Estimation Method, Device, Equipment and Medium for Bridge Weight Limit

Abstract

The present disclosure provides a method, apparatus, device, and medium for estimating the weight limit of a bridge, relating to the field of computer technologies, and particularly to the fields of autonomous driving, high-precision maps, and navigation technologies. The implementation solution is as follows: based on the location information of the bridge, at least one first trajectory data passing through the bridge within a predetermined time range is obtained, wherein each first trajectory data in the at least one first trajectory data includes the total weight information of the corresponding vehicle; and based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data, the weight limit value of the bridge is estimated.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technologies, and in particular to the fields of autonomous driving, high-precision maps, and navigation technologies. Specifically, it relates to a method, apparatus, electronic device, computer-readable storage medium, and computer program product for estimating the weight limit of a bridge. Background Art

[0002] The wide application of satellite positioning technology and mobile Internet technology has promoted the development of industries related to electronic map navigation services and location services. Whether the information in the electronic map used by navigation software is comprehensive and accurate directly affects the rationality and accuracy of navigation routes. At the same time, considering the navigation of large vehicles, some passing reference information also needs to be added to the corresponding sections in the electronic map. For example, for bridge sections, the weight limit information of the bridge needs to be added accordingly; for tunnel sections, the height limit information of the tunnel needs to be added accordingly.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or adopted. Unless otherwise specified, no method described in this section should be considered prior art solely because it is included in this section. Similarly, unless otherwise specified, the problems mentioned in this section should not be considered to have been recognized in any prior art. Summary of the Invention

[0004] The present disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for estimating the weight limit of a bridge.

[0005] According to one aspect of the present disclosure, there is provided a method for estimating the weight limit of a bridge, including: obtaining at least one first trajectory data passing through the bridge within a predetermined time range based on the location information of the bridge, where each first trajectory data in the at least one first trajectory data includes the total weight information of the corresponding vehicle; and estimating the weight limit value of the bridge based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data.

[0006] According to another aspect of the present disclosure, there is provided an apparatus for estimating the weight limit of a bridge, including: a first obtaining unit configured to obtain at least one first trajectory data passing through the bridge within a predetermined time range based on the location information of the bridge, where each first trajectory data in the at least one first trajectory data includes the total weight information of the corresponding vehicle; and an estimating unit configured to estimate the weight limit value of the bridge based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data.

[0007] According to another aspect of the present disclosure, there is provided an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-described method for estimating the bridge weight limit.

[0008] According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the above-described method for estimating the bridge weight limit.

[0009] According to another aspect of the present disclosure, there is provided a computer program product, including a computer program, wherein the computer program, when executed by a processor, implements the above-described method for estimating the bridge weight limit.

[0010] According to one or more embodiments of the present disclosure, it is possible to save the time cost and labor cost of manual on-site measurement, efficiently estimate a relatively reasonable and accurate bridge weight limit value, thereby improving the electronic map information, avoiding large vehicles passing through the bridge without clearly knowing the weight limit of the bridge, and enhancing the safety of vehicle driving.

[0011] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings exemplarily illustrate embodiments and constitute a part of the specification, and are used together with the written description of the specification to explain the exemplary embodiments. The illustrated embodiments are only for illustrative purposes and do not limit the scope of the claims. In all the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1 A schematic diagram showing an exemplary system in which the various methods described herein can be implemented according to an embodiment of the present disclosure;

[0014] Figure 2 A flowchart showing a method for estimating the bridge weight limit according to an embodiment of the present disclosure;

[0015] Figure 3 A schematic diagram showing the excavation of bridge locations based on water system data and road network data according to an exemplary embodiment of the present disclosure;

[0016] Figure 4 A flowchart showing the acquisition of at least one first trajectory data according to an embodiment of the present disclosure;

[0017] Figure 5Shows a flowchart for estimating the weight limit value of a bridge according to an embodiment of the present disclosure;

[0018] Figure 6 Shows a flowchart for estimating the weight limit value based on a weight limit reference value according to an embodiment of the present disclosure;

[0019] Figure 7 Shows a flowchart for estimating the weight limit value of a bridge according to an embodiment of the present disclosure;

[0020] Figure 8 Shows a flowchart of a method for estimating the weight limit of a bridge according to an exemplary embodiment of the present disclosure;

[0021] Figure 9 Shows a structural block diagram of an apparatus for estimating the weight limit of a bridge according to an exemplary embodiment of the present disclosure;

[0022] Figure 10 Shows a structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure. Detailed implementation manners

[0023] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to assist in understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0024] In the present disclosure, unless otherwise specified, the terms "first", "second", etc. are used to describe various elements and are not intended to limit the positional relationship, timing relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, and in certain cases, based on the context description, they may also refer to different instances.

[0025] In the description of various examples in the present disclosure, the terms used are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in the present disclosure covers any one of the listed items and all possible combinations.

