer storage medium having a computer program stored therein, which when executed by a processor, implements the steps of the above-described updating method.

As described above, the update method, apparatus, and computer storage medium of the present application include: acquiring at least one crowdsourcing road data acquired by at least one information acquisition terminal to a target road; associating the crowdsourcing road data according to a preset strategy to obtain an association relation between the crowdsourcing road data; and merging the crowdsourcing road data according to the incidence relation, and updating the target road based on a merging result. Therefore, roads in the map can be accurately updated in a low-cost and rapid mode, and user experience is improved.

Drawings

Fig. 1 is a schematic flow chart of an updating method according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of an update system according to an embodiment of the present invention;

fig. 3 is a schematic flowchart illustrating an updating method according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of an updating apparatus according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the recitation of an element by the phrase "comprising an … …" does not exclude the presence of additional like elements in the process, method, article, or apparatus that comprises the element, and further, where similarly-named elements, features, or elements in different embodiments of the disclosure may have the same meaning, or may have different meanings, that particular meaning should be determined by their interpretation in the embodiment or further by context with the embodiment.

It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope herein. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context. Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some way.

It should be understood that, although the steps in the flowcharts in the embodiments of the present application are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, in different orders, and may be performed alternately or at least partially with respect to other steps or sub-steps of other steps.

It should be noted that step numbers such as S101 and S102 are used herein for the purpose of more clearly and briefly describing the corresponding contents, and do not constitute a substantial limitation on the sequence, and those skilled in the art may perform S102 first and then S101 in specific implementations, but these steps should be within the scope of the present application.

It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no specific meaning in themselves. Thus, "module", "component" or "unit" may be used mixedly.

Referring to fig. 1, for the update method provided in the embodiment of the present application, the update method may be executed by an update apparatus provided in the embodiment of the present application, the apparatus may be implemented in a software and/or hardware manner, and the apparatus may specifically be a cloud server, a terminal, and the like, in this embodiment, taking an example in which the update method is applied to the cloud server, the update method includes the following steps:

step S101: and acquiring at least one crowdsourcing road data acquired by the at least one information acquisition terminal on the target road.

It should be noted that the at least one crowd-sourced road data may be collected by at least one information collection terminal at the same position or different positions of the target road, for example, collected within the same or different distance ranges in the same lane, or collected within the same distance ranges in different lanes. The information acquisition terminal can be vehicle mounted terminal, also can equipment such as positioner, distance sensor, camera, radar sensor, crowd-sourced road data can include trajectory data and perception data. It can be understood that, because the influence of factors such as human or environment may be received, crowdsourcing road data collected by the information collection terminal may not really contain road characteristic information, and therefore, after the crowdsourcing road data is collected by the information collection terminal, the crowdsourcing road data can be firstly subjected to quality inspection, namely, whether the quality of the crowdsourcing road data meets a preset quality condition or not is detected, if the quality of the crowdsourcing road data meets the preset quality condition, the crowdsourcing road data is reported to the cloud server, and otherwise, the crowdsourcing road data is not reported to the cloud server. For example, if it is detected that the crowd-sourced road data does not include preset features such as lane lines, the crowd-sourced road data may not be reported to the cloud server. Here, the cloud server may acquire at least one crowdsourcing road data acquired by the at least one information acquisition terminal for the target road, and may receive the at least one crowdsourcing road data acquired by the at least one information acquisition terminal for the target road.

Step S102: and associating the crowdsourcing road data according to a preset strategy to obtain an association relation between the crowdsourcing road data.

The associating of the crowdsourced road data according to the preset strategy can be regarded as analyzing the corresponding road section positions of the crowdsourced road data on the target road and the relationship between the crowdsourced road data and the target road, including whether the crowdsourced road data comprises data of the same road section, whether the crowdsourced road data is data of an adjacent road section, and the like.

In an embodiment, the associating the crowdsourced road data according to a preset policy to obtain an association relationship between the crowdsourced road data includes:

optimizing each crowdsourcing road data to obtain optimized crowdsourcing road data;

and performing feature extraction and comparison on each optimized crowdsourcing road data, and determining an incidence relation between the crowdsourcing road data based on an obtained feature comparison result.