[0026] In the related art, the weight limit value of a bridge in an electronic map is generally obtained by recognizing the information on the bridge sign. Specifically, the road image can be collected on-site by a collection vehicle, a test vehicle, etc. Subsequently, image recognition is performed on the bridge sign in the image to obtain information such as the name and weight limit value of the bridge.

[0027] In actual scenarios, for some small bridges without signs and without maintenance, their weight limit information cannot be obtained through the above-mentioned scheme. This results in the lack of the weight limit value of these bridges in the electronic map, or in the process of making the electronic map, bridges of the above types cannot be marked, leading to incomplete and inaccurate information in the electronic map, and there are certain safety hazards in the navigation routes passing through the above bridges.

[0028] According to an embodiment of the present disclosure, by obtaining the vehicle trajectory data passing on a certain bridge within a predetermined time, and based on the total weight information of each vehicle in the trajectory data, the weight limit value of the bridge is estimated. This method does not require manual on-site measurement, can save the time cost and labor cost of manual on-site measurement, efficiently estimate a relatively reasonable and accurate weight limit value of the bridge, and then improve the electronic map information, avoid large vehicles passing through the bridge without knowing the weight limit of the bridge, and enhance the safety of vehicle driving.

[0029] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0030] Figure 1 FIG. shows a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein can be implemented according to an embodiment of the present disclosure. Referring to Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105 and 106, a server 120, and one or more communication networks 110 that couple the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105 and 106 can be configured to execute one or more applications.

[0031] In an embodiment of the present disclosure, the server 120 can run one or more services or software applications that enable the execution of the above-described method for estimating the weight limit of a bridge.

[0032] In certain embodiments, the server 120 can also provide other services or software applications, which can include non-virtual environments and virtual environments. In certain embodiments, these services can be provided as web-based services or cloud services, for example, provided to users of the client devices 101, 102, 103, 104, 105 and / or 106 under a software as a service (SaaS) model.

[0033] InFigure 1 In the configuration shown, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof that may be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may in turn utilize one or more client applications to interact with server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Thus, Figure 1 is an example of a system for implementing the various methods described herein and is not intended to be limiting.

[0034] Users may use client devices 101, 102, 103, 104, 105, and / or 106 to upload vehicle trajectory data and parameter information such as total vehicle weight and axle weight. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although Figure 1 only six client devices are depicted, those skilled in the art will be able to understand that the present disclosure may support any number of client devices.

[0035] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors, or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (such as GOOGLE Chrome OS); or include various mobile operating systems, such as MICROSOFT WindowsMobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular phones, smart phones, tablets, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, Internet-enabled gaming devices, etc. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and may use various communication protocols.

[0036] Network 110 can be any type of network well-known to those skilled in the art, which can support data communication using any one of a variety of available protocols (including but not limited to TCP / IP, SNA, IPX, etc.). By way of example only, one or more networks 110 can be a local area network (LAN), an Ethernet-based network, token ring, wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (such as Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0037] Server 120 can include one or more general-purpose computers, dedicated server computers (such as PC (personal computer) servers, UNIX servers, midrange servers), blade servers, mainframes, server clusters, or any other suitable arrangement and / or combination. Server 120 can include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (such as one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices of the server). In various embodiments, server 120 can run one or more services or software applications that provide the functions described below.

[0038] The computing units in server 120 can run one or more operating systems including any of the above operating systems as well as any commercially available server operating systems. Server 120 can also run any one of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0039] In some embodiments, server 120 can include one or more applications to analyze and combine data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and / or 106. Server 120 can also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and / or 106.

[0040] In some embodiments, server 120 can be a server of a distributed system, or a server combined with a blockchain. Server 120 can also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in the cloud computing service system, which solves the defects of difficult management and weak business scalability existing in traditional physical hosts and virtual private server (VPS, Virtual Private Server) services.

[0041] System 100 may also include one or more databases 130. In some embodiments, these databases can be used to store data and other information. For example, one or more of the databases 130 can be used to store information such as audio files and video files. The databases 130 can reside in various locations. For example, the database used by the server 120 can be local to the server 120, or can be remote from the server 120 and can communicate with the server 120 via a network-based or dedicated connection. The databases 130 can be of different types. In some embodiments, the database used by the server 120 can be, for example, a relational database. One or more of these databases can store, update, and retrieve data to and from the database in response to commands.

[0042] In some embodiments, one or more of the databases 130 can also be used by an application to store application data. The databases used by the application can be different types of databases, such as key-value repositories, object repositories, or conventional repositories supported by a file system.

[0043] Figure 1 The system 100 can be configured and operated in various ways to enable the application of the various methods and apparatuses described according to the present disclosure.

[0044] According to some embodiments, as Figure 2 shown, a method for estimating the weight limit of a bridge is provided, including: step S201, based on the location information of the bridge, obtaining at least one first trajectory data passing through the bridge within a predetermined time range, wherein each first trajectory data in the at least one first trajectory data includes the total weight information of the corresponding vehicle; and step S202, estimating the weight limit value of the bridge based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data.

[0045] Thereby, it is possible to save the time cost and labor cost of on-site manual measurement, efficiently estimate a more reasonable and accurate weight limit value of the bridge, further improve the electronic map information, avoid large vehicles passing through the bridge without clearly knowing the weight limit of the bridge, and enhance the safety of vehicle driving.

[0046] Among them, the location information of each bridge can be obtained based on existing map data. In some embodiments, before obtaining the trajectory data of the bridge, it can also be first determined whether the bridge already has weight limit value information. If such information already exists, subsequent operations may not be performed on it.

[0047] In some embodiments, for some small bridges, the corresponding road sections are often not marked as bridges in the map data. For such bridges not marked in the map data, the position information of the above types of bridges can be obtained by performing spatial position difference processing on the water system data and road network data in the map data.

[0048] Figure 3 FIG. shows a schematic diagram of bridge position mining based on water system data and road network data according to an exemplary embodiment of the present disclosure.

[0049] In some exemplary embodiments, as Figure 3 shown, first, the water system data and the road network data can be spatially superimposed to obtain the road sections (such as road section 303) in the road (such as road 301) in the road network that are on the water system (such as water system 302). Then, this road section can be marked as a bridge type in the map data, and the position of the bridge can be obtained accordingly. Thus, the bridges marked in the map can be obtained, further improving the accuracy and integrity of the map data.

[0050] In some embodiments, based on the above method, the width and length information of the corresponding bridge can also be further determined to further improve the map data, provide data support for subsequent steps, and make the estimation of the bridge weight limit more accurate.

[0051] Subsequently, based on the position information of the bridge, at least one first trajectory data passing through the bridge within a predetermined time range can be obtained. The predetermined time range can be, for example, half a year or one year. In some embodiments, relevant technicians can also extend or shorten the predetermined time range according to the number of trajectories passing through the bridge within the predetermined time range, so as to further improve the data collection efficiency while ensuring the data accuracy.

[0052] In some embodiments, each first trajectory data may include the driving trajectory of the corresponding vehicle and the relevant vehicle parameters of the corresponding vehicle (such as the total weight value information of the vehicle).

[0053] In some embodiments, as Figure 4As shown, based on the location information of the bridge, obtaining at least one first trajectory data of vehicles passing through the bridge within a predetermined time range may include: Step S401, obtaining the driving trajectory of each vehicle among at least one vehicle passing through the bridge within the predetermined time range, where the driving trajectory is obtained from the corresponding navigation application of the corresponding vehicle; Step S402, obtaining the total weight information of each vehicle among at least one vehicle, where the total weight information is preset information in the corresponding navigation application of the corresponding vehicle; and Step S403, obtaining at least one first trajectory data, where at least one first trajectory data corresponds to at least one vehicle respectively, and each first trajectory data in the at least one first trajectory data includes the driving trajectory and total weight information of the corresponding vehicle.

[0054] Thus, while obtaining the user trajectory data through the navigation software, the vehicle information (including the total weight information of the vehicle) pre-entered by the user in the navigation software is obtained. Thus, the data acquisition efficiency can be improved, and there is no need for manual data collection, further simplifying the estimation process of the bridge weight limit.

[0055] In some embodiments, the acquisition of the first trajectory data may include: in response to the user using the navigation software for route navigation, collecting the driving trajectory data in the navigation software within a predetermined time range, and then, based on the above-mentioned location information of the bridge, determining the driving trajectories of the vehicles passing through the bridge within the predetermined time range, and determining the number of vehicles passing through the bridge within the predetermined time range based on the number of driving trajectories.

[0056] During the process of the user using the navigation software, to ensure driving safety, the user usually pre-enters information such as the vehicle model, length, width, total weight information, and axle weight information of their vehicle into the navigation software, so as to prevent the navigation software from guiding them to an impassable route.

[0057] In some embodiments, after determining the driving trajectories of the vehicles passing through the bridge within the predetermined time range, based on the corresponding relationship between the driving trajectories and the corresponding navigation software, the relevant parameter information of the vehicles driven by the corresponding users can be determined, such as including the total weight information of the vehicles. The above-mentioned driving trajectories and total weight information constitute a first trajectory data.

[0058] In some embodiments, such as Figure 5As shown, estimating the weight limit value of a bridge based on the total weight information corresponding to each first trajectory data in at least one first trajectory data may include: Step S501, determining a weight limit reference value of the bridge based on the total weight information corresponding to each first trajectory data in at least one first trajectory data; Step S502, obtaining the length information and width information of the bridge; Step S503, determining a load-bearing reference value of the bridge based on the length information and width information, where the load-bearing reference value is the maximum weight that the bridge can theoretically bear; Step S504, comparing the numerical values of the load-bearing reference value and the weight limit reference value to verify the weight limit reference value; and Step S505, in response to the weight limit reference value being verified, estimating the weight limit value based on the weight limit reference value.