It can be understood that, since different crowdsourcing road data are collected by different information collection terminals and are influenced by factors such as equipment performance and environment, tracks corresponding to the crowdsourcing road data may not be smooth, and there may be a deviation between the tracks corresponding to the crowdsourcing road data, and in order to accurately perform feature extraction and comparison on the crowdsourcing road data, optimization processing may be performed on the crowdsourcing road data, for example, a track corresponding to the crowdsourcing road data is optimized to be a smooth track in target crowdsourcing road data whose adjustment track has a deviation. After the optimization processing is performed on each crowdsourced road data, feature extraction may be performed on each optimized crowdsourced road data, for example, features such as a dotted line end point, a stop line, a lane line, a traffic signal lamp, a curvature inflection point and the like are extracted, then a feature comparison operation is performed, that is, each crowdsourced road data is matched based on the extracted features, and an association relationship between each crowdsourced road data is determined based on an obtained feature comparison result. For example, assuming that the position trajectories of two crowdsourced road data are in the same segment range, if the lane line and the stop line included in one crowdsourced road data are completely the same as the lane line and the stop line included in the other crowdsourced road data, it is indicated that the two crowdsourced road data may be data collected for the same road segment; if the lane lines and the stop lines included in one piece of crowd-sourced road data are the same as or different from the lane lines and the stop lines included in the other piece of crowd-sourced road data, it is indicated that the two pieces of crowd-sourced road data may be data collected for adjacent road segments. Therefore, after the optimization processing is carried out on the crowdsourced road data, the incidence relation among the crowdsourced road data is obtained, the accuracy of the obtained incidence relation can be effectively improved, and the operation speed is higher.

In an embodiment, the optimizing each piece of crowd-sourced road data to obtain each optimized crowd-sourced road data includes:

determining target crowdsourcing road data from each of the crowdsourcing road data, wherein the sum of deviations between a trajectory corresponding to the target crowdsourcing road data and trajectories corresponding to other crowdsourcing road data in each of the crowdsourcing road data is equal to or greater than a preset threshold;

optimizing the target crowd-sourced road data based on the other crowd-sourced road data to obtain the optimized target crowd-sourced road data.

It can be understood that, because some information acquisition terminals may have problems such as positioning misalignment, and the like, a trajectory corresponding to the obtained crowdsourcing road data may have a deviation from a trajectory corresponding to the crowdsourcing road data obtained by another information acquisition terminal, for all crowdsourcing road data, a sum of deviations between the trajectory corresponding to each crowdsourcing road data and trajectories corresponding to other crowdsourcing road data may be calculated, so that the crowdsourcing road data with the sum of deviations equal to or greater than a preset threshold is determined as target crowdsourcing road data, and the target crowdsourcing road data is optimized based on the other crowdsourcing road data, so that the deviation between the trajectory corresponding to the target crowdsourcing road data and the trajectory corresponding to the other crowdsourcing road data is within a reasonable range. The preset threshold may be set according to actual requirements, for example, may be set to 5 meters, 10 meters, and the like. For example, assuming that all the crowd-sourced road data includes the crowd-sourced road data A, B, C and D, respectively, if the sum of deviations between the trajectory corresponding to the crowd-sourced road data a and the trajectory corresponding to the crowd-sourced road data B, C, D is greater than 10 meters, the crowd-sourced road data a is determined as the target crowd-sourced road data, and the target crowd-sourced road data a is optimized based on the crowd-sourced road data B, C, D. In this way, by optimizing the crowdsourcing road data with the track deviation, the operation speed of associating each crowdsourcing road data can be increased, and the accuracy of the acquired association relationship can be improved.

In an embodiment, before the associating the crowd-sourced road data according to a preset policy and obtaining an association relationship between the crowd-sourced road data, the method further includes: detecting whether the crowdsourcing road data meets preset requirements or not; and deleting the crowdsourcing road data which does not meet preset requirements. It can be understood that, under the influence of human operation, environment, equipment performance, and the like, the at least one crowdsourced road data collected by the at least one information collection terminal on the target road may not be all available for updating the target road, and the at least one crowdsourced road data needs to be filtered first. The preset requirement may be set according to actual requirements, for example, the track types are consistent, the length of the road segment corresponding to the data is greater than a preset length threshold, and the like. In an embodiment, the detecting whether each of the crowd-sourced road data meets a preset requirement includes: and detecting whether the track type corresponding to each crowdsourcing road data is consistent with the track type of the target road. It can be understood that the track type of the target road may be known according to information such as existing map data, for example, it may be a straight line, a curve, and the like, and the track type corresponding to the crowd-sourced road data may be known according to track data in the crowd-sourced road data, and if the track type corresponding to the crowd-sourced road data is not consistent with the track type of the target road, it indicates that the crowd-sourced road data cannot be used to update the target road, and at this time, the crowd-sourced road data that does not meet the preset requirement may be deleted, so as to perform subsequent processing operation based on the remaining crowd-sourced road data. In this way, the accuracy of updating the roads in the map can be further improved.

Step S103: and merging the crowdsourcing road data according to the incidence relation to obtain the updated data of the target road.