[0059] Thus, by first determining the weight limit reference value based on the total weight value of each first vehicle, and then verifying the rationality of the reference value based on the obtained bridge length and width information, the accuracy and rationality of the estimated weight limit value can be improved, and further the traffic safety of the bridge can be enhanced.

[0060] In some embodiments, the weight limit reference value of the bridge may first be determined based on the total weight information corresponding to each first trajectory data in at least one first trajectory data. In some exemplary embodiments, the mean value of the total weight information in all first trajectory data may be considered as the weight limit reference value. In some exemplary embodiments, the maximum value of the total weight information in all first trajectory data may also be considered as the weight limit reference value.

[0061] In some embodiments, before calculating the weight limit reference value, the confidence level of the vehicle total weight value information corresponding to each first trajectory data may first be determined. For example, a total weight threshold interval may be preset in advance, and the vehicle total weight value information not within this total weight threshold interval may be deleted, so as to screen out the total weight value information that is not within a reasonable range and has obvious errors. In some embodiments, the length, width, height, etc. information of the vehicle corresponding to the first trajectory data may further be obtained based on the preset information in the navigation software, and the vehicle model may be estimated based on the above information, and then the reference total weight value of the vehicle model may be obtained. For example, if the total weight value information of a vehicle is 30 tons, but the length of the vehicle is 6 meters, it can be determined that it is a small truck, and then the total weight value information is obviously unreasonable and can be screened out.

[0062] After screening the total weight value information in each of the above first trajectory data in the above manner, the calculation of the reference weight limit value can be performed based on the remaining total weight value information. Thus, the obtained reference weight limit value can be more accurate.

[0063] In some embodiments, after obtaining the reference weight limit value, a load-bearing reference value for verifying the reference weight limit value may be further obtained based on information about the bridge itself, such as the length information and width information of the bridge, that is, the maximum weight that the bridge can theoretically bear.

[0064] In some embodiments, the length information and width information of the bridge may be obtained based on map data. In some embodiments, for small bridges not marked in the map data, the length information and width information of the bridge may also be mined based on the above-mentioned bridge location mining method and the spatial difference between water system data and road network data.

[0065] In some embodiments, determining the load-bearing reference value of the bridge based on the length information and width information may include: determining the vehicle types that can pass through the bridge based on the width information; and determining the load-bearing reference value based on the length information and the reference length and reference weight of the vehicle types that can pass through.

[0066] In some embodiments, the vehicle types that can pass through the bridge may be first determined by the width information of the bridge. For example, the vehicle types that can pass through may be determined based on the regulations on the length and width of each vehicle type in relevant national standards.

[0067] After obtaining the vehicle types that can pass through the bridge, the number of vehicles that can pass through the bridge at the same time may be further obtained based on the reference length of the vehicle types that can pass through and the length of the bridge, and the load-bearing reference value of the bridge may be calculated by multiplying the number of vehicles by the reference weight of the vehicles. Among them, the reference length and reference weight of the vehicles may be obtained, for example, based on the regulations on the length, width and other information of each vehicle type in relevant national standards.

[0068] Subsequently, the corresponding reference weight limit value may be verified based on the above load-bearing reference value. In response to the reference weight limit value passing the verification, the reference weight limit value may be determined as the weight limit value of the bridge. In some embodiments, the verification of the reference weight limit value may be performed by determining whether the reference weight limit value does not exceed the load-bearing reference value. In response to the reference weight limit value not exceeding the load-bearing reference value, it is determined that the reference weight limit value passes the verification.

[0069] Thus, based on the length and width information of the bridge, the vehicle types that can pass through the bridge are determined, and the total weight of the vehicles that the bridge can carry at the same time is judged through the length information, and the reference value is verified based on this value, thereby further improving the rationality of the reference value and the accuracy and rationality of the estimated weight limit information.

[0070] In some embodiments, such as Figure 6As shown, in response to the verification of the weight limit reference value, based on the weight limit reference value, estimating the weight limit value may also include: Step S601, in response to the verification of the weight limit reference value, obtaining at least one bridge image of the bridge; Step S602, based on the at least one bridge image, obtaining the bridge quality grade information of the bridge; and Step S603, based on the bridge quality grade information and the weight limit reference value, estimating the weight limit value.

[0071] Thus, by further obtaining the image of the bridge and analyzing the quality grade of the bridge; on the basis of the reference value, adjusting based on the quality grade information, so as to determine the weight limit information. Thus, by further introducing the bridge quality grade information, the weight limit value is adjusted, thereby further improving the safety and rationality of the weight limit value.

[0072] In some embodiments, in response to the verification of the reference weight limit value of the bridge, it is also possible to further obtain the bridge image and apply image recognition technology to perform predictive analysis on the bridge image to obtain the bridge quality grade information of the bridge. Subsequently, based on the bridge quality grade information, the reference weight limit value of the bridge is adjusted to estimate the weight limit value of the bridge.

[0073] In some embodiments, at least one bridge image can be input into a trained neural network model (such as a CNN model can be applied) for predictive analysis, so as to obtain the corresponding bridge quality grade label of the bridge.

[0074] In some embodiments, based on the at least one bridge image, obtaining the bridge quality grade information of the bridge may include: performing predictive analysis on the at least one bridge image to obtain at least one bridge information, where the at least one bridge information includes at least one of the bridge laying state, the bridge structure, and the bridge maintenance condition; and based on the at least one bridge information, determining the bridge quality grade information.

[0075] In some embodiments, at least one bridge image can be input into a trained neural network model (such as a CNN model can be applied) for predictive analysis, so as to obtain the corresponding bridge quality grade label of the bridge.

[0076] In some embodiments, the above at least one bridge image can also be respectively input into multiple trained neural network models (for example, a CNN model can be applied) for predictive analysis. Among them, the above multiple neural network models can be respectively used for predictive analysis of multiple different quality dimensions of the bridge, such as the laying state of the bridge (for example, laying based on sand, cement or asphalt), the bridge structure (for example, steel structure or concrete, etc.), and the bridge maintenance situation (for example, good road surface, damaged road surface, dangerous bridge, etc.), so as to obtain bridge information for the above multiple different quality dimensions. Subsequently, based on the above multiple bridge information, the bridge quality grade information of the bridge can be comprehensively determined.

[0077] In some embodiments, the corresponding bridge quality grade information can be determined based on the above multiple bridge information and the corresponding preset rules. In some embodiments, the above multiple bridge information can also be input into a trained neural network together for predictive analysis to obtain the bridge quality grade information of the bridge.

[0078] Thus, based on the bridge image, the laying state, bridge structure, maintenance situation, etc. of the bridge are respectively identified, and based on the identification situations of each dimension, the bridge quality grade is comprehensively analyzed and obtained, so as to efficiently and accurately obtain the actual situation of the bridge, and the weight limit value can be adjusted more accurately and reasonably.

[0079] Subsequently, based on the bridge quality grade information of the bridge, the weight limit reference value can be adjusted to estimate the weight limit value of the bridge.

[0080] In some embodiments, based on the bridge quality grade information and the weight limit reference value, estimating the weight limit value may include: determining the weight limit adjustment value corresponding to the bridge quality grade information; and estimating the weight limit value based on the weight limit reference value and the weight limit adjustment value.

[0081] Thus, different weight limit adjustment values can be determined according to different bridge quality grades, and the weight limit value can be adjusted based on the corresponding depression values, thereby further improving the safety and reasonableness of the weight limit value.

[0082] In some embodiments, different bridge quality grades correspond to different weight limit adjustment values. After the bridge quality grade is determined, the corresponding weight limit adjustment value can be subtracted from the corresponding weight limit reference value to obtain the weight limit value of the bridge.

[0083] In some embodiments, different bridge quality grades correspond to different weight limit adjustment ratios. After the bridge quality grade is determined, the corresponding weight limit reference value can be adjusted downward according to the corresponding weight limit adjustment ratio to obtain the weight limit value of the bridge.

[0084] In some embodiments, after obtaining the bridge quality level, the corresponding weight limit adjustment value may also be adjusted based on a preset rule in combination with the length and width information of the bridge (for example, when the bridge width is less than a preset width threshold, the weight limit adjustment value may be increased based on the corresponding preset adjustment value), and the weight limit value of the bridge may be estimated based on the adjusted weight limit adjustment value and the weight limit reference value.

[0085] In some embodiments, as Figure 7 shown, estimating the weight limit value of a bridge based on the total weight information corresponding to each first trajectory data in at least one first trajectory data may further include: in response to the weight limit reference value failing to pass verification, performing the following operations to obtain an updated weight limit reference value until the updated weight limit reference value passes verification: Step S701, adjusting the predetermined time range; Step S702, based on the position information of the bridge, obtaining at least one second trajectory data passing through the bridge within the adjusted predetermined time range, wherein each second trajectory data in the at least one second trajectory data includes the total weight information of the corresponding vehicle; and Step S703, updating the weight limit reference value based on the total weight information corresponding to each second trajectory data in the at least one second trajectory data.