Since the association relationship includes a positional relationship between the crowdsourced road data, such as whether the crowdsourced road data is data of the same road segment, whether the crowdsourced road data includes data of the same road segment, whether the crowdsourced road data is data of an adjacent road segment, and the like, the crowdsourced road data can be merged according to the association relationship to obtain the updated data of the target road. Specifically, each crowdsourcing road data is grouped according to the incidence relation; the crowdsourcing road data of the same group comprises data corresponding to the same road section; and carrying out road characteristic alignment on the crowdsourcing road data in each group, and merging the crowdsourcing road data after alignment among the groups. Here, after the cloud server groups the crowdsourcing road data according to the association relationship, the cloud server performs road feature alignment, such as stop line alignment, traffic signal lamp alignment, lane line alignment, and the like, on the crowdsourcing road data in each group, so that only one crowdsourcing road data exists in each group after the road feature alignment, and then merges the crowdsourcing road data after alignment among the groups.

In summary, the updating method provided in the above embodiments includes: acquiring at least one crowdsourcing road data acquired by at least one information acquisition terminal to a target road; associating the crowdsourcing road data according to a preset strategy to obtain an association relation between the crowdsourcing road data; and merging the crowdsourcing road data according to the incidence relation, and updating the target road based on a merging result. Therefore, roads in the map can be accurately updated in a low-cost and rapid mode, and user experience is improved.

In one embodiment, the method further comprises: and verifying the updated data of the target road according to a preset verification mode. Here, the preset verification method may be various, including but not limited to a self-checking method and an other-checking method, where the self-checking method is to verify the update data of the target road by using the vehicle where the at least one information collection terminal is located, and the other-checking method is to verify the update data of the target road by using another vehicle except for the vehicle where the at least one information collection terminal is located. Therefore, the adaptability and the accuracy of the updated data of the target road are detected by verifying the updated data of the target road, and the updating accuracy is further improved.

In one embodiment, the method further comprises: and after the verification of the update data of the target road is passed, updating the target road in the map according to the update data of the target road.

Specifically, after the verification of the update data of the target road is confirmed, the cloud server updates the target road in the map according to the update data of the target road, so that the target road is updated in time, a user using the map can know the relevant information of the target road in time conveniently, and the accuracy of map updating is ensured.

The foregoing embodiments are specifically described below by way of specific examples based on the same inventive concept as the foregoing embodiments.

Referring to fig. 2, for the structure schematic diagram of an update system that this application embodiment provided, the update system include at least one car end 1 and with cloud server 2 that car end 1 is connected, at least one car end 1 is handled the data of gathering on same target road respectively to obtain the crowd-sourced road data that contains orbit and perception result, and will crowd-sourced road data upload to cloud server 2. Specifically, the vehicle end 1 includes an inertia measurement unit 10, a positioning system 11, a driving assistance system 12, an image acquisition unit 13, a motion estimation unit 14, and a sensing unit 15, where the inertia measurement unit 10, the positioning system 11, the driving assistance system 12, and the image acquisition unit 13 are respectively connected to the motion estimation unit 14 and the sensing unit 15, the motion estimation unit 14 performs motion estimation according to a sensor signal acquired by the information acquisition device to obtain a trajectory, and the sensing unit 15 performs image sensing based on deep learning according to the sensor signal acquired by the information acquisition device to obtain a sensing result. The vehicle end 1 may upload crowd-sourced road data including a track and a perception result to the cloud server at a preset time node (for example, when the vehicle is parked). In addition, the vehicle end 1 can obtain the track and the perception result after filtering useless information, so as to reduce the data transmission cost. The track refers to a path, and the sensing result is road surface information such as traffic lights, road lines and the like.

Based on the same inventive concept as the foregoing embodiment, referring to fig. 3, a specific flowchart of an updating method provided in the embodiment of the present application is applied to the cloud server 2, and includes the following steps:

step S201: performing quality inspection on the crowdsourcing road data uploaded by each vehicle end;

specifically, the cloud server (hereinafter referred to as the cloud) performs quality inspection on the crowdsourcing road data uploaded by each vehicle end, and filters out the crowdsourcing road data with obvious problems (such as obvious drift, track type mismatch and the like) to perform data screening, so as to obtain the crowdsourcing road data after quality inspection.

Step S202: optimizing the crowd-sourced road data after quality inspection;

here, since the trajectories corresponding to the crowd-sourced road data may not be smooth, and there may be a deviation between the trajectories corresponding to the crowd-sourced road data, in order to accurately obtain the correlation between the crowd-sourced road data, optimization processing may be performed on the crowd-sourced road data after quality inspection, for example, the trajectories corresponding to the crowd-sourced road data may be optimized to be smooth, and trajectory data, lane correspondence, lane line correction, and the like in the crowd-sourced road data whose trajectories deviate are adjusted.