[0086] In response to the weight limit reference value failing to pass verification, the predetermined time range may be further extended to lengthen the acquisition period of vehicle trajectories, so as to obtain more vehicle trajectory data passing through the bridge, and based on the re-acquired trajectory data (second trajectory data), the weight limit value of the bridge may be estimated again based on the above method until the updated weight limit reference value passes verification. Thus, the rationality and accuracy of the weight limit reference value are further improved.

[0087] In some embodiments, after estimating the weight limit value, the relevant information such as the weight limit value, the reference weight limit value, and the bridge image may also be uploaded to the map production department for further manual review to further ensure the accuracy and timeliness of the relevant data.

[0088] Figure 8 shows a flowchart of a method for estimating the weight limit of a bridge according to an exemplary embodiment of the present disclosure.

[0089] In some exemplary embodiments, as Figure 8As shown, the bridge weight limit estimation method may include: Step S801, mining the bridge location from water system data and road network data to obtain the bridge location; Step S802, based on the location information of the bridge, obtaining at least one first trajectory data passing through the bridge within a predetermined time range; Step S803, based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data, determining the weight limit reference value; Step S804, based on the length and width of the bridge, determining the load-bearing reference value to verify the weight limit reference value based on the load-bearing reference value; Step S805, in response to the weight limit reference value failing to pass the verification, adjusting the predetermined time range and re-executing the above steps until the updated weight limit reference value passes the verification; Step S806, in response to the weight limit reference value passing the verification, performing image recognition on the bridge image, determining the bridge quality grade, and obtaining the corresponding weight limit adjustment value; Step S807, adjusting the weight limit reference value based on the weight limit adjustment value to determine the adjusted weight limit reference value as the weight limit value of the bridge; Step S808, outputting the weight limit value and supplementing this information to the map data.

[0090] According to some embodiments, as Figure 9 shown, there is provided an estimation device 900 for the weight limit of a bridge, including: a first acquisition unit 910 configured to obtain at least one first trajectory data passing through the bridge within a predetermined time range based on the location information of the bridge, wherein each first trajectory data in the at least one first trajectory data includes the total weight information of the corresponding vehicle; and an estimation unit 920 configured to estimate the weight limit value of the bridge based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data.

[0091] Among them, the operations performed by the unit 910 and the unit 920 in the estimation device 900 for the weight limit of the bridge are similar to the operations of Step S201 and Step S202 in the above-mentioned bridge weight limit estimation method, and will not be elaborated here.

[0092] In some embodiments, the estimation unit may include: a first determination subunit configured to determine the weight limit reference value of the bridge based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data; a first acquisition subunit configured to obtain the length information and width information of the bridge; a second determination subunit configured to determine the load-bearing reference value of the bridge based on the length information and width information, wherein the load-bearing reference value is the maximum weight that the bridge can theoretically bear; a verification subunit configured to compare the numerical magnitudes of the load-bearing reference value and the weight limit reference value to verify the weight limit reference value; and an estimation subunit configured to, in response to the weight limit reference value passing the verification, estimate the weight limit value based on the weight limit reference value.

[0093] In some embodiments, the second determination subunit may include: a first determination module configured to determine the passable vehicle types of the bridge based on the width information; and a second determination module configured to determine a load-bearing reference value based on the length information and the reference length and reference weight of the passable vehicle types.

[0094] In some embodiments, the estimation subunit may include: a first acquisition module configured to acquire at least one bridge image of the bridge in response to the passing of the weight limit reference value verification; a second acquisition module configured to acquire bridge quality grade information of the bridge based on the at least one bridge image; and an estimation module configured to estimate a weight limit value based on the bridge quality grade information and the weight limit reference value.

[0095] In some embodiments, the estimation module may be further configured to: determine a weight limit adjustment value corresponding to the bridge quality grade information; and estimate a weight limit value based on the weight limit reference value and the weight limit adjustment value.

[0096] In some embodiments, the second acquisition module may be further configured to: perform a predictive analysis on the at least one bridge image to acquire at least one bridge information, where the at least one bridge information includes at least one of a bridge laying state, a bridge structure, and a bridge maintenance condition; and determine the bridge quality grade information based on the at least one bridge information.

[0097] In some embodiments, the estimation unit may further include: an execution subunit configured to perform operations of the following modules to acquire an updated weight limit reference value in response to the failure of the weight limit reference value verification until the updated weight limit reference value passes the verification. The execution subunit includes: an adjustment module configured to adjust a predetermined time range; a third acquisition module configured to acquire at least one second trajectory data passing through the bridge within the adjusted predetermined time range based on the position information of the bridge, where each second trajectory data in the at least one second trajectory data includes the total weight information of the corresponding vehicle; and an update module configured to update the weight limit reference value based on the total weight information corresponding to each second trajectory data in the at least one second trajectory data.

[0098] In some embodiments, the first acquisition unit may include: a second acquisition subunit configured to acquire the driving trajectory of each vehicle among at least one vehicle passing through the bridge within a predetermined time range, wherein the driving trajectory is acquired from the navigation application corresponding to the respective vehicle; a third acquisition subunit configured to acquire the total weight information of each vehicle among at least one vehicle, wherein the total weight information is preset information in the navigation application corresponding to the respective vehicle; and a fourth acquisition subunit configured to acquire at least one first trajectory data, wherein at least one first trajectory data corresponds to at least one vehicle respectively, and each first trajectory data in the at least one first trajectory data includes the driving trajectory and the total weight information of the corresponding vehicle.

[0099] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0100] According to an embodiment of the present disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0101] Referring Figure 10 , the structural block diagram of the electronic device 1000 that can be used as the server or client of the present disclosure will now be described. It is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processing device, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0102] As Figure 10 shown, the electronic device 1000 includes a computing unit 1001, which can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 1002 or the computer program loaded from the storage unit 1008 into the random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the electronic device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other through a bus 1004. The input / output (I / O) interface 1005 is also connected to the bus 1004.

[0103] Multiple components in the electronic device 1000 are connected to the I / O interface 1005, including: an input unit 1006, an output unit 1007, a storage unit 1008, and a communication unit 1009. The input unit 1006 can be any type of device capable of inputting information into the electronic device 1000. The input unit 1006 can receive input digital or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device, and can include, but is not limited to, a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and / or a remote control. The output unit 1007 can be any type of device capable of presenting information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1008 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 1009 allows the electronic device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a BluetoothTM device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0104] The computing unit 1001 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1001 executes the various methods and processes described above, such as the method for estimating the bridge weight limit described above. For example, in some embodiments, the method for estimating the bridge weight limit described above can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the method for estimating the bridge weight limit described above can be executed. Alternatively, in other embodiments, the computing unit 1001 can be configured to execute the method for estimating the bridge weight limit described above in any other suitable manner (e.g., by means of firmware).

[0105] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0109] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0110] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0111] It should be understood that the various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this is not limited herein.

[0112] Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above methods, systems, and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only defined by the authorized claims and their equivalent scope. Various elements in the embodiments or examples may be omitted or replaced by their equivalent elements. In addition, the steps may be executed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. Importantly, with the evolution of technology, many of the elements described herein may be replaced by equivalent elements that emerge after the present disclosure.

Claims

1. A method for estimating the weight limit of a bridge, the method comprises: Based on the location information of the bridge, obtaining at least one first trajectory data passing through the bridge within a predetermined time range, wherein each first trajectory data in the at least one first trajectory data includes the total weight information of the corresponding vehicle; and Based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data, estimating the weight limit value of the bridge, including: Based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data, determining the weight limit reference value of the bridge; Obtaining the length information and width information of the bridge; Based on the length information and the width information, determining the load-bearing reference value of the bridge, wherein the load-bearing reference value is the maximum weight that the bridge can theoretically bear; Comparing the numerical magnitudes of the load-bearing reference value and the weight limit reference value to verify the weight limit reference value; and In response to the weight limit reference value being verified, estimating the weight limit value based on the weight limit reference value.

2. The method according to claim 1, wherein, The determining the load-bearing reference value of the bridge based on the length information and the width information includes: Based on the width information, determining the vehicle types that can pass through the bridge; and Based on the length information and the reference length and reference weight of the vehicle types that can pass through, determining the load-bearing reference value.

3. The method according to claim 1 or 2, wherein, The estimating the weight limit value based on the weight limit reference value in response to the weight limit reference value being verified includes: In response to the weight limit reference value being verified, obtaining at least one bridge image of the bridge; Based on the at least one bridge image, obtaining the bridge quality grade information of the bridge; and Based on the bridge quality grade information and the weight limit reference value, estimating the weight limit value.

4. The method according to claim 3, wherein, The estimating the weight limit value based on the bridge quality grade information and the weight limit reference value includes: Determining the weight limit adjustment value corresponding to the bridge quality grade information; and Based on the weight limit reference value and the weight limit adjustment value, estimating the weight limit value.

5. The method according to claim 3, the obtaining the bridge quality grade information of the bridge based on the at least one bridge image comprises: Performing predictive analysis on the at least one bridge image to obtain at least one bridge information, wherein the at least one bridge information includes at least one of the bridge laying state, bridge structure, and bridge maintenance condition; and Based on the at least one bridge information, determining the bridge quality grade information.

6. The method according to claim 1, wherein, The estimating the weight limit value of the bridge based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data further includes: In response to the weight limit reference value not being verified, performing the following operations to obtain an updated weight limit reference value until the updated weight limit reference value is verified: Adjusting the predetermined time range; Based on the position information of the bridge, obtain at least one second trajectory data of vehicles passing through the bridge within the adjusted predetermined time range, where each second trajectory data in the at least one second trajectory data includes the total weight information of the corresponding vehicle; and Update the weight limit reference value based on the total weight information corresponding to each second trajectory data in the at least one second trajectory data.