Step S203: matching and associating the optimized crowdsourcing road data, and determining an association relation between the crowdsourcing road data;

here, feature extraction may be performed on each of the optimized crowd-sourced road data, such as extracting features of a dotted end point, a stop line, a lane line, and the like, and then each of the crowd-sourced road data may be matched based on the extracted features, thereby determining an association relationship between each of the crowd-sourced road data. When the relevant data is matched and associated, feature extraction can be adopted, such as the determination of the features of the end points of the dotted lines, and when the solid lines are determined, the data can be matched by referring to the information of the solid lines, such as the curvature change of the road lines.

Step S204: and merging the optimized crowd-sourced road data based on the incidence relation to obtain the updated data of the target road.

Here, when merging, the overall characteristics of the data need to be considered, for example, the road feature alignment is performed on the crowd-sourced road data corresponding to the same road segment, and then the crowd-sourced road data after alignment of different road segments is merged, so as to obtain the updated data of the target road. In addition, the data can be merged by adopting a clustering algorithm, an extraction technology and other modes during merging.

Step S205: performing self-checking on the updated data of the target road;

here, the update data of the target road obtained after the merging may be self-checked by an internal quantization index, specifically, the update data may be detected by a vehicle that provides the crowd-sourced road data, and it may be determined whether the update data satisfies a requirement based on the internal quantization index.

Step S206: performing other inspection on the updated data of the target road;

here, the simulation test may be performed by introducing an external parameter to perform a test on the update data, specifically, the update data is detected by a vehicle that does not provide the crowd-sourced road data, and whether the update data satisfies a requirement is determined based on the external parameter.

Step S207: and outputting the updated data of the target road to a manual operation end for detection.

Here, the updated data of the target road may also be output to a manual operation side to manually detect whether the updated data satisfies a requirement. And when the updating data meet the requirements, updating the updating data of the target road into a high-precision map.

Therefore, quality inspection, optimization and combination operation are carried out on a large amount of road data reported by the vehicle end, and data capable of being used for updating the map are obtained, namely, under the condition that the vehicle data acquisition cost is not increased, a large amount of data are processed, and the characteristics that the data volume collected by crowdsourcing vehicles is large, the data dimensionality is rich, data generation period blocks are generated are utilized, so that the accuracy of the finally obtained map is effectively improved, the problem that the data accuracy collected by the vehicles is low is solved, and then the high-precision map is obtained, a good basis is provided for the automatic driving performance and the driving experience, and the wide applicability is realized.

Based on the same inventive concept as the previous embodiment, an embodiment of the present invention provides an updating apparatus, as shown in fig. 4, including: a processor 310 and a memory 311 storing computer programs; the processor 310 illustrated in fig. 4 is not used to refer to the number of the processors 310 as one, but is only used to refer to the position relationship of the processor 310 relative to other devices, and in practical applications, the number of the processors 310 may be one or more; similarly, the memory 311 shown in fig. 4 is also used in the same sense, i.e. it is only used to refer to the position relationship of the memory 311 with respect to other devices, and in practical applications, the number of the memory 311 may be one or more. The updating method applied to the updating apparatus described above is implemented when the computer program is executed by the processor 310.

The updating apparatus may further include: at least one network interface 312. The various components in the update apparatus are coupled together by a bus system 313. It will be appreciated that the bus system 313 is used to enable communications among the components connected. The bus system 313 includes a power bus, a control bus, and a status signal bus in addition to the data bus. For clarity of illustration, however, the various buses are labeled as bus system 313 in FIG. 4.

The memory 311 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the nonvolatile Memory may 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 (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration 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), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memory 311 described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.

The memory 311 in the embodiment of the present invention is used to store various types of data to support the operation of the update apparatus. Examples of such data include: any computer program for operating on the updating means, such as operating systems and application programs; contact data; telephone book data; a message; a picture; video, etc. The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application programs may include various application programs such as a Media Player (Media Player), a Browser (Browser), etc. for implementing various application services. Here, the program that implements the method of the embodiment of the present invention may be included in an application program.

Based on the same inventive concept of the foregoing embodiments, this embodiment further provides a computer storage medium, where a computer program is stored in the computer storage medium, where the computer storage medium may be a Memory such as a magnetic random access Memory (FRAM), 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 Flash Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read Only Memory (CD-ROM), and the like; or may be a variety of devices including one or any combination of the above memories, such as a mobile phone, computer, tablet device, personal digital assistant, etc. The computer program stored in the computer storage medium implements the updating method applied to the above-described updating apparatus when executed by a processor. Please refer to the description of the embodiment shown in fig. 1 for a specific step flow realized when the computer program is executed by the processor, which is not described herein again.

The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

As used herein, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, including not only those elements listed, but also other elements not expressly listed.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.