7. The method according to claim 1, wherein the obtaining, based on the position information of the bridge, of at least one first trajectory data of vehicles passing through the bridge within a predetermined time range includes: Obtain the driving trajectory of each vehicle in at least one vehicle passing through the bridge within the predetermined time range, where the driving trajectory is obtained from the corresponding navigation application of the corresponding vehicle; Obtain the total weight information of each vehicle in the at least one vehicle, where the total weight information is preset information in the corresponding navigation application of the corresponding vehicle; and Obtain the at least one first trajectory data, where the at least one first trajectory data corresponds to the at least one vehicle respectively, and each first trajectory data in the at least one first trajectory data includes the driving trajectory and total weight information of the corresponding vehicle.

8. An apparatus for estimating the weight limit of a bridge, the apparatus comprises: A first obtaining unit, configured to obtain at least one first trajectory data of vehicles passing through the bridge within a predetermined time range based on the position information of the bridge, where each first trajectory data in the at least one first trajectory data includes the total weight information of the corresponding vehicle; and An estimating unit, configured to estimate the weight limit value of the bridge based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data, where the estimating unit includes: A first determining subunit, configured to determine the weight limit reference value of the bridge based on the total weight information corresponding to each first trajectory data in the at least one first trajectory data; A first obtaining subunit, configured to obtain the length information and width information of the bridge; A second determining subunit, configured to determine the load-bearing reference value of the bridge based on the length information and the width information, where the load-bearing reference value is the maximum weight that the bridge can theoretically bear; A verifying subunit, configured to compare the numerical magnitudes of the load-bearing reference value and the weight limit reference value to verify the weight limit reference value; and An estimating subunit, configured to, in response to the weight limit reference value passing the verification, estimate the weight limit value based on the weight limit reference value.

9. The apparatus according to claim 8, wherein the second determining subunit includes: A first determining module, configured to determine the vehicle types that can pass through the bridge based on the width information; and A second determining module, configured to determine the load-bearing reference value based on the length information and the reference length and reference weight of the vehicle types that can pass through.

10. The apparatus according to claim 8 or 9, wherein the estimating subunit includes: A first obtaining module, configured to, in response to the weight limit reference value passing the verification, obtain at least one bridge image of the bridge; A second acquisition module, configured to acquire bridge quality grade information of the bridge based on the at least one bridge image; and An estimation module, configured to estimate the weight limit value based on the bridge quality grade information and the weight limit reference value.

11. The apparatus according to claim 10, wherein the estimation module is further configured to:[[]] Determine a weight limit adjustment value corresponding to the bridge quality grade information; and Estimate the weight limit value based on the weight limit reference value and the weight limit adjustment value.

12. The apparatus according to claim 10, wherein the second acquisition module is further configured to:[[]] Perform predictive analysis on the at least one bridge image to acquire at least one piece of bridge information,[[]] wherein the at least one piece of bridge information includes at least one of a bridge laying state, a bridge structure, and a bridge maintenance condition; and Determine the bridge quality grade information based on the at least one piece of bridge information.

13. The apparatus according to claim 8, wherein the estimation unit further includes:[[]] An execution subunit, configured to, in response to the weight limit reference value failing verification, perform operations of the following modules to acquire an updated weight limit reference value until the updated weight limit reference value passes verification. The execution subunit includes:[[]] An adjustment module, configured to adjust the predetermined time range; A third acquisition module, configured to acquire at least one second trajectory data passing through the bridge within the adjusted predetermined time range based on the position information of the bridge, wherein each second trajectory data in the at least one second trajectory data includes total weight information of a corresponding vehicle; and An update module, configured to update the weight limit reference value based on the total weight information corresponding to each second trajectory data in the at least one second trajectory data.

14. The apparatus according to claim 8, wherein the first acquisition unit includes:[[]] A second acquisition subunit, configured to acquire the driving trajectory of each vehicle in at least one vehicle passing through the bridge within the predetermined time range, wherein the driving trajectory is acquired from a navigation application corresponding to the vehicle; A third acquisition subunit, configured to acquire the total weight information of each vehicle in the at least one vehicle, wherein the total weight information is preset information in a navigation application corresponding to the vehicle; and A fourth acquisition subunit, configured to acquire the at least one first trajectory data, wherein the at least one first trajectory data corresponds to the at least one vehicle respectively, and each first trajectory data in the at least one first trajectory data includes the driving trajectory and total weight information of a corresponding vehicle.

15. An electronic device,[[]] comprising:[[]] At least one processor; and A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1-7.

16. A non-transitory computer-readable storage medium storing computer instructions,[[]] wherein The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

17. A computer program product, comprising a computer program, wherein, the computer program, when executed by a processor, implements the method according to any one of claims 1-7.

